The Population Biology of Tuberculosis [Course Book ed.] 9781400866571

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The Population Biology of Tuberculosis

MONOGRAPHS IN POPULATION BIOLOGY EDITED BY SIMON A. LEVIN AND HENRY S. HORN

A complete series list follows the index.

The Population Biology of Tuberculosis Christopher Dye

PRINCETON UNIVERSITY PRESS Princeton and Oxford

Copyright © 2015 by Princeton University Press Published by Princeton University Press, 41 William Street, Princeton, New Jersey 08540 In the United Kingdom: Princeton University Press, 6 Oxford Street, Woodstock, Oxfordshire OX20 1TW press.princeton.edu All Rights Reserved Library of Congress Cataloging-­in-­Publication Data Dye, Christopher, author.   The population biology of tuberculosis / Christopher Dye.     p. ; cm. — (Monographs in population biology ; 54)   Includes bibliographical references and index.   ISBN 978-­0-­691-­15462-­6 (hardcover : alk. paper)   I. Title.  II. Series: Monographs in population biology ; 54.   [DNLM:  1. Mycobacterium tuberculosis—genetics.  2. Tuberculosis—epidemiology.  3. Population Dynamics.  4. Systems Biology.  WF 205.1]  RA644.T7 614.5'42—dc23 2014037803 British Library Cataloging-­in-­Publication Data is available This book has been composed in Times LT Std Printed on acid-­free paper. ∞ Printed in the United States of America 10 9 8 7 6 5 4 3 2 1

Contents

Preface vii Chapter 1  Tuberculosis Undefeated

1

Chapter 2  Concepts and Models

26

Chapter 3  Risk and Variation

64

Chapter 4  Interventions and Control

100

Chapter 5  Strains and Drug Resistance

138

Chapter 6  TB and HIV/AIDS

162

Chapter 7  Elimination and Eradication

190

Chapter 8  Populations and Social Diseases

207

Appendix 1  Derivation of the Basic Case Reproduction Number and Epidemic Doubling Time Appendix 2  Formal Description of the Standard Model

219 222

References 227 Index 271

Preface O what can ail thee, knight-­at-­arms, Alone and palely loitering? —John Keats, La Belle Dame sans Merci Is there no respect of place, persons, nor time in you? —William Shakespeare, Twelfth Night (Malvolio, Act 2, Scene 3)

Consumption, galloping and otherwise, asthenia, gibbus, lupus, phthisis, scrofula, tabes, hectic and gastric fever, Pott’s and Koch’s disease: tuberculosis in all its various guises killed more than 100 million people in the twentieth century and uncounted numbers in the preceding millennia. Presently, there are about 9 million new episodes of TB each year, probably a little lower than the maximum reached around the turn of the millennium. After 90 years of vaccination and more than 60 years of drug therapy, TB is still among the top 10 causes of human mortality. And while TB cases and deaths have fallen markedly over the past century in the rich world, no country has yet come close to eliminating the disease. The goal of this book is to explain why TB is so persistent and to describe what must be done if we are to eliminate the disease during the course of the twenty-­first century. Shelves of books have been written about TB, mostly on the historical, cultural, clinical and microbiological aspects of the disease. The classic exposition of TB epidemiology in a social context is The White Plague by René and Jean Dubos (1952). The work of Karel Styblo (1991), giant among European public health physicians, has been brought together in a collection of papers that any TB specialist should still keep to hand. George Comstock (1980), during a lifetime’s work on TB in North America, succeeded in his ambition never to write a book, but his 1980 overview of landmarks in TB epidemiology is compulsory reading. Based on his vast experience at the International Union Against Tuberculosis and Lung Disease, Hans Rieder (1999, 2002) has written indispensable guides to TB epidemiology (1999) and control (2002) systematically examining the factors that determine infection, disease, and death and reviewing the efficacy and effectiveness of interventions based on drug treatment and vaccination.

viii

Preface

The approach taken in this book is different. My goal is to put the key characters in epidemiology—person, place and time—in the context of population biology, that is, demography, ecology, evolution, and population genetics. Aided by simple mathematical models, the aim is to use the insights that come from thinking about Mycobacterium tuberculosis and its human host as dynamic, interacting populations to describe how, within a few decades, TB cases and deaths could be brought down to much lower levels and how a combination of novel interventions could eventually lead to elimination. The outlook is more strategic than tactical, exploring general patterns and principles rather than addressing detailed epidemiological questions that apply to specific settings. This strategic approach is different in flavor from much of the recent analytical literature on tuberculosis, which explores detailed solutions to specific or local problems. Although the story is told with the aid of models, this book is not intended to be a primer on mathematical or statistical methods in epidemiology; the particulars of the methods used can be found in the sources cited. The conventional tools of epidemiology include cross-­sectional, case-­ control, and cohort studies and, the ultimate investigative method, controlled experimental trials. With these techniques we can assess, for example, whether drug-­resistant M. tuberculosis is associated with certain genotypes, the relative risk of TB among cigarette smokers, and whether new drugs are effective treatments for individual patients. These studies tend to be static in outlook. For instance, a new TB vaccine that is found to have, in a randomized, controlled trial, a protective efficacy of 70% against pulmonary TB in adults would be a breakthrough for TB control. But knowing only the protective efficacy, we could neither predict nor retrospectively understand the community-­wide impact of a vaccination program over 10 years. That understanding requires a knowledge of events that happen through population interactions and across bacterial and human generations—of processes that can be understood and measured in terms of case reproduction numbers, heterogeneity in transmission, herd immunity, feedback loops, equilibria, and evolutionary selective pressures. These concepts of population biology allow us to address questions that are important for the control of infectious diseases but which lie outside the linear, time-­bound traditions of descriptive, risk-­factor epidemiology. This outlook is influenced by and shares a common aim with Infectious Diseases of Humans by Roy Anderson and Robert May (1991) Their 1991 book marked a new era in epidemiology, drawing widely on ideas across the population sciences, and developing mathematical theory that was always closely linked to data. The greatest defect of their 700-­page book, however, is that it contains just a single mention of TB—and then only for historical background. We are two decades into the biggest-­ever attempt to scale up drug treatment for TB control and have arrived at the United Nations 2015 deadline for reaching

Preface ix

the Millennium Development Goals (MDGs). The global HIV/AIDS epidemic has peaked, and more money is now being spent on TB research and development than ever before. And yet the number of TB cases is falling slowly, if at all, in many low-­income, high-­burden countries, and new strains of drug-­resistant TB are being discovered each year. Optimists have set 2050 as the date for TB elimination; pessimists speak of the global spread of TB strains resistant to all antibiotics. Both outcomes are possible; this book is intended as an aid to achieving the former and avoiding the latter. The thread of the argument is as follows. Chapter 1 sets the scene with an empirical account of the evolutionary and epidemiological history of TB in populations around the world. Chapter 2 captures the key concepts underpinning this history in a formal mathematical framework—a family of dynamic models that are used, in this and subsequent chapters, to investigate the key questions about TB epidemiology and control. While Chapter 2 sets out the principles by focusing on the average characteristics of individuals in populations, a central goal of epidemiology, and fundamental to host and pathogen evolution, is to explain differences—why some people acquire infection and disease and not others. Chapter 3 therefore explores the factors that affect the risk of exposure to M. tuberculosis infection and the risk of developing TB disease. A key finding is that the known risks are numerous but small, and each of these small risks typically explains only a small fraction of TB cases in any chosen population. This has implications for TB control, the subject of Chapter 4. Without a better understanding of who is most at risk of infection and disease, the opportunities for targeted prevention and treatment are limited. This is why dominant control efforts today do not focus on hotspots but rather confront the disease in whole populations. The principal approach to TB control, drug treatment of active disease, has saved millions of lives but has been much less effective in stopping transmission. A central conclusion of Chapter 4 is that while the mitigation of risk factors such as diabetes, undernutrition, or tobacco smoking is helpful as an accessory to TB control, the greater opportunity for immediate impact lies with the early diagnosis and curative treatment of TB disease. Chapters 5–7 deal with three specific and important topics in TB epidemiology and control: multiple TB strains and drug resistance; the dual epidemic of TB and HIV/AIDS; and the prospects for elimination and eradication. Resistance to TB drugs is a threat everywhere, but the continued spread of resistance is not inevitable. By adopting today’s best management practices, both the number and prevalence of resistant cases can be reversed in principle and in practice, even in settings where resistant strains have become common. The epidemic of HIV/AIDS has increased the incidence of TB by a factor of three in southern Africa. But given the combined potential effectiveness of preventive and curative treatments for both infections, plus the fact that HIV epidemics have peaked in the worst-­affected

x

Preface

countries, there is huge and unrealized potential for TB-­HIV control. For both drug-­resistant and HIV-­related TB the immediate priority is, once again, on early and strain-­specific diagnosis coupled with curative treatment. Here the spotlight is on the way TB control programs work along with the essential functions of health services. The conclusions of Chapters 4–7 are mutually reinforcing. Although this is a positive outlook, the post-­2015 targets for TB control present a hugely demanding task for the coming decades. Looking ahead in Chapter 7, it is clear that new methods of control are needed, not just the vital procedures of health services but also technologies to prevent new infections and to neutralize established infections, if we are to stand any chance of eliminating TB by midcentury. To conclude, Chapter 8 draws together the epidemiological insights that come from population biology and considers the context in which TB remains a “social disease” in the early twenty-­first century. It argues that the label social disease is a stimulus for thinking widely about the (risk) factors that govern the distribution and abundance of TB cases and deaths in populations. The distinctive contribution of population biology is to range quantitatively across demography, ecology, epidemiology, environmental science, and evolution. Widening the horizon will bring into view a greater range of options for tackling TB in the post-­2015 era of health and sustainable development. Chapter 8 argues that the striking gap between the actual and potential impact of drug treatment suggests placing greater emphasis right now on the early detection and effective treatment of active or incipient TB. That means finding ways to boost the demand for—and supply of—health services. Much emphasis has been placed on developing new technology for TB control; too little attention has been paid to the design of health and social systems that can make best use of technology. This monograph is largely an exercise in applied population biology. That said, it is worth reflecting that the generalities sought in any branch of science often emerge from the study of specific examples. That was true of the seminal contributions to epidemiology made by Daniel Bernouilli, William Hamer, Anderson Gray McKendrick, Ronald Ross, and Fred Soper as they conceived of better ways to understand and control smallpox, measles, and malaria. Tuberculosis research, too, has uncovered some basic truths about diseases in populations. Some of the fundamental epidemiological processes discussed in this book are: how diseases can persist with low case reproduction numbers; the epidemiological consequences of partial immunity; the implications of a spectrum of more or less infectious clinical conditions; disease persistence and extinction in the presence of a large reservoir of latent infection; the similarities and differences of drugs and vaccines and their effectiveness in combined interventions; and the dynamics of TB during epidemiologic and demographic transitions.

Preface xi

This book displays the inherited features of its forebears, but it has also been shaped by the kindness of my contemporaries. Those who generously provided me with source materials, original data and ideas, editorial help, and constructive criticism include Tomas Allen, Martien Borgdorff, Nathan Carr, Andy Cliff, Ted Cohen, Iñaki Comas, Tom Frieden, Quinn Fusting, Sebastien Gagneux, Philippe Glaziou, Hans Heesterbeek, Philip Hill, Rein Houben, Ali­ son Kalett, Knut Lonnnroth, Clarisse Mason, Ian Orme, Nim Pathy, Antonio Pio, Mario Raviglione, Hans Rieder, Joel Spicer, Hazim Timimi, Gian van der Spuy, Brian Williams, Mark Woolhouse, and Douglas Young. My wife, Enricke Bouma, has always found the right way to provide the right support at the right time. My deepest gratitude to her comes with a promise: that the consumption of excess time will be cured by the rediscovery of lost weekends. Finally, the opinions expressed in this monograph do not necessarily reflect the opinions of reviewers or the decisions, policies, or views of my employer, the World Health Organization. If this is not a book of “words in their best order,”1 the fault is entirely mine.

1 Samuel Taylor Coleridge, “Prose: words in their best order; poetry: the best words in the best order.”

The Population Biology of Tuberculosis

CHAPTER 1

Tuberculosis Undefeated There may be 12 to 25 million infectious cases of tuberculosis in the world. Confronted with such an astronomical computation of distress, it is clear that tuberculosis is very far from being defeated. —John Crofton (1960) In 2012, an estimated 8.6 million people developed TB and 1.3 million died from the disease. There were 12 million prevalent cases. —World Health Organization (2013a)

“Tuberculosis Undefeated” was the title chosen by Crofton for his 1960 Marc Daniels lecture, given in memory of a colleague who made a vital contribution to the development of combination drug therapy against tuberculosis (Crofton 1960). Much has happened in the population biology of TB since 1960, but Crofton’s title still reflects the state of TB in the world today, as reflected in WHO statistics (World Health Organization 2013a). Despite the development of highly efficacious drug therapy to prevent and cure TB, 8–10 million people developed some form of the disease in 2012, there were 11–14 million extant (prevalent) cases, and 1.2–1.5 million people died. The number of TB episodes each year has probably now fallen below an all-­time high, reached in the decade 2000–2009, but TB remains the largest cause of death from a single, curable infectious agent.1 The challenge we face in the twenty-­first century is to control—and ultimately eliminate—a pathogen that has inhabited human populations (including the earliest hominids) for tens of thousands and possibly millions of years. To begin to understand how elimination could be achieved, the purpose of this

1

HIV/AIDS causes more deaths; HIV is treatable with antiretroviral drugs but is not yet curable.

2

Chapter 1

opening chapter is to define the problem. The following sections describe the essential characteristics of the pathogen and the collection of illnesses it causes, its origins and distribution in human populations, the dominant trends through time, and the burden of disease today. This is an explicitly descriptive account, which prefigures the more analytical investigation of TB epidemiology and control in subsequent chapters.

MYCOBACTERIA Everything about the biology of Mycobacterium tuberculosis happens slowly. Whereas other bacteria divide in minutes, M. tuberculosis takes hours. Most bacteria can be cultured in hours, but M. tuberculosis takes weeks. Most outbreaks of bacterial infections run over days or weeks, but an epidemic of M. tuberculosis takes years, decades or centuries. M. tuberculosis is a small (measured in m), rod-­shaped, aerobic, mostly nonmotile, non-­spore-­forming bacterium. Following convention in bacteriology, mycobacteria are classified according to their reactions to standard stains used in microscopy. M. tuberculosis is bracketed with gram-­positive bacteria, although its waxy cell wall, rich in fatty acids, is Gram neutral (Hinson et al. 1981). Nevertheless, once the poorly absorptive cell wall has been successfully impregnated with concentrated dyes (fuchsin), it withstands decolorization by acids—hence the use of an acid-­fast stain in procedures such as Ziehl-­Neelsen, which renders all mycobacteria bright red on a blue background when viewed under a light microscope. Mycobacterial species are distinguished by their appearance—M. tuberculosis is comparatively “rough” among its congeners— and by their response to biochemical tests (Ait-­Khlaed and Enarson 2003). The slow growth of mycobacteria in culture is somewhat less slow in liquid (days) than on solid media (weeks on the traditional Löwenstein-­Jensen slopes). In diagnostic laboratories that rely on culture, the slow growth can cause significant diagnostic delays, with clinical and epidemiological consequences, including the loss of patients and continuing transmission before the start of treatment. In partial compensation, the variable growth rates of mycobacteria species are an asset in differential diagnosis. Mycobacteria are widespread in the environment and commonly found in soil and water. Not all are obligate parasites. The M. tuberculosis complex (MTBC) of species contains the major mammalian pathogens, including M. tuberculosis sensu stricto and M. africanum. It also includes the mainly animal pathogens M. bovis (cattle), M. caprae (goats), M. microti (voles), and M. pinnipedii (seals, sea lions; Smith et al. 2006). As a cause of human TB, M. bovis is more commonly

Tuberculosis Undefeated 3

associated with extrapulmonary than pulmonary disease (Durr et al. 2013). With improved management of infection in cattle, plus the pasteurization of milk, there are nowadays few human cases due to M. bovis (median 1. When infection spreads, R falls from its maximum average value of R0 as the epidemic uses up susceptibles. The implication is that a, the fraction of people developing TB

Concepts and Models 37

when exposed to infection, is higher than 0.05 among uninfected people, lower than 0.05 in those already infected, and 0.05 on average for a population in which TB is stable. If the proportion of people that develops TB on first exposure is, say, 0.1 then R0 = 2. When R0 = 2 in this deterministic model, an introduced infection will certainly spread and, in the early stages of the epidemic, the proportion of people infected doubles every 2ln(2) = 1.4 years (Figure 2.2A, upper line). This model therefore describes a slow-­moving epidemic of an infectious disease that is relatively uncontagious, as compared, for example, with measles (R0 = 5–20, with an infectious period of about 1 week; Anderson and May 1991). How many people will be affected in a setting where TB is endemic? The number of secondary cases arising from each primary case is initially R0U = 2, with U = 1. Through time, the proportion of people infected increases monotonically toward the steady endemic state. In that steady state each case generates one further case on average, so R0U* = R* = 1 and U* = 1/R0 = 1/2 = 0.5. Those individuals who are not uninfected must be infected, so 1–U* = I* = 0.5 (Figure 2.2A). This is the equilibrium obtained as the balance between the rate at which cases are produced and the rate at which they are removed. If R0 is not much bigger than the threshold value of 1, the equilibrium prevalence of infection is sensitive to changes in the processes that affect transmission and susceptibility. For R0 = 2, TB could be eliminated from a population just by halving the transmission coefficient (b), or the proportion of infected people who progress from infection to disease (a), or the duration of infectiousness (1/[ + I]). Considering, for example, the contact rate, if R0 = 2 for b = 10, then R0 > 1 requires b > 5 for infection to spread and persist. The estimates of b below 5 obtained from TB in populations today are consistent with the observation that TB is in decline in many countries (though the reasons for the decline need a fuller investigation considering all system parameters). To illustrate the effects of varying b and R0, Figure 2.2A shows the evolution of epidemics with b set at 6, 8, and 10. The sensitivity of the endemic state to variation in R0 is reflected in the disproportionate effect on equilibrium prevalence: a 40% reduction in b from 10 to 6 reduces the prevalence of infection 66% from 0.5 to 0.17. The proposition in model (2.1) that TB epidemiology can be represented by two equations with four parameters is in one sense a reductio ad absurdum. The model is a tool for understanding TB epidemiology as much because of what it cannot do as what it can do. One of the absurdities is that all TB deaths are immediately replaced by uninfected people. This is a critical assumption because infection dynamics are sensitive to the supply of susceptibles. If TB deaths were not immediately replaced in the population, TB dynamics would be characterized by outbreaks at intervals of several decades (Figure 2.2B).

(a)

0.6

Prevalence infected

0.5

R0 = 2.0

0.4

R0 = 1.6

0.3 0.2

R0 = 1.2

0.1 0.0

0

50

100

150

200

Years (time) (b)

1.0

0.20 Uninfected Infected 0.15

0.6 0.10 0.4 0.05

0.2

0.0

Prevalence infected

Prevalence uninfected

0.8

0

50

100

0.00 200

150

Years (time) (c)

1.0

Uninfected Infected

Prevalence uninfected or infected

0.8

0.6

0.4

p = 0.19

0.2

0.0

0

10

20

30

Years (age)

Figure 2.2.  Dynamics of infection in the two-compartment model A (Figure 2.1A). A.  Prevalence of infection through time for various values of R0 obtained by setting b to 6, 8 or 10. Other parameter values are a = 0.1,  = 0.02, I = 0.48. B.  Prevalence of uninfected and infected people with a restricted supply of susceptibles. C.  Prevalence of infection by age generated by an annual risk of infection (ARI, or force of infection) of 2%/year. ARI can be estimated from the prevalence of infection of p = 0.19 at 10 years of age, as –ln(1 – 0.19)/10 = 0.02.

Concepts and Models 39

The interepidemic period is the time required to replenish the population of susceptibles so that R > 1. TB epidemics do not behave like this, which raises the question of how endemic stability is maintained. Before addressing this and other features of TB epidemics, the next section examines one of the key assumptions behind many epidemiological models of this kind: transmission by random or homogeneous mixing.

TRANSMISSION Minimal models make bold assumptions, often concealed within innocently simple formulations of key processes (Mollison 1984). The formulation of transmission as bUI, the nonlinear ingredient of model (2.1), follows the “mass action” assumption commonplace in infectious disease epidemiology. Common usage is not, however, sufficient justification to proceed without reflection, and there are two important aspects of population mixing that need to be understood before following tradition here. First, mass action, or frequency-­dependent transmission, assumes that, among the infectious contacts made with other individuals in the population, a fraction U leads to new infections. An alternative is “pseudo mass action,” or density-­ dependent transmission, where infection depends on the number (density) rather than the proportion (frequency) of uninfected individuals (Keeling and Rohani 2008). If U and I in model (2.1) are numbers of individuals (call them Û and Î ) rather than proportions in a population of size N, so that Û + Î = N, then mass action is abÎÛ/N, and pseudo mass action is abÎÛ. Pseudo mass action seems the less plausible of the two representations because it supposes that the number of people who acquire infection from each infectious case increases proportionally with population size. As others have pointed out, it seems unlikely that an infectious TB case in the United Kingdom would infect twice as many people in Birmingham (population 1M) as in Sheffield (population 0.5M; Anderson and May 1991; Keeling and Rohani 2008). Of course the form abÎÛ could give the right result if we knew precisely how TB cases mix with others in a large population, so that Û could be adjusted appropriately. To proceed without that information and yet retain the convenience of random mixing, the more reasonable approximation is that the number of people who acquire infection depends on Û/N rather than Û (Castillo-­Chavez and Song 2004). Mass action is, in the language of Anderson and May (1991), “strong homogenous mixing”. In contrast, under “weak homogeneous mixing” the rate of acquisition of new infections depends only on the proportion of people uninfected (abU) and not on the proportion infected (I). In developing the first

40

Chapter 2

epidemiological models of TB, Brogger (1967) and Waaler and others (Waaler 1968; Waaler and Piot 1969) followed Hamer (1906) in assuming that transmission does depend on the proportion infected, abI (Bailey 1975b), and that the pool of uninfected people is constant. The product of the contact rate and the prevalence of TB (b × I) is generally known as the force of infection and is called the annual risk of infection (ARI) in TB epidemiology (Chapter 1). In framing their TB transmission model, Revelle et al. (1967) were the first to adopt mass action and strong homogeneous mixing with I and U as dynamic variables as in model (2.1). The models in this and subsequent chapters adopt mass action on grounds of plausibility and simplicity, but also because M. tuberculosis transmission evidently does depend on the distribution of both infectious and infectible people, as will become clear shortly. There is, however, one special exception that is nicely illustrated with the two-­compartment model. When thinking about the acquisition of infection with age, it is the force of infection, abI, that counts because everyone is uninfected (U = 1) and assumed to be susceptible to infection at birth. Model (2.1) has no age structure, but it can be reformulated to describe the change of infection with age at equilibrium, where individuals are exposed to a constant risk of infection from birth:



dU = −αβUI − µU, da (2.3) dI = αβUI − µI I. da

Now the variables U and I depend on age (a) rather than time. The average age at which infection is acquired is A = 1/ab. The force of infection is  = abI, which is the same as the ARI. For children at age a, with prevalence of infection p, the average annual risk of infection over life up to that age is  = –ln(1 – p)/a or, considering infection as a process that happens in discrete time steps rather than continuously,  = 1 – (1 – p)1/a (Rieder 1999). The prevalence of infection is typically measured in schoolchildren aged 6–12 years by tuberculin skin test surveys. In settings where TB is highly endemic, that age group of children is chosen because the proportion infected is neither too small (so that measures are imprecise) nor too great (as p approaches 1,  becomes indiscriminately large), and studies of 6–12 year olds give recent estimates of the risk of infection, for the last 3–6 years on average (Figure 2.2C). Considering the infection rate by age also offers a surprisingly simple and elegant way to estimate R0, first revealed by Dietz (1975). If the force of infection is  in a population with life and expectancy 1/, then the proportion of people uninfected across all age groups in the steady state is /( + ) = 1/ R0. This

Concepts and Models 41

formulation essentially says that, for infection to persist (R0 > 1), the average person has to live long enough to acquire infection and develop infectious disease. This leads on to the standard result, R0 = 1+ L = 1 + L/A (Dietz 1975; Anderson and May 1991). In a population with life expectancy 50 years experiencing a typical annual risk of M. tuberculosis infection of, say, 2%, R0 = 1 + 0.02 × 50 = 2. Another feature of transmission formulated as bUI is the supposition that contacts between infected and uninfected people occur at random. Although patterns of contact are obviously more complex in reality, mass action can be defended as a special case of Einstein’s dictum that “everything should be made as simple as possible, but no simpler.”3 In the context of mixing models, Haldane famously made the case for “beanbag genetics” in terms of the powerful insights that come from simple models (Haldane 1964; Crow 2001). The mass action of model (2.1) is beanbag epidemiology. There is an additional important point about mixing with reference to the transmission of infection. It is that apparently complex patterns of contact may be formally the same as random mixing in the context of specific questions. Thus for insect vectors of diseases such as malaria, where vectors and hosts live in interconnected patches, host-­patch fidelity by mosquitoes (one group of mosquitoes closely but not exclusively linked to one group of hosts) does not affect R0 if vector biting rates are homogenous across all patches (Dye and Hasibeder 1986; Hasibeder and Dye 1988), though it does affect the spatial diffusion of infection. This is a reminder that any assumption needs to be robust only with respect to the particular question under investigation. It is also a further justification for the use of simple, but carefully formulated, testable models in epidemiology.

LATENT INFECTION AND ACTIVE DISEASE The high prevalence of infection shown in Figure 2.2A (up to 50%) is typical of settings in which TB is highly endemic (Sudre et al. 1992 Dye et al. 1999). However I, as defined in model (2.1), does not make the distinction, critical for TB, between those individuals who are infected and those who have active disease. To separate infection from illness, the UI model needs to include another class of people with latent infection. The mass action model of Revelle and others is a convenient way to explore the consequences of latency (Figure 2.1B; Revelle and Male 1970). In this model every episode of TB disease is preceded by a period of latent infection, 3 Ironically, Einstein’s original, longer, and more convoluted statement is usually paraphrased for simplicity.

42

Chapter 2

and everyone infected can potentially progress to TB disease. A proportion of TB cases also recovers spontaneously and may later relapse to suffer a second or subsequent episode of TB. Relapse from the recovered state is included at this stage in order to explore the behavior of Revelle’s model with published parameter values; the significance of relapse itself is examined shortly. Strictly speaking, the latent period is defined in epidemiology as the time from acquisition of infection to the onset of infectiousness, whereas the incubation period is the time from infection to the onset of illness. For cases of pulmonary TB that become infectious, the incubation period is a little shorter than the latent period. Here, however, we shall follow convention in the TB literature and use latency instead of incubation to refer to the state of subclinical or asymptomatic infection before TB disease. A series of studies on latency has emphasized that there is a continuum between early infection and active disease (Young et al. 2008; Barry et al. 2009). Nevertheless, it remains analytically convenient to put people with infection and illness in two separate compartments, as is still done when deciding on clinical treatment. With these simplifications, and using the same parameter values as Revelle and others, the most striking effect of including latency is that a high prevalence of infection (73% at equilibrium) is accompanied by a comparatively low prevalence of TB disease (5%, Figure 2.3A). The slow progression through the latent state keeps the prevalence of TB disease low because, for infected individuals, there is no consequence of reinfection (they are effectively immune), and most infected people die of other causes before they have a chance to develop active disease. With the parameter values in Figure 2.3A, the average time from infection to disease is 132 years, but life expectancy is only 70 years. In addition, this formulation of latency causes the prevalence of TB disease to saturate well below 1 as R0 increases; the upper line in Figure 2.3B has an asymptote at around 0.1. While Revelle’s model is an advance on the UI model (2.1) in separating infection and disease, it fails, of course, to represent other aspects of TB epidemiology. Compared with the typical values encountered in TB epidemiology, the calculations shown in Figure 2.3A yield a high equilibrium prevalence of active disease (5%), high R0 (7.5), an excessive lifetime risk of TB after infection (35%), a long duration of illness (6.9 years), a high prevalence/incidence ratio (9.0), and an exceedingly large annual risk of infection (15%/year at equilibrium). We can try to obtain lower and more realistic estimates of these key indicators by adjusting parameters to lower R0. However, this is not entirely successful because any reduction in R0 also lengthens the time course of an epidemic. One way to reduce R0 is to slow the rate of progression to active disease. If, for example, that rate is halved so that R0 falls from 11.0 to 8.2 (Figure 2.3C), the epidemic takes more than 200 years to reach a steady state. Another option is to shorten the average

0.06

1

0.05

Prevalence uninfected or latent infection

0.8

0.04 0.6

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0.03

Infectious

0.4

0.02 0.2

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(a)

0.01

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0.10 Zero immunity 90% immunity

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0.08

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0.04 0.03

R0 = 8.2

0.02

R0 = 4.3

0.01 0

0

100

200

300

400

Years

Figure 2.3.  Dynamics of infection and disease generated by the four-compartment model B (Figure 2.1B). A.  Proportion of people uninfected, latently infected, and with TB disease over 400 years, with parameter values b = 2.62, a = 0.1,  = 0.014, I = 0.07,  = 0.0076,  = 0.06,  = 0.017, R0 = 11.0, as given by Revelle et al. (1967). B.  Equilibrium prevalence in relation R0, varied by adjusting b, assuming levels of innate immunity of zero or 90%. C.  Prevalence of TB through time for three different values of R0.

44

Chapter 2

duration of active disease by increasing mortality and recovery rates while keeping the case fatality rate at a realistic 50%. The example in Figure 2.3C shortens the average duration of illness to 2 years, lowers R0 to 4.3 and prevalence to 1.4%. Now the epidemic takes nearly 400 years to reach equilibrium (Figure 2.3C). This is an unsatisfactory representation of TB epidemiology because we know that epidemics can spread within a few decades and stabilize at prevalence rates below 1% (Chapter 1). To align model and data more precisely requires more than the adjustment of parameter values. Further structural changes are needed. To this end, the next four sections introduce four more key facts about the biology of TB: variation in the rate of progression from infection to disease, TB arising from reinfection and by relapse after spontaneous recovery, and variation among cases in their infectiousness.

VARIATION IN THE LATENT PERIOD The rate of progression from infection to active disease is not homogenous, for two reasons. First, some infected people are less susceptible to developing TB disease on any time scale. A coarse correction to Revelle’s model accommodates this idea by taking a proportion of people to be completely immune to developing disease after infection. The assumption that, for example, only 10% are susceptible yields the lower saturation curve in Figure 2.3B. Now the equilibrium prevalence of TB is only 0.6%, or 600/100,000 of population, which is the right order of magnitude for a setting with highly-­endemic TB today. The second possibility is that every infected person will eventually develop TB disease, but some people do so more slowly than others. One way to represent this in models is to make the rate of progression a function of time since infection (Waaler 1968; Vynnycky and Fine 1997b; Feng et al. 2001; Colijn et al. 2007). Another is to postulate that there are two classes of people, fast and slow progressors to active disease (Blower et al. 1995; Dye et al. 1998; Colijn et al. 2007; Sutherland et al. 1982). “Fast” is intended to represent disease arising from a first infection or reinfection and, by convention, occurs within 5 years. The 5-­year cutoff was originally chosen as the time at which an infected child reaches the median cumulative lifetime risk of TB (Holm 1969; Sutherland et al. 1982). In the control arms of trials of BCG vaccination and isoniazid preventive therapy (IPT), where the incidence of TB was monitored in tuberculin skin test–positive people, there is no evidence that the rate of breakdown changed at 5 years—or at any other time since enrollment in the trial (which was necessarily shorter than the time since infection, Figure 2.4A; D’Arcy Hart and Sutherland, 1977; Sutherland 1968; Ferebee 1969). By contrast, in a cohort

(a) BCG and IPT trials

1

Relative TB incidence

BCG

IPT

0.1

0.33/yr 0.01

0

1

5

10 Years since enrollment

0.19/yr 15

20

(b) Netherlands cohort

TB-free survival

0.53/yr

0.27/yr 0.1

0.50/yr

0.01

0

2

4

6 8 Years since infection

10

12

Figure 2.4. A.  Incidence of TB in cohorts of tuberculin-positive individuals who were followed in the control arms of trials of BCG vaccination (D’Arcy Hart and Sutherland 1977; Sutherland 1968) and isoniazid preventive therapy (IPT; Ferebee 1969). TB incidence (log scale) is relative to that reported during the first year of observation. The rates of breakdown to TB differ between the trials, but there is no evidence of a discrete change in the rate of breakdown through time (constant slope). B.  Tuberculosis-free survival probability among 1095 epidemiologically confirmed secondary cases in the Netherlands, 1993–2007 (Borgdorff et al. 2011). Breakdown to TB slowed after 1.5 years (less negative slope) and then appeared to accelerate again after 8 years, perhaps because it became harder with time to verify links between cases and contacts, thus underestimating the size of the denominator.

46

Chapter 2

of contacts of cases in the Netherlands, TB developed more quickly for the first 1.5 years after infection but then slowed (Figure 2.4B; the acceleration after 8 years is perhaps explained by the fact that it became harder with time to verify links between cases and contacts, thus underestimating the size of the denominator; Borgdorff et al. 2011). Although the distribution of M. tuberculosis latent periods (time between infection and the onset of infectious disease) is continuous rather than discrete, compartmental models of TB epidemiology usually opt for convenience and divide slow and fast progressors. Slow progressors include those who are immune to developing TB disease after infection (taken to be very slow progression) and those who reactivate by progressing gradually from early to advanced sub­ clinical infection to active disease. An innate mechanism could have a genetic or physiological basis and could be partial or complete, temporary or permanent. The model depicted in Figure 2.1C (see also Appendix 2) incorporates this two-­speed latent process (plus other features discussed shortly), so that R0 is given by

R0 =

αν f (1 − α) νs βσ F . (2.4) < + νs + µ (µ + µI) (1 − ω) ν f + µ

The term in square brackets is the sum of the proportions of infected people who pass through fast (a) and slow (1 - a) latent states at rates f and s, respectively. Each of the components of R0—describing transmission, susceptibility, and duration—can be partitioned like this to accommodate any distinctive characteristics of subpopulations. The total value of R0 is then the sum of the weighted average across subpopulations. The proportion a is also now distinguished from , the proportion of TB cases that becomes infectious. The other new ingredient in (2.4) is , the proportion of active cases that recovers spontaneously and later relapses to active disease. This value of  is calculated from

ω=

η ρ , (2.5) η + µ + µI ρ + µ

in which  is the per capita rate of spontaneous recovery and  is the rate of relapse. Each case of TB could recover and relapse one or more times, extending the duration of infectiousness on each occasion. The duration of an infectious episode, 1/(  + I), is therefore multiplied by the factor 1+ ω + ω 2 +g=1/ (1− ω). In this example—and, in general, for TB arising in adults—R0 is dominated by the fast progressors. The reactivation of latent infection makes a relatively small contribution. With the parameter values in Figure 2.5, these proportions are 0.14 and 0.02. The components of R0 attributable to each are 1.50 and 0.26, which makes R0 = 1.76 in total.

Concepts and Models 47

Equation (2.4) can be used to calculate R0 provided we know the average values of parameters for the period during which secondary cases arise from the primary case. For slow progressors that period is many decades, during which contact rates and susceptibility may change. For fast and slow progressors, we need the weighted average for all ages and both sexes, and for any other collection of subgroups characterized by different patterns of contact and susceptibility. Equation (2.4) also ignores the fact that some people infected by the primary case will develop TB, not from the primary infection, but from an intercepting secondary infection. Reinfection (see the next section) thus reduces the number of secondary cases arising from the primary case. This is an additional reason why R0, when applied to M. tuberculosis, is effectively an estimate of the maximum average ratio of secondary to primary cases (Vynnycky and Fine 1998). With those caveats and the choice of parameter values in Figure 2.5A (line 1), a TB epidemic generated by a mix of fast and slow progressors can reach peak prevalence within 100 years and yet settle to a lower prevalence than in Revelle’s model. In the example in Figure 2.5A (line 1), the steady-­state prevalence is less than 0.5% (< 500/100,000/year), which is well within the range observed in highly endemic countries. Variation in the rate of progression has one other distinctive epidemiological effect in a model of fast and slow progressors. In the early stages of an epidemic, fast progression generates more prevalent cases than can be supported at an equilibrium that is also determined by the number of slow progressors in the population. Consequently, prevalence initially overshoots the level supportable in the stable endemic state and is pulled back down while the slow progressors catch up. With two-­speed latency, the pathway to equilibrium is a damped oscillation (Figure 2.5A). If, in reality, latency is not a two-speed but a multispeed process, with a distribution of time delays, the system would be less prone to oscillation.

REINFECTION Under the assumption that infected people have a variable rate of progression to active disease—some faster, some slower—it is possible to generate a realistic value of prevalence for endemic TB (Figure 2.5, line 1). However, we are still assuming that all episodes of TB arise from the first infection—a view once promoted by Stead (1967) in his “unitary concept of pathogenesis.” And yet there is abundant and long-­standing evidence that a first episode of TB and subsequent episodes can follow reinfection (Canetti 1939, Chiang and Riley 2005). One line of evidence comes from observations made on clusters of TB cases that carry the same M. tuberculosis strain. Members of a cluster are likely to have

48

0.03

Chapter 2

(a)

0.1

0.02 3. Relapse

0.01

2. Reinfection

0

50

100

150

200

250

0.06 0.04 0.02

1. Fast and slow latent 0

x = 0.35 x=0

0.08

Prevalence TB

Prevalence TB

4. Infectious and noninfectious

(b)

0

300

1

2

Years 0.14

3

4

R0

(c)

0.16

(d)

0.12

0.1

Prevalence TB

Prevalence TB

0.12

0.08 0.06 0.04

Relapse = 0.02/yr 0.08

No relapse

0.04

0.02 0

0

0.5

1

x

1.5

0

0

50

100

150

200

250

Years

Figure 2.5.  Dynamics of TB generated by various permutations of model C (Figure 2.1C). A.  Including additively (1) variation in the rate of progression from latent to active TB disease, (2) reinfection, (3) relapse, and (4) variation in infectiousness. B.  Equilibrium prevalence in relation to R0 showing the sensitivity to x. C.  Reinfection parameter x as a nonlinear determinant of equilibrium prevalence. D.  Relapse as a factor that increases and stabilizes endemic prevalence. Unless otherwise specified, parameter values are: b = 10/year, a = 0.14,  = 0.02/ year, I = 0.35/year, N = 0.0375year, f = 0.667/year, s = 0.0005/year (50/100k/ year),  = 0.5,  = 0.02/year,  = 0.15/year, x = 0.35, R0 = 1.76. Competing rates of mortality and recovery give case fatality rates of 0.7 for I and 0.2 for N and durations of illness of 2.0 years for I and 2.6 years for N. The lifetime risk of developing TB for an infected person is 0.16. Before distinguishing infectious from noninfectious TB in A (line 4), I = N = 0.19/year.

acquired TB as a result of local and recent transmission, but some cases found in clusters are known from tuberculin skin tests to have had earlier infections (Heldal et al. 2000; de Boer et al. 2003). A less direct but nevertheless consistent argument is that epidemiological models cannot explain long-­term trends in incidence (Sutherland et al. 1982; Vynnycky and Fine 1997b; Dye et al. 1998; Grzybowski et al. 1976) and

300

Concepts and Models 49

mortality (Vynnycky and Fine 1999) without including some mechanism of reinfection. In models that exclude reinfection, the decline in TB cases in Europe and North America during the twentieth century is slower than observed, especially in older people with long-­standing infections, and TB mortality rates are too low (Chapter 3). More than this, the statistical fitting of epidemiological models to data provides quantitative estimates of the risk of TB from reinfection (among estimates of other parameters, Table 2.2). As a simple and logical extension to mass action, abUI, reinfection can be expressed as abxLI, where the proportion of infectious contacts between I and L that leads to TB disease (a) is increased or decreased by factor x. The estimates of x for adults in Table 2.2 (final column) all lie between 0 and 1, so that 1 - x measures the degree of protection given by a prior infection. By contrast, the three estimates of x for children exceed 1. One implication is that a first infection predisposes children to TB on reinfection. A more likely explanation, however, is that children are older when reinfected than when first infected, and susceptibility to TB increases with age. While an established infection, as measured by a tuberculin skin test, appears to be a marker of protection, the mechanism of protection is not known. Individuals who do not progress to TB disease on first exposure to infection could be innately immune to TB or just lucky. The first infection could stimulate an acquired immune response, which protects against future infections. Whatever the process, x < 1 means that individuals with a prior infection are less susceptible to TB than the average uninfected individual (see also Andrews et al. 2012). This protective effect does not necessarily apply to infected people who develop active TB disease (see below). Understanding the mechanism of protection is important, for example, in the context of vaccine development, but here we are concerned with understanding the consequences of reinfection for TB epidemiology. When TB can arise from reinfection, in addition to a first infection and reactivation, the equilibrium prevalence of TB will be higher (all other parameter values being the same; Figure 2.5A, line 2) and is sensitive to the value of x. For instance, when R0 = 3.5 equilibrium prevalence is sixfold larger for x = 0.35 (7.1%) as compared with x = 0 (1.1%, Figure 2.5B). With x set at the best estimate of 0.35, prevalence does not saturate for R0 up to 4 (Figure 2.5B, upper line). Under these circumstances, endemic TB prevalence is determined both by the number of infections and by the proportion of people who can be infected or reinfected (strong homogenous mixing). By contrast, without reinfection (x = 0), endemic prevalence would be determined mostly by the proportion of people susceptible (in effect, weak homogeneous mixing; Figure 2.5B, lower line). This is another way to explain why the inclusion of reinfection in TB models allows faster rates of decline in TB, in line with the data from Europe and North

United States and Europe

England and Wales

Netherlands

Country

(Blower et al. 1995)

(Vynnycky and Fine 1997b)

(Dye et al. 1998)

—

—

> 20 Adults

—

10–20

—

—

≥ 15 < 10

—

< 15

—

—

> 20

0–10

(Vynnycky and Fine 1997b)

— —

15–69

(Sutherland et al. 1982)

0.85 (meningitis) 0.32 (miliary)

Meningeal and miliary TB (% for children < 5 years)

10–20

1 so that prior exposure increases susceptibility to TB. In general, x < 1 and the conditions for multiple equilibria are unlikely to occur in reality. There is, however, a particular case in which x > 1, discovered when investigating cases of recurrent TB: individuals who are susceptible to TB on first exposure are more susceptible than the average person to TB on reexposure (Verver et al. 2005). If this is generally true, it would require an additional refinement to the model in Figure 2.1C.

RELAPSE Before the discovery of TB drugs, about half of all cases died and half spontaneously recovered (Figure 1.5). But self-­cure is, for many patients, a temporary respite and carries a risk of relapse of 2%–4%/year, tending to be higher shortly after recovery (Dye et al. 1998). This may seem a small percentage each year, but it comes from a large reservoir of infected people. With the accumulation of recovered TB cases over decades, a significant proportion of all cases arising each year would be due to relapse. In the example in Figure 2.5A, 40% of TB episodes spontaneously resolve. If 2% of these relapse each year, the lifetime risk of relapse is 50%, and the prevalence of TB doubles from 0.8% to 1.6% (compare lines 2 and 3). In this scenario, the total annual incidence is about 1%/year (1000/100,000/ year), of which 0.21% is due to relapse and only 0.03% to reactivation. The level of endemicity is thus sensitive to whether the rate of relapse is zero, 1%, 2%, or more.

52

Chapter 2

A comparison of lines 2 and 3 in Figure 2.5A shows a second effect of relapse, which is to damp the oscillatory approach to equilibrium in a system with fast and slow progressors. This stabilizing effect and the interaction between relapse, reinfection, and the variation in progression become clearer if we choose the unrealistically high value of x = 1, whereupon a first infection offers no protection against reinfection (Figure 2.5D). When the relapse rate is zero, the pathway to equilibrium is a prolonged oscillation, damping over hundreds of years. However, with a relapse rate of 2%/year, there is just a brief “downward limb to the cycle” (Comstock 1975a); the pathway to equilibrium is monotonic after the initial overshoot. In sum, TB is essentially a relapsing disease (Davies 2011), and, before the availability of drugs, relapse from self-­cure would have made an important contribution to the annual incidence of disease—probably more important than reactivation. Relapse has stabilizing effects, both by adding to the supply of infectious cases (mitigating against elimination) and by doing so on another time scale, smoothing over differences between fast and slow progression and tending to damp oscillations.

VARIATION IN INFECTIOUSNESS Pulmonary tuberculosis is a spectrum of clinical conditions. Cavitary lung disease is more severe and more infectious than infection contained within lung tubercles. Extrapulmonary disease is not infectious at all, at least not via respiration. Traditionally, the “infector pool” (Canetti 1962) has been identified by bacilli detectable in microscopically examined sputum smears. Smear-­positive TB is certainly the most infectious form of disease, but some infections are transmitted by smear-­negative cases too (Rieder 1999; Behr et al. 1999; Elwood et al. 2005). Smear-­negative cases are often confirmed as TB by more sensitive diagnostic techniques. The long-­established method is bacterial culture, though nucleic acid amplification methods are also sensitive, detecting 70%–80% of smear-­negative, culture-­positive cases (Boehme et al. 2011). The new generation of molecular diagnostics has the major advantage of diagnosing a higher proportion of pulmonary TB cases but also the disadvantage that no distinction is made between more and less infectious cases. Just as variable rates of progression have been dichotomized into fast and slow classes, so too have compartmental mathematical models adopted the dichotomy of infectious and noninfectious TB, where the infectious class includes some smear-­negative disease. The distinction between infectious and noninfectious TB is included as proportion  in the expression for R0 (2.4). Although R0 is the same for any given value of the product a ×  in equation (2.4),  independently affects the prevalence of TB because infectious

Concepts and Models 53 (b) 2500

0.8

0.02

2000

0.6

0.015

0.4

0.01 Uninfected Latent Active TB

0.2 0.0

0

100

200

0.005

Cases/100,000 population

0.025

Prevalence TB

Prevalence uninfected or latent infection

(a) 1.0

0 300

Incidence Mortality Prevalence

1500 1000 500 0

0

100

Year

200

300

100

150

Year (d)

(c) 80

40

4 Styblo Framingham ARI

20

0

0

100

200 Year

2

0 300

TB incidence/100,000/yr

6

ARI or Framinghma factor

Styblo ratio

60

Infection Reinfection Relapse Reactivation Total

500

8

400 300 200 100 0

0

50 Year

Figure 2.6.  Further elaboration of TB dynamics generated by model C (Figure 2.1C), including all four refinements in Figure 2.5A . A.  Proportion of people uninfected, infected and with TB disease over 300 years. B.  TB incidence, prevalence, and mortality per 100,000 population. C.  Time trends in key indicators: the annual risk of infection (ARI), Styblo ratio, and Framingham factor. D.  Four etiological pathways contribute to overall TB incidence by different magnitudes and on different time scales.

and noninfectious cases may differ in the duration of illness. In other words, prevalence is not uniquely determined by R0 and, in the example in Figure 2.5A (line 4), the inclusion of  increases prevalence because the duration of noninfectious TB is longer (lower mortality rate) than for infectious disease (higher mortality rate). With all its various structural refinements, the most advanced model of the three models in Figure 2.1 (model C) can replicate many of the characteristics of TB epidemics before the widespread use of drugs around 1950. In Figure 2.6A, the prevalence of TB disease is 1.6%, the annual risk of infection is 4.5%, the Styblo ratio is 1:53 (compared with the 1:50 rule of thumb), and

54

Chapter 2

0.6 R0

Regression coefficient

0.4

Prevalence

0.2 0 –0.2 –0.4 –0.6

α

σ

μI

β

x

ρ

η

νs

μ

μN

νf

Model parameters

Figure 2.7.  Multivariate sensitivity analysis showing how equilibrium TB prevalence (black bars) and R0 (grey bars) depend on parameters of the model in Figure 2.1C. Each parameter is given a uniform distribution ranging from –10% to +10% of the point estimate, and 5000 random samples are taken from this distribution by Latin hypercube sampling. Sensitivity is measured by a regression of each outcome variable (R0, prevalence) on the changing value of each parameter (partial rank correlation coefficients give similar results). Parameter x affects prevalence but not the value of R0, which measures the initial spread of infection into an otherwise uninfected population.

the Framingham factor is 1:8 (compared with 1:9 in the original study; Figure 2.6C). Total TB incidence is 0.50%/year, of which 0.25%/year is due to new infections, 0.12%/year to reinfection, 0.10%/year to relapse, and 0.03%/year to reactivation (Figure 2.6D). In all, the effect of adding in reinfection, relapse and variation in infectiousness is to increase TB prevalence more than fivefold. The majority of cases come from new infections and reinfection rather than from relapse and reactivation. This deviates from the common view that about half of all TB cases arise within 5 years of infection and half come from older infections. This observation came originally from studies on a cohort of infected children 1–3 years old (Comstock et al. 1974a). Infected children are at high risk of developing TB before age 5, especially miliary or meningeal disease, and then again during adolescence and adulthood. In contrast, the contribution of different etiological pathways shown in Figure 2.6D represents the whole population, in which most TB cases are in adults. A sensitivity analysis of the results in Figure 2.7 shows which of the biological processes most affect R0 and equilibrium TB prevalence. Both R0 and

Concepts and Models 55

prevalence are most sensitive to changes in: a (proportion of new infections leading to primary disease), b (contact rate),  (the proportion of cases that becomes infectious), and the TB mortality rate (I), where lower death rates lead to higher R0 and prevalence if not balanced by self-­cure (Figure 2.7). These are the processes that most sensitively determine the rate of spread of an epidemic and the ultimate prevalence of endemic TB.

SURVIVAL OF M. TUBERCULOSIS IN SMALL HUMAN POPULATIONS As described in Chapter 1, M. tuberculosis appears to have been a human pathogen for hundreds of thousands of years, since humans lived in small, widely dispersed and actively dispersing groups, spreading within and out of Africa. This long association between host and pathogen prompts the question of how M. tuberculosis avoided extinction and coevolved in once-­small human populations and how selection pressures have changed in the world’s large interconnected human populations today. Among the life history strategies that could help to avoid extinction, it has been suggested that the latent state enables M. tuberculosis to skip generations of human hosts as a mechanism for survival in small populations (Blaser and Kirschner 2007; Ernst et al. 2007). From the perspective of quantitative epidemiology, this seems unlikely. Equation (2.4) showed that TB cases arising from the reactivation of latent infections make a small contribution to R0. With the parameter values used in conjunction with equation (2.4), TB would not be self-­ sustaining solely through reactivation (that component of R0 much less than 1). For this reason alone, latency does not appear to be a long-­term survival strategy. Extinction can be investigated more explicitly with a stochastic realization of the model in Figure 2.1C (Dye 2014b). Figure 2.8A and B show five 100-­year runs of the stochastic version of the model to illustrate the dynamic behavior of TB in a small population (500 people). In two of the runs, infections go extinct. In three, infection is maintained. The three in which infections persist generate large fluctuations in numbers of cases, but TB is in no danger of extinction during the 100-­year period because the number of infected people remains high. Figure 2.8C shows, more systematically, how population size is critical for persistence. For any chosen set of birth and death rates, TB is more likely to go extinct in a smaller population. In this model, with these parameter values, population size becomes important when there are fewer than approximately 1000 individuals. When there is a risk of extinction in small populations, the best way to mitigate that risk is by maximizing R0. In Figure 2.8D, for example, when a higher proportion of infections becomes fast progressors, R0 increases and so does the prevalence of TB (notice that prevalence increases sharply when R0  1.3 because

56

Chapter 2

(a) TB Cases

(b) Infected people 80

Number of infected people

Number of cases

30

20

10

0

0

20

40

60

80

60

40

20

0

100

0

20

40

Years (c) Extinction and population size

80

100

(d) Latency, progression, prevalence

0.007

0.03

5 Latency

0.006

0.004 0.003

Higher risk of extinction below population ≈ 1000

0.002

Progression

4

0.02

3 R0

0.005

Prevalence TB

Prevalence TB

60 Years

2

0.01

1

0.001 0

0

1000

2000

3000

Population size

4000

5000

0.00 0.05

0.1

0.15

0 0.2

Proportion fast progressors

Figure 2.8.  Five examples of time series, as described. A.  Prevalent TB cases. B.  Infected people in a model population of 500 people, after the introduction of two index cases, generated by a stochastic realization of the model in Figure 2.1C. C.  TB prevalence in relation to population size, ranging from 10 to 4900. Each point marks the average prevalence of TB for one model run in years 100–500. Extinction becomes more likely at population sizes 6% in adults ≥ 15 years) and a large relative risk (k > 20) gives a population ­attributable fraction in excess of 0.6 for these nine countries (Figure 3.2). The contribution to R0 (as distinct from case incidence) will not, however, be so large because HIV-­positive TB cases transmit fewer infections per unit time than HIV negatives and have a shorter period of infectivity (untreated HIV-­positive TB is rapidly fatal; Chapter 6). One weakness of the compilation in Figure 3.2 is that it examines each risk factor separately. There have been few studies of multiple risk factors for TB and their interactions, but combinations of presently known risk factors are unlikely to deviate from the general pattern in Figure 3.2. As postulated in Figure 3.1D and E, certain risk factors will indeed be associated (e.g., HIV infection and silicosis in South African gold miners; Corbett et al. 2000). Individuals who are exposed to multiple risk factors will be at higher risk of TB (Patra et al. 2014), but they will also form a smaller fraction of the overall population. So the inverse relation between prevalence and risk is likely to be preserved (Figure 3.2). The observation made here that most known risk factors for TB account for only a small fraction of cases sets TB apart from some other infectious diseases. Woolhouse and others reviewed heterogeneous transmission among a variety of infectious diseases and found that 20% of the host population contributed at least 80% of the transmission potential (dubbed the “20:80 rule”), as measured by the contribution to R0 (Woolhouse et al. 1997). In terms of equation (3.4), the same result for TB would require k ≥ 21 for  = 0.2, giving F ≥ 0.8. As we have seen, except only for HIV in sub-­Saharan Africa, the best-­known risk factors

74

Chapter 3

for TB do not generate that much heterogeneity (Figure 3.2). The implication of the 20:80 rule, where it holds, is that a core group at high risk can be targeted so as to effectively control infectious disease on a large scale. The potential for such targeted interventions against TB is presently restricted: HIV infection in southern Africa appears to be one of the few examples. So far, this discussion of risk has classified people as affected or not affected (exposed or not exposed, susceptible or not susceptible). And yet the risk of infection or disease often depends on the magnitude or intensity of the exposure (i.e., response depends on dose). Taking undernutrition as an example, there is a general relationship between body mass index (BMI) and the risk of TB to individuals in which incidence falls by approximately 14% for every unit increase in BMI (Figure 3.3; Lönnroth, Williams, et al. 2010). By applying this relationship, for example, to the distribution of BMI among women in India, it is possible to relate risk and attributable fraction to the prevalence of undernutrition without an arbitrary cutoff in BMI (Figure 3.4A). The 7% of women with the lowest average BMI of 14 account for 13% of TB cases, while the 47% of women with BMI ≤ 18 account for 26% of TB. Once again there is an inverse relation between prevalence and relative risk, with population attributable fraction fluctuating around 0.2. Treating BMI as a continuous variable is more informative about the role of undernutrition (and, indeed, overnutrition) in TB epidemiology and control. For instance, as a way of finding TB cases in the community, screening

TB incidence/100,000/year

1000

100

10

1 15

20

25

30

35

BMI (kg/m2)

Figure 3.3.  The relationship between TB incidence and body mass index (BMI) in six cohort studies reviewed by Lönnroth, Williams, et al. (2010). Error bars indicate 95% confidence intervals.

Risk and Variation 75

(a) Undernutrition 0.2

10

(b) Crowding

0.4

0.6

0.8

10

0.01

0.05

0.2

0.4

0.6

0.8

Relative risk

Relative risk

0.1

14

0.05

17 1 0.01

18

20 22

0.1

1

0.01

Proportion population 0.8

10

0.4

Relative risk

Relative risk

1

(d) London boroughs

0.6

0.2 1105 0.1

0.1

0.4

Proportion population

(c) Poverty 10

0.6 0.7 0.5

1 0.001

1

0.8

0.9

729

0.05 1 0.1

548

314

201 1

Proportion population

0.6

0.8

0.4

0.2 424 0.05

246

215 185

118 47 42

0.01 1 0.1

1 Proportion population

Figure 3.4.  As for Figure 3.2, but for risks with variable levels of exposure. A.  Undernutrition among Indian women aged 15–49 years (BMI) (Dye et al. 2011). International Institute for Population Sciences (IIPS) and Macro International (2007) National Family Health Survey (NFHS-3), 2005–06, India: Key Findings. Mumbai: IIPS. B.  Crowding in households, Canadian First Nations communities (persons per room; Clark et al. 2002). C.  Poverty in India (self-reported TB prevalence/100,000 population). D.  Thirty-two London boroughs ranked by incidence, from highest to lowest (average reported cases/100,000 population/year from 1982–9191; Mangtani et al. 1995).

only the lowest BMI group for TB is likely to be more cost effective but less effective than targeting the larger group of women with BMI ≤ 18. Figure 3.4 B–D gives three other examples: for poverty in India, crowding in Canadian households, and for London boroughs ranked by reported cases per capita. In all these examples, there is convergence toward a relative risk of 1 at a population prevalence of 1 (at the bottom right of each graph, when the population is compared with itself). Only for crowding in households does the

76

Chapter 3

attributable fraction exceed 0.4, when the data include fewer than 0.8 persons per room (Figure 3.4B). Further up the causal chain, moving away from etiology and toward large-­ scale environmental effects, risks become multifactorial. Poverty, for instance, encompasses a variety of disadvantages and is almost always associated with TB (Dye, Lönnroth, et al. 2009). A key question is whether there are any outstandingly important components of poverty that could be modified to aid TB control. Oxlade and Murray (2012) examined a series of exogenous and endogenous factors affecting TB that were linked to poverty in India, including: a rural setting, educational achievement, health insurance, gender, intake of fresh food, milk and protein, BMI, anemia, tobacco and alcohol use, and indoor air pollution. Only BMI had a strong mediating effect on the association between poverty and the prevalence of TB. This study once again highlights the dilemma for epidemiology and control: many factors account for the burden of TB in populations; to focus on a few modifiable factors is to focus on just a small fraction of the causes of TB. In sum, comparative risk assessment reveals few risk factors that are sufficiently common and sufficiently risky to account for a large fraction of TB cases in any population.

VARIATION IN RISK WITH AGE AND SEX One of the most remarkable facts in the biology of M. tuberculosis is that children aged 5–15 years have a much lower risk of developing TB and of dying with TB than younger children and adults (Figure 3.5A; Marais, Gie, Schaaf, Hesseling, Obihara, Starke, et al. 2004; Newton et al. 2008; Donald et al. 2010). This is not because they are less frequently exposed to infection, but because infection in 5–15 year-olds is less likely to progress to the forms of TB disease characteristic of early childhood or to typical adult pulmonary TB. The risk of exposure to infection does vary somewhat with age, according to whether children and adolescents spend more time at home, at school, or elsewhere in the community. For example, Wood et al. (2010) estimated the annual risk of infection for children aged 0–4, 5–9, and 10–15 years in Cape Town to be 3.9%, 3.9% and 4.8%. Borgdorff et al. (1999) found that infections in the Netherlands were preferentially transmitted among people of similar ages. Working in Lima, Peru, Zelner et al (2014) found that the risk of infection from both household and community sources increased from birth until 20 years of age. Clearly, the age-­specific pattern of exposure will change from one setting to another, but the variation in risk of infection with age is not large—perhaps typically less than a factor of 2 between lowest and highest. This is consistent

14

(a) Risk of TB after infection

Risk of TB after infection (%)

12 10 8 6 4 2 0

0–1

1–4

5–9

10–14

15–24 M

15–24 F

15–24 M

15–24 F

Age group (years) 60

(b) Risk of death after TB

Risk of death after TB (%)

50 40 30 20 10 0

0–1

1–4

5–9 10–14 Age group (years)

Figure 3.5. A.  The percentage of persons aged up to 24 years who develop TB disease following infection. B.  The percentage of TB cases that dies, in the absence of treatment. For ages 0–14, males (M) and females (F) are aggregated; for age class 15–24 years, they are separated. Based on a collection of studies carried out during the prechemotherapy era (1920–1950), summarized by Marais et al. (2004).

78

Chapter 3

with a view of TB transmission is which there are multiple and widely dispersed sources of infection in any community. After exposure to infection, young children—and especially infants—are at relatively high risk of tuberculous meningitis and miliary disease. Between the ages of 5 and 10 years, infection may be enough to cause some radiological abnormalities, which are commonly missed in routine diagnosis, but children in this age group are relatively refractory to pulmonary TB. In the data shown in Figure 3.5A, TB was 3.4 times less common in 5–8 ­year-­olds than in women 15–29 years old. Above 10 years of age and toward adolescence (≥ 15 years), the risk of TB following infection increases earlier for girls than boys, which coincides with the earlier age of puberty in girls (Grigg 1958a; Donald et al. 2010; Marais, Gie, Schaaf, Hesseling, Obihara, Nelson, et al. 2004). In the absence of treatment, severe forms of TB in early childhood are associated with high case fatality (Figure 3.5B). Children older than 15 years and adults are more likely to develop cavitary pulmonary disease than younger children, and cavitary disease is also associated with a higher case fatality rate. The growing risk of pulmonary disease during and after puberty suggests a mechanism linked to endocrine changes, but the mechanism remains obscure (Donald et al. 2010). In the absence of a definitive explanation for the rising risk of pulmonary TB during adolescence, it has even been suggested that the development of pulmonary TB is facilitated by a sexually transmitted agent (Nagelkerke 2011). One frequently reproduced illustration of changing TB incidence with age comes from a study by Comstock et al. (1974a), who followed a cohort of tuberculin-­positive children and adolescents aged 1–18 years, from 1949 to 1969 in Puerto Rico (Figure 3.6A). The great majority of cases (92%) were pulmonary TB, though 10% of cases among children 1–6 years old were due to meningeal and miliary disease. The pattern of TB incidence by age in Figure 3.6A depends on the changing risk of disease following infection, but that is not the only explanation. A simplified version of model C (Figure 2.1C) helps to disentangle the underlying processes. Let the rate of change of uninfected (U), latently infected (L) and TB cases (I) with age (a) be



dU = − λU, da dL = (1 − σ) λU − (ν + σλx) L, (3.7) da dI = σλU + (υ + σλx) L. da

The analytic procedure is to estimate the initial risk of infection to the cohort from a tuberculin survey carried out in 1949–1951 (5.3%/year, Figure 3.6B; Comstock et al. 1974b) and then numerically fit model (3.7) to the profile of TB

400

(a)

TB cases/100,000/yr

300

200

100

0

Tuberculin positive (%)

80

0

10

20 Age (years)

30

40

5

10 Age (years)

15

20

(b)

60

40

20

0

0

Figure 3.6. A.  Incidence of TB by age in a cohort of children and adolescents 1–18 years old in Puerto Rico, followed from 1949 to 1969 (continuous line; dotted line, model (3.7), Comstock et al. 1974a). B.  The percent of tuberculin-positive children is urban and rural areas at the beginning of the study. A least squares fit of the accumulation of infection with age a, where percent infected = (1− e−λa) × 100, suggests that the annual risk of infection (, ARI) was 6.5% in urban areas (data points shown), 4.1% in rural areas, and 5.3% on average (Comstock et al. 1974b). With this initial average rate of infection, model (3.7) (line, fitted by maximum likelihood as described by Dye et al. (1998); see also Figure 3.7) can represent the character of the data in panel A only by assuming that ARI was falling throughout the period of observation (at an estimated 14%/year) and that the proportion of children ( 0). If x is set to zero, it is not possible to account for the persistent decline of TB among older people (Figure 3.8; Dye et al. 1998; Vynnycky and Fine 1997b). A finite rate of reinfection (x > 0) is also needed to explain high TB-­incidence rates in countries of the Asia-­Pacific region (Trauer et al. 2014). A rearrangement of the data in Figure 3.7 gives the characteristic distribution of TB cases by age and through time (Figure 3.9). When transmission was high in the Netherlands during the 1950s, primary infection and reinfection generated the highest incidence rates in adults aged 15–24 years. As transmission subsided over subsequent decades, so did the overall incidence rate. Through time, a progressively higher proportion of cases came from the reactivation of older infections and, by the 1970s, peak incidence had shifted to the elderly. These variations in rates of infection and disease by age differ between sexes. Surveys of the prevalence of infection and disease carried out in Bangalore,

1990

Risk and Variation 81

2500

(a) Age 25–34 yr

TB cases/100,000/yr

2000 1500 1000 500 0 1950

TB cases/100,000/yr

800

1960

1970 Year

1980

1990

1970 Year

1980

1990

(b) Age 65+ yr

600

400

200

0 1950

1960

Figure 3.8.  A model without reinfection (x = 0) can replicate the decline of TB in younger adults (25–34 years, Part A) in the Netherlands, but not in older adults (65+ years, Part B). Adapted from Dye et al. (1998).

India, between 1961 and 1968 show some typical patterns (National Tuberculosis Institute Bangalore 1974). The prevalence of infection and disease was no different between boys and girls aged 5–14 years (Figure 3.10A–D). However, adult men had higher incidence and prevalence rates of TB than women (Figure 3.10B and C), both because they were more frequently exposed to infection (Figure 3.10A) and because they were more susceptible to TB following infection (Figure 3.10D). Broadly, the same patterns were reported from the Republic of Korea in 1965 and 1970, though the prevalence of infection and disease and

82

Chapter 3

80 Model Data

TB incidence/100,000/yr

70 60

1958

50 40 30 1970

20

1974

10 0

1988 0–14

15–24

25–34

35–44 45–54 Year (age)

55–64

65+

Figure 3.9.  Reported TB cases per 100,000 population by age in the Netherlands, 1958–1988 (grey), with the fitted model 3.7 (black).

of TB cases per infection were far greater in Korea than in Bangalore (Figure 3.11A–C).1 Routine surveillance also typically reports more TB cases in men than women. Such data are subject to reporting biases of various kinds, but Borgdorff et al. (2000), comparing case notifications with the results of prevalence surveys in 14 countries, found that the predominance of male cases did reflect true epidemiological differences and not differences in access to health care. A subsequent review of 137 separate studies also found, for the most part, no quantitative gender-­related differences in access to TB services (Yang et al. 2014). However, in the few studies where differences were identified, women were at a disadvantage. But TB case rates are not always higher in men than women. In Denmark in 1950–1952, men had higher infection rates than women and yet there were more TB cases among women (Groth-­Petersen et al. 1959). Higher TB prevalence rates among women were also found among Alaskan Eskimos during the 1950s (Comstock et al. 1967). Although the ratio of TB prevalence to infection prevalence is sometimes greater for men than women, and vice versa, women are more susceptible to extrapulmonary TB following infection (Rieder 1999).

1 Infection rates saturated at less than 100% in men and women during this period of high transmission in Korea (ARI 5.3% in 1965) implying that tuberculin positivity is not lifelong, i.e. some people revert from positive to negative (Figure 3.8A). It is not known whether this signals the loss of bacterial viability.

Risk and Variation 83

(a)

0.4 0.2

Males Females

1000

5–14

15–24 35–54 Age group (years)

(c)

600 400 200 5–14

15–24 35–54 Age group (years)

55+

(b)

300 200 100 0

55+

800

0

Incidence TB/100,000/yr

0.6

0.0

Prevalence TB/100,000

400

Prevalence TB/1000 infections

Prevalence infection

0.8

15

5–14

15–24 35–54 Age group (years)

55+

5–14

15–24 35–54 Age group (years)

55+

(d)

10

5

0

Figure 3.10.  Infection and disease by age and sex in Bangalore, India, in four prevalence surveys carried out over the period 1961–1968 (National Tuberculosis Institute Bangalore 1974). A.  Prevalence of infection, as judged by tuberculin skin test (induration ≥ 10 mm). B.  Incidence of culture-positive TB between surveys 3 and 4. C.  Prevalence of smear- and culture-positive TB in all four surveys. D.  Prevalence of TB per 1000 infected people over four surveys. In D, the ratio prevalence/infection is taken to be a measure of progression to TB disease following infection, assuming no difference in the duration of disease between sexes in this untreated population.

There is another possible risk associated with age that is often overlooked—the risk of not being diagnosed with TB. Kim et al. (1995) carried out an unusual nationwide study of TB incidence among civil servants (at all grades) in the Republic of Korea, which can be compared with the findings of a contemporary prevalence survey (Figure 3.12A; Hong et al. 1998). Incidence surveys are rarely done because TB is a rare disease: two surveys are needed, and low case rates require large sample sizes or long periods of follow-­up for accurate measurement. The striking feature of these Korean surveys is that the

1.0

(a)

Prevalence infection

0.8 0.6 0.4 0.2

Males Females

0.0

Prevalence TB/100,000

4000

15–24 35–54 Age group (years)

55+

5–14

15–24 35–54 Age group (years)

55+

5–14

15–24 35–54 Age group (years)

55+

(b)

3000

2000

1000

0

50 Prevalence TB/1,000 infections

5–14

(c)

40 30 20 10 0

Figure 3.11.  As for Figure 3.10 A, C, and D, but for the Republic of Korea, from surveys carried out in 1965 and 1970 (Ministry of Health and Social Affairs 1971). Parts B and C report the prevalence of bacteriologically positive TB.

800

20

Prevalence/incidence Incidence Prevalence

15

600 10

400

5

200

0

3

20–29

30–39

40–49

50–59

60–69

Ratio prevalence/incidence

Cases per 100,000 population

(a) Republic of Korea

0

(b) China Prevalence c+/prevalence s+ Prevalence s+/notified s+

Ratios

2

1

0

15–24

25–34

35–44 45–54 55–64 Age group (years)

65+

Figure 3.12.  The apparent increase in duration of TB episodes with age as indicated by each of the following. A.  The ratio of prevalence/incidence in Republic of Korea 1995 (Kim et al. 1995; Hong et al. 1998). B.  The ratio of smear-positive (s+) prevalent/notified cases in China in 2010 (World Health Organization 2013; Wang et al. 2014). In China, the ratio of culture-positive (c+) to smear-positive (s+) cases did not change systematically with age.

86

Chapter 3

62.5

25.8

25.6 61.5 25.4

61.0 60.5

25.2

60.0 59.5 59.0 1994

25.0

Average age cases 20–39 years

Average age cases 50+ years

62.0

Adults 50+ years Adults 20–39 years 1998

2002

2006

2010

24.8 2014

Year

Figure 3.13. Aging and rejuvenation of the TB epidemic in China. As the risk of infection declines, the average age of reported smear-positive TB cases increases among adults ≥ 50 years but falls among adults 30–39 years. Average ages in each year are calculated from case rates rather than numbers to remove the effect of aging in the human population. Data from WHO (World Health Organization 2013a).

prevalence/incidence ratio, which measures the average duration of an episode of TB, increases with age to values exceeding 10 years among adults aged 50 years and older. The implication is that chronic, undiagnosed TB was typical among older people in Korea during the 1990s. A similar pattern emerges from a comparison of prevalence and case notifications (reported, incident cases) in China (Figure 3.12B; World Health Organization 2013a; Wang et al. 2014). There was no systematic trend with age in the ratio of prevalences measured by culture and smear microscopy (open bars) in the 2010 national survey, but the ratio of smear-­positive prevalent to reported cases was higher in older people (filled bars). One explanation is that routine surveillance tends to miss cases among older people; the other is that the average duration of a TB episode was longer in older people. Either way, older people appear to be at a disadvantage. In some countries where TB is in decline, there are clear deviations from the expected pattern of aging epidemics in aging populations (Dye and Williams 2010). In China, the average age of older adults (> 55 years) with smear-­positive TB has been steadily increasing, but the reverse is true for younger adults with TB (Figure 3.13). Mexico, Myanmar, Sri Lanka, and Vietnam have shown the

Risk and Variation 87

same phenomenon (Dye and Williams 2010). There are at least four possible explanations. First, a factor affecting susceptibility to TB, such as HIV coinfection, generates a disproportionate number of cases among young men and women. Second, infections are imported into cities by young, foreign-­born adults. Third, there are higher contact and transmission rates among young adults migrating internally to high-­density urban areas. Fourth, novel strains like Beijing are found predominantly among younger cases and could have higher rates of transmission than other strains. Whatever the cause, young adults in some settings are partially rejuvenating TB epidemics. In conclusion, boys and girls are typically exposed to infection at about the same rate. The risk of TB following infection is relatively high at birth and decreases through childhood until the onset of puberty, which occurs earlier for girls than for boys. During adulthood, men are usually more exposed to infection than women; men or women may suffer higher rates of TB following infection, depending on the setting, but women are consistently more vulnerable to extrapulmonary TB.

VARIATION IN RISK THROUGH TIME One aim of TB epidemiology is to understand why the burden of disease changes through time, most often from year to year but also on shorter or longer timescales. The goal is to understand which risk factors change, why they change, by how much, and with what effects. Risks that can be modified directly are potential instruments for TB control. Risks that have increasingly adverse effects must be overcome by tackling the cause directly or by counteracting it with more powerful control measures. The quantitative challenge is to understand the separate and combined effects of numerous risks, which occur with different magnitudes on different time scales. These changes might involve any of the risk factors already discussed: HIV coinfection, chronic diseases (e.g., diabetes, undernutrition), and demographic (e.g., birth and death rates, population growth), social (e.g., urbanization), and economic (e.g., income) determinants. These factors could act independently, or they could reinforce or counteract one another. A comparative study of India and the Republic of Korea found a mixture of reinforcing and counteracting effects acting over the decade 1998–2008 (Dye et al. 2011). In India, TB incidence would have increased faster than population size (Figure 3.14A) because of the adverse effects of aging, urbanization, changing body mass index (BMI), and rising diabetes prevalence (Figure 3.15), generating an increase in TB incidence per capita of 6% in 10 years (Figure 3.14A).

Change annual TB incidence (%/10 yr)

80

TB cases TB cases/capita 60

40

20

0

20 Change annual TB incidence (%/10 yr)

(a) India

TB among all adults

TB among adults with diabetes

(b) Republic of Korea TB cases TB cases/capita

10 0 –10 –20 –30 –40

TB among all adults

TB among adults with diabetes

Figure 3.14.  The net effects of nutritional and demographic changes on TB and TB among people with diabetes in (A) India and (B) Republic of Korea, expressed as the change over 10 years (1998–2008) in annual incidence (filled bars) and annual incidence per capita (open bars). Errors on each bar are 95%CL. Adapted from Dye et al. (2011).

Risk and Variation 89

Change annual TB incidence (%/10 yr)

25 India 20

Korea

15 10 5 0 –5 –10

Population growth

Aging

BMI

Diabetes

Urbanization

Figure 3.15.  Changes in the annual number of new TB cases between 1998 and 2008 in India (filled bars) and Korea (open bars) attributable to each of five factors (horizontal axis) acting separately. Adapted from Dye et al. (2011).

Widespread nutritional improvements in India (Naandi Foundation 2011) were offset by a fall in BMI among the majority of men who live in rural areas. Although the number of people with diabetes in India is set to increase at 2.6%/ year between 2011 and 2030 and more than 100 million adults will suffer from diabetes by 2030, this chronic disease is expected to have a small effect on TB incidence overall (Figure 3.15). Two statistics explain why: given a relative risk of TB among people with diabetes of 2.6, the fraction of TB cases attributable to diabetes in India would rise from 11.7% in 2011 to 13.7% in 2030. Over the same period, the value of R0 would increase by only 2.2% of its initial value (based on equation (3.4)). Diabetes is a growing risk for TB in India, not so much because the prevalence is increasing in any age group but because more people are surviving to the age at which they will develop diabetes (Figure 3.16A). By 2030, there will be more people with diabetes in urban than rural India. But this is mainly because India’s urban population will grow faster than the rural population, and the risk of infection is higher in urban than in rural areas (Chadha et al. 2005); it is not because the prevalence of diabetes will increase more quickly in urban areas (Figure 3.16B). Thus the link between TB and diabetes is primarily a demographic rather than an epidemiological problem.

80

(a) DM prevalence Population 2011 base

70

DM cases (000s)

60 50 40 30 20 10 0

70

20–39

40–59 Age group (years)

60–79

(b)

60

DM cases (000s)

50 40 30 20 10 0

Rural

Urban Rural or urban area

Figure 3.16.  Expected changes in the numbers of people with diabetes in India between 2011 and 2030 (A) by age group and (B) in rural and urban areas. Black bars show the numbers in 2011; grey bars mark the increases between 2011 and 2030 due to population growth (dark grey) and to the expected rise in diabetes prevalence (light grey). Data from the International Diabetes Federation (2011).

Risk and Variation 91

In the Republic of Korea, by contrast, the number of TB cases would have increased more slowly than population size because of positive effects of urbanization, increasing BMI and falling diabetes prevalence (Figure 3.15). Consequently, TB incidence per capita is estimated to have fallen by 8% between 1998 and 2008 (Figure 3.15). In the context of TB epidemiology, Korea’s most significant problem is rapid population aging (Figure 3.15; Park et al. 2013). The aging of TB epidemics in aging human populations is expected to slow decline of TB everywhere. Vynnycky et al. (2008) and Wu et al. (2010) have highlighted the deceleration in Hong Kong linked to a growing fraction of cases arising by reactivation in older adults (Figure 3.17). The effect could be exacerbated by reactivation rates in elderly people that are higher than those previously observed in Europe (Vynnycky et al. 2008). The epidemiology of TB in Hong Kong bears the hallmarks of the aging phenomenon, although processes other than the age shift may have contributed to the slowdown. Most strikingly, there appears to have been a sudden deceleration in the decline of TB around 1978, just at the moment when China’s Open Door Policy fostered trade, investment, and migration between Hong Kong and the mainland (Figure 3.17A,B). Similarly, the rate of decline of TB in Portugal slowed markedly after the so-­called Carnation Revolution in 1974. From 1960–1974, the case notification rate fell at an average of 7%/year; from 1976 to 2004 the decline was only 3%/year. The transition was marked by an abrupt slowdown in of national economic growth and mass immigration of Portuguese citizens from Angola and Mozambique, creating a large impoverished refugee population. Precisely how these events in Hong Kong and Portugal affected TB transmission and susceptibility is not known, but they evidently did. They are a reminder that patterns in TB epidemiology do not usually have a single dominant cause. Although nutritional and demographic changes have had stronger adverse effects in India than Korea, the magnitude of the effects in both countries was small compared to the potential for control by chemotherapy. Incremental improvements in TB case detection and cure have the potential to reduce incidence by 5%–10% per year and by 40%–60% over a decade (Chapter 4; Dye et al. 1998; Dowdy and Chaisson 2009). For a given fraction of cases detected and cured, the decline tends to be faster where the risk of TB is predominantly from recent transmission (as in India) and slower where more cases arise from the reactivation of latent infection (as in the Republic of Korea). For either India or the Republic of Korea, the comparatively weak adverse effects identified here ought to be surmountable by early case detection and treatment, though the challenge is greater for India.

(a) 1000

TB cases/100,000/yr

65–74 yr 55–64 yr

45–54 yr 25–34 yr

1978

35–44 yr 100 1970

1975

1980

1985

1990

1995

2000

Year (b) 0

Age group (years) 25–34

35–44

45–54

55–64

65–74

Change in TB cases (%/yr)

–2 –4 –6 –8 –10

1970–78 1978–98

–12

Figure 3.17.  The slowing decline of TB in Hong Kong 1970–1998. A.  Reported TB cases (per 100,000 per year) in five age groups. B.  Average rates of change in the five age groups over the periods 1970–1978 and 1978–1998. The deceleration in decline was greater in younger than older adults. 1978 was the year in which China declared an Open Door Policy, increasing migration between the mainland and Hong Kong. Data from Vynnycky et al. (2008).

Risk and Variation 93

In India, the risks associated with adverse nutritional and demographic changes are greater than portrayed by the preceding analysis because the pace of aging and urbanization in India is accelerating. For example, the proportion of India’s population living in urban areas increased from 13% in 1990 to 15% in 2010 but is expected to reach 20% by 2030 (Dye et al. 2011). The average age of the Indian population increased by 3 years between 1990 and 2010 (24.9 to 27.9 years); it will increase by a further 5 years by 2030 (to 32.9 years). In theory, any process that increases incidence per capita will also increase the risk of infection through transmission, leading to a further increase in case incidence. This positive feedback process would be checked somewhat by acquired (partial) immunity. Nevertheless, control efforts would need to be more forceful to push down the burden of disease. Tobacco smoking was not included in this study of India and the Republic of Korea because comparable survey data were not available from both countries. However, if 19% of adults (≥ 15 years) smoke in India and the relative risk for TB is 2, then 11% of all TB cases would be attributable to smoking (Lönnroth, Castro, et al. 2010). However, in terms of trends, tobacco is less likely than diabetes to increase the burden of TB. The number of smokers is increasing in growing populations, like that of India, but the prevalence of smoking is falling in most countries, albeit slowly (World Health Organization 2010a; Shafey et al. 2010; Qian et al. 2010). Consequently, smoking trends are expected to have favorable but small effects on trends in TB incidence in all regions of the world (Basu et al. 2011). The striking contrast in trends in lung cancer mortality in the United Kingdom (down) and France (up) between 1970 and 2000, compared with the similarity in TB trends in the two countries, indirectly suggests that smoking has not been a strong determinant of TB trends in these two countries (Figure 3.18). Moreover, a cross-­country comparison of TB trends in Central and Eastern Europe found that smoking (and diabetes) was associated with declining TB incidence (Dye, Lönnroth, et al. 2009). It may be that these factors reflect social patterns that are linked to health, health services, and economic development in ways that override their importance as risk factors for TB. None of this is to deny that a significant fraction of TB cases is linked to smoking or that tobacco control will also help to reduce TB cases and deaths (Lin et al. 2008). But the impact of changes in tobacco smoking on TB time trends will not be large. While most of the recognized risk factors appear to be weak determinants of TB trends, there are at least three that have much stronger and more rapid effects. The first is HIV coinfection, as depicted in Figure 3.2. The relative risk of TB in people with advanced HIV infection is high (k = 10–30) and, in southern African countries where the prevalence of HIV is also high, the majority of TB

94

Chapter 3

cases are attributable to HIV. The biggest epidemiological changes caused by HIV happened quickly in comparison with other events in TB epidemiology, mainly within the decade 1990–2000 (Chapter 6). The second is the combination of factors that arise during periods of social and economic breakdown. Being present throughout every country, M. tuberculosis is a ubiquitous sentinel of failing health and health services. The effects of the First and Second World Wars (plus an influenza pandemic in 1918; Noymer 2011) on TB mortality can be seen in the time series presented in Chapter 1 (Figures 1.4 and 1.5), and increases in TB transmission, incidence and mortality have been reported during other armed conflicts since 1945 (Barr and Menzies 1994; M’Boussa et al. 2002). But the resurgence of TB does not require all-­out war. The United States suffered a resurgence of TB during the 1980s and early 1990s due to the deterioration of TB program infrastructure, the development of hot spots (e.g., hospitals, shelters, prisons) where tuberculosis flourished, the spread of HIV/AIDS and drug-­resistant strains, and a growing number of cases among foreign-­born persons (Bloom and Murray 1992; Schneider et al. 2005). The most profound effects of social change on TB in recent history are those that accompanied the collapse of the Soviet Union in 1991. The collapse was associated with widespread economic recession across Central and Eastern Europe during the early 1990s and an upsurge of TB cases and deaths. The effects were less severe in the USSR’s western neighbors, and more severe in former Soviet states and their overseas dependencies. In Hungary, for example, a temporary dip in GDP after 1990 was mirrored by a rise in TB incidence (measured as notified cases) that took a decade to return to the 1990 level (Figure 3.19A); Arinaminpathy and Dye 2010). In Russia, the recession was deeper and longer, with grave consequences for TB (Figure 3.18B). Reported TB cases peaked in 2000 and have since fallen slowly. In 2010, Russia reported 83 cases per 100,000 population (119,000 cases in total), more than twice the rate in 1990 (34 per 100,000). Across 15 Central and Eastern European countries, there was a tight correlation between lost economic productivity over the period of recession for each country and excess numbers of TB cases and deaths (Figure 3.20). Even Cuba, which had close economic and political ties with the Soviet Union, conformed with this pattern (Rodriguez-­Ojea et al. 2002; Gonzalez et al. 2007; Figure 3.20). The principal limitation of such “ecologic” analysis (a term disfavored by this ecologist), which makes comparisons among groups, is that it does not identify underlying causes. Alcohol-­related deaths certainly became more frequent in Russia during the 1990s (Leon et al. 2007; Zaridze et al. 2009; Leon et al. 1997). Body mass index appears to have remained stable (Finucane 2011). Health services deteriorated, including those responsible for TB control. Active screening

TB cases/100,000/yr (log10)

1.8

(a) TB cases

1.6

1.4

1.2

1

France England & Wales

0.8 1970

18

1975

1980

1985 Year

1990

1995

2000

1985 Year

1990

1995

2000

(b) Lung cancer mortality

Deaths/100,000 men/yr

16 14 12 10 8 6 4 2 1970

France United Kingdom 1975

1980

Figure 3.18. A.  Trends in reported TB cases per 100,000 population per year in France and in England & Wales, 1972–2000 (www.invs.sante.fr and www.hpa.org). B.  Trends in lung cancer mortality among men 35–44 years in France and the United Kingdom, 1970–2000. Data from Jamison and others (2006).

96

Chapter 3

(a) Hungary

70 60

GDP ($000s/person)

20

50 15

40 30

10

20 5

TB cases/100,000/yr

25

10

0 1980

1985

1990

1995

2000

2005

0

Year (b) Russia

100

20

15 60 10

40

5

0 1980

TB cases/100,000/yr

GDP ($000s/person)

80

20

1985

1990

1995

2000

2005

0

Year

Figure 3.19.  Comparisons of GDP per capita (grey) and TB case notifications for (A) Hungary and (B) Russia, before and after the collapse of the Soviet Union, 1980 to 2006 (Arinaminpathy and Dye 2010).

for TB was falling in the late 1980s. During the 1990s, a higher proportion of cases developed severe cavitary disease than in earlier years, treatment success declined, and the case fatality rate rose (Shilova and Dye 2001). Clearly, a combination of factors exacerbated the TB epidemic in Russia. The contribution of each factor is not known, but together they caused an epidemiological disaster.

Risk and Variation 97

40

Excess TB cases (standardized/person)

Georgia Moldova

30

Ukraine

20

Cuba Russia

10

0

Latvia

Bulgaria Uzbekistan Albania 0

Kyrgyz Republic

Czech Republic Estonia Hungary Poland Romania Slovak Republic

5 10 15 20 Lost economic productivity (standardized GDP/person)

25

Figure 3.20.  Excess TB cases in relation to loss of GDP (standardized index) for 15 countries in Eastern Europe (black) during the 1990s, plus Cuba (grey), following the collapse of the Soviet Union. Adapted from Arinaminpathy and Dye (2010).

A third important factor is immigration. For countries in west and central Europe, 40% of the variation in TB time trends can be explained by the fraction of cases that are foreign born (Figure 3.21). Immigration from high-­incidence countries is a significant obstacle to TB control and elimination in low-­incidence countries (Chapter 7). As the movement of people from poorer to richer countries continues to increase, migration is set to become a greater obstacle to TB control in future.

FEW PEOPLE AT HIGH RISK, MANY PEOPLE AT LOW RISK The exploration of risk in this chapter has exposed a central conundrum of tuberculosis epidemiology: based on decades of investigation, it is possible accurately to predict how many people get TB but not who gets TB. The reason is that the known risk factors for infection and disease are actually not very risky (relative risk 2–3) and typically exist at low prevalence. Consequently, they explain only a small fraction of cases (usually 1–2 years) are those who did not progress to TB disease soon after infection. Their apparent immunity to reinfection could be a preinfection characteristic of either host or pathogen or a host response to infection. From the perspective of developing new vaccines, the distinction is important: the latter implies that infection stimulates a protective immune response, which might be replicated with a new vaccine; the former does not. Whichever explanation is correct, a meta-­analysis of a series of studies shows that the risk of TB following infection in TST-­positive individuals averages only 21% (95%CI 14%–30%) of the risk in TST-­negative individuals (Andrews et al. 2012). This is consistent with some estimates obtained from mathematical modeling (Dye et al. 1998; Clark and Vynnycky 2004; Sutherland et al. 1982) but lower than others (Vynnycky and Fine 1997b; Brooks-­Pollock et al. 2011). Clearly, a better vaccine than BCG is needed, one that protects uninfected or infected people against infectious pulmonary TB and which has high efficacy in all settings. Notwithstanding the disappointing results of a trial with vaccine MVA85A (Tameris et al. 2013), the pipeline for vaccine development contains more candidates than ever before (Lalvani et al. 2013; Aeras 2013; McShane

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et al. 2012; Brennan et al. 2012; Knudsen et al. 2014; Kaufmann et al. 2014; Groschel et al. 2014). Because the mode of action and potential efficacy of a new vaccine are unknown, there are few barriers to speculation in theoretical analysis. If a new vaccine with high lifelong efficacy against pulmonary TB (80%, whether or not it also prevents infection) were administered only to infants, the effect on incidence would be modest by 2030 (Figure 4.7D). The effect would actually be less than shown in Figure 4.7D because this simple model of TB epidemiology without age structure excludes the 15-­year time delay from birth to adulthood, after which the risk for pulmonary TB increases markedly (Chapter 3; Young and Dye 2006). Much greater benefits would be obtained sooner in a mass vaccination campaign, even with a vaccine of lower efficacy. A campaign that continuously vaccinated uninfected people so as to immunize a fraction rising to 30% in 2035 would exceed the best effect of neonatal vaccination in Figure 4.7D. Higher immunization rates would, of course, be better still: immunizing 80% of uninfected people by 2035 would have an effect comparable to the best scenario for drug treatment of active disease (cf Figure 4.7A and D). At present, it is completely unknown whether these possibilities can be realized because there is no way to predict the efficacy of future TB vaccines.

NEUTRALIZING OR ELIMINATING LATENT INFECTION TB disease can be prevented by neutralizing or eliminating latent infection, in addition to preventing infection. The treatment of latent TB infection (TLTI) could be carried out with a drug or a vaccine. However, while there are highly efficacious drugs (especially isoniazid) and drug combinations for preventive therapy, there is currently no vaccine that is targeted at people who are already infected. Individuals at high risk of TB who have a positive tuberculin skin test (e.g., contacts of active cases, immigrants to low-­incidence countries) but not active disease can be offered treatment for latent TB infection (TLTI), most commonly with the inexpensive drug isoniazid. Studies among contacts of active cases have demonstrated that 12 months of daily isoniazid gives 30%–100% protection against developing TB disease (Cohn and El-­Sadr 2000; Comstock 1999). Drug combinations, particularly rifampicin and pyrazinamide for 2–3 months, can be as effective as 12 months of isoniazid but are not as safe (Jasmer et al. 2002; Centers for Disease Control and Prevention 2001; Centers for Disease Control and Prevention 2002). And yet TLTI is not widely used. The main reason is that compliance with long-­term daily treatment tends to be poor among healthy people—a relatively

Interventions and Control 125

high risk of TB for infected as compared with uninfected people is usually still a low risk in absolute terms. In addition, a survey must be carried out to find who is eligible for treatment because they have been exposed to infection (positive on a tuberculin skin test, TST, or by an interferon gamma release assay, IGRA), but active disease must be excluded (e.g., by radiography) before isoniazid is taken alone, and side effects include a hepatitis risk of approximately 1% for each course of treatment. Where TLTI has been carried out, it is mostly through isoniazid preventive therapy (IPT). The epidemiological literature on IPT contains mixed reports of success and failure, with outcomes that are not always predictable. In the United States, for example, the practice of contact tracing and IPT has fallen short of recommendations (Reichler et al. 2002); some high-­risk groups, such as the elderly (Reichler et al. 2002; Sorresso et al. 1995), do not receive the full benefits that IPT can provide. IPT can be hard to manage in the groups that most need it, such as illegal immigrants (Matteelli et al. 2000), though supervision has helped drug users (Gourevitch et al. 1998; Chaisson et al. 2001), and financial incentives have improved completion rates among the homeless (Tulsky et al. 2000). TLTI has been used as a component of intensive, local control campaigns, such as those carried out for North American and Greenland Eskimos, but probably had effects secondary to the prompt treatment of active disease (Grzybowski et al. 1976; Styblo 1991; Comstock et al. 1979). At present, TLTI plays no more than an accessory role in TB control, though the number of recipients around the world has been neither directly quantified nor indirectly estimated (this is in contrast to TLTI for HIV-­positive people; Chapter 6). To compare the effects of treatment postinfection (drug or vaccine) and preinfection (vaccine), the rates of removal from target groups (U versus Ls plus Lf) are made the same in Figure 4.7D and E. The proportions of the population treated are lower in Figure 4.7E because fewer people are infected than uninfected. Where transmission is higher, it is more effective to treat preinfection because a higher proportion of cases come from recent infection. Where transmission is lower, it is better to treat postinfection because a higher proportion of cases are due to reactivation. Reducing Susceptibility to Infection and Disease Vaccination is one way to lower susceptibility to infection. Another is by mitigating the effects of endogenous risk factors, such as diabetes, undernutrition, or tobacco smoking. Such efforts will certainly be helpful in reducing the risk of TB, but they are likely to play only a supplementary role in control programs (Odone et al. 2014). This is because, in various combinations, a small proportion

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of people are affected, the relative risk is modest, and the scope for reducing those at risk is limited. The prevalence of tobacco smoking and diabetes can be reduced only slowly at the level of national populations. For example, the prevalence of tobacco smoking among adults in the United States fell at an average of 1.7%/year between 1965 and 2011 (Centers for Disease Control and Prevention 2014). In the example shown in Figure 4.7F, the effects of mitigation are, for a risk factor with prevalence 0.2 and relative risk 2, R0 enhanced by 1.2, made comparable to those for vaccination preinfection (Figure 4.2D) and for TLTI. Even though the reduction of 16%/year is an ambitious program of risk mitigation, the effect is modest. As expected, the impact of treating only part of a population at moderate risk is not as great as treating the whole population pre-­or postinfection. When risk factors such as diabetes increase in prevalence, drug treatment can, in principle, easily offset their effects. As shown in Chapter 3, India’s rising prevalence of diabetes would increase the value of R0 by only 2.2% between 2011 and 2030. In contrast, 70% case detection and 95% cure would cut R0 from 1.24 to 0.86—that is, by 30%.

REWARD FOR EFFORT IN TB CONTROL The preceding comparison of interventions reveals some of the strengths and weaknesses of different approaches to control. However, a comparative study of effectiveness is not, on its own, sufficient to choose between interventions. Faced with limited resources, the choice is often guided by the relationship between reward and effort, typically expressed as the ratio of cost to benefits, effectiveness, or utility. A full economic analysis of TB interventions is outside the scope of this discussion of population biology, but there are some dynamic features of the relationship between reward and effort that highlight the main differences among strategies and which can inform more detailed economic evaluations. The choice among options for control is frequently made by carrying out detailed and specific studies to evaluate which intervention will yield the greatest reward (usually morbidity or mortality as a measure of effectiveness, E) for least effort (usually cost, C) on a given epidemiological background. However, the transmission models used in this and preceding chapters offer a simple and more general view, allowing different kinds of interventions to be compared within a single, unifying analytical framework (Dye and Floyd 2006). There are two complementary ways to allow for the extra (multiplicative) effects of reducing transmission. The first and simplest is to compare equilibria

Interventions and Control 127

before and after an intervention. For TB models in which infection confers partial immunity, R 0 . 1/ [U* + x (1−U*)], where U* is the fraction uninfected in the steady state (Chapter 2). Alternatively, U* . (1- xR 0) / [(1- x) R 0], so that U* = 1/R 0 when x = 1 (fully protective acquired immunity) and U* = 1 when R0 = 1. Any TB intervention will reduce R0, and this formula gives the expected reduction in the number of people infected (1 - U*) when the ensuing fall in transmission has taken the system to a new steady state. If R0 is reduced to a fraction q of its initial value (the measure of effort), then the proportion of people infected (1 - U*) increases nonlinearly with q (down to a minimum value qR0 ≥ 1 when U* = 1, the measure of reward). The change (1 - U*) is always greater than the change measured by q because reducing transmission is disproportionately beneficial, so the ratio (1 - U*)/q is the multiplicative effect of transmission on the effort/reward ratio. When scaled by the ratio of the price per person treated (P) to the per capita efficacy () of the treatment with respect to the chosen outcome, cost/effectiveness, C/E  (P/)[(1 - U*)/q]. Although this approximation has the virtue of being easy to compute and has didactic value, it overestimates the effect of an intervention in the long transition (decades) between steady states. For TB, the early part of the transition phase is more important because there is a strong preference for evaluating the short-­term effectiveness of interventions, as reflected in the time-­based discount rates used in economic analysis. The essential simplifying observation is this: although these models describe nonlinear biological processes, effort and reward are, to a good approximation, linearly related for a range of different control methods under a wide variety of epidemiological circumstances. This is the basis of the second method. For the first few years of an intervention, the cumulative number of people effectively treated (T) during any intervention (drugs, vaccines, etc.) and the cumulative number of cases prevented (S) are in general related by

S . kITt. (4.7)

For any intervention, k is the proportion of cases or deaths prevented (reward or effectiveness) divided by the proportion of persons effectively treated (effort). Larger values of k prevent more cases or deaths per person treated, giving more favorable (lower) effort/reward ratios (T/S). Equation (4.7) is derived from the empirical relationships shown in Figure 4.11, which apply, in this formulation, to preinfection vaccination, to the treatment of TB disease after active case finding, and to the mitigation of an endogenous risk factor that enhances the risk of infection of disease following exposure. The difference in T/S (effort/reward) for these three kinds of intervention is captured in the different values of k (Table 4.3).

128

(b)

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Chapter 4

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10 20 30 40 Infections prevented (000s)

50

50 100 150 200 250 Initial incidence/100,000/yr

300

(d)

2.0 1.5 1.0 0.5 0.0

0

Figure 4.11. Reward for effort in TB control, when active cases are prevented by vaccinating preinfection. A.  Effort is the cumulative number of infected people effectively treated over time (T). Reward is measured as the cumulative number of new TB cases prevented (S). Changes in T and S are driven by changes in the per capita treatment rate. Vaccination is more effective per unit effort when initial incidence is higher and when the intervention proceeds for longer (declining slopes show effectiveness and effort for t = 2, 4, 8 12 and 16 years). B.  Cases prevented are a constant fraction (approximately) of infections prevented. The ratio T/S is proportional to (C) the duration of the intervention and (D) the initial, equilibrium incidence rate. Adapted from Dye and Floyd (2006).

In Figure 4.11A, the number of cases prevented or saved (S) is, to a good approximation, linearly related to the number of people successfully vaccinated preinfection (T). Because fewer TB cases generate fewer infections in the population, S increases through time. The number of cases prevented is a constant proportion of the number of infections prevented ( 0.21, Figure 4.11B). The rate of increase of S with T is also approximately linear (Figure 4.11C), and that rate itself varies more or less linearly with the initial incidence (Figure

Interventions and Control 129

4.11D). Assembling this series of linear relations leads to equation (4.7) and to the surface plots in Figure 4.11. The approximations in Figure 4.11 eventually break down when other, nonlinear effects become important, notably in transition to the low-­incidence phase in which cases arise mainly by reactivation rather than recent infection (Figure 4.8). However, in the short term, the linear approximation in equation (4.7) will be good enough to make the right choice among distinct options for control and to express these as a cost/effectiveness ratio using C/E = (P/)(T/S). For vaccination preinfection, least squares regression gives a value of k = 0.80/person/year when the outcome is incidence and k = 0.17 when the outcome is mortality, in each instance with a 95% CL of less than 1% of these values. The small error attached to the estimates of k accounts not only for the linear approximation but also for the uncertainty in model parameter values, which are randomly selected from the range plausible for TB (Dye and Floyd 2006). A preinfection vaccine would be given to a large number of people, many of whom would never be infected or develop TB following infection. For this reason, given for example I = 100/100,000/year and T = 10 years, immunization (i.e., efficacious vaccination) prevents only 0.8 cases, 0.17 deaths, and 2.9 DALYs for every 100 people successfully vaccinated. Estimates of k are similar for the treatment of TB disease (with active case detection) and for methods of prevention or treatment for an endogenous risk factor (Table 4.3; Figure 4.12C). In principle, this analysis can be applied to a vaccine of any specified efficacy, since efficacy is just a scale factor acting on T, though an important one in practice. However, it does make the optimistic assumption that a vaccine or vaccination protocol (e.g., with periodic boosters) gives lifelong protection. For shorter durations of protection, which are likely in practice, k would have to be recalibrated (to a smaller value). In the limit where a vaccine has no effect on transmission, such as neonatal BCG protecting young children against noninfectious forms of TB (causing meningeal and miliary disease), then k  0. Equation (4.7) needs to be modified for interventions in which the method of treatment acts directly on the outcome measure, so effort and reward are tightly correlated. For example, the treatment of TB disease by passive case detection is focused on patients who, as a result of treatment, become fewer in number through time, so costs and effects are associated. Then

S . kTt. (4.8)

So T/S is independent of the initial incidence. Constant k in equation (4.8) differs in magnitude (and units) when it replaces kI and so is not directly comparable to estimates that come from applying equation (4.7). For the treatment of TB disease, k = 0.83/year. Then a 10-­year control program prevents

Uninfected or infected person at risk Uninfected or infected person at risk

Mitigate risk factor (HIV)

Infected person

Treat latent infection by drug therapy or post-infection vaccination

Mitigate risk factor (other than HIV)

Uninfected person

Prevent infection among uninfected people by preinfection vaccination

kIt

kIt**

kt

kIt

kt

kIt

6.25

0.77

3.04E-03

0.80

0.13

0.63

3.12

0.11

4.26E-04

0.17

0.02

0.18

100

100

—

100

—

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—

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10

10

10

10

10

10

6.3

0.8

3.0

0.80

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0.63

827

3.12

0.11

0.43

0.17

20

0.18

200

50

2.0

7.9

2.9

365

3.1

3414

* S/T does not depend on initial incidence, I; ** For risk factors, k = A/p; in this example prevalence p = 0.2, relative risk = 2, and attributable fraction A = 0.17; *** DALY calculations assume each case loses 0.5 yr due to disability, each death loses 15 yr healthy life; Estimates are given to ≥2 significant figures.

Reduce susceptibility to infection and disease

Neutralize early or latent infection

Prevent infection and reinfection

Infective bacteria in environment

Reduce infections in environment

Cases or deaths averted/ person treated, S/T/

TB case, among all examined

0.20

Efficacy (deaths), k

Diagnosis and cure by active case detection

Efficacy (cases), k 0.83

Initial incidence/100,000/yr, I

kt*

Duration of intervention, t

TB case, among self-reporting symptomatics

Unit protected, prevented or treated

Cases averted/100 persons “treated”/10 yr

Diagnosis and cure by passive case detection

Subtype of intervention

Deaths averted/100 persons “treated”/10 yr

Treat TB disease

Type of intervention

Table 4.3. Reward for effort given to a range of TB interventions. DALYs averted/100 persons “treated”/10 yr***

Interventions and Control 131 (a)

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800 600 400 200 0

0

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Initial incidence/100,000/yr

Figure 4.12. As for Figure 4.8 but for the treatment of TB disease. While the average T/S ratio is proportional to the duration of the intervention, as in Figure 4.8, T/S is nearly independent of the initial incidence rate (C, D). Adapted from Dye and Floyd (2006).

827 cases, 200 deaths, and 3414 DALYs per 100 cases successfully treated (Figure 4.12, Figure 4.13A, Table 4.3). This is hugely more effective than vaccination per person treated. Not only is the number of cases prevented greater, but the number of deaths and DALYs averted are also magnified because drug treatment reduces case fatality, in addition to reducing transmission. Even without attaching costs, these figures help to explain why the treatment of TB is regarded as highly cost effective. Among the interventions shown in Table 4.3, only infection control is anywhere near as effective, per infective contact prevented, as drug treatment for TB disease. This analysis can be applied to any intervention against TB disease (an improved diagnostic, drug, or detection method) and to the treatment of any form of TB, including drug-­resistant forms. If, for example, an epidemic of

300

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220 180

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32

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140 100

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Figure 4.13. Reward for effort in TB control. The panels are shaded and contoured to show the number of people eligible for treatment (TB patients, uninfected or latently infected people at risk) that have to be treated (drug, vaccine, etc.) for each TB case prevented, in relation to the duration of a control program (x-axis, years) and initial incidence (y-axis, cases/100,000/year). The number of persons treated per case saved (T/S) tends to be lower when the intervention continues for longer because the benefits of interrupting transmission grow through time and when initial incidence is higher (except for the treatment of TB disease). A.  The treatment of TB gives a much better ratio of effort/reward than any other intervention. After about 12 years T/S falls to less than 0.1, so that the number of cases saved exceeds those permanently cured by a factor of 10. B.  For the treatment of latent infection (TLTI), T/S is also independent of the initial incidence rate, but, compared with (A), many more people have to be treated for each case prevented. For both (C) vaccination preinfection and (D) mitigation of a risk factor, T/S does depend on initial incidence. T/S ratios are similar for these two interventions, so preference depends on treatment costs per eligible person and on treatment efficacy.

Initial incidence 100,000/yr

0.32

Initial incidence 100,000/yr

300

Initial incidence 100,000/yr

(b) Treatment of latent infection

(a) Treatment of active TB

Interventions and Control 133

drug-­resistant TB can be considered independently from that of drug-­susceptible TB and epidemiological parameters are the same (e.g., transmission rate b or susceptibility parameter p), then this requires only the adjustment of treatment efficacy. More realistically, the dynamic interactions between drug-­susceptible and drug-­resistant epidemics would require a structurally different epidemiological model (Chapter 5). The treatment of latent infection, in comparison with vaccination, focuses on people who are at higher risk of TB because they are already infected. Consequently, a 10-­year control program prevents more cases (3.9), deaths (0.43), and DALYs (7.9) per 100 infected people successfully treated (Table 4.3, Figure 4.13B). If the effect of TLTI is to remove infection temporarily and at the same time eliminate the partial immunity that latent infection provides (unknown), then TLTI would be a less effective intervention and could actually increase TB incidence in areas of relatively high transmission. Under these circumstances, the cost per DALY recovered by a new preinfection vaccine would be more favorable than for TLTI provided the cost per person immunized is about threefold less (2.8). A second modification of equation (4.7) applies to the mitigation of endogenous risk factors. For this type of intervention, constant k can be related to the prevalence of the risk factor (p), the relative risk (R), and the population-­ attributable fraction (A) by

k=

A R−1 = . (4.9) p [1 + p (R − 1)]

This allows calculation of T/S for any control method with this mode of action, whatever the prevalence and relative risk. With an initial incidence of 100/100,000/year, roughly 1 TB case would be saved for every 100 smokers that quit over a 10-­year period, assuming that quitting removes all previous risk (Table 4.3; Figure 4.13D). The effect would be greater if R or p is greater. Rearranging equation (4.7) also gives the proportion of cases prevented, S/ It. This is the population preventable fraction (P) for a given number of people treated and is proportional to k:

P=

S = It

kT. (4.10)

For treatment of TB disease (equation (4.8)), the equivalent is

P=

S = It

kT . (4.11) I

Equations (4.7) and (4.8) capture the effects of any intervention, both as direct benefits to individuals receiving treatment and as population benefits that come

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from reducing transmission. When the benefits are measured as reduced mortality, the ratio of benefits including and excluding transmission lie in the range 1.5–2.2. This is a guide to the multiplicative effect of transmission on T/S or C/E ratios; the effect of reducing transmission, adding population benefits to individual benefits, is to make interventions about twice as cost effective. Although the relation between reward and effort for each intervention varies among regions, which differ by wealth and health, the variation among strategies is far greater, whatever the outcome measure (Dye and Floyd 2006). These results justify the maintenance and expansion of programs based on combination chemotherapy as the dominant mode of TB control around the world. However, in seeking effectiveness in TB control and not merely cost effectiveness, more effort (and money) will have to be expended to achieve the desirable rewards. This is illustrated by the difference between passive and active case detection. The former is relatively cost effective, but the latter may be essential for high impact.

FROM EFFICACY TO EFFECTIVENESS IN TB CONTROL The tools available for TB control today are essentially the same as they were two decades ago (Zumla, Schito, et al. 2014; Zumla, Gillespie, et al. 2014; Kaufmann et al. 2014). And because TB epidemics in most high-­burden countries are driven mainly by persistent transmission, Styblo and Rouillon’s 1992 assertion (quoted at the start of this chapter) still applies: the combination of early case detection and cure should be the most effective approach to control. In practice, combination chemotherapy has cured millions of cases and saved millions of lives, but the reduction in incidence has been disappointing (Dye, Lonnroth, et al. 2009). The gap between the actual and potential impact of drug treatment is a strong argument for finding new ways to achieve early diagnosis, linked to effective treatment. As a general investigative strategy, the present weaknesses of control programs should be considered as risk factors for TB and quantified in the same way as other conventional risks, such as diabetes, undernutrition, and tobacco smoking (Chapter 3; Dye and Raviglione 2013). The health penalties incurred by risks of all kinds and the health benefits of overcoming them need to be evaluated in like-­with-­like comparisons. Studies such as that by Claassens and others (Claassens, du Toit, et al. 2013), which measured the preventable fraction of newly diagnosed patients that was lost to follow-­up (21% in five provinces of South Africa), should be replicated elsewhere and extended to evaluate the consequences for transmission. This is one of a range of pragmatic approaches to evaluating the impact of interventions that improve case detection (Blok et al. 2014). Among all the possibilities, too little attention has been paid to the

Interventions and Control 135

potential epidemiological benefits of repairing and upgrading health services for TB and for other conditions of ill health (Kim et al. 2013; Farmer 2013). Considering the step from passive to active case detection, the failure to find much benefit in recent practical experience (Kranzer et al. 2013) is another expression of unfulfilled potential. In line with other analyses (Yaesoubi and Cohen 2013), the application of equations (4.1)–(4.6) to prevalence survey data suggests that active case finding could markedly reduce transmission and incidence. The logic is simple: infectious cases spend months or years in the community, usually much longer than the self-­reported duration of illness, and removing them will reduce the risk of infection for everyone. It must be emphasized, however, that this conclusion rests on the deduction that most cases in high-­incidence countries are still due to persistent transmission rather than to the reactivation of old infections. This inference is so important that it needs to be reexamined wherever possible. For control strategies based on drug treatment, new tools will aid both passive and active case finding, but some technologies will be more effective than others. Where cure rates for drug-­susceptible TB already exceed 90% for patients that complete treatment, new drugs have little to offer, except the convenience of shorter regimens (e.g., 2–3 months). Shorter regimens could elevate cure rates in settings where treatment outcomes are poor because patients default from or die during treatment (Salomon et al. 2006; Massire et al. 2011). The potential benefits of shorter regimens could be assessed by finding out when most default or death occurs. This information is already available from patient records in health facilities, which are an underexploited source of data. New drugs are likely to be more important for the treatment of resistant M. tuberculosis strains, where current first-­or second-­line drugs cannot or should not be used (Chapter 5). In contrast to drugs, the potential impact of new diagnostic tests is large because early detection (e.g., at “point of care”; McNerney and Daley 2011; Mwaba et al. 2011; Pai and Pai 2012; McNerney et al. 2012; Cobelens et al. 2012; Weyer et al. 2011; Sreeramareddy et al. 2009; Storla et al. 2008), when coupled with the best treatment, could interrupt a high proportion of infections (Dye 2012; Millen et al. 2008; Small and Pai 2010; Evans 2011). Recently developed diagnostics, including Xpert MTB/RIF, promise to facilitate both (Steingart et al. 2013; Boehme et al. 2011; Boehme et al. 2010; Walusimbi et al. 2013; Lawn et al. 2013; Weyer et al. 2012; Meyer-­Rath et al. 2012; Menzies et al. 2012). So far, field studies with Xpert MTB/RIF have yielded variable results: the number of extra cases diagnosed depends on the current standard of care and may not be high in settings where the quality of existing procedures is good or where “empiric,” or presumptive treatment based on clinical diagnosis, is the norm (Chapter 6; Theron et al. 2014). When a higher proportion of cases is found and successfully

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treated, more infections should be averted, though no study has yet demonstrated a reduction in transmission or incidence. There are major challenges in all such studies to quantify the time delays to diagnosis, infectiousness during the period of delay, and the proportion of cases that is eventually detected. From the perspective of TB population dynamics, the main aim of control is to neutralize the principal source of cases, namely, those arising by transmission (exogenous), or by fast progression from infection to active disease (endogenous). The present focus of control programs on the former is simply because the drugs are available. In principle, however, mass treatment preinfection (with a vaccine) or postinfection (with a drug or vaccine) could be just as effective; but neither strategy will be possible until there is a new vaccine or a method of diagnosing and safely treating large numbers of infected people (Abu-­Raddad et al. 2009; Knight et al. 2014). Both these methods work in principle because, like the treatment of TB disease, they interrupt infection before it causes disease. For a given fraction of eligible people protected, treatment postinfection (TLTI) is more effective because it targets those who are already carry M. tuberculosis. The difference between approaches grows as incidence falls because TLTI removes long-­standing as well as recent infections, and reactivation becomes a more important source of cases at low incidence. As a method of preventing (exogenous) exposure, infection control is probably limited because the methods available—masks, ventilation, ultraviolet light—apply mostly to settings such as health facilities in which a minority of transmission events takes place. However, the potential impact of infection needs more empirical work. The outstanding question is what proportion of all transmitted infections can be interrupted in any setting and in the population at large. Styblo and Bumgarner (1991) have pointed out that, while TB declined significantly in the predrug era, control strategies cannot rely on gradual social and economic development. Extending the investigation in Chapter 3, the analysis here suggests that the mitigation of endogenous risk factors (diabetes, undernutrition, tobacco smoking) can play no more than a supporting role in TB control. That said, there is international agreement to cut mortality from major noncommunicable diseases (NCDs), including diabetes and diseases linked to tobacco and alcohol consumption, with targets set for 2025 (Kontis et al. 2014). The benefits for TB control of meeting these new NCD targets are likely to be modest (Figure 4.8F), but they should be evaluated. It follows, too, that the slow decline in TB burden globally is due mainly to failures to exploit the full potential of drug treatment rather than to the rise of epidemiological and demographic risk factors. But there are exceptions, including

Interventions and Control 137

dramatic economic collapse, immigration to low-­incidence countries (Chapter 3), and the spread of drug resistance (Chapter 5) and HIV/AIDS (Chapter 6). The WHO post-­2015 targets for global TB control also highlight the gap between what could be achieved in theory and what has been achieved in practice (World Health Organization 2014b). These targets can be reached only by pushing current technology to the limit, by using all possible methods in combination, and by developing more effective technologies within the next two decades. That progress is vital in keeping alive the hope for elimination by midcentury (Chapter 7).

CHAPTER 5

Strains and Drug Resistance The speed with which [India’s National Plan] has been introduced might lead to so much drug resistance to isoniazid, to rifampicin and to both drugs in patients newly presenting for treatment as eventually to render short-­course chemotherapy ineffective. —Wallace Fox (1990) TB—and the resistant forms of the disease, multi-­, extensively, and even totally drug-­resistant TB—is out of control due to neglect and inattention. —Lee Reichman (2013)

The view of India from the P. D. Hinduja Hospital in Mumbai, where more than half the TB cases are multidrug resistant (MDR-­TB) and more than a third are resistant to fluoroquinolones (Agrawal et al. 2009) fortifies Fox’s fears for the whole subcontinent. And Mumbai is not unique: high rates of drug-­resistant TB have been found in numerous settings around the world. But to judge whether resistance is “out of control,” sensu Reichman, requires a more comprehensive assessment of the geographical distribution of resistant forms of M. tuberculosis and their time trends. That is the goal of this chapter. Apart from drug resistance, there are plenty of other enticing questions about M. tuberculosis population genetics. Some of these are described in the next section, but the focus here is on drug resistance, arguably the most important evolutionary problem in TB research today. To combat epidemics of drug-­resistant TB, it is vital to understand why some resistant strains have greater reproductive fitness—a greater propensity to spread through bacterial and human populations— than drug-­susceptible strains (Dye, Williams, et al. 2002; Dye and Williams 2009; Dye 2009). This is not simply a question of whether drug-­resistant cases can infect others and generate secondary resistant cases. Clearly they can, but how many? If public health (mal)practice has been a more important determinant

Strains and Drug Resistance 139

of reproductive success than genetic mechanisms, then improved diagnostic and treatment practices could keep the frequency of resistant strains among TB cases low in any population. It has been argued that intensive drug therapy will simply increase the selection pressure on resistant pathogen genotypes and that transmission can be minimized with intermediate drug concentrations (Read et al. 2011; Huijben et al. 2013). However, a growing body of evidence suggests that the effective and intensive use of existing TB diagnostic procedures and drugs can indeed postpone and even reverse epidemics of multidrug-­resistant TB.

GENETIC VARIATION IN M. TUBERCULOSIS Mutations conferring resistance to antibiotics are conspicuous because they undermine the efficacy of drug treatment. But there are other genetic variants within the M. tuberculosis complex that have important effects. For instance, infections with West African M. africanum are less virulent than infections with M. tuberculosis; that is, they progress more slowly or less frequently to TB disease, although they are no less transmissible (de Jong et al. 2008; de Jong et al. 2010; Anderson and May 1991). Based on phylogenetic analysis, M. africanum is thought to be more ancient than other M. tuberculosis lineages that have spread around the world. Taken together, these observations have led to speculation, as yet unverified, that enhanced virulence explains the greater abundance and wider geographical distribution of modern M. tuberculosis strains (Gagneux 2012). Surrounding this proposition are numerous unanswered questions, such as why M. africanum persists in West Africa but not in South America, to where it must surely have been transported via the slave trade. Among M. tuberculosis variants, members of the Beijing family have acquired greater notoriety (Merker et al. 2015). Beijing was originally defined on the basis of IS6110 RFLP patterns (van Soolingen et al. 1995), and some family members are hypervirulent in laboratory animals (Parwati et al. 2010; Gagneux 2013). In humans, Beijing strains have been associated with HIV infection (Viegas et al. 2013) with enhanced ability to cause secondary cases (Tuite et al. 2013; Yang et al. 2012; Nodieva et al. 2010) and with relapse after treatment (Huyen et al. 2013). The fact that Beijing strains tend to be more common in younger people points to the recent epidemic spread of these strains across Asia and Africa (Hanekom et al. 2007; Yang et al. 2012). However, in New South Wales, Australia, M. tuberculosis strains of the East African Indian lineage (EAI, lineage 1, Figure 1.1) tuberculosis have overtaken strains in the Beijing family as the more prevalent cause of tuberculosis (Gurjav et al. 2014). In this setting at least, the fitness of Beijing strains appears to be lower than that of EAI strains, for unknown reasons.

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Some Beijing strains (but not all) are associated with drug resistance, in which case resistance may be the attribute that governs epidemiological behavior, rather than other characteristics of the genotype (Viegas et al. 2013; Borrell and Gagneux 2009; Buu et al. 2012; European Concerted Action on New Generation Genetic Markers and Techniques for the Epidemiology and Control of Tuberculosis 2006; Sun et al. 2007; Cox et al. 2005; Marais et al. 2006; Klopper et al. 2013; Yang et al. 2012). Beijing strains have displayed a variety of epidemiological behaviors even in the same setting. Between 1993 and 2004 in Cape Town, one group of drug-­sensitive strains spread rapidly while a group of MDR strains remained stable and uncommon (Figure 5.1A; van der Spuy et al. 2008). Within the MDR Beijing group, however, one particular strain (R220) did increase in frequency in the early 2000s, its behavior having been masked by other members of the group (Johnson et al. 2010). An increase in the frequency of resistant strains under drug pressure is expected, but the force of selection clearly depends on other unknown biological attributes of these strains. The changing frequency of M. tuberculosis genotypes might also be determined by the changing characteristics of their human hosts; that is, evolution is actually coevolution. Certain M. tuberculosis lineages are associated with human genetic variants, which may reflect functional coadaptation of host and bacterial genotypes through long association (Gagneux 2012; Realpe et al. 2014) or simply that M. tuberculosis and H. sapiens are cotraveling commensals that have each diverged in their own ways. Some possible examples of coadaptation are beginning to emerge: in Vietnam, individuals carrying the C allele at the TLR-­2 T597C locus are more likely to have TB caused by the East-­Asian/Beijing genotype than by other genotypes (Caws et al. 2008). Individuals infected with the Euro-­American lineage of M. tuberculosis have developed fewer cases of meningeal than pulmonary TB, suggesting that these strains are less capable of extrapulmonary dissemination within hosts (Figure 5.1B). With increasingly rapid and refined methods of genetic investigation, especially whole-­genome sequencing, the description of genotypes has outpaced functional studies of phenotypes (Coscolla and Gagneux 2010; Gagneux 2013). Consequently, much remains to be discovered about the clinical, epidemiological, and evolutionary consequences of all the genetic variants in the thickening M. tuberculosis catalogue.

RESISTANCE, NEW AND ACQUIRED, TO FIRST-­AND SECOND-­LINE DRUGS Individuals who carry drug-­resistant strains obtain them either by infection from other TB cases or by mutation and selection during the course of infection

80

(a) Beijing drug sensitive Beijing drug resistant

Cases

60

40

20

0

2.5

0

2

4

6 Years

8

10

12

(b)

Odds ratio

2

1.5

1

0.5

Carriers of TLR-2 C allele TB meningitis

0

Euro-American

E Asian/Beijing Strain

Figure 5.1.  Evolution and coevolution of human M. tuberculosis. A.  Two strains of M. tuberculosis with different dynamics. Annual number of drug-sensitive (black) and drug-resistant (grey) cases of the Beijing family. The number of cases caused by the drug-susceptible strain rose steeply after year 8 (van der Spuy et al. 2008). B.  Interactions between host and bacterial genotypes affect the risk of TB. In Vietnam, individuals carrying the C allele at the TLR-2 T597C locus appeared more likely to have TB caused by the East-Asian/Beijing genotype than by other genotypes (black). Individuals infected with the Euro-American lineage of M. tuberculosis were less likely to have meningeal than pulmonary tuberculosis (grey; Caws et al. 2008).

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(horizontal gene transfer is probably uncommon and unproven as a mechanism of acquiring resistance) (Martinez et al. 2007; Rosas-­Magallanes et al. 2006; Coros et al. 2008; Becq et al. 2007; Muller, Borrell, et al. 2013; Wang and Behr 2014). The former are called new or primary cases of resistance, and are usually identified as new cases of TB. The latter are cases of acquired resistance, where resistant genotypes are amplified by selection during a failed course of drug treatment. In routine practice, sensitive or resistant phenotypes of M. tuberculosis, derived from the genotypes, are defined mainly by laboratory drug susceptibility tests, but direct genotyping technologies are now being used more frequently, including GenoType MTBDRplus and Xpert MTB/RIF (World Health Organization 2013a; Steingart et al. 2013; Arentz et al. 2013). The clinical and epidemiological characteristics of any given phenotype vary from one setting to another, for at least three reasons. First, a diversity of mutations causes resistance to any one drug; these mutations arise at variable rates (McGrath et al. 2013; Ford et al. 2011) and are present at varying frequency (Muller, Borrell, et al. 2013; Sun et al. 2012; Merker et al. 2013). Hundreds of alleles have been associated with resistance to isoniazid, many of them affecting the function of the katG gene, which encodes the catalyse-­peroxidase enzyme required for isoniazid activation (Hazbon et al. 2006; Muller, Borrell, et al. 2013). Resistance to rifampicin is due to mutations in rpoB genes, which encode various RNA polymerase subunits (Prammananan et al. 2008; Muller, Borrell, et al. 2013). The consequence of this diversity is that genotypes do not precisely map onto phenotypes, even to the extent that resistance mutations can be found in isolates that are categorized as drug susceptible in laboratory tests (Somoskovi et al. 2013; Van Deun et al. 2013; Ahmad Khan and Behr 2014; Ocheretina et al. 2014). Second, resistance to any one drug can occur in combination with resistance to other drugs. MDR strains are, by definition, resistant to isoniazid and rifampicin, but they are commonly insensitive to other drugs too. Isoniazid has been used as a first-­line TB drug since the 1950s and rifampicin, only since the 1970s. This may explain why MDR-­TB usually arises when isoniazid resistant strains also acquire resistance to rifampicin, although the higher mutation rate of isoniazid resistant alleles could also play a role (Valcheva and Mokrousov 2011). Cross-­country comparisons have found that the proportion of patients carrying MDR strains tends to be higher where rifampicin has been used for longer (World Health Organization and International Union Against Tuberculosis And Lung Disease 2004). More than this, the effects of multiple resistance mutations on the response to drug treatment are interactive, or epistatic, and depend on genetic background (Muller, Borrell, et al. 2013; Fenner et al. 2012; Gagneux 2013; Borrell and Gagneux 2011; Trauner et al. 2014). For instance, in rural Vietnam, Beijing strains are strongly associated with MDR-­TB but

Strains and Drug Resistance 143

only in the presence of streptomycin resistance (Buu et al. 2012). Such effects, along with the choice of drug combinations, determine the selection pressure on genotypes within individual infections (the chance of developing acquired resistance) and, consequently, on the genotypes that are transmitted from one person to another. Third, drug selection acting on genotypic variation, both within and among cases, will cause a shift in the average characteristics of each phenotype (group of genotypes; Perez-­Lago et al. 2014; Cohen and Murray 2004). For example, new mutations and other novel genetic rearrangements are often deleterious to mycobacteria (such as rifampicin-­resistant rpoB mutants; Mariam et al. 2004; Gagneux et al. 2006; Strauss et al. 2008), though strains carrying some resistance genes can, through mutation and selection, compensate for the initial loss of fitness (Gagneux et al. 2006; Gagneux 2009; Smith et al. 2014), while circulating in populations (Gagneux et al. 2006; Gagneux 2013; Muller, Borrell, et al. 2013; Comas et al. 2012). Drug combinations are an effective way to prevent the spread of resistance because, while the probability of one resistance mutation arising is low (of the order 10–­8 per generation), the probability of two resistance mutations arising to two drugs with different modes of action is much smaller. The chance of two mutations arising independently in the same genetic division event is, for example, 10–­8 × 10–­8 = 10–­16 per generation; allowing for two mutations to arise over a sequence of divisions in a growing bacterial population increases the chance of multiple resistance arising, perhaps to as much as 10–­4 or 10–­5 (Colijn et al. 2011). Although TB patients are routinely treated with drug combinations because multiple-­resistance mutations are less common than single mutations, drug-­ specific pharmacodynamics, low drug quality, incorrect prescriptions, and poor adherence can expose resistant genotypes to nonlethal drug concentrations. Given a mutation rate of the order of 10–­8 per generation, the emergence of a resistant genotype is almost inevitable in a population of 100 million bacteria, and that genotype will become more common under drug-­selection pressure. The cure rates of patients carrying MDR-­TB strains, when treated with isoniazid and rifampicin, are lower than drug-­susceptible strains (but not zero), and the relapse rates may be higher (Espinal, Kim, et al. 2000; Mak et al. 2008; Cox et al. 2006, 2008; Keshavjee et al. 2008; Mitnick 2008; Green Light Committee Initiative 2007; Kwon et al. 2008; Bonilla et al. 2008). The cure rates associated with a phenotype are also highly variable from one setting to another because of the underlying genotypic variation, because of comorbidities (e.g., related to HIV infection and alcohol or tobacco use), and because of the diversity of treatment protocols, with variable levels of adherence (Falzon et al. 2013; Kurbatova et al. 2012; Franke et al. 2008; Orenstein et al. 2009).

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The failure of treatment with first-­line drugs—or the threat of failure—invites the use of second-­line drugs, so some strains become resistant to these second-­ line drugs as well. Extensively drug-­resistant (XDR) strains are refractory not only to isoniazid and rifampicin (MDR), but also to one or more fluoroquinolones (such as ciprofloxacin, levofloxacin, moxifloxacin, and ofloxacin) and to injectables (the polypeptide capreomycin, or the aminoglycosides kanamycin and amikacin; Singh et al. 2007; Koenig 2008; Jones et al. 2008; World Health Organization 2008; Cegielski et al. 2014). Combinations of fluoroquinolones and injectables, among others, have been reserved for second-­line treatment because they are, variously, more costly, less efficacious, harder to administer, and more toxic. Nevertheless, second-­line drugs are gaining wider usage, so more strains will inevitably become XDR. The move to second-­line drugs is also a move toward broader-­spectrum antibiotics. A wider concern about their use is that treatment for TB will select for resistance among other coexisting pathogens. For example, invasive streptococcal infection that is resistant to levofloxacin has been associated with a history of TB treatment in South Africa (von Gottberg et al. 2008). Another risk is that drugs such as fluoroquinolones, which are routinely used in the treatment of community-­acquired bacterial infections, are also inadvertently used to treat undiagnosed TB, effectively by monotherapy, which promotes the multiplication and spread of resistant strains. For TB, as for other infectious diseases, the escalation of resistance from MDR to XDR and beyond feeds concern that, with a diminishing armory of effective treatments, the end of the antibiotic era is in sight (Hancock 2007; Raviglione 2006; Laxminarayan et al. 2013). Despite the genetic complexity behind labels such as MDR or XDR, the analysis in this chapter focuses on these phenotypes rather than on the diversity of underlying genotypes. There are three practical reasons for doing this: First, genotypes are not yet routinely identified in epidemiological studies; second, there is presently insufficient information to assign different therapeutic, clinical and epidemiological characteristics to all genotypes; and third, the intent here is to understand the epidemiology and evolution of strains as they have been, and are currently, defined.

THE LINK BETWEEN DRUG RESISTANCE AND HIV COINFECTION People who are infected with both M. tuberculosis and HIV are at progressively higher risk of developing TB as their immunity breaks down (Chapter 6). In settings where a high proportion of TB cases are drug resistant, HIV coinfection will obviously increase the number of resistant cases. Much less clear are

Strains and Drug Resistance 145

the circumstances under which HIV coinfection could exacerbate the resistance problem, either by increasing the proportion of cases carrying resistant strains,or by having detrimental effects on treatment. There are at least five reasons, in principle, why antibiotic resistance might be found more often among HIV-­infected TB patients (Dye, Williams, et al. 2002). First, M. tuberculosis strains with lower genetic fitness, manifest as a lower capacity to cause disease following infection (i.e., they are, in this sense, less virulent), may appear only in immunosuppressed people (Strauss et al. 2008). The second possibility is that TB among HIV-­infected persons is due to recent infection, where a higher fraction of recent infections is drug resistant. Third, there may be shared risk factors for infection with HIV and drug-­resistant strains of M. tuberculosis, such as injecting drug use and hospitalization. This is likely to be the main reason for the numerous reported nosocomial outbreaks of MDR among AIDS patients. Fourth, the treatment of immunosuppressed TB patients might fail because such patients carry a larger number of bacteria. On top of this, resistance would emerge more readily if the larger population of microorganisms were also genetically more diverse. Finally, HIV-­infected TB patients may be subjected more often to functional monotherapy (Vernon et al. 1999). There are various mechanisms by which bacteria may be exposed to one drug only, even while a patient is under treatment with the recommended drug combination. For HIV-­uninfected patients carrying drug-­susceptible strains, bacterial replication is normally suppressed with four drugs during the first 2 months of intensive phase treatment. Patients then typically switch to a combination of isoniazid and rifampicin during the 4-­month continuation phase. If M. tuberculosis in HIV/AIDS patients can continue replicating into the continuation phase, then replicating organisms might be exposed to rifampicin alone because isoniazid has a shorter half-­life and there are no other supporting drugs. Resistance to rifamycins has been seen repeatedly in HIV-­infected TB patients, perhaps for this reason. One general empirical observation is that MDR appears to be uncommon in sub-­Saharan Africa, the epicenter of the AIDS pandemic (Figure 5.2B). More specifically, a review of 24 observational studies found that the frequency of MDR-­TB tended to be higher in people with HIV infection but only by 24% on average (pooled odds ratio, OR = 1.24; Mesfin et al. 2014). The effect was smaller in institution-­based as compared with population-­based studies and was bigger than average for primary MDR-­TB (OR = 2.28). These results are in line with a separate compilation of data from 24 countries, in which the majority showed a positive association between HIV infection and MDR-­TB, but fewer than half were statistically significant by the usual criterion (p < 0.05; Dean et al. 2014). The mechanisms behind these weak effects and behind the variation among

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New Retreatment 60

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2 4 8 16 32 Estimated MDR-TB (%, upper limit of range)

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Estimated incidence new MDR-TB (per 100,000/yr)

AFR AMR EMR EUR

10

1%

SEA WPR 0.1%

1

0.1

1

10 100 1000 Estimated incidence new TB cases (per 100,000/yr)

Figure 5.2. A.  Frequency distribution of MDR-TB percentage among all new (black) and retreatment (grey) cases. Median and weighted mean estimated MDR-TB percentages in 216 countries and territories in 2012 were 1.8% and 3.6% among new cases and 14.0% and 18.2% among retreatment cases. B.  Estimated incidence of new MDR-TB cases in relation to all new TB cases for the six WHO regions of the world. Only countries with populations greater than 1 million are represented. Diagonal lines represent a fixed percentage of new TB cases carrying MDR strains. African countries (filled circles) and European countries (mainly former Soviet) tend to have higher rates. Data from WHO (World Health Organization 2013a).

Strains and Drug Resistance 147

studies remain to be properly explored. Among various possible methodological difficulties (Mesfin et al. 2014), one is concerned with population dynamics: real biological associations between HIV coinfection and drug resistance could be hard to detect because each fluctuates in a different way through time (Sergeev et al. 2012). Although nonlinear dynamic modeling is not standardized in the same way as linear correlation analysis, it is potentially a more sensitive tool to be applied in future studies.

GLOBAL DISTRIBUTION OF DRUG-­RESISTANT TB Most of the current information about the frequency of resistance (phenotypes) around the world comes from testing patients who are diagnosed in public health facilities, although some data have been obtained from national TB prevalence surveys. The measures and estimates of resistance derived from routine surveillance are approximate and possibly biased (Cohen et al. 2008; Ben Amor et al. 2008; Cohen et al. 2010), but these samples provide the best available measure of the frequency of drug-­resistant genotypes, defined as the proportion of TB patients who carry the various resistant phenotypes (World Health Organization 2013a). Given the widespread and often inappropriate use of isoniazid and rifampicin for more than four decades, it is perhaps surprising how little MDR-­TB there is worldwide. In 2012, the estimated frequency of MDR among new TB cases was just 3.6% and among previously treated cases, 20.2% (Figure 5.2A; World Health Organization 2013a), figures that have remained quite stable over repeated estimation exercises (Dye, Espinal, et al. 2002, Zignol et al. 2006, World Health Organization 2014c). The estimate of MDR among new cases was corroborated by an independent estimate of 3.2% among children under 15 years old (Jenkins et al. 2014). But there is important geographical variation around the average: resistance rates are far higher in eastern Europe and central Asia, where some countries report MDR-­TB in more than 20% of new cases and more than 50% of previously treated cases (Figure 5.2B; van der Werf et al. 2014; Zignol et al. 2014). In numbers rather than rates, there were an estimated 450,000 new cases of MDR-­TB worldwide in 2012, of which more than half were living in India, China, or Russia. About 94,000 TB cases eligible for MDR-­TB treatment were notified globally, mostly by European countries, India, and South Africa, and 77,000 were started on second-­line treatment. Up to 2012, XDR-­TB had been found in 92 countries, and approximately 10% of MDR-­TB cases were also XDR-­TB (World Health Organization 2013a; Dheda et al. 2014). The percentage of cases diagnosed with MDR-­TB that were also enrolled on treatment was

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especially low in African countries (51%; Kidenya et al. 2013).Treatment success for MDR-­TB exceeded 75% in 34 of 107 countries that provided data but averaged 48% overall (World Health Organization 2013a), about the same as the 54% reported in a separate 9000-­patient meta-­analysis (Ahuja et al. 2012). The high frequency of MDR-­TB in some settings, coupled with poor treatment outcomes, underlines a key question for TB control: do drug-­genotype interactions make it inevitable that resistant strains will spread through M. tuberculosis and human populations? Or can resistant strains be contained by prompt diagnosis and treatment and by achieving the highest possible cure rates with drugs (Dye and Espinal 2001b)? One way to answer to these questions is through empirical observations, preferably in controlled experiments. Another complementary method is by quantitative investigation of the relative and absolute fitness of drug resistant strains, as follows.

RELATIVE REPRODUCTIVE FITNESS Taking the units of investigation to be TB cases (rather than M. tuberculosis bacteria), the question of whether drug-­resistant strains will spread through a drug-­susceptible population, and how fast, is determined by the relative and absolute reproductive fitness of drug-­sensitive and drug-­resistant cases (Dye and Williams 2009; Dye 2009; Dye and Espinal 2001a; Dye, Williams, et al. 2002). In a population that consists of only two types of cases, carrying sensitive (S) and resistant (R) strains, the basic case reproduction number (R0) for S and R can each be greater or less than 1 and greater or less than each other, giving six permutations (Table 5.1). If the reproduction numbers of S and R are both less than 1, TB is condemned to extinction (conditions 1 and 2 in Table 5.1), which is the ultimate aim of control. Sensitive strains will persist in the long run if R0S >1 and R0S >R0R (conditions 5 and 6). So long as TB persists, there will be some drug resistance, because resistant genotypes arise by mutation and will be transmitted at least occasionally. However, if R0R 1: then resistant cases not only persist, but they persist in self-­sustaining transmission cycles (conditions 3–5). Whenever R0R >1 and R0R >R0S, resistant cases out-­compete sensitive cases and completely replace them. This outcome, however, take decades to reach. Under most circumstances away from equilibrium, the proportion of new cases that is resistant will increase through time (Table 5.1). That can happen, in principle, even when a control program is driving both resistant and sensitive cases to extinction. To cut the fraction as well as the number of resistant cases

Strains and Drug Resistance 149

Table 5.1. Six criteria governing the long-term dynamics of drug-sensitive (S) and drug-resistant (R) tuberculosis depending on the basic case reproduction numbers, R0. Outcomes

1

Condition on R0S

Condition on R0R

R0S

Extinction of S and R

Rising

3

>1

Extinction of S; R persists in self-sustaining transmission cycles

Rising to fixation

> R0S

Extinction of S; R persists in self-sustaining transmission cycles

Rising to fixation

Persistence of S and R

Rising then steady

Extinction of S, persistence of R

Rising then steady but low

4

>1

5

< R0S

6

10%) and in how many countries. The data presented here from countries as different as Estonia and Hong Kong suggest that, with the use of DST and a choice of first-­and second-­line drugs, both the incidence of MDR per capita and the proportion of patients carrying MDR strains can be reduced, the former more quickly than the latter. The optimistic interpretation of these data is that good program management, including prompt diagnosis and appropriate treatment of drug-­resistant TB, can indeed keep the case reproduction number of MDR strains below their replacement rate and even below that of non-­MDR strains. MDR-­TB can be forced into decline although, as for antibiotic resistance in other bacteria, the rate of decline will be slow (Andersson and Hughes 2010). Before reaching the firm conclusion that drug-­resistant TB is controllable, a fuller explanation is needed for why MDR-­TB incidence is falling in some places and not in others. Notwithstanding reports from Mexico (DeRiemer et al. 2005; Espinal and Dye 2005) and Taiwan (Liao and Lin 2012) that DOTStype control programs can be effective even in the presence of MDR-TB, the question of whether second-­line drugs are both necessary and sufficient to bring down the incidence of MDR and to reduce MDR frequency among all TB cases has not yet been convincingly resolved. From a clinical perspective, the goal in treating patients with MDR and XDR is to achieve early detection and high cure rates, preferably above 75%, to maximize survival for patients and minimize transmission to others. Where MDR and XDR are not under control, as in Russia and possibly Botswana (Casali et al. 2014; Zignol et al. 2012), is the frequency of resistance increasing because of poor diagnostic and treatment practices? Or do the circulating genotypes have intrinsically higher survival and multiplication rates, perhaps due to the type and combination of resistance genes, or because of HIV coinfection? Net reproductive fitness is not, of course, determined by genes or environment acting independently but by the interaction between the

Strains and Drug Resistance 161

two. The diversity of time trends in drug-­susceptible and drug-­resistant cases (Figures 5.5–5.7) reflect the ways in which these interactions play out in different settings around the world. The main conclusion of this chapter, that drug resistance is preventable and reversible, must be corroborated and expanded with longer series of data from a wider range of countries (including India and China), countries with high rates of HIV infection (notably Botswana and South Africa), and those reporting cases of extensively drug resistant TB (XDR-­TB). If, despite compensatory mutations, epistasis and apparently high selection pressures, drug-­resistant strains do not spread to fixation in M. tuberculosis populations, we need to understand why. In short, the optimistic view presented here must be tested against a bleaker imagined future, where virulent, untreatable, extensively drug-­resistant TB spreads out of “hot zones,” foreshadowing a return to the dark ages that preceded chemotherapy (Fox 1990; Blower and Chou 2004; Reichman 2013).

CHAPTER 6

TB and HIV/AIDS Although a sharp increase in tuberculosis incidence has been observed where both tuberculous and HIV infections are prevalent, it is very probable that the transmission of tuberculous infection may be much less affected. —Karel Styblo and Donald Enarson (1991) Will DOTS do it? —Kevin de Cock and Richard Chaisson (1999)

Contemplating the few data at hand in 1991, Styblo and Enarson (1991) presciently judged some of the consequences of the unfolding TB-­HIV epidemic. Writing in the same year, Styblo and Bumgarner (1991) concluded that the 70% case detection and 85% cure rates—to be achieved under the DOTS strategy— “may contain the excess infections caused by HIV.” At the end of that decade, during which HIV infection spread most rapidly in Africa, De Cock and Chaisson (1999) offered a reappraisal of TB control for the AIDS era. They predicted that DOTS (Chapter 4) as a single control strategy would not succeed, especially in sub-­Saharan Africa. Looking back in 2011, De Cock observed that DOTS-­type programs had indeed been less successful than hoped, attributing the cause to inadequate technology and poor coordination between TB and HIV/AIDS control programs (De Cock et al. 2011). Notwithstanding the limitations of DOTS, there have been some major successes in the control of both HIV/AIDS and TB. The discovery and widespread use of antiretroviral therapy (ART) is among the greatest advances in public health during the past 30 years. The scale-­up of ART put 10 million HIV-­positive people in low-­and middle-­income countries on treatment by the end of 2012, 7.5 million in the African region (World Health Organization 2013b). AIDS-­ related mortality is falling rapidly; for those who start ART early, life expectancy has returned to around 80% of normal (World Health Organization 2013b) and is likely to get better still. In Kwa-­Zulu Natal, ART increased lifespans by

TB and HIV/AIDS 163

a remarkable 11.3 years in the 8 years from 2003 to 2011 (Bor et al. 2013), with effects of similar size replicated across South Africa (April et al. 2014). A growing number of pregnant women are receiving ART as prophylaxis, so mother-­to-­child HIV transmission is falling, too (World Health Organization 2013b). Nearly half (46%) of all notified TB patients were tested for HIV in 2012, but more than 85% were tested in 15 countries, with the largest estimated numbers of HIV-­positive cases, mainly in Africa (World Health Organization 2013a). Fifty-­seven percent of HIV-­positive TB cases were put on ART, and 80% received cotrimoxazole (a broad-­spectrum antibiotic) preventive therapy (CPT). Of 1.6 million people enrolled in HIV care in 2012, 520,000 received isoniazid preventive therapy (IPT) to reduce the risk of developing TB disease. These observations on the rise of TB cases in Africa and elsewhere and on the discovery and implementation of control measures raise a series of questions about the population biology of TB linked to HIV/AIDS. Why did TB case incidence increase by a factor of 2–3 in some African populations? Was it simply because a large number of coinfected people were immunosuppressed, or did HIV-­positive TB cases also contribute to M. tuberculosis transmission? Are most TB-­HIV cases caused by acquiring HIV on top of existing M. tuberculosis infections or the other way around? Is the decline in TB, now seen in most countries with high HIV prevalence, due to the fall in HIV incidence and prevalence or also to specific TB control measures? Can the diagnosis and treatment of TB disease (the essence of the original DOTS approach) be effective even in settings of high HIV prevalence, and what could be added by other preventive and curative methods, including ART and IPT? These questions are the subject of this chapter. The answers will guide the choice of control measures for TB and HIV/AIDS in any setting and calibrate expectations of success.

HIV INFECTION AS A RISK FACTOR FOR TB Absolute and Relative Risk From the perspective of individuals (rather than populations), HIV coinfection is the most powerful known risk factor for susceptibility to M. tuberculosis infection and progression to active disease (Chapter 3). Without ART, the incidence rate of TB among coinfected people increases exponentially with advancing immunosuppression (Williams and Dye 2003; Williams et al. 2010; Lodi et al. 2012). The relative risk of TB among HIV-­positive people is, on average, 10–15 times higher than for HIV negatives. That risk is commonly measured as the incidence rate ratio (IRR), the TB incidence rate in HIV positives (I+) divided

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by that in HIV negatives (I–; Corbett et al. 2003). IRR depends on the prevalence of M. tuberculosis in HIV-­positive (m+) and HIV-­negative populations (m–) and on the relative rate of progression from infection to TB disease (r+ and r–). Thus

I+ m+ r+ h . (6.1) − = − − I m r (1− h)

Let r+ = kr–, with k the relative progression rate (using the same notation as in Chapter 3), so the proportion of incident TB cases that is HIV-­positive is

I+ m+ hk . (6.2) = + I m hk + m− (1− h)

If the prevalence of M. tuberculosis infection is the same in HIV-­positive and HIV-­negative individuals (m+ = m–), then

I+ hk = . (6.3) I 1 + h (k − 1)

Equation (6.3) is the usual formula for the prevalence of a risk factor among all incident cases, but the distinction between equations (6.2) and (6.3) highlights the fact that M. tuberculosis infection may not be independent of HIV infection. The fraction of cases attributable to HIV, which is smaller than the proportion that is HIV positive, is similar to equation (6.3) but with k replaced by k – 1 in the numerator, so as to count only the excess due to HIV (Chapter 3). The rates of progression from M. tuberculosis infection to disease vary with time since infection, both for HIV-­negative and HIV-­positive people. The quantity k is the ratio of these variable rates, so it too changes with time since infection. For HIV negatives, progression is faster on average among people who have recently been infected; the initial mix of fast and slow progressors later becomes dominated by slow progressors, so the average rate of progression decelerates with time since acquiring M. tuberculosis infection (Chapter 2). For HIV positives, the rate of progression to TB depends on time since acquiring HIV infection, and increases with immunosuppression, as signaled by falling CD4+ cell count. The changing risk of TB during the course of HIV infection was summarized by Williams et al. (2010) as

I t = I 0 e α [C − C ] = I 0 kt . (6.4) 0

t

In a combined analysis of three separate studies, they found that TB incidence (It ) in HIV-­positive people increased from initial incidence (I0) at a rate a = 0.36 ± 0.12 (95%CL) per 100-­unit reduction in CD4+ cells/L (Ct) from its starting value (C0).

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The CD4+ cell count among HIV-­negative people differs not only among individuals but also among populations, for reasons that are still unknown (Williams et al. 2006). For example, four surveys in Africa recorded: Botswana, 626 ± 26 (95% CL), South Africa, 1179 ± 36, Tanzania, 911 ± 38, and Zambia, 840 ± 60. The mean CD4+ count in 18 African populations was 879 cells/L. The life expectancy of HIV-­positive people, approximately 10 years, is independent of the initial CD4+ count, so in individuals (and populations) where the initial value is higher, the rate of decline is steeper. Curiously, this means that, for any given CD4+ count, the life expectancy of people in Botswana is greater than that in South Africa. One implication is that guidelines on when to start antiretroviral therapy, which are based on threshold CD4+ counts (e.g., 500 cells /mL; World Health Organization 2013b), need local adaptation. Shortly after HIV seroconversion, during the 1–3-­week intensive phase of HIV infection (Cohen et al. 2012), CD4+ cell count drops by about  = 0.25 of its initial value. It then falls linearly with time from a fraction 1 -  of its initial value, C0, so that

Ci = (1 − ∆) C 0 (9 − 2i) / 7 (6.5)

Here i is one of n = 4 progressive stages of HIV infection. Each stage is assumed to last 2.5 years, so Ct = 4Ci (cf equation (6.4)). With these formulas, Ci falls to 0.1 of its initial value after stage 4 (year 10) (Figure 6.1A). Thus, with 0.36/100 cells/L and  = 0.25  879, the incidence of TB at the end of the acute phase of HIV infection is 2.2 times the initial incidence in a HIV-­negative population. The incidence rate of TB at 2, 5, and 10 years after becoming infected is 3.8, 8.5, and 33.1 times its initial value (Figure 6.1A). The IRR depends on all the elements of equation (6.1), and these elements vary from place to place and from time to time, so it is not surprising that measurements of IRR differ among countries. For instance, based on national data from more than 100 countries with generalized HIV epidemics (>1% HIV prevalence in 15–49-­year-­olds), median values and ranges of IRR have been calculated as 5.9 (range 3.5–8.0 across regional groups of countries; Corbett et al. 2003a), 21 (interquartile range, IQR 14–25; World Health Organization 2011), and 14 (IQR 12–20; World Health Organization 2012a). Which, among all the possible determinants of IRR, are the most important determinants of this variation? The numerator of IRR (TB incidence in the HIV-­positive population) is expected to increase as HIV epidemics age, when a higher proportion of people (untreated with ART) move into the advanced stages of infection (Figure 6.1B). But the denominator of IRR (TB incidence in the HIV-­negative population) varies too. Where TB incidence fell to its lowest levels in southern Africa before the introduction of HIV, we can speculate that

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incidence was also relatively low in relation to the reservoir of M. tuberculosis infection, so that a higher proportion of cases came from reactivation rather than recent infection. These are the characteristics of TB epidemics in decline (Chapter 2) and could explain why, for example, IRR is larger for Zimbabwe (TB incidence 35/100,000 in 1980, IRR = 35 in 2010) than for Botswana (TB incidence 218/100,000 in 1980, IRR = 5 in 2010; Figure 6.1C). For countries with low-­prevalence or concentrated HIV epidemics, IRR tends to be higher than in generalized HIV epidemics (Corbett et al. 2003a); for example, it was found to be 60 (range 41–77) based on data from the United States (Corbett et al. 2003a) and 34 (IQR 20–34) for more than 100 countries investigated by WHO in 2010 (World Health Organization 2011). One explanation, as for Zimbabwe, is that the denominator of the IRR is low because a high proportion of cases come from reactivation in countries with low TB incidence. Another is that the prevalence of M. tuberculosis infection is greater in HIV-­positive than in HIV-­negative populations (m+ > m–, and equation 6.2 is preferred to equation 6.3). In other words, there are shared risk factors for HIV and M. tuberculosis infection, for example, among intravenous drug users. In sum, measures of the IRR give some useful insights into the structure of TB-­HIV epidemics, but they also raise questions that are a point of entry for deeper investigations. Transmission and the Infectious Period HIV-­positive TB cases with M. tuberculosis in sputum almost certainly contribute to transmission, but the contribution from each case is probably smaller than for HIV-­negative cases. This is, firstly, because the infectious period is typically shorter. Progression to severe disease is rapid. Without treatment for HIV and TB, life expectancy can be measured in months (Corbett et al. 2003a: Straetemans et al. 2010); with TB treatment, cases rapidly become non­infectious (Frieden 2004). In addition, TB cases with advanced immunosuppression (low CD4+ counts) are less likely to have cavitary, pulmonary disease and are probably therefore less infectious per unit time (Figure 6.2; Affusim et al. 2013; Kwan and Ernst 2011; Perlman et al. 1997; Munthali et al. 2014; Gupta et al. 2013). Household contact studies of the relative infectiousness of HIV-­positive as compared with HIV-­negative TB cases have measured either fewer transmitted infections (Espinal, Peréz, et al. 2000; Crampin et al. 2008; Carvalho et al. 2001) or no difference (Cruciani et al. 2001; Kifai and Bakari 2009). However, more discriminating studies of infectiousness by HIV stage have found that HIV-­positive TB cases are less likely to transmit infection to others during the

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Figure 6.2.  The frequency of cavitation in pulmonary TB is correlated with CD4+ cell count; cases with low CD4+ cell counts are likely to be less infectious per unit time. Adapted from Kwan and Ernst (2011).

advanced stages of HIV infection (e.g., CD4+ < 250 cells/L), consistent with the picture in Figure 6.2 (Kenyon et al. 2002; Huang et al. 2013). A small number of investigations have attempted to measure the ratio of infectious periods in HIV-­positive and HIV-­negative people, with variable results: three estimates are 0.13 (0.02-­0.75), 0.25 (0.11–0.39), and 1.34 (0.41-­6.56). The average of such disparate estimates, calculated as 0.31 (0.18–0.53), is of doubtful utility (Williams et al. 2010). Nevertheless, putting these data alongside those from the household contact studies, it seems very likely that HIV-­positive TB cases, treated or untreated for TB and HIV, typically transmit fewer infections than HIV negatives. To illustrate the effects of individual risk and infectiousness at population level, consider South Africa as an example. If 16% of adults are HIV positive (and thus 84% HIV negative), each at 15 times the risk of TB, and each HIV-­ positive TB case transmits 0.3 times as many infections as a HIV-­negative case, then the HIV epidemic generates 3.2 times the number of TB cases in the adult population (>60% HIV positive) but only 1.6 times the number of infections. The consequences of these observations for the TB-­HIV epidemic in South Africa and other countries are examined in greater detail shortly, after an overview of TB-­HIV epidemiology worldwide.

TB and HIV/AIDS 169

GLOBAL EPIDEMIOLOGY OF TB LINKED TO HIV/AIDS In 2012, an estimated 1.1 million (13%) of 8.6 million people who developed TB worldwide were HIV positive and 320,000 died, a case fatality rate of 30% (World Health Organization 2013a). The WHO African region had by far the highest proportion of TB cases infected with HIV (37%), followed by the Americas (11%), Europe (5%), and South-­East Asia (5%). In nine countries of eastern and southern Africa, the majority of TB cases were HIV positive, including Botswana (62%), South Africa (63%), Zimbabwe (71%), and Swaziland (77%). Nine of the 10 countries with the largest numbers of new HIV-­positive TB cases were in Africa, with South Africa ranked first (330,000 cases) and India ranked second (130,000). In sub-­Saharan Africa, TB incidence was forced upward by the spread of HIV infection, with visible effects from the 1980s onward. TB prevalence also increased but less than TB incidence because HIV coinfection has shortened the average duration of illness. HIV incidence peaked during the 1990s, earlier in some countries (Zambia 1992) than others (South Africa 1998; Figure 6.3). HIV prevalence has also peaked, typically 2–3 years after incidence, but has been maintained at high levels by the introduction of antiretroviral therapy. TB case notification rates have peaked too, 7–12 years after HIV incidence, declining thereafter (at 4–6% annually in the examples in Figure 6.3). This delay is consistent with the fact that most TB cases arise during the later stages of HIV infection. It also suggests that the rise and fall of HIV incidence has been the principal determinant of TB trends in southern Africa rather than the activities of TB control programs. This question is considered further in the next section. Besides the spectacular HIV-­driven rise of TB in sub-­Saharan Africa, HIV coinfection has had locally important epidemiological effects in many other parts of the world, especially in subpopulations at high risk of coinfection. To pick just one example, only 7% of all TB cases tested in Vietnam were HIV positive in 2012. But in some provinces HIV has slowed, and even reversed the decline of TB due to unusually high infection rates among young men. Cantho and Haiphong provinces are examples, where HIV and TB are strongly associated with intravenous drug use (Figure 6.4; Thanh et al. 2010; Kato et al. 2013).

ANATOMY OF A TB-­HIV EPIDEMIC Modeling TB-­HIV interactions links the TB risk to individuals with the risk to populations (previous two sections) and provides a way to explore the options for control, building on the general account of control in Chapter 4. The following investigation

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centers on South Africa, the country that has not only the largest numbers of HIV-­ positive TB cases each year, but also very high rates per capita of TB-­HIV incidence (630/100,000 in 2012) and mortality (88/100,000; Reid et al. 2014). Figure 6.5 shows how the standard TB model (Chapter 2), extended to include HIV infection as a risk factor, describes the dual epidemic in South Africa (Figure 6.5). The time series of reported TB cases (points) is scaled to 3/4 of estimated cases (i.e., an assumed case detection rate of 75%, lines in Figure 6.5A). The number of reported cases increased by a factor of 3.3 between 1990 and 2012, but most of the increase was in HIV-­positive cases. Consequently, the proportion of cases HIV positive rose to around two-­thirds by 2012 (64% in Figure 6.5B). The high infection rate of HIV among TB cases, and the limited increase in HIV-­negative cases, implies that HIV-­positive cases are not responsible for much additional transmission, as predicted by Styblo and Enarson (1991), and as reflected in the studies of household contacts and infectious periods reviewed before. In this model for South Africa, the average duration of an episode of infectious TB is calculated, approximately, to be 1.9 years for HIV negatives and 0.9 years for HIV positives. In 2012, the estimated annual incidence rates of HIV-­negative and HIV-­positive cases were 304 and 540 per 100,000, and the annual risks of infection generated by these cases were 2.2% and 0.7%, respectively. The basic case reproduction numbers in the absence of any drug treatment were 4.2 for HIV-­negative TB cases but only 0.4 for HIV-­positive cases. The implication is that HIV-­positive TB is not self-­sustaining in this population (R0 < 1); that is, TB persistence in the HIV-­positive population relies on transmission from the HIV-­negative population. Among the large number of extra HIV-­positive TB cases, the majority were due to new HIV infections acquired by people already infected with M.  tuberculosis (Figure 6.5C). Much smaller numbers of cases were attributable to new M. tuberculosis infections or to reinfection of people who were already coinfected. Since year 2000, the second-­largest source of cases was from new M. tuberculosis infections of HIV-­negative people, and 80% of these infections were transmitted by HIV-­negative cases. Even though HIV incidence in South Africa peaked before 2000 (Figure 6.3), the predominance of cases arising from new HIV infections is expected to persist until 2015 and beyond unless there is a further sharp decline in HIV incidence (see the following). On the face of it, this analysis agrees partly, but not wholly, with the findings of genotyping studies (mostly restriction fragment-­length polymorphisms of the insertion sequence IS6110), which have examined clustering patterns to distinguish cases arising from more and less recent transmission. In these empirical studies, HIV-­positive TB cases among younger people (50 years) who are already carrying M. tuberculosis infections, the acquisition of HIV accelerates the progression of old (latent) infections to TB and increases susceptibility to reinfection. Due to reinfection, HIV-­positive TB cases in older people are caused by infections that are more recent, on average, than HIV-­negative cases in older people and are, therefore, expected to be found more often in clusters. All this is consistent with the data, but one other observation is not. Even if HIV-­positive cases are caused by a higher proportion of recent infections than HIV-­negative cases, the majority still come from acquiring HIV infection, not from reinfection. And yet Houben et al. (2011) found HIV-­positive and -­negative cases to be equally clustered among people who had evidence of a prior latent M. tuberculosis infection (tuberculin positive). If the same observation is made in other settings, it will require deeper investigation. These epidemiological studies of M. tuberculosis strains deserve careful consideration because they provide a check on the hypothetical balance (derived from models) of cases due to infection, reinfection, and reactivation. This understanding of TB-­HIV epidemiology determines the expected effectiveness of control measures, both for the treatment of latent infection and TB disease (see the following section). Besides these questions about case incidence, the measurement of mortality is critical during TB-­HIV epidemics because TB as an AIDS-­related illness has a high case fatality rate. The death rates of HIV-­positive and HIV-­negative cases in South Africa were calculated to be 155 and 55 per 100,000 population, respectively, in 2012, corresponding to case fatality rates of 29% and 18%, similar to WHO estimates (Figure 6.5D; World Health Organization 2013a). If the fatality of untreated HIV-­positive cases is close to 100%, prompt diagnosis and high cure rates are needed to keep the fraction dying as low as 29%. The provision of treatment for TB with HIV coinfection is formally synergistic because, without both together, HIV-­infected TB cases have a high risk of death. The importance of coupling early diagnosis with the right treatment, in the context of TB-­HIV control, is further explored in the next section.

TB CONTROL IN THE PRESENCE OF HIV The HIV-­induced upward trends in TB incidence and mortality have been a setback for TB control. However, the fact that national TB control programs have not been able to prevent the excess cases and deaths does not mean that

TB and HIV/AIDS 175

chemotherapy is completely ineffective. Moreover, while HIV is an extra complication in TB control, it also provides an additional target for intervention. The various options for controlling HIV-­related TB (here “TB-­HIV control,” for short) considered singly and in combination, are open to quantitative investigation. In addition to the usual approach to TB chemotherapy (Chapter 4), the recommended methods for TB-­HIV control center on the diagnosis of (1) HIV among TB cases and (2) TB among people infected with HIV. These are entry points for preventive and curative drug treatment (BCG vaccination is not expected to be effective). Number (2) embraces the “three Is,” which are intensive TB case finding among HIV-­positive people, isoniazid preventive therapy for HIV-­ positive people who do not have TB disease, and infection control in health care and congregate settings (World Health Organization 2012b; Gupta et al. 2014). Building on the overview of TB control in Chapter 4, the following sections of this chapter explore the effectiveness of the first two of these control methods on TB-­population dynamics. Infection control is omitted; as discussed in Chapter 4, although measures to prevent infection in health facilities are essential to protect staff and patients (Bock et al. 2007), they are not likely to have major effects at the level of whole populations. Drug Treatment of TB Disease The standard model, extended here to describe TB-­HIV epidemiology (Appendix 2), shows that combining the early detection of TB with a high cure rate should be among the most effective ways to cut TB burden, even in the midst of a major HIV epidemic. Beginning with TB patients who present at clinics (passive case detection), the primary goal is to permanently cure and prevent mortality among the highest possible fraction. Successful treatment requires the correct diagnosis, the correct drug combinations for TB and HIV, and adherence to treatment. The key questions about diagnosis are: What is the fraction of true TB cases among all patients presenting with signs and symptoms, how many of these cases are found by existing diagnostic procedures, and how many are put on treatment? Micro­ scopy identifies a large fraction of cases with bacteria in sputum, but cases with light bacterial loads are often sputum smear negative. Smear-­negative TB is more common among people with HIV infection (Getahun et al. 2007; Lawn and Wood 2011). Bacterial culture is a more sensitive diagnostic tool than microscopy, detecting a high proportion of cases, but is slow: culture in a liquid medium typically requires 2 weeks and on a solid medium, 4–8 weeks, although refined procedures are now shortening these delays (Ghodbane et al. 2014). For HIV-­positive TB cases with rapidly progressive disease, an immediate diagnostic decision saves lives. Consequently, where microscopy

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is the only instrument for bacteriological confirmation, the diagnosis is often made empirically or presumptively; that is, on the basis of chest radiography and symptoms (cough, chest pain, fever, weight loss, night sweats, HIV test), which are less sensitive and/or specific than bacteriological methods (Lawn, Ayles, et al. 2011; Theron et al. 2014). In view of the diversity of diagnostic tools and clinical practices, the scope for improving cure through better diagnosis varies from one setting to another (Lin et al. 2012). Without considering all the possibilities here, there are three general points to be made. First, in relation to empiric treatment, the effect of introducing new and more sensitive tools for diagnosis, notably nucleic acid amplification methods, depends on the prevailing quality of clinical diagnosis. The use of, for example, Xpert MTB/RIF for TB diagnosis could simply confirm TB among suspected cases already put on treatment, with no additional clinical or epidemiological benefit at all (aside from its use in detecting rifampicin-­ resistant TB; Chapter 5; Theron et al. 2014; Churchyard, Stevens, et al. 2014). Second, the broad question about diagnosis asks not simply what proportion of TB cases are correctly diagnosed, but what proportion starts treatment. The answer depends partly on technology and medical skills, but it also depends on methods of patient care that influence, for example, the rate of default (Botha et al. 2008; Churchyard, Stevens, et al. 2014; Khan et al. 2009; Macpherson et al. 2014). Third, where a high proportion of TB cases are HIV positive, raising diagnostic sensitivity is expected to have a smaller effect on transmission and case incidence (Figure 6.6A, compare lines 0 and 1) than on mortality; Figure 6.6B, line 1). This is because the improvement in sensitivity is least for infectious (principally smear-­positive) HIV-­negative cases that contribute most to transmission and most for HIV-­positive cases that are at greatest risk of death. Whether this difference exists in practice depends on local circumstances. Here the model in Figure 6.6 offers not a firm prediction, but a testable hypothesis. If a higher proportion of TB cases are correctly diagnosed, a higher proportion can be put on treatment and cured. However, treatment must also maximize the cure rate per se—that is, success among those put on treatment. Apart from the usual prescription that TB patients should adhere to the right drug regimen, those that test positive for HIV require antiretroviral therapy (ART) and cotrimoxazole (CPT) early during the course of TB treatment and after TB treatment has been completed (World Health Organization 2012b; Saraceni et al. 2014; Massire et al., 2011) As with diagnosis, the scope for increasing treatment success depends on current practice. However, Figure 6.6A and B (line 2) shows again that whatever the reduction in case incidence, the reduction in mortality is expected to be greater. The cure rate cannot exceed 100%, so further gains must be made by improving case detection, that is, by drawing a higher proportion of patients to clinics and sooner (improved passive case finding) or by seeking TB cases outside

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Figure 6.6.  Modeled impact of combined control measures on TB incidence (A, C) and mortality (B, D) in South Africa. Solid lines are principal strategies for control; the three dotted lines in each of (C) and (D) are counterfactuals. (A, B) 0, baseline; 1, increased diagnostic sensitivity; 2, increased cure rate; 3, earlier detection of all TB cases; 4, additional TB case finding among HIV-positive people. Grey lines show trajectories towards the post-2015 targets of reducing (A) incidence by half and (B) deaths by one-fourth between 2015 and 2025; the targets actually require a steeper decline in incidence after 2025. (C, D) 0, baseline with ART for HIV-positive people at risk of TB, scaling up from zero in 2000 to 70% coverage by 2020; –1, no ART; 1 (dotted), ART for HIV-positive people, reducing TB incidence but with no increase in survival; 2, lifelong IPT for HIV-positive people at risk of TB; 3 (dotted), 6 months of IPT; 4, ART plus other means of interrupting HIV transmission, so HIV incidence falls at 3%/year after 2015. The model and the parameter values for panels A and B are outlined in Appendix 2.

clinics (active or enhanced case finding). Either way, finding HIV-­positive and -­negative cases earlier can add significantly to the benefits obtained in clinics (Figure 6.6A and B, line 3). In theory the extra benefit could be large; in practice it will depend on the details of the case-­finding methods. Some methods of active TB case finding in populations, carried out with mobile vans or by door-­ to-­door visits (Corbett et al. 2010) and through household contacts of patients

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Chapter 6

(Rosencrantz 1987), do suggest that transmission can be reduced (albeit with some ambiguities) and that what works best depends on the setting. All these calculations show, like other analyses with similar models (Currie et al. 2003), that TB incidence can be reduced by chemotherapy even where high HIV prevalence is high. Two questions follow: Could chemotherapy have prevented the rise in TB incidence and, now that TB incidence has reached nearly 1% (1000/100,000) annually with two-­thirds of cases HIV-­positive, has the efficacy of chemotherapy been impaired? Or, to paraphrase De Cock and Chaisson, could DOTS have done it? For South Africa, the answer to both questions is: partly, but only with greater effort. First, Figure 6.7A shows that, even if the case detection rate had been improved in 1980 so as to halve the number of infections transmitted by each HIV-­negative case (from 18 to 9), TB incidence would still have increased to 440/100,000 in 2012, with 75% of cases HIV positive. Figure 6.7B compares the rate of decline in TB incidence following enhanced case detection in populations with and without HIV for the same initial steady-­state TB incidence (600/100,000/ year). When the rate of case finding is doubled in 2015 (cutting infections from HIV-­negative cases from 24 to 18), TB incidence falls at 4.7%/year with no HIV but at only 2.8%/year when HIV incidence is 0.5% annually. Figure 6.7A and B show, as expected, that HIV coinfection is a significant impediment to TB control. In comparison with South Africa, India has a smaller burden per capita of TB and of HIV/AIDS, and the former is not mainly attributable to the latter. The estimated incidence of TB was less than 200/100,000 population in 2012 (cf 1000/100,000, or 1%, in South Africa), and peak HIV incidence in the adult population (15–49 years) was 0.7% in 1998 (South Africa reached a maximum of 2.9% in in the same year). In the face of India’s unfolding HIV epidemic, Williams et al. (2005) investigated whether the national TB control program (NTP) could reduce TB incidence and halve prevalence and mortality between 1990 and 2015 to satisfy international targets. Unlike southern African countries, routine TB case notifications show no discernible signal of HIV as incidence rose to its peak toward the end of the 1990s (cf Figure 6.3). The expected, small rise in HIV-­positive TB cases (up to about 5% of all incident cases) may not have been visible in the national statistics, but it has certainly diminished the impact of the NTP (Figure 6.8A). However, by scaling up the detection of smear-­positive cases to 70% and the cure rate to 85% nationally by 2005 and maintaining these rates thereafter, the NTP should have been able to maintain a downward trend in incidence that began around year 2000 (Figure 6.8A). In line with the calculations for South Africa, the bigger challenge would be to prevent deaths among HIV-­positive cases (Figure 6.8B), notably in the south of the country, where HIV prevalence has been higher. HIV is not the main obstacle to TB control in India (Chapter 4), but neither is it a negligible problem.

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Figure 6.7.  Could DOTS have done it? A.  The effect of halving (from 18 to 9) the number of infections transmitted by each HIV-negative case (black), as compared with the observed trajectory of the epidemic. B.  Comparing the decline in TB incidence, from an initial steady state of 600 cases/100,000/year, when the rate of case finding is doubled in populations with (grey) and without HIV (black).

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Figure 6.8.  TB drug treatment to overcome the adverse effects of India’s HIV epidemic, showing (A) case incidence and (B) mortality. Lines compare the effects of HIV with no additional effort from the national control program (NTP, black), the NTP assuming that there was no HIV epidemic (light grey), and both together (dark grey). Adapted from Williams et al. (2005).

TB and HIV/AIDS 181

Intensive TB Case Finding among HIV-­positive People Going beyond attempts to find and cure more cases, the three Is focus specifically on TB linked to HIV. The first of these, intensive TB case finding among HIV-­positive people, would, of course, enhance the impact of control overall, but it is also expected to magnify the differential effect on case incidence (smaller, because HIV-­positive cases contribute little to transmission) and mortality (larger, because HIV-­positive cases are at greater risk of death; Figure 6.6A and B, lines 4). In this setting with high HIV prevalence, modeled on South Africa, all four interventions together are not expected to halve TB incidence by 2025, as demanded by the post-­2015 international targets for TB control (Chapter 4, Figure 6.6A, grey line). There is a greater chance of cutting deaths by 75%, but that would require a very rapid scale-­up of this combination of interventions (Figure 6.6B). Quite clearly, the effort to reach these targets requires more than the treatment of TB disease—it needs to be supported by methods of TB prevention. Antiretroviral Therapy for TB Prevention ART for HIV-­positive people reduces the per capita incidence rate of TB by about two-­thirds (65%, 95% CL 56%–72%, i.e., reduced to one-­third of its value without ART), There is some evidence that the beneficial effect of ART is greater for people with low CD4+ cell counts (84% reduction, range 74%–93%, for 350 CD4+ cells/L reduces the lifetime risk of TB, and