Mapping high or low mortality places across time in the United States: a research note on a health visualization and analysis project

Geospatial and Machine Learning Regression Techniques for Analyzing Food Access Impact on Health Issues in Sustainable Communities

Food access is a major key component in food security, as it is every individual’s right to proper access to a nutritious and affordable food supply. Low access to healthy food sources influences people’s diet and activity habits. Guilford County in North Carolina has a high ranking in low food security and a high rate of health issues such as high blood pressure, high cholesterol, and obesity. Therefore, the primary objective of this study was to investigate the geospatial correlation between health issues and food access areas. The secondary objective was to quantitatively compare food access areas and heath issues’ descriptive statistics. The tertiary objective was to compare several machine learning techniques and find the best model that fit health issues against various food access variables with the highest performance accuracy. In this study, we adopted a food-access perspective to show that communities that have residents who have equitable access to healthy food options are typically less vulnerable to health-related disasters. We propose a methodology to help policymakers lower the number of health issues in Guilford County by analyzing such issues via correlation with respect to food access. Specifically, we conducted a geographic information system mapping methodology to examine how access to healthy food options influenced health and mortality outcomes in one of the largest counties in the state of North Carolina. We created geospatial maps representing food deserts—areas with scarce access to nutritious food; food swamps—areas with more availability of unhealthy food options compared to healthy food options; and food oases—areas with a relatively higher availability of healthy food options than unhealthy options. Our results presented a positive correlation coefficient of R2 = 0.819 among obesity and the independent variables of transportation access, and population. The correlation coefficient matrix analysis helped to identify a strong negative correlation between obesity and median income. Overall, this study offers valuable insights that can help health authorities develop preemptive preparedness for healthcare disasters.

Decomposition of the US black/white inequality in premature mortality, 2010-2015: an observational study

OBJECTIVE

Decompose the US black/white inequality in premature mortality into shared and group-specific risks to better inform health policy.

SETTING

All 50 US states and the District of Columbia, 2010 to 2015.

PARTICIPANTS

A total of 2.85 million non-Hispanic white and 762 639 non-Hispanic black US-resident decedents.

PRIMARY AND SECONDARY OUTCOME MEASURES

The race-specific county-level relative risks for US blacks and whites, separately, and the risk ratio between groups.

RESULTS

There is substantial geographic variation in premature mortality for both groups and the risk ratio between groups. After adjusting for median household income, county-level relative risks ranged from 0.46 to 2.04 (median: 1.03) for whites and from 0.31 to 3.28 (median: 1.15) for blacks. County-level risk ratios (black/white) ranged from 0.33 to 4.56 (median: 1.09). Half of the geographic variation in white premature mortality was shared with blacks, while only 15% of the geographic variation in black premature mortality was shared with whites. Non-Hispanic blacks experience substantial geographic variation in premature mortality that is not shared with whites. Moreover, black-specific geographic variation was not accounted for by median household income.

CONCLUSION

Understanding geographic variation in mortality is crucial to informing health policy; however, estimating mortality is difficult at small spatial scales or for small subpopulations. Bayesian joint spatial models ameliorate many of these issues and can provide a nuanced decomposition of risk. Using premature mortality as an example application, we show that Bayesian joint spatial models are a powerful tool as researchers grapple with disentangling neighbourhood contextual effects and sociodemographic compositional effects of an area when evaluating health outcomes. Further research is necessary in fully understanding when and how these models can be applied in an epidemiological setting.

Methods and tools for geographical mapping and analysis in primary health care

AIM

The purpose of this paper is to review methods and tools for mapping, visualising and exploring geographic information to aid in primary health care (PHC) research and development.

BACKGROUND

Mapping and spatial analysis of indicators of locality health profiles and healthcare needs assessment are well-established facets of health services research and development. However, while there are a range of different methods and tools used for these purposes, non-specialists responsible for managing the use of such information systems may find knowing where to start and what can be done a relatively steep learning curve. In this review, health and sociodemographic datasets are used to illustrate some key methods, tools and organisational issues, and builds upon two recent reviews in this journal, respectively, focusing on geographic data sources and geographic concepts. Those familiar with mapping and spatial analysis should find this a useful review of current matters.

METHOD

A thematic review is presented with illustrative case studies relevant to PHC. It begins with a section on visualising and interpreting geographic information. This is followed by a section critiquing analytical methods. Consideration is given to software and deployment issues in a third section. Content is based on domain knowledge of the authors as a team of geographic information scientists and a public health practitioner working in tandem, with its scope restricted to routine applications of mapping and analysis. Advanced techniques such as spatio-temporal modelling are not considered, neither are methodological technicalities, although guidance on further reading is provided.

SUMMARY

Geographical perspectives are now playing a significant role in PHC delivery, and for those engaged in informatics and/or managing population-level care, understanding key geographic information systems methods and terminologies are important as is gaining greater familiarity with institutional aspects of implementation.

Mortality and physician supply: does region hold the key to the paradox?

OBJECTIVE

. To determine if the supply of physicians has a consistent relationship with mortality across regions.

DATA SOURCES

County-level data describing the supply of physicians, mortality, and socioeconomic conditions of the population as provided in the Area Resource File (BHPr, HRSA) and the Compressed Mortality File (NCHS, CDC).

STUDY DESIGN

Ordinary least squares and geographically weighted regression models with age-adjusted all-cause and disease-specific mortality as the dependent variables were specified using pooled data from 1996 to 2000 to test for the relationship with primary care and specialist physician-population ratios. The residuals from the OLS models were mapped and examined for potential clustering. A series of geographically weighted regression models were run for all 3,070 counties and the z-scores and significance of the models mapped.

PRINCIPAL FINDINGS

The association between primary care physician supply and mortality was not observed in contrast to other studies; mapping the residuals of those models suggested regional clustering. When weighted geographically, the relationship between primary care and specialist physician supply and mortality presents a mixed pattern. The results show strong regional patterns that may explain the lack of a consistent national association. Primary care physicians are associated with decreased mortality on the east coast and upper midwest, but that correlation disappears or is reversed in the west (with the exception of Washington State) and south central states.

CONCLUSIONS

We find evidence that there are regionally focused association between physician supply and mortality, holding constant population characteristics that reflect the influence of social and economic characteristics. However, these relationships are not consistent across the United States; there are regions where there are stronger and weaker associations between type of practitioner and mortality and other regions where no association is apparent. This suggests that the direction for further analysis lies in the understanding of the regional differences and whether there are policy alternatives to address these different patterns.

Persistent clusters of mortality in the United States

We explored how place shapes mortality by examining 35 consecutive years of US mortality data. Mapping age-adjusted county mortality rates showed both persistent temporal and spatial clustering of high and low mortality rates. Counties with high mortality rates and counties with low mortality rates both experienced younger population out-migration, had economic decline, and were predominantly rural. These mortality patterns have important implications for proper research model specification and for health resource allocation policies.

The cumulative effect of rural and regional residence on the health of older adults

OBJECTIVE

This study considers whether rural status or living in a particular region of the United States has effects on mental and physical health. The analyses examine whether there are independent and interactive effects of rural status and region of residence on health, beyond individual level factors related to poverty.

METHODS

Ordinary least squares (OLS) regression models are used to examine the relationships between rural and regional residence and health across three time periods.

RESULTS

Negative health effects of rural residence were found only in the South region. Positive health effects of rural residence were found only in the Midwest region. There are no observed health risks associated with rural or regional residence across group.

DISCUSSION

The results indicate a cumulative risk of rural and Southern residence for older men and women. Living in a rural place in the midwestern United States seems to provide unique sources of health benefits.