Sub-Saharan African (SSA) cities have high air and noise pollution levels, yet limited information exists on children's joint exposures at home and school locations - the two most important environments where children spend much of their time. This study characterised schoolchildren's exposure to multiple air pollutants and environmental noise and sound sources within the Accra School Health and Environment Study (ASHES). ASHES involved 1034 children aged 8-12 years from 90 public (73%) and private schools in Accra, Ghana, a major SSA city. Annual mean concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC), as well as environmental noise (L day, L night, L den) and sound sources, were derived from land use regression models and linked to geocoded school and home locations for 919 children with valid data. A time-location weighted total exposure estimate was calculated for the air and noise pollution metrics. We also examined children's responses to a noise annoyance survey in relation to their noise exposure levels at home. For air pollutants, median home exposures were 33.8 μg m-3 (PM2.5), 55.5 μg m-3 (NO2), and 5.2 × 10-5m-1 (BC), and corresponding median school exposures were 29.9 μg m-3, 53.5 μg m-3, and 4.9 × 10-5m-1. The time location-weighted total air pollutant exposures for most children surpassed the respective World Health Organization (WHO) guideline. Similarly, median environmental noise levels (in dBA) at home (L den = 66.3 and L night = 54.2) and at school (L day = 62.4) exceeded WHO thresholds for road-traffic noise and Ghana's noise standard for areas with educational facilities, respectively. Pollution levels at home and school were moderately correlated (r [PM2.5] = 0.56; r [NO2] = 0.33; r [L den]= 0.47). On average, children attending public schools or living in lower income areas had higher exposures to both air and noise pollution than their private school (fee-paying) or higher-income counterparts. In contrast, nature-based sounds were more common in higher income areas. Among children exposed to L den noise levels between 65 and 70 dBA, 35% reported being highly annoyed to road traffic noise. Schoolchildren in Accra experience air and noise pollution levels exceeding health-based guidelines and standards. Exposures are unequally distributed, highlighting environmental inequalities with implications for child health, development, and learning.

INTRODUCTION:The lack of a robust and comprehensive civil registration system undermines efforts to develop policies that address emerging health issues in a population. While Ghana has made significant strides in birth registration, progress in death registration has lagged despite the system's introduction in 1888. Death records are dispersed across institutions and hampered by weak coordination and inconsistent data sharing. The study aims to evaluate the completeness of death registration using both demographic and empirical methods. METHODS:We assessed the quality of mortality data using demographic methods and further applied Death Distribution Methods (DDM) as well as the Empirical Completeness Method (ECM) to evaluate the completeness of death registration from the 2022 mortality register and two rounds of population and housing censuses. FINDINGS:Age reporting ranged between fairly accurate and rough, with noticeable under-reporting of under-five deaths. Death registration completeness was estimated at 28.7 percent and 56 percent using the ECM and DDM, respectively. DDM results reveal that male completeness was highest (101%) in the Volta Region and lowest (42%) in the Western Region, while female completeness was highest in Upper East (89%) and lowest in Western (35%) regions. Sub-national variations in completeness were further highlighted by the ECM, with completeness ranging from nearly 70 percent in Greater Accra to barely 8 percent in the Oti Region. CONCLUSIONS:The study revealed that completeness of death registration remains low and incomplete, with minimal improvement over the past two decades. The findings underscore the urgent need to adopt innovative, targeted and coordinated approaches to improve completeness, ensuring Ghana can meet international commitments such as SDG 17.19.2.

Fine-scale, geocoded neighborhood socioeconomic status (nSES) data are foundational inputs for urban studies and spatial inequality research, yet remain scarce in emerging economies that together account for over 70% of the global population. Here, we use publicly available satellite and street view imagery to estimate nSES at fine spatial resolution, proposing an ensemble regression framework that unites multi-view images to measure intra-urban inequality. We estimate eight indicators—spanning household registration (hukou), housing, income, employment, and education—as numeric values rather than broad quantile bands, enabling direct cross-neighborhood comparison. Family and individual SES from a representative survey serve as training data. The proposed fusion model outperforms single ensemble regression models and single-view image-based models in mean absolute percentage error. To interpret image-based prediction, we apply SHAP-based feature attribution and modality-contribution analysis, identifying the built-environment features that drive predictions and revealing that satellite and street view imagery play complementary rather than redundant roles. Neighborhood inequalities at traffic analysis zone and township scales in central Beijing are visualized for the first time. We further transfer the trained model to Shanghai, where no comparable public nSES dataset exists, providing a limited proof-of-concept against real-estate data for two of the eight indicators and outlining a replicable pathway for data-scarce cities.

Kigali, like many cities in sub-Saharan Africa (SSA), must balance rapid urban growth and the provision of essential services with the need to curb environmental pollution and protect public health in the context of its unique topography. Although the city has implemented policies aimed at reducing emissions from multiple sectors, systematic data on oxides of nitrogen (NO X ; NO 2 and NO), key markers of combustion-related urban air pollution, have been limited. We applied a standardized measurement protocol previously used in Accra, Ghana, to characterize city-scale spatial and temporal patterns of NO X pollution in Kigali. Between November 2022 and December 2023, we deployed Ogawa passive samplers to collect weekly integrated NO 2 (n = 630) and NO (n = 630) samples across 130 sites (10 year-long and 120 rotating week-long locations) representing diverse land-use types and source characteristics. Weekly NO 2 and NO concentrations ranged from approximately 2 to 62 µg/m3 (mean [SD]: 13.9 [11.4]) and approximately 1 to 49 µg/m³, respectively. Although nearly all background sites recorded NO 2 concentrations below the World Health Organization (WHO) annual guideline of 10 µg/m3, exceedances were common in more urbanized settings, occurring in 39% of samples from sparsely residential areas, 89% from commercial, business, and industrial (CBI) areas, and 99% from densely populated residential areas. Mean NO 2 concentrations were significantly higher in urban compared with rural neighborhoods (18.2 vs. 6.3 µg/m³; p < 0.001), at sites located within 200 m of primary roads compared with those farther away (19.9 vs. 11.6 µg/m3; p < 0.001), and at lower compared with higher elevations (15.4 vs. 9.0 µg/m3; p < 0.001). The levels were higher and exceeded the WHO annual guideline in the more densely populated and urbanized districts of Kicukiro and Nyarugenge, compared with the more rural Gasabo district. Similar spatial patterns were observed for NO. Overall, NO 2 and NO concentrations across Kigali were strongly patterned by land use, traffic proximity, population density, and topography, with the highest levels observed in traffic-dominated, densely populated, low-elevation areas. These city-wide measurement data provide critical evidence to inform land-use planning, air quality management, and regulatory strategies in a rapidly urbanizing, landlocked city characterized by complex topography.

Kigali, like many cities in sub-Saharan Africa, faces rapid urban growth alongside the need to manage air pollution and protect public health. Despite its policy efforts, systematic data on nitrogen oxides (NOx: NO2 and NO), key indicators of combustion-related pollution, have been limited. We applied a standardized protocol to characterize city-scale spatial and temporal patterns of NOx across Kigali. Between November 2022 and December 2023, we collected weekly integrated NO2 and NO samples (n = 630 each) at 130 sites representing diverse land-use types. NO2 concentrations ranged from 1.3 to 61.9 µg/m3 (mean 13.9 µg/m3), with annual-equivalent frequently exceeding the WHO annual guideline (10 µg/m3) in urban areas. Exceedances occurred in 39% of sparsely residential, 89% of commercial/industrial, and 99% of densely populated residential sites. NO2 concentrations were significantly higher in urban versus rural areas (18.2 vs. 6.3 µg/m3), near major roads (19.9 vs. 11.6 µg/m3), and at lower elevations (15.4 vs. 9.0 µg/m3). The highest levels were observed in the densely populated districts of Kicukiro and Nyarugenge. Overall, NO2 and NO exhibited strong spatial gradients related to land use, traffic, population density, and topography, highlighting the importance of targeted urban planning and air quality management in rapidly growing cities.

Summary: Background: Visceral adipose tissue (VAT) may causally contribute to cardiovascular disease (CVD); however, evidence in Latin America is limited. Here, we evaluated the association between estimated VAT (eVAT) and incident CVD among individuals without diabetes and estimated its population-attributable fraction (PAF) using data from the Cohorts Consortium of Latin America and the Caribbean (CC-LAC). Methods: We pooled data from 15 prospective cohorts across 7 countries (n = 23,097; 62% women [n = 14,383], median age 51 years). Baseline eVAT (in grams [g]) was estimated using the Metabolic Score for Visceral Fat (METS-VF) index, incorporating age, sex, an insulin resistance index, and waist-to-height ratio. The primary outcome was incident CVD events, including fatal and non-fatal outcomes. Cause-specific Cox proportional hazards models estimated adjusted hazard ratios (aHR). PAF estimates used scenario-based eVAT quartile reductions. Findings: Over a median follow-up of 4 years (113,622 person-years), 436 participants (1.9%) experienced an incident CVD event (174 fatal; 262 non-fatal). Uniformly age- and sex-standardized incidence rates were 3.8 (95% CI: 3.5–4.2) per 1000 person-years. Each 100 g increase in eVAT was associated with a 4% higher CVD hazard (aHR 1.04, 1.03–1.06). Compared with Q1 (<735 g), those in Q3 (1069–1441 g) and Q4 (≥1441 g) had a higher CVD hazard (Q3 aHR: 1.78 [1.28–2.49]; Q4 aHR: 2.03 [1.48–2.79]; p-for-trend <0.001). Associations were stronger for non-fatal events. In PAF estimates, shifting individuals from Q4 to lower quartiles could prevent 8.8% (2.8%–14.7%) of CVD events over 10 years. Interpretation: Higher eVAT was associated with increased CVD risk in Latin America, supporting the view that modest VAT reductions could decrease regional CVD burden. Funding: CC-LAC was funded by the Wellcome Trust.

Cities have complex dynamics on timescales from hourly and daily changes to monthly shifts and annual trends. We used time-lapsed street-view imagery (SVI) to capture and analyse temporal trends of urban environmental features in Accra, Ghana. We collected a novel dataset of 6.8 million street-view images (SVI) at five-minute intervals over five years at ten representative locations in Accra, Ghana. We used a fine-tuned YOLOv7 object detection model to detect and obtain counts of people, large vehicles, small vehicles, two-wheelers, market-related objects, refuse and animals in all images. We used a mixed-effects zero-inflated negative binomial model with indicators for hour of day, day of week, week of year, and year to consistently and coherently identify temporal patterns of object counts across time scales and sites. People and small vehicles were most prevalent in mid-morning and early evening, and market-related objects peaked in early afternoon. The number of people, vehicles and market-related objects declined on weekends at most sites, although two residential sites showed an inverse trend, peaking in all three categories on weekends. Long-term trends over the years indicate a rise in people at high-density residential sites. Over the same period, market-related objects, two-wheelers and small vehicles declined at several locations with different land-use characteristics, suggesting broad shifts in transport and commercial activity. These results demonstrate the potential of SVI and computer vision for urban monitoring to support strategies for improving mobility, traffic congestion, pollution, access to goods and services, and waste management.

Elementary school and early education are crucial for children’s cognitive and social development, as well as lifetime health and well-being. For children in cities, urban schools present numerous advantages in education quality and access to resources and opportunities that stimulate learning and improve health. In Sub-Saharan African (SSA) cities, the complexity of the urban environment requires careful consideration of school environments in enhancing child health and development. Yet, little is known about environmental conditions in schools and schoolchildren’s health in rapidly urbanizing SSA cities. This paper describes the various datasets captured within the Accra School Health and Environment Study (ASHES), a study platform designed to characterize air and noise pollution at elementary schools and for schoolchildren, and their influence on key markers of childhood health and development. We outline environmental exposures and health and developmental outcomes among children living in a major metropolitan area in SSA, along with preliminary results and planned analyses. ASHES was implemented in Accra, one of the fastest growing metropolises in SSA. Between July 2022 and May 2023, 1,037 children (∼60% female) aged 8-12 were recruited from 90 public (74%) and private primary schools. Weeklong fine particulate matter (PM 2.5 ), black carbon (BC), and sound pressure levels were measured in the schoolyards. Homes of the children were geocoded and linked with spatial prediction models to estimate ambient pollutant concentrations at each child’s residence. Data were also captured on anthropometry, blood pressure, respiratory function, cognitive and behavioral functions, and sleep quality. Questionnaires gathered additional information on school, household, and sociodemographic factors. Preliminary results suggest that a third of children were hypertensive, 30% were overweight or obese, and 14% had behavioral problems. PM 2.5 and noise levels across schools exceeded local and international standards. Several ongoing epidemiologic analyses will examine the key exposures in relation to the major outcomes.

Abstract Ambient air pollution has been linked to elevated blood pressure (BP) in adults, but research is limited among children, particularly in sub-Saharan Africa (SSA). We investigated the potential effects of ambient fine particulate matter (PM 2.5 ), black carbon (BC), and nitrogen dioxide (NO 2 ) exposures on BP in school-aged children in Accra, Ghana’s capital and one of the fastest growing cities in SSA. We performed a cross-sectional analysis among 919 (60% girls) schoolchildren aged 7–14 years across 90 elementary schools. Following NIH guideline, we define elevated BP as age, sex, and height specific systolic and/or diastolic BP values at ≥ 90th percentile. PM 2.5 , BC, and NO 2 concentrations at homes and schools were estimated using spatiotemporal land-use regression models developed specifically for Accra using measurement data from 146 sites. Covariate-adjusted associations were estimated using multivariable mixed-effects linear and logistic regression models. The mean SBP and DBP among the children were 107.2 (9.7) and 69.1 (7.1) mmHg, respectively, with one-third of the children having elevated BP. Children’s PM 2.5 exposure at both home and school were 5–8 times the World Health Organization (WHO) annual guideline of 5 µg/m 3 , BC levels were above the maximum value in the “good practice statement for BC” (5.1 µg/m 3 ), and NO 2 concentrations exceeded the WHO annual guideline of 10 µg/m 3 by 11-fold at some locations. Overall, PM 2.5 , BC, and NO 2 were negatively associated with BP after adjustment for demographic and lifestyle covariates. For instance, a 10 unit increase in air pollution at homes was associated with a small decrease in SBP (PM 2.5 : −0.70 mmHg (95% CI −3.34, 1.94), BC: −4.61 mmHg (95% CI −9.59, 0.37), NO 2 : -0.19 mmHg (95% CI −0.60, 0.22) and DBP (PM 2.5 : −0.83 mmHg (95% CI −2.93, 1.28), BC: −4.35 mmHg (95% CI −8.19, −0.50), NO 2 : −0.14 mmHg (95% CI −0.46, 0.18)). Furthermore, home-level PM 2.5 was associated with reduced odds of having elevated BP (OR 0.75 (95% CI 0.39, 1.41)), as was BC (OR 0.35 (95% CI 0.09, 1.39)). This study showed no evidence of pro-hypertensive effect of exposure to PM 2.5 , BC, and NO 2 pollution among schoolchildren in Accra.

Air and noise pollution are significant emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data are a major barrier to the formulation and evaluation of policies to reduce air and noise pollution.We designed and carried out an innovative 3-year measurement campaign to characterise air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area (GAMA), Ghana. Our design used a combination of fixed (3 year-long, n=10) and rotating (week-long over 1 year, n =136) sites, selected to represent a range of land uses and source influences. We collected data on PM2.5, black carbon (BC), nitrogen oxides (NOx), weather variables, noise pollution, along with street level time-lapse images with cameras. To do this, we strategically deployed low-cost, low-power, lightweight monitoring devices in an integrated station that was robust, socially unobtrusive, and able to function in the West African coastal climate. We used spatiotemporal land use regression models to predict PM2.5, NO2, BC and noise pollution across the city in high spatial resolution, and state-of-the-art methods in deep learning to predict pollution levels in high temporal resolution by training classification algorithms on 2 million time-lapse images captured at street level with corresponding pollution measurements. Most measurement sites recorded air pollution and noise levels above the WHO health-based guidelines. Spatiotemporal LUR models achieved good out of sample R2’s of 0.51-0.54 (noise), 0.58 – 0.83 (PM2.5), 0.78 - 0.80 (NO2) and 0.79 – 0.88 (BC). From the deep learning image-based analysis, the classification (prediction) accuracy of noise levels in space and time was higher (40-70%) than PM2.5 (30-55%), due to the localised nature of noise source emissions, and the fine-grained nature of our classes, which distinguish between small changes than previous studies. Our approach to monitoring and modelling air and noise pollution can be scaled up in other SSA cities to fill critical data gaps, and is already being successfully piloted in Kigali, Rwanda. The exposure surfaces developed with the LUR models are now supporting ongoing epidemiological studies assessing the impact of exposure to air and noise on birth outcomes and child health and development in Accra. Street view imagery are an increasingly available resource in cities around the world (from CCTV; Google Street View), and results from our deep learning image-based analysis show that the time lapsed images are a uniquely informative source of data for predicting high resolution temporal change in exposure, simultaneously with the presence or absence of potential determinants, though integrating with high spatial resolution remains a challenge.

CONTEXT:There is large variation in the individual risk of developing obesity-associated comorbidities. While obesity is highly prevalent in Mexico, data on the extent and heterogeneity of its associated comorbidities are lacking. OBJECTIVE:We estimated the prevalence of different obesity-associated comorbidities, and how they have changed over 15 years. METHODS:We gathered data from different editions of nationally representative health and nutrition surveys (ENSANUT) from 2006 to 2022. The prevalence of obesity and the coexistence with diabetes, dyslipidemia, hypertension, depression, and impaired mobility, which are outcomes used in the Edmonton Obesity Staging System (EOSS), which assesses 3 dimensions (medical, mental, and functional) across 5 incremental severity stages, by sex and age groups, were estimated across all included surveys. Metabolically healthy obesity (MHO) was defined as the absence of diabetes, dyslipidemia, and hypertension. RESULTS:A total of 20 758 participants were analyzed. Mean body mass index (BMI) increased progressively at all ages from 30.2 to 31.0 across survey rounds. Depression and impaired mobility were highly prevalent even among MHO individuals. While most people with obesity had at least one detectable abnormality, there was large heterogeneity in the presented comorbidities. The most prevalent EOSS categories were stage 2 for the medical dimension (90.1%), and stage 1 for the functional and mental dimensions (75.1% and 62.9%, respectively). The prevalence of obesity-related comorbidities increased with age but was similar across all surveys. In both sexes, MHO was less likely as age and BMI increased. CONCLUSION:The prevalence of obesity comorbidities has been stable over time in Mexico but increases with age. The rising prevalence of obesity and the aging of the population will cause additional burdens to the population and the health system.

The surge in global electric bicycle ownership has exerted immense pressure on bicycle infrastructure. Theoretically, there’s a need to reassess the risk factors associated with multiple bike lane users. Based on this, there’s a practical need to re-evaluate the safety and quality of outdated infrastructure. This paper aims to reconsider risk factors related to bicycle infrastructure safety in the context of electric bicycles sharing lanes with traditional bicycles. Moreover, many countries lack precise spatial data concerning bicycle infrastructure. This study introduces a mobile sensing method based on bicycles, aiming to acquire daytime and nighttime bike lane datasets in a cost-effective, efficient, and large-scale manner. A computer vision-based bicycle risk factor assessment model was established, and the distribution of bicycle safety risk factors was visually analyzed. Research data was collected from a representative 59.5-kilometer bicycle lane area in Beijing. The results confirm the significant impact of the surge in electric bicycles, with electric bike users accounting for 72.1% of cyclists, 32.3% wearing helmets, and 8.4% riding against traffic. During the day, the highest-ranking risk factors include the type of bicycle lanes (half lacking dedicated lanes or being shared), roadside parking, and subpar road conditions. At night, insufficient street lighting are notable concerns. The research methodology is easily replicable and can be extended to new multi-user coexistence cycling environments or countries without bicycle spatial data, offering insights for bicycle safety policies and road design.

BACKGROUND:Nutrition is a critical determinant of tuberculosis (TB), providing a protective effect at high body mass index (BMI) and incurring an increased risk of TB disease at low BMI. Global nutritional transition and interventions to end hunger could directly affect the TB epidemic in high TB burden countries. METHODS:We constructed dynamic TB transmission models for 12 high TB burden countries with low HIV prevalence. We explicitly accounted for the effects of BMI on TB disease progression and treatment outcomes using a meta-analysis of longitudinal cohort studies, incorporating the effect of BMI mediated through diabetes. The models were calibrated to historical trends in TB epidemiology and mean BMI. We estimated potential changes in TB incidence and mortality between 2015 and 2030 under different scenarios of population nutrition. FINDINGS:Compared with a scenario where mean BMI remained at 2015 levels, if past trends in mean BMI continued then by 2030 TB incidence and mortality would decline by a cumulative 14.7% (95% credible interval: 12.7%-16.7%) and 15.6% (12.5%-19.2%), respectively. In comparison, achieving zero hunger by 2030 would reduce incidence and mortality by 32.0% (20.0%-43.8%) and 37.3% (26.1%-49.6%), respectively. If past trends continued and zero hunger was also achieved, incidence and mortality would be reduced by 38.2% (27.0%-49.1%) and 42.4% (32.1%-53.5%), respectively, equivalent to preventing 20.6 million people developing TB disease and averting 5.4 million TB deaths over 15 years in the 12 high-burden countries. CONCLUSIONS:Nutrition transitions and interventions to end hunger could have a major impact on the future epidemiology of TB in high-burden countries. Investment is urgently required to implement and scale up nutritional interventions.

The unprecedented and drastic emergency responses that accompanied the declaration of COVID-19 as a pandemic have highlighted and intensified mobility injustices worldwide. Most of the global interest in the impact of COVID-19 on mobility patterns has come from developed countries, leaving a gap in literature specifically focused on Africa. This paper aims to fill that gap by examining the effects of government-imposed travel restrictions on people's attitudes and mobility behavior in urban Ghana. Using a combination of data sources, including surveys and photographic evidence, we analyze the spatial variations in mobility patterns during the lockdown. Our findings from statistical analyses and time-lapsed images indicate that many young people, informal sector workers, and individuals living in disadvantaged neighborhoods largely ignored the lockdown order. In contrast, most formal sector employees utilized internet-enabled telecommuting, e-learning opportunities, and telephone communications during the lockdown period. The paper concludes with policy recommendations aimed at enhancing mobility justice for all in the face of future public health crises and social emergencies that may require physical mobility restrictions.

BACKGROUND:Understanding the changing metabolic health burden among children and adolescents is crucial for current and future public health resource allocation in China, particularly given rapid population ageing. We aimed to estimate trends in the metabolic burden in children and adolescents aged 7-18 years from 2000 to 2030, using overweight, obesity, and hypertension as proxy indicators. METHODS:We extracted age, sex, height, weight, and blood pressure data for Han children and adolescents aged 7-18 years, as recorded in five cycles of the Chinese National Surveys on Students Constitution and Health in the years 2000, 2005, 2010, 2014, and 2019. We used demographic indicators reported by the Seventh National Population Census in 2020 to represent the demographic situation in 2019 and UN population estimates and projections for China to derive the national age structure from 2000 to 2030. We calculated the 2019 age-standardised prevalence rates of overweight and obesity, hypertension, comorbid overweight and obesity with hypertension, severe obesity, and severe hypertension. Direct standardisation was applied to adjust for the effect of changes in population structures and derive age-specific prevalence estimates from 2000 to 2030. A population development index that captures demographic trends while accounting for the influence of age structure was calculated from birth rate, death rate, and proportions of the population aged 0-14 years and older than 65 years. Correlation coefficients (r) and corresponding p values for the association between the population development index and metabolic burden were calculated with general linear regression models. Multinomial regressions were applied to model age-specific and sex-specific prevalence rates as a function of time. We used decomposition analysis to evaluate the individual contributions of age-specific prevalence, age distribution, and population growth to the net change in case numbers. FINDINGS:The final analysis of national survey data included 1 106 416 observations. In 2019, the age-standardised prevalence rates were 21·5% (95% CI 21·3-21·7) for overweight and obesity, 16·6% (16·4-16·8) for hypertension, 5·5% (5·4-5·6) for overweight and obesity with hypertension, 1·6% (1·5-1·6) for severe obesity, and 2·1% (2·0-2·2) for severe hypertension. China's population of children and adolescents aged 7-18 years is predicted to decrease from 276 million in 2000 to 181 million in 2030 (-34·4%). Between 2000 and 2030, we estimate increases of 39·0 million (180·6%) cases of overweight and obesity, 7·1 million (131·5%) cases of overweight and obesity with hypertension, 4·3 million (430·0%) cases of severe obesity, and 1·2 million (34·3%) cases of severe hypertension. Between 2000 and 2030, we estimate a slight decrease of 0·3 million (-0·8%) cases of hypertension. A significant negative association between population development index and metabolic burden was observed for 2019 (r=-0·485, p=0·0062) and projected for 2030 (r=-0·417, p=0·020). Decomposition analysis indicated that rising age-specific prevalence is the primary driver of increasing numbers of metabolic cases, partially offset by population decline. INTERPRETATION:In the context of China's declining youth populations, increases in the prevalence, clinical severity, and absolute case numbers of overweight and obesity with hypertension signal a worsening metabolic health burden. Beyond public health policies to shape healthier lifestyle patterns, enhanced efforts are needed to prepare China's primary health-care system and optimise the allocation of paediatric health-care resources. FUNDING:National Key R&D Program of China, National Natural Science Foundation of China, Beijing Natural Science Foundation, Peking University Talent Introduction Program Project, Clinical Medicine Plus X-Young Scholars Project of Peking University, UK Medical Research Council, and the Abdul Latif Jameel Institute for Disease and Emergency Analytics at Imperial College London, funded by a donation from Community Jameel.

Cities in sub-Saharan Africa (SSA) are undergoing significant economic and urban expansion. The rapid urban growth is shaping land use, housing, transportation, and energy for household and commercial use. Consequently, air pollution from diverse local and regional sources and with complex space-time patterns has emerged as a major environmental health concern for cities in SSA. Yet, limited city-scale data are a barrier to climate and health impact assessment as well as policy formulation and evaluation to reduce air pollution. We are implementing and testing the transferability of Pathways to Equitable Health Cities measurement protocol for Accra in Kigali, Rwanda. The protocol is designed to generate rich environmental pollution data in SSA cities. Both Accra and Kigali are representative of the rapid urbanization and economic transformation that are happening across SSA. We have assembled and integrated multiple low-cost, low-power, lightweight sensors that have been validated in prior studies to measure integrated and real-time fine particulate matter (PM2.5), black carbon (BC), and oxides of nitrogen (NO2 and NO) concentrations at city-scale. Initiated in November 2022, our year-long measurement campaign utilizes a network and combination of ‘fixed’ (n=10) and ‘rotating’ (n =120) monitoring sites. The sites represent variety of land uses and emission sources, including background, road traffic, commercial, industrial and residential areas, and neighbourhood socioeconomic classes. The fixed sites are monitored continuously for one year to capture temporal (annual and seasonal) patterns, whereas the rotating sites are monitored for one week (in groups of four per week) to capture spatial variations in the pollutant concentrations. In addition to the air pollutants, we are also collecting data on environmental noise and weather variables (i.e. temperature, relative humidity and wind speed/direction) to aid in the analyses. The Kigali initiative is being implemented in partnership with AIMS-Rwanda and the Rwanda Environment Management Authority (REMA) to promote capacity building within the government. Planned analyses involve the use state-of-the-art models, including spatial statistics, deep/machine learning approaches, to capture highly resolved temporal and spatial variations as well as socioeconomic inequalities in pollution levels across Kigali city and to identify sources and their relative contributions. The data form the basis of future climate change and air pollution forecasting and health impact assessment as well as policy evaluation and emission reduction scenarios in the city.

Cities encompass a mixture of artificial, human, animal, and nature-based sounds, which through long and short-term exposures, can impact on physical and mental health. Yet, most epidemiological research has focused on only transportation noise, leaving a significant gap in understanding the health impacts of other urban sound types, especially in sub-Saharan Africa (SSA). We conducted a large-scale measurement campaign in Accra, Ghana, collecting audio recordings and sound levels from 129 locations between April 2019-June 2020. We classified sound types with a neural network model and then used Random Forest land use regression to predict prevalences of different sound types citywide. We then developed a composite metric integrating sound levels with the prevalence of sound types. Road traffic sounds dominated the urban core, while human and animal sounds were prominent in high-density and peri-urban areas, respectively. Our high-resolution approach provides a comprehensive characterization of the complexity of urban sounds in a major SSA city, paving the way for new epidemiological studies on the health impacts of exposure to diverse sound sources in the future.

In Sub-Saharan African (SSA) cities, elementary school environments may significantly contribute to children’s exposure to environmental pollution, potentially affecting their health, development, and learning. Despite children spending much of their day at school, limited data exists regarding levels, inequalities, and determinants of air and noise pollution in school settings, particularly in rapidly urbanizing regions. As part of the Accra School Health and Environment Study (ASHES), we assessed air and noise pollution in primary schools across the Greater Accra Metropolitan Area, one of SSA’s fastest-growing metropolises, and explored determinants of pollution levels around these schools. We conducted weeklong measurements of fine particulate matter (PM _2.5 ), black carbon (BC), and sound pressure levels in 90 schoolyards (74% public, 26% private). We assessed schoolyard characteristics (surface type, greenness, road proximity) and examined their associations with pollutants using generalized additive models. Additionally, we evaluated 1037 child responses to noise annoyance surveys. Annual equivalent PM _2.5 concentrations exceeded WHO guidelines by 2–13 times (11–65 µ g m ^−3 ). Median noise levels (57 dBA) surpassed Ghana EPA standards at >60% of schools, coinciding with 60% of students reporting high noise annoyance. BC and noise were higher in public and more urban schools. In the most urbanized district, all pollutants were inversely associated with neighborhood socioeconomic status. Lower greenness correlated with higher BC levels; associations with other spatial factors were weak or not statistically significant. These findings underscore the need to reduce air and noise pollution at urban SSA schools and promote healthier, quieter environments that support learning and development.