Place, displacement, and health-seeking behaviour among the Ugandan Batwa: A qualitative study.

For many Indigenous Peoples, relationships to the land are inherent in identity and culture, and to all facets of health and wellbeing, physically, emotionally, psychologically, and spiritually. The Batwa are Indigenous Peoples of rural, southwest Uganda who have experienced tremendous social and economic upheaval, due to relatively recent forced displacement and land dispossession. This loss of physical connection to their ancestral lands has significantly impacted Batwa health, and also affected available healthcare options for Batwa. This exploratory study (1) identified and characterized factors that influence Batwa health-seeking behaviour, using acute gastrointestinal illness, a critical public health issue, as a focal point for analysis; and (2) explored possible intersections between the Batwa's connection to place-and displacement-and their health-seeking behaviour for acute gastrointestinal illness. Twenty focus group discussions, stratified by gender, were conducted in ten Batwa settlements in Kanungu District, Uganda and eleven semi-structured interviews were conducted with primary healthcare workers, community health coordinators, clinical officers, and development program coordinators. Qualitative data were thematically analyzed using a constant comparative method. Batwa identified several significant motivators to engage with Indigenous and/or biomedical forms of healthcare, including transition to life outside the forest and their reflections on health in the forest; 'intellectual access' to care and generational knowledge-sharing on the use of Indigenous medicines; and Batwa identity and way of life. These nuanced explanations for health-seeking behaviour underscore the significance of place-and displacement-to Batwa health and wellbeing, and its relationship to their health-seeking behaviour for acute gastrointestinal illness. As such, the results of this study can be used to inform healthcare practice and policy and support the development of a culturally- and contextually-appropriate healthcare system, as well as to reduce the burden of acute gastrointestinal illness among Batwa.

High-resolution spatial and spatiotemporal modelling of air pollution using fixed site and mobile monitoring in a Canadian city.

The development of high-resolution spatial and spatiotemporal models of air pollutants is essential for exposure science and epidemiological applications. While fixed-site sampling has conventionally provided input data for statistical predictive models, the evolving mobile monitoring method offers improved spatial resolution, ideal for measuring pollutants with high spatial variability such as ultrafine particles (UFP). The Quebec Air Pollution Exposure and Epidemiology (QAPEE) study measured and modelled the spatial and spatiotemporal distributions of understudied pollutants, such as UFPs, black carbon (BC), and brown carbon (BrC), along with fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in Quebec City, Canada. We conducted a combined fixed-site (NO2 and O3) and mobile monitoring (PM2.5, BC, BrC, and UFPs) campaign over 10-months. Mobile monitoring routes were monitored on a weekly basis between 8am-10am and designed using location/allocation modelling. Seasonal fixed-site sampling campaigns captured continuous 24-h measurements over two-week periods. Generalized Additive Models (GAMs), which combined data on pollution concentrations with spatial, temporal, and spatiotemporal predictor variables were used to model and predict concentration surfaces. Annual models for PM2.5, NO2, O3 as well as seven of the smallest size fractions in the UFP range, had high out of sample predictive accuracy (range r2: 0.54-0.86). Varying spatial patterns were observed across UFP size ranges measured as Particle Number Counts (PNC). The monthly spatiotemporal models for PM2.5 (r2 = 0.49), BC (r2 = 0.27), BrC (r2 = 0.29), and PNC (r2 = 0.49) had moderate or moderate-low out of sample predictive accuracy. We conducted a sensitivity analysis and found that the minimum number of 'n visits' (mobile monitoring sessions) required to model annually representative air pollution concentrations was between 24 and 32 visits dependent on the pollutant. This study provides a single source of exposure models for a comprehensive set of air pollutants in Quebec City, Canada. These exposure models will feed into epidemiological research on the health impacts of ambient UFPs and other pollutants.

Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO2 concentrations in Accra, Ghana.

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) µg m-3, respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] µg 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 µg m-3. Significant disparities in NO2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 µg 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.

Beyond here and now: Evaluating pollution estimation across space and time from street view images with deep learning.

Advances in computer vision, driven by deep learning, allows for the inference of environmental pollution and its potential sources from images. The spatial and temporal generalisability of image-based pollution models is crucial in their real-world application, but is currently understudied, particularly in low-income countries where infrastructure for measuring the complex patterns of pollution is limited and modelling may therefore provide the most utility. We employed convolutional neural networks (CNNs) for two complementary classification models, in both an end-to-end approach and as an interpretable feature extractor (object detection), to estimate spatially and temporally resolved fine particulate matter (PM2.5) and noise levels in Accra, Ghana. Data used for training the models were from a unique dataset of over 1.6 million images collected over 15 months at 145 representative locations across the city, paired with air and noise measurements. Both end-to-end CNN and object-based approaches surpassed null model benchmarks for predicting PM2.5 and noise at single locations, but performance deteriorated when applied to other locations. Model accuracy diminished when tested on images from locations unseen during training, but improved by sampling a greater number of locations during model training, even if the total quantity of data was reduced. The end-to-end models used characteristics of images associated with atmospheric visibility for predicting PM2.5, and specific objects such as vehicles and people for noise. The results demonstrate the potential and challenges of image-based, spatiotemporal air pollution and noise estimation, and that robust, environmental modelling with images requires integration with traditional sensor networks.

Spatial assessment of the attributable burden of disease due to transportation noise in England.

BACKGROUND: Noise pollution from transportation is one of the leading contributors to the environmental disease burden in Europe. We provide a novel assessment of spatial variations of these health impacts within a country, using England as an example. METHODS: We estimated the burden of annoyance (highly annoyed), sleep disturbance (highly sleep disturbed), ischemic heart disease (IHD), stroke, and diabetes attributable to long-term transportation noise exposures in England for the adult population in 2018 down to local authority level (average adult population: 136,000). To derive estimates, we combined literature-informed exposure-response relationships, with population data on noise exposures, disease, and mortalities. Long-term average noise exposures from road, rail and aircraft were sourced from strategic noise mapping, with a lower exposure threshold of 50 dB (decibels) Lden and Lnight. RESULTS: 40 %, 4.5 % and 4.8 % of adults in England were exposed to road, rail, and aircraft noise exceeding 50 dB Lden. We estimated close to a hundred thousand (∼97,000) disability adjusted life years (DALY) lost due to road-traffic, ∼13,000 from railway, and âˆ¼ 17,000 from aircraft noise. This excludes some noise-outcome pairs as there were too few studies available to provide robust exposure-response estimates. Annoyance and sleep disturbance accounted for the majority of the DALYs, followed by strokes, IHD, and diabetes. London, the South East, and North West regions had the greatest number of road-traffic DALYs lost, while 63 % of all aircraft noise DALYs were found in London. The strategic noise mapping did not include all roads, which may still have significant traffic flows. In sensitivity analyses using modelled noise from all roads in London, the DALYs were 1.1x to 2.2x higher. CONCLUSION: Transportation noise exposures contribute to a significant and unequal environmental disease burden in England. Omitting minor roads from the noise exposure modelling leads to underestimation of the disease burden.

High-resolution patterns and inequalities in ambient fine particle mass (PM2.5) and black carbon (BC) in the Greater Accra Metropolis, Ghana.

Growing cities in sub-Saharan Africa (SSA) experience high levels of ambient air pollution. However, sparse long-term city-wide air pollution exposure data limits policy mitigation efforts and assessment of the health and climate effects. In the first study of its kind in West Africa, we developed high resolution spatiotemporal land use regression (LUR) models to map fine particulate matter (PM2.5) and black carbon (BC) concentrations in the Greater Accra Metropolitan Area (GAMA), one of the fastest sprawling metropolises in SSA. We conducted a one-year measurement campaign covering 146 sites and combined these data with geospatial and meteorological predictors to develop separate Harmattan and non-Harmattan season PM2.5 and BC models at 100 m resolution. The final models were selected with a forward stepwise procedure and performance was evaluated with 10-fold cross-validation. Model predictions were overlayed with the most recent census data to estimate the population distribution of exposure and socioeconomic inequalities in exposure at the census enumeration area level. The fixed effects components of the models explained 48-69 % and 63-71 % of the variance in PM2.5 and BC concentrations, respectively. Spatial variables related to road traffic and vegetation explained the most variability in the non-Harmattan models, while temporal variables were dominant in the Harmattan models. The entire GAMA population is exposed to PM2.5 levels above the World Health Organization guideline, including even the Interim Target 3 (15 µg/m3), with the highest exposures in poorer neighborhoods. The models can be used to support air pollution mitigation policies, health, and climate impact assessments. The measurement and modelling approach used in this study can be adapted to other African cities to bridge the air pollution data gap in the region.

Perceived social support: A study of genetic counseling graduate students in the United States (US) and Canada.

Social support is described as having positive psychological and physical outcomes and offers some protective benefits against mental illness. However, research has not addressed social support for genetic counseling graduate students, although this population is prone to elevated levels of stress in addition to field-specific phenomena like compassion fatigue and burnout. Therefore, an online survey was distributed to genetic counseling students in accredited programs in the United States and Canada to synthesize information about (1) demographic information, (2) self-identified sources of support, and (3) the availability of a strong support network. In total, 238 responses were included in the analysis, yielding a mean social support score of 3.84 on a 5-point scale, where higher scores indicate increased social support. The identification of friends or classmates as forms of social support significantly increased social support scores (p < 0.001; p = 0.006, respectively). There was also a positive correlation between increased social support scores and the number of social support outlets (p = 0.01). Subgroup analysis focused on potential differences in social support for racially/ethnically underrepresented participants (comprising less than 22% of respondents), revealing that this population identified friends as a form of social support significantly less often than their White counterparts; and mean social support scores were also significantly lower. Our study underscores the importance of classmates as a source of social support for genetic counseling graduate students while uncovering discrepancies that exist in social support sources between White and underrepresented students. Ultimately, stakeholders in genetic counseling student success should foster a community and culture of support within a training program (whether traditionally in-person or online) to encourage success among all students.

Environmental noise and health in low-middle-income-countries: A systematic review of epidemiological evidence.

Evidence of the health impacts from environmental noise has largely been drawn from studies in high-income countries, which has then been used to inform development of noise guidelines. It is unclear whether findings in high-income countries can be readily translated into policy contexts in low-middle-income-countries (LMICs). We conducted this systematic review to summarise noise epidemiological studies in LMICs. We conducted a literature search of studies in Medline and Web of Science published during 2009-2021, supplemented with specialist journal hand searches. Screening, data extraction, assessment of risk of bias as well as overall quality and strength of evidence were conducted following established guidelines (e.g. Navigation Guide). 58 studies were identified, 53% of which were from India, China and Bulgaria. Most (92%) were cross-sectional studies. 53% of studies assessed noise exposure based on fixed-site measurements using sound level meters and 17% from propagation-based noise models. Mean noise exposure among all studies ranged from 48 to 120 dB (Leq), with over half of the studies (52%) reporting the mean between 60 and 80 dB. The most studied health outcome was noise annoyance (43% of studies), followed by cardiovascular (17%) and mental health outcomes (17%). Studies generally reported a positive (i.e. adverse) relationship between noise exposure and annoyance. Some limited evidence based on only two studies showing that long-term noise exposure may be associated with higher prevalence of cardiovascular outcomes in adults. Findings on mental health outcomes were inconsistent across the studies. Overall, 4 studies (6%) had "probably low", 18 (31%) had "probably high" and 36 (62%) had "high" risk of bias. Quality of evidence was rated as 'low' for mental health outcomes and 'very low' for all other outcomes. Strength of evidence for each outcome was assessed as 'inadequate', highlighting high-quality epidemiological studies are urgently needed in LMICs to strengthen the evidence base.

Characterisation of urban environment and activity across space and time using street images and deep learning in Accra.

The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy.

Spatial modelling and inequalities of environmental noise in Accra, Ghana.

Noise pollution is a growing environmental health concern in rapidly urbanizing sub-Saharan African (SSA) cities. However, limited city-wide data constitutes a major barrier to investigating health impacts as well as implementing environmental policy in this growing population. As such, in this first of its kind study in West Africa, we measured, modelled and predicted environmental noise across the Greater Accra Metropolitan Area (GAMA) in Ghana, and evaluated inequalities in exposures by socioeconomic factors. Specifically, we measured environmental noise at 146 locations with weekly (n = 136 locations) and yearlong monitoring (n = 10 locations). We combined these data with geospatial and meteorological predictor variables to develop high-resolution land use regression (LUR) models to predict annual average noise levels (LAeq24hr, Lden, Lday, Lnight). The final LUR models were selected with a forward stepwise procedure and performance was evaluated with cross-validation. We spatially joined model predictions with national census data to estimate population levels of, and potential socioeconomic inequalities in, noise levels at the census enumeration-area level. Variables representing road-traffic and vegetation explained the most variation in noise levels at each site. Predicted day-evening-night (Lden) noise levels were highest in the city-center (Accra Metropolis) (median: 64.0 dBA) and near major roads (median: 68.5 dBA). In the Accra Metropolis, almost the entire population lived in areas where predicted Lden and night-time noise (Lnight) surpassed World Health Organization guidelines for road-traffic noise (Lden <53; and Lnight <45). The poorest areas in Accra also had significantly higher median Lden and Lnight compared with the wealthiest ones, with a difference of ∼5 dBA. The models can support environmental epidemiological studies, burden of disease assessments, and policies and interventions that address underlying causes of noise exposure inequalities within Accra.

'We don't use the same ways to treat the illness:' A qualitative study of heterogeneity in health-seeking behaviour for acute gastrointestinal illness among the Ugandan Batwa.

The Batwa (Twa), an Indigenous People of southwest Uganda, were evicted from their ancestral forest lands in 1991 due to establishment of the Bwindi Impenetrable Forest. This land dispossession forced Batwa to transition from a semi-nomadic, hunting-gathering livelihood to an agricultural livelihood; eliminated access to Indigenous food, medicines, and shelter; and shifted their healthcare options. Therefore, this exploratory study investigated why Batwa choose Indigenous or biomedical treatment, or no treatment, when experiencing acute gastrointestinal illness. Ten gender-stratified focus groups were conducted in five Batwa settlements in Kanungu District, Uganda (n = 63 participants), alongside eleven semi-structured interviews (2014). Qualitative data were analysed thematically, using a constant comparative method. Batwa emphasised that health-seeking behaviour for acute gastrointestinal illness was diverse: some Batwa used only Indigenous or biomedical healthcare, while others preferred a combination, or no healthcare. Physical and economic access to care, and also perceived efficacy and quality of care, influenced their healthcare decisions. This study provides insight into the Kanungu District Batwa's perceptions of biomedical and Indigenous healthcare, and barriers they experience to accessing either. This study is intended to inform public health interventions to reduce their burden of acute gastrointestinal illness and ensure adequate healthcare, biomedical or Indigenous, for Batwa.

Nitrogen oxides (NO and NO2) pollution in the Accra metropolis: Spatiotemporal patterns and the role of meteorology.

Economic and urban development in sub-Saharan Africa (SSA) may be shifting the dominant air pollution sources in cities from biomass to road traffic. Considered as a marker for traffic-related air pollution in cities, we conducted a city-wide measurement of NOx levels in the Accra Metropolis and examined their spatiotemporal patterns in relation to land use and meteorological factors. Between April 2019 to June 2020, we collected weekly integrated NOx (n = 428) and NO2 (n = 472) samples at 10 fixed (year-long) and 124 rotating (week-long) sites. Data from the same time of year were compared to a previous study (2006) to assess changes in NO2 concentrations. NO and NO2 concentrations were highest in commercial/business/industrial (66 and 76 µg/m3, respectively) and high-density residential areas (47 and 59 µg/m3, respectively), compared with peri-urban locations. We observed annual means of 68 and 70 µg/m3 for NO and NO2, and a clear seasonal variation, with the mean NO2 of 63 µg/m3 (non-Harmattan) increased by 25-56% to 87 µg/m3 (Harmattan) across different site types. The NO2/NOx ratio was also elevated by 19-28%. Both NO and NO2 levels were associated with indicators of road traffic emissions (e.g. distance to major roads), but not with community biomass use (e.g. wood and charcoal). We found strong correlations between both NO2 and NO2/NOx and mixing layer depth, incident solar radiation and water vapor mixing ratio. These findings represent an increase of 25-180% when compared to a small study conducted in two high-density residential neighborhoods in Accra in 2006. Road traffic may be replacing community biomass use (major source of fine particulate matter) as the prominent source of air pollution in Accra, with policy implication for growing cities in SSA.

What you see is what you breathe? Estimating air pollution spatial variation using street level imagery.

High spatial resolution information on urban air pollution levels is unavailable in many areas globally, partially due to high input data needs of existing estimation approaches. Here we introduce a computer vision method to estimate annual means for air pollution levels from street level images. We used annual mean estimates of NO2 and PM2.5 concentrations from locally calibrated models as labels from London, New York, and Vancouver to allow for compilation of a sufficiently large dataset (~250k images for each city). Our experimental setup is designed to quantify intra and intercity transferability of image-based model estimates. Performances were high and comparable to traditional land-use regression (LUR) and dispersion models when training and testing on images from the same city (R2 values between 0.51 and 0.95 when validated on data from ground monitoring stations). Like LUR models, transferability of models between cities in different geographies is more difficult. Specifically, transferability between the three cities i.e., London, New York, and Vancouver, which have similar pollution source profiles were moderately successful (R2 values between zero and 0.67). Comparatively, performances when transferring models trained on these cities with very different source profiles i.e., Accra in Ghana and Hong Kong were lower (R2 between zero and 0.21) suggesting the need for local calibration with local calibration using additional measurement data from cities that share similar source profiles.

Small area variations and factors associated with blood pressure and body-mass index in adult women in Accra, Ghana: Bayesian spatial analysis of a representative population survey and census data.

BACKGROUND: Body-mass index (BMI) and blood pressure (BP) levels are rising in sub-Saharan African cities, particularly among women. However, there is very limited information on how much they vary within cities, which could inform targeted and equitable health policies. Our study aimed to analyse spatial variations in BMI and BP for adult women at the small area level in the city of Accra, Ghana. METHODS AND FINDINGS: We combined a representative survey of adult women's health in Accra, Ghana (2008 to 2009) with a 10% random sample of the national census (2010). We applied a hierarchical model with a spatial term to estimate the associations of BMI and systolic blood pressure (SBP) and diastolic blood pressure (DBP) with demographic, socioeconomic, behavioural, and environmental factors. We then used the model to estimate BMI and BP for all women in the census in Accra and calculated mean BMI, SBP, and DBP for each enumeration area (EA). BMI and/or BP were positively associated with age, ethnicity (Ga), being currently married, and religion (Muslim) as their 95% credible intervals (95% CrIs) did not include zero, while BP was also negatively associated with literacy and physical activity. BMI and BP had opposite associations with socioeconomic status (SES) and alcohol consumption. In 2010, 26% of women aged 18 and older had obesity (BMI ≥ 30 kg/m2), and 21% had uncontrolled hypertension (SBP ≥ 140 and/or DBP ≥ 90 mm Hg). The differences in mean BMI and BP between EAs at the 10th and 90th percentiles were 2.7 kg/m2 (BMI) and in BP 7.9 mm Hg (SBP) and 4.8 mm Hg (DBP). BMI was generally higher in the more affluent eastern parts of Accra, and BP was higher in the western part of the city. A limitation of our study was that the 2010 census dataset used for predicting small area variations is potentially outdated; the results should be updated when the next census data are available, to the contemporary population, and changes over time should be evaluated. CONCLUSIONS: We observed that variation of BMI and BP across neighbourhoods within Accra was almost as large as variation across countries among women globally. Localised measures are needed to address this unequal public health challenge in Accra.

Personal exposure to PM2.5 of indoor and outdoor origin in two neighboring Chinese communities with contrasting household fuel use patterns.

The Chinese government is replacing high-polluting, household coal heating stoves with electricity- and natural gas-powered heaters to improve ambient air quality. The evaluation of the intervention impact on outdoor PM2.5 and personal exposure in community level are in their initial stages. We compared outdoor air pollution and personal exposure in two neighboring communities (~2 km) in northern China with contrasting household fuel use patterns: one where residents primarily used coal for cooking and heating ("coal village") and one where natural gas was the dominant fuel ("gas village"). We collected 24-h outdoor gravimetric PM2.5 samples in each village and concurrently measured 48-h integrated exposures among 71 participants from 41 and 30 households in the coal and gas villages, respectively. PM2.5 samples were analyzed for mass and chemical composition. Daily outdoor PM2.5 concentrations in the coal village (mean ± standard deviation: 109 ± 41 µg/m3) were, on average, 1.3 ± 0.2 times higher than in the gas village (88 ± 38 µg/m3). However, personal PM2.5 exposures were lower in the coal village (116 ± 121 µg/m3 versus 156 ± 106 µg/m3). PM2.5 species that can serve as tracers for residential coal combustion (e.g., S, Se, Zn, Pb, etc.) and dust (Al, Ca, Mg, Fe, Si and Ti) were higher in the coal village, and the proportion of personal PM2.5 of outdoor origin in the coal village was nearly 2 times higher than the gas village. Our results suggest that ambient PM2.5 and its chemical composition can vary at relatively small spatial scales and may reflect community-level differences in the household energy use. However, personal PM2.5 exposures may not mirror between-village differences in outdoor air pollution if only PM2.5 mass is evaluated. Individual chemical composition of PM2.5 exposure can provide important insight in future studies on the effectiveness of source-targeted air quality interventions.

Spatial-temporal patterns of ambient fine particulate matter (PM2.5) and black carbon (BC) pollution in Accra.

Sub-Saharan Africa (SSA) is rapidly urbanizing, and ambient air pollution has emerged as a major environmental health concern in growing cities. Yet, effective air quality management is hindered by limited data. We deployed robust, low-cost and low-power devices in a large-scale measurement campaign and characterized within-city variations in fine particulate matter (PM2.5) and black carbon (BC) pollution in Accra, Ghana. Between April 2019 and June 2020, we measured weekly gravimetric (filter-based) and minute-by-minute PM2.5 concentrations at 146 unique locations, comprising of 10 fixed (∼1 year) and 136 rotating (7 day) sites covering a range of land-use and source influences. Filters were weighed for mass, and light absorbance (10-5m-1) of the filters was used as proxy for BC concentration. Year-long data at four fixed sites that were monitored in a previous study (2006-2007) were compared to assess changes in PM2.5 concentrations. The mean annual PM2.5 across the fixed sites ranged from 26 µg m-3 at a peri-urban site to 43 µg m-3 at a commercial, business, and industrial (CBI) site. CBI areas had the highest PM2.5 levels (mean: 37 µg m-3), followed by high-density residential neighborhoods (mean: 36 µg m-3), while peri-urban areas recorded the lowest (mean: 26 µg m-3). Both PM2.5 and BC levels were highest during the dry dusty Harmattan period (mean PM2.5: 89 µg m-3) compared to non-Harmattan season (mean PM2.5: 23 µg m-3). PM2.5 at all sites peaked at dawn and dusk, coinciding with morning and evening heavy traffic. We found about a 50% reduction (71 vs 37 µg m-3) in mean annual PM2.5 concentrations when compared to measurements in 2006-2007 in Accra. Ambient PM2.5 concentrations in Accra may have plateaued at levels lower than those seen in large Asian megacities. However, levels are still 2- to 4-fold higher than the WHO guideline. Effective and equitable policies are needed to reduce pollution levels and protect public health.

Space-time characterization of community noise and sound sources in Accra, Ghana.

Urban noise pollution is an emerging public health concern in growing cities in sub-Saharan Africa (SSA), but the sound environment in SSA cities is understudied. We leveraged a large-scale measurement campaign to characterize the spatial and temporal patterns of measured sound levels and sound sources in Accra, Ghana. We measured sound levels and recorded audio clips at 146 representative locations, involving 7-days (136 locations) and 1-year measurements between 2019 and 2020. We calculated metrics of noise levels and intermittency and analyzed audio recordings using a pre-trained neural network to identify sources. Commercial, business, and industrial areas and areas near major roads had the highest median daily sound levels (LAeq24hr: 69 dBA and 72 dBA) and the lowest percentage of intermittent sound; the vice-versa was found for peri urban areas. Road-transport sounds dominated the overall sound environment but mixtures of other sound sources, including animals, human speech, and outdoor music, dominated in various locations and at different times. Environmental noise levels in Accra exceeded both international and national health-based guidelines. Detailed information on the acoustical environmental quality (including sound levels and types) in Accra may guide environmental policy formulation and evaluation to improve the health of urban residents.

A feasibility study of metabolic phenotyping of dried blood spot specimens in rural Chinese women exposed to household air pollution.

BACKGROUND: Exposure-response studies and policy evaluations of household air pollution (HAP) are limited by current methods of exposure assessment which are expensive and burdensome to participants. METHODS: We collected 152 dried blood spot (DBS) specimens during the heating and non-heating seasons from 53 women who regularly used biomass-burning stoves for cooking and heating. Participants were enrolled in a longitudinal study in China. Untargeted metabolic phenotyping of DBS were generated using ultra-high performance liquid chromatography coupled with mass spectrometry to exemplify measurement precision and assessment for feasibility to detect exposure to HAP, evaluated by season (high pollution vs. low pollution) and measured personal exposure to fine particulate matter <2.5 µm diameters (PM2.5) and black carbon (BC) in the 48-h prior to collecting the DBS specimen. RESULTS: Metabolites e.g., amino acids, acyl-carnitines, lyso-phosphorylcholines, sphinganine, and choline were detected in the DBS specimens. Our approach is capable of detecting the differences in personal exposure to HAP whilst showing high analytical reproducibility, coefficient of variance (CV) <15%, meeting the U.S. Food and Drug Administration criteria. CONCLUSIONS: Our results provide a proof of principle that high-resolution metabolic phenotypic data can be generated using a simple DBS extraction method thus suitable for exposure studies in remote, low-resource settings where the collection of serum and plasma is logistically challenging or infeasible. The analytical run time (19 min/specimen) is similar to most global phenotyping methods and therefore suitable for large-scale application.

High-resolution spatiotemporal measurement of air and environmental noise pollution in Sub-Saharan African cities: Pathways to Equitable Health Cities Study protocol for Accra, Ghana.

INTRODUCTION: Air and noise pollution are emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data are a barrier to the formulation and evaluation of policies to reduce air and noise pollution. METHODS AND ANALYSIS: We designed a year-long measurement campaign to characterise air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area (GAMA), Ghana. Our design uses a combination of fixed (year-long, n=10) and rotating (week-long, n =~130) sites, selected to represent a range of land uses and source influences (eg, background, road traffic, commercial, industrial and residential areas, and various neighbourhood socioeconomic classes). We will collect data on fine particulate matter (PM2.5), nitrogen oxides (NOx), weather variables, sound (noise level and audio) along with street-level time-lapse images. We deploy low-cost, low-power, lightweight monitoring devices that are robust, socially unobtrusive, and able to function in Sub-Saharan African (SSA) climate. We will use state-of-the-art methods, including spatial statistics, deep/machine learning, and processed-based emissions modelling, to capture highly resolved temporal and spatial variations in pollution levels across the GAMA and to identify their potential sources. This protocol can serve as a prototype for other SSA cities. ETHICS AND DISSEMINATION: This environmental study was deemed exempt from full ethics review at Imperial College London and the University of Massachusetts Amherst; it was approved by the University of Ghana Ethics Committee (ECH 149/18-19). This protocol is designed to be implementable in SSA cities to map environmental pollution to inform urban planning decisions to reduce health harming exposures to air and noise pollution. It will be disseminated through local stakeholder engagement (public and private sectors), peer-reviewed publications, contribution to policy documents, media, and conference presentations.

Acute gastrointestinal illness in an African Indigenous population: the lived experience of Uganda's Batwa.

INTRODUCTION: Indigenous populations in low-income regions are often the most acutely affected by social gradients that impact health, including high burdens of infectious disease. Using a mixed methods approach, this study characterized the lived experience of acute gastrointestinal illness (AGI) in an Indigenous Batwa population in south-western Uganda. METHODS: Quantitative data analyses were conducted on data from three cross-sectional census surveys of Batwa conducted in January 2013 (n=583), January 2014 (n=569) and April 2014 (n=540). Using a 14-day recall period, cases of AGI were defined as three or more loose stools or any vomiting in a 24-hour period. These analyses were supplemented by qualitative data from key informant interviews (n=11 interviews) and Batwa focus group discussions (n=61 participants). RESULTS: From the surveys, episodes of diarrhea and episodes of vomiting lasted on average 3.6 (95%CI 2.3-4.3) and 3.0 (95%CI 2.1-3.9) days, and individuals experienced an average of 4.3 (95%CI 3.9-4.8) and 2.6 (95%CI 2.1-3.1) loose stools and vomiting episodes in 24 hours. Focus group participants and key informants indicated that episodes of AGI for Batwa were not limited to symptom-based consequences for the individual, but also had economic, social and nutritional impacts. CONCLUSION: Despite efforts to increase health literacy in disease transmission dynamics, risks and prevention measures, the perceived barriers and a lack of benefits still largely underscored adopting positive AGI prevention behaviors. This study moved beyond surveillance and provided information on the broader community-level burden of AGI and highlighted the current challenges and opportunities for improved uptake of AGI prevention measures for the Batwa.