Infectious Diseases of Poverty

In recent years, detection of local dengue cases in Florida have increased in both frequency and geographical extent. From 2022 to 2024, consecutive outbreaks in Miami-Dade County were mainly caused by a single lineage of dengue virus (DENV) serotype 3, prompting questions about changing epidemiology and a transition towards endemicity. In this study, we used mathematical modeling and genomic epidemiology to reveal the spatiotemporal dynamics and drivers of local dengue cases in Florida. We found that annual clusters and outbreaks were caused by frequent short-lived DENV introductions, primarily from the Caribbean, and did not find evidence for local trans-seasonal DENV lineage persistence. Further, we show that the climate-driven increases in local suitability for Aedes aegypti transmission and travel-associated cases were the greatest risk factors for outbreaks in Miami-Dade and the geographic expansion of dengue in Florida. Overall, while we do not yet find evidence for endemicity, we demonstrate how climatic trends are enhancing the local public health risk caused by dengue in Florida.

Background Childhood overweight and obesity represent major public health challenges, shaped by socio-economic and environmental factors. This study investigates the mediating and moderating role of urban environmental exposures in socio-economic disparities in childhood excess weight. Methods Data was drawn from a population-based sample of children (2-9 years) and adolescents (10-17 years) living in Geneva, Switzerland. Parents reported household financial situation and children's height and weight, from which excess weight (i.e. overweight or obesity) was derived. Residential exposures to air pollution (PM2.5, NO2), noise (daytime, nighttime), and neighborhood greenness (green areas, canopy coverage) were estimated based on geocoded residential addresses. The association between household financial situation and excess weight was evaluated, as well as the mediating and moderating roles of urban environmental exposures. Results The analysis included 1006 children and 1154 adolescents. Among children, an average-to-poor household financial situation was associated with higher odds of excess weight in children (adjusted odds ratio [aOR]: 1.79, 95% confidence interval [CI]: 1.13; 2.84). Higher noise exposure was associated with excess weight (daytime: aOR: 1.40, 95% CI: 1.10; 1.77, nighttime: aOR: 1.37, 95% CI: 1.08; 1.74), while the association with PM2.5 appeared stronger among socio-economically disadvantaged children, though the interaction did not reach statistical significance (financial situation x PM2.5 interaction: aOR: 1.59, 95% CI: 0.98; 2.59). No significant associations were observed among adolescents. Conclusion These findings highlight the joint influence of social and environmental inequalities on childhood excess weight and stress the need to address these interconnected determinants to design equitable, targeted public health interventions.

The emergence of a hantavirus variant aboard a commercial cruise ship presents a significant public health concern. This study develops a discrete-time stochastic Susceptible-Exposed-Infectious-Recovered-Dead model to estimate transmission dynamics, hidden exposed infections, and outbreak risk among passengers and crew. Epidemiological parameters and latent disease states were inferred using an Ensemble Adjustment Kalman Filter calibrated to reported case data from WHO and ECDC situation reports. The estimated basic reproduction number was 2.76, with a 95% confidence interval of 2.52-2.99, indicating substantial potential for sustained onboard transmission before strict quarantine measures. Simulations further suggest that several exposed individuals may remain unidentified during the early outbreak phase, creating a hidden reservoir that symptom-based surveillance alone may fail to detect. These findings highlight the importance of rapid surveillance, widespread testing, targeted quarantine, and active monitoring of exposed individuals in confined travel settings. The proposed modeling framework can support timely outbreak assessment and intervention planning for infectious-disease events in similarly dense and spatially constrained populations.

Successive dengue virus (DENV) outbreaks can progressively reshape population immunity influencing disease expression and diagnostic performance. Objectives The aim was to evaluate the impact of secondary infections across sequential outbreaks on clinical severity, serotype dynamics and diagnostic concordance. Methods This retrospective study analyzed 976 febrile-stage samples from three sequential outbreaks in Misiones, Argentina. For serotyping and clinical analyses, 869 viremic samples confirmed by at least one direct method were included (2016: n=512; 2019: n=148; 2024: n=209). Additionally, 318 samples, including 107 non-viremic cases, were used to compare NS1 rapid diagnostic tests (NS1 Ag) and RT-PCR. Viral serotyping and clinical and laboratory markers of disease severity were evaluated. Results Secondary infections increased from 31.05% (2016) to 43.24% (2019) and 53.87% (2024) (p<0.0010). Serotype distribution shifted from DENV-1 predominance in 2016 (95.12%), DENV-1/DENV-4 co-circulation in 2019 (60.71%/39.29%), and DENV-2 predominance in 2024 (97.60%). Secondary infections were associated with more severe disease manifestations, particularly in 2024, with higher hematocrit (p=0.0120) and hemoglobin (p=0.0080), lower white blood cells (p=0.020) and platelet counts (p=0.0030), and elevated AST (p=0.0007) and ALT (p=0.0130). Concordance between NS1 Ag and RT-PCR was lower in secondary infections (k=0.457 vs k=0.759, p=0.0013). Conclusions The rising frequency of secondary infections may affect both clinical severity and diagnostic performance during outbreaks. The clinical impact was more evident in 2024, likely associated with the introduction of a new serotype. These findings highlight the need for optimized surveillance and diagnostic strategies to improve case detection and patient management during epidemics.

Frequent HIV testing, or "retesting," the practice of regular HIV testing following a negative test result, among persons at high risk of HIV exposure is critical for initiating treatment early among newly infected persons and reducing the risk of HIV transmission. However, barriers to HIV retesting, such as fear of stigma, underestimating risk after a prior negative HIV test, and navigating the logistics of accessing an HIV test, have contributed to lower-than-desired retesting rates in Sub-Saharan Africa, where median time from infection to diagnosis is over 2.5 years. The Innovative Behavioral Intervention Strategies (IBIS) study aims to encourage re-testing by utilizing principles of behavioral economics and human-centered-design in a many-arm randomized trial (known as a "megatrial") of avatar-delivered video-based messages and text messages to promote HIV retesting. In 2025, we conducted two-day focus groups in Kenya and Uganda to prototype the messages among community members and healthcare workers. An expert team engaged participants in various activities and discussions to elicit their feedback, where they reflected on factors such as local relevance, clarity, and visual appeal for each prototype. Key changes as a result of workshop feedback include standardized greetings for each arm, clearer language and refined translations, SMS language which protects participant privacy, and avatar updates for local acceptability, while maintaining core behavioral theory. The workshops generated important insights that shaped the final avatars, scripts, and messages encouraging HIV retesting which will be incorporated in the eventual trial. This study demonstrates the value of engaging end-users early in the intervention development process, and gives insight into the application of artificial intelligence (AI) to improve health behaviors in resource-limited settings.

BackgroundHepatitis B virus infection remains a major public health challenge, particularly among people living with human immunodeficiency virus, due to shared transmission routes and the potential for accelerated liver disease progression. Molecular characterization of circulating HBV strains is essential for understanding viral epidemiology, mutation patterns, and implications for diagnostics and vaccination. MethodsThis study investigated the prevalence of hepatitis B infection and molecular characteristics of the hepatitis B virus surface gene among HIV-infected individuals receiving antiretroviral therapy in Nairobi County, Kenya. Plasma samples were screened for hepatitis B surface antigen using enzyme-linked immunosorbent assay. Hepatitis B viral DNA was extracted from HBsAg-positive samples and the surface gene region amplified by polymerase chain reaction. Amplified products were subjected to Sanger sequencing. Sequence assembly, genotype determination, and mutation analysis. ResultsThe prevalence of HIV/HBV co-infection among HIV-positive individuals was determined to be 8.97%. Genotype analysis revealed the circulation of HBV genotype A (sub-genotypes A1 and A4) and genotype D (sub-genotypes D4 and D10) among the studied population. Amino acid sequence analysis of the major hydrophilic region of the surface gene identified several mutations, with R122K and Y134F being the most frequently observed substitutions. ConclusionHepatitis B infection remains prevalent among HIV-infected individuals receiving antiretroviral therapy in Nairobi County. The circulation of multiple hepatitis B virus genotypes and the presence of mutations within the surface gene highlight the importance of continuous molecular surveillance to monitor viral evolution and its potential implications for hepatitis B virus diagnosis, vaccination strategies, and clinical management in HIV-infected populations

Leishmaniasis, a climate-sensitive zoonotic neglected tropical disease, is transmitted by Phlebotomine sand flies and closely linked to socio-economic inequities. Understanding its spatio-temporal dynamics under environmental and social change is critical for effective control. A machine learning framework (XGBoost) was developed to map the global and European distribution of leishmaniasis, incorporating climatic indicators, land cover, elevation, and socio-economic indices (Human Development Index, AROPE). For Europe, five proven vector species (Phlebotomus perniciosus, P. ariasi, P. perfiliewi, P. neglectus, and P. tobbi) were modelled alongside cutaneous and visceral leishmaniasis. Across both analyses, land use features, particularly shrubland and forest cover, had the greatest explanatory power, reflecting their role in providing microclimates and vertebrate hosts for sand flies. Climatic factors, notably mean temperature of the coldest quarter and humidity of the warmest/driest quarters, were also influential, as these facilitate sand fly survival. Socio-economic predictors consistently improved model performance, confirming the role of poverty and inequity as determinants of disease distribution. Globally, leishmaniasis risk increased by ~17% since the 1990s, with Africa, Asia, and the Americas experiencing the greatest rise. In Europe, modest continental-scale increases (CL +1.28%; VL +2.47%) masked strong sub-national heterogeneity, including northward expansion of visceral leishmaniasis and increases in cutaneous leishmaniasis in southern and eastern regions. Sand fly projections indicated expansion of warm-adapted species (P. ariasi, P. perniciosus, P. neglectus) and contraction of species preferring cooler, more humid niches (P. perfiliewi, P. tobbi). These findings highlight climate change, land use, and inequity as interacting drivers of leishmaniasis, emphasising the need for enhanced surveillance, integrated vector management, and targeted support for vulnerable populations, including refugees and migrants.

Purpose: This study developed a Bayesian hierarchical spatio-temporal modeling framework to analyze factors and trends in malaria risk across Ghana's 16 administrative regions from 2020 to 2024. The aim was to identify statistically significant areas with elevated or persistent malaria risk, to inform targeted intervention planning and support the National Malaria Elimination Program. Methods: This study utilized malaria incidence data from the Ghana Health Service's District Health Information Management System-II covering the years 2020 to 2024. Meteorological data were sourced from the Visual Crossing Weather Data, and regional population estimates were obtained from the Ghana Statistical Service. To analyze the data, a Bayesian hierarchical spatiotemporal model with a Negative Binomial (NB) likelihood was implemented using Integrated Nested Laplace Approximation to account for overdispersion. The model included Conditional Autoregressive priors for structured spatial effects, first-order random walk priors for temporal dependence, and spatio-temporal interaction terms. Additionally, Local Indicators of Spatial Association (LISA) analysis with 999 conditional permutations was conducted to identify statistically significant spatial clusters, including high-high hotspots and low-low cold spots. Results: The NB model significantly outperformed the Poisson model, leading to a reduction in the dispersion statistic from 9,227.55 to 1.11. Humidity with a 1-month lag showed the strongest positive association with malaria risk, while the ultraviolet index had the greatest protective effect. Predictive relative risk maps identified persistent high-risk clusters in the northern and northwestern regions, specifically Upper West, Upper East, Bono, Ahafo, and Western North. LISA analysis indicated that Bono-Ahafo has been a stable high-high cluster from 2020 to 2023, while Ashanti has remained a consistent low-high anomaly. Additionally, Greater Accra and Central regions formed a significant low-low cluster in 2024. Conclusion: The Bayesian hierarchical spatio-temporal framework effectively characterized the complex transmission dynamics of malaria in Ghana. It revealed significant spatial dependence, temporal correlation, and interactions between these factors. By identifying persistent high-risk clusters and statistically significant spatial associations, this framework provides essential evidence to guide resource allocation. These findings support Ghana's National Malaria Elimination Program Strategic Plan (2024-2028) by enabling targeted interventions in hotspots and optimizing the use of limited resources to sustain progress in low-transmission areas.

Background: The lifespan of insecticide-treated nets (ITNs) varies widely across settings, reflecting both intrinsic product characteristics and external factors related to use, care, and environment. While the resistance to damage (RD) score captures intrinsic product durability, there is no standardized metric to quantify contextual risks. This study presents a proof of concept for the Risk Index (RI), a composite measure of site-level risk factors for ITN physical durability and survival. Methods: We conducted a secondary analysis of durability monitoring data from 44 sites across 15 countries in sub-Saharan Africa, covering 14 ITN products. The RI was calculated as a weighted composite of 12 indicators spanning net handling, net care attitudes, and use environment. Associations between RI and median ITN survival were assessed using weighted linear regression and multivariable mixed-effects models adjusting for RD score, with country included as a random effect. Results: RI scores ranged from 25.1 to 83.7 across study sites. In bivariable analysis, a 10-point decrease in RI was associated with a 4.0-month increase in median ITN survival (95% CI: 1.7-6.3; p=0.001). In multivariable analysis adjusting for RD, this association remained significant but attenuated to 2.2 months (95% CI: 0.1-4.2; p=0.037). Independently, a 10-point increase in RD score was associated with a 3.5-month increase in survival (95% CI: 1.3-5.7; p=0.001). No interaction was observed between RI and RD. Predicted survival differed by approximately one year between the lowest- and highest-risk settings. Conclusion: The RI provides a standardized measure of contextual risk factors affecting ITN lifespan, independent of ITN product type. When used alongside a product's RD score, the RI enables improved interpretation of expected site-level variation in net performance. This combined framework offers a practical basis for incorporating behavioural and environmental risk into vector control planning and for tailoring ITN strategies to local conditions.

Background: Dengue is a major public health problem in Brazil, and Minas Gerais is one of the states with the highest burden. In January 2019, the Brumadinho dam collapse released about 12 million cubic meters of iron ore tailings into the Paraopeba River basin, causing environmental disturbance that could plausibly affect vector habitats and dengue transmission. We evaluated the spatiotemporal dynamics of dengue in Minas Gerais from 2014 to 2023 and tested whether the disaster was associated with changes in affected municipalities. Methods: We performed an ecological spatiotemporal analysis using dengue notifications from SINAN for all municipalities in Minas Gerais (2014-2023). Municipalities were classified as Paraopeba basin, regional controls, or state controls. Temporal similarity was assessed using Pearson correlation-based hierarchical clustering and non-metric multidimensional scaling (NMDS). Sources of variation were examined with PERMANOVA and principal component analysis (PCA). A linear mixed-effects model with municipality as a random effect was used to test changes after 2019, with pre/post contrasts estimated from marginal means. Results: Dengue showed strong temporal synchrony across the state, with major epidemic peaks in 2015-2016, 2019, and 2023. Health region explained 31.5% of the variation in temporal incidence profiles (p = 0.001), whereas Paraopeba basin status explained no significant variation (p = 0.998). No temporal cluster was enriched for municipalities in the Paraopeba basin. PCA identified 2023, 2019, and 2016 as the main years driving variability. In the mixed model, year was significant (p < 0.001), but Paraopeba basin status and its interaction with time were not. Incidence increased significantly after 2019 in non-exposed municipalities (p < 0.001), but not in basin municipalities (p = 0.088). Conclusions: Dengue dynamics in Minas Gerais were driven mainly by regional and state-wide epidemic processes, with no significant independent effect of the Brumadinho dam collapse on notified dengue patterns.

Malaria transmission in Nigeria is highly seasonal and climate-sensitive, yet routine surveillance and meteorological datasets remain underutilized for predictive modelling at subnational levels. This study modelled seasonal malaria incidence trends in Nasarawa State, Nigeria using routine surveillance and climatic data. A retrospective ecological time-series study was conducted using monthly confirmed malaria incidence data from all 13 Local Government Areas of Nasarawa State between 2021 and 2025. Rainfall and temperature were examined as the climatic predictors. Seasonal decomposition and cross-correlation analyses were performed to identify the temporal patterns and lag structures. Seasonal Autoregressive Integrated Moving Average (SARIMA) and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models were developed using the Box-Jenkins framework. Model performance was evaluated using the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Malaria incidence showed pronounced seasonal peaks, with the highest transmission occurring during the rainy season. Cross-correlation analysis identified rainfall at a one-month lag and contemporaneous temperature as significant predictors of malaria incidence. The SARIMAX model outperformed the univariate SARIMA model, achieving strong predictive accuracy (MAPE = 8.7%). Forecast projections indicate sustained transmission with a peak incidence expected between June and August 2026. Malaria transmission in Nasarawa follows a predictable seasonal pattern that is influenced by climatic variability. Incorporating rainfall and temperature into SARIMAX models improves the forecasting performance and provides evidence supporting climate-informed malaria surveillance and preparedness in endemic settings.

Introduction: Dengue, chikungunya, and hand, foot, and mouth disease (HFMD) are priority notifiable infections in Thailand. Whether vector-borne and contact-mediated diseases responded differently to the coronavirus disease 2019 pandemic has not been quantified within a unified national surveillance framework over an extended period. Methods: We conducted an ecological interrupted time-series analysis using weekly province-level notifiable disease surveillance data from epidemiological week 1 of 2016 to week 53 of 2025 across all 77 Thai provinces. Incidence per 100,000 population was calculated using year-specific civil registration population denominators. Segmented quasi-Poisson regression with two Fourier harmonics for annual seasonality was fitted, with the primary pandemic onset defined as week 1 of 2020 and two alternative onset definitions prespecified for sensitivity analysis. Results: The analysis included 40,579 province-week observations across 527 epidemiological weeks, comprising 790,263 dengue, 32,265 chikungunya, and 713,822 HFMD cases nationally. Immediate incidence rate ratios at pandemic onset were 0.39, 0.54, and 0.51 for dengue, chikungunya, and HFMD, respectively. Sustained post-onset trends diverged across diseases, with declining trajectories for the two vector-borne infections and a positive post-onset slope for hand, foot, and mouth disease. Dengue rebounded above pre-pandemic levels by 2023, chikungunya remained quiescent through 2025, and HFMD exceeded its pre-pandemic baseline by approximately 26%. Conclusion: Vector-borne and contact-mediated diseases in Thailand followed sharply contrasting decadal trajectories that tracked the transmission ecologies of each pathogen. These findings support transmission-mode-specific pandemic-resilient surveillance, accelerated arboviral and enteroviral vaccine deployment, and integrated vector management.

Canine rabies persists in NDjamena (Chad) despite vaccination campaigns exceeding 70% coverage, suggesting a role for dog mobility and spatial heterogeneity. We propose a metapopulation SEIR model incorporating distance-modulated dog movements and an explicit vaccinated class. Analysis of the isolated patch establishes global stability of the disease-free equilibrium via a Lyapunov function. For the metapopulation, a composite Lyapunov function shows that elimination is governed by a reproduction number [R]v. Calibrated with field data (2012-2022), simulations reveal that uniform vaccination of both patches reduces [R]v by 46% (from 2.84 to 1.52) but does not achieve elimination, while targeted strategies are less effective. These results demonstrate that exhaustive vaccination coverage across the entire urban network and increased vaccination intensity are necessary to eliminate canine rabies in NDjamena. Our model provides a quantitative framework for planning effective control strategies.

Understanding the immune response against HIV-1 and the natural resistance exhibited by HIV-exposed Seronegative Individuals (HESN) offers the possibility of proposing new control strategies. Several studies suggest an important role of HLA and KIR genes in protecting against HIV-1 infection. Moreover, there is an important gap in the knowledge of these genetic factors in seronegative Latin American men who have sex with men (MSM), a population largely underrepresented in HIV immunogenetic studies. This study aimed to identify HLA and KIR genetic profile associated with potential resistance to HIV-1 acquisition, in a cross-sectional study including a cohort of 60 HIV-1-seronegative Colombian MSM at low and high risk of HIV-1 infection. The high-risk group showed a higher frequency of the HLA-B*18 allele, and a lower frequency of the HLA*B35, which have been previously associated with protection and susceptibility to HIV-1 infection respectively. Likewise, the high-risk group exhibited a low frequency of Bx haplotypes, a higher frequency of one AA haplotype and differences in KIR gene profile, with a low frequency of the inhibitory KIR2DL5 and both activating KIR2DS1, KIR2DS2 and KIR2DS5 genes. These findings suggest that host immunogenetic factors may contribute to resistance to HIV-1 acquisition in highly exposed individuals.

Between 2015 and 2025, Cambodia reported a 99.9% decline in the number of cases of malaria. To aid acceleration of elimination, the National Center for Parasitology, Entomology and Malaria Control (CNM) implemented a package of interventions known as the Last Mile (LM) elimination program. The aim of this study was to determine the impact of the LM program on case numbers and evaluate the coverage of interventions. LM was rolled out between November 2020 and December 2023 in villages reporting a locally acquired case of Plasmodium falciparum or mixed infection with P. falciparum and P. vivax and included combinations of targeted drug administration (TDA), intermittent preventative treatment for forest goers (IPTf), active fever screening (AFS), the recruitment of a village or mobile malaria worker (VMW/MMW) and the top-up of insecticide-treated bed nets (ITN) depending on the vulnerability and receptivity of the village. A total of 103 full and 82 partial villages in seven provinces were included. Two rounds of TDA were administered, with a total of 10,678 individuals (67.6%) given during the first round and 9,678 (62.3%) during the second round. Coverage varied by province with none meeting the recommended threshold of 80%. IPTf was implemented each month among 35% (n=35) of full LM villages and 56% (n=42) of partial LM villages. A total of 11.7% (n=12) of full LM villages implemented AFS consistently on a weekly basis. Controlled interrupted time series showed no statistically significant difference in the number of malaria cases before and after the implementation of LM. Although we were unable to prove a statistically significant impact of LM, likely due to the small number of cases prior to LM, it is important to add to the limited evidence-based for Accelerator Strategies in countries approaching the elimination of malaria. Furthermore, findings from the feasibility and impact of individual interventions were used to change policy at the national level.

Background In 2017, Zambia adopted surveillance as a core intervention towards achieving malaria elimination. Among the surveillance strategies is the malaria case investigation and response 1-3-7 (MCIR 1-3-7), which has been piloted in two low-incidence districts in the Southern Province since 2021. The study aimed to assess the implementation of MCIR 1-3-7 under programmatic conditions. It examined the timeliness, and completeness of the MCIR 1-3-7 activities, including the completeness of data entry in surveillance forms, and explored the experiences and perspectives of healthcare workers involved in the pilot. Methods A mixed-methods design was employed to assess the MCIR 1-3-7. Using a descriptive cross-sectional design, quantitative data were collected from 19 healthcare facilities in the two districts to assess the timeliness and completeness of MCIR 1-3-7. Additionally, 12 qualitative interviews were conducted with 29 healthcare workers from 11 of the 19 healthcare facilities. The interviews were voice-recorded and then transcribed manually. A codebook was developed using an iterative process to explore the facilitators and barriers encountered by healthcare workers in implementing the MCIR 1-3-7 intervention. All the visited facilities were purposively selected based on logistical convenience. Results This study retrospectively assessed 510 malaria cases that were diagnosed between January 2022 and June 2023, presenting at 19 health facilities: 283 cases in Chikankata and 227 in Mazabuka districts. A total of 278 cases (54.5%) were deemed to have been imported from outside the district, province, or country, while 45.5% (232/510) of the cases were classified as transmitted locally. Overall, 29.6% of case notification forms were found to be complete. Twelve interviews with 29 healthcare workers revealed a lack of transportation modalities as the main obstacle in executing the MCIR 1-3-7 intervention. The healthcare workers also indicated that monetary incentives, and supportive supervision would help them succeed in implementing this intervention. Conclusions The MCIR 1-3-7 has the potential to accelerate elimination in areas with low-transmission of malaria in Zambia. This study highlights opportunities to improve future implementation of the MCIR 1-3-7 intervention via strengthening supportive supervision, availing job aids, and ensuring access to malaria commodities as the intervention expands.

Background India accounts for nearly one-fourth of the global tuberculosis (TB) burden. The country's progress towards elimination of TB is hindered by considerable heterogeneity in behavioural, social, and health system determinants, which influence transmission dynamics and care access. Evidence from the recent national TB prevalence survey showed that almost half of individuals with active disease were asymptomatic, underscoring the limitations of symptom -based case finding. Achieving the End TB targets will therefore require strategies that simultaneously address the substantial pool of individuals with undiagnosed, asymptomatic disease and those symptomatic individuals who do not seek care. Methods We developed a transmission model of TB that explicitly incorporates individuals with asymptomatic disease, and those who do not seek care. Model calibration was performed within a Bayesian framework using epidemiological and programmatic data for India. The calibrated model was then used to project the potential impact of intervention on TB incidence and mortality. Results Under the baseline scenario, the estimated TB incidence and mortality rates for 2024 were 180 (163-203) and 24 (18-31) per 100,000 population, respectively. Across all intervention scenarios targeting improved diagnosis, active case finding, nutrition support and their combination the reduction in incidence rate by 2030 ranged from 13% to 60% compared with 2025, while the corresponding decline in mortality rate ranged from 16% to 66%. Conclusion While individual interventions yield measurable reductions in TB incidence and mortality, but greater impact is achieved when implemented in combination reflecting the need for a comprehensive, multi-component response towards TB elimination.

Reproductive tract infections (RTI) are associated with adverse outcomes in pregnant African women. However, many diagnostic strategies are unaffordable or perform poorly. Here, we assess RTI prevalence and predictors of chlamydia/gonorrhea (CT/NG) in pregnant women reporting vaginal discharge and the performance of our previously published CT/NG risk algorithm in this population versus Rwandan National Guidelines (RNG). From 2017-2020, free sexually transmitted infections (STI) services were provided to residents in Kigali, Rwanda. Medical history and gynecologic examination were done. Laboratory assessments included HIV; syphilis; microscopy for trichomoniasis, bacterial vaginosis (BV), and candida; and PCR for CT/NG. Eighty-seven pregnant women received STI services. Prevalence was 28% for CT/NG, 15% for trichomoniasis, 24% for BV, 39% for candida, and 79% for any RTI. Predictors of CT/NG were age <=25 (adjusted prevalence odds ratio [aPOR]=4.92; 95% confidence interval [CI]: 1.52-15.90; p=0.008), inconsistent condom use (aPOR=4.86; 95%CI: 0.98-24.10; p=0.053), absence of candida (aPOR=4.23; 95%CI: 1.13-15.82; p=0.032), and endocervical inflammation/discharge (aPOR=4.91; 95%CI: 1.40-17.20; p=0.013). Our algorithm outperformed the 2019 and 2024 RNG (sensitivity: 92% versus 46% and 35% respectively). Pregnant women seeking STI services had high RTI prevalence. Our algorithm performed well. Algorithms tailored for pregnant women and including male partner risk factors should be explored.

BackgroundStigma remains a pervasive barrier to curbing the spread of human immunodeficiency virus (HIV) among adolescents and young adults in Lima, Peru. Social media offers a promising avenue for scalable, youth-centered stigma reduction, but few interventions have been rigorously evaluated in this context. ObjectiveWe evaluated the potential of a social media campaign to reduce perceived HIV-related stigma among young adults living with HIV. This involved a sequential explanatory mixed-methods study, including a randomized evaluation, followed by focus groups to understand the findings. Methods150 young adults (aged 18-29 years) living with HIV (YLWH) were randomized to receive information on social media from one of the following: (1) the control account; (2) the control account and the social media campaign accounts (Instagram and TikTok); or (3) the control account, the campaign accounts, and the accounts of participating influencers. Perceived stigma was measured via pre- and post-campaign surveys using Spanish versions of the abridged Berger HIV Stigma Scale and the Stigma Stress Scale. Focus groups and interviews were conducted with a purposive sample of participants to contextualize quantitative results. Qualitative data were analyzed using Framework Analysis. ResultsMean changes in HIV Stigma and Stigma Stress scores were small and not statistically significant. Post-hoc as-treated analyses supported these findings. Fidelity to intervention allocation was low to moderate, depending on the metric considered. Qualitative data suggested that the campaign positively impacted participants perceived stigma and that personal circumstances, crossover, frequency of exposure to content, and issues related to completing study questionnaires contributed to the lack of meaningful change in stigma scores. ConclusionsWhile quantitative data did not support that exposure to a social media campaign led to meaningful reductions in HIV-related stigma, qualitative data suggested that the campaign had a positive impact and that limitations in the study design, together with external factors, may have obscured benefits in quantitative analyses.

Background: Incomplete childhood vaccination undermines individual and herd immunity and increases vulnerability to vaccine-preventable diseases. Understanding local determinants of vaccination adherence is essential for targeted interventions. This study assessed routine immunization completion and dropout patterns among children aged 0-15 months in Bayelsa State, Nigeria. Objectives: To determine vaccination completion rates, identify factors influencing adherence, analyze temporal patterns across immunization milestones, and provide evidence-based recommendations for improving coverage. Methods: A comparative longitudinal study was conducted from March 2023 to July 2024 across three Local Government Areas (LGAs), representing each senatorial district. A total of 369 mother-child pairs (123 per LGA) were enrolled. Data were obtained from health facility immunization registers and supplemented with semi-structured questionnaires. Children were followed through the 6th week, 10th week, 14th week, 9th month, and 15th month immunization visits. Completion rates were analyzed using descriptive statistics and chi-square tests. Ethical approval was obtained from the State Ministry of Health, and informed consent was obtained from all mothers. Results: Completion rates varied across LGAs, with the highest in LGA C (86.2%) and lowest in LGA B (61.0%). Phone-based reminders achieved the highest adherence, outperforming routine and home visit strategies. Progressive attrition was observed along the immunization schedule, with dropout exceeding completion by the 15th month. Principal reasons for non-completion included forgetfulness, travel, and caregiver busyness. Maternal age, education, and occupation significantly influenced adherence, indicating disparities across LGAs. Conclusion: Vaccination adherence is shaped by maternal characteristics and operational strategies. While early-stage coverage is high, attrition increases at later milestones, particularly in LGAs with lower resource engagement. Recommendations: Implement targeted phone-based reminders, milestone-specific outreach, and community engagement programs to reduce dropout, enhance timely completion, and strengthen childhood immunity.