When homogeneity meets heterogeneity: the geographically weighted regression with spatial lag approach to prenatal care utilization

Evaluating spatial accessibility to COVID-19 vaccine resources in diversely populated counties in the United States

This study examines the accessibility to COVID-19 vaccination resources in two counties surrounding Newark, NJ in the New York Metropolitan Area, United States. The study area represents diverse population makeups. COVID-19 vaccines were made available by different types of vaccination sites including county mass vaccination sites, medical facilities and pharmacies, and a FEMA community vaccination center in spring 2021. We used the two-step floating catchment area method to measure accessibility and calculated the average accessibility scores of different population groups. We examined the patterns and tested the significance of the differences in accessibility across population groups. The results showed clear spatial heterogeneity in the accessibility to vaccine resources with the existing infrastructure (medical/pharmacy vaccine sites). Accessibility patterns changed with the introduction of county mass sites and the FEMA community site. The county mass vaccination sites in one county greatly increased accessibilities for populations of minority and poverty. The FEMA community site in the other county accomplished the same. Both the local health department and the federal government played an important role in mitigating pre-existing inequalities during the vaccination campaign. Our study shows that social determinants of health need to be addressed and taken into explicit consideration when planning resource distribution during the pandemic.

Using geographic information systems to assess community-level vulnerability to housing insecurity in rural areas

Research examining homelessness in rural areas has been sparse. The current study aims to expand conceptions of rural homelessness by mapping community-level risk factors related to housing insecurity. Geographic information systems (GIS) techniques were used to map the distribution of select community-level risk indicators in the State of Maine. Three methodological choices related to this process are demonstrated: (1) selection and distribution of housing insecurity risk indicators; (2) use of location quotients; and (3) use of spatial lags. After examining and mapping selected risk factors against the location of homeless service supports, four areas in Maine were identified as communities of concern for housing insecurity. Better understanding the extent and location of areas of high need that are resource poor can help service and funding agencies to plan for the more efficient and effective distribution of homeless prevention and mitigation services. Implications for research in rural areas are discussed.

Older Adult Mortality From COVID-19: Food Access as a Determinant Within a Socio-ecological Framework

BACKGROUND AND OBJECTIVES

Low access to food can have an adverse impact on health yet there is limited research on how it is related to coronavirus disease 2019 (COVID-19). The objective of this study was to (a) better understand how inadequate food access was associated with older adult mortality from COVID-19 and (b) determine the spatial distribution of mortality from low food access utilizing a socio-ecological framework.

RESEARCH DESIGN AND METHODS

This study area was the larger Midwest, a region of the United States, which included the following states: Minnesota, Wisconsin, Iowa, Illinois, Indiana, Michigan, Ohio, and Pennsylvania. Data were aggregated from multiple sources at the county-level. Because the spatial data used in this study violated several assumptions of the global regression framework, geographically weighted regression (GWR) was employed.

RESULTS

Results from GWR revealed low access to food was positively associated with mortality from COVID-19 for older adults but the association varied in (a) magnitude and (b) significance across the larger Midwest. More specifically, the socio-ecological framework suggested low access to food, female-headed households, and percentage Hispanic played a meaningful role in explaining older adult mortality for the western region of the larger Midwest. This was not as evident for the eastern portion.

DISCUSSION AND IMPLICATIONS

Such a finding calls attention to the importance of capturing the local context when devising policies to reduce mortality for older adults from COVID-19. Regional policymakers can collaborate with public health professionals when applying these results to formulate local action plans that recognize variations across geographic space.

Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling

The coronavirus disease of 2019 (COVID-19) pandemic is currently a global challenge, with 210 countries, including Indonesia, seeking to minimize its spread. Therefore, this study aims to determine the spatiotemporal spread pattern of this virus in Surabaya using various data on confirmed cases from 28 April to 26 October 2021. It also aims to determine the relationship between pollutant parameters, such as carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃), as well as the government’s high social restrictions policy in Java-Bali. Several methods, such as the weighted mean center, directional distribution, Getis–Ord Gi*, Moran’s I, and geographically weighted regression, were used to identify the spatial spread pattern of the virus. The weighted mean center indicated that the epicenter location of the outbreak moved randomly. The directional distribution demonstrated a decrease of 21 km² at the end of the study phase, which proved that its spread has significantly reduced in Surabaya. Meanwhile, the Getis–Ord Gi* results demonstrated that the eastern and southern parts of the study region were highly infected. Moran’s I demonstrate that COVID-19 cases clustered during the spike. The geographically weighted regression model indicated a number of influence zones in the northeast, northwest, and a few in the southwest parts at the peak of R² 0.55. The relationship between COVID-19 cases and air pollution parameters proved that people living at the outbreak’s center have low pollution levels due to lockdown. Furthermore, the lockdown policy reduced CO, NO₂, SO₂, and O₃. In addition, increase in air pollutants; namely, NO₂, CO, SO₂ and O₃, was recorded after 7 weeks of lockdown implementation (started from 18 August).

"Distance-Driven" Versus "Density-Driven": Understanding the Role of "Source-Case" Distance and Gathering Places in the Localized Spatial Clustering of COVID-19-A Case Study of the Xinfadi Market, Beijing (China)

The frequent occurrence of local COVID-19 today gives a strong necessity to better understand the effects of "source-case" distance and gathering places, which are often considered to be the key factors of the localized spatial clustering of an epidemic. In this study, the localized spatial clustering of COVID-19 cases, which originated in the Xinfadi market in Beijing from June-July 2020, was investigated by exploring the spatiotemporal characteristics of the clustering using descriptive statistics, point pattern analysis, and spatial autocorrelation calculation approaches. Spatial lag zero-inflated negative binomial regression model and geographically weighted Poisson regression with spatial effects were also introduced to explore the factors which influenced the clustering of COVID-19 cases at the micro spatial scale. It was found that the local epidemic can be significantly divided into two stages which are asymmetric in time. A significant spatial spillover effect of COVID-19 was identified in both global and local modeling estimation. The dominant role of the "source-case" distance effect, which was reflected in both global and local scales, was revealed. Relatively, the role of gathering places is not significant at the initial stage of the epidemic, but the upward trend of the significance of some places is obvious. The trend from "distance-driven" to "density-driven" of the localized spatial clustering of COVID-19 was predicted. The effectiveness of blocking the transformation trend will be a key issue for the global response to the local COVID-19.

Association of Travel Distance to Nearest Abortion Facility With Rates of Abortion

IMPORTANCE

Travel distance to abortion services varies widely in the US. Some evidence shows travel distance affects use of abortion care, but there is no national analysis of how abortion rate changes with travel distance.

OBJECTIVE

To examine the association between travel distance to the nearest abortion care facility and the abortion rate and to model the effect of reduced travel distance.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional geographic analysis used 2015 data on abortions by county of residence from 1948 counties in 27 states. Abortion rates were modeled using a spatial Poisson model adjusted for age, race/ethnicity, marital status, educational attainment, household poverty, nativity, and state abortion policies. Abortion rates for 3107 counties in the 48 contiguous states that were home to 62.5 million female residents of reproductive age (15-44 years) and changes under travel distance scenarios, including integration into primary care (<30 miles) and availability of telemedicine care (<5 miles), were estimated. Data were collected from April 2018 to October 2019 and analyzed from December 2019 to July 2020.

EXPOSURES

Median travel distance by car to the nearest abortion facility.

MAIN OUTCOMES AND MEASURES

US county abortion rate per 1000 female residents of reproductive age.

RESULTS

Among the 1948 counties included in the analysis, greater travel distances were associated with lower abortion rates in a dose-response manner. Compared with a median travel distance of less than 5 miles (median rate, 21.1 [range, 1.2-63.6] per 1000 female residents of reproductive age), distances of 5 to 15 miles (median rate, 12.2 [range, 0.5-23.4] per 1000 female residents of reproductive age; adjusted coefficient, -0.05 [95% CI, -0.07 to -0.03]) and 120 miles or more (median rate, 3.9 [range, 0-12.9] per 1000 female residents of reproductive age; coefficient, -0.73 [95% CI, -0.80 to -0.65]) were associated with lower rates. In a model of 3107 counties with 62.5 million female residents of reproductive age, 696 760 abortions were estimated (mean rate, 11.1 [range, 1.0-45.5] per 1000 female residents of reproductive age). If abortion were integrated into primary care, an additional 18 190 abortions (mean rate, 11.4 [range, 1.1-45.5] per 1000 female residents of reproductive age) were estimated. If telemedicine were widely available, an additional 70 920 abortions were estimated (mean rate, 12.3 [range, 1.4-45.5] per 1000 female residents of reproductive age).

CONCLUSIONS AND RELEVANCE

These findings suggest that greater travel distances to abortion services are associated with lower abortion rates. The results indicate which geographic areas have insufficient access to abortion care. Modeling suggests that integrating abortion into primary care or making medication abortion care available by telemedicine may decrease unmet need.

Spatial heterogeneity in the association between particulate matter air pollution and low birth weight in South Korea

As many studies showed the spatial heterogeneity in the association between particulate matter (PM) air pollution and low birth weight (LBW), few studies focused on the variation of local associations at the national scale and related areal characteristics. This study aimed to explore different approaches to estimating local effects of PM with an aerodynamic diameter ≤10 μm (PM₁₀) on LBW across 235 districts in South Korea, to investigate the spatial pattern of local associations, and to examine the relationship with local socio-demographic and environmental characteristics. LBW was identified in 5,692,650 mothers from birth certificate data for 2001-2013. We estimated individual annual-average concentrations of PM₁₀ at centroids of mothers' residential districts by using a previously-validated prediction model. Then, we estimated district-specific odds ratios of LBW for PM₁₀ using modified geographically weighted logistic regression. Here, we applied four approaches with different neighborhood definitions: the distance-based approach within 20- and 40-km bandwidth and the hybrid approach replacing with adjacent districts for urban districts <100 km². In addition, we compared district-specific socioeconomic indicators and emission estimates across three groups of districts that showed significantly positive, no, and significantly negative associations. Medians of district-specific estimates of four approaches were similar to the global estimate and between each other. However, their variability differed with some unreasonably high estimates when a small distance was applied as the neighborhood definition, although spatial pattern was generally similar among the four. The hybrid approach based on the different neighborhood definition by urban and rural areas provided stable risk estimates. Higher risk districts in rural areas were found in more socioeconomically-deprived areas, whereas urban areas showed higher risk districts when their air pollution emissions were higher. Our approach and findings will help identify high risk areas and enhance understanding of geographic determinants.

Exploring heterogeneities with geographically weighted quantile regression: An enhancement based on the bootstrap approach

Geographically weighted quantile regression (GWQR) has been proposed as a spatial analytical technique to simultaneously explore two heterogeneities, one of spatial heterogeneity with respect to data relationships over space and one of response heterogeneity across different locations of the outcome distribution. However, one limitation of GWQR framework is that the existing inference procedures are established based on asymptotic approximation, which may suffer computation difficulties or yield incorrect estimates with finite samples. In this paper, we suggest a bootstrap approach to address this limitation. Our bootstrap enhancement is first validated by a simulation experiment and then illustrated with an empirical US mortality data. The results show that the bootstrap provides a practical alternative for inference in GWQR and enhances the utilization of GWQR.

Global and Geographically Weighted Quantile Regression for Modeling the Incident Rate of Children's Lead Poisoning in Syracuse, NY, USA

Objective: The purpose of this study was to explore the full distribution of children's lead poisoning and identify "high risk" locations or areas in the neighborhood of the inner city of Syracuse (NY, USA), using quantile regression models. Methods: Global quantile regression (QR) and geographically weighted quantile regression (GWQR) were applied to model the relationships between children's lead poisoning and three environmental factors at different quantiles (25th, 50th, 75th, and 90th). The response variable was the incident rate of children's blood lead level ≥ 5 µg/dL in each census block, and the three predictor variables included building year, town taxable values, and soil lead concentration. Results: At each quantile, the regression coefficients of both global QR and GWQR models were (1) negative for both building year and town taxable values, indicating that the incident rate of children lead poisoning reduced with newer buildings and/or higher taxable values of the houses; and (2) positive for the soil lead concentration, implying that higher soil lead concentration around the house may cause higher risks of children's lead poisoning. Further, these negative or positive relationships between children's lead poisoning and three environmental factors became stronger for larger quantiles (i.e., higher risks). Conclusions: The GWQR models enabled us to explore the full distribution of children's lead poisoning and identify "high risk" locations or areas in the neighborhood of the inner city of Syracuse, which would provide useful information to assist the government agencies to make better decisions on where and what the lead hazard treatment should focus on.

Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques

Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban and socio-economic parameters. This paper elucidates the use of the Geographic Information System (GIS), Principal Component Analysis (PCA) and Geographically-Weighted Regression (GWR) techniques to integrate various parameters and estimate the UEQ of two major cities in Ontario, Canada. Remote sensing, GIS and census data were first obtained to derive various environmental, urban and socio-economic parameters. The aforementioned techniques were used to integrate all of these environmental, urban and socio-economic parameters. Three key indicators, including family income, higher level of education and land value, were used as a reference to validate the outcomes derived from the integration techniques. The results were evaluated by assessing the relationship between the extracted UEQ results and the reference layers. Initial findings showed that the GWR with the spatial lag model represents an improved precision and accuracy by up to 20% with respect to those derived by using GIS overlay and PCA techniques for the City of Toronto and the City of Ottawa. The findings of the research can help the authorities and decision makers to understand the empirical relationships among environmental factors, urban morphology and real estate and decide for more environmental justice.

A scoping review of geographic information systems in maternal health

Background

Geographic information systems (GIS) are increasingly recognized tools in maternal health.

Objectives

To evaluate the use of GIS in maternal health and to identify knowledge gaps and opportunities.

Search strategy

Keywords broadly related to maternal health and GIS were used to search for academic articles and gray literature.

Selection criteria

Reviewed articles focused on maternal health, with GIS used as part of the methods.

Data collection and analysis

Peer reviewed articles (n = 40) and gray literature sources (n = 30) were reviewed.

Main results

Two main themes emerged: modeling access to maternal services and identifying risks associated with maternal outcomes. Knowledge gaps included a need to rethink spatial access to maternal care in low- and middle-income settings, and a need for more explicit use of GIS to account for the geographical variation in the effect of risk factors on adverse maternal outcomes. Limited evidence existed to suggest that use of GIS had influenced maternal health policy. Instead, application of GIS to maternal health was largely influenced by policy priorities in global maternal health.

Conclusions

Investigation of the role of GIS in contributing to future policy directions is warranted, particularly for elucidating determinants of global maternal health.

GEOGRAPHICALLY-WEIGHTED REGRESSION ANALYSIS OF PERCENTAGE OF LATE-STAGE PROSTATE CANCER DIAGNOSIS IN FLORIDA

This study assessed spatial context and the local impacts of putative factors on the proportion of prostate cancer diagnosed at late-stages in Florida during the period 2001-2007. A logistic regression was performed aspatially and by geographically-weighted regression (GWR) at the nodes of a 5 km spacing grid overlaid over Florida and using all the cancer cases within a radius of 125 km of each node. Variables associated significantly with high percentages of late-stage prostate cancer included having comorbidities, smoking, being Black and living in census tracts with farmhouses. Having private or public insurance, being married or diagnosed in a for-profit facility, as well as living in census tracts with high household income reduced significantly this likelihood. Geographically-weighted regression allowed the identification of areas where the local odds ratio is significantly different from the ratio estimated using aspatial regression (State-level). For example, the local odds ratios for the comorbidity covariates were significantly smaller than the State-level odds ratio in Tallahassee and Pensacola, while they were significantly larger in Palm Beach. This emphasizes the need for local strategies and cancer control interventions to reduce the percentage of prostate cancer diagnosed at late-stages and ultimately eliminate health disparities.