Using GIS Mapping to Target Public Health Interventions: Examining Birth Outcomes Across GIS Techniques

Spatial autocorrelation and stratified heterogeneity in the evaluation of breast cancer risk inequity and socioeconomic factors analysis in China: Evidence from Nanchang, Jiangxi Province

Study of socioeconomic factors can play an important role in the spatial distribution of breast cancer by leading to a better understanding of its spatial pattern and assist breast cancer screening and early diagnosis. Taking Nanchang, a major city in central China, as an example, spatial autocorrelation and stratified heterogeneity were applied using a 10 10 km grid division to analyse breast cancer risk and socioeconomic factors. The research results showed that the median incidence rate of female breast cancer in Nanchang from 2016 to 2018 was 6.6/100,000 with a standard deviation of 12.3/100,000. Areas with higher incidence rates were mainly located in the central urban area and the major county towns. Spatial regression analysis showed that there was a statistically significant correlation between the spatial patterns of breast cancer incidence on the one hand, and on the other socioeconomic factors, such as total gross domestic product (GDP), per capita GDP and density of places of social and economic activities, i.e. points of interest. In addition, the normalized difference vegetation index also played a part in this respect. This research could serve as a reference for regional public health policy formulation and breast cancer screening.

Identifying priority and bright spot areas for improving diabetes care: a geospatial approach

The objective of this study was to describe a novel geospatial methodology for identifying poor-performing (priority) and well-performing (bright spot) communities with respect to diabetes management at the ZIP Code Tabulation Area (ZCTA) level. This research was the first phase of a mixed-methods approach known as the focused rapid assessment process (fRAP). Using data from the Lehigh Valley Health Network in eastern Pennsylvania, geographical information systems mapping and spatial analyses were performed to identify diabetes prevalence and A1c control spatial clusters and outliers. We used a spatial empirical Bayes approach to adjust diabetes-related measures, mapped outliers and used the Local Moran's I to identify spatial clusters and outliers. Patients with diabetes were identified from the Lehigh Valley Practice and Community-Based Research Network (LVPBRN), which comprised primary care practices that included a hospital-owned practice, a regional practice association, independent small groups, clinics, solo practitioners and federally qualified health centres. Using this novel approach, we identified five priority ZCTAs and three bright spot ZCTAs in LVPBRN. Three of the priority ZCTAs were located in the urban core of Lehigh Valley and have large Hispanic populations. The other two bright spot ZCTAs have fewer patients and were located in rural areas. As the first phase of fRAP, this method of identifying high-performing and low-performing areas offers potential to mitigate health disparities related to diabetes through targeted exploration of local factors contributing to diabetes management. This novel approach to identification of populations with diabetes performing well or poor at the local community level may allow practitioners to target focused qualitative assessments where the most can be learnt to improve diabetic management of the community.

Models for Heart Failure Admissions and Admission Rates, 2016 through 2018

BACKGROUND

Approximately 6.5 to 6.9 million individuals in the United States have heart failure, and the disease costs approximately $43.6 billion in 2020. This research provides geographical incidence and cost models of this disease in the U.S. and explanatory models to account for hospitals' number of heart failure DRGs using technical, workload, financial, geographical, and time-related variables.

METHODS

The number of diagnoses is forecast using regression (constrained and unconstrained) and ensemble (random forests, extra trees regressor, gradient boosting, and bagging) techniques at the hospital unit of analysis. Descriptive maps of heart failure diagnostic-related groups (DRGs) depict areas of high incidence. State- and county-level spatial and non-spatial regression models of heart failure admission rates are performed. Expenditure forecasts are estimated.

RESULTS

The incidence of heart failure has increased over time with the highest intensities in the East and center of the country; however, several Northern states have seen large increases since 2016. The best predictive model for the number of diagnoses (hospital unit of analysis) was an extremely randomized tree ensemble (predictive R² = 0.86). The important variables in this model included workload metrics and hospital type. State-level spatial lag models using first-order Queen criteria were best at estimating heart failure admission rates (R² = 0.816). At the county level, OLS was preferred over any GIS model based on Moran's I and resultant R²; however, none of the traditional models performed well (R² = 0.169 for the OLS). Gradient-boosted tree models predicted 36% of the total sum of squares; the most important factors were facility workload, mean cash on hand of the hospitals in the county, and mean equity of those hospitals. Online interactive maps at the state and county levels are provided.

CONCLUSIONS

Heart failure and associated expenditures are increasing. Costs of DRGs in the study increased $61 billion from 2016 through 2018. The increase in the more expensive DRG 291 outpaced others with an associated increase of $92 billion. With the increase in demand and steady-state supply of cardiologists, the costs are likely to balloon over the next decade. Models such as the ones presented here are needed to inform healthcare leaders.

Is Crowdsourcing a Reliable Method for Mass Data Acquisition? The Case of COVID-19 Spread in Greece During Spring 2020

We present a GIS-based crowdsourcing application that was launched soon after the first COVID-19 cases had been recorded in Greece, motivated by the need for fast, location-wise data acquisition regarding COVID-19 disease spread during spring 2020, due to limited testing. A single question was posted through a web App, to which the anonymous participants subjectively answered whether or not they had experienced any COVID-19 disease symptoms. Our main goal was to locate geographical areas with increased number of people feeling the symptoms and to determine any temporal changes in the statistics of the survey entries. It was found that the application was rapidly disseminated to the entire Greek territory via social media, having, thus, a great public reception. The higher percentages of participants experiencing symptoms coincided geographically with the highly populated urban areas, having also increased numbers of confirmed cases, while temporal variations were detected that accorded with the restrictions of activities. This application demonstrates that health systems can use crowdsourcing applications that assure anonymity, as an alternative to tracing apps, to identify possible hot spots and to reach and warn the public within a short time interval, increasing at the same time their situational awareness. However, a continuous reminder for participation should be scheduled.

Preterm birth disparities between states in the United States: an opportunity for public health interventions

Objective: To examine associations between statelevel characteristics and state-level preterm birth rates.Study design: We conducted a retrospective ecological cross-sectional study using statelevel data from 2013 to 2014 extracted from publicly available sources -the March of Dimes PeriStats database, the U.S. Census Bureau, the US Department of Education, and the US Department of Justice.Results: State-level preterm birth rates correlated with the following state characteristics: poverty rate, obesity rate, percentage of non-Hispanic Black women residents, smoking rate, percent of C - section deliveries, percent of births to women <20 years old, pregnancies receiving late/no prenatal care, and violent crimes per capita. Linear regression analysis found that only the percent of non-Hispanic Black women by state remained a significant predictor of state-level preterm birth rates after adjusting for other risk factors.Conclusions: States with higher percentages of non-Hispanic Black women had higher rates of preterm birth, even after adjusting for sociodemographic characteristics, prenatal care, and maternal health by state. These findings suggest that public health interventions that target contextual and environmental risk factors affecting non-Hispanic Black women may help to curb rising rates of preterm birth in the United States.

Behavioral mapping of children's physical activities and social behaviors in an indoor preschool facility: methodological challenges in revealing the influence of space in play

BACKGROUND

GIS (Geographic Information Systems) based behavior maps are useful for visualizing and analyzing how children utilize their play spaces. However, a GIS needs accurate locational information to ensure that observations are correctly represented on the layout maps of play spaces. The most commonly used tools for observing and coding free play among children in indoor play spaces require that locational data be collected alongside other play variables. There is a need for a practical, cost-effective approach for extending most tools for analyzing free play by adding geospatial locational information to children's behavior data collected in indoor play environments.

RESULTS

We provide a non-intrusive approach to adding locational information to behavior data acquired from video recordings of preschool children in their indoor play spaces. The gridding technique showed to be a cost-effective method of gathering locational information about children from video recordings of their indoor physical activities and social behaviors. Visualizing the proportions of categories and observed intervals was done using bubble pie charts which allowed for the merging of multiple categorical information on one map. The addition of locational information to other play activity and social behavior data presented the opportunity to assess what types of equipment or play areas may encourage different physical activities and social behaviors among preschool children.

CONCLUSIONS

Gridding is an effective method for providing locational data when analyzing physical activities and social behaviors of preschool children in indoor spaces. It is also reproducible for most GIS behavior mapping focusing on indoor environments. This bypasses the need to have positioning devices attached to children during observations, which can raise ethical considerations regarding children's privacy and methodological implications with children playing less naturally. It also supports visualizations on behavior maps making them easier to interpret.

Hospital-Based Back Surgery: Geospatial-Temporal, Explanatory, and Predictive Models

Background

Hospital-based back surgery in the United States increased by 60% from January 2012 to December 2017, yet the supply of neurosurgeons remained relatively constant. During this time, adult obesity grew by 5%.

Objective

This study aimed to evaluate the demand and associated costs for hospital-based back surgery by geolocation over time to evaluate provider practice variation. The study then leveraged hierarchical time series to generate tight demand forecasts on an unobserved test set. Finally, explanatory financial, technical, workload, geographical, and temporal factors as well as state-level obesity rates were investigated as predictors for the demand for hospital-based back surgery.

Methods

Hospital data from January 2012 to December 2017 were used to generate geospatial-temporal maps and a video of the Current Procedural Terminology codes beginning with the digit 63 claims. Hierarchical time series modeling provided forecasts for each state, the census regions, and the nation for an unobserved test set and then again for the out-years of 2018 and 2019. Stepwise regression, lasso regression, ridge regression, elastic net, and gradient-boosted random forests were built on a training set and evaluated on a test set to evaluate variables important to explaining the demand for hospital-based back surgery.

Results

Widespread, unexplained practice variation over time was seen using geographical information systems (GIS) multimedia mapping. Hierarchical time series provided accurate forecasts on a blind dataset and suggested a 6.52% (from 497,325 procedures in 2017 to 529,777 in 2018) growth of hospital-based back surgery in 2018 (529,777 and up to 13.00% by 2019 [from 497,325 procedures in 2017 to 563,023 procedures in 2019]). The increase in payments by 2019 are estimated to be US $323.9 million. Extreme gradient-boosted random forests beat constrained and unconstrained regression models on a 20% unobserved test set and suggested that obesity is one of the most important factors in explaining the increase in demand for hospital-based back surgery.

Conclusions

Practice variation and obesity are factors to consider when estimating demand for hospital-based back surgery. Federal, state, and local planners should evaluate demand-side and supply-side interventions for this emerging problem.

Geospatial-Temporal and Demand Models for Opioid Admissions, Implications for Policy

Background: As the opioid epidemic continues, understanding the geospatial, temporal, and demand patterns is important for policymakers to assign resources and interdict individual, organization, and country-level bad actors. Methods: GIS geospatial-temporal analysis and extreme-gradient boosted random forests evaluate ICD-10 F11 opioid-related admissions and admission rates using geospatial analysis, demand analysis, and explanatory models, respectively. The period of analysis was January 2016 through September 2018. Results: The analysis shows existing high opioid admissions in Chicago and New Jersey with emerging areas in Atlanta, Salt Lake City, Phoenix, and Las Vegas. High rates of admission (claims per 10,000 population) exist in the Appalachian area and on the Northeastern seaboard. Explanatory models suggest that hospital overall workload and financial variables might be used for allocating opioid-related treatment funds effectively. Gradient-boosted random forest models accounted for 87.8% of the variability of claims on blinded 20% test data. Conclusions: Based on the GIS analysis, opioid admissions appear to have spread geographically, while higher frequency rates are still found in some regions. Interdiction efforts require demand-analysis such as that provided in this study to allocate scarce resources for supply-side and demand-side interdiction: Prevention, treatment, and enforcement.

Intraurban differentials of perinatal mortality: modeling for identifying priority areas

Abstract Objective: To analyze the intraurban spatial distribution of perinatal mortality, its avoidability, and relationship with socioeconomic indicators in Recife, Pernambuco, Brazil, in the period from 2013 to 2015. Method: An ecological study with data from the Information Systems on Mortality and Live Births and the Brazilian Institute of Geography and Statistics, using neighborhoods as the analysis unit. We elaborated an indicator of social deprivation formed by variables from the demographic census. We estimated the Kernel density of the deaths and calculated the Moran index of the perinatal mortality coefficients in the spatial analysis. We elaborated thematic maps of avoidable perinatal mortality and social deprivation. Results: The global statistical analysis of the mortality distribution indicated evidence of spatial aggregation. Moran's index was 0.18. We found clusters of perinatal mortality in neighborhoods of the Central, North, Northwest, and South Regions. In the North, Northwest, Southwest, and South Regions we identified neighborhoods with greater social deprivation and avoidable mortality coefficients. The primary cause of death was of fetuses and newborns affected by hypertensive maternal disorders. Conclusion: We demonstrated intraurban differentials in perinatal mortality among neighborhoods. The stratification of the urban space according to the social deprivation indicator presented a relation with the perinatal mortality and its avoidability.

Are We Serving the Most At-Risk Communities? Examining the Reach of a South Carolina Home Visiting Program

In addition to individual-level characteristics, characteristics of the social and physical environments in which individuals reside may adversely impact health outcomes. Careful attention to the role of "place" can result in programs that successfully deliver services to those most at risk. This retrospective, cross-sectional study used geocoded residential addresses from 3090 households enrolled in a South Carolina (SC) home visiting program, 2013-2016, and corresponding years of data for maternal and child health outcomes obtained from vital records data. ZIP Code Tabulation Areas (ZCTAs) served as the primary geographic unit of analysis. ZCTAS with high volumes of birth or adverse maternal and child health outcomes for any of 10 indicators were flagged. Distribution of enrolled households across highest-risk ZCTAs was calculated. Of 379 ZCTAS with reported data, 152 had 8 or more risk flags. Of the 152 highest-risk ZCTAs, 33 also had high birth volumes. Fifty-seven of the 152 highest-risk ZCTAs had no enrollees; seven of the 33 highest-risk/highest-volume ZCTAS had no enrollees. Service delivery gaps existed despite a statewide, county-level needs assessment conducted prior to program implementation. This study suggests methods to identify service areas of need, as an ongoing effort toward program improvement.

Emergency Medical Services Utilization Is Associated With Community Deprivation in Children

BACKGROUND

Pediatric emergency medical services (EMS) utilization is costly and resource intensive; significant variation exists across large-scale geographies. Less is known about variation at smaller geographic levels where factors including lack of transportation, low health literacy, and decreased access to medical homes may be more relevant. Our objective was to determine whether pediatric EMS utilization varied across Hamilton County, Ohio, census tracts and whether such utilization was associated with socioeconomic deprivation.

METHODS

This was a retrospective analysis of children living in Hamilton County, Ohio, transported by EMS to the Cincinnati Children's emergency department between July 1, 2014, and July 31, 2016. Participants' addresses were assigned to census tracts and an EMS utilization rate and deprivation index were calculated for each. Pearson's correlation coefficients evaluated relationships between tract-level EMS utilization and deprivation. Tract-level deprivation was used as a predictor in patient-level evaluations of acuity.

RESULTS

During the study period, there were 4,877 pediatric EMS transports from 219 of the 222 county census tracts. The county EMS utilization rate during the study period was 2.4 transports per 100 children (range 0.2-11). EMS utilization rates were positively correlated with increasing deprivation (r = 0.72, 95% confidence interval [CI], 0.65-0.77). Deprivation was associated with lower illness severity at triage, fewer transports resulting in resuscitation suite use, and fewer transports resulting in hospitalizations (all p < 0.05).

CONCLUSIONS

EMS utilization varied substantially across census tracts in Hamilton County, Ohio. A deeper understanding into why certain socioeconomically deprived areas contribute to disproportionately high rates of EMS utilization could support development of targeted interventions to improve use.

Pockets of progress amidst persistent racial disparities in low birthweight rates

Racial disparities persist in adverse perinatal outcomes such as preterm birth, low birthweight (LBW), and infant mortality across the U.S. Although pervasive, these disparities are not universal. Some communities have experienced significant improvements in black (or African American) birth outcomes, both in absolute rates and in rate ratios relative to whites. This study assessed county-level progress on trends in black and white LBW rates as an indicator of progress toward more equal birth outcomes for black infants. County-level LBW data were obtained from the 2003 to 2013 U.S. Natality files. Black LBW rates, black-white rate ratios and percent differences over time were calculated. Trend lines were first assessed for significant differences in slope (i.e., converging, diverging, or parallel trend lines). For counties with parallel trend lines, intercepts were tested for statistically significant differences (sustained equality vs. persistent disparities). To assess progress, black LBW rates were compared to white LBW rates, and the trend lines were tested for significant decline. Each county's progress toward black-white equality was ultimately categorized into five possible trend patterns (n = 408): (1) converging LBW rates with reductions in the black LBW rate (decreasing disparities, n = 4, 1%); (2) converging LBW rates due to worsening white LBW rates (n = 5, 1%); (3) diverging LBW rates (increasing disparities, n = 9, 2%); (4) parallel LBW rates (persistent disparities, n = 373, 91%); and (5) overlapping trend lines (sustained equality, n = 18, 4%). Only four counties demonstrated improvement toward equality with decreasing black LBW rates. There is significant county-level variation in progress toward racial equality in adverse birth outcomes such as low birthweight. Still, some communities are demonstrating that more equitable outcomes are possible. Further research is needed in these positive exemplar communities to identify what works in accelerating progress toward more equal birth outcomes.

Data Visualization Promotes Sound Public Health Practice: The AIDSvu Example

The ability to depict surveillance and other complex health-related data in a visual manner promotes sound public health practice by supporting the three core functions of public health: assessment, policy development, and assurance. Further, such efforts potentiate the use of surveillance data beyond traditional public health audiences and venues, thus fostering a "culture of health." This practice report provides several recent examples of how data from AIDSVu-an interactive map of the U.S. showing the impact of HIV at national, state, and local levels-has been used to: fine tune the assessment of HIV-related disparities at a community level, educate and empower communities about HIV and its consequences, and better target HIV interventions to reach underserved, vulnerable populations.

Applying Precision Public Health to Prevent Preterm Birth

Preterm birth (PTB) is one of the major health-care challenges of our time. Being born too early is associated with major risks to the child with potential for serious consequences in terms of life-long disability and health-care costs. Discovering how to prevent PTB needs to be one of our greatest priorities. Recent advances have provided hope that a percentage of cases known to be related to risk factors may be amenable to prevention; but the majority of cases remain of unknown cause, and there is little chance of prevention. Applying the principle of precision public health may offer opportunities previously unavailable. Presented in this article are ideas that may improve our abilities in the fields of studying the effects of migration and of populations in transition, public health programs, tobacco control, routine measurement of length of the cervix in mid-pregnancy by ultrasound imaging, prevention of non-medically indicated late PTB, identification of pregnant women for whom treatment of vaginal infection may be of benefit, and screening by genetics and other “omics.” Opening new research in these fields, and viewing these clinical problems through a prism of precision public health, may produce benefits that will affect the lives of large numbers of people.