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Drivers of Tuberculosis Transmission
research-article
Author(s):
Barun Mathema
1 ,
Jason R Andrews
2 ,
Ted Cohen
3 ,
Martien W Borgdorff
4
,
5 ,
Marcel Behr
6 ,
Judith R Glynn
7 ,
Roxana Rustomjee
8 ,
Benjamin J Silk
9 ,
Robin Wood
10
Publication date (Electronic):
03 November 2017
Journal:
The Journal of Infectious Diseases
Publisher:
Oxford University Press
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Abstract
Measuring tuberculosis transmission is exceedingly difficult, given the remarkable variability in the timing of clinical disease after Mycobacterium tuberculosis infection; incident disease can result from either a recent (ie, weeks to months) or a remote (ie, several years to decades) infection event. Although we cannot identify with certainty the timing and location of tuberculosis transmission for individuals, approaches for estimating the individual probability of recent transmission and for estimating the fraction of tuberculosis cases due to recent transmission in populations have been developed. Data used to estimate the probable burden of recent transmission include tuberculosis case notifications in young children and trends in tuberculin skin test and interferon γ–release assays. More recently, M. tuberculosis whole-genome sequencing has been used to estimate population levels of recent transmission, identify the distribution of specific strains within communities, and decipher chains of transmission among culture-positive tuberculosis cases. The factors that drive the transmission of tuberculosis in communities depend on the burden of prevalent tuberculosis; the ways in which individuals live, work, and interact (eg, congregate settings); and the capacity of healthcare and public health systems to identify and effectively treat individuals with infectious forms of tuberculosis. Here we provide an overview of these factors, describe tools for measurement of ongoing transmission, and highlight knowledge gaps that must be addressed.
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Diabetes Mellitus Increases the Risk of Active Tuberculosis: A Systematic Review of 13 Observational Studies
Christie Y. Jeon, Megan Murray (2008)
Introduction Despite the availability of effective therapy, tuberculosis (TB) continues to infect an estimated one-third of the world's population, to cause disease in 8.8 million people per year, and to kill 1.6 million of those afflicted [1]. Current TB control measures focus on the prompt detection and treatment of those with infectious forms of the disease to prevent further transmission of the organism. Despite the enormous success of this strategy in TB control, the persistence of TB in many parts of the world suggests the need to expand control efforts to identify and address the individual and social determinants of the disease. Since the early part of the 20th century, clinicians have observed an association between diabetes mellitus (DM) and TB, although they were often unable to determine whether DM caused TB or whether TB led to the clinical manifestations of DM [2–6]. Furthermore, these reports did not address the issues of confounding and selection bias. More recently, multiple rigorous epidemiological studies investigating the relationship have demonstrated that DM is indeed positively associated with TB [7–11]. While the investigators suggested that the association reflects the effect of DM on TB, some controversy over the directionality of the association remains due to observations that TB disease induces temporary hyperglycemia, which resolves with treatment [12,13]. A causal link between DM and TB does not bode well for the future, as the global burden of DM is expected to rise from an estimated 180 million prevalent cases currently to a predicted 366 million by 2030 [14]. Experts have raised concerns about the merging epidemics of DM and TB [15–17], especially in low- to middle-income countries, such as India and China, that are experiencing the fastest increase in DM prevalence [18] and the highest burden of TB in the world [19]. Given the public health implications of a causal link between DM and TB, there is a clear need for a systematic assessment of the association in the medical literature. We undertook a systematic review to qualitatively and quantitatively summarize the existing evidence for the association between DM and TB, to examine the heterogeneity underlying the different studies, and to evaluate the methodological quality of the studies. As our aim was to summarize the effect of DM on TB, we did not include studies that investigated the reverse association. Methods We conducted our systematic review according to the guidelines set forth by the Meta-analysis of Observational Studies in Epidemiology (MOOSE) group for reporting of systematic reviews of observational studies (see Text S2 for the MOOSE Checklist) [20]. Search Strategy and Selection Criteria We searched the PubMed database from 1965 to March 2007 and the EMBASE database from 1974 to March 2007 for studies of the association between DM and TB disease; our search strategy is detailed in Box 1. We also hand-searched bibliographies of retrieved papers for additional references and contacted experts in the field for any unpublished studies. Since we speculated that studies that examined the association between DM and TB may not have referred to the term “diabetes” in the title or abstract, we also searched for studies that examined any risk factors for active TB. We restricted our analysis to human studies, and placed no restrictions on language. We included studies if they were peer-reviewed reports of cohort, case-control, or cross-sectional studies that either presented or allowed computation of a quantitative effect estimate of the relationship between DM and active TB and that controlled for possible confounding by age or age groups. We also included studies that compared prevalence or incidence of DM or TB of an observed population to a general population as long as they had performed stratification or standardization by age groups. We excluded studies if they were any of the following: case studies and reviews; studies among children; studies that did not provide effect estimates in odds ratios, rate ratios, or risk ratios, or did not allow the computation of such; studies that did not adjust for age; studies that employed different methods for assessing TB among individuals with and without DM or for assessing DM among TB patients and controls; studies that investigated the reverse association of the impact of TB disease or TB treatment on DM; anonymous reports; and duplicate reports on previously published studies. Box 1. Search Strategy to Identify Observational Studies on the Association of Diabetes and Active Tuberculosis PubMed MeSH terms 1. “tuberculosis” 2. “diabetes mellitus” 3. “cohort studies” OR “case-control studies” OR “cross-sectional studies” OR “epidemiologic studies” OR “follow-up studies” OR “longitudinal studies” OR “prospective studies” OR “retrospective studies” Text terms 1. “tuberculosis” 2. “diabetes” OR “glucose intolerance” OR “glucose tolerance” OR “insulin resistance” 3. “chronic disease(s)” OR “non-communicable disease(s)” 4. “risk factor(s)” Search strings (all inclusive): 1. 1 AND 2 2. 1 AND 3 AND 5 3. 1 AND 3 AND 6 4. 1 AND 3 AND 7 5. 4 AND 5 (for the year preceding 3/2/07 for articles that may not have been assigned MeSH terms) EMBASE Text terms 1. “tuberculosis”, major subject 2. “diabetes mellitus” 3. “risk factor(s)” 4. “observational study” OR “longitudinal study” OR “prospective study” OR “case-control study” OR “cross-sectional study” Search strings (all inclusive): 1. 1 AND 2 2. 1 AND 3 3. 1 AND 4 Data Extraction The two investigators (CJ, MM) independently read the papers and extracted information on the year and country of the study, background TB incidence, study population, study design, number of exposed/unexposed people or cases/controls, definitions and assessment of DM and TB, statistical methods, effect estimates and their standard errors, adjustment and stratification factors, response rates, the timing of diagnosis of DM relative to that of TB, and the potential duplication of data on the same individuals. Differences were resolved by consensus. For the studies that did not directly report the background TB incidence, we obtained data for the closest matching year and state (or country) made available by public databases (WHO global tuberculosis database, http://www.who.int/globalatlas/dataQuery/; CDC Wonder, http://wonder.cdc.gov/TB-v2005.html). Data Analysis We separated the studies by study design and assessed heterogeneity of effect estimates within each group of studies using the Cochrane Q test for heterogeneity [21] and the I2 statistic described by Higgins et al. [22]. We determined the 95% confidence intervals (CIs) for the I2 values using the test-based methods [22]. We performed meta-analysis for computation of a summary estimate only for the study design (i.e., cohort) that did not show significant heterogeneity. Effect estimates of other study designs were not summarized due to significant heterogeneity. For those studies that reported age, sex, race, or region stratum-specific effects, we calculated an overall adjusted effect estimate for the study using the inverse-variance weighting method, then included this summary estimate in the meta-analyses and sensitivity analyses. We decided a priori to use the Dersimonian and Laird random effects method to pool the effect estimates across studies for the meta-analyses, because the underlying true effect of DM would be expected to vary with regard to underlying TB susceptibility and the severity of DM, and because it would yield conservative 95% confidence intervals [23]. In order to identify possible sources of heterogeneity and to assess the effect of study quality on the reported effect estimates, we performed sensitivity analyses in which we compared pooled effect estimates for subgroups categorized by background TB incidence, geographical region, underlying medical conditions of the population under study, and the following quality-associated variables: time of assessment of DM in relation to TB diagnosis, method of DM assessment (self-report or medical records versus laboratory tests), method of TB assessment (microbiologically confirmed versus other), adjustment for important potential confounders, and the potential duplication of data on the same individuals. To determine whether the effect estimates varied significantly by the above-mentioned factors, we performed univariate meta-regressions, in which we regressed the study-specific log-transformed relative risks (RRs) by the variables representing the study characteristics, weighting the studies by the inverse of the sum of within-study and between-study variance for all studies within the comparison. For background TB incidence, we created an ordinal variable, 1 representing < 10/100,000 person-years to 3 representing ≥ 100/100,000 person-years. Coefficients of meta-regression represent differences in log-transformed RRs between the subgroups; we tested the significance of these coefficients by Student t-test, and significance was set at p < 0.10. We considered studies to be of higher quality if they specified that DM be diagnosed prior to the time of TB diagnosis; used blood glucose tests for diagnosis of DM; used a microbiological definition of TB; adjusted for at least age and sex; were cohort, nested case-control, or population-based case-control studies; or did not have the potential for duplication of data. As the average background incidence rate of TB did not exceed 2 per 100 person-years in any of the of the case-control studies that had not employed incidence density sampling, we assumed TB to be sufficiently rare that the odds ratios would estimate the risk ratios [24], and that it would therefore be valid to compute summary RR in the sensitivity analyses regardless of the measure of association and design of the study. We explored possible effect modification by age by examining the three studies that reported results by age groups [7,9,25]. For this analysis, we graphed the stratum-specific estimates in a forest plot, and tested for heterogeneity of the effects within each study by the Q-test and I2 value. We also performed meta-regression within each study in which we regressed the log-transformed RRs by the mid-points of the age-bands. For the unbound age group, ≥ 60 y, we added half the range of the neighboring age-band, or 5 y, to the cutoff. We computed the factor reduction in RR with 10 y increases in age, and reported the p-value for significance of trend. We assessed publication bias using the Begg test and Egger test [26,27]. Statistical procedures were carried out using R version 2.5.1 [28]. 95% CI of the I2 value was computed using the “heterogi” module in STATA version 10 [29]. Results We identified and screened 3,701 papers by titles and abstracts; of these, 3,378 were excluded because they did not study risk factors for TB, were studies among children, were case reports, reviews, or studies of TB treatment outcome (Figure 1). Of the remaining 323 articles, 232 studies were excluded because they did not report on the association between DM and TB, and 56 studies were excluded because they were review articles (12) or ecological studies (2); studied the clinical manifestations of TB in people with diabetes (11); studied the association of DM and TB treatment outcome (6); assessed latent, relapsed, clustered, or drug-resistant TB as the outcome (6); studied the reverse association of the effect of TB on DM (5); had no comparison group (5); were case reports (3); did not give a quantitative effect estimate (3); had collapsed DM and other chronic diseases into a single covariate (2); or was a study that had been reported elsewhere (1). We contacted the authors of four papers that reported including DM in a multivariate analysis but that did not provide the adjusted effect estimate for DM; we included the papers of the two authors who responded and provided these adjusted estimates [30,31]. Further exclusion of studies that did not adjust for age (11), studies that used a general population as the comparison group for TB incidence or DM prevalence without standardization by age (9), and studies that used different methods for ascertaining TB in the people with diabetes and control group (2), left 13 eligible studies. These included three prospective cohort studies [7,30,32], eight case-control studies [8,11,31–37], and two studies for which study design could not be classified as either cohort or case control, as TB case accrual occurred prospectively while the distribution of diabetes in the population was assessed during a different time period after baseline [9,25]. The studies were set in Canada (1), India (1), Mexico (1), Russia (1), South Korea (1), Taiwan (1), the UK (1), and the US (6), and were all reported in English and conducted in the last 15 y. Two of the cohort studies were among renal transplant patients [30,32], and three of the case-control studies were hospital-based or based on discharge records [8,11,35]. The studies are summarized in Table 1. Figure 1 Flow Chart of Literature Search for Studies on the Association between Diabetes Mellitus and Active Tuberculosis Table 1 Summary of the 13 Observational Studies of Association between Diabetes and Active Tuberculosis Included in the Meta-analysis Table 1 Extended. Figure 2 summarizes the adjusted effect estimates of the 13 studies categorized by the study design. We found substantial heterogeneity of effect estimates from studies within each study design; between-study variance accounted for 39% of the total variance among cohort studies, 68% of the total variance among case-control studies, and 99% of the total variance in the remaining two studies. Despite this heterogeneity, the forest plot shows that DM is positively associated with TB regardless of study design, with the exception of the study by Dyck et al. [25]. DM was associated with a 3.11-fold (95% CI 2.27–4.26) increased risk of TB in the cohort studies. Of note, the study conducted within a nontransplant population provided greater weight (63%) to the summary estimate than the other two cohort studies combined. The effect estimates in the remaining studies were heterogeneous and varied from a RR of 0.99 to 7.83. Figure 2 Forest Plot of the 13 Studies That Quantitatively Assessed the Association between Diabetes and Active Tuberculosis by Study Designs Size of the square is proportional to the precision of the study-specific effect estimates, and the bars indicate the corresponding 95% CIs. Arrows indicate that the bars are truncated to fit the plot. The diamond is centered on the summary RR of the cohorts studies, and the width indicates the corresponding 95% CI. *Other: The studies by Ponce-de-Leon et al. [7] and Dyck et al. [25] were not specified as prospective cohort or case-control. TB case accrual occurred prospectively, while the underlying distribution of diabetes was determined during a different time period after baseline. Table 2 shows that there is an increased risk of active TB among people with diabetes regardless of background incidence, study region, or underlying medical conditions in the cohort. In the sensitivity analyses, we noticed that the strength of association increased from a RR of 1.87 to a RR of 3.32 as background TB incidence of the study population increased from < 10/100,000 person-years to ≥ 100/100,000 person-years, but the trend was not significant (trend p = 0.229). Effect estimates were heterogeneous within each category of background TB incidence (I2 = 60%, 98%, and 76% from highest to lowest background TB incidence category). Table 2 Results of Sensitivity Analyses to Identify Sources of Heterogeneity in the Magnitudes of the Association between Diabetes and Active Tuberculosis We also found that the associations of DM and TB in the study populations from Central America [9], Europe [33,37], and Asia [7,30,32] (RRCentralAm = 6.00, RREurope=4.40, RRAsia = 3.11) were higher than those of North American studies [8,11,33,34–36] (RRNA = 1.46) (meta-regression p CentralAm = 0.006, p Europe = 0.004, p Asia = 0.03). Among North American studies, the pooled estimate of the relative risks for Hispanics from two studies [8,11] was higher (RR = 2.69) than that of non-Hispanics from the same study [8] and other North American studies (RR = 1.23) (meta-regression p = 0.060) (Table 2). In general, stratification of the studies by quality-associated variables did not reduce the heterogeneity of effect estimates. Nonetheless, DM remained positively associated with TB in all strata. Studies that explicitly reported that DM was diagnosed prior to TB showed stronger associations (RR = 2.73) [7,31–34] than those that did not establish the temporal order of DM and TB diagnosis (RR = 2.10) [8,9,11,25,30,35–37], although the difference was not significant (meta-regression p = 0.483). Associations were stronger in studies that classified DM exposure through empirical testing (RR = 3.89) [7,9,32,34] rather than medical records (RR = 1.61) (meta-regression p = 0.051) [8,11,25,30,31,33]; and in those that confirmed TB status using microbiological diagnosis (RR = 4.91) [7,9,35,37] than in the studies that did not confirm by microbiological tests (RR = 1.66) (meta-regression p = 0.015) [8,11,25,30–34,36]. Among case-control studies, those that were nested in a clearly identifiable population or were population-based also reported stronger associations (RR = 3.36) [31,33,34,37] than those that used hospital based controls (RR = 1.62) [8,11,37], but the difference was not significant (meta-regression p = 0.321). Studies that had adjusted for smoking showed stronger associations (RR = 4.40) [33,37], while studies in which an individual may have contributed more than one observation to the data revealed weaker associations (RR = 1.62) [8,11]. Although these results suggest that higher-quality studies gave stronger estimates of association, we also found that the association was weaker in studies that adjusted for socioeconomic status (RR = 1.66) (Table 2) [8,11,37]. Figure 3 presents the summary measures of the association between DM and TB by age group based on the data from the three studies that presented age-stratified RRs. The plots from Kim et al. [7] and Ponce-de-Leon et al. [9] demonstrate stronger associations of DM and TB under the age of 40 y and declining RR with increasing age in age groups over 40 y (trend p Kim = 0.014, p Ponce-de-Leon = 0.184). Each 10 y increase in age was associated with a 0.6-fold reduction in magnitude of association in the study by Kim et al. [7]. This trend was not apparent in the study by Dyck et al. (Figure 3) [25]. Figure 3 Forest Plot of Age-Specific Association between Diabetes and Active Tuberculosis from Kim et al. [7], Ponce-de-Leon et al. [9], and Dyck et al. [25] Size of the square is proportional to the precision of the study-specific effect estimates, and the bars indicate 95% CI of the effect estimates. Arrows indicate that the bars are truncated to fit the plot. *Meta-regression: Factor reduction in RR with 10 y increase in age; p-values are given for test of linear trend. HR, hazard ratio. Both the Egger test and Begg test for publication bias were insignificant (p = 0.37, p = 0.14). Discussion Summary of Findings Our meta-analysis shows that DM increases the risk of TB, regardless of different study designs, background TB incidence, or geographic region of the study. The cohort studies reveal that compared with people who do not have diabetes, people with diabetes have an approximately 3-fold risk of developing active TB. Higher increases in risk were seen among younger people, in populations with high background TB incidence, and in non-North American populations. Heterogeneity of strengths of association may reflect true geographic/ethnic differences in severity of DM, transmission dynamics of TB, and the distribution of effect modifiers such as age, or it may be due to differences in study methodology or rigor. Given this heterogeneity of the RR estimates and the fact that all the cohort studies were conducted in Asia, we note that the summary estimate may not be applicable to other populations and study types. While the included studies covered a relatively broad range of geographic areas, there were none from Africa, where TB incidence is high. Nonetheless, a positive association of DM and TB was noted in two African studies [38,39] and several other studies that we excluded from the meta-analysis [10,40–42], as well as in a previous narrative review [43] of the association of DM and TB. Unlike the previous review, our systematic review identified five additional studies that had examined the association of DM and TB, computed a pooled summary estimate among the cohort studies, and determined important sources of heterogeneity through rigorous sensitivity analyses. Public Health Implication With an estimated 180 million people who have diabetes, a figure expected to double by year 2030, it is clear that DM constitutes a substantial contributor to the current and future global burdens of TB. For example, if we assume a RR of 3 and a prevalence of DM in Mexico of 6%, we can conclude that DM accounts for 67% of active TB cases among people with diabetes, and 11% of cases among the entire Mexican population (see Text S1 for the calculation) [44]. The contribution of DM to the burden of TB may be even higher in countries such as India and China where the incidence TB is greater and mean age is lower. In fact, a recent study by Stevenson et al. determined that DM accounts for 80.5% of incident pulmonary TB among people with diabetes, and 14.8% of incident TB in the total population in India [16]. The population-attributable risk for diabetes is comparable to that of HIV/AIDS; while HIV/AIDS is strong risk factor for TB (RRHIV = 6.5–26 [45], approximately 2–9 times greater than the RRDM estimated in this study), it is a less prevalent medical condition (33 million people infected in 2007 [46], approximately 5–6 times less prevalent than DM). Given these figures it may be puzzling to observe a decrease in TB in those areas that have experienced a growing burden of DM. We attribute this observation to negative confounding by factors such as improved nutrition and TB control measures in the areas of increasing DM such as India and China. Were these other factors to remain the same, we would expect to see a TB incidence trend reflecting that of DM in accordance with the positive association. Biological Plausibility Numerous studies have presented convincing biological evidence in support of the causal relationship between DM and impaired host immunity to TB. Studies in animal models have demonstrated that diabetic mice experimentally infected with M. tuberculosis have higher bacterial loads compared to euglycemic mice, regardless of the route of inoculation of M. tuberculosis [47,48]. Compared to euglycemic mice, chronically diabetic mice also had significantly lower production of interferon-γ (IFN-γ) and interleukin-12 (IL-12) and fewer M. tuberculosis antigen (ESAT-6)-responsive T cells early in the course of M. tuberculosis infection, marking a diminished T helper 1 (Th1) adaptive immunity, which plays a crucial role in controlling TB infection [48]. In experimental studies of human plasma cells, high levels of insulin have been shown to promote a decrease in Th1 immunity through a reduction in the Th1 cell to Th2 cell ratio and IFN-γ to IL-4 ratio [49]. Additionally, an ex vivo comparison study of production of Th1 cytokines showed that nonspecific IFN-γ levels were significantly reduced in people with diabetes compared to controls without diabetes [50]. Another study indicated a dose–response relationship; levels of IFN-γ were negatively correlated with levels of HbA1c (a measure of serum glucose levels over time in humans) [51]. Furthermore, neutrophils from people with diabetes had reduced chemotaxis and oxidative killing potential than those of nondiabetic controls [52], and leukocyte bactericidal activity was reduced in people with diabetes, especially those with poor glucose control [53]. Taken together, these studies strongly support the hypothesis that DM directly impairs the innate and adaptive immune responses necessary to counter the proliferation of TB. Limitations There are several potential limitations to this study. Our analysis was based on estimates derived from observational studies that are vulnerable to confounding by variables associated with both DM and TB. To address the issue of potential confounding, we performed a sensitivity analysis in which we reported separate summary estimates for the studies that adjusted for important potential confounders and those that did not. Studies that controlled for socioeconomic status in a multivariable model found that the adjusted effect of DM was reduced, but not eliminated. Crude effect estimates were not provided in two of the larger studies that adjusted for socioeconomic status, thus the direction of bias cannot be determined. The three studies that did report both crude and the adjusted estimates [33,34,37] found that the adjusted RRs for DM were higher. Although we could not exclude the possibility of residual confounding by unmeasured confounders in these observational studies, such as other chronic diseases that often coexist with diabetes, we found that the effect of DM on TB risk persisted even after adjustment for multiple potential confounders that are likely to be correlated with unmeasured factors. Eight of the studies included in this meta-analysis were case-control studies. Control selection strategies included sampling from hospitals, discharge records, department of health records, the general population, and the cohort in which the study was nested. Sampling controls from hospital or discharge records may have introduced a Berkson bias—a selection bias that can occur when both the exposure and the outcome are associated with attendance at a health-care facility from which cases and controls are recruited [54]. Since DM can lead to multiple health problems, the prevalence of DM is likely to be higher among persons attending clinics or being admitted to hospitals than it is in the general population. This bias would be expected to result in an underestimation of the effect of DM on TB, an expectation that was consistent with our finding that studies using hospital-based controls reported lower effect estimates [54]. Other sources of potential bias include misclassification of either exposure or outcome, such as may have occurred in studies that did not employ laboratory tests to diagnose DM or TB. When we restricted our analysis to studies that used glucose tests to determine DM status, we found that effect estimates were higher than in the studies that relied on less-rigorous methods, consistent with our expectation of a bias toward the null among studies that nondifferentially misclassify the exposure. Studies that utilized glucose tests to classify the exposure may also have reported higher RRs of TB among people with diabetes, since they may have identified undiagnosed people with diabetes who remained untreated and therefore may have had higher glycemic levels that those who self-reported their status. Those studies that confirmed TB through microbiological diagnosis also reported stronger associations, suggesting that diabetes may have a stronger impact on smear-positive and thus transmissible forms of TB. Our result underscores the conclusion by Stevenson et al. that DM accounts for a greater proportion of smear-positive TB than of other forms [17]. In short, we found that magnitudes of association varied by the quality of the studies; at the same time, variations may have been influenced by differences in population characteristics that are correlated with quality-associated variables. Another important limitation of our systematic review is that most of the studies we included failed to examine age as an effect modifier of the relationship between DM and TB. The studies by Kim et al. [7] and Ponce-de-Leon et al. [9] found that estimates varied markedly by age, with substantially higher estimates among younger people. This finding may be explained by heterogeneity of the individuals without diabetes between the age groups. Because baseline glucose tolerance is lower in older persons without diabetes, elderly controls may have had an elevated risk of TB compared to younger ones [55], thus reducing the apparent effect of DM. It is possible that younger people with diabetes might have had type I diabetes, a more severe form of diabetes with a juvenile onset; however, because most studies did not distinguish between type I and type II diabetes we cannot conclude whether the effect modification by age would have been due to differences in types of diabetes. Notably, the study by Dyck et al. [25] did not demonstrate this trend in the age-specificity of the effect of DM on TB and in fact showed a negative association among the elderly. The authors of the study note that results may have been biased by differential mortality in the elderly since individuals with diabetes who would have been most at risk for TB would have already died. Moreover, this study also differed from the others in that it relied on medical records rather than laboratory tests to determine DM status, and it had not included DM occurring in the last six of the 16 y during which TB case accrual occurred. Conclusions In summary, we found consistent evidence for an increased risk of TB among people with diabetes despite heterogeneity in study design, geographic area, underlying burden of TB, assessment of exposure and outcome, and control of potential confounders. Data from these human studies are consistent with emerging information on the biological mechanisms by which hyperglycemia may affect the host immune response to TB. Our findings suggest that TB controls programs should consider targeting patients with diabetes for interventions such as active case finding and the treatment of latent TB and, conversely, that efforts to diagnose, detect, and treat DM may have a beneficial impact on TB control. We also recommend further studies investigating how TB risk varies by type, duration, and severity of DM, for a more thorough understanding of the association that could be translated to a clear public health message. Supporting Information Text S1 Calculation of Attributable Risk Fraction of TB among Patients with Diabetes and Population-Attributable Risk Fraction of TB Due to Diabetes (17 KB DOC) Click here for additional data file. Text S2 MOOSE Checklist (61 KB DOC) Click here for additional data file.
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Tuberculosis Incidence in Prisons: A Systematic Review
Iacopo Baussano, Brian Williams, Paul Nunn … (2010)
Introduction Occurrence of active tuberculosis (TB) in prisons is usually reported to be much higher than the average levels reported for the corresponding general population [1],[2]. In prisons located in developing countries TB has been reported as the most common cause of death [3]. High levels of TB in prison populations are likely to be attributable to the fact that a disproportionate number of prisoners are from population groups already at high risk of TB infection and TB disease (for example, alcohol or drug users, homeless people, mentally ill individuals, former prisoners, and illegal immigrants from areas characterized by high TB prevalence). Furthermore, the prison setting, where segregation criteria are based on crime characteristics rather than on public health concerns, may facilitate transmission. In addition, overcrowding, late case detection, inadequate treatment of infectious cases, high turnover of prisoners, and poor implementation of TB infection control measures are all known factors contributing to transmission of Mycobacterium tuberculosis. Finally, prisoners may be at risk of rapid progression of latent TB infection (LTBI) to TB disease following recent infection or reactivation of latent infection through coexisting pathology, particularly HIV infection, intravenous drug use, and poor nutritional status [3],[4]. Moreover, prisons represent a reservoir for disease transmission to the community at large; the TB infection may spread into the general population through prison staff, visitors, and close contacts of released prisoners [5]. The transmission dynamics between prisoners and the general population has been hypothesized to play a key role in driving overall population-level TB incidence, prevalence, and mortality rates [4]. Overlooking TB prevention and control in prisons settings can carry serious consequences for both prisoners and the general community, in particular in those countries where poor TB control, lack of TB infection control measures, and incarceration rates are high [3]. The main objectives of the present study were to assess, by reviewing the published literature, the consequences of within-prison spread of TB, estimating the relative risk and risk difference for incident latent TB infection (LTBI) and TB disease in prisons worldwide, as compared to the incidence in the corresponding local general population and the fraction (percent) of LTBI and TB in the general population attributable (PAF%) to the exposure in prisons. The aim of this study is to provide relative and absolute estimates of the risk of TB associated with incarceration, and of the potential impact of specific preventive measures to control TB transmission in the prison setting. Methods Search Strategy An initial search of the available literature for systematic reviews or meta-analyses reporting estimates of the occurrence of LTBI and TB incidence in prisons did not identify potentially relevant studies. Details on the search strategy adopted to identify original primary studies in English, French, Italian, Spanish, Portuguese, or Russian, published since January 1980 through June 2010, and reporting data on the incidence of LTBI and TB in prisons, are reported in Text S2. Study Selection The PRISMA checklist is in Text S1. Studies were eligible for inclusion if they reported the incidence of LTBI and TB disease in prisons or if they reported the number of incident LTBI and TB cases identified in the study along with the overall number of inmates or prison personnel investigated or the person-years of follow-up. LTBI incidence has been defined as tuberculin conversion, that is newly positive tuberculin skin test (TST) after a documented negative-baseline TST as reported in the original study [6]. For TB disease incident cases we included both definitive (microbiologically confirmed) and presumptive (based on clinical, imaging, or pathology criteria) diagnoses. In order to include studies of comparable quality, we considered only data published in peer-reviewed journals. Thus data from unpublished literature, such as Ministry of Health or Justice reports, were not included. We excluded studies with the following characteristics: (1) reporting only case series; (2) reporting only outbreak investigations; (3) reporting only prevalence of LTBI and TB in prisons; (4) reporting investigations targeted only to multi-drug resistant TB, (5) case-control studies, (6) those starting before 1980. All duplicate citations were eliminated from the initial database. Three reviewers screened these citations by reviewing titles and abstracts to identify potentially relevant studies. Disagreements between the reviewers were resolved by consensus. The database was then screened again to include only primary research articles, and the full text of each citation was obtained and reviewed. Data Extraction A data extraction form was designed by three reviewers, then all the papers were independently reviewed and data extraction was cross-checked. Disagreements between the reviewers were resolved by consensus. The following datasets were collected from each study: country where the study was performed, study period, incidence of LTBI/TB and corresponding confidence intervals and/or the number of incident LTBI and TB cases identified, and the overall number of inmates or prison personnel investigated or the person-years of follow-up, and if reported the incidence of LTBI and/or TB in a comparison group, such as the local general population or prison administrative workers not exposed to TB in the setting under investigation. To estimate TB incidence among the general populations in the host countries, we used estimates provided by the WHO for the corresponding study period (WHO Global Health Atlas [7]). To estimate LTBI incidence among the general populations in the host countries, we used estimates provided in the original papers or, alternatively, as reported in the literature. Data Analysis For each study the incidence rate ratio (IRR) for LTBI and TB in prison compared to the incidence in the general population was calculated. The presence of heterogeneity across studies was assessed by the conventional chi-squared test for heterogeneity (we regarded a p-level below 0.05 as indicating significant heterogeneity in the data), and by calculating the I 2 statistic, which accounts for the number of studies included in the meta-analysis and provides a direct measure of the variability not explained by the information included in the analysis [8]. We used STATA version 9.2 (StataCorp, College Station, TX, USA) software for statistical analysis. In order to assess the fraction of LTBI or TB in the population attributable to the exposure to prison settings, we calculated the population attributable fraction percent (PAF%) using Levin's formula [9] , where IRR is the LTBI or TB IRR measured from each study and Pe is the proportion of the population in prisons as given in the Human Development Report (year 2007/08) [10]. Other sources of information provide slightly different figures, for example those reported by the “International Centre for Prison Studies” of the King's College in London [11] are usually slightly higher than those reported by the United Nations [10]; however, the data from the two sources are consistent. To investigate possible sources of heterogeneity, we stratified the analysis according to income of the population in which the study was conducted. In particular, we defined two strata, high- and middle/low-income countries as classified by the World Bank [12]. Furthermore, to investigate potential sources of heterogeneity, we tested, by means of univariate meta-regression analyses, the possible effect of between-study variance of overcrowding, presence/absence of ventilation systems, strategies of isolation of suspected TB cases, and TST testing at entry as reported in each study. We also tested the effect of study quality, which was assessed using the Newcastle-Ottawa scoring scale for cohort studies [13]. In brief, the quality of the studies was assessed considering the definition and representativeness of the cohort of inmates or prison personnel, the diagnostic criteria for cases of active TB, and the comparability of the cohorts on the basis of the study design or analysis. Results The study selection process is shown in Figure 1. We identified 582 potentially relevant unique citations from all literature searches. From 256 original primary studies, 23 studies [14]–[36] were included, accounting overall for 670 cases of LTBI with 31,404 person-years of follow-up and for 1,710 cases of TB with 512,780 person-years of follow-up. Steenland et al. reported LTBI cases among prison personnel separately, according to their “high” or “low” risk of being exposed to inmate cases of TB cases [30]; Russkikh et al. reported TB incidence among prison personnel in Udmurt Republic (Russian Federation) during and following the socioeconomic crisis that occurred in Russia in the late 1990s [35]; whereas Klopf et al. reported TB incidence rates before and after the implementation of a TB control program in New York State Department of Correctional Services, separately [21]. We kept these distinctions in our analyses. None of the selected studies reported data from short-term correctional facilities. 10.1371/journal.pmed.1000381.g001 Figure 1 Flow diagram for study selection. The median number of cases per study of LTBI in prisons was 86 (interquartile range [IQR]: 49–169) and 68 for TB (IQR: 23–214), while the median number of person-years of follow-up in each study was 8,027 (IQR: 1,027–9,746) for LTBI and 13,869 (IQR: 3,927–81,759) for TB. For studies reporting LTBI data collected since 1991, five studies were from the US (high-income country) and one study was from Brazil (middle-income country). For studies reporting TB data collected since 1981, 13 studies were from high-income countries, six studies were from countries with an estimated middle/low-income [16],[22],[29]. The geographic distribution of studies reporting TB incidence was more heterogeneous. Tables 1 and 2 summarize the findings of the six and 19 studies that reported LTBI and TB incident cases in prisons, respectively. In particular, for each study included in the review, we have reported the period under investigation, the number of LTBI or TB cases and the person-years at risk, the LTBI or TB incidence for the comparison group representing the local general population, the estimated rate difference, the estimated IRR with the corresponding 95% confidence intervals (95%CIs), the incarcerated population (per 1,000 inhabitants), and the estimated PAF%. 10.1371/journal.pmed.1000381.t001 Table 1 Studies reporting LTBI incidence in prisons. Author, Year(Country) Period Cases, n(At Risk) Incidence in Prisons, % Incidence in General Population, %a Incidence Rate Difference IRR(95%CI) Incarcerated Population, ×1,000 Inhabitantsb PAF% Ferreira et al., 1996 (Brazil) 1992–1993 21 (68) 30.9 0.5 30.4 61.76 (40.27–94.73) 1.91 10.4 Hung et al., 2003 (USA) 2000–2001 49 (9,746) 0.53 0.1 0.43 5.03 (3.8–6.65) 7.38 2.9 Koo et al., 1997 (USA) 1989–1991 130 (2,201) 5.91 0.1 5.81 59.06 (49.74–70.14) 7.38 30.0 MacIntyre et al., 1997 (USA) 1993–1994 86 (1,027) 8.37 0.1 8.27 83.74 (67.79–103.45) 7.38 37.9 Mitchell et al., 2005 (USA) 1999–2000 3 (231) 1.30 0.1 1.20 12.99 (4.19–40.27) 7.38 8.1 Steenland et al., 1997 (USA) (high) 1991–1992 169 (10,104) 1.67 0.1 1.57 16.73 (14.39–19.45) 7.38 10.4 Steenland et al., 1997 (USA) (low) 1991–1992 212 (8,027) 2.64 0.1 2.54 26.41 (23.08–30.22) 7.38 15.8 Characteristics of the study, estimated annual incidence of LTBI in prisons, estimated annual incidence of LTBI in the general population, estimated annual incidence of LTBI difference, estimated annual incidence of LTBI ratio, fraction of the population in prison, fraction of LTBI in the population attributable to the exposure in prisons. a As reported in Menzies et al., 2007 [49]; Steenland et al. reported LTBI cases among prisons personnel separately according to their “high” or “low” risk of being exposed to inmate cases of tuberculosis cases [30]. b As reported in the the Human Development Report (year 2007/08) [47]. 10.1371/journal.pmed.1000381.t002 Table 2 Studies reporting TB incidence in prisons, by income area according to the World Bank classification. Income Category Author, Year(Country) Period Cases, n(At Risk) Incidence in Prisons, ×100,000 Incidence in General Population, ×100,000 Incidence Rate Difference IRR(95%CI) Incarcerated Population, ×1,000 Inhabitants‡ PAF% High-income countries Martin et al., 2001 (Spain) 1991–1999 NR 639† 45 594 14.2(9.2–21.8) 1.45 1.88 Mor et al., 2008 (Israel) 1998–2004 23(91,000) 25.3 10 15.3 2.5(1.7–3.8) 2.09 0.32 Wong et al., 2008 (Hong Kong) 1999–2005 214(82,406) 259.7 76 183.7 3.4(3.0–3.9) 1.68 0.40 Ijaz et al., 2004 (USA) 1992–2000 58(81,759) 70.9 10 60.9 7.1(5.48–9.18) 7.38 4.30 Hanau-Bercot et al., 2000 (France) 1991–1995 68(31,546) 215.5 25 190.6 8.6(6.8–10.9) 0.85 0.64 Valway et al., 1994 (USA) 1990–1992 171(109,475) 156.2 9 147.2 17.3(14.9–20.2) 7.38 10.77 Koo et al., 1997 (USA) 1991–1991 10(5,421) 184.5 17.4 167.1 18.4(9.9–34.3) 7.38 11.41 Klopf et al., 1998 (USA) 1991–1997* NR 225 9 216.0 25.0(NA) 7.38 62.3 Klopf et al., 1998 (USA) 1991–1997** NR 61 9 52.0 6.8(NA) 7.38 30.7 Fernandez de la Hoz et al., 2001 (Spain) 1997–1997 97(7,524) 1,289.2 40 1,249.2 32.2(26.4–39.3) 1.45 4.33 Jones et al., 1999 (USA) 1995–1997 38(13,869) 274.0 8 266.0 34.2(24.9–47.1) 7.38 19.70 March et al., 2000 (Spain) 1994–1996 267(3,927) 6,799.1 45 6,754.1 151.1(134.0–170.3) 1.45 17.87 Chaves et al., 1997 (Spain) 1993–1994 216(9,461) 2,283.1 30.4 2,252.7 75.1(48.8–115.4) 1.45 9.70 Braun, 1989 (USA) 1984–1986 39(36,967) 105.5 9 96.5 11.7(8.6–16.0) 7.38 7.33 Middle/low income countries Ferreira et al., 1996 (Brazil)# 1992–1993 20(720) 2,777. 8 77 2,700.8 36.1(23.3–55.9) 1.91 6.28 de Oliveira et al., 2004 (Brazil) 1993–2000 359(34,344) 1,045.3 67.75 977.5 15.4(13.9–17.1) 1.91 2.68 Russkikh et al., 2007 (Russia) 1996–2000† NR 2,035.3 58.0 1,977.3 35.1(NA) 6.11 17.2 Russkikh et al., 2007 (Russia) 2001–2005‡ NR 1,649.9 71.6 1,578.3 23.0(NA) 6.11 11.9 Pavlov et al., 2003 (Russia) 1998–2000 NR 1,942.8 49.6 1,893.2 39.1(NA) 6.11 18.9 Slavuckij et al, 2002 (Russia) 1998–1998 22(2,500) 880 100 780 8.8(5.8–13.4) 6.11 4.55 Koffi et al., 1997 (Ivory Coast) 1990–1992 108(1,861) 5,803.3 177 5,626.3 32.8(27.1–39.6) 0.49 1.53 Source: [12]. Characteristics of the study, estimated annual tuberculosis (TB) incidence in prisons, estimated annual TB incidence in the general population, estimated annual TB incidence difference, estimated annual TB incidence ratio, fraction of the population in prison, fraction of TB in the population attributable to the exposure in prisons. ‡ As reported in the Human Development Report (year 2007/08) [47]. †: As reported in Martin et al., 2001 [26]. # Female inmates only. Klopf et al. reported TB incidence *before and **after the implementation of a TB control program in New York State Department of Correctional Services and prisons personnel separately [21]. Russkikh et al. reported TB incidence among prison personnel †during and ‡following the socioeconomic crisis occurred in Russia in the late 1990s [35]. NA, not applicable; NR, not reported. The median estimated annual incidence of LTBI in prisons was 2.6% (IQR: 1.3%–8.4%) overall and 2.1% (IQR: 1.3%–5.9%) for studies from the US. The IRR for LTBI was 26.4 (IQR: 13.0–61.8) overall and 21.6 (IQR: 13.0–59.1) for studies from the US. Figure 2 shows the distribution of the IRR for LTBI by income area. 10.1371/journal.pmed.1000381.g002 Figure 2 Forest plot showing the study-specific estimates of the IRRs for LTBI in prisons as compared to corresponding general populations, by income area according to the World Bank classification. Source: [12]. The median estimated annual incidence of TB in prisons was 237.6 per 100,000 persons (IQR: 156–639) for studies from high-income countries and 1,942.8 per 100,000 persons (IQR: 1,045.3–2,777.8) for studies from middle/low-income countries. The median estimated IRR for TB were 17.9 (IQR: 8.6–61) and 32.8 (IQR: 15.4–36.1), respectively. The median difference between annual incidence of LTBI measured in prisons as compared with that measured in the general population was 2.5% (IQR: 1.2%–8.3%). Since all the studies reporting data about LTBI incidence in prisons from high-income countries were from the US, we restricted the analysis of TB incidence to studies from the US, in order to compare incidence ratio ratios for LTBI and TB. The calculated median estimate for TB was 48 (IQR: 24–114.5), which was higher than the IRR of 32 (IQR: 19.6–44.3) found for LTBI, though the distribution of these estimates largely overlapped (see above). Figure 3 shows the distribution of the IRR for TB by income area. 10.1371/journal.pmed.1000381.g003 Figure 3 Forest plot showing the study-specific estimates of the IRR for tuberculosis in prisons as compared to the corresponding general populations, by income area according to the World Bank classification. Source: [12]. NA, not applicable. Finally, using the estimated annual incidence of LTBI and annual TB IRR and the reported population proportion of inmates for each country of interest we estimated the PAF%. The median population in prison (per 1,000 inhabitants) was 4.7 (IQR: 1.4–7.4) for high-income countries and 6.1 (IQR: 1.9–6.1) for middle/low-income countries. The median estimated PAF% for LTBI was 13.1% (IQR: 8.1%–30.0%) for studies from high-income countries (US) and 10.4% for the only study from a middle-income country. The median estimated PAF% for TB was 8.5% (IQR: 1.9%–17.9%) for studies from high-income countries and 6.3% (IQR: 2.7%–17.2%) for studies from middle-low–income countries. Figure 4 shows the PAF% (on a log scale) for TB as a function of both the proportion of population in prison and the IRR between prisoners and general population. As an example, Jones [20] and Koffi [22] reported similar IRRs—34 and 33 respectively—but due to the different proportions of the population that are in prison the estimated PAF% diverge widely (19.7% versus 1.5%). On the other hand, Fernandez de la Hoz and Wong [32] reported from countries with similar proportions of incarcerated population, 1.4 and 1.7 per 1000 population respectively, but the large difference in IRR produces a substantial shift in the estimated PAF% (4% versus 0.4%). 10.1371/journal.pmed.1000381.g004 Figure 4 Contour plot showing the relationship between the proportion of exposed population, IRR, and PAF%. X-axis reports the proportion of the population in prison, Y-axis reports the PAF on a log scale calculated using the Levin's formula [9]. The isoclines represent different levels of IRR. Klopf et al. reported TB incidence *before and **after implementation of a TB control program in New York State Department of Correctional Services and prisons personnel separately.[21] Russkikh et al. reported TB incidence among prison personnel †during and ‡following the socioeconomic crisis occurred in Russia in the late 1990s [35]. The between-study heterogeneity was considerable. In particular, the overall I 2 statistic was 98% (95%CIs: 98%–99%); it was 98% (95%CIs: 97%–98%) for data from high-income countries and 94% (95%CIs: 87%–97%) for data from middle/low-income countries. The heterogeneity did not decrease significantly after stratification by income of the countries. Similarly, accounting in univariate metaregression analyses for overcrowding of the prison setting, presence/absence of ventilation systems, strategies of isolation of suspected TB cases, and TST or TB testing at entry into prison did not show any significant effect on decreasing the between-study variance. However, the IRR estimated from Wong et al. [32], the study with the highest quality scoring, differed significantly from the IRR estimated from studies with the lowest quality scoring. However, no other significant difference was attributable to studies' quality scoring. Discussion In this study we attempt to summarize the published evidence of incidence of both LTBI and TB in prisons. The present systematic review confirms, using peer-reviewed data from both high- and middle/low-income countries, that the risk for TB is at least one order of magnitude greater in prisons than in the general population, as reported by Aerts et al. [37] in a questionnaire-based survey from the WHO European Region. Analogous results have been reported by Zarate et al. [38] in a review summarizing data from international organizations such as the International Committee of the Red Cross and the WHO. The magnitudes of IRR for LTBI and TB estimated in the present systematic review are consistent with each other. Furthermore the finding that the median IRR for LTBI (26.4; IQR: 13.0–61.8) is comparable to that for TB (23.0; IQR: 11.7–36.1) is in line with findings from previous reports and suggests that incident TB cases have a greater impact on subsequent transmission than does importation of LTBI [39]. However, in settings where screening is performed at entry in prison, incident TB cases may represent some LTBI importation. Direct evidence, based on molecular genotyping and drug susceptibility testing, of TB transmission in prisons was recently provided by Matthys et al. [40]. Although in some countries the number of TB cases in prisons represents a relevant proportion of the overall burden of the disease, data on TB in prisons are not always reported to ministries of health [41]. Thus, the TB incidence statistics used for international reporting may be flawed. This underreporting may help to limit a potential bias in our estimates for the IRR (and therefore PAF%), with prisoners being compared to a truly unexposed population; by contrast, if in some countries data from prisons were merged with that of the general population, IRR could have been underestimated. However, those who enter and exit prisons are more likely to belong to population subgroups at a higher risk for LTBI and TB disease than the general population, such as illegal immigrants, hard-to-reach people (such as the homeless), and underserved ethnic-social minorities. A higher risk of transmission outside of the prisons with respect to the general population may lead to an overestimation of IRRs, since a fraction of the transmission occurring within the community would be attributed to the prison setting. The PAF% values given here should be considered estimates of the real impact of transmission of TB within prisons, depicting the two main forces acting on such an impact: the proportion of the population in prison and the role of measures to control transmission. The method adopted to estimate the PAF% was developed to measure the impact on a population of risk factors for noncommunicable diseases [9] and does not account for the transmission dynamics of infectious diseases. In particular, it does not capture the indirect effects of preventive strategies devised to interrupt the chain of disease transmission. Thus, our estimates cannot capture the consequences of introducing TB control measures on transmission dynamics within a prison or between a prison and the local community [1]. Incarcerated people and prison staff can move to different institutions within the judiciary system and to health centers. Plus, prisoners and prison staff have contact with visitors, and prisoners can be freed without a diagnosis or before having completed therapy [3],[41]. As a consequence, not only have prison outbreaks of TB been linked to an increased incidence of TB in local communities, but mass incarceration in Central Asian and Eastern European countries has been associated with the increase of TB rates in the general population [4]. Education on early identification of TB and early case management, screening of inmates at arrival, isolation of cases with positive sputum smears—within the framework of community health services when necessary [5],[42]—all represent potentially effective measures. Their implementation is, however often hampered by resource constraints specific to the prison setting. Nevertheless, emphasis should always be placed on control of TB transmission, especially in periods of growth of prison populations [4]. In high- and middle/low-income countries, the maximum possible reduction of the median TB annual incidence in prisons was estimated to be 187 and 1,893 per 100,000 population, respectively. Although assessing the cost-effectiveness of the introduction of TB transmission control measures is beyond the scope of this paper, such a potential reduction of TB incidence in prisons would make attractive a range of infection control strategies. The PAF% for TB and LTBI in high- and medium/low-income countries ranges from 4.5% to 10.4%; however, figures for high-income countries are driven by data from the US, the country with the largest prison population. Nonetheless, data from the US could provide useful insights into the epidemiology of TB in prisons: The IRRs for LTBI and TB are consistent. Furthermore, data reported by Klopf et al. [21] showed that a reduction of IRR from 25 to 6.8 may have halved the PAF% from 62.3% to 30.7%. Unfortunately, there is a lack of data from Africa and Central Asia, so it is unclear to what extent these findings can be generalized to other countries, with factors such as inadequate nutrition and HIV prevalence [43] possibly playing substantial roles. Limited data from the Russian Federation seem to support our findings. A limitation of the present study is that few reviewed papers stratified the prison population for relevant risk factors such as HIV status. The present findings should be interpreted in the light of some study limitations. The high heterogeneity between studies did not allow a pooled analysis of the data; similar levels of heterogeneity have been observed in other systematic reviews focusing on control of TB transmission and those analyzing observational studies [44],[45]. Such heterogeneity can be due to differences in methodological quality, study design, sampling variability, and study populations across studies. Unfortunately, the meta-regression analyses testing for the potential effect on the between-study heterogeneity of prison overcrowding, implementation of TB infection control interventions, strategies of isolation of suspected TB cases, TST or TB testing upon entry into prison, and study quality scoring did not show any significant role for these factors. In particular, we were unable to account for the duration of time that inmates spend in prisons. Furthermore, we could not account for specific patterns of incarceration, since the classification and organization of detention centers differ between countries. It has been shown that the direct comparison of the TB rates estimated in prisons from the same area but with different characteristics may differ significantly [46]. Furthermore, the best available estimates of LTBI incidence in general population should be regarded cautiously, since they are not drawn from random samples of the population. Meanwhile, information on factors potentially affecting the TST result interpretation such as BCG (bacille Calmette-Guérin) status and nontuberculous mycobacteria distribution in the local population are not available. The PAF% estimates given here rely on a few key assumptions that cannot be assessed directly. The first assumption is that the proportion of the population in prisons as reported by the Human Development Report [47] is reliable and applicable to the specific prison setting investigated in the reviewed studies. The second is that the IRR for TB remains relatively constant over time; in fact, fluctuations of the IRR may occur within a decade or more [48]. In conclusion, these findings provide a detailed summary of the evidence on LTBI and TB risk and incidence in prisons attributable to within-prison spread of TB and make it possible to estimate the impact at a population level. These data may prove useful to inform the development of rational policies to control TB transmission in correctional facilities. Future studies should assess the population attributable risk of prison-to-community spread and describe the conditions in the prison that influence TB transmission. Reporting on the factors potentially affecting the rates of transmission within the different prisons should reduce the heterogeneity of the reported findings and may help us understand the main reasons for the differences in transmission in different settings. Supporting Information Text S1 PRISMA checklist. (0.07 MB DOC) Click here for additional data file. Text S2 Search details. (0.02 MB DOC) Click here for additional data file.
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Transmission of multidrug-resistant Mycobacterium tuberculosis in Shanghai, China: a retrospective observational study using whole-genome sequencing and epidemiological investigation.
Chongguang Yang, Tao Luo, Xin Shen … (2017)
Multidrug-resistance is a substantial threat to global elimination of tuberculosis. Understanding transmission patterns is crucial for control of the disease. We used a genomic and epidemiological approach to assess recent transmission of multidrug-resistant (MDR) tuberculosis and identify potential risk factors for transmission.
Author and article information
Journal
Journal ID (nlm-ta): J Infect Dis
Journal ID (iso-abbrev): J. Infect. Dis
Journal ID (publisher-id): jid
Title:
The Journal of Infectious Diseases
Publisher:
Oxford University Press
(US
)
ISSN
(Print):
0022-1899
ISSN
(Electronic):
1537-6613
Publication date
(Print):
01
October
2017
Publication date
(Electronic):
03
November
2017
Publication date PMC-release: 03
November
2018
Volume: 216
Issue: Suppl 6
,
Towards Zero New TB Infections: Research Needs for Halting TB Transmission
Pages: S644-S653
Affiliations
[1
]
Department of Epidemiology, Mailman School of Public Health, Columbia University , New York, New York;
[3
]
Department of Epidemiology of Microbial Diseases, Yale School of Public Health , New Haven, Connecticut;
[5
]
Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Academic Medical
Center, University of Amsterdam , the Netherlands;
[6
]
McGill International TB Centre, Research Institute of the McGill University Health
Centre , Montreal,Canada;
[7
]
Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical
Medicine , United Kingdom;
[8
]
Tuberculosis Clinical Research Branch, Therapeutics Research Program, Division of
AIDS, National Institute of Allergy and Infectious Diseases, National Institutes of
Health, Department of Health and Human Services , Rockville, Maryland;
Author notes
Correspondence: B. Mathema, PhD, Department of Epidemiology, Mailman School of Public
Health, Columbia University, 722 West 168th Street, New York, NY 10032 (
bm2055@
123456columbia.edu
).
Article
Publisher ID: jix354DOI: 10.1093/infdis/jix354
PMC ID: 5853844
PubMed ID: 29112745
SO-VID: 6cfc916e-d00c-43f5-904e-a9ec674c2ad0
Copyright © © The Author 2017. Published by Oxford University Press for the Infectious Diseases
Society of America.
License:
This is an Open Access article distributed under the terms of the Creative Commons Attribution 3.0 IGO (CC BY 3.0 IGO) License ( https://creativecommons.org/licenses/by/3.0/igo/) which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
History
Page count
Pages: 10
Funding
Funded by:
National Institutes of Health
10.13039/100000002
Categories
Subject:
Supplement Articles
ScienceOpen disciplines: Infectious disease & Microbiology
Data availability:
ScienceOpen disciplines: Infectious disease & Microbiology