Using data from online geocoding services for the assessment of environmental obesogenic factors: a feasibility study

The influence of social and environmental determinants of health on hospitalizations for pediatric asthma

BACKGROUND

Asthma is the most common chronic disease of childhood, and has several social, environmental, and demographic factors potentially influential to its disease burden. This study sought to determine the influence of these factors on hospital admissions and readmissions for pediatric asthma.

METHODS

This was a retrospective cohort study using data from the Indiana Network for Patient Care, a state-wide health information exchange in the United States. Study participants were children 2-18 years old admitted to the hospital with a respiratory diagnostic code between 2010 and 2021. Clinical variables were obtained from electronic health record data, and social and environmental determinants of health data were obtained from the Indiana Social Assets and Vulnerabilities Indicators using geocoding systems. Negative binomial models were used to examine community level social and environmental risk factors modifying the relationship between patient characteristics and the risk of asthma-related hospitalizations and 30-day readmissions.

RESULTS

The study sample included 25,063 patients with an average follow-up of 9 (SD = 5) years. Of these, there were 17,816 asthma-related admissions. There were a total of 1,037 asthma-related 30-day readmissions, with an incidence rate of readmissions relative to total visits of 0.028 per person-year. A high social vulnerability index (SVI) was associated with an increased rate of hospital admissions (Proportion attributable ratio: 1.09, 95%CI (1.03,1.15), p < 0.05). No environmental determinants of health were significantly associated with hospitalization rate.

CONCLUSION

High SVI was significantly associated with increased risk of total hospital admissions for pediatric asthma.

GIS-Based Assessments of Neighborhood Food Environments and Chronic Conditions: An Overview of Methodologies

The industrial revolution and urbanization fundamentally restructured populations' living circumstances, often with poor impacts on health. As an example, unhealthy food establishments may concentrate in some neighborhoods and, mediated by social and commercial drivers, increase local health risks. To understand the connections between neighborhood food environments and public health, researchers often use geographic information systems (GIS) and spatial statistics to analyze place-based evidence, but such tools require careful application and interpretation. In this article, we summarize the factors shaping neighborhood health in relation to local food environments and outline the use of GIS methodologies to assess associations between the two. We provide an overview of available data sources, analytical approaches, and their strengths and weaknesses. We postulate next steps in GIS integration with forecasting, prediction, and simulation measures to frame implications for local health policies.

A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation

BACKGROUND

Overweight and obesity are severe public health problems worldwide. Obesity can lead to chronic diseases such as type 2 diabetes mellitus. Environmental factors may affect lifestyle aspects and are therefore expected to influence people's weight status. To assess environmental risks, several methods have been tested using geographic information systems. Freely available data from online geocoding services such as OpenStreetMap (OSM) can be used to determine the spatial distribution of these obesogenic factors. The aim of our study was to develop and test a spatial obesity risk score (SORS) based on data from OSM and using kernel density estimation (KDE).

METHODS

Obesity-related factors were downloaded from OSM for two municipalities in Bavaria, Germany. We visualized obesogenic and protective risk factors on maps and tested the spatial heterogeneity via Ripley's K function. Subsequently, we developed the SORS based on positive and negative KDE surfaces. Risk score values were estimated at 50 random spatial data points. We examined the bandwidth, edge correction, weighting, interpolation method, and numbers of grid points. To account for uncertainty, a spatial bootstrap (1000 samples) was integrated, which was used to evaluate the parameter selection via the ANOVA F statistic.

RESULTS

We found significantly clustered patterns of the obesogenic and protective environmental factors according to Ripley's K function. Separate density maps enabled ex ante visualization of the positive and negative density layers. Furthermore, visual inspection of the final risk score values made it possible to identify overall high- and low-risk areas within our two study areas. Parameter choice for the bandwidth and the edge correction had the highest impact on the SORS results.

DISCUSSION

The SORS made it possible to visualize risk patterns across our study areas. Our score and parameter testing approach has been proven to be geographically scalable and can be applied to other geographic areas and in other contexts. Parameter choice played a major role in the score results and therefore needs careful consideration in future applications.

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information

BACKGROUND

Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study's population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants' protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects.

METHODS

This protocol demonstrates how to: (1) securely geocode patients' residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality.

RESULTS

Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients' coded census tract locations.

CONCLUSIONS

This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.

Health geo-governance to assess the vulnerability of Batna, Algeria to COVID-19: the role of GIS in the fight against a pandemic

The health systems in many countries are still unable to control the evolution and persistence of the COVID-19 pandemic despite the large mobilisation of national resources. International attention has focussed on finding a cure, and preventive measures and national and international strategies to be adopted and implemented with regard to other future pandemics have been neglected despite their predictability and high probability of occurrence. This work aims to anticipate a reading on experience feedback in light of the current pandemic situation, and to identify the main spatial elements of vulnerability in Batna, Algeria, which seems to control the ability of an urban area to prevent the spread of the COVID-19 virus. We used a digital model based on a multi-criteria approach implemented in a geo-decisional GIS database to serve as a decision support tool for dealing with an epidemiological situation as a preventive or curative action. The results from the model seem to adequately reflect the reality of confirmed incidents in Batna. In addition, the results of the analysis of the spatiotemporal evolution of the virus clearly confirm that the urban sectors characterised by high vulnerability are those that have recorded an increasing number of confirmed COVID-19 incidents since the start of the epidemic until December 2020.

Diabetes mellitus and comorbidities - A cross-sectional study with control group based on nationwide ambulatory claims data

As a condition, diabetes mellitus is associated with risk factors and diseases such as obesity. At the same time, cardiovascular diseases are a frequent consequence of diabetes. There have yet to be any findings on the Germany-wide prevalence of diabetes and diabetes comorbidities based on statutory health insurance data. This study estimates the documented prevalence of diabetes in 2019 on the basis of all ambulatory physicians' claims data of German statutory health insurance. In addition, the prevalence of obesity, high blood pressure, coronary heart disease, heart failure, stroke and depression is calculated for diabetes and non-diabetes patients, and the prevalence ratio (PR) is determined as a quotient. The approach used was a case-control design, which assigns a control person without diabetes to each diabetes patient who is similar in terms of age, region and sex. In diabetes patients, a PR greater than 1 was observed for all examined diseases across all age groups, thus demonstrating a higher prevalence compared to persons without diabetes. The highest PR across all age groups for women (3.8) and men (3.7) was found for obesity. In a comparison over time, documented prevalence figures of diabetes in Germany stagnate. With the exception of depression, the documented prevalences of comorbidities correspond well with the prevalences found in population-wide examination surveys.

Web Data Mining: Validity of Data from Google Earth for Food Retail Evaluation

To overcome the challenge of obtaining accurate data on community food retail, we developed an innovative tool to automatically capture food retail data from Google Earth (GE). The proposed method is relevant to non-commercial use or scholarly purposes. We aimed to test the validity of web sources data for the assessment of community food retail environment by comparison to ground-truth observations (gold standard). A secondary aim was to test whether validity differs by type of food outlet and socioeconomic status (SES). The study area included a sample of 300 census tracts stratified by SES in two of the largest cities in Brazil, Rio de Janeiro and Belo Horizonte. The GE web service was used to develop a tool for automatic acquisition of food retail data through the generation of a regular grid of points. To test its validity, this data was compared with the ground-truth data. Compared to the 856 outlets identified in 285 census tracts by the ground-truth method, the GE interface identified 731 outlets. In both cities, the GE interface scored moderate to excellent compared to the ground-truth data across all of the validity measures: sensitivity, specificity, positive predictive value, negative predictive value and accuracy (ranging from 66.3 to 100%). The validity did not differ by SES strata. Supermarkets, convenience stores and restaurants yielded better results than other store types. To our knowledge, this research is the first to investigate using GE as a tool to capture community food retail data. Our results suggest that the GE interface could be used to measure the community food environment. Validity was satisfactory for different SES areas and types of outlets.

Racial and socioeconomic disparity associates with differences in cardiac DNA methylation among men with end-stage heart failure

Heart failure (HF) is a multifactorial syndrome that remains a leading cause of worldwide morbidity. Despite its high prevalence, only half of patients with HF respond to guideline-directed medical management, prompting therapeutic efforts to confront the molecular underpinnings of its heterogeneity. In the current study, we examined epigenetics as a yet unexplored source of heterogeneity among patients with end-stage HF. Specifically, a multicohort-based study was designed to quantify cardiac genome-wide cytosine-p-guanine (CpG) methylation of cardiac biopsies from male patients undergoing left ventricular assist device (LVAD) implantation. In both pilot (n = 11) and testing (n = 31) cohorts, unsupervised multidimensional scaling of genome-wide myocardial DNA methylation exhibited a bimodal distribution of CpG methylation found largely to occur in the promoter regions of metabolic genes. Among the available patient attributes, only categorical self-identified patient race could delineate this methylation signature, with African American (AA) and Caucasian American (CA) samples clustering separately. Because race is a social construct, and thus a poor proxy of human physiology, extensive review of medical records was conducted, but ultimately failed to identify covariates of race at the time of LVAD surgery. By contrast, retrospective analysis exposed a higher all-cause mortality among AA (56.3%) relative to CA (16.7%) patients at 2 yr following LVAD placement (P = 0.03). Geocoding-based approximation of patient demographics uncovered disparities in income levels among AA relative to CA patients. Although additional studies are needed, the current analysis implicates cardiac DNA methylation as a previously unrecognized indicator of socioeconomic disparity in human heart failure outcomes.

NEW & NOTEWORTHY A bimodal signature of cardiac DNA methylation in heart failure corresponds with racial differences in all-cause mortality following mechanical circulatory support. Racial differences in promoter methylation disproportionately affect metabolic signaling pathways. Socioeconomic factors are associated with racial differences in the cardiac methylome among men with end-stage heart failure.

Listen to this article’s corresponding podcast at https://ajpheart.podbean.com/e/racial-socioeconomic-determinants-of-the-cardiac-epigenome/.