Large age shifts in HIV-1 incidence patterns in KwaZulu-Natal, South Africa.

Recent declines in adult HIV-1 incidence have followed the large-scale expansion of antiretroviral therapy and primary HIV prevention across high-burden communities of sub-Saharan Africa. Mathematical modeling suggests that HIV risk will decline disproportionately in younger adult age-groups as interventions scale, concentrating new HIV infections in those >age 25 over time. Yet, no empirical data exist to support these projections. We conducted a population-based cohort study over a 16-y period (2004 to 2019), spanning the early scale-up of antiretroviral therapy and voluntary medical male circumcision, to estimate changes in the age distribution of HIV incidence in a hyperepidemic region of KwaZulu-Natal, South Africa, where adult HIV incidence has recently declined. Median age of HIV seroconversion increased by 5.5 y in men and 3.0 y in women, and the age of peak HIV incidence increased by 5.0 y in men and 2.0 y in women. Incidence declined disproportionately among young men (64% in men 15 to 19, 68% in men 20 to 24, and 46% in men 25 to 29) and young women (44% in women 15 to 19, 24% in women 20 to 24) comparing periods pre- versus post-universal test and treat. Incidence was stable (<20% change) in women aged 30 to 39 and men aged 30 to 34. Age shifts in incidence occurred after 2012 and were observed earlier in men than in women. These results provide direct epidemiological evidence of the changing demographics of HIV risk in sub-Saharan Africa in the era of large-scale treatment and prevention. More attention is needed to address lagging incidence decline among older individuals.

Changes of Grocery Shopping Frequencies and Associations with Food Deserts during the COVID-19 Pandemic in the United States.

The COVID-19 pandemic has dramatically altered people's lives in multiple aspects, including grocery shopping behaviors. Yet, the changing trend of grocery shopping frequencies during the COVID-19 and its associations with food deserts remain unclear. We aimed to (1) examine variations of grocery shopping frequencies at county level in the USA during the COVID-19 pandemic from March 2020 to December 2021; (2) investigate associations between grocery shopping frequencies and food deserts during the COVID-19 pandemic; and (3) explore heterogeneity in grocery shopping frequencies-food desert associations across urban and rural areas. The county-level grocery shopping frequencies were derived from a grocery pattern dataset obtained from SafeGraph. We divided the 22-month period into 5 stages and employed the growth curve modeling to estimate the trajectories of grocery shopping frequencies and the associations between grocery shopping frequencies and food deserts in each stage, separately. Results revealed that grocery shopping frequencies experienced a "W-shaped" pattern from March 2020 to December 2021. Counties with the least percent of food deserts had slower decrease in grocery shopping frequencies at the initial stage and recovered more rapidly at later stages. Counties with the highest percent of food deserts were subject to deprivation amplification as a result of the pandemic. We also found differences existed in the grocery shopping frequencies-food desert associations between metropolitan counties and rural counties. Our findings suggest the impacts of COVID-19 on grocery shopping frequencies varied across different time periods, shedding light on designing different strategies to reduce the risk of contagion while shopping inside of grocery stores. Further, our findings highlight an urgent need to help people living in food deserts (especially in rural counties) to procure healthy foods safely during health emergencies like COVID-19 pandemic which disrupt mobility and social behaviors.

Water, sanitation and depression in rural communities: evidence from nationally representative study data in South Africa.

Clean water and sanitation provisions are essential for good hygiene and health, with rural South Africa facing a simultaneous access crisis of both, the direct health effect of restricted access to both on mental health remaining scarce and largely overlooked. This study investigated the association between access to clean water and sanitation on depression in rural South Africa utilizing the most recent data (year 2017) from the South African National Income Dynamics Study. Our study outcome was depression, based on the 10-item abridged version of the Center for Epidemiologic Studies Depression Scale (data available in SA-NIDS), the main exposures being access to clean water and adequate sanitation facilities (i.e. flushing toilets). Two types of analyses were conducted: first, adjusted logistic regression models were fitted to assess the relationship between lack of access to clean water and adequate sanitation to depression. Second, we conducted mediation analysis to investigate whether access to clean water mediated the relationship between lack of access to toilets and depression. A high proportion of rural participants lacked access to clean water (n = 6,188, 47.6%) and adequate toilets (n = 9,797, 81.6%). The regression analyses indicated that lack of access to both clean water (OR = 1.21, 95% CI: 1.06-1.39) and adequate sanitation (OR = 1.36, 95% CI: 1.10-1.69) were significantly associated with greater odds of depression. The mediation analysis indicated that access to clean water partially mediated the relationship between lack of access to adequate sanitation and depression, the total mediated effect being 18.2% (95% CI: 11.0%-51.0%). Most rural communities in South Africa lack access to basic services that are essential for human dignity and a decent quality of life, leading to opportunities for poor mental health, with its various consequences for socio-economic development and personal wellbeing, including avoidable depression.

Predicting the Risk of Human Immunodeficiency Virus Type 1 (HIV-1) Acquisition in Rural South Africa Using Geospatial Data.

BACKGROUND: Accurate human immunodeficiency virus (HIV) risk assessment can guide optimal HIV prevention. We evaluated the performance of risk prediction models incorporating geospatial measures. METHODS: We developed and validated HIV risk prediction models in a population-based cohort in South Africa. Individual-level covariates included demographic and sexual behavior measures, and geospatial covariates included community HIV prevalence and viral load estimates. We trained models on 2012-2015 data using LASSO Cox models and validated predictions in 2016-2019 data. We compared full models to simpler models restricted to only individual-level covariates or only age and geospatial covariates. We compared the spatial distribution of predicted risk to that of high incidence areas (≥ 3/100 person-years). RESULTS: Our analysis included 19 556 individuals contributing 44 871 person-years and 1308 seroconversions. Incidence among the highest predicted risk quintile using the full model was 6.6/100 person-years (women) and 2.8/100 person-years (men). Models using only age group and geospatial covariates had similar performance (women: AUROC = 0.65, men: AUROC = 0.71) to the full models (women: AUROC = 0.68, men: AUROC = 0.72). Geospatial models more accurately identified high incidence regions than individual-level models; 20% of the study area with the highest predicted risk accounted for 60% of the high incidence areas when using geospatial models but only 13% using models with only individual-level covariates. CONCLUSIONS: Geospatial models with no individual measures other than age group predicted HIV risk nearly as well as models that included detailed behavioral data. Geospatial models may help guide HIV prevention efforts to individuals and geographic areas at highest risk.

Coronavirus Disease 2019 (COVID-19) Response in Zimbabwe: A Call for Urgent Scale-up of Testing to meet National Capacity.

Control of coronavirus disease 2019 (COVID-19) heavily relies on universal access to testing in order to identify who is infected; track them to make sure they do not spread the disease further; and trace those with whom they have been in contact. The recent surge in COVID-19 cases in Zimbabwe is an urgent national public health concern and requires coordinated efforts to scale up testing using the capacity already in existence in the country. There is a need for substantial decentralization of testing, investment in better working conditions for frontline health workers, and the implementation of measures to curb corruption within government structures.

Patterns of multimorbidity and their association with hospitalisation: a population-based study of older adults in urban Tanzania.

BACKGROUND: while the HIV epidemic remains a considerable challenge in sub-Saharan Africa, a dramatic reduction in the associated mortality has led to a fundamental shift in the public health priorities aimed at tackling multimorbidity. Against the unprecedented level of urbanisation taking place in Tanzania, the burden of multimorbidity and its consequences among ageing adults, in the form of costly inpatient hospitalisation, remain unquantified. METHODS: we used data from one of Africa's largest urban population cohort, the Dar es Salaam Health and the Demographic Surveillance System, to quantity the extent of multimorbidity (occurrence of 2 ≥ health conditions) and discordant multimorbidity (occurrence of conditions in 2 ≥ domains in mental health, non-communicable and communicable health) among 2,299 adults aged ≥40 years in Dar es Salaam, Tanzania. We fitted logistic regression models to investigate the association between multimorbidity and inpatient hospitalisation. RESULTS: the prevalence of multimorbidity and discordant multimorbidity were 25.3 and 2.5%, respectively. Although the severe forms of multimorbidity (2.0% with ≥4 health conditions) and discordancy were low, hospitalisation was significantly higher based on the regression analyses. Household food insecurity was the only socio-economic variable that was significantly and consistently associated with a greater hospitalisation. CONCLUSION: we found an alarmingly high degree of multimorbidity among this ageing urban population where hospitalisation was driven by multimorbidity. As public health resources remain scarce, reducing costly inpatient hospitalisation requires multilevel interventions that address clinical- and structural-level challenges (e.g. food insecurity) to mitigate multimorbidity and promote long-term healthy independent living among older adults in Tanzania.

Understanding HIV and associated risk factors among religious groups in Zimbabwe.

BACKGROUND: The influence of religion and belief systems is widely recognized as an important factor in understanding of health risk perception and myths in the general fight against the HIV pandemic. This study compares the understanding of HIV risk factors and utilization of some HIV services among religious groups in Zimbabwe. METHODS: We conducted secondary data statistical analysis to investigate the understanding of HIV and associated risk factors among religious groups in Zimbabwe using 2015-2016 Zimbabwe Demographic and Health Survey (ZDHS) data. We began by investigating associations between understanding of HIV and associated risk factors among religious groups. A multivariate stepwise backward elimination method was carried out to explore factors determining understanding of HIV risk after controlling for confounding factors using the most recent ZDHS data (2015-2016). RESULTS: The results from the three surveys showed that, in general apostolic sector had low understanding of HIV and associated risk factors compared to other religious groups. Analysis of the 2015-2016 ZDHS data showed that women belonging to the apostolic sector were less likely to know where to get an HIV test odds ratio (OR) and 95% confidence interval, 0.665 (0.503-0.880) and to know that male circumcision reduces HIV transmission OR 0.863 (0.781-0.955). Women from this group had no knowledge that circumcised men can be infected if they do not use condoms OR 0.633 (0.579-0.693), nor that it is possible for a healthy-looking person to have HIV, OR 0.814 (0.719-0.921). They would not buy vegetables from a vendor with HIV OR 0.817 (0.729-0.915) and were less likely to support that HIV positive children should be allowed to attend school with HIV negative children OR 0.804 (0.680-0.950). Similar results were obtained for men in the apostolic sector. These men also did not agree that women were justified to use condoms if the husband has an Sexually Transmitted Infection (STI) OR 0.851 (0.748-0.967). CONCLUSIONS: Our results suggest that apostolic sector lack adequate knowledge of HIV and associated risk factors than other religious groups. Targeting HIV prevention programmes by religious groups could be an efficient approach for controlling HIV in Zimbabwe.

Spatiotemporal analysis of insecticide-treated net use for children under 5 in relation to socioeconomic gradients in Central and East Africa.

BACKGROUND: Insecticide-treated net (ITN) use is the core intervention among the strategies against malaria in sub-Saharan Africa (SSA) and the percentage of ITN ownership has increased from 47% in 2010 to 72% in 2017 across countries in SSA. Regardless of this massive expansion of ITN distribution, considerable gap between ownership and use of ITNs has been reported. Using data from more than 100,000 households in Central and East Africa (CEA) countries, the main aim of this study was to identify barriers associated with low ITN use and conduct geospatial analyses to estimate numbers and locations of vulnerable children living in areas with high malaria and low ITN use. METHODS: Main sources of data for this study were the Demographic and Health Surveys and Malaria Indicator Surveys conducted in 11 countries in CEA. Logistic regression models for each country were built to assess the association between ITN ownership or ITN use and several socioeconomic and demographic variables. A density map of children under 5 living in areas at high-risk of malaria and low ITN use was generated to estimate the number of children who are living in these high malaria burden areas. RESULTS: Results obtained suggest that factors such as the number of members in the household, total number of children in the household, education and place of residence can be key factors linked to the use of ITN for protecting children against malaria in CEA. Results from the spatiotemporal analyses found that although total rates of ownership and use of ITNs across CEA have increased up to 70% and 48%, respectively, a large proportion of children under 5 (19,780,678; 23% of total number of children) still lives in high-risk malaria areas with low use of ITNs. CONCLUSION: The results indicate that despite substantial progress in the distribution of ITNs in CEA, with about 70% of the households having an ITN, several socioeconomic factors have compromised the effectiveness of this control intervention against malaria, and only about 48% of the households protect their children under 5 with ITNs. Increasing the effective ITN use by targeting these factors and the areas where vulnerable children reside can be a core strategy meant to reducing malaria transmission.

HIV incidence declines in a rural South African population: a G-imputation approach for inference.

BACKGROUND: Ad hoc assumptions about the unobserved infection event, which is known only to occur between the latest-negative and earliest-positive test dates, can lead to biased HIV incidence rate estimates. Using a G-imputation approach, we infer the infection dates from covariate data to estimate the HIV incidence rate in a hyper-endemic South African setting. METHODS: A large demographic surveillance system has annually tested a cohort of HIV-uninfected participants living in the KwaZulu-Natal province. Using this data, we estimated a cumulative baseline hazard function and the effects of time-dependent covariates on the interval censored infection dates. For each HIV-positive participant in the cohort, we derived a cumulative distribution function and sampled multiple infection dates conditional on the unique covariate values. We right censored the data at the imputed dates, calculated the annual HIV incidence rate per 100 person-years, and used Rubin's rules to obtain the 95% confidence intervals. RESULTS: A total of 20,011 uninfected individuals with a repeat HIV test participated in the incidence cohort between 2005 and 2018. We observed 2,603 infections per 58,769 person-years of follow-up among women and 845 infections per 41,178 person-years of follow-up among men. Conditional on age and circumcision status (men only), the female HIV incidence rate declined by 25%, from 5.0 to 3.7 infections per 100 person-years between 2014 and 2018. During this period, the HIV incidence rate among men declined from 2.1 to 1.1 infections per 100 person-years-a reduction of 49%. We observed similar reductions in male and female HIV incidence conditional on condom-use, marital status, urban residential status, migration history, and the HIV prevalence in the surrounding community. CONCLUSION: We have followed participants in one of the world's largest and longest running HIV cohorts to estimate long-term trends in the population-wide incidence of infection. Using a G-imputation approach, we present further evidence for HIV incidence rate declines in this hyper-endemic South African setting.

Capturing the spatial variability of HIV epidemics in South Africa and Tanzania using routine healthcare facility data.

BACKGROUND: Large geographical variations in the intensity of the HIV epidemic in sub-Saharan Africa call for geographically targeted resource allocation where burdens are greatest. However, data available for mapping the geographic variability of HIV prevalence and detecting HIV 'hotspots' is scarce, and population-based surveillance data are not always available. Here, we evaluated the viability of using clinic-based HIV prevalence data to measure the spatial variability of HIV in South Africa and Tanzania. METHODS: Population-based and clinic-based HIV data from a small HIV hyper-endemic rural community in South Africa as well as for the country of Tanzania were used to map smoothed HIV prevalence using kernel interpolation techniques. Spatial variables were included in clinic-based models using co-kriging methods to assess whether cofactors improve clinic-based spatial HIV prevalence predictions. Clinic- and population-based smoothed prevalence maps were compared using partial rank correlation coefficients and residual local indicators of spatial autocorrelation. RESULTS: Routinely-collected clinic-based data captured most of the geographical heterogeneity described by population-based data but failed to detect some pockets of high prevalence. Analyses indicated that clinic-based data could accurately predict the spatial location of so-called HIV 'hotspots' in > 50% of the high HIV burden areas. CONCLUSION: Clinic-based data can be used to accurately map the broad spatial structure of HIV prevalence and to identify most of the areas where the burden of the infection is concentrated (HIV 'hotspots'). Where population-based data are not available, HIV data collected from health facilities may provide a second-best option to generate valid spatial prevalence estimates for geographical targeting and resource allocation.

Hepatitis B in Egypt: A cross-sectional analysis of prevalence and risk factors for active infection from a nationwide survey.

BACKGROUND & AIMS: Viral hepatitis is a major public health challenge in Egypt but little is known about the epidemiology of the hepatitis B virus (HBV) infection, its geographical distribution, or risk factors for infection in the Egyptian context. This study addresses this deficit using data from a nationally representative survey. METHODS: Cross-sectional analysis of data gathered from men and women aged 15-59 in the Egypt Health Issues Survey (EHIS) 2015, including logistic regression to evaluate the contribution of different factors to risk of HBV infection. This was supplemented by spatial analysis of the distribution of acute or chronic HBV infection at governorate level, and the ecological relationship between HBV and HCV infections. RESULTS: Population HBV prevalence was 1.4% (95% confidence interval [CI] 1.2-1.6), with a HBV-HCV co-infection rate of 0.06%. Spatial analysis showed localisation of HBV infected individuals primarily to urban areas of Upper Egypt (in contrast to HCV for which prevalence is highest in rural Lower Egypt), and those in early middle age (Adjusted Odds Ratio [AOR] 3.32, 95% CI: 1.66-6.63). HBV positive status among other household members emerged as a powerful driver of infection risk in this analysis (AOR=10.75, 95% CI: 4.98-23.24). CONCLUSION: Spatial distribution of HBV infection in Egypt differs markedly from HCV and co-infection rates are low. Within-household transmissions appear to be particularly important in explaining the persistence of HBV infection in the general population. Prevention strategies should focus on urban Upper Egypt, and particularly those households with documented cases of infection.

Proximity to healthcare clinic and depression risk in South Africa: geospatial evidence from a nationally representative longitudinal study.

Proximity to primary healthcare facilities may be a serious barrier to accessing mental health services in resource-limited settings. In this study, we examined whether the distance to the primary healthcare clinic (PHCC) was associated with risk of depression in KwaZulu-Natal Province, South Africa. Depressive symptoms and household coordinates data were accessed from the nationally representative South African National Income Dynamics Study. Distances between households and their nearest PHCCs were calculated and mixed-effects logistic regression models fitted to the data. Participants residing <6 km from a PHCC (aOR = 0.608, 95% CI 0.42-0.87) or 6-14.9 km (aOR = 0. 612, 95% CI 0.44-0.86) had a lower depression risk compared to those residing ≥15 km from the nearest PHCC. Distance to the PHCC was independently associated with increased depression risk, even after controlling for key socioeconomic determinants. Minimizing the distance to PHCC through mobile health clinics and technology could improve mental health.

Spatial epidemiology of hepatitis C virus infection in Egypt: analyses and implications.

UNLABELLED: Egypt has the highest hepatitis C virus (HCV) prevalence in the world (14.7%). The drivers of the HCV epidemic in Egypt are not well understood, but the mass parenteral antischistosomal therapy (PAT) campaigns in the second half of the 20th century are believed to be the determinant of the high prevalence. We studied HCV exposure in Egypt at a microscale through spatial mapping and epidemiological description of HCV clustering. The source of data was the 2008 Egypt Demographic and Health Survey. We identified clusters with high and low HCV prevalence and high and low PAT exposure using Kulldorff spatial scan statistics. Correlations across clusters were estimated, and each cluster age-specific HCV prevalence was described. We identified six clusters of high HCV prevalence, three clusters of low HCV prevalence, five clusters of high PAT exposure, and four clusters of low PAT exposure. HCV prevalence and PAT exposure were not significantly associated across clusters (Pearson correlation coefficient [PCC] = 0.36; 95% confidence interval [CI] -0.12 to 0.71). Meanwhile, there was a strong association between HCV prevalence in individuals older than 30 years of age (who could have been exposed to PAT) and HCV prevalence in individuals 30 years of age or younger (who could not have been exposed to PAT) (PCC = 0.81; 95% CI 0.55-0.93). CONCLUSION: The findings illustrate a spatial variation in HCV exposure in Egypt. The observed clustering was suggestive of an array of iatrogenic risk factors, besides past PAT exposure, and ongoing transmission. The role of PAT exposure in the HCV epidemic could have been overstated. Our findings support the rationale for spatially prioritized interventions.

Moving beyond hotspots of HIV prevalence to geospatial hotspots of UNAIDS 95-95-95 targets in sub-Saharan Africa.

The HIV epidemic in sub-Saharan Africa displays a varied geographical distribution, with particular regions termed as HIV hotspots due to a higher prevalence of infection. Addressing these hotspots is essential for controlling the epidemic. However, these regions, influenced by historical factors, challenge standard interventions. Legacy effects-the lasting impact of past events-play a substantial role in the persistence of these hotspots. To address this challenge of the standard interventions, we propose a shift towards the UNAIDS 95-95-95 targets. Spatial analysis of HIV viral load and antiretroviral therapy coverage can provide a more comprehensive perspective on the epidemic's dynamics. Studies in Zambia and Zimbabwe, using this approach, have revealed disparities in HIV care metrics across regions. By focusing on the UNAIDS 95-95-95 targets, more effective control strategies can be designed, with consideration of both historical and current factors. This approach would offer a solution-oriented strategy, emphasising tailored interventions based on specific regional needs.

Convergence of HIV and non-communicable disease epidemics: geospatial mapping of the unmet health needs in an HIV hyperendemic community in South Africa.

INTRODUCTION: As people living with HIV (PLHIV) are experiencing longer survival, the co-occurrence of HIV and non-communicable diseases has become a public health priority. In response to this emerging challenge, we aimed to characterise the spatial structure of convergence of chronic health conditions in an HIV hyperendemic community in KwaZulu-Natal, South Africa. METHODS: In this cross-sectional study, we used data from a comprehensive population-based disease survey conducted in KwaZulu-Natal, South Africa, which collected data on HIV, diabetes and hypertension. We implemented a novel health needs scale to categorise participants as: diagnosed and well-controlled (Needs Score 1), diagnosed and suboptimally controlled (Score 2), diagnosed but not engaged in care (Score 3) or undiagnosed and uncontrolled (Score 4). Scores 2-4 were indicative of unmet health needs. We explored the geospatial structure of unmet health needs using different spatial clustering methods. RESULTS: The analytical sample comprised 18 041 individuals. We observed a similar spatial structure for HIV among those with combined needs Score 2-3 (diagnosed but uncontrolled) and Score 4 (undiagnosed and uncontrolled), with most PLHIV with unmet needs clustered in the southern urban and peri-urban areas. Conversely, a high prevalence of need Scores 2 and 3 for diabetes and hypertension was mostly distributed in the more rural central and northern part of the surveillance area. A high prevalence of need Score 4 for diabetes and hypertension was mostly distributed in the rural southern part of the surveillance area. Multivariate clustering analysis revealed a significant overlap of all three diseases in individuals with undiagnosed and uncontrolled diseases (unmet needs Score 4) in the southern part of the catchment area. CONCLUSIONS: In an HIV hyperendemic community in South Africa, areas with the highest needs for PLHIV with undiagnosed and uncontrolled disease are also areas with the highest burden of unmet needs for other chronic health conditions, such as diabetes and hypertension. Our study has revealed remarkable differences in the distribution of health needs across the rural to urban continuum even within this relatively small study site. The identification and prioritisation of geographically clustered vulnerable communities with unmet health needs for both HIV and non-communicable diseases provide a basis for policy and implementation strategies to target communities with the highest health needs.

HIV transmission dynamics and population-wide drug resistance in rural South Africa.

Despite expanded antiretroviral therapy (ART) in South Africa, HIV-1 transmission persists. Integrase strand transfer inhibitors (INSTI) and long-acting injectables offer potential for superior viral suppression, but pre-existing drug resistance could threaten their effectiveness. In a community-based study in rural KwaZulu-Natal, prior to widespread INSTI usage, we enroled 18,025 individuals to characterise HIV-1 drug resistance and transmission networks to inform public health strategies. HIV testing and reflex viral load quantification were performed, with deep sequencing (20% variant threshold) used to detect resistance mutations. Phylogenetic and geospatial analyses characterised transmission clusters. One-third of participants were HIV-positive, with 21.7% having detectable viral loads; 62.1% of those with detectable viral loads were ART-naïve. Resistance to older reverse transcriptase (RT)-targeting drugs was found, but INSTI resistance remained low (<1%). Non-nucleoside reverse transcriptase inhibitor (NNRTI) resistance, particularly to rilpivirine (RPV) even in ART-naïve individuals, was concerning. Twenty percent of sequenced individuals belonged to transmission clusters, with geographic analysis highlighting higher clustering in peripheral and rural areas. Our findings suggest promise for INSTI-based strategies in this setting but underscore the need for RPV resistance screening before implementing long-acting cabotegravir (CAB) + RPV. The significant clustering emphasises the importance of geographically targeted interventions to effectively curb HIV-1 transmission.

Prison as a driver of recent transmissions of multidrug-resistant tuberculosis in Callao, Peru: a cross-sectional study.

Background: We sought to identify resistance patterns and key drivers of recent multidrug-resistant tuberculosis (MDR-TB) transmission in a TB-prevalent area in Peru. Methods: Cross-sectional study including MDR Mycobacterium tuberculosis complex (Mtbc) strains identified in Callao-Peru between April 2017 and February 2019. Mtbc DNA was extracted for whole genome sequencing which was used for phylogenetic inference, clustering, and resistance mutation analyses. Clusters indicative of recent transmission were defined based on a strain-to-strain distance of ≤5 (D5) single nucleotide polymorphisms (SNPs). Epidemiologic factors linked to MDR-TB clustering were analyzed using Poisson regression. Findings: 171 unique MDR-Mtbc strains were included; 22 (13%) had additional fluoroquinolone resistance and were classified as pre-XDR. Six strains (3.5%) harboured bedaquiline (BDQ) resistance mutations and were classified as MDR + BDQ. 158 (92%) Mtbc strains belonged to lineage 4 and 13 (8%) to lineage 2. Using a cluster threshold of ≤5 SNPs, 98 (57%) strains were grouped in one of the 17 D5 clusters indicative of recent transmission, ranging in size from 2 to the largest cluster formed by 53 4.3.3 strains (group_1). Lineage 4.3.3 strains showed the overall highest cluster rate (43%). In multivariate analyses, current or previous imprisonment was independently associated with being part of any MDR-TB transmission clusters (adjusted prevalence ratio [aPR], 1.45; 95% CI, 1.09-1.92). Interpretation: Pre-XDR-TB emerged in more than 10% of the MDR-TB strains investigated. Transmission of 4.3.3 Mtbc strains especially of the dominant group_1 clone is a major driver of the MDR-TB epidemic in Callao. Current or previous imprisonment was linked to recent MDR-TB transmissions, indicating an important role of prisons in driving the MDR-TB epidemic. Funding: This work was supported in part by the ERANet-LAC Network of the European Union, Latin America and the Caribbean Countries on Joint Innovation and Research Activities, and FONDECYT. Additional support was received from Leibniz Science Campus Evolutionary Medicine of the Lung, the Deutsche Forschungsgemeinschaft (German Research Foundation, under Germany's Excellence Strategy-EXC 2167 Precision Medicine in Inflammation), and the Research Training Group 2501 TransEvo.

Mapping HIV clustering: a strategy for identifying populations at high risk of HIV infection in sub-Saharan Africa.

BACKGROUND: The geographical structure of an epidemic is ultimately a consequence of the drivers of the epidemic and the population susceptible to the infection. The 'know your epidemic' concept recognizes this geographical feature as a key element for identifying populations at higher risk of HIV infection where prevention interventions should be targeted. In an effort to clarify specific drivers of HIV transmission and identify priority populations for HIV prevention interventions, we conducted a comprehensive mapping of the spatial distribution of HIV infection across sub-Saharan Africa (SSA). METHODS: The main source of data for our study was the Demographic and Health Survey conducted in 20 countries from SSA. We identified and compared spatial clusters with high and low numbers of HIV infections in each country using Kulldorff spatial scan test. The test locates areas with higher and lower numbers of HIV infections than expected under spatial randomness. For each identified cluster, a likelihood ratio test was computed. A P-value was determined through Monte Carlo simulations to evaluate the statistical significance of each cluster. RESULTS: Our results suggest stark geographic variations in HIV transmission patterns within and across countries of SSA. About 14% of the population in SSA is located in areas of intense HIV epidemics. Meanwhile, another 16% of the population is located in areas of low HIV prevalence, where some behavioral or biological protective factors appear to have slowed HIV transmission. CONCLUSIONS: Our study provides direct evidence for strong geographic clustering of HIV infection across SSA. This striking pattern of heterogeneity at the micro-geographical scale might reflect the fact that most HIV epidemics in the general population in SSA are not far from their epidemic threshold. Our findings identify priority geographic areas for HIV programming, and support the need for spatially targeted interventions in order to maximize the impact on the epidemic in SSA.

Narrative minireview of the spatial epidemiology of substance use disorder in the United States: Who is at risk and where?

Drug overdose is the leading cause of death by injury in the United States. The incidence of substance use disorder (SUD) in the United States has increased steadily over the past two decades, becoming a major public health problem for the country. The drivers of the SUD epidemic in the United States have changed over time, characterized by an initial heroin outbreak between 1970 and 1999, followed by a painkiller outbreak, and finally by an ongoing synthetic opioid outbreak. The nature and sources of these abused substances reveal striking differences in the socioeconomic and behavioral factors that shape the drug epidemic. Moreover, the geospatial distribution of the SUD epidemic is not homogeneous. The United States has specific locations where vulnerable communities at high risk of SUD are concentrated, reaffirming the multifactorial socioeconomic nature of this epidemic. A better understanding of the SUD epidemic under a spatial epidemiology framework is necessary to determine the factors that have shaped its spread and how these patterns can be used to predict new outbreaks and create effective mitigation policies. This narrative minireview summarizes the current records of the spatial distribution of the SUD epidemic in the United States across different periods, revealing some spatiotemporal patterns that have preceded the occurrence of outbreaks. By analyzing the epidemic of SUD-related deaths, we also describe the epidemic behavior in areas with high incidence of cases. Finally, we describe public health interventions that can be effective for demographic groups, and we discuss future challenges in the study and control of the SUD epidemic in the country.

Progress Towards UNAIDS's 95-95-95 Targets in Zimbabwe: Sociodemographic Constraints and Geospatial Heterogeneity.

The HIV/AIDS epidemic remains critical in sub-Saharan Africa, with UNAIDS establishing "95-95-95" targets to optimize HIV care. Using the Zimbabwe Population-based HIV Impact Assessment (ZIMPHIA) geospatial data, this study aimed to identify patterns in these targets and determinants impacting the HIV care continuum in underserved Zimbabwean communities. Analysis techniques, including Gaussian kernel interpolation, optimized hotspot, and multivariate geospatial k-means clustering, were utilized to establish spatial patterns and cluster regional HIV care continuum needs. Further, we investigated healthcare availability, access, and social determinants and scrutinized the association between socio-demographic and behavioral covariates with HIV care outcomes. Disparities in progress toward the "95-95-95" targets were noted across different regions, with each target demonstrating unique geographic patterns, resulting in four distinct clusters with specific HIV care needs. Key factors associated with gaps in achieving targets included younger age, male sex, employment, and minority or no religious affiliation. Our study uncovers significant spatial heterogeneity in the HIV care continuum in Zimbabwe, with unique regional patterns in "95-95-95" targets. The spatial analysis of the UNAIDS targets presented here could prove instrumental in designing effective control strategies by identifying vulnerable communities that are falling short of these targets and require intensified efforts. Our result provides insights for designing region-specific interventions and enhancing community-level factors, emphasizing the need to address regional gaps and improve HIV care outcomes in vulnerable communities lagging behind.