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Mortality during and after incarceration is poorly understood in low- and middle-income countries (LMICs). The need to address this knowledge gap is especially urgent in South America, which has the fastest growing prison population in the world. In Brazil, insufficient data have precluded our understanding of all-cause and cause-specific mortality during and after incarceration. We linked incarceration and mortality databases for the Brazilian state of Mato Grosso do Sul to obtain a retrospective cohort of 114,751 individuals with recent incarceration. Between January 1, 2009 and December 31, 2018, we identified 3,127 deaths of individuals with recent incarceration (705 in detention and 2,422 following release). We analyzed age-standardized, all-cause, and cause-specific mortality rates among individuals detained in different facility types and following release, compared to non-incarcerated residents. We additionally modeled mortality rates over time during and after incarceration for all causes of death, violence, or suicide. Deaths in custody were 2.2 times the number reported by the national prison administration (n = 317). Incarcerated men and boys experienced elevated mortality, compared with the non-incarcerated population, due to increased risk of death from violence, suicide, and communicable diseases, with the highest standardized incidence rate ratio (IRR) in semi-open prisons (2.4; 95% confidence interval [CI]: 2.0 to 2.8), police stations (3.1; 95% CI: 2.5 to 3.9), and youth detention (8.1; 95% CI: 5.9 to 10.8). Incarcerated women experienced increased mortality from suicide (IRR = 6.0, 95% CI: 1.2 to 17.7) and communicable diseases (IRR = 2.5, 95% CI: 1.1 to 5.0). Following release from prison, mortality was markedly elevated for men (IRR = 3.0; 95% CI: 2.8 to 3.1) and women (IRR = 2.4; 95% CI: 2.1 to 2.9). The risk of violent death and suicide was highest immediately post-release and declined over time; however, all-cause mortality remained elevated 8 years post-release. The limitations of this study include inability to establish causality, uncertain reliability of data during incarceration, and underestimation of mortality rates due to imperfect database linkage. Incarcerated individuals in Brazil experienced increased mortality from violence, suicide, and communicable diseases. Mortality was heightened following release for all leading causes of death, with particularly high risk of early violent death and elevated all-cause mortality up to 8 years post-release. These disparities may have been underrecognized in Brazil due to underreporting and insufficient data.

Background Each year 3–6 million people develop life-threatening severe dengue (SD). Clinical warning signs for SD manifest late in the disease course and are nonspecific, leading to missed cases and excess hospital burden. Better SD prognostics are urgently needed. Methods We integrated 11 public datasets profiling the blood transcriptome of 365 dengue patients of all ages and from seven countries, encompassing biological, clinical, and technical heterogeneity. We performed an iterative multi-cohort analysis to identify differentially expressed genes (DEGs) between non-severe patients and SD progressors. Using only these DEGs, we trained an XGBoost machine learning model on public data to predict progression to SD. All model parameters were “locked” prior to validation in an independent, prospectively enrolled cohort of 377 dengue patients in Colombia. We measured expression of the DEGs in whole blood samples collected upon presentation, prior to SD progression. We then compared the accuracy of the locked XGBoost model and clinical warning signs in predicting SD. Results We identified eight SD-associated DEGs in the public datasets and built an 8-gene XGBoost model that accurately predicted SD progression in the independent validation cohort with 86.4% (95% CI 68.2–100) sensitivity and 79.7% (95% CI 75.5–83.9) specificity. Given the 5.8% proportion of SD cases in this cohort, the 8-gene model had a positive and negative predictive value (PPV and NPV) of 20.9% (95% CI 16.7–25.6) and 99.0% (95% CI 97.7–100.0), respectively. Compared to clinical warning signs at presentation, which had 77.3% (95% CI 58.3–94.1) sensitivity and 39.7% (95% CI 34.7–44.9) specificity, the 8-gene model led to an 80% reduction in the number needed to predict (NNP) from 25.4 to 5.0. Importantly, the 8-gene model accurately predicted subsequent SD in the first three days post-fever onset and up to three days prior to SD progression. Conclusions The 8-gene XGBoost model, trained on heterogeneous public datasets, accurately predicted progression to SD in a large, independent, prospective cohort, including during the early febrile stage when SD prediction remains clinically difficult. The model has potential to be translated to a point-of-care prognostic assay to reduce dengue morbidity and mortality without overwhelming limited healthcare resources.

BackgroundTuberculosis (TB) disproportionately affects people deprived of liberty (PDL). Prior studies have shown higher TB treatment completion rates among PDL compared to the general population. However, little is known about how incarceration-related movements such as transfers between facilities and releases to the community affect TB treatment outcomes.MethodsWe linked person-level incarceration data with TB notifications data from the Notifiable Disease Information System for the Brazilian state of Mato Grosso do Sul between January 2006 and December 2018. We constructed a cohort of PDL who were newly diagnosed with drug-susceptible TB and initiated treatment. We compared treatment outcomes between individuals who remained in the same carceral facility and those who were transferred to other facilities or released from incarceration during treatment. We computed the covariate-adjusted relative risk of unfavorable treatment outcomes for individuals transferred or released during treatment.ResultsWe identified 1261 PDL who initiated TB treatment. Of these individuals, 842 (66.8%) remained in the same carceral facility, 256 (20.3%) were transferred to other facilities, and 163 (12.9%) were released to the community during treatment. Among those who remained in the same carceral facility, 72.9% (614/842) were successfully treated within 8 months following treatment initiation. In contrast, only 61.7% (158/256) of those who were transferred and 50.3% (82/163) of those who were released achieved TB treatment success within 8 months. After adjusting for covariates, the risk of unfavorable treatment outcomes was 1.4 (95% CI: 1.2 to 1.7) times as high for individuals transferred to other facilities and 1.6 (95% CI: 1.3 to 2.0) times as high for individuals released from incarceration, compared to those who remained in the same facility during treatment. For individuals released less than 2 months into treatment, the risk of unfavorable treatment outcomes was twice as high (adjusted relative risk [aRR]: 2.1, 95% CI: 1.6–2.7).ConclusionsTransfers between facilities and releases from incarceration are common and may pose barriers to TB treatment completion. Strategies for ensuring continuity of care across carceral facilities and between prison and community health systems are urgently needed to improve TB outcomes for individuals impacted by incarceration.

Correspondence to Dr Alberto L Garcia-Basteiro; alberto.garcia-basteiro@manhica.net Globally, incarceration is a well-documented risk factor for Mycobacterium tuberculosis infection and tuberculosis (TB) disease.1 Persons deprived of liberty (PDLs) in Latin America (LA) experience incidence rates of TB that are 26 times higher (95% CI 17.1 to 40.1) than those in the general population, and this disparity is the largest in the world.2 Over the last decade, the prison population in LA has more than doubled, which now has some of the highest incarceration rates in the world, has not been accompanied by concomitant improvements in physical or healthcare infrastructure, creating conditions for intensified TB transmission.3 4 The heightened risk of TB has long been a part of the sentence received by PDLs.5 Every year that a PDL spends in prison increases their risk of developing TB.6 The cumulative risk of TB, although decreasing once a person is released from prison, consistently remains higher than the general population rates for years afterward.6 7 Studies indicate that prisons are an important driver of TB epidemics, whereby rising incarceration and high transmission rates in prisons are amplifying TB at the population level, undermining the progress of TB programmes in the general population.6 8 Most national TB programmes (NTPs) in the LA region define PDLs as one of the high-risk populations (such as indigenous population, drug users, immigrants, among others). History of incarceration is typically not an element of notification databases, so cases occurring in the community among individuals with prior incarceration are not currently recognised by the NTPs as being related to prisons. [...]there is evidence from molecular epidemiology studies indicating that genomic clusters of TB occurring in the community are shared among individuals with and without incarceration history, suggesting onward community transmission of prison-related cases.9 10 A straightforward but crucial surveillance change is that NTP notification forms must include incarceration history, specifying facility, duration and dates. [...]appropriate questions should be developed with relevant stakeholders and focus groups. Table 1 The features of prisons in 14 Latin American countries are compared in terms of incarceration rates, the ratio of TB notification rates between prisons and the community, surveillance of prison history in community TB cases by national TB programmes and the institution responsible for managing health issues within correctional facilities in each country Country Prison TB incidence* General population TB incidence* Incarceration rate† TB incidence rate ratio in prisons/ community* Incarcerated population* Total TB cases‡ Proportion of TB in prison† Incarceration history reported in TB surveillance§ Governance of health services within prisons§ Paraguay 3211.6 48 (40–59) 234 62.5 15 000 3480 16.9 No Ministry of Justice Chile 87.6 16 (13–18) 259 5.0 51 113 3184 1.3 No Ministry of Justice Dominican Republic 1075.1 45 (33–57) 223 48.9 25 416 4881 7.3 No Public ministry Uruguay 712.7 32 (27–37) 408 34.4 14 965 1112 7.3 No Ministry of Interior El Salvador 5151.6 49 (36–64) 1086 51.0 71 000 3136 55.5 No Ministry of Justice Guatemala 829.4 27 (20–35) 142 40.7 23 765 5090 5.9 No Government Ministry Ecuador 1592.3 48 (35–62) 224 41.7 31 143 8793 10.7 No Ministry of Health Peru 3566.3 130 (102–161) 262 20.8 92 352 43 094 10.3 No Ministry of Justice Costa Rica 166.4 11 (7.8–14) 301 334.9 15 700 560 8.0 No Ministry of Justice Colombia 633.9 41 (32–51) 196 29.3 102 168 20 862 6.0 No Ministry of Justice Argentina 267.3 30 (26–36) 249 10.0 114 074 14 198 2.5 No Security Secretary Brazil 1250.4 48 (42–56) 389 31.3 835 643 102 029 11.3 No Ministry of Justice Honduras 1160.6 33 (24–42) 218 33.3 19 619 3268 2.4 No Security Secretary Panamá 525.0 42 (32–53) 499 16.0 22 239 1812 5.2 No Ministry of Health *https://www.paho.org/es/documentos/tuberculosis-americas-informe-regional-2021 †https://www.prisonstudies.org/world-prison-brief-data ‡Global TB Report 2022. §In May 2023, we interviewed representatives from several countries who are involved in TB control efforts.

Between December 22, 2020, and March 4, 2021, the BNT162b2 or mRNA-1273 vaccine was offered to two thirds of prisoners in California, and 66.5% of those offered a vaccine accepted at least one dose. Acceptance was highest among Hispanic residents (72.6%) and White residents (72.1%) and was lowest among Black residents (54.9%).

A subset of individuals exposed to Mycobacterium tuberculosis ( Mtb ) that we refer to as ‘resisters’ (RSTR) show evidence of IFN-γ − T cell responses to Mtb -specific antigens despite serially negative results on clinical testing. Here we found that Mtb -specific T cells in RSTR were clonally expanded, confirming the priming of adaptive immune responses following Mtb exposure. RSTR CD4 + T cells showed enrichment of T H 17 and regulatory T cell-like functional programs compared to Mtb -specific T cells from individuals with latent Mtb infection. Using public datasets, we showed that these T H 17 cell-like functional programs were associated with lack of progression to active tuberculosis among South African adolescents with latent Mtb infection and with bacterial control in nonhuman primates. Our findings suggested that RSTR may successfully control Mtb following exposure and immune priming and established a set of T cell biomarkers to facilitate further study of this clinical phenotype. Seshadri, Davis and colleagues show that individuals who do not develop an infection with Mycobacterium tuberculosis ( Mtb ), despite exposure to the bacteria and expansion of CD4 + T cell clones specific to Mtb antigens, show enrichment of T H 17 cell and T regulatory functional programs.

Predicting the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. We developed a logistic regression-based classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N = 705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.94 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N = 97) and retrospectively (N = 100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.

Formerly incarcerated people have exceptionally poor health profiles and are at increased risk of preventable mortality when compared with their general population peers. However, not enough is known about the epidemiology of mortality in this population—specifically the rates, causes, and timing of death in specific subgroups and regions—to inform the development of targeted, evidence-based responses. We aimed to document the incidence, timing, causes, and risk factors for mortality after release from incarceration. We analysed linked administrative data from the multi-national Mortality After Release from Incarceration Consortium (MARIC) study. We examined mortality outcomes for 1 471 526 people released from incarceration in eight countries (Australia, Brazil, Canada, New Zealand, Norway, Scotland, Sweden, and the USA) from 1980 to 2018, across 10 534 441 person-years of follow-up (range 0–24 years per person). We combined data from 18 cohort studies using two-step individual participant data meta-analyses to estimate pooled all-cause and cause-specific crude mortality rates (CMRs) per 100 000 person-years, for specific time periods (first, daily from days 1–14; second, weekly from weeks 3–12; third, weeks 13–52 combined; fourth, weeks 53 and over combined; and fifth, total follow-up) after release, overall and stratified by age, sex, and region. 75 427 deaths were recorded. The all-cause CMR during the first week following release (1612 [95% CI 1048–2287]) was higher than during all other time periods (incidence rate ratio [IRR] compared with week 2: 1·5 [95% CI 1·2–1·8], I2=26·0%, weeks 3–4: 2·0 [1·5–2·6], I2=53·0%, and weeks 9–12: 2·2 [1·6–3·0], I2=70·5%). The highest cause-specific mortality rates during the first week were due to alcohol and other drug poisoning (CMR 657 [95% CI 332–1076]), suicide (135 [36–277]), and cardiovascular disease (71 [16–153]). We observed considerable variation in cause-specific CMRs over time since release and across regions. Pooled all-cause CMRs were similar between males (731 [95% CI 630–839]) and females (660 [560–767]) and were higher in older age groups. The markedly elevated rate of death in the first week post-release underscores an urgent need for investment in evidence-based, coordinated transitional healthcare, including treatment for mental illness and substance use disorders to prevent post-release deaths due to suicide and overdose. Temporal variations in rates and causes of death highlight the need for routine monitoring of post-release mortality. Australia's National Health and Medical Research Council.

Tuberculosis (TB) and COVID-19 are the two leading causes of infectious death, claiming approximately 2.6 million lives in 2022. Addressing these and other epidemics requires a multi-pronged approach consisting of biomedical innovations, supportive policies and systems, and successful implementation. In this dissertation, I apply diverse epidemiologic and data science methods to inform responses on each of these three fronts. In chapter two, to guide the development of a new TB vaccine, I identify blood-based correlates of protection against TB in rhesus macaques. I show that innate immune transcriptional signatures induced early after vaccination can accurately predict antigen-specific, adaptive immune responses in the lung, as well as protection against Mtb challenge. In chapter three, to understand TB drivers at the population level, I quantify the burden of TB attributable to decades of mass incarceration policies in Latin America. Using mathematical transmission modeling, I estimate that in 2019, more than 1 in 4 of the region’s new TB cases can be attributed to incarceration. Further, I show that decarceration interventions can have substantial impacts on future population TB incidence. In chapter four, I conduct a prospective, stakeholder-informed observational study to evaluate the implementation of COVID-19 preventive measures in Northern California jails. I find prevalent sentiments among incarcerated individuals of an inadequate pandemic response by the jails. Moreover, restrictive measures like lockdowns and suspension of visitation were cited by jail residents as deterrents to care-seeking and reasons for worsened mental health during the pandemic. Together, the findings from this dissertation provide critical insights on the molecular- to population-level determinants of TB and COVID-19. They will inform effective strategies for an integrated response to these epidemics to advance global health equity and justice.