A multicomponent method assessing healthy cardiovascular urban environments: The Heart Healthy Hoods Index

Modifying effect of urban parks on socioeconomic inequalities in diabetes prevalence: a cross-sectional population study of Madrid City, Spain

BACKGROUND

Evidence has shown contradicting results on how the density of urban green spaces may reduce socioeconomic inequalities in type 2 diabetes (equigenic hypothesis). The aim of this study is to test whether socioeconomic inequalities in diabetes prevalence are modified by park density.

METHODS

We designed a population-wide cross-sectional study of all adults registered in the primary healthcare centres in the city of Madrid, Spain (n=1 305 050). We obtained georeferenced individual-level data from the Primary Care Electronic Health Records, and census-tract level data on socioeconomic status (SES) and park density. We modelled diabetes prevalence using robust Poisson regression models adjusted by age, country of origin, population density and including an interaction term with park density, stratified by gender. We used this model to estimate the Relative Index of Inequality (RII) at different park density levels.

FINDINGS

We found an overall RII of 2.90 (95% CI 2.78 to 3.02) and 4.50 (95% CI 4.28 to 4.74) in men and women, respectively, meaning that the prevalence of diabetes was three to four and a half times higher in low SES compared with high SES areas. These inequalities were wider in areas with higher park density for both men and women, with a significant interaction only for women (p=0.008).

INTERPRETATION

We found an inverse association between SES and diabetes prevalence in both men and women, with wider inequalities in areas with more parks. Future works should study the mechanisms of these findings, to facilitate the understanding of contextual factors that may mitigate diabetes inequalities.

Uses of Social Determinants of Health Data to Address Cardiovascular Disease and Health Equity: A Scoping Review

Background Cardiovascular disease is the leading cause of morbidity and mortality worldwide. Prior research suggests that social determinants of health have a compounding effect on health and are associated with cardiovascular disease. This scoping review explores what and how social determinants of health data are being used to address cardiovascular disease and improve health equity. Methods and Results After removing duplicate citations, the initial search yielded 4110 articles for screening, and 50 studies were identified for data extraction. Most studies relied on similar data sources for social determinants of health, including geocoded electronic health record data, national survey responses, and census data, and largely focused on health care access and quality, and the neighborhood and built environment. Most focused on developing interventions to improve health care access and quality or characterizing neighborhood risk and individual risk. Conclusions Given that few interventions addressed economic stability, education access and quality, or community context and social risk, the potential for harnessing social determinants of health data to reduce the burden of cardiovascular disease remains unrealized.

Role of Built Environments on Physical Activity and Health Promotion: A Review and Policy Insights

As urbanization and motorization continue worldwide, various health issues have emerged as a burden between individuals, families and governments at all levels. Under the prevalence of chronic disease, this review synthesizes research on the impact of the various built environments on the multiple health outcomes from a methodological and mechanistic perspective. Besides, it attempts to provide useful planning and policy implications to promote physical activity and health benefits. The finds show that: (1) Current literature has used a variety of dataset, methods, and models to examine the built environment-health benefit connections from the perspective of physical activity; (2) The prevalence of chronic diseases is inextricably linked to the built environment, and policy interventions related to physical activity and physical and mental wellbeing of urban residents should be emphasized; (3) The impact of the built environment on health is manifested in the way various elements of the physical environment guide the lifestyle of residents, thereby influencing physical activity and travel; (4) Given the changes that have occurred in the built environment during the current urban expansion, the link between urban planning and the public health sector should be strengthened in the future, and the relevant authorities should actively pursue policies that promote urban public health in order to improve the health of residents. Finally, it proposes potential policy insights for urban planning and development toward a healthier city and society.

Temporal trends in within-city inequities in COVID-19 incidence rate by area-level deprivation in Madrid, Spain

Patterns of exposure and policies aiming at reducing physical contact might have changed the social distribution of COVID-19 incidence over the course of the pandemic. Thus, we studied the temporal trends in the association between area-level deprivation and COVID-19 incidence rate by Basic Health Zone (minimum administration division for health service provision) in Madrid, Spain, from March 2020 to September 2021. We found an overall association between deprivation and COVID-19 incidence. This association varied over time; areas with higher deprivation showed higher COVID-19 incidence rates from July to November 2020 and August-September 2021, while, by contrast, higher deprivation areas showed lower COVID-19 incidence rates in December 2020 and July 2021.

Understanding the role of urban social and physical environment in opioid overdose events using found geospatial data

Opioid use disorder is a serious public health crisis in the United States. Manifestations such as opioid overdose events (OOEs) vary within and across communities and there is growing evidence that this variation is partially rooted in community-level social and economic conditions. The lack of high spatial resolution, timely data has hampered research into the associations between OOEs and social and physical environments. We explore the use of non-traditional, "found" geospatial data collected for other purposes as indicators of urban social-environmental conditions and their relationships with OOEs at the neighborhood level. We evaluate the use of Google Street View images and non-emergency "311" service requests, along with US Census data as indicators of social and physical conditions in community neighborhoods. We estimate negative binomial regression models with OOE data from first responders in Columbus, Ohio, USA between January 1, 2016, and December 31, 2017. Higher numbers of OOEs were positively associated with service request indicators of neighborhood physical and social disorder and street view imagery rated as boring or depressing based on a pre-trained random forest regression model. Perceived safety, wealth, and liveliness measures from the street view imagery were negatively associated with risk of an OOE. Age group 50-64 was positively associated with risk of an OOE but age 35-49 was negative. White population, percentage of individuals living in poverty, and percentage of vacant housing units were also found significantly positive however, median income and percentage of people with a bachelor's degree or higher were found negative. Our result shows neighborhood social and physical environment characteristics are associated with likelihood of OOEs. Our study adds to the scientific evidence that the opioid epidemic crisis is partially rooted in social inequality, distress and underinvestment. It also shows the previously underutilized data sources hold promise for providing insights into this complex problem to help inform the development of population-level interventions and harm reduction policies.

Developing an index to estimate the association between the food environment and CVD mortality rates

The food environment has been shown to influence dietary patterns, which ultimately affects nutrition-related diseases such as diabetes, obesity, and cardiovascular disease (CVD). Measures of food accessibility and socioeconomics were combined to develop the Food Environment Index (FEI), characterizing all U.S. counties between 2008 and 2016. Multi-level regression models showed that this index is significantly negatively associated with CVD death rates across the two time periods studied (2008-2010 and 2013-2016). The FEI may be a useful proxy for identifying differences in the food environment to inform future interventions.

Using socially-sensed data to infer ZIP level characteristics for the spatiotemporal analysis of drug-related health problems in Maryland

This research investigated how socially sensed data can be used to detect ZIP level characteristics that are associated with spatial and temporal patterns of Emergency Department patients with a chief complaint and/or diagnosis of overdose or drug-related health problems for four hospitals in Baltimore and Anne Arundel County, MD during 2016-2018. Dynamic characteristics were identified using socially-sensed data (i.e., geo-tagged Twitter data) at ZIP code level over varying temporal resolutions. Data about three place-based variables including comments and concerns about crime, drug use, and negative or depressed sentiments, were extracted from tweets, along with data from four socio-environmental variables from the American Community Survey were collected to explore socio-environmental characteristics during the same period. Our study showed a statistically significant increase in adjusted rates of Emergency Department (ED) visits occurred between June and November 2017 for patients residing in ZIP codes in western Baltimore and northeastern Anne Arundel County. During this period, the three topics extracted from Twitter data were highly correlated with the ZIP codes where the patients were residing. Exploring the dynamic spatial associations between socio-environmental variables and ED visits for acute overdose assists local health officials in optimizing interventions for vulnerable locations.

A COMPARATIVE CASE STUDY OF WALKING ENVIRONMENT IN MADRID AND PHILADELPHIA USING MULTIPLE SAMPLING METHODS AND STREET VIRTUAL AUDITS

The objective of this study is to quantify, using virtual audits in Madrid and Philadelphia, cross-city differences in the walking environment and to test whether differences vary by sampling method. We used two sampling methods; first, a contiguous area combining census units (~15.000 population area for each setting) was selected using the Median Neighborhood Index (MNI). MNI is a summary index that averages Euclidean distances of sociodemographic and urban form features, used to select the median neighborhood for a given city. Second, we selected a population-density stratified sampling of the same number of census units as above. M-SPACES audit tool was deployed, using street virtual audits to measure function, safety, aesthetics, and destinations along each street segment. Madrid streets had lower scores for function (b=-0.29 CI95% -0.55;-0.31) and safety (b=-0.38 CI95% -0.61;-0.14). Madrid had a greater proportion of streets having at least one walking destination in the street segment (PR=1.92 95% CI 1.55; 2.39). We did not find a significant difference between Madrid and Philadelphia in aesthetics. We found an interaction between safety and sampling methods. This approach can reveal which elements of the built environment account for between-city differences, key to mass influences that operate at the city level.

Density of Green Spaces and Cardiovascular Risk Factors in the City of Madrid: The Heart Healthy Hoods Study

The aim of this study is to evaluate the relationship between the density of green spaces at different buffer sizes (300, 500, 1000 and 1500 m) and cardiovascular risk factors (obesity, hypertension, high cholesterol, and diabetes) as well as to study if the relationship is different for males and females. We conducted cross-sectional analyses using the baseline measures of the Heart Healthy Hoods study (N = 1625). We obtained data on the outcomes from clinical diagnoses, as well as anthropometric and blood sample measures. Exposure data on green spaces density at different buffer sizes were derived from the land cover distribution map of Madrid. Results showed an association between the density of green spaces within 300 and 500 m buffers with high cholesterol and diabetes, and an association between the density of green spaces within 1500 m buffer with hypertension. However, all of these associations were significant only in women. Study results, along with other evidence, may help policy-makers creating healthier environments that could reduce cardiovascular disease burden and reduce gender health inequities. Further research should investigate the specific mechanisms behind the differences by gender and buffer size of the relationship between green spaces and cardiovascular risk factors.