Patient- and area-level predictors of prostate cancer among South Carolina veterans: a spatial analysis

A standard procedure for constructing a multi-level social vulnerability index using CLSA and SOS data as working examples

BACKGROUND

The construct of social vulnerability attempts to understand social circumstances not merely as a descriptor, but as a predictor of adverse health events. It can be measured by aggregating social deficits in a social vulnerability index (SVI). We describe a standard procedure for constructing a multi-level SVI using two working examples.

METHODS

First, we describe a six-step approach to constructing a SVI. Then, we conducted a secondary analysis of a clinical dataset (Canadian Immunization Research Network's Serious Outcomes Surveillance Network (SOS)) and a population-based dataset (Canadian Longitudinal Study on Aging (CLSA)). In both datasets, we construct SVIs, use descriptive statistics to report distributions by age and sex, and perform a multivariable linear regression of social vulnerability on frailty.

RESULTS

Procedures for drafting a list of candidate social items, selecting deficits for inclusion, and screening deficits to meet inclusion criteria were applied to yield a 18-deficit SVI for the SOS and 74-deficit SVI for the CLSA. Deficits in each SVI were re-scored between 0 and 1, where 1 indicates the greater risk. Finally, the sum of all deficits is calculated into an index. In the SOS, SVI was associated with age only for females and was weakly associated with frailty (r = 0.26, p<0.001). In the CLSA, SVI was associated with age for both sexes and moderately associated with frailty (r = 0.41, p<0.001).

CONCLUSION

We present a standard method of constructing a SVI by incorporating factors from multiple social domains and levels in a social-ecological model. This SVI can be used to improve our understanding of social vulnerability and its impacts on the health of communities and individuals.

Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review

BACKGROUND

Electronic health records (EHRs) 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 EHRs 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 used individual-level health data from EHRs within the United States to characterize patient phenotypes.

METHODS

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

RESULTS

A substantial proportion of studies (>85%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86%) in publications was observed after 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 limited.

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. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support.

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.

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.

Deconstructing, Addressing, and Eliminating Racial and Ethnic Inequities in Prostate Cancer Care

CONTEXT

Men of African ancestry have demonstrated markedly higher rates of prostate cancer mortality than men of other races and ethnicities around the world. In fact, the highest rates of prostate cancer mortality worldwide are found in the Caribbean and Sub-Saharan West Africa, and among men of African descent in the USA. Addressing this inequity in prostate cancer care and outcomes requires a focused research approach that creates durable solutions to address the structural, social, environmental, and health factors that create racial disparities in care and outcomes.

OBJECTIVE

To introduce a conceptual model for evaluating racial inequities in prostate cancer care to facilitate the development of translational research studies and interventions.

EVIDENCE ACQUISITION

A collaborative review of literature relevant to racial inequities in prostate cancer care and outcomes was performed. Existing literature was used to highlight various components of the conceptual model to inform future research and interventions toward equitable care and outcomes.

EVIDENCE SYNTHESIS

Racial inequities in prostate cancer outcomes are driven by a series of structural and social determinants of health that impact exposures, mediators, and outcomes. Social determinants of equity, such as laws/policies, economic systems, and structural racism, affect the inequitable access to environmental and neighborhood exposures, in addition to health care access. Although the incidence disparity remains problematic, various studies have demonstrated parity in outcomes when social and health factors, such as access to equitable care, are normalized. Few studies have tested interventions to reduce inequities in prostate cancer among Black men.

CONCLUSIONS

Worldwide, men of African ancestry demonstrate worse outcomes in prostate cancer, a phenomenon driven largely by social factors that inform biologic, environmental, and health care risks. A conceptual model was presented that organizes the many factors that influence prostate cancer incidence and mortality. Within that framework, we must understand the current state of inequities in clinical prostate cancer practice, the optimal state of what equitable practice would be, and how achieving equity in prostate cancer care balances costs, benefits, and harms. More robust characterization of the sources of prostate cancer inequities should inform testing of ambitious and innovative interventions as we work toward equity in care and outcomes.

PATIENT SUMMARY

Men of African ancestry demonstrate the highest rates of prostate cancer mortality, which may be reduced through social interventions. We present a framework for formalizing the identification of the drivers of prostate cancer inequities to facilitate the development of interventions and trials to eradicate them.

A spatial assessment of prostate cancer mortality-to-incidence ratios among South Carolina veterans: 1999-2015

PURPOSE

To assess veteran-specific prostate cancer (PrCA) mortality-to-incidence ratios (MIR) in South Carolina's (SC) veteran population.

METHODS

U.S. Veterans Health Administration electronic medical records from January 1999 to December 2015 identified 3,073 PrCA patients residing in 345 ZIP code tabulation areas (ZCTA) within SC. MIRs were calculated for all SC ZCTAs and by key patient- and neighborhood-level risk factors for PrCA. Comparisons between ZCTAs identified as part of a spatial cluster were compared with non-significant ZCTAs using t tests.

RESULTS

The MIR was 0.17 overall, ranging from a low of 0.15 among Black men to 0.20 among White men. Among metropolitan ZCTAs, the MIR was 0.18 compared to 0.16 in non-metropolitan ZCTAs. Two clusters of higher-than-expected MIRs were found in the Upstate region.

CONCLUSIONS

Identification of spatial clusters of higher- or lower-than-expected MIRs allows for further testing of possible explanatory factors, and the capacity to target resources and policies according to greatest need.