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COVID-19 deaths in nursing homes accounted for 30.2% of all COVID-19 deaths in the United States during the early weeks (1-January to 26-July, 2020) of the pandemic. This study presents the geographic diffusion of COVID-19 cases and deaths in nursing homes during this time period, while also providing explanation of regional risk factors. Nursing home COVID-19 data on confirmed cases (n = 173,452) and deaths (n = 46,173) were obtained from the Centers for Medicare and Medicaid Services. Weekly COVID-19 case counts were spatially smoothed to identify nursing homes in areas of high COVID-19 infection. Bivariate spatial autocorrelation was used to visualize High vs. Low-case counts and related deaths. Zero-inflated negative binomial models were estimated within Health and Human Service (HHS) Regions at three-week intervals to evaluate facility and area-level risk factors. The first reported nursing home resident to die of COVID-19 was in the state of Washington on 28-February, 2020. By 24-May, 2020 there were simultaneous epicenters in the Northeast (HHS Regions 1 and 2) and Midwest (HHS Region 5) with diffusion into the South (HHS Regions 4 and 6) from 15-June to 5-July, 2020. The case-fatality rate was highest from 25-May to 14-June, 2020 (30.9 deaths per 1000 residents); thereafter declining to 24.1 (15-June to 5-July, 2020) and 19.4 (6-July to 26-July, 2020) (overall case-fatality rate 1-January to 26-July = 26.6). Statistically significant risk factors for COVID-19 deaths were admission of patients with COVID-19 into nursing homes, staff confirmed infections and nursing shortages. COVID-19 deaths were likely to occur in nursing homes in high minority and non-English speaking neighborhoods and neighborhoods with a high proportion of households with disabilities. Enhanced communication between HHS regional administrators about \"lessons learned\" could provide receiving state health departments with timely information to inform clinical practice to prevent premature death in nursing homes in future pandemics.

Adult men living with HIV are less likely than women to adhere to antiretroviral therapy (ART). This highlights the need to understand factors driving nonadherence and how adherence behaviors and barriers change with age. However, existing HIV surveillance data often collapse ages into broad groups, limiting life-course analysis. This study addresses this gap by examining the time from HIV diagnosis to ART initiation and subsequent adherence barriers among men, using women as a comparison group, in Greater Gaborone, Botswana. To explore these dynamics, we employed a cross-sectional survey design tailored to capture age-specific patterns of ART initiation and adherence. A cross-sectional survey was conducted with a stratified random sample of 239 men and 428 women attending 21 HIV treatment clinics. Semistructured questionnaires captured life-course characteristics, including the stage of HIV disease at ART initiation and individual-level factors affecting adherence. The results showed that men (  = 239) initiated ART medication during Stage 1 (  = 165, 61.9%), early Stage 2 (  = 46, 19.2%), late Stage 2 (  = 17, 7.1%), and Stage 3 (  = 11, 4.6%). Most men (69.1%) were diagnosed in Stage 1 of HIV disease compared to early Stage 2 (19.2%), late Stage 2 (7.1%), and Stage 3 (4.6%). Majority of the men (  = 63, 38.2%) diagnosed in Stage 1 were aged 40-49 years, followed by those aged 30-39 years (  = 38, 23.0%). ART nonadherence rates were 46.0% for men and 29.1% for women. Men who were more likely to be ART nonadherent (compared to women) were aged 30-39 years (OR = 2.05, 1.06-3.56), 40-49 years (OR = 1.91, 1.06-3.45), and 50+ years (OR = 3.95, 1.73-8.97). Other risk factors for ART nonadherence were recently sick, comorbidities, and taking other medications. Despite free ART and medical care in Botswana, men face barriers related to comorbidities, highlighting the need for targeted gender-specific interventions to improve ART adherence.

Background Inequalities in geographic access to health care result from the configuration of facilities, population distribution, and the transportation infrastructure. In recent accessibility studies, the traditional distance measure (Euclidean) has been replaced with more plausible measures such as travel distance or time. Both network and raster-based methods are often utilized for estimating travel time in a Geographic Information System. Therefore, exploring the differences in the underlying data models and associated methods and their impact on geographic accessibility estimates is warranted. Methods We examine the assumptions present in population-based travel time models. Conceptual and practical differences between raster and network data models are reviewed, along with methodological implications for service area estimates. Our case study investigates Limited Access Areas defined by Michigan’s Certificate of Need (CON) Program. Geographic accessibility is calculated by identifying the number of people residing more than 30 minutes from an acute care hospital. Both network and raster-based methods are implemented and their results are compared. We also examine sensitivity to changes in travel speed settings and population assignment. Results In both methods, the areas identified as having limited accessibility were similar in their location, configuration, and shape. However, the number of people identified as having limited accessibility varied substantially between methods. Over all permutations, the raster-based method identified more area and people with limited accessibility. The raster-based method was more sensitive to travel speed settings, while the network-based method was more sensitive to the specific population assignment method employed in Michigan. Conclusions Differences between the underlying data models help to explain the variation in results between raster and network-based methods. Considering that the choice of data model/method may substantially alter the outcomes of a geographic accessibility analysis, we advise researchers to use caution in model selection. For policy, we recommend that Michigan adopt the network-based method or reevaluate the travel speed assignment rule in the raster-based method. Additionally, we recommend that the state revisit the population assignment method.

Background While previous studies have shown that regular physical activity can delay the onset of certain chronic diseases; less is known about the changes in physical activity practices following chronic disease diagnoses. China is experiencing a rapid aging transition, with physical activity an important routine in many older people’s lives. This study utilizes the Health Belief Model to better understand the bidirectional relationships and bipolar effects between physical activity and chronic disease burden in Huainan City, a mid-sized city in China. Methods Longitudinal health survey data (2010–2015) from annual clinic visits for 3198 older people were obtained from a local hospital, representing 97% of the older population in three contiguous neighborhoods in Huainan City. The chronic diseases studied included obesity, hypertension, diabetes, hyperlipidemia, cardiovascular diseases, liver and biliary system diseases, and poor kidney function. Multilevel logistic regression was used to examine differences in physical activity levels across socio-demographic groups. Cox proportional hazards models were used to examine the impacts of physical activity practice levels on chronic disease onsets. Logistic regression was used to estimate the effects of chronic disease diagnosis on physical activity practice levels. Results The prevalence of chronic diseases increased with increasing age, among men, and those with a lower education. Older people who were physically active experienced a later onset of chronic disease compared to their sedentary counterparts, particularly for obesity and diabetes. Following diagnosis of a chronic disease, physically active older people were more likely to increase their physical activity levels, while sedentary older people were less likely to initiate physical activity, demonstrating bipolar health trajectory effects. Conclusions Health disparities among older people may widen as the sedentary experience earlier onsets of chronic diseases and worse health trajectories, compared to physically active people. Future health education communication and programmatic interventions should focus on sedentary and less healthy older populations to encourage healthy aging. These lessons from China may be applied to other countries also experiencing an increasing aging population.

Roemer's Law, a widely cited principle in health care policy, states that hospital beds that are built tend to be used. This simple but powerful expression has been invoked to justify Certificate of Need regulation of hospital beds in an effort to contain health care costs. Despite its influence, a surprisingly small body of empirical evidence supports its content. Furthermore, known geographic factors influencing health services use and the spatial structure of the relationship between hospital bed availability and hospitalization rates have not been sufficiently explored in past examinations of Roemer's Law. We pose the question, \"Accounting for space in health care access and use, is there an observable association between the availability of hospital beds and hospital utilization?\" We employ an ecological research design based upon the Anderson behavioral model of health care utilization. This conceptual model is implemented in an explicitly spatial context. The effect of hospital bed availability on the utilization of hospital services is evaluated, accounting for spatial structure and controlling for other known determinants of hospital utilization. The stability of this relationship is explored by testing across numerous geographic scales of analysis. The case study comprises an entire state system of hospitals and population, evaluating over one million inpatient admissions. We find compelling evidence that a positive, statistically significant relationship exists between hospital bed availability and inpatient hospitalization rates. Additionally, the observed relationship is invariant with changes in the geographic scale of analysis. This study provides evidence for the effects of Roemer's Law, thus suggesting that variations in hospitalization rates have origins in the availability of hospital beds. This relationship is found to be robust across geographic scales of analysis. These findings suggest continued regulation of hospital bed supply to assist in controlling hospital utilization is justified.

Toxics Release Inventory (TRI) facilities release, manage, and transport toxic chemicals. Studies of human exposure to TRI sites have used area and distance spatial measures that may lead to misclassification bias. This study used the risk-screening environmental indicator (RSEI) model of toxicity-weighted concentrations to estimate relative human health risks at the census tract level in Michigan between 2008 and 2017 by (1) reporting TRI chemicals and facilities that contributed the most to elevated relative human health risks; (2) mapping the annual relative human health risks at the census tract level over the study period; and (3) documenting the racial and ethnic composition and poverty levels of residents living in tracts with exceedingly high relative human health risks. Findings indicate that 186 chemicals were attributed to n  = 1048 TRI facilities in Michigan during the study period. The urban areas of Detroit and Grand Rapids contained 81.1% of tracts with the highest relative human health risks. African Americans, Hispanics, and residents living near and below poverty were most likely to live in these highest tracts. A growing disparity was observed in the Detroit urban area, whereas a declining disparity trend was observed in the Grand Rapids urban area over time. These findings demonstrate environmental injustices in the two largest urban areas in Michigan.

Lung cancer is the most common cause of cancer-related death in Michigan. Most patients are diagnosed at advanced stages of the disease. There is a need to detect clusters of lung cancer incidence over time, to generate new hypotheses about causation and identify high-risk areas for screening and treatment. The Michigan Cancer Surveillance database of individual lung cancer cases, 1985 to 2018 was used for this study. Spatial and spatiotemporal clusters of lung cancer and level of disease (localized, regional and distant) were detected using discrete Poisson spatial scan statistics at the zip code level over the study time period. The approach detected cancer clusters in cities such as Battle Creek, Sterling Heights and St. Clair County that occurred prior to year 2000 but not afterwards. In the northern area of the lower peninsula and the upper peninsula clusters of late-stage lung cancer emerged after year 2000. In Otter Lake Township and southwest Detroit, late-stage lung cancer clusters persisted. Public and patient education about lung cancer screening programs must remain a health priority in order to optimize lung cancer surveillance. Interventions should also involve programs such as telemedicine to reduce advanced stage disease in remote areas. In cities such as Detroit, residents often live near industry that emits air pollutants. Future research should therefore, continue to focus on the geography of lung cancer to uncover place-based risks and in response, the need for screening and health care services.

We investigated the effects of improved indoor environmental quality (IEQ) on perceived health and productivity in occupants who moved from conventional to green (according to Leadership in Energy and Environmental Design ratings) office buildings. In 2 retrospective–prospective case studies we found that improved IEQ contributed to reductions in perceived absenteeism and work hours affected by asthma, respiratory allergies, depression, and stress and to self-reported improvements in productivity. These preliminary findings indicate that green buildings may positively affect public health.

Under-five child mortality declined 47% since 2000 following the implementation of the United Nation’s (UN) Millennium Development Goals. To further reduce under-five child mortality, the UN’s Sustainable Development Goals (SDGs) will focus on interventions to address neonatal mortality, a major contributor of under-five mortality. The African region has the highest neonatal mortality rate (28.0 per 1000 live births), followed by that of the Eastern Mediterranean (26.6) and South-East Asia (24.3). This study used the Demographic and Health Survey Birth Recode data (http://dhsprogram.com/data/File-Types-and-Names.cfm) to identify high-risk districts and countries for neonatal mortality in two sub-regions of Africa – East Africa and West Africa. Geographically weighted Poisson regression models were estimated to capture the spatially varying relationships between neonatal mortality and dimensions of potential need i) care around the time of delivery, ii) maternal education, and iii) women’s empowerment. In East Africa, neonatal mortality was significantly associated with home births, mothers without an education and mothers whose husbands decided on contraceptive practices, controlling for rural residency. In West Africa, neonatal mortality was also significantly associated with home births, mothers with a primary education and mothers who did not want or plan their last child. Importantly, neonatal mortality associated with home deliveries were explained by maternal exposure to unprotected water sources in East Africa and older maternal age and female sex of infants in West Africa. Future SDG-interventions may target these dimensions of need in priority high-risk districts and countries, to further reduce the burden of neonatal mortality in Africa.