Urbanization and physical activity in the global Prospective Urban and Rural Epidemiology study.

Urbanization may influence physical activity (PA) levels, although little evidence is available for low- and middle- income countries where urbanization is occurring fastest. We evaluated associations between urbanization and total PA, as well as work-, leisure-, home-, and transport-specific PA, for 138,206 adults living in 698 communities across 22 countries within the Prospective Urban and Rural Epidemiology (PURE) study. The 1-week long-form International PA Questionnaire was administered at baseline (2003-2015). We used satellite-derived population density and impervious surface area estimates to quantify baseline urbanization levels for study communities, as well as change measures for 5- and 10-years prior to PA surveys. We used generalized linear mixed effects models to examine associations between urbanization measures and PA levels, controlling for individual, household and community factors. Higher community baseline levels of population density (- 12.4% per IQR, 95% CI - 16.0, - 8.7) and impervious surface area (- 29.2% per IQR, 95% CI - 37.5, - 19.7), as well as the rate of change in 5-year population density (- 17.2% per IQR, 95% CI - 25.7, - 7.7), were associated with lower total PA levels. Important differences in the associations between urbanization and PA were observed between PA domains, country-income levels, urban/rural status, and sex. These findings provide new information on the complex associations between urbanization and PA.

Bring Your Own Location Data: Use of Google Smartphone Location History Data for Environmental Health Research.

BACKGROUND: Environmental exposures are commonly estimated using spatial methods, with most epidemiological studies relying on home addresses. Passively collected smartphone location data, like Google Location History (GLH) data, may present an opportunity to integrate existing long-term time-activity data. OBJECTIVES: We aimed to evaluate the potential use of GLH data for capturing long-term retrospective time-activity data for environmental health research. METHODS: We included 378 individuals who participated in previous Global Positioning System (GPS) studies within the Washington State Twin Registry. GLH data consists of location information that has been routinely collected since 2010 when location sharing was enabled within android operating systems or Google apps. We created instructions for participants to download their GLH data and provide it through secure data transfer. We summarized the GLH data provided, compared it to available GPS data, and conducted an exposure assessment for nitrogen dioxide (NO2) air pollution. RESULTS: Of 378 individuals contacted, we received GLH data from 61 individuals (16.1%) and 53 (14.0%) indicated interest but did not have historical GLH data available. The provided GLH data spanned 2010-2021 and included 34 million locations, capturing 66,677 participant days. The median number of days with GLH data per participant was 752, capturing 442 unique locations. When we compared GLH data to 2-wk GPS data (∼1.8 million points), 95% of GPS time-activity points were within 100m of GLH locations. We observed important differences between NO2 exposures assigned at home locations compared with GLH locations, highlighting the importance of GLH data to environmental exposure assessment. DISCUSSION: We believe collecting GLH data is a feasible and cost-effective method for capturing retrospective time-activity patterns for large populations that presents new opportunities for environmental epidemiology. Cohort studies should consider adding GLH data collection to capture historical time-activity patterns of participants, employing a "bring-your-own-location-data" citizen science approach. Privacy remains a concern that needs to be carefully managed when using GLH data. https://doi.org/10.1289/EHP10829.

A multiscale analysis of social and spatial determinants of cancer and noncancer hazards from on-road air pollution in Texas.

Evidence on environmental justice studies linking adverse health effects and on-roadair pollution showing spatial nonstationarity is limited.This study uses cancer and noncancer risk estimates from on-road sources of hazardous air pollutants modeled by the National-Scale Air Toxics Assessment (NATA) at the census tract (N = 5265) level and sociodemographic variables from U.S. Census Bureau to examine the nonstationarity spatial relationship by comparing aspatial and spatial regression modelsglobal ordinary least squares, spatial error model, geographically weighted regression, and multiscale geographically weighted regression. At first glance, census tracts within the highest quartile of cancer and noncancer risks were clustered in the major urban areas. Spatial regression indicates that cancer and non-cancer risk were associated with census tract level percentages of Black, Indigenous, and People of Color (BIPOC). These findings can serve as geospatial guidance for intervening in the processes that drive socio-spatial disparity in air pollution exposure.

A 2-year pandemic period analysis of facility and county-level characteristics of nursing home coronavirus deaths in the United States, January 1, 2020-December 18, 2021.

Nursing home residents are highly susceptible to COVID-19 infection and complications. We used a generalized linear mixed Poisson model and spatial statistics to examine the determinants of COVID-19 deaths in 13,350 nursing homes in the first 2-year pandemic period using the Centers for Medicare and Medicaid Services and county-level related data. The average prevalence of COVID-19 mortality among residents was 9.02 (Interquartile range = 10.18) per 100 nursing home beds in the first 2-year of the pandemic. Fully-adjusted mixed model shows that nursing homes COVID-19 deaths reduced by 5% (Q2 versus Q1: IRR = 0.949, 95% CI 0.901- 0.999), 14.4% (Q3 versus Q1: IRR = 0.815, 95% CI 0.718 - 0.926), and 25% (Q2 versus Q1: IRR = 0.751, 95% CI 0.701- 0.805) of facility ratings. Spatial analysis showed a significant hotspot of nursing home COVID-19 deaths in the Northeast US. This study contributes to nursing home quality assessment for improving residents' health.

Accumulation and bioaccessibility of toxic metals in root tubers and soils from gold mining and farming communities in the Ashanti region of Ghana.

The presence of metal contaminants in agricultural soils and subsequent uptake by food crops can pose serious human health risk. In this study, we assessed the levels of toxic metals - arsenic, chromium, copper, iron, manganese, nickel, and zinc - in soils and some edible root tuber crops from two gold mining and two non-mining communities in Ghana to evaluate the potential human health risks associated with exposure to these metals. Concentrations of the metals in 154 soil and edible root tuber samples were analyzed using field portable x-ray fluorescence spectrometer prior to confirmation by inductively coupled plasma mass spectrometry. Bioaccessibility of the metals was determined using an in vitro physiologically based extraction technique. Concentrations of the metals were generally higher in the gold mining communities than in the non-mining communities. The contamination indices indicated low to moderate contamination of the soil and food crops. Bioaccessibility for the metals varied from 1.7% (Fe) to 62.3 (Mn). Overall, the risks posed by the metals upon consumption of the tubers were low.

Racial/Ethnic Heterogeneity and Rural-Urban Disparity of COVID-19 Case Fatality Ratio in the USA: a Negative Binomial and GIS-Based Analysis.

The 2019 coronavirus disease (COVID-19) has exacerbated inequality in the United States of America (USA). Black, indigenous, and people of color (BIPOC) are disproportionately affected by the pandemic. This study examines determinants of COVID-19 case fatality ratio (CFR) based on publicly sourced data from January 1 to December 18, 2020, and sociodemographic and rural-urban continuum data from the US Census Bureau. Nonspatial negative binomial Poisson regression and geographically weighted Poisson regression were applied to estimate the global and local relationships between the CFR and predictors-rural-urban continuum, political inclination, and race/ethnicity in 2407 rural counties. The mean COVID-19 CFR among rural counties was 1.79 (standard deviation (SD) = 1.07; 95% CI 1.73-1.84) higher than the total US counties (M = 1.69, SD = 1.18; 95% CI: 1.65-1.73). Based on the global NB model, CFR was positively associated with counties classified as "completely rural" (incidence rate ratio (IRR) = 1.24; 95% CI: 1.12-1.39) and "mostly rural" (IRR = 1.26; 95% CI: 1.15-1.38) relative to "mostly urban" counties. Nonspatial regression indicates that COVID-19 CFR increases by a factor of 8.62, 5.87, 2.61, and 1.36 for one unit increase in county-level percent Blacks, Hispanics, American Indians, and Asian/Pacific Islanders, respectively. Local spatial regression shows CFR was significantly higher in rural counties with a higher share of BIPOC in the Northeast and Midwest regions, and political inclination predicted COVID-19 CFR in rural counties in the Midwest region. In conclusion, spatial and racial/ethnic disparities exist for COVID-19 CFR across the US rural counties, and findings from this study have implications for public health.

GPS-based built environment measures associated with adult physical activity.

Studies often rely on home locations to access built environment (BE) influences on physical activity (PA). We use GPS and accelerometer data collected for 288 individuals over a two-week period to examine eight GPS-derived BE characteristics and moderate-to-vigorous PA (MVPA) and light-to-moderate-vigorous PA (LMVPA). NDVI, parks, blue space, pedestrian-orientated intersections, and population density were associated with increased odds of LMVPA and MVPA, while traffic air pollution and noise were associated with decreased odds of LMVPA and MVPA. Associations varied by population density and when accounting for multiple BE measures. These findings provide further information on where individuals choose to be physically active.

Determinants of Gender-Based Violence and Its Physiological Effects Among Women in 12 African Countries.

Goals 3 and 5 of the United Nations Sustainable Development Goals are to promote good health and well-being and to achieve gender equality, respectively. To successfully move toward these goals in the area of gender equality, there is the need to understand the underlying legislative or laws that protect women and girls from all forms of domestic violence (DV), including gender-based violence (GBV). The cardinal objective of this study, therefore, was to examine the risk factors of GBV and the physiological effects of GBV. To date, few studies have quantified the relationship between laws on DV and the incidence of DV/GBV. This article fills that gap by using Demographic and Health Surveys (DHS) data of 12 African countries. We applied multivariate logistic regression to estimate the association of the absence of laws on DV, men dominant power, history of violence, alcohol consumption, women's attitude toward men's violence perpetration, and decision-making power with the scores of GBV and physiological effects of GBV. Group Kruskal-Wallis Rank test was used to determine the variation of the two outcomes among the 12 countries. Results show significant disparities in the score of GBV, H test (11) = 168,217, p < .001, and score of physiological effects, H test (11) = 122,127, p < .001, among the 12 countries. Specifically, Ghana, Namibia, Rwanda, Mozambique, Zimbabwe, Malawi, Sierra Leone, and Togo reported the highest physiological effect of GBV. Presence of DV laws, male dominance, alcohol consumption, history of abuse, and women empowerment predict GBV and the physiological effect of GBV. Thus, building strong legal frameworks against all forms of DV and empowering women may reduce the incidence of GBV and physiological effects of GBV for all African women.

Regional variation and demographic factors associated with knowledge of malaria risk and prevention strategies among pregnant women in Nigeria.

Pregnant women and children are the most vulnerable populations for malaria infection. Yet, knowledge of risk, and preventive measures are poor among this population. Using the 2015 Nigeria Malaria Indicator Survey, we applied logit link function to estimate the associations of wealth status, educational attainment, and region of residence with malaria risk knowledge and prevention strategies (using a treated mosquito net and malaria drugs) among 739 Nigerian pregnant women aged 15-49 years. Urban women who had obtained a secondary school education (Adjusted odds ratio [aOR] = 2.12; 95% confidence interval [CI] 1.09-4) or higher (aOR = 8.31; 95% CI 3.2-22) had more knowledge of malaria risk. Urban women in the South-West (aOR = 5.02; [CI] 2.02-12.50) and South-East (aOR = 2.68; 95% CI 1.19-6.06) were more likely to use treated mosquito nets during pregnancy. Women in the urban South-West (aOR = 4.04; 95% CI 1.5-11) were more likely to use malaria drugs during pregnancy than those in the North-Central. A wide regional disparity in the knowledge of malaria risks and use of preventive measures exists. Thus, promoting equal access to malaria preventive measures as well as improving knowledge about malaria transmission by mosquitoes should be considered as essential components of ongoing malaria control and elimination efforts in Nigeria.

Human Health Risk and Bioaccessibility of Toxic Metals in Topsoils from Gbani Mining Community in Ghana.

BACKGROUND: Anthropogenic activities such as artisanal mining pose a major environmental health concern due to the potential for discharge of toxic metals into the environment. OBJECTIVES: To determine the distribution and pollution patterns of arsenic (As), iron (Fe), nickel (Ni), cobalt (Co), chromium (Cr), manganese (Mn), copper (Cu) and zinc (Zn) in the topsoil of a mining community in Ghana, along with potential human health risks and in vitro bioaccessibility. METHODS: Concentrations of metals were determined using X-ray fluorescence techniques and validated using inductively coupled plasma-mass spectrometry. RESULTS: Concentrations of the metals in topsoil were in the order of magnitude of Cu (31.38 mg/kg) < Ni (45.39 mg/kg) < As (59.66 mg/kg) < Cr (92.87 mg/kg) < Zn (106.98 mg/kg) < Mn (1195.49 mg/kg) < Fe (30061.02 mg/kg). Geo-statistical and multivariate analyses based on hazard indices including contamination, ecological risks, geo-accumulation, and pollution load suggest that the topsoils are contaminated in the study area. The potential ecological risk index (PERI) showed high ecological risk effects (PERI=269.09), whereas the hazard index (1×10-7) and carcinogenic risk index (1×10-5) indicated low human health risks. Elevated levels of As, Cr, Ni, and Zn were found to emanate from anthropogenic origins, whereas Fe, Mn, and Cu levels were attributed mainly to geological and atmospheric depositions. Physicochemical parameters (pH, electrical conductivity and total organic carbon) showed weak positive correlations to the metal concentrations. Elemental bioaccessibility was variable, decreasing in the order of Mn (35± 2.9%) > Cu (29± 2.6%) > Ni (22± 1.3%) > As (9± 0.5%) > Cr (4± 0.6%) > Fe (2± 0.4%). CONCLUSIONS: Incorporation of in-vitro bioaccessibility into the risk characterization models resulted in a hazard index of less than 1, implying low human health risks. However, due to accumulation effects of the metals, regular monitoring is required. COMPETING INTERESTS: The authors declare no competing financial interests.