Characteristics of the built environment and spatial patterning of type 2 diabetes in the urban core of Durham, North Carolina

Contribution of environmental determinants to the risk of developing type 2 diabetes mellitus in a life-course perspective: a systematic review protocol

BACKGROUND

Prevention policies against type 2 diabetes mellitus (T2DM) focus solely on individual healthy lifestyle behaviours, while an increasing body of research recognises the involvement of environmental determinants (ED) (cultural norms of land management and planning, local foodscape, built environment, pollution, and neighbourhood deprivation). Precise knowledge of this relationship is essential to proposing a prevention strategy integrating public health and spatial planning. Unfortunately, issues related to the consistency and synthesis of methods, and results in this field of research limit the development of preventive strategies. This systematic review aims to improve knowledge about the relationship between the risk of developing T2DM in adulthood and long-term exposure to its ED during childhood or teenage years.

METHODS

This protocol is presented according to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) tools. PubMed, Embase, CINAHL, Web of Science, EBSCO, and grey literature from the Laval University Libraries databases will be used for data collection on main concepts such as 'type 2 diabetes mellitus', 'zoning' or 'regional, urban, or rural areas land uses', 'local food landscape', 'built environment', 'pollution', and 'deprivation'. The Covidence application will store the collected data for selection and extraction based on the Population Exposure Comparator Outcome and Study design approach (PECOS). Studies published until December 31, 2023, in English or French, used quantitative data about individuals aged 18 and over that report on T2DM, ED (cultural norms of land management and planning, local foodscape, built environment, and neighbourhood deprivation), and their association (involving only risk estimators) will be included. Then, study quality and risk of bias will be conducted according to the combined criteria and ratings from the ROBINS-E (Risk of Bias in Non-randomised Studies-of Exposures) tools and the 'Effective Public Health Practice Project' (EPHPP). Finally, the analytical synthesis will be produced using the 'Synthesis Without Meta-analysis' (SWiM) guidelines.

DISCUSSION

This systematic review will summarise available evidence on ED associated with T2DM. The results will contribute to improving current knowledge and developing more efficient cross-sectoral interventions in land management and public health in this field of research.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42023392073.

Application of Spatial Analysis for Electronic Health Records: Characterizing Patient Phenotypes and Emerging Trends

Background

Electronic health records (EHR) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHR in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.

Objective

This study reviews advanced spatial analyses that employed individual-level health data from EHR within the US to characterize patient phenotypes.

Methods

We systematically evaluated English-language peer-reviewed articles from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on time, study design, or specific health domains.

Results

Only 49 articles met the eligibility criteria. These articles utilized diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were relatively underexplored. A noteworthy surge (n = 42, 85.7%) in publications was observed post-2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains, such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were rarely utilized.

Conclusions

This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. Additionally, this review proposes guidelines for harnessing the potential of spatial analysis to enhance the context of individual patients for future clinical decision support.

Methods in Public Health Environmental Justice Research: a Scoping Review from 2018 to 2021

PURPOSE OF REVIEW

The volume of public health environmental justice (EJ) research produced by academic institutions increased through 2022. However, the methods used for evaluating EJ in exposure science and epidemiologic studies have not been catalogued. Here, we completed a scoping review of EJ studies published in 19 environmental science and epidemiologic journals from 2018 to 2021 to summarize research types, frameworks, and methods.

RECENT FINDINGS

We identified 402 articles that included populations with health disparities as a part of EJ research question and met other inclusion criteria. Most studies (60%) evaluated EJ questions related to socioeconomic status (SES) or race/ethnicity. EJ studies took place in 69 countries, led by the US (n = 246 [61%]). Only 50% of studies explicitly described a theoretical EJ framework in the background, methods, or discussion and just 10% explicitly stated a framework in all three sections. Among exposure studies, the most common area-level exposure was air pollution (40%), whereas chemicals predominated personal exposure studies (35%). Overall, the most common method used for exposure-only EJ analyses was main effect regression modeling (50%); for epidemiologic studies the most common method was effect modification (58%), where an analysis evaluated a health disparity variable as an effect modifier. Based on the results of this scoping review, current methods in public health EJ studies could be bolstered by integrating expertise from other fields (e.g., sociology), conducting community-based participatory research and intervention studies, and using more rigorous, theory-based, and solution-oriented statistical research methods.

Spatial clusters of diabetes: individual and neighborhood characteristics in the ELSA-Brasil cohort study

This study identified spatial clusters of type 2 diabetes mellitus among participants of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) residing in two cities and verified individual and neighborhood socioeconomic environmental characteristics associated with the spatial clusters. A cross-sectional study was conducted with 4,335 participants. Type 2 diabetes mellitus was defined as fasting blood glucose ≥ 126mg/dL (7.0mmol/L), oral glucose tolerance test ≥ 200mg/dL (11.1mmol/L), or glycated hemoglobin ≥ 6.5% (48mmol/L); by antidiabetic drug use; or by the self-reported medical diagnosis of type 2 diabetes mellitus. Neighborhood socioeconomic characteristics were obtained from the 2011 Brazilian census. A spatial data analysis was conducted with the SaTScan method to detect spatial clusters. Logistic regression models were fitted to estimate the magnitude of associations. In total, 336 and 343 participants had type 2 diabetes mellitus in Belo Horizonte, Minas Gerais State (13.5%) and Salvador, Bahia State (18.5%), respectively. Two cluster areas showing a high chance of type 2 diabetes mellitus were identified in Belo Horizonte and Salvador. In both cities, participants living in the high type 2 diabetes mellitus cluster area were more likely to be mixed-race or black and have a low schooling level and manual work; these were also considered low-income areas. On the other hand, participants in the low type 2 diabetes mellitus cluster area of Salvador were less likely to be black and have low schooling level (university degree) and live in a low-income area. More vulnerable individual and neighborhood socioeconomic characteristics were associated with living in clusters of higher type 2 diabetes mellitus occurrence , whereas better contextual profiles were associated with clusters of lower prevalence.