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.

Systematic assessments of climate change adaptation are critical for monitoring progress and planning effectively, but current approaches are limited in their scope, accuracy, and relevance to local contexts. Here, we present an improved approach using coproduction to quantitively assess adaptation based on local knowledge and priorities. This is applied to locally led adaptation (LLA) to flood risk in Tamale, Ghana, to provide the first quantitative assessments of this increasingly common adaptation practice. Through a multi-year process, including community marble distribution, focus groups, and household surveys, 11 LLA solutions were assessed. Assessments were based on adaptation success criteria that mattered most to local communities and included important considerations that are commonly missing from technical assessments, including multiple risk-reduction mechanisms, equity, sustainability, and co-impacts. Community-based and behavioural LLA solutions, such as collective action and tree planting, were deemed most effective, whilst structural and technical solutions were ranked lower. By integrating these assessments into a flood risk model, we show that LLA approaches significantly reduced flood risk overall but did not address existing inequalities. Our results showcase the potential of coproduction to increase the scope and robustness of adaptation assessments and highlight practical challenges of delivering on the LLA principles in real-world settings.

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.

Current climate change adaptation assessments are limited in their scope, accuracy, and relevance to local contexts. Here, we present an improved assessment approach using coproduction, applied to locally led adaptation (LLA) to flood risk in Tamale, Ghana. Through a multi-year process, including participatory ranking, focus groups, and household surveys, 11 solutions were assessed. Assessments considered multiple risk-reduction mechanisms, equity, sustainability, and co-impacts. Community-based and behavioural solutions, such as collective action and tree planting, were deemed most effective, whilst structural and technical solutions were ranked lower. By integrating these assessments into a flood risk model, we show that LLA approaches significantly reduced flood risk overall but did not address existing inequalities. Our results showcase the potential of coproduction to increase the scope and robustness of adaptation assessments and highlight practical challenges of delivering on the LLA principles in real-world settings.

Principles of dense, mixed-use environments and pedestrianisation are influential in urban planning practice worldwide. A key outcome espoused by these principles is generating "urban vitality", the continuous use of street sidewalk infrastructure throughout the day, to promote safety, economic viability and attractiveness of city neighbourhoods. Vitality is hypothesised to arise from a nearby mixture of primary uses, short blocks, density of buildings and population and a diversity in the age and condition of surrounding buildings. To investigate this claim, we use a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the city of Accra, Ghana. We developed a measure of urban vitality for each location based on the coefficient of variation in pedestrian volume over time in our images, obtained from counts of people identified using object detection. We also construct measures of "generators of diversity": mixed-use intensity, building, block and population density, as well as diversity in the age of buildings, using data that are available across multiple cities and perform bivariate and multivariate regressions of our urban vitality measure against variables representing generators of diversity to test the latter's association with vitality. We find that two or more unique kinds of amenities accessible within a five-minute walk from a given location, as well as the density of buildings (of varying ages and conditions) and short blocks, are associated with more even footfall throughout the day. Our analysis also indicates some potential negative trade-offs from dense and mixed-use neighbourhoods, such as being associated with more continuous road traffic throughout the day. Our methodological approach is scalable and adaptable to different modes of image data capture and can be widely adopted in other cities worldwide.

Spousal age differences are highest in Sub-Saharan Africa, where trends in age at first marriage indicate an increase for both men and women. However, the net outcome for spousal age difference is difficult to predict without explicit analysis of these distributions. This study examines differentials in spousal age for women in first union. Further, it examines differences within population sub-groups and across countries, focusing on the influence of age at marriage and educational attainment. The analysis pools 144 survey datasets from the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) conducted between 1980 and 2020 in 34 countries. OLS regression analysis was used to predict the spousal age difference at marriage with five-year cohorts used to study time trends. The analysis finds a decline of 1.7 years in the spousal age difference at first marriage between the earliest (1980-1984) and most recent (2014-2019), indicating that there has been minimal reduction in age at first marriage over four decades, despite rising ages at marriage for females. Increasing age at first marriage and educational attainment of women partly explain the decline observed over time.

Sub-Saharan Africa and other developing regions have urbanized extensively, leading to complex urban features with varying presence and types of roads, buildings and vegetation. We use a novel hierarchical deep learning framework and high-resolution satellite images to characterize multidimensional urban environments in multiple cities. Application of the model to images from Accra, Dakar, and Dar es Salaam identified areas with analogous patterns of building density, roads and vegetation. These included dense settlements within the metropolitan boundary (20-54% of urban area), peri-urban intermix of natural and built environment (21-44%), natural vegetation (9-13%) and agricultural land (8-15%). Kigali, with its mountainous geography and post-colonial expansion, exhibited unique urban characteristics including a sparser urban core (23%) and significant wildland-urban intermix (19% of vegetation). Other notable clusters were water (2% of area of Accra) and empty land (8-10% of Accra and Dakar). Our results demonstrate that unlabeled satellite images with unsupervised deep learning can be used for consistent and coherent near-real-time urban monitoring, particularly in regions where traditional data are scarce.

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.

Residents of urban informal settlements are among the most at-risk of climate change-exacerbated hazards. Yet, traditional approaches to adaptation have failed to reduce risk sustainably and equitably. In contrast, transformative adaptation recognizes the inextricable nature of complex climate risk and social inequality, embedding principles of social justice in pathways to societal resilience. Its potential for impact may be greatest in informal settlements, but its application in this context introduces a new set of challenges and remains largely aspirational. To address this missed opportunity, in this focus article we provide clarity on how transformative adaptation can manifest in informal settlements. Although context-dependency precludes the formulation of specific guidelines, we identify four principles which are foundational to its deployment in these settings. Acknowledging constraints, we define levels of achievement of the principles and suggest how they might be reached in practice. Achieving transformative adaptation in informal settlements is complex, but we argue that it is already achievable and could represent a prime opportunity to accelerate the rate of adaptation to build a climate resilient society.

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.

Local social and ecological contexts influence the experience of poverty and inequality in a number of ways that include shaping livelihood opportunities and determining the available infrastructure, services and environmental resources, as well as people’s capacity to use them. The metrics used to define poverty and inequality function to guide local and international development policy but how these interact with the local ecological contexts is not well explored. We use a social-ecological systems (SES) lens to empirically examine how context relates to various measures of human well-being at a national scale in Ghana. Using a novel dataset constructed from the 100% Ghanian Census, we examine poverty and inequality at a fine population level across and within multiple dimensions of well-being. First, we describe how well-being varies within different Ghanian SES contexts. Second, we ask whether monetary consumption acts a good indicator for well-being across these contexts. Third, we examine measures of inequality in various metrics across SES types. We find consumption distributions differ across SES types and are markedly distinct from regional distributions based on political boundaries. Rates of improved well-being are positively correlated with consumption levels in all SES types, but correlations are weaker in less-developed contexts like, rangelands and wildlands. Finally, while consumption inequality is quite consistent across SES types, inequality in other measures of living standards (housing, water, sanitation, etc) increases dramatically in SES types as population density and infrastructural development decreases. We advocate that SES types should be recognized as distinct contexts in which actions to mitigate poverty and inequality should better incorporate the challenges unique to each.

Road traffic has become the leading source of air pollution in fast-growing sub-Saharan African cities. Yet, there is a dearth of robust city-wide data for understanding space-time variations and inequalities in combustion related emissions and exposures. We combined nitrogen dioxide (NO2) and nitric oxide (NO) measurement data from 134 locations in the Greater Accra Metropolitan Area (GAMA), with geographical, meteorological, and population factors in spatio-temporal mixed effects models to predict NO2 and NO concentrations at fine spatial (50 m) and temporal (weekly) resolution over the entire GAMA. Model performance was evaluated with 10-fold cross-validation (CV), and predictions were summarized as annual and seasonal (dusty [Harmattan] and rainy [non-Harmattan]) mean concentrations. The predictions were used to examine population distributions of, and socioeconomic inequalities in, exposure at the census enumeration area (EA) level. The models explained 88% and 79% of the spatiotemporal variability in NO2 and NO concentrations, respectively. The mean predicted annual, non-Harmattan and Harmattan NO2 levels were 37 (range: 1-189), 28 (range: 1-170) and 50 (range: 1-195) mu g m(-3), respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] mu g m(-3)). Road traffic was the dominant factor for both pollutants, but NO2 had higher spatial heterogeneity than NO. For both pollutants, the levels were substantially higher in the city core, where the entire population (100%) was exposed to annual NO2 levels exceeding the World Health Organization (WHO) guideline of 10 mu g m(-3). Significant disparities in NO2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 mu g m(-3) higher compared with the wealthiest (p < 0.001). The results showed the important role of road traffic emissions in air pollution concentrations in the GAMA, which has major implications for the health of the city's poorest residents. These data could support climate and health impact assessments as well as policy evaluations in the city.

Climate change is changing physical and social risks facing people in African cities. Emerging awareness is beginning to stimulate a wide range of adaptive responses. These responses are playing out in a complex institutional and governance context which shape their effectiveness and legitimacy. Employing a hybrid governance approach, we investigate the development of flooding and flood protection in the context of urban development in Tamale, Ghana. We argue that the interplay between traditional and state-based authority shapes the market for land, the regulation of land use and the provision of urban services, including flood protection. Hybrid governance influences the types of knowledge applied to urban problem-solving, the legitimacy of choices made, the human and other resources that can be deployed in building community resilience and the willingness to act in the provision of public goods by communities. We suggest how the existing hybrid governance setting could be strengthened to achieve more effective and legitimate adaptation to dynamic flood risks under climate change in Tamale, with lessons for other West African contexts.

Globally, millions of individuals access services and opportunities on a daily basis using different modes of motorized and non-motorized transportation. However, in the global south, the role of public transport in providing access to services is relatively under-researched due to non-functional public transport services and poor infrastructure. This paper uses data from the Greater Accra Metropolitan Area (GAMA) to empirically contribute to the discourse on how public transport availability varies across different residential locations and assess if there is equitable access to Public Transport Infrastructure and Services (PTIS) across urban and peri-urban areas. A household questionnaire survey was designed to collect data on public transport access based on socioeconomic, socio-cultural, personal preferences/experiences and residential location in the Greater Accra Metropolitan Area (GAMA). A sample size of 1340 respondents, consisting of males and females between the ages of 18 and 70 residing in GAMA, was achieved. The paper also used data from the public and open databases. The Three-Step Floating Catchment Area (3SFCA) and geospatial methods were used to estimate spatial accessibility.The study found a significantly high disparity in accessibility to public transport in the Greater Accra Metropolitan Area (GAMA). There is also significant spatial inequality in the level of access to Public Transport Infrastructure and Services (PTIS) in GAMA. The study revealed that the planning and provisioning of public transport infrastructure in GAMA has left areas with inequitable access to transport services. There is the need for increased investment in public transport infrastructure in EAs where Public Transport Infrastructure and Services (PTIS) were found to be very low or low in GAMA.

Adaptation is essential to mitigate the effects of climate change, such as increasing flood risk. In response to widespread maladaptation, citizen-led approaches are increasingly championed, whereby people on the frontline of climate change determine their own objectives and strategies of adaptation. Enabling equitable and effective citizen-led adaptation requires an understanding of the barriers for different groups of people but this is currently lacking, especially in low- and middle-income countries. Using responses to a co-produced household survey (n = 286) in Tamale, Ghana, we show that barriers to citizen-led adaptation interventions (n = 11) differ between households which we relate to important components of adaptive capacity. Overall, awareness, education, and networks are the most important barriers, but resources and time are important for poor households of fewer members. Barriers also differ between interventions and overall structural interventions are preferred over behavioural. This work can inform policies and actions to support effective and equitable citizen-led adaptation.