Disparities in Access to Robotic Knee Arthroplasty: A Geospatial Analysis.

BACKGROUND: The utilization of robotic knee arthroplasty (RKA) continues to increase across the United States. The aim of this geospatial analysis was to elucidate if RKA is distributed uniformly across the United States or if disparities exist in patient access. METHODS: Publicly available provider-finding functions for 5 major manufacturers of RKA systems were used to obtain the practice locations of surgeons performing RKA along with their associated RKA system manufacturer. The average travel distance for each county to the nearest RKA surgeon was calculated and Moran's index clustering analysis was used to find hotspots and coldspots of RKA access. A logistic regression model was used to identify the predictive odds ratios between robotic hotspots and coldspots with county-level sociodemographic variables. Of the 34,216 currently practicing orthopedic surgeons in 2022, 2,571 have access to robotic assistance for knee arthroplasty. RESULTS: Hotspots of increased travel time were predominantly in West South Central and West North Central census regions. Hotspots were significantly more rural and consisted of predominantly White populations, with lower median income and health insurance coverage. CONCLUSIONS: The results of the current study align with existing literature, demonstrating absolute geographic access disparities for rural and economically disadvantaged populations. Additionally, relative access disparities persist for minority populations and individuals with high comorbidity burdens residing in urban areas.

Geographic variation in HCV treatment penetration among people who inject drugs in Baltimore, MD.

We evaluated geographic heterogeneity in hepatitis C virus (HCV) treatment penetration among people who inject drug (PWID) across Baltimore, MD since the advent of direct-acting antivirals (DAAs) using space-time clusters of HCV viraemia. Using data from a community-based cohort of PWID, the AIDS Linked to the IntraVenous Experience (ALIVE) study, we identified space-time clusters with higher-than-expected rates of HCV viraemia between 2015 and 2019 using scan statistics. We used Poisson regression to identify covariates associated with HCV viraemia and used the regression-fitted values to detect adjusted space-time clusters of HCV viraemia in Baltimore city. Overall, in the cohort, HCV viraemia fell from 77% in 2015 to 64%, 49%, 39% and 36% from 2016 to 2019. In Baltimore city, the percentage of census tracts where prevalence of HCV viraemia was ≥85% dropped from 57% to 34%, 25%, 22% and 10% from 2015 to 2019. We identified two clusters of higher-than-expected HCV viraemia in the unadjusted analysis that lasted from 2015 to 2017 in East and West Baltimore and one adjusted cluster of HCV viraemia in West Baltimore from 2015 to 2016. Neither differences in age, sex, race, HIV status, nor neighbourhood deprivation were able to explain the significant space-time clusters. However, residing in a cluster with higher-than-expected viraemia was associated with age, sex, educational attainment and higher levels of neighbourhood deprivation. Nearly 4 years after DAAs became available, HCV treatment has penetrated all PWID communities across Baltimore city. While nearly all census tracts experienced improvements, change was more gradual in areas with higher levels of poverty.

Geospatial clustering in sugar-sweetened beverage consumption among Boston youth.

The objective was to detect geospatial clustering of sugar-sweetened beverage (SSB) intake in Boston adolescents (age = 16.3 ± 1.3 years [range: 13-19]; female = 56.1%; White = 10.4%, Black = 42.6%, Hispanics = 32.4%, and others = 14.6%) using spatial scan statistics. We used data on self-reported SSB intake from the 2008 Boston Youth Survey Geospatial Dataset (n = 1292). Two binary variables were created: consumption of SSB (never versus any) on (1) soda and (2) other sugary drinks (e.g., lemonade). A Bernoulli spatial scan statistic was used to identify geospatial clusters of soda and other sugary drinks in unadjusted models and models adjusted for age, gender, and race/ethnicity. There was no statistically significant clustering of soda consumption in the unadjusted model. In contrast, a cluster of non-soda SSB consumption emerged in the middle of Boston (relative risk = 1.20, p = .005), indicating that adolescents within the cluster had a 20% higher probability of reporting non-soda SSB intake than outside the cluster. The cluster was no longer significant in the adjusted model, suggesting spatial variation in non-soda SSB drink intake correlates with the geographic distribution of students by race/ethnicity, age, and gender.

Factors influencing geographic gender disparity in neurosurgery: a nationwide geospatial clustering analysis.

OBJECTIVE: Women neurosurgeons (WNs) continue to remain a minority in the specialty despite significant initiatives to increase their representation. One domain less explored is the regional distribution of WNs, facilitated by the hiring practices of neurosurgical departments across the US. In this analysis, the authors coupled the stated practice location of WNs with regional geospatial data to identify hot spots and cold spots of prevalence and examined regional predictors of increases and decreases in WNs over time. METHODS: The authors examined the National Provider Identifier (NPI) numbers of all neurosurgeons obtained via the National Plan and Provider Enumeration System (NPPES), identifying the percentage of WNs in each county for which data were appended with data from the US Census Bureau. Change in WN rates was identified by calculating a regression slope for all years included (2015-2022). Hot spots and cold spots of WNs were identified through Moran's clustering analysis. Population and surgeon features were compared for hot spots and cold spots. RESULTS: WNs constituted 10.73% of all currently active neurosurgical NPIs, which has increased from 2015 (8.81%). Three hot spots were found-including the Middle Atlantic and Pacific divisions-that contrasted with scattered cold spots throughout the East Central regions that included Memphis as a major city. Although relatively rapidly growing, hot spots had significant gender inequality, with a median WN percentage of 11.38% and a median of 0.61 WNs added to each respective county per year. CONCLUSIONS: The authors analyzed the prevalence of WNs by using aggregated data from the NPPES and US Census Bureau. The authors also show regional hot spots of WNs and that the establishment of WNs in a region is a predictor of additional WNs entering the region. These data suggest that female neurosurgical mentorship and representation may be a major driver of acceptance and further gender diversity in a given region.

Geospatially clustered low COVID-19 vaccine rates among adolescents in socially vulnerable US counties.

COVID-19 vaccinations are widely available across the United States (U.S.), yet little is known about the spatial clustering of COVID-19 vaccinations. This study aimed to test for geospatial clustering of COVID-19 vaccine rates among adolescents aged 12-17 across the U.S. counties and to compare these clustering patterns by sociodemographic characteristics. County-level data on COVID-19 vaccinations and sociodemographic characteristics were obtained from the COVID-19 Community Profile Report up to April 14, 2022. A total of 3,108 counties were included in the analysis. Global Moran's I statistic and Anselin Local Moran's analysis were used, and clustering patterns were compared to sociodemographic variables using t-tests. Counties with low COVID-19 vaccinated clusters were more likely, when compared to unclustered counties, to have higher numbers of individuals in poverty and uninsured individuals, and higher values of Social Vulnerability Index (SVI) and COVID-19 Community Vulnerability Index (CCVI). While high COVID-19 vaccinated clusters, compared to neighboring counties, had lower numbers of Black population, individuals in poverty, and uninsured individuals, and lower values of SVI and CCVI, but a higher number of Hispanic population. This study emphasizes the importance of addressing systemic barriers, such as poverty and lack of health insurance, which were found to be associated with low COVID-19 vaccination coverage.

Geospatial clustering and modelling provide policy guidance to distribute funding for active TB case finding in Ethiopia.

Tuberculosis (TB) exhibits considerable spatial heterogeneity, occurring in clusters that may act as hubs of community transmission. We evaluated the impact of an intervention targeting spatial TB hotspots in a rural region of Ethiopia. To evaluate the impact of targeted active case finding (ACF), we used a spatially structured mathematical model that has previously been described. From model equilibrium, we simulated the impact of a hotspot-targeted strategy (HTS) on TB incidence ten years from intervention commencement and the associated cost-effectiveness. HTS was also compared with an untargeted strategy (UTS). We used logistic cost-coverage analysis to estimate cost-effectiveness of interventions. At a community screening coverage level of 95 % in a hotspot region, which corresponds to screening 20 % of the total population, HTS would reduce overall TB incidence by 52 % compared with baseline. For UTS to achieve an equivalent effect, it would be necessary to screen more than 80 % of the total population. Compared to the existing passive case detection strategy, the HTS at a CDR of 75 percent in hotspot regions is expected to avert 1,023 new TB cases over ten years saving USD 170 per averted case. Similarly, at the same CDR, the UTS will detect 1316 cases over the same period saving USD 3 per averted TB case. The incremental-cost effectiveness-ratio (ICER) of UTS compared with HTS is USD 582 per averted case corresponding to 293 more TB cases averted at an additional cost of USD 170,700. Where regional TB program spending was capped at current levels, maximum gains in incidence reduction were seen when the regional budget was shared between hotspots and non-hotspot regions in the ratio of 40% : 60%. Our analysis suggests that a spatially targeted strategy is efficient and cost-saving, with the potential for significant reduction in overall TB burden.

Geospatial clustering of meningitis: an early warning system (hotspot) for potential meningitis outbreak in upper east region of Ghana.

OBJECTIVE: We mapped and generated hot spots for potential meningitis outbreak from existing data in Upper East region, Ghana. DESIGN: This was a cross-sectional study conducted in 2017. DATA SOURCE: Meningitis data in the Upper East Region from January 2007, to December 2016. MAIN OUTCOME MEASURE: We used spatial tools in Quantum Geographic Information System (QGIS) and Geoda to draw choropleth map of meningitis incidence, case fatality and hotspot for potential meningitis outbreak. RESULTS: A total of 2312 meningitis cases (suspected and confirmed) were recorded from 2016-2017 with median incidence of 15.0cases/100,000 population (min 6.3, max 47.8). Median age of cases was 15 years (IQR: 6-31 years). Most (44.2%) of those affected were 10 years and below. Females (51.2%) constituted the highest proportion. Median incidence from 2007-2011 was 20cases/100,000 population (Min 11.3, Max 39.9) whilst from 2012-2016 was 11.1cases/100,000 populations (Min 6.3, Max 47.8). A total of 28 significant hotspot sub-districts clusters (p=0.024) were identified with 7 High-high risk areas as potential meningitis outbreak spots. CONCLUSION: The occurrence of meningitis is not random, spatial cluster with high -high-risk exist in some sub-districts. Overall meningitis incidence and fatality rate have declined in the region with district variations. Districts with high meningitis incidence and fatality rates should be targeted for intervention. FUNDING: Author EA was supported by the West Africa Health Organization (Ref.: Prog/A17IEpidemSurveillN°57212014/mcrt).

Evaluating for a geospatial relationship between radon levels and thyroid cancer in Pennsylvania.

OBJECTIVES/HYPOTHESIS: To determine whether there is an association between radon levels and the rise in incidence of thyroid cancer in Pennsylvania. STUDY DESIGN: Epidemiological study of the state of Pennsylvania. METHODS: We used information from the Pennsylvania Cancer Registry and the Pennsylvania Department of Energy. From the registry, information regarding thyroid incidence by county and zip code was recorded. Information regarding radon levels per county was recorded from the state. Poisson regression models were fit predicting county-level thyroid incidence and change as a function of radon/lagged radon levels. To account for measurement error in the radon levels, a Bayesian Model extending the Poisson models was fit. Geospatial clustering analysis was also performed. RESULTS: No association was noted between cumulative radon levels and thyroid incidence. In the Poisson modeling, no significant association was noted between county radon level and thyroid cancer incidence (P = .23). Looking for a lag between the radon level and its effect, no significant effect was seen with a lag of 0 to 6 years between exposure and effect (P = .063 to P = .59). The Bayesian models also failed to show a statistically significant association. A cluster of high thyroid cancer incidence was found in western Pennsylvania. CONCLUSIONS: Through a variety of models, no association was elicited between annual radon levels recorded in Pennsylvania and the rising incidence of thyroid cancer. However, a cluster of thyroid cancer incidence was found in western Pennsylvania. Further studies may be helpful in looking for other exposures or associations.