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Background Integration of HIV services with other health services has been proposed as an important strategy to boost the sustainability of the global HIV response. We conducted a systematic and comprehensive synthesis of the existing scientific evidence on the impact of service integration on the HIV care cascade, health outcomes, and cost-effectiveness. Methods and findings We reviewed the global quantitative empirical evidence on integration published between 1 January 2010 and 10 September 2021. We included experimental and observational studies that featured both an integration intervention and a comparator in our review. Of the 7,118 unique peer-reviewed English-language studies that our search algorithm identified, 114 met all of our selection criteria for data extraction. Most of the studies (90) were conducted in sub-Saharan Africa, primarily in East Africa (55) and Southern Africa (24). The most common forms of integration were (i) HIV testing and counselling added to non-HIV services and (ii) non-HIV services added to antiretroviral therapy (ART). The most commonly integrated non-HIV services were maternal and child healthcare, tuberculosis testing and treatment, primary healthcare, family planning, and sexual and reproductive health services. Values for HIV care cascade outcomes tended to be better in integrated services: uptake of HIV testing and counselling (pooled risk ratio [RR] across 37 studies: 1.67 [95% CI 1.41–1.99], p < 0.001), ART initiation coverage (pooled RR across 19 studies: 1.42 [95% CI 1.16–1.75], p = 0.002), time until ART initiation (pooled RR across 5 studies: 0.45 [95% CI 0.20–1.00], p = 0.050), retention in HIV care (pooled RR across 19 studies: 1.68 [95% CI 1.05–2.69], p = 0.031), and viral suppression (pooled RR across 9 studies: 1.19 [95% CI 1.03–1.37], p = 0.025). Also, treatment success for non-HIV-related diseases and conditions and the uptake of non-HIV services were commonly higher in integrated services. We did not find any significant differences for the following outcomes in our meta-analyses: HIV testing yield, ART adherence, HIV-free survival among infants, and HIV and non-HIV mortality. We could not conduct meta-analyses for several outcomes (HIV infections averted, costs, and cost-effectiveness), because our systematic review did not identify sufficient poolable studies. Study limitations included possible publication bias of studies with significant or favourable findings and comparatively weak evidence from some world regions and on integration of services for key populations in the HIV response. Conclusions Integration of HIV services and other health services tends to improve health and health systems outcomes. Despite some scientific limitations, the global evidence shows that service integration can be a valuable strategy to boost the sustainability of the HIV response and contribute to the goal of ‘ending AIDS by 2030’, while simultaneously supporting progress towards universal health coverage., Caroline Bulstra and co-workers assess evidence on the benefits of service integration in the HIV care cascade., Author summary Why was this study done? The rapid scale-up of HIV testing and antiretroviral therapy (ART) in many countries and communities over the past 2 decades has been largely achieved with stand-alone HIV programmes. Increasing life expectancy and the side effects of ART are leading to more co-morbidities among people living with HIV, suggesting that ART programmes that also offer other treatments could improve both healthcare effectiveness and the patient experience. Other reasons for integration of services include the hope that joint delivery of services will increase coverage and reduce costs. The global evidence on integration of HIV services and other health services, to our knowledge, has never been synthesised, and it is thus unclear what the empirical effects of integration are. What did the researchers do and find? We conducted a systematic review and meta-analysis to synthesise the results of integrating HIV services and other health services for HIV care cascade outcomes (testing, linkage to care, treatment initiation, treatment adherence, retention, and viral suppression), HIV health outcomes (new infections and mortality), non-HIV health outcomes, and costs and cost-effectiveness. In most of the 114 studies that our systematic review identified most outcomes were better in integrated compared to separate services. What do these findings mean? Integration of HIV services and other health services tends to improve health and health systems outcomes. The success of integration strategies is highly context-specific, and more evidence is needed on integration in specific geographical areas and for key populations in the HIV response. Despite such limitations, our systematic review and meta-analysis support the case for integration as a valuable and viable strategy to boost the sustainability of the HIV response and contribute to the goal of ‘ending AIDS by 2030’, while simultaneously supporting progress towards universal health coverage.

Background The recent Lancet Commission on Legal Determinants of Global Health argues that governance can provide the framework for achieving sustainable development goals. Even though over 90% of fatal road traffic injuries occur in low- and middle-income countries (LMICs) primarily affecting motorcyclists, the utility of helmet laws outside of high-income settings has not been well characterized. We sought to evaluate the differences in outcomes of mandatory motorcycle helmet legislation and determine whether these varied across country income levels. Methods and findings A systematic review and meta-analysis were completed using the PRISMA checklist. A search for relevant articles was conducted using the PubMed, Embase, and Web of Science databases from January 1, 1990 to August 8, 2021. Studies were included if they evaluated helmet usage, mortality from motorcycle crash, or traumatic brain injury (TBI) incidence, with and without enactment of a mandatory helmet law as the intervention. The Newcastle–Ottawa Scale (NOS) was used to rate study quality and funnel plots, and Begg’s and Egger’s tests were used to assess for small study bias. Pooled odds ratios (ORs) and their 95% confidence intervals (CIs) were stratified by high-income countries (HICs) versus LMICs using the random-effects model. Twenty-five articles were included in the final analysis encompassing a total study population of 31,949,418 people. There were 17 retrospective cohort studies, 2 prospective cohort studies, 1 case–control study, and 5 pre–post design studies. There were 16 studies from HICs and 9 from LMICs. The median NOS score was 6 with a range of 4 to 9. All studies demonstrated higher odds of helmet usage after implementation of helmet law; however, the results were statistically significantly greater in HICs (OR: 53.5; 95% CI: 28.4; 100.7) than in LMICs (OR: 4.82; 95% CI: 3.58; 6.49), p-value comparing both strata < 0.0001. There were significantly lower odds of motorcycle fatalities after enactment of helmet legislation (OR: 0.71; 95% CI: 0.61; 0.83) with no significant difference by income classification, p-value: 0.27. Odds of TBI were statistically significantly lower in HICs (OR: 0.61, 95% CI 0.54 to 0.69) than in LMICs (0.79, 95% CI 0.72 to 0.86) after enactment of law (p-value: 0.0001). Limitations of this study include variability in the methodologies and data sources in the studies included in the meta-analysis as well as the lack of available literature from the lowest income countries or from the African WHO region, in which helmet laws are least commonly present. Conclusions In this study, we observed that mandatory helmet laws had substantial public health benefits in all income contexts, but some outcomes were diminished in LMIC settings where additional measures such as public education and law enforcement might play critical roles., In a systematic review and meta-analysis, Jacob Lepard and colleagues evaluate the differences in outcomes of mandatory motorcycle helmet legislation by country-income level., Author summary Why was this study done? The utility of mandatory motorcycle helmet legislation has been studied at length in high-income country (HIC) settings, with extensive evidence demonstrating improvement in mortality and morbidity from road traffic accidents. Prior to this study, there was very limited discussion regarding the utility of helmet laws specifically in low-resource settings, despite the fact that over 90% of fatal road traffic injuries occurred in low- and middle-income countries (LMICs) each year. Based upon this, we sought to evaluate all available literature using a systematic review and meta-analysis to determine the benefit of helmet legislation in LMICs in comparison to HICs. What did the researchers do and find? A systematic review and meta-analysis were conducted, which demonstrated that road users in countries with mandatory motorcycle helmet legislation were significantly more likely to wear a helmet and significantly less likely to experience motorcycle fatality or traumatic brain injury (TBI). We identified a disparity in legislative benefit, showing lower usage of motorcycle helmets and less reduction in brain injuries, including severe TBI in LMICs compared with HICs. Notably though, the reduction in motorcycle fatalities was similar between income contexts and overall greater in LMICs when controlling for study quality and years since law enactment, indicating that the overall goal of the law is achieved regardless of income context. What do these findings mean? Our research addresses a significant gap in the literature regarding the utility of helmet laws in low- and middle-income settings, where they are unequivocally needed the most. These findings indicate that helmet laws reduce mortality and have significant benefit in all income contexts. The presence of some disparities in legislative outcomes in LMICs highlights that additional measures beyond legislation may be needed in low-resource settings to ensure the greatest protection to the most vulnerable populations of road users worldwide. Our study is limited in that no studies from the lowest income countries met criteria for inclusion. This is likely a result of minimal research output or resources from these regions. Additionally, few low-income countries (13.9%) have helmet laws to be studied. We thus had to infer our findings from middle-income countries onto the lowest income countries.

Background Worldwide, nearly 800,000 individuals die by suicide each year; however, longitudinal prediction of suicide attempts remains a major challenge within the field of psychiatry. The objective of the present research was to develop and evaluate an evidence-based suicide attempt risk checklist [i.e., the Durham Risk Score (DRS)] to aid clinicians in the identification of individuals at risk for attempting suicide in the future. Methods and findings Three prospective cohort studies, including a population-based study from the United States [i.e., the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) study] as well as 2 smaller US veteran cohorts [i.e., the Assessing and Reducing Post-Deployment Violence Risk (REHAB) and the Veterans After-Discharge Longitudinal Registry (VALOR) studies], were used to develop and validate the DRS. From a total sample size of 35,654 participants, 17,630 participants were selected to develop the checklist, whereas the remaining participants (N = 18,024) were used to validate it. The main outcome measure was future suicide attempts (i.e., actual suicide attempts that occurred after the baseline assessment during the 1- to 3-year follow-up period). Measure development began with a review of the extant literature to identify potential variables that had substantial empirical support as longitudinal predictors of suicide attempts and deaths. Next, receiver operating characteristic (ROC) curve analysis was utilized to identify variables from the literature review that uniquely contributed to the longitudinal prediction of suicide attempts in the development cohorts. We observed that the DRS was a robust prospective predictor of future suicide attempts in both the combined development (area under the curve [AUC] = 0.91) and validation (AUC = 0.92) cohorts. A concentration of risk analysis found that across all 35,654 participants, 82% of prospective suicide attempts occurred among individuals in the top 15% of DRS scores, whereas 27% occurred in the top 1%. The DRS also performed well among important subgroups, including women (AUC = 0.91), men (AUC = 0.93), Black (AUC = 0.92), White (AUC = 0.93), Hispanic (AUC = 0.89), veterans (AUC = 0.91), lower-income individuals (AUC = 0.90), younger adults (AUC = 0.88), and lesbian, gay, bisexual, transgender, and queer or questioning (LGBTQ) individuals (AUC = 0.88). The primary limitation of the present study was its its reliance on secondary data analyses to develop and validate the risk score. Conclusions In this study, we observed that the DRS was a strong predictor of future suicide attempts in both the combined development (AUC = 0.91) and validation (AUC = 0.92) cohorts. It also demonstrated good utility in many important subgroups, including women, men, Black, White, Hispanic, veterans, lower-income individuals, younger adults, and LGBTQ individuals. We further observed that 82% of prospective suicide attempts occurred among individuals in the top 15% of DRS scores, whereas 27% occurred in the top 1%. Taken together, these findings suggest that the DRS represents a significant advancement in suicide risk prediction over traditional clinical assessment approaches. While more work is needed to independently validate the DRS in prospective studies and to identify the optimal methods to assess the constructs used to calculate the score, our findings suggest that the DRS is a promising new tool that has the potential to significantly enhance clinicians’ ability to identify individuals at risk for attempting suicide in the future., Using data from three prospective cohorts in the US, Nathan Kimbrel and colleagues report on the development and validation of an evidence-based checklist to identify those at risk of future suicide attempts., Author summary Why was this study done? Nearly 800,000 individuals die by suicide each year worldwide; however, longitudinal prediction of suicide attempts remains a major challenge within the field of psychiatry. Current clinical risk instruments and assessments to detect risk for future suicide attempts lack sufficient diagnostic accuracy to guide treatment decisions. What did the researchers do and find? The goal of this study was to develop and evaluate a risk score (the Durham Risk Score or DRS) to aid clinicians in identifying individuals at risk for attempting suicide. Secondary analyses were conducted on 3 prospective cohort studies from the US (total sample size = 35,654 participants), including a large general population study and 2 smaller veteran cohorts. The risk score was a strong predictor of future suicide attempts in both the combined development (area under the curve [AUC] = 0.91) and validation (AUC = 0.92) cohorts. Moreover, 82% of prospective suicide attempts occurred among individuals in the top 15% of risk scores, whereas 27% occurred among individuals scoring in the top 1% of risk scores. The risk score also performed well among important subgroups, including women, men, Black, White, Hispanic, veterans, lower-income individuals, younger adults, and lesbian, gay, bisexual, transgender, and queer or questioning (LGBTQ) individuals. What do these findings mean? Our findings suggest that the DRS is a promising new tool that has the potential to enhance clinicians’ ability to identify individuals at risk for attempting suicide. The primary limitation of this work is its reliance on secondary data analyses to develop and validate the score. More work is needed to independently validate the DRS in prospective studies and to identify the optimal methods to assess each of the constructs used to calculate the score.

Background Schools play a key role in supporting the well-being and resettlement of refugee children, and parental engagement with the school may be a critical factor in the process. Many resettlement countries have policies in place to support refugee parents’ engagement with their children’s school. However, the impact of these programs lacks systematic evaluation. This study first aimed to validate self-report measures of parental school engagement developed specifically for the refugee context, and second, to identify parent characteristics associated with school engagement, so as to help tailor support to families most in need. Methods and findings The report utilises 2016 baseline data of a cohort study of 233 Arabic-speaking parents (77% response rate) of 10- to 12-year-old schoolchildren from refugee backgrounds across 5 schools in Sydney, Australia. Most participants were born in Iraq (81%) or Syria (11%), and only 25% spoke English well to very well. Participants’ mean age was 40 years old, and 83% were female. Confirmatory factor analyses were run on provisional item sets identified from a literature review and separate qualitative study. The findings informed the development of 4 self-report tools assessing parent engagement with the school and school community, school belonging, and quality of the relationship with the schools’ bilingual cultural broker. Cronbach alpha and Pearson correlations with an established Teacher–Home Communication subscale demonstrated adequate reliability (α = 0.67 to 0.80) and construct and convergent validity of the measures (p < 0.01), respectively. Parent characteristics were entered into respective least absolute shrinkage and selection operator (LASSO) regression analyses. The degree of parents’ psychological distress (as measured by the Kessler10 self-report instrument) and postmigration living difficulties (PLMDs) were each associated with lower school engagement and belonging, whereas less time lived in Australia, lower education levels, and an unemployed status were associated with higher ratings in relationship quality with the schools’ cultural broker. Study limitations include the cross-sectional design and the modest amount of variance (8% to 22%) accounted for by the regression models. Conclusions The study offers preliminary refugee-specific measures of parental school engagement. It is expected they will provide a resource for evaluating efforts to support the integration of refugee families into schools. The findings support the need for initiatives that identify and support parents with school-attending children from refugee backgrounds who are experiencing psychological distress or resettlement stressors. At the school level, the findings suggest that cultural brokers may be effective in targeting newly arrived families., Jess Baker and co-workers study engagement of Arabic-speaking refugees with their children’s schools in Australia., Author summary Why was this study done? Current measures of parent school engagement do not consider the unique experiences of families from refugee backgrounds. This study was undertaken to develop appropriate measures of refugee parents’ engagement with school. It is expected that specific measures to ascertain refugee parents’ engagement will help support the development and evaluation of school-based refugee programs. What did the researchers do and find? Using data from a cohort study of 233 Arabic-speaking refugee families, the study developed and validated 4 self-reports tools to assess parent school engagement and their sense of belonging. The study also explored the quality of the relationship between parents and the bilingual officer employed by schools to act as a cultural broker. Our findings suggest that parents who were experiencing psychological distress or postmigration living difficulties (PLMDs) were less engaged with their child’s school. What do these findings mean? These new refugee-specific measures of parent engagement are expected to assist in evaluating programs aimed at supporting the integration of refugee parents and their children into schools. The cross-sectional design precludes causal interpretations. Nonetheless, the findings highlight a need to consider the identification and capacity building of parents who are experiencing psychological distress or resettlement stressors.

Background Reducing meat consumption could bring health and environmental benefits, but there is little research to date on effective interventions to achieve this. A non-randomised controlled intervention study was used to evaluate whether prominent positioning of meat-free products in the meat aisle was associated with a change in weekly mean sales of meat and meat-free products. Methods and findings Weekly sales data were obtained from 108 stores: 20 intervention stores that moved a selection of 26 meat-free products into a newly created meat-free bay within the meat aisle and 88 matched control stores. The primary outcome analysis used a hierarchical negative binomial model to compare changes in weekly sales (units) of meat products sold in intervention versus control stores during the main intervention period (Phase I: February 2019 to April 2019). Interrupted time series analysis was also used to evaluate the effects of the Phase I intervention. Moreover, 8 of the 20 stores enhanced the intervention from August 2019 onwards (Phase II intervention) by adding a second bay of meat-free products into the meat aisle, which was evaluated following the same analytical methods. During the Phase I intervention, sales of meat products (units/store/week) decreased in intervention (approximately −6%) and control stores (−5%) without significant differences (incidence rate ratio [IRR] 1.01 [95% CI 0.95–1.07]. Sales of meat-free products increased significantly more in the intervention (+31%) compared to the control stores (+6%; IRR 1.43 [95% CI 1.30–1.57]), mostly due to increased sales of meat-free burgers, mince, and sausages. Consistent results were observed in interrupted time series analyses where the effect of the Phase II intervention was significant in intervention versus control stores. Conclusions Prominent positioning of meat-free products into the meat aisle in a supermarket was not effective in reducing sales of meat products, but successfully increased sales of meat-free alternatives in the longer term. A preregistered protocol (https://osf.io/qmz3a/) was completed and fully available before data analysis., Carmen Piernas and co-workers study positioning and marketing of meat-free products in supermarkets., Author summary Why was this study done? Reducing meat consumption could bring health and environmental benefits, but there is limited evidence of interventions that may be effective. Physical environments within which food choices are made can exert a significant influence on food selection. Supermarkets account for a large proportion of all United Kingdom retail grocery sales, and interventions to reduce meat selection store have the potential to reach a large number of consumers. But evidence on the effectiveness of in-store “nudges” to shift meat purchasing behaviours is lacking. We assessed the immediate and longer-term effects of repositioning meat-free products (plant-based mince, burgers, meatballs, and sausages) into the meat aisle in a major UK supermarket on the purchases of meat and meat-free products. What did the researchers do and find? Prominent positioning of meat-free products in the meat aisle did not result in decreased sales of equivalent meat products (the primary outcome measure). However, the intervention led to a significant increase in sales of meat-free products, which was sustained over time. Intensification of the intervention in the Phase II period further increased sales of meat-free products, with no impact on sales of meat products. A post hoc analysis by store affluence suggested that the effects on sales of meat-free products were of greater magnitude in stores located in areas of average or below average affluence. What do these findings mean? Redesigning microenvironments in grocery stores could help shift habitual purchasing behaviours to reduce the demand for meat. The limited effectiveness of this positioning intervention in meeting the primary goal, reduced meat consumption, suggests that it may be necessary to make structural changes that explicitly target a reduction in meat purchases to achieve global targets that are compatible with human health and environmental sustainability.

Background Timely interventions in women presenting with preterm labour can substantially improve health outcomes for preterm babies. However, establishing such a diagnosis is very challenging, as signs and symptoms of preterm labour are common and can be nonspecific. We aimed to develop and externally validate a risk prediction model using concentration of vaginal fluid fetal fibronectin (quantitative fFN), in combination with clinical risk factors, for the prediction of spontaneous preterm birth and assessed its cost-effectiveness. Methods and findings Pregnant women included in the analyses were 22+0 to 34+6 weeks gestation with signs and symptoms of preterm labour. The primary outcome was spontaneous preterm birth within 7 days of quantitative fFN test. The risk prediction model was developed and internally validated in an individual participant data (IPD) meta-analysis of 5 European prospective cohort studies (2009 to 2016; 1,783 women; mean age 29.7 years; median BMI 24.8 kg/m2; 67.6% White; 11.7% smokers; 51.8% nulliparous; 10.4% with multiple pregnancy; 139 [7.8%] with spontaneous preterm birth within 7 days). The model was then externally validated in a prospective cohort study in 26 United Kingdom centres (2016 to 2018; 2,924 women; mean age 28.2 years; median BMI 25.4 kg/m2; 88.2% White; 21% smokers; 35.2% nulliparous; 3.5% with multiple pregnancy; 85 [2.9%] with spontaneous preterm birth within 7 days). The developed risk prediction model for spontaneous preterm birth within 7 days included quantitative fFN, current smoking, not White ethnicity, nulliparity, and multiple pregnancy. After internal validation, the optimism adjusted area under the curve was 0.89 (95% CI 0.86 to 0.92), and the optimism adjusted Nagelkerke R2 was 35% (95% CI 33% to 37%). On external validation in the prospective UK cohort population, the area under the curve was 0.89 (95% CI 0.84 to 0.94), and Nagelkerke R2 of 36% (95% CI: 34% to 38%). Recalibration of the model’s intercept was required to ensure overall calibration-in-the-large. A calibration curve suggested close agreement between predicted and observed risks in the range of predictions 0% to 10%, but some miscalibration (underprediction) at higher risks (slope 1.24 (95% CI 1.23 to 1.26)). Despite any miscalibration, the net benefit of the model was higher than “treat all” or “treat none” strategies for thresholds up to about 15% risk. The economic analysis found the prognostic model was cost effective, compared to using qualitative fFN, at a threshold for hospital admission and treatment of ≥2% risk of preterm birth within 7 days. Study limitations include the limited number of participants who are not White and levels of missing data for certain variables in the development dataset. Conclusions In this study, we found that a risk prediction model including vaginal fFN concentration and clinical risk factors showed promising performance in the prediction of spontaneous preterm birth within 7 days of test and has potential to inform management decisions for women with threatened preterm labour. Further evaluation of the risk prediction model in clinical practice is required to determine whether the risk prediction model improves clinical outcomes if used in practice. Trial registration The study was approved by the West of Scotland Research Ethics Committee (16/WS/0068). The study was registered with ISRCTN Registry (ISRCTN 41598423) and NIHR Portfolio (CPMS: 31277)., Sarah J Stock and colleagues develop and validate a risk prediction model of spontaneous preterm birth for women with preterm labour symptoms, based on vaginal fluid fetal fibronectin and clinical risk factors., Author summary Why was this study done? Preterm labour is notoriously challenging for clinicians to diagnose due to the nonspecific nature of presenting signs and symptoms. The outcomes and health of preterm babies can be significantly improved by well-timed and appropriate interventions, but unnecessary overtreatment of women is common and costly. Qualitative fetal fibronectin (fFN) is one test used to aid the diagnosis of preterm labour. What did the researchers do and find? We developed and validated a risk prediction model to improve the prediction of impending preterm birth using a combination of qualitative fFN results and additional clinical risk factors. The primary outcome was spontaneous preterm birth within 7 days of fFN testing. We developed and internally validated the model using data from 1783 European women and externally validated the model in a prospective cohort study of 2,924 UK women. Women with signs and symptoms of preterm labour were included in the analysis between 22+0 to 34+6 weeks gestation. At a threshold of ≥2% risk of birth within 7 days of testing, we found the prognostic model to be cost effective in comparison to fFN alone. What do these findings mean? Our findings indicate that the prognostic model has promising potential to improve prediction of spontaneous preterm birth. The model is well placed to aid management decisions and discussions with women presenting with signs and symptoms of preterm labour. Limitations were that there were low numbers of participants in the studies who were not White, and there were some missing data in the risk predictor development cohort.

Tuberculosis preventive therapy (TPT) reduces TB-related morbidity and mortality in people living with HIV (PLHIV). Cascade-of-care analyses help identify gaps and barriers in care and develop targeted solutions. A previous latent tuberculosis infection (LTBI) cascade-of-care analysis showed only 18% of persons in at-risk populations complete TPT, but a similar analysis for TPT among PLHIV has not been completed. We conducted a meta-analysis to provide this evidence.We first screened potential articles from a LTBI cascade-of-care systematic review published in 2016. From this study, we included cohorts that reported a minimum of 25 PLHIV. To identify new cohorts, we used a similar search strategy restricted to PLHIV. The search was conducted in Medline, Embase, Health Star, and LILACS, from January 2014 to February 2021. Two authors independently screened titles and full text and assessed risk of bias using the Newcastle-Ottawa Scale for cohorts and Cochrane Risk of Bias for cluster randomized trials. We meta-analyzed the proportion of PLHIV completing each step of the LTBI cascade-of-care and estimated the cumulative proportion retained. These results were stratified based on cascades-of-care that used or did not use LTBI testing to determine eligibility for TPT. We also performed a narrative synthesis of enablers and barriers of the cascade-of-care identified at different steps of the cascade. A total of 71 cohorts were included, and 70 were meta-analyzed, comprising 94,011 PLHIV. Among the PLHIV included, 35.3% (33,139/94,011) were from the Americas and 29.2% (27,460/94,011) from Africa. Overall, 49.9% (46,903/94,011) from low- and middle-income countries, median age was 38.0 [interquartile range (IQR) 34.0;43.6], and 65.9% (46,328/70,297) were men, 43.6% (29,629/67,947) were treated with antiretroviral therapy (ART), and the median CD4 count was 390 cell/mm3 (IQR 312;458). Among the cohorts that did not use LTBI tests, the cumulative proportion of PLHIV starting and completing TPT were 40.9% (95% CI: 39.3% to 42.7%) and 33.2% (95% CI: 31.6% to 34.9%). Among cohorts that used LTBI tests, the cumulative proportions of PLHIV starting and completing TPT were 60.4% (95% CI: 58.1% to 62.6%) and 41.9% (95% CI:39.6% to 44.2%), respectively. Completion of TPT was not significantly different in high- compared to low- and middle-income countries. Regardless of LTBI test use, substantial losses in the cascade-of-care occurred before treatment initiation. The integration of HIV and TB care was considered an enabler of the cascade-of-care in multiple cohorts. Key limitations of this systematic review are the observational nature of the included studies, potential selection bias in the population selection, only 14 cohorts reported all steps of the cascade-of-care, and barriers/facilitators were not systematically reported in all cohorts.Although substantial losses were seen in multiple stages of the cascade-of-care, the cumulative proportion of PLHIV completing TPT was higher than previously reported among other at-risk populations. The use of LTBI testing in PLHIV in low- and middle-income countries was associated with higher proportion of the cohorts initiating TPT and with similar rates of completion of TPT.

Background Human immunodeficiency virus (HIV) is the strongest known risk factor for tuberculosis (TB) through its impairment of T-cell immunity. Tuberculosis preventive treatment (TPT) is recommended for people living with HIV (PLHIV) by the World Health Organization, as it significantly reduces the risk of developing TB disease. We conducted a systematic review and meta-analysis of modeling studies to summarize projected costs, risks, benefits, and impacts of TPT use among PLHIV on TB-related outcomes. Methods and findings We searched MEDLINE, Embase, and Web of Science from inception until December 31, 2020. Two reviewers independently screened titles, abstracts, and full texts; extracted data; and assessed quality. Extracted data were summarized using descriptive analysis. We performed quantile regression and random effects meta-analysis to describe trends in cost, effectiveness, and cost-effectiveness outcomes across studies and identified key determinants of these outcomes. Our search identified 6,615 titles; 61 full texts were included in the final review. Of the 61 included studies, 31 reported both cost and effectiveness outcomes. A total of 41 were set in low- and middle-income countries (LMICs), while 12 were set in high-income countries (HICs); 2 were set in both. Most studies considered isoniazid (INH)-based regimens 6 to 2 months long (n = 45), or longer than 12 months (n = 11). Model parameters and assumptions varied widely between studies. Despite this, all studies found that providing TPT to PLHIV was predicted to be effective at averting TB disease. No TPT regimen was substantially more effective at averting TB disease than any other. The cost of providing TPT and subsequent downstream costs (e.g. post-TPT health systems costs) were estimated to be less than $1,500 (2020 USD) per person in 85% of studies that reported cost outcomes (n = 36), regardless of study setting. All cost-effectiveness analyses concluded that providing TPT to PLHIV was potentially cost-effective compared to not providing TPT. In quantitative analyses, country income classification, consideration of antiretroviral therapy (ART) use, and TPT regimen use significantly impacted cost-effectiveness. Studies evaluating TPT in HICs suggested that TPT may be more effective at preventing TB disease than studies evaluating TPT in LMICs; pooled incremental net monetary benefit, given a willingness-to-pay threshold of country-level per capita gross domestic product (GDP), was $271 in LMICs (95% confidence interval [CI] −$81 to $622, p = 0.12) and was $2,568 in HICs (−$32,115 to $37,251, p = 0.52). Similarly, TPT appeared to be more effective at averting TB disease in HICs; pooled percent reduction in active TB incidence was 20% (13% to 27%, p < 0.001) in LMICs and 37% (−34% to 100%, p = 0.13) in HICs. Key limitations of this review included the heterogeneity of input parameters and assumptions from included studies, which limited pooling of effect estimates, inconsistent reporting of model parameters, which limited sample sizes of quantitative analyses, and database bias toward English publications. Conclusions The body of literature related to modeling TPT among PLHIV is large and heterogeneous, making comparisons across studies difficult. Despite this variability, all studies in all settings concluded that providing TPT to PLHIV is potentially effective and cost-effective for preventing TB disease., Aashna Uppal and co-workers report on predicted costs and benefits of tuberculosis preventive therapy for people with HIV infection., Author summary Why was this study done? Human immunodeficiency virus (HIV) is the strongest know risk factor for tuberculosis (TB). While the uptake of tuberculosis preventive treatment (TPT) has increased in recent years, a summarization of costs, risks, benefits, and impacts of TPT among people living with HIV (PLHIV) does not exist. What did the researchers do and find? We conducted a systematic review and meta-analysis to synthesize data on costs and cost-effectiveness, as well as risks and impact on TB morbidity and mortality associated with TPT provided to PLHIV. Our search identified 6,615 titles; 61 full texts were included in the final review. We performed quantile regression and a random effects meta-analysis to describe key trends in cost, effectiveness, and cost-effectiveness outcomes across studies and to identify key determinants of these outcomes. Values of model parameters varied widely. In our quantile regression analyses, we found that TPT appeared to be more effective reducing at active TB incidence and more cost-effective in high-income countries (HICs), compared to low- and middle-income countries (LMICs). The pooled incremental net monetary benefit, given a willingness-to-pay threshold of country-level gross domestic product (GDP) per capita, was positive for both LMICs and HICs, meaning that TPT was potentially cost-effective compared to no TPT, regardless of study setting. Aside from our quantitative results, individual study conclusions found that providing TPT to PLHIV was predicted to be effective at averting TB disease and was predicted to be cost-effective compared to not providing TPT. What do these findings mean? Heterogeneity and inconsistent reporting of model parameters made it difficult to summarize these studies and limited the extent of pooling through meta-analytical techniques. This underscores the need for better standardization of models for TB. The findings of this review support greater resource allocation in all settings to expand programs that deliver TPT to PLHIV.

Background Effective health system interventions may help address the disproportionate burden of diabetes in low- and middle-income countries (LMICs). We assessed the impact of health system interventions to improve outcomes for adults with type 2 diabetes in LMICs. Methods and findings We searched Ovid MEDLINE, Cochrane Library, EMBASE, African Index Medicus, LILACS, and Global Index Medicus from inception of each database through February 24, 2020. We included randomized controlled trials (RCTs) of health system interventions targeting adults with type 2 diabetes in LMICs. Eligible studies reported at least 1 of the following outcomes: glycemic change, mortality, quality of life, or cost-effectiveness. We conducted a meta-analysis for the glycemic outcome of hemoglobin A1c (HbA1c). GRADE and Cochrane Effective Practice and Organisation of Care methods were used to assess risk of bias for the glycemic outcome and to prepare a summary of findings table. Of the 12,921 references identified in searches, we included 39 studies in the narrative review of which 19 were cluster RCTs and 20 were individual RCTs. The greatest number of studies were conducted in the East Asia and Pacific region (n = 20) followed by South Asia (n = 7). There were 21,080 total participants enrolled across included studies and 10,060 total participants in the meta-analysis of HbA1c when accounting for the design effect of cluster RCTs. Non-glycemic outcomes of mortality, health-related quality of life, and cost-effectiveness had sparse data availability that precluded quantitative pooling. In the meta-analysis of HbA1c from 35 of the included studies, the mean difference was −0.46% (95% CI −0.60% to −0.31%, I2 87.8%, p < 0.001) overall, −0.37% (95% CI −0.64% to −0.10%, I2 60.0%, n = 7, p = 0.020) in multicomponent clinic-based interventions, −0.87% (−1.20% to −0.53%, I2 91.0%, n = 13, p < 0.001) in pharmacist task-sharing studies, and −0.27% (−0.50% to −0.04%, I2 64.1%, n = 7, p = 0.010) in trials of diabetes education or support alone. Other types of interventions had few included studies. Eight studies were at low risk of bias for the summary assessment of glycemic control, 15 studies were at unclear risk, and 16 studies were at high risk. The certainty of evidence for glycemic control by subgroup was moderate for multicomponent clinic-based interventions but was low or very low for other intervention types. Limitations include the lack of consensus definitions for health system interventions, differences in the quality of underlying studies, and sparse data availability for non-glycemic outcomes. Conclusions In this meta-analysis, we found that health system interventions for type 2 diabetes may be effective in improving glycemic control in LMICs, but few studies are available from rural areas or low- or lower-middle-income countries. Multicomponent clinic-based interventions had the strongest evidence for glycemic benefit among intervention types. Further research is needed to assess non-glycemic outcomes and to study implementation in rural and low-income settings., In this meta-analysis of published studies, David Flood and colleagues assess the impact of health system interventions to improve outcomes for type 2 diabetes patients., Author summary Why was this study done? Approximately 80% of the 463 million adults with type 2 diabetes worldwide live in low- and middle-income countries (LMICs). Evidence-based treatments for diabetes exist, but health systems in LMICs have difficulty meeting diabetes patients’ needs. Health system interventions can help address this gap by improving the delivery of diabetes care within health systems. What did the researchers do and find? We conducted a systematic review and meta-analysis of 39 health system interventions aiming to improve outcomes of glycemic (i.e., blood glucose) control, mortality, quality of life, or cost-effectiveness for people with type 2 diabetes in LMICs. We found that health system interventions for type 2 diabetes may be effective in improving glycemic control in LMICs, but few studies were available from rural areas or low- or lower-middle-income countries. Among intervention types, multicomponent clinic-based interventions had the strongest evidence for improving glycemic control. What do these findings mean? Our findings support the scaling up of diabetes health system interventions to improve patients’ glycemic control in LMICs. Further research is needed to assess other outcomes beyond glycemic control, especially in rural areas and in low- or lower-middle-income countries.

Background Suicide is a major public health concern globally. Accurately predicting suicidal behavior remains challenging. This study aimed to use machine learning approaches to examine the potential of the Swedish national registry data for prediction of suicidal behavior. Methods and findings The study sample consisted of 541,300 inpatient and outpatient visits by 126,205 Sweden-born patients (54% female and 46% male) aged 18 to 39 (mean age at the visit: 27.3) years to psychiatric specialty care in Sweden between January 1, 2011 and December 31, 2012. The most common psychiatric diagnoses at the visit were anxiety disorders (20.0%), major depressive disorder (16.9%), and substance use disorders (13.6%). A total of 425 candidate predictors covering demographic characteristics, socioeconomic status (SES), electronic medical records, criminality, as well as family history of disease and crime were extracted from the Swedish registry data. The sample was randomly split into an 80% training set containing 433,024 visits and a 20% test set containing 108,276 visits. Models were trained separately for suicide attempt/death within 90 and 30 days following a visit using multiple machine learning algorithms. Model discrimination and calibration were both evaluated. Among all eligible visits, 3.5% (18,682) were followed by a suicide attempt/death within 90 days and 1.7% (9,099) within 30 days. The final models were based on ensemble learning that combined predictions from elastic net penalized logistic regression, random forest, gradient boosting, and a neural network. The area under the receiver operating characteristic (ROC) curves (AUCs) on the test set were 0.88 (95% confidence interval [CI] = 0.87–0.89) and 0.89 (95% CI = 0.88–0.90) for the outcome within 90 days and 30 days, respectively, both being significantly better than chance (i.e., AUC = 0.50) (p < 0.01). Sensitivity, specificity, and predictive values were reported at different risk thresholds. A limitation of our study is that our models have not yet been externally validated, and thus, the generalizability of the models to other populations remains unknown. Conclusions By combining the ensemble method of multiple machine learning algorithms and high-quality data solely from the Swedish registers, we developed prognostic models to predict short-term suicide attempt/death with good discrimination and calibration. Whether novel predictors can improve predictive performance requires further investigation., In a study of national registry data, Qi Chen and colleagues use machine learning models to predict suicidal behavior following a visit to psychiatric specialty care among adults in Sweden., Author summary Why was this study done? Suicidal behavior is overrepresented in people with mental illness and contributes to the substantial public health burden of psychiatric conditions. Accurately predicting suicidal behavior has long been challenging. The potential of applying machine learning to linked national datasets to predict suicidal behavior remains unknown. What did the researchers do and find? We identified a sample of 541,300 inpatient and outpatient visits to psychiatric specialty care in Sweden during 2011 and 2012. The sample was then divided into a training dataset and a test dataset. We first trained prediction models separately for suicide attempt/death within 90 days and 30 days following a visit to psychiatric specialty care, using 4 different machine learning algorithms. We then used an ensemble method to combine the performance of the trained models with the intention to achieve an overall performance superior than each individual model. The final model based on the ensemble method achieved the best predictive performance. This model was applied to test dataset and showed good model discrimination and calibration for both the 90-day and 30-day outcomes. What do these findings mean? Our findings suggest that combining machine learning with registry data has the potential to accurately predict short-term suicidal behavior. An approach combining 4 machine learning methods showed an overall predictive performance slightly better than each individual model.

Background In an effort to prevent and reduce the prevalence rate of people with obesity and diabetes, South Africa implemented a sugar-content-based tax called the Health Promotion Levy in April 2018, one of the first sugar-sweetened beverage (SSB) taxes to be based on each gram of sugar (beyond 4 g/100 ml). This before-and-after study estimated changes in taxed and untaxed beverage intake 1 year after the tax, examining separately, to our knowledge for the first time, the role of reformulation distinct from behavioral changes in SSB intake. Methods and findings We collected single-day 24-hour dietary recalls from repeat cross-sectional surveys of adults aged 18–39 years in Langa, South Africa. Participants were recruited in February–March 2018 (pre-tax, n = 2,459) and February–March 2019 (post-tax, n = 2,489) using door-to-door sampling. We developed time-specific food composition tables (FCTs) for South African beverages before and after the tax, linked with the diet recalls. By linking pre-tax FCTs only to dietary intake data collected in the pre-tax and post-tax periods, we calculated changes in beverage intake due to behavioral change, assuming no reformulation. Next, we repeated the analysis using an updated FCT in the post-tax period to capture the marginal effect of reformulation. We estimated beverage intake using a 2-part model that takes into consideration the biases in using ordinary least squares or other continuous variable approaches with many individuals with zero intake. First, a probit model was used to estimate the probability of consuming the specific beverage category. Then, conditional on a positive outcome, a generalized linear model with a log-link was used to estimate the continuous amount of beverage consumed. Among taxed beverages, sugar intake decreased significantly (p < 0.0001) from 28.8 g/capita/day (95% CI 27.3–30.4) pre-tax to 19.8 (95% CI 18.5–21.1) post-tax. Energy intake decreased (p < 0.0001) from 121 kcal/capita/day (95% CI 114–127) pre-tax to 82 (95% CI 76–87) post-tax. Volume intake decreased (p < 0.0001) from 315 ml/capita/day (95% CI 297–332) pre-tax to 198 (95% CI 185–211) post-tax. Among untaxed beverages, sugar intake increased (p < 0.0001) by 5.3 g/capita/day (95% CI 3.7 to 6.9), and energy intake increased (p < 0.0001) by 29 kcal/capita/day (95% CI 19 to 39). Among total beverages, sugar intake decreased significantly (p = 0.004) by 3.7 (95% CI −6.2 to −1.2) g/capita/day. Behavioral change accounted for reductions of 24% in energy, 22% in sugar, and 23% in volume, while reformulation accounted for additional reductions of 8% in energy, 9% in sugar, and 14% in volume from taxed beverages. The key limitations of this study are an inability to make causal claims due to repeat cross-sectional data collection, and that the magnitude of reduction in taxed beverage intake may not be generalizable to higher income populations. Conclusions Using a large sample of a high-consuming, low-income population, we found large reductions in taxed beverage intake, separating the components of behavioral change from reformulation. This reduction was partially compensated by an increase in sugar and energy from untaxed beverages. Because policies such as taxes can incentivize reformulation, our use of an up-to-date FCT that reflects a rapidly changing food supply is novel and important for evaluating policy effects on intake., Rina Swart and co-authors use real world data to demonstrate that sugar-sweetened beverage tax was associated with reduced sugary drink intake in a low-income population within a middle-income country., Author summary Why was this study done? In 2018, South Africa became the first sub-Saharan African country to implement a sugary beverage tax to reduce consumption of added sugars. Sugar-sweetened beverage (SSB) taxes have been shown to reduce purchases, but few studies have included dietary intake, and they are focused in higher income countries like the United States. To our knowledge, this is the first study to examine the effects of an SSB tax using detailed 24-hour dietary recall data focused on a low-income population from the Global South. To our knowledge, this is the first study to evaluate real-world changes from an SSB tax policy by empirically quantifying how much of the overall change in sugar intake from taxed beverages came from consumers’ behavioral changes versus reformulation of beverages. What did the researchers do and find? Using dietary intake data collected from a low-income South African township before and after the SSB tax was implemented, we examined changes in sugar, calorie, and volume intake from taxed and untaxed beverages. We used detailed brand-product-specific nutrition facts panel data collected at baseline and a year after tax implementation to allow us to examine both behavioral change and reformulation. We found the intake of sugar, calories, and volume of taxed beverages decreased by 9.0 g (31%), 39 kcal (33%), and 117 ml (37%) per capita per day, respectively. The intake of sugar, calories, and volume of untaxed beverages increased by 5.3 g (36%), 30 kcal (29%), and 339 ml (58%) per capita per day, respectively. Water accounted for the majority (52%) of the pre–post difference in volume of untaxed beverage intake. Of the 9.0 g per capita per day (31%) total reduction in sugar from taxed beverages, 6.4 g per capita per day (22%) was due to behavioral differences. When accounting for reformulation, there was an additional 2.6 g per capita per day (9%) reduction in the post-tax period. What do these findings mean? SSB taxes were associated with reduced sugary drink intake in a low-income population within a middle-income country. We quantified the observed changes in beverage intake due to reformulation and behavior change, thereby examining the 2 primary goals of an SSB tax based on sugar concentration. Future research will be needed to understand how responses to sugar-based beverage taxes may vary by socioeconomic status.

Background People living in sub-Saharan Africa (SSA) are disproportionately exposed to trauma and may be at increased risk for posttraumatic stress disorder (PTSD). However, a dearth of population-level representative data from SSA is a barrier to assessing PTSD. This manuscript sought to calculate pooled PTSD prevalence estimates from nationally and regionally representative surveys in SSA. Methods and findings The search was conducted in PubMed, Embase, PsycINFO, and PTSDpubs and was last run between October 18, 2019, and November 11, 2019. We included studies that were published in peer-reviewed journals; used probabilistic sampling methods and systematic PTSD assessments; and included ≥ 450 participants who were current residents of an SSA country, at least 50% of whom were aged between 15 and 65 years. The primary outcomes were point prevalence estimates of PTSD across all studies, and then within subgroups. The protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (registration number CRD42016029441). Out of 2,825 unique articles reviewed, 25 studies including a total of 58,887 eligible participants (54% female) in 10 out of the 48 countries in SSA were identified. Most studies enrolled any adult aged 18 years or older. However, some studies only enrolled specific age brackets or persons as young as 15 years old. Six studies were national surveys, and 19 were regional. There were 4 key findings in the meta-analysis: (1) the overall pooled prevalence of probable PTSD was 22% (95% CI 13%–32%), while the current prevalence—defined as 1 week to 1 month—was 25% (95% CI 16%–36%); (2) prevalence estimates were highly variable, ranging from 0% (95% CI 0%–0%) to 74% (95% CI 72%–76%); (3) conflict-unexposed regions had a pooled prevalence of probable PTSD of 8% (95% CI 3%–15%), while conflict-exposed regions had a pooled prevalence of probable PTSD of 30% (95% CI 21%–40%; p < 0.001); and (4) there was no significant difference in the pooled prevalence of PTSD for men and women. The primary limitations of our methodology are our exclusion of the following study types: those published in languages other than English, French, and Portuguese; smaller studies; those that focused on key populations; those that reported only on continuous measures of PTSD symptoms; and unpublished or non–peer-reviewed studies. Conclusions In this study, PTSD symptoms consistent with a probable diagnosis were found to be common in SSA, especially in regions exposed to armed conflict. However, these studies only represent data from 10 of the 48 SSA countries, and only 6 studies provided national-level data. Given the enormous heterogeneity expected across the continent, and also within countries and regions, this review cannot speak to rates of PTSD in any regions not included in this review. Thus, substantial gaps in our knowledge of PTSD prevalence in SSA remain. More research on population-level prevalence is needed to determine the burden of trauma symptoms and PTSD in SSA and to identify acceptable and feasible approaches to address this burden given limited mental healthcare resources., Lauren Ng and co-workers investigate the prevalence of post-traumatic stress disorder in countries in sub-Saharan Africa., Author summary Why was this study done? Repeated and prolonged exposure to violence, armed conflict, and mass-casualty events, combined with a lack of access to mental health treatment, may result in a substantial effect on the population burden of posttraumatic stress disorder (PTSD) in sub-Saharan Africa (SSA). While many studies of PTSD have been conducted in SSA, most of these studies derived their estimates from nonrepresentative samples or specific populations. Population-representative epidemiologic data are critical to understand the burden of PTSD in SSA and develop national and regional policies to address that burden. What did the researchers do and find? We conducted a systematic review and meta-analysis of the prevalence of PTSD from representative national and regional studies in SSA. Pooled prevalence estimates were calculated across all studies, and then within subgroups including by sex, assessment time frame (i.e., 1 week, 1 month, 1 year), use of a screening or diagnostic measure, and whether populations were affected or not affected by mass-casualty war or armed conflict. We identified 25 unique studies (N = 58,887) across 10 of the 48 SSA countries. The pooled prevalence of probable PTSD across all studies was 22% (95% CI 13%–32%). The pooled prevalence of probable PTSD in participants from war-exposed regions was 30% (95% CI 20%–40%), while the estimate from war-unexposed regions was 8% (95% CI 3%–15%; p = 0.01). What do these findings mean? These data suggest that PTSD symptoms and probable PTSD are common in SSA. However, information was only found on 10 of the 48 SSA countries, and only 6 studies provided national-level data. Only one study used a measure of PTSD symptoms whose reliability and validity had been assessed previously in the population of interest. Our results suggest both that PTSD is a major public health problem in SSA and that large gaps in our knowledge of this problem remain.

Background Policymakers worldwide are considering requiring warnings for sugary drinks. A growing number of experimental studies have examined sugary drink warnings’ impacts, but no research to our knowledge has synthesized this literature. To inform ongoing policy debates, this study aimed to identify the effects of sugary drink warnings compared with control conditions. Methods and findings We systematically searched 7 databases on June 21, 2019, and October 25, 2019. We also searched reference lists of relevant articles. Two investigators independently screened titles, abstracts, and full texts to identify peer-reviewed articles that used an experimental protocol to examine the effects of sugary drink warnings compared to a control condition. Two investigators independently extracted study characteristics and effect sizes from all relevant full-text articles. We meta-analyzed any outcome assessed in at least 2 studies, combining effect sizes using random effects meta-analytic procedures. Twenty-three experiments with data on 16,241 individuals (mean proportion female, 58%) were included in the meta-analysis. Most studies took place in Latin America (35%) or the US or Canada (46%); 32% included children. Relative to control conditions, sugary drink warnings caused stronger negative emotional reactions (d = 0.69; 95% CI: 0.25, 1.13; p = 0.002) and elicited more thinking about the health effects of sugary drinks (d = 0.65; 95% CI: 0.29, 1.01; p < 0.001). Sugary drink warnings also led to lower healthfulness perceptions (d = −0.22; 95% CI: −0.27, −0.17; p < 0.001) and stronger disease likelihood perceptions (d = 0.15; 95% CI: 0.06, 0.24; p = 0.001). Moreover, sugary drink warnings reduced both hypothetical (d = −0.32; 95% CI: −0.44, −0.21; p < 0.001) and actual consumption and purchasing behavior (d = −0.17; 95% CI: −0.30, −0.04; p = 0.012). Statistically significant effects were not observed for perceptions of added sugar or positive sugary drink attitudes (p’s > 0.10). Moderation analyses revealed that health warnings (e.g., “Beverages with added sugar contribute to obesity”) led to greater reductions in hypothetical sugary drink purchases than did nutrient warnings (e.g., “High in sugar”; d = −0.35 versus −0.18; Qb = 4.04; p = 0.04). Limitations of this study include that we did not review grey literature and that we were unable to conduct moderation analyses for several prespecified moderators due to an insufficient number of studies. Conclusions This international body of experimental literature supports sugary drink warnings as a population-level strategy for changing behavior, as well as emotions, perceptions, and intentions. Protocol Registry PROSPERO ID 146405., Anna Grummon and colleagues reveal the potential of warnings labels on sugary drinks to deter consumption., Author summary Why was this study done? Policies requiring warnings for sugary drinks could discourage sugary drink consumption and reduce diet-related disease. A growing number of experimental studies have examined consumers’ responses to sugary drink warnings, but no research to our knowledge has synthesized these studies, making it difficult for policymakers to anticipate the impacts of sugary drink warning policies. What did the researchers do and find? Using a comprehensive search strategy, we identified and meta-analyzed 23 experiments, representing more 16,000 individuals, that assessed the impact of sugary drink warnings versus control conditions. We found that sugary drink warnings reduced both self-reported and objectively measured purchases of sugary drinks. Warnings also led to beneficial changes in mechanisms underlying longer-term behavior change, including causing stronger emotional responses, increasing perceptions that sugary drinks contribute to disease, and reducing intentions to purchase or consume sugary drinks. What do these findings mean? Requiring warnings for sugary drinks is a promising policy strategy for informing consumers and reducing consumption of sugary drinks. Future research will clarify optimal warning design, the trajectory of warnings’ effects over time, and warnings’ impacts on key population subgroups.

Freya Shearer and co-authors discuss the use of decision analysis in planning for infectious disease pandemics.

Background Among people living with HIV (PLHIV), more flexible and sensitive tuberculosis (TB) screening tools capable of detecting both symptomatic and subclinical active TB are needed to (1) reduce morbidity and mortality from undiagnosed TB; (2) facilitate scale-up of tuberculosis preventive therapy (TPT) while reducing inappropriate prescription of TPT to PLHIV with subclinical active TB; and (3) allow for differentiated HIV–TB care. Methods and findings We used Botswana XPRES trial data for adult HIV clinic enrollees collected during 2012 to 2015 to develop a parsimonious multivariable prognostic model for active prevalent TB using both logistic regression and random forest machine learning approaches. A clinical score was derived by rescaling final model coefficients. The clinical score was developed using southern Botswana XPRES data and its accuracy validated internally, using northern Botswana data, and externally using 3 diverse cohorts of antiretroviral therapy (ART)-naive and ART-experienced PLHIV enrolled in XPHACTOR, TB Fast Track (TBFT), and Gugulethu studies from South Africa (SA). Predictive accuracy of the clinical score was compared with the World Health Organization (WHO) 4-symptom TB screen. Among 5,418 XPRES enrollees, 2,771 were included in the derivation dataset; 67% were female, median age was 34 years, median CD4 was 240 cells/μL, 189 (7%) had undiagnosed prevalent TB, and characteristics were similar between internal derivation and validation datasets. Among XPHACTOR, TBFT, and Gugulethu cohorts, median CD4 was 400, 73, and 167 cells/μL, and prevalence of TB was 5%, 10%, and 18%, respectively. Factors predictive of TB in the derivation dataset and selected for the clinical score included male sex (1 point), ≥1 WHO TB symptom (7 points), smoking history (1 point), temperature >37.5°C (6 points), body mass index (BMI) 10) yielded TB prevalence of 1%, 1%, 2%, and 6% in the lowest risk group and 33%, 22%, 26%, and 32% in the highest risk group for XPRES, XPHACTOR, TBFT, and Gugulethu cohorts, respectively. At clinical score ≥2, the number needed to screen (NNS) ranged from 5.0 in Gugulethu to 11.0 in XPHACTOR. Limitations include that the risk score has not been validated in resource-rich settings and needs further evaluation and validation in contemporary cohorts in Africa and other resource-constrained settings. Conclusions The simple and feasible clinical score allowed for prioritization of sensitivity and NPV, which could facilitate reductions in mortality from undiagnosed TB and safer administration of TPT during proposed global scale-up efforts. Differentiation of risk by clinical score cutoff allows flexibility in designing differentiated HIV–TB care to maximize impact of available resources., Andrew Auld and colleagues evaluate a clinical score for active tuberculosis in persons with HIV infection., Author summary Why was this study done? Tuberculosis (TB) remains the most common cause of death among people living with HIV (PLHIV) and is often undiagnosed at time of death. Rapid scale-up of tuberculosis preventive therapy (TPT) to 13 million PLHIV in low- and middle-income countries (LMICs) has been proposed for 2021; however, active TB is commonly asymptomatic and therefore missed by current WHO-recommended 4-symptom TB screening rules. Therefore, more sensitive TB screening tools are needed to better facilitate early TB diagnosis and safer scale-up of TPT to PLHIV by avoiding TPT prescription to clients with asymptomatic active TB, who need TB treatment. What did the researchers do and find? We derived a TB risk score for PLHIV from XPRES trial data and validated the score on 3 external datasets. We prioritized high sensitivity and ability to correctly rule out TB (i.e., high negative predictive value (NPV)) at key time points in care such as HIV clinic enrollment and before TPT prescription. Both logistic regression and random forest machine learning approaches were used to identify the 6 most important predictors, commonly available in LMIC clinic settings. In the external datasets, TB risk score ≥2 had higher sensitivity (87% to 97%) than WHO 4-symptom screening rule and increased NPV by 0.3% to 1.7%. Three risk groups were identified by the score, with active TB prevalence in external datasets ranging from 1% to 6% in the lowest to 22% to 32% in the highest risk groups. What do these findings mean? Following further validation, this clinical score could improve early detection of active TB to reduce morbidity and mortality from undiagnosed TB. Use of the clinical score cutoff of ≥2 during the proposed TPT scale-up for 13 million PLHIV could potentially avoid many thousands of PLHIV with active TB being inappropriately prescribed TPT. By differentiating 3 risk groups, the score also allows for the development of differentiated service delivery models suitable for LMIC.

Background Despite the sharp decline in global under-5 deaths since 1990, uneven progress has been achieved across and within countries. In sub-Saharan Africa (SSA), the Millennium Development Goals (MDGs) for child mortality were met only by a few countries. Valid concerns exist as to whether the region would meet new Sustainable Development Goals (SDGs) for under-5 mortality. We therefore examine further sources of variation by assessing age patterns, trends, and forecasts of mortality rates. Methods and findings Data came from 106 nationally representative Demographic and Health Surveys (DHSs) with full birth histories from 31 SSA countries from 1990 to 2017 (a total of 524 country-years of data). We assessed the distribution of age at death through the following new demographic analyses. First, we used a direct method and full birth histories to estimate under-5 mortality rates (U5MRs) on a monthly basis. Second, we smoothed raw estimates of death rates by age and time by using a two-dimensional P-Spline approach. Third, a variant of the Lee–Carter (LC) model, designed for populations with limited data, was used to fit and forecast age profiles of mortality. We used mortality estimates from the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) to adjust, validate, and minimize the risk of bias in survival, truncation, and recall in mortality estimation. Our mortality model revealed substantive declines of death rates at every age in most countries but with notable differences in the age patterns over time. U5MRs declined from 3.3% (annual rate of reduction [ARR] 0.1%) in Lesotho to 76.4% (ARR 5.2%) in Malawi, and the pace of decline was faster on average (ARR 3.2%) than that observed for infant (IMRs) (ARR 2.7%) and neonatal (NMRs) (ARR 2.0%) mortality rates. We predict that 5 countries (Kenya, Rwanda, Senegal, Tanzania, and Uganda) are on track to achieve the under-5 sustainable development target by 2030 (25 deaths per 1,000 live births), but only Rwanda and Tanzania would meet both the neonatal (12 deaths per 1,000 live births) and under-5 targets simultaneously. Our predicted NMRs and U5MRs were in line with those estimated by the UN IGME by 2030 and 2050 (they overlapped in 27/31 countries for NMRs and 22 for U5MRs) and by the Institute for Health Metrics and Evaluation (IHME) by 2030 (26/31 and 23/31, respectively). This study has a number of limitations, including poor data quality issues that reflected bias in the report of births and deaths, preventing reliable estimates and predictions from a few countries. Conclusions To our knowledge, this study is the first to combine full birth histories and mortality estimates from external reliable sources to model age patterns of under-5 mortality across time in SSA. We demonstrate that countries with a rapid pace of mortality reduction (ARR ≥ 3.2%) across ages would be more likely to achieve the SDG mortality targets. However, the lower pace of neonatal mortality reduction would prevent most countries from achieving those targets: 2 countries would reach them by 2030, 13 between 2030 and 2050, and 13 after 2050., Iván Mejía-Guevara and colleagues predict neonatal and under-5 mortality in sub-Sahara Africa by 2030, with most countries missing Millennium Development Goals for child mortality, Author summary Why was this study done? Under-5 mortality remains high in sub-Saharan Africa (SSA), and it is declining unevenly across countries and age groups. Only a few countries in SSA met the Millennium Development Goals (MDGs) for child mortality reduction. A new analysis that explores sources of variation is needed to address concerns on whether the region would be able to reach the Sustainable Development Goals (SDGs). What did the researchers do and find? Using data from full birth histories, we developed a new demographic model to assess age patterns of mortality, fit mortality profiles across age and time, and forecast under-5 mortality. We found marked differences in mortality profiles by age and levels across countries over time. The average annual rate of reduction (ARR) for under-5 mortality rates (U5MRs) between 1990 and 2017 was 3.2%, but it varied from 0.1% in Lesotho to 5.2% in Malawi. Based on our prediction model, we assessed the achievability of SDGs mortality targets by 2030 (2050 when possible) for 31 SSA countries. Countries with a rapid pace of mortality decline would be more likely to meet the sustainable mortality goals (e.g., Rwanda and Tanzania), as opposed to those with a slower pace (e.g., Chad and Nigeria). However, the slow pace in neonatal mortality would prevent most countries from reaching the SDGs by 2030, even countries like Ethiopia that have met the MDGs before the 2015 deadline. What do these findings mean? Our analytical approach aims to provide insights for the assessment of sustainable targets of child mortality and to identify the range of epidemiological situations and trajectories. The acceleration of neonatal mortality decline is key to advancing the agenda for sustainable development and should be considered in future programmatic assessments.

Background It has been hypothesized that prisons serve as amplifiers of general tuberculosis (TB) epidemics, but there is a paucity of data on this phenomenon and the potential population-level effects of prison-focused interventions. This study (1) quantifies the TB risk for prisoners as they traverse incarceration and release, (2) mathematically models the impact of prison-based interventions on TB burden in the general population, and (3) generalizes this model to a wide range of epidemiological contexts. Methods and findings We obtained individual-level incarceration data for all inmates (n = 42,925) and all reported TB cases (n = 5,643) in the Brazilian state of Mato Grosso do Sul from 2007 through 2013. We matched individuals between prisoner and TB databases and estimated the incidence of TB from the time of incarceration and the time of prison release using Cox proportional hazards models. We identified 130 new TB cases diagnosed during incarceration and 170 among individuals released from prison. During imprisonment, TB rates increased from 111 cases per 100,000 person-years at entry to a maximum of 1,303 per 100,000 person-years at 5.2 years. At release, TB incidence was 229 per 100,000 person-years, which declined to 42 per 100,000 person-years (the average TB incidence in Brazil) after 7 years. We used these data to populate a compartmental model of TB transmission and incarceration to evaluate the effects of various prison-based interventions on the incidence of TB among prisoners and the general population. Annual mass TB screening within Brazilian prisons would reduce TB incidence in prisons by 47.4% (95% Bayesian credible interval [BCI], 44.4%–52.5%) and in the general population by 19.4% (95% BCI 17.9%–24.2%). A generalized model demonstrates that prison-based interventions would have maximum effectiveness in reducing community incidence in populations with a high concentration of TB in prisons and greater degrees of mixing between ex-prisoners and community members. Study limitations include our focus on a single Brazilian state and our retrospective use of administrative databases. Conclusions Our findings suggest that the prison environment, more so than the prison population itself, drives TB incidence, and targeted interventions within prisons could have a substantial effect on the broader TB epidemic., From a large cohort study in Brazil, Tarub Mabud and colleagues reveal that the prison environment, rather than inmates themselves, drives up TB infection rates that spill over into communities., Author summary Why was this study done? Tuberculosis (TB) remains a leading cause of infectious disease mortality worldwide, despite global efforts to combat it. Certain environments such as prisons predispose individuals to higher than normal risk of TB. High-risk prison environments may sustain and amplify TB transmission in their surrounding communities as individuals are incarcerated and released. The time course of TB risk following incarceration and release has not been well characterized, and the impact of interventions conducted in prisons upon the community TB epidemic is not known. What did the researchers do and find? We used Brazilian TB and prisoner databases to quantify the TB risk for individuals throughout incarceration and release from prison. We created a mathematical model to simulate prison-based TB interventions among prisoners, ex-prisoners, and the general population in a Brazilian community and then generalized this model to a wide range of epidemiological contexts. After incarceration, the TB risk for a prisoner rises to 30 times that of a community member, and this risk remains elevated for 7 years after release from prison. A comprehensive intervention including mass screening, entry and exit screening, improved diagnostics, and isoniazid preventive therapy conducted within Brazilian prisons would reduce active TB incidence by 79% in the prisons and by 40% in the general population. Across global contexts, prison-based TB interventions would have maximum effectiveness in reducing community incidence of TB in populations with a high concentration of disease in prisons and greater degrees of mixing between ex-prisoners and community members. What do these findings mean? The prison environment, more so than an inherently high-risk prison population, drives TB incidence in prisons. Targeted interventions within prisons could substantially impact TB epidemics in the general population.

Background With enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare. Methods and findings We introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002–2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6–148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5–80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102–575) than those made with the baseline model (CRPS = 125, 95% CI 120–168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data. Conclusions This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems., Author summary Why was this study done? A climate-driven dengue early warning system would allow public health decision-makers to design, implement, and target timely interventions to the most at-risk places. Dengue is sensitive to changes in temperature, rainfall, humidity, and wind speed. Therefore, a dengue prediction model driven by seasonal climate forecasts offers a valuable tool for predicting dengue risk in advance. What did the researchers do and find? We developed a superensemble of probabilistic models to predict dengue incidence across Vietnam up to 6 months ahead. The predictive ability of the superensemble was assessed using multiple verification metrics and compared to a baseline model at multiple lead times, seasons, and locations. We found that the model superensemble generated more accurate predictions than the baseline model 1 to 3 months ahead but not 4 to 6 months ahead. The model superensemble, however, was better able to predict outbreaks than the baseline model across all lead times. The predictive ability of the model varied with geographic location, forecast horizon, and time of the year and performed best in the peak season in areas experiencing a high level of transmission. What do these findings mean? While outbreaks of infectious diseases are difficult to predict, particularly several months ahead, model superensembles, which combine multiple climatic drivers with dengue surveillance data at the time of forecast issue date, provide a useful decision-support tool. Early warning systems driven by seasonal climate forecasts could shift dengue control towards a more preventative approach, guiding bespoke and targeted public health interventions and a more efficient allocation of scarce resources.

Background Sugar-sweetened beverage (SSB) consumption is positively associated with obesity, type 2 diabetes, and cardiovascular disease. The World Health Organization recommends that member states implement effective taxes on SSBs to reduce consumption. The United Kingdom Soft Drinks Industry Levy (SDIL) is a two-tiered tax, announced in March 2016 and implemented in April 2018. Drinks with ≥8 g of sugar per 100 ml (higher levy tier) are taxed at £0.24 per litre, drinks with ≥5 to 1.2% alcohol by volume are exempt. We aimed to determine if the announcement of the SDIL was associated with anticipatory changes in purchases of soft drinks prior to implementation of the SDIL in April 2018. We explored differences in the volume of and amount of sugar in household purchases of drinks in each levy tier at 2 years post announcement. Methods and findings We used controlled interrupted time series to compare observed changes associated with the announcement of the SDIL to the counterfactual scenario of no announcement. We used data from Kantar Worldpanel, a commercial household purchasing panel with approximately 30,000 British members that includes linked nutritional data on purchases. We conducted separate analyses for drinks liable for the SDIL in the higher, lower, and no-levy tiers controlling with household purchase volumes of toiletries. At 2 years post announcement, there was no difference in volume of or sugar from purchases of higher-levy-tier drinks compared to the counterfactual of no announcement. In contrast, a reversal of the existing upward trend in volume (ml) of and amount of sugar (g) in purchases of lower-levy-tier drinks was seen. These changes led to a −96.1 ml (95% confidence interval [CI] −144.2 to −48.0) reduction in volume and −6.4 g (95% CI −9.8 to −3.1) reduction in sugar purchased in these drinks per household per week. There was a reversal of the existing downward trend in the amount of sugar in household purchases of the no-levy drinks but no change in volume purchased. At 2 years post announcement, these changes led to a 6.1 g (95% CI 3.9–8.2) increase in sugar purchased in these drinks per household per week. There was no evidence that volume of or amount of sugar in purchases of all drinks combined was different from the counterfactual. This is an observational study, and changes other than the SDIL may have been responsible for the results reported. Purchases consumed outside of the home were not accounted for. Conclusions The announcement of the UK SDIL was associated with reductions in volume and sugar purchased in lower-levy-tier drinks before implementation. These were offset by increases in sugar purchased from no-levy drinks. These findings may reflect reformulation of drinks from the lower levy to no-levy tier with removal of some but not all sugar, alongside changes in consumer attitudes and beliefs. Trial registration ISRCTN Registry ISRCTN18042742., David Pell and colleagues report differences in the volume of household purchases of drinks and sugar in these drinks 2 years after the sugar levy in the UK., Author summary Why was this study done? Consumption of sugar-sweetened beverages (SSBs) is associated with dental caries, obesity, type 2 diabetes, and cardiovascular disease. In March 2016, the UK government announced a tax on SSB manufacturers and producers, called the Soft Drinks Industry Levy (SDIL). The SDIL has a higher tier of £0.24 per litre on drinks containing ≥8 g of sugar per 100 ml and a low tier of £0.18 per litre on drinks containing 5 to

Background Low circulating vitamin D levels have been associated with risk of asthma, atopic dermatitis, and elevated total immunoglobulin E (IgE). These epidemiological associations, if true, would have public health importance, since vitamin D insufficiency is common and correctable. Methods and findings We aimed to test whether genetically lowered vitamin D levels were associated with risk of asthma, atopic dermatitis, or elevated serum IgE levels, using Mendelian randomization (MR) methodology to control bias owing to confounding and reverse causation. The study employed data from the UK Biobank resource and from the SUNLIGHT, GABRIEL and EAGLE eczema consortia. Using four single-nucleotide polymorphisms (SNPs) strongly associated with 25-hydroxyvitamin D (25OHD) levels in 33,996 individuals, we conducted MR studies to estimate the effect of lowered 25OHD on the risk of asthma (n = 146,761), childhood onset asthma (n = 15,008), atopic dermatitis (n = 40,835), and elevated IgE level (n = 12,853) and tested MR assumptions in sensitivity analyses. None of the four 25OHD-lowering alleles were associated with asthma, atopic dermatitis, or elevated IgE levels (p ≥ 0.2). The MR odds ratio per standard deviation decrease in log-transformed 25OHD was 1.03 (95% confidence interval [CI] 0.90–1.19, p = 0.63) for asthma, 0.95 (95% CI 0.69–1.31, p = 0.76) for childhood-onset asthma, and 1.12 (95% CI 0.92–1.37, p = 0.27) for atopic dermatitis, and the effect size on log-transformed IgE levels was −0.40 (95% CI −1.65 to 0.85, p = 0.54). These results persisted in sensitivity analyses assessing population stratification and pleiotropy and vitamin D synthesis and metabolism pathways. The main limitations of this study are that the findings do not exclude an association between the studied outcomes and 1,25-dihydoxyvitamin D, the active form of vitamin D, the study was underpowered to detect effects smaller than an OR of 1.33 for childhood asthma, and the analyses were restricted to white populations of European ancestry. This research has been conducted using the UK Biobank Resource and data from the SUNLIGHT, GABRIEL and EAGLE Eczema consortia. Conclusions In this study, we found no evidence that genetically determined reduction in 25OHD levels conferred an increased risk of asthma, atopic dermatitis, or elevated total serum IgE, suggesting that efforts to increase vitamin D are unlikely to reduce risks of atopic disease., In a Mendelian randomization analysis, J. Brent Richards and colleagues investigate possible relations between genetic elements associated with vitamin D levels and atopic susceptibility., Author summary Why was this study done? Observational epidemiological studies have reported associations of low vitamin D levels with risk of asthma, atopic dermatitis, and elevated immunoglobulin E (IgE) levels. However, these studies are susceptible to confounding and reverse causation, and thus it remains unclear whether these associations are genuine. The randomized controlled trials published to date on this topic have been inconclusive. If vitamin D insufficiency did cause atopic diseases, this would be of clinical relevance, since vitamin D insufficiency is common and safely correctable. What did the researchers do and find? We applied a Mendelian randomization study design, which greatly limits bias due to confounding and prevents bias due to reverse causation, to understand if vitamin D levels are associated with a higher risk of adult and pediatric asthma, atopic dermatitis, or elevated IgE levels. Despite high statistical power, our study showed no evidence of an unconfounded association between vitamin D and the studied outcomes. What do these findings mean? Our findings suggest that the previous epidemiological associations between vitamin D and atopic diseases could be due to confounding. Efforts to increase vitamin D levels will probably not result in decreased risk of adult and pediatric asthma, atopic dermatitis, or elevated IgE levels.

Background High body mass index (BMI) is an important contributor to the global burden of ill-health and health inequality. Lower socioeconomic position (SEP) in both childhood and adulthood is associated with higher adult BMI, but how these associations have changed across time is poorly understood. We used longitudinal data to examine how childhood and adult SEP relates to BMI across adulthood in three national British birth cohorts. Methods and Findings The sample comprised up to 22,810 participants with 77,115 BMI observations in the 1946 MRC National Survey of Health and Development (ages 20 to 60–64), the 1958 National Child Development Study (ages 23 to 50), and the 1970 British Cohort Study (ages 26 to 42). Harmonized social class-based SEP data (Registrar General’s Social Class) was ascertained in childhood (father’s class at 10/11 y) and adulthood (42/43 years), and BMI repeatedly across adulthood, spanning 1966 to 2012. Associations between SEP and BMI were examined using linear regression and multilevel models. Lower childhood SEP was associated with higher adult BMI in both genders, and differences were typically larger at older ages and similar in magnitude in each cohort. The strength of association between adult SEP and BMI did not vary with age in any consistent pattern in these cohorts, but were more evident in women than men, and inequalities were larger among women in the 1970 cohort compared with earlier-born cohorts. For example, mean differences in BMI at 42/43 y amongst women in the lowest compared with highest social class were 2.0 kg/m2 (95% CI: −0.1, 4.0) in the 1946 NSHD, 2.3 kg/m2 (1.1, 3.4) in the 1958 NCDS, and 3.9 kg/m2 (2.3, 5.4) the in the 1970 BCS; mean (SD) BMI in the highest and lowest social classes were as follows: 24.9 (0.8) versus 26.8 (0.7) in the 1946 NSHD, 24.2 (0.4) versus 26.5 (0.4) in the 1958 NCDS, and 24.2 (0.3) versus 28.1 (0.8) in the 1970 BCS. Findings did not differ whether using overweight or obesity as an outcome. Limitations of this work include the use of social class as the sole indicator of SEP—while it was available in each cohort in both childhood and adulthood, trends in BMI inequalities may differ according to other dimensions of SEP such as education or income. Although harmonized data were used to aid inferences about birth cohort differences in BMI inequality, differences in other factors may have also contributed to findings—for example, differences in missing data. Conclusions Given these persisting inequalities and their public health implications, new and effective policies to reduce inequalities in adult BMI that tackle inequality with respect to both childhood and adult SEP are urgently required, In a harmonized analysis of socioeconomic and anthropometric data from three cohorts, David Bann and colleagues trace the relationship between socioeconomic status and BMI over time in the UK., Author Summary Why Was This Study Done? High body mass index (BMI) is thought to be harmful to human health—in most adults, a high BMI is due to having high amounts of fat mass in the body. Previous studies have found that those with fewer socioeconomic resources—both as children and as adults—are more likely, on average, to have a higher BMI as adults. Reducing these socioeconomic inequalities in BMI is an important health policy goal, yet there is limited existing data to help us understand comprehensively how these inequalities have changed across time. What Did the Researchers Do and Find? We used data from three national British birth cohort studies of those born in 1946, 1958, and 1970—these studies contain comparable data on social class in childhood and adulthood, and on BMI across adult life. We confirm that large inequalities in BMI exist, according to both childhood and adult SEP—these were stronger among women, but also found among men. Inequalities according to childhood SEP generally become progressively larger at older ages in all cohorts and in both genders; inequalities according to adult SEP were larger among more recently born generations of women. What Do These Findings Mean? The fact that BMI inequalities have persisted or increased across different generations, despite policies designed to reduce them, suggests that new policies are required. Results support the need to intervene earlier rather than later in adult life, since inequalities tend to become larger at older ages. Limitations include the use of only one aspect of socioeconomic circumstances (social class), and the fact that not all participants continue to provide data in longitudinal studies—this may have led us to underestimating the size of BMI inequalities.

Background Torture and other forms of ill treatment have been reported in at least 141 countries, exposing a global crisis. Survivors face multiple physical, psychological, and social difficulties. Psychological consequences for survivors are varied, and evidence on treatment is mixed. We conducted a systematic review and meta-analysis to estimate the benefits and harms of psychological, social, and welfare interventions for torture survivors. Methods and findings We updated a 2014 review with published randomised controlled trials (RCTs) for adult survivors of torture comparing any psychological, social, or welfare intervention against treatment as usual or active control from 1 January 2014 through 22 June 2019. Primary outcome was post-traumatic stress disorder (PTSD) symptoms or caseness, and secondary outcomes were depression symptoms, functioning, quality of life, and adverse effects, after treatment and at follow-up of at least 3 months. Standardised mean differences (SMDs) and odds ratios were estimated using meta-analysis with random effects. The Cochrane tool was used to derive risk of bias. Fifteen RCTs were included, with data from 1,373 participants (589 females and 784 males) in 10 countries (7 trials in Europe, 5 in Asia, and 3 in Africa). No trials of social or welfare interventions were found. Compared to mostly inactive (waiting list) controls, psychological interventions reduced PTSD symptoms by the end of treatment (SMD −0.31, 95% confidence interval [CI] −0.52 to −0.09, p = 0.005), but PTSD symptoms at follow-up were not significantly reduced (SMD −0.34, 95% CI −0.74 to 0.06, p = 0.09). No significant improvement was found for PTSD caseness at the end of treatment, and there was possible worsening at follow-up from one study (n = 28). Interventions showed no benefits for depression symptoms at end of treatment (SMD −0.23, 95% CI −0.50 to 0.03, p = 0.09) or follow-up (SMD −0.23, 95% CI −0.70 to 0.24, p = 0.34). A significant improvement in functioning for psychological interventions compared to control was found at end of treatment (SMD −0.38, 95% CI −0.58 to −0.18, p = 0.0002) but not at follow-up from only one study. No significant improvement emerged for quality of life at end of treatment (SMD 0.38, 95% CI −0.28 to 1.05, p = 0.26) with no data available at follow-up. The main study limitations were the difficulty in this field of being certain of capturing all eligible studies, the lack of modelling of maintenance of treatment gains, and the low precision of most SMDs making findings liable to change with the addition of further studies as they are published. Conclusions Our findings show evidence that psychological interventions improve PTSD symptoms and functioning at the end of treatment, but it is unknown whether this is maintained at follow-up, with a possible worsening of PTSD caseness at follow-up from one study. Further interventions in this population should address broader psychological needs beyond PTSD while taking into account the effect of multiple daily stressors. Additional studies, including social and welfare interventions, will improve precision of estimates of effect, particularly over the longer term., Aseel Hamid and colleagues show that psychological interventions help alleviate PTSD symptoms in the short term for torture victims; long-term effects are unknown., Author summary Why was this study done? Torture occurs in the majority of countries around the world, often leaving survivors with prolonged physical and psychological problems. We still do not know what treatment for psychological problems is effective. This review aimed to calculate the effects of psychological, social, and welfare interventions on the mental health, functioning, and quality of life of torture survivors. What did the researchers do and find? Published data from 15 randomised controlled trials (RCTs)—all of psychological interventions, including 1,373 participants across 10 countries—were systematically reviewed and analysed. Compared to control conditions, psychological interventions significantly reduced symptoms of post-traumatic stress disorder (PTSD) and improved functioning at the end of treatment, but not at follow-up. Psychological interventions did not significantly improve depression symptoms or quality of life. Psychological interventions did not significantly reduce the incidence of PTSD diagnosis, and one study, with 28 participants, showed an increase of PTSD diagnosis at follow-up compared to control conditions. What do these findings mean? More effective interventions are needed with this population. Future interventions for torture survivors should be more collaborative and should address broader psychological problems beyond PTSD as well as social and welfare needs that maintain psychological problems.

Background There has been a growing interest in understanding the effects of social networks on health-related behaviour, with a particular backdrop being the emerging prominence of complexity or systems science in public health. Social network interventions specifically use or alter the characteristics of social networks to generate, accelerate, or maintain health behaviours. We conducted a systematic review and meta-analysis to investigate health behaviour outcomes of social network interventions. Methods and findings We searched eight databases and two trial registries from 1990 to May 28, 2019, for English-language reports of randomised controlled trials (RCTs) and before-and-after studies investigating social network interventions for health behaviours and outcomes. Trials that did not specifically use social networks or that did not include a comparator group were excluded. We screened studies and extracted data from published reports independently. The primary outcome of health behaviours or outcomes at ≤6 months was assessed by random-effects meta-analysis. Secondary outcomes included those measures at >6–12 months and >12 months. This study is registered with the International Prospective Register of Systematic Reviews, PROSPERO: CRD42015023541. We identified 26,503 reports; after exclusion, 37 studies, conducted between 1996 and 2018 from 11 countries, were eligible for analysis, with a total of 53,891 participants (mean age 32.4 years [SD 12.7]; 45.5% females). A range of study designs were included: 27 used RCT/cluster RCT designs, and 10 used other study designs. Eligible studies addressed a variety of health outcomes, in particular sexual health and substance use. Social network interventions showed a significant intervention effect compared with comparator groups for sexual health outcomes. The pooled odds ratio (OR) was 1.46 (95% confidence interval [CI] 1.01–2.11; I2 = 76%) for sexual health outcomes at ≤6 months and OR 1.51 (95% CI 1.27–1.81; I2 = 40%) for sexual health outcomes at >6–12 months. Intervention effects for drug risk outcomes at each time point were not significant. There were also significant intervention effects for some other health outcomes including alcohol misuse, well-being, change in haemoglobin A1c (HbA1c), and smoking cessation. Because of clinical and measurement heterogeneity, it was not appropriate to pool data on these other behaviours in a meta-analysis. For sexual health outcomes, prespecified subgroup analyses were significant for intervention approach (p < 0.001), mean age of participants (p = 0.002), and intervention length (p = 0.05). Overall, 22 of the 37 studies demonstrated a high risk of bias, as measured by the Cochrane Risk of Bias tool. The main study limitations identified were the inclusion of studies of variable quality; difficulty in isolating the effects of specific social network intervention components on health outcomes, as interventions included other active components; and reliance on self-reported outcomes, which have inherent recall and desirability biases. Conclusions Our findings suggest that social network interventions can be effective in the short term (6 months) for sexual health outcomes. Intervention effects for drug risk outcomes at each time point were not significant. There were also significant intervention effects for some other health outcomes including alcohol misuse, well-being, change in HbA1c, and smoking cessation., Ruth Hunter and colleagues report a systematic review and meta-analysis of behavioural interventions employing social networks, along with outcomes., Author summary Why was this study done? Social network interventions specifically use or alter the characteristics of social networks to generate, accelerate, or maintain health behaviours and positive health outcomes. Results from previous systematic reviews provided some evidence that social network interventions were effective for improving social support and haemoglobin A1c (HbA1c) outcomes; however, the few studies identified had a high risk of bias. The optimal way to apply social network intervention approaches to various health interventions remains unknown. What did the researchers do and find? We conducted a systematic review and meta-analyses of 37 studies investigating the effectiveness of social network interventions for health behaviours and outcomes (or their surrogates). Our findings show a significant effect of social network interventions for a range of health behaviours and outcomes, in particular for sexual health outcomes, both in the short and longer term. Subgroup analyses were significant for the intervention approach and when trials were grouped on the basis of mean age and percentage of females. In total, 22 out of the 37 studies identified had a high risk of bias, and included studies employed different study designs of variable quality. What do these findings mean? Evidence from this study suggests that social network interventions are associated with positive health behaviours and outcomes. Researchers and public health practitioners should consider how to use the social networks of their populations when delivering health behaviour interventions in order to maximise effectiveness. We recommend that the scientific community should move beyond individual-level approaches to design and test interventions that use the largely untapped potential of social networks to improve health behaviours and outcomes.

Background Low back pain (LBP) is a major health problem. Globally it is responsible for the most years lived with disability. The most problematic type of LBP is chronic LBP (pain lasting longer than 3 mo); it has a poor prognosis and is costly, and interventions are only moderately effective. Targeting interventions according to risk profile is a promising approach to prevent the onset of chronic LBP. Developing accurate prognostic models is the first step. No validated prognostic models are available to accurately predict the onset of chronic LBP. The primary aim of this study was to develop and validate a prognostic model to estimate the risk of chronic LBP. Methods and Findings We used the PROGRESS framework to specify a priori methods, which we published in a study protocol. Data from 2,758 patients with acute LBP attending primary care in Australia between 5 November 2003 and 15 July 2005 (development sample, n = 1,230) and between 10 November 2009 and 5 February 2013 (external validation sample, n = 1,528) were used to develop and externally validate the model. The primary outcome was chronic LBP (ongoing pain at 3 mo). In all, 30% of the development sample and 19% of the external validation sample developed chronic LBP. In the external validation sample, the primary model (PICKUP) discriminated between those who did and did not develop chronic LBP with acceptable performance (area under the receiver operating characteristic curve 0.66 [95% CI 0.63 to 0.69]). Although model calibration was also acceptable in the external validation sample (intercept = −0.55, slope = 0.89), some miscalibration was observed for high-risk groups. The decision curve analysis estimated that, if decisions to recommend further intervention were based on risk scores, screening could lead to a net reduction of 40 unnecessary interventions for every 100 patients presenting to primary care compared to a “treat all” approach. Limitations of the method include the model being restricted to using prognostic factors measured in existing studies and using stepwise methods to specify the model. Limitations of the model include modest discrimination performance. The model also requires recalibration for local settings. Conclusions Based on its performance in these cohorts, this five-item prognostic model for patients with acute LBP may be a useful tool for estimating risk of chronic LBP. Further validation is required to determine whether screening with this model leads to a net reduction in unnecessary interventions provided to low-risk patients., Adrian Traeger and colleagues report the development and validation of a prognostiv model (PICKUP) for estimating risk of developing chronic low back pain., Author Summary Why Was This Study Done? A minority of patients who experience an episode of low back pain develop persistent (chronic) pain. Offering tests and treatments to all these patients exposes high numbers of low-risk patients to unnecessary intervention, which is very costly and potentially harmful. A tool to help healthcare practitioners accurately predict whether a patient with a recent episode of low back pain will develop persistent pain stands to greatly reduce the burden of low back pain on the health system and on patients. What Did the Researchers Do and Find? We developed a five-item screening questionnaire using study data from 1,230 patients with a recent episode of low back pain. We tested how well this screening questionnaire could predict the onset of persistent pain in a separate sample of 1,528 patients. We found that the screening questionnaire could predict the onset of persistent pain with acceptable levels accuracy (area under the receiver operating characteristic curve = 0.66 [95% CI 0.63 to 0.69]; intercept = 0.55, slope = 0.89). What Do These Findings Mean? This brief, easy-to-use screening questionnaire could help healthcare practitioners and researchers make an early estimate of a patient’s risk of persistent low back pain. The screening questionnaire predicted outcome more accurately in patients with low risk scores than in those with high risk scores. Screening patients with a recent episode of low back pain could reduce the number of unnecessary interventions provided to low-risk patients.

Background Neurogenic bladder dysfunction represents one of the most common and devastating sequelae of traumatic spinal cord injury (SCI). As early prediction of bladder outcomes is essential to counsel patients and to plan neurourological management, we aimed to develop and validate a model to predict urinary continence and complete bladder emptying 1 y after traumatic SCI. Methods and Findings Using multivariate logistic regression analysis from the data of 1,250 patients with traumatic SCI included in the European Multicenter Spinal Cord Injury study, we developed two prediction models of urinary continence and complete bladder emptying 1 y after traumatic SCI and performed an external validation in 111 patients. As predictors, we evaluated age, gender, and all variables of the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) and of the Spinal Cord Independence Measure (SCIM). Urinary continence and complete bladder emptying 1 y after SCI were assessed through item 6 of SCIM. The full model relies on lower extremity motor score (LEMS), light-touch sensation in the S3 dermatome of ISNCSI, and SCIM subscale respiration and sphincter management: the area under the receiver operating characteristics curve (aROC) was 0.936 (95% confidence interval [CI]: 0.922–0.951). The simplified model is based on LEMS only: the aROC was 0.912 (95% CI: 0.895–0.930). External validation of the full and simplified models confirmed the excellent predictive power: the aROCs were 0.965 (95% CI: 0.934–0.996) and 0.972 (95% CI 0.943–0.999), respectively. This study is limited by the substantial number of patients with a missing 1-y outcome and by differences between derivation and validation cohort. Conclusions Our study provides two simple and reliable models to predict urinary continence and complete bladder emptying 1 y after traumatic SCI. Early prediction of bladder function might optimize counselling and patient-tailored rehabilitative interventions and improve patient stratification in future clinical trials., Thomas Kessler and colleagues identify and validate two models to predict urinary continence and complete bladder emptying one year after traumatic spinal cord injury., Author Summary Why Was This Study Done? Neurogenic bladder dysfunction is one of the most common and devastating sequelae of traumatic spinal cord injury. Recovery of bladder function represents an absolute priority for individuals affected by spinal cord injury, and it is often considered to be more important than recovery of ambulation. There is a valid rule to predict locomotion and upper limb function in patients with spinal cord injury, but no similar tool for bladder function is available. What Did the Researchers Do and Find? Based on data from 1,250 patients with traumatic spinal cord injury included in the European Multicenter Spinal Cord Injury (www.emsci.org) study, we derived two simple and reliable models to predict urinary continence and complete bladder emptying 1 y after traumatic spinal cord injury. Our models have been validated in an independent cohort of 111 patients with traumatic spinal cord injury. What Do These Findings Mean? Our models allow early prediction of bladder outcome after traumatic spinal cord injury, which is crucial to counsel patients, to set rehabilitative goals, and to orient patient-tailored interventions. Our models could help promptly identifying patients who are not likely to have favourable bladder outcomes despite standard therapies and who could benefit more from investigational therapies. Our models may improve patient stratification in future clinical trials assessing treatments for bladder dysfunction in patients with traumatic spinal cord injury.