Disparities in Cancer Stage Outcomes by Catchment Areas for a Comprehensive Cancer Center.

Importance: The National Cancer Institute comprehensive cancer centers (CCCs) lack spatial and temporal evaluation of their self-designated catchment areas. Objective: To identify disparities in cancer stage at diagnosis within and outside a CCC's catchment area across a 10-year period using spatial and statistical analyses. Design, Setting, and Participants: This cross-sectional, population-based study conducted between 2010 and 2019 utilized cancer registry data for the Johns Hopkins Sidney Kimmel CCC (SKCCC). Eligible participants included patients with cancer in the contiguous US who received treatment for cancer, a diagnosis of cancer, or both at SKCCC. Patients were geocoded to zip code tabulation areas (ZCTAs). Individual-level variables included sociodemographic characteristics, smoking and alcohol use, treatment type, cancer site, and insurance type. Data analysis was performed between March and July 2023. Exposures: Distance between SKCCC and ZCTAs were computed to generate a catchment area of the closest 75% of patients and outer zones in 5% increments for comparison. Main Outcomes and Measures: The primary outcome was cancer stage at diagnosis, defined as early-stage, late-stage, or unknown stage. Multinomial logistic regression was used to determine associations of catchment area with stage at diagnosis. Results: This study had a total of 94 007 participants (46 009 male [48.94%] and 47 998 female [51.06%]; 30 195 aged 22-45 years [32.12%]; 4209 Asian [4.48%]; 2408 Hispanic [2.56%]; 16 004 non-Hispanic Black [17.02%]; 69 052 non-Hispanic White [73.45%]; and 2334 with other or unknown race or ethnicity [2.48%]), including 47 245 patients (50.26%) who received a diagnosis of early-stage cancer, 19 491 (20.73%) who received a diagnosis of late-stage cancer , and 27 271 (29.01%) with unknown stage. Living outside the main catchment area was associated with higher odds of late-stage cancers for those who received only a diagnosis (odds ratio [OR], 1.50; 95% CI, 1.10-2.05) or only treatment (OR, 1.44; 95% CI, 1.28-1.61) at SKCCC. Non-Hispanic Black patients (OR, 1.16; 95% CI, 1.10-1.23) and those with Medicaid (OR, 1.65; 95% CI, 1.46-1.86) and no insurance at time of treatment (OR, 2.12; 95% CI, 1.79-2.51) also had higher odds of receiving a late-stage cancer diagnosis. Conclusions and Relevance: In this cross-sectional study of CCC data from 2010 to 2019, patients residing outside the main catchment area, non-Hispanic Black patients, and patients with Medicaid or no insurance had higher odds of late-stage diagnoses. These findings suggest that disadvantaged populations and those living outside of the main catchment area of a CCC may face barriers to screening and treatment. Care-sharing agreements among CCCs could address these issues.

Case-area targeted interventions during a large-scale cholera epidemic: A prospective cohort study in Northeast Nigeria.

BACKGROUND: Cholera outbreaks are on the rise globally, with conflict-affected settings particularly at risk. Case-area targeted interventions (CATIs), a strategy whereby teams provide a package of interventions to case and neighboring households within a predefined "ring," are increasingly employed in cholera responses. However, evidence on their ability to attenuate incidence is limited. METHODS AND FINDINGS: We conducted a prospective observational cohort study in 3 conflict-affected states in Nigeria in 2021. Enumerators within rapid response teams observed CATI implementation during a cholera outbreak and collected data on household demographics; existing water, sanitation, and hygiene (WASH) infrastructure; and CATI interventions. Descriptive statistics showed that CATIs were delivered to 46,864 case and neighbor households, with 80.0% of cases and 33.5% of neighbors receiving all intended supplies and activities, in a context with operational challenges of population density, supply stock outs, and security constraints. We then applied prospective Poisson space-time scan statistics (STSS) across 3 models for each state: (1) an unadjusted model with case and population data; (2) an environmentally adjusted model adjusting for distance to cholera treatment centers and existing WASH infrastructure (improved water source, improved latrine, and handwashing station); and (3) a fully adjusted model adjusting for environmental and CATI variables (supply of Aquatabs and soap, hygiene promotion, bedding and latrine disinfection activities, ring coverage, and response timeliness). We ran the STSS each day of our study period to evaluate the space-time dynamics of the cholera outbreaks. Compared to the unadjusted model, significant cholera clustering was attenuated in the environmentally adjusted model (from 572 to 18 clusters) but there was still risk of cholera transmission. Two states still yielded significant clusters (range 8-10 total clusters, relative risk of 2.2-5.5, 16.6-19.9 day duration, including 11.1-56.8 cholera cases). Cholera clustering was completely attenuated in the fully adjusted model, with no significant anomalous clusters across time and space. Associated measures including quantity, relative risk, significance, likelihood of recurrence, size, and duration of clusters reinforced the results. Key limitations include selection bias, remote data monitoring, and the lack of a control group. CONCLUSIONS: CATIs were associated with significant reductions in cholera clustering in Northeast Nigeria despite operational challenges. Our results provide a strong justification for rapid implementation and scale-up CATIs in cholera-response, particularly in conflict settings where WASH access is often limited.

Healthcare utilization in women diagnosed with endometrial cancer: A survey-based study.

OBJECTIVE: Despite similar incidence, non-Hispanic Black women are twice as likely to die of endometrial cancer as non-Hispanic White women. The social determinants of health may contribute to this disparity. We studied barriers to care and social needs of endometrial cancer patients. METHODS: In a cohort of patients with endometrial cancer from the All of Us study, participants self-reported demographics and completed validated surveys (access to medical care, transportation, caregiving, finances, medication, general care, specialty care, housing insecurity). Univariate and multivariate logistic regression models evaluated demographic and access factors associated with any need. RESULTS: Of 568 participants, 77.7% identified as non-Hispanic White, 7.5% Black, and 8.8% Hispanic. 59% were > 65 years and 95.8% insured. Contributors to delays in care were paying out of pocket (9.9%), provider anxiety (7.6%), transportation (6.3%), cost of copay (6.2%), and insufficient leave from work (5.6%). To mitigate healthcare costs, 16.2% of participants inquired about lower-cost medications, 11.1% reported delaying filling prescriptions, 7.6% taking fewer prescribed medications, and 6.5% skipped doses. Regarding multivariate analysis, participants earning <$25,000 had a 7.3 (95% CI 1.7-31.7) higher adjusted odds of transportation needs and 3.6 (95% CI 1.4-9.7) higher difficulty accessing specialists. No racial/ethnic disparities were identified. CONCLUSIONS: Social needs and barriers to care are most pronounced among endometrial cancer survivors earning <$25,000. Unexpectedly, and possibly related to sample size or survey tool, race/ethnicity were not zassociated with barriers to care. Further studies on health-related social needs, optimal screening tools, and effective interventions are needed in order to achieve equity in cancer outcomes for endometrial cancer patients.

Evaluating co-occurring space-time clusters of depression and suicide-related outcomes before and during the COVID-19 pandemic.

Rapidly emerging research on the mental health consequences of the COVID-19 pandemic shows increasing patterns of psychological distress, including anxiety and depression, and self-harming behaviors, particularly during the early months of the pandemic. Yet, few studies have investigated the spatial and temporal changes in depressive disorders and suicidal behavior during the pandemic. The objective of this retrospective analysis was to evaluate geographic patterns of emergency department admissions for depression and suicidal behavior in North Carolina before (March 2017-February 2020) and during the COVID-19 pandemic (March 2020 - December 2021). Univariate cluster detection examined each outcome separately and multivariate cluster detection was used to examine the co-occurrence of depression and suicide-related outcomes in SatScan; the Rand index evaluated cluster overlap. Cluster analyses were adjusted for age, race, and sex. Findings suggest that the mental health burden of depression and suicide-related outcomes remained high in many communities throughout the pandemic. Rural communities exhibited a larger increase in the co-occurrence of depression and suicide-related ED visits during the pandemic period. Results showed the exacerbation of depression and suicide-related outcomes in select communities and emphasize the need for targeted and sustained mental health interventions throughout the many phases of the COVID-19 pandemic.

Geographic Disparities in Potential Accessibility to Gynecologic Oncologists in the United States From 2001 to 2020.

OBJECTIVE: To use a spatial modeling approach to capture potential disparities of gynecologic oncologist accessibility in the United States at the county level between 2001 and 2020. METHODS: Physician registries identified the 2001-2020 gynecologic oncology workforce and were aggregated to each county. The at-risk cohort (women aged 18 years or older) was stratified by race and ethnicity and rurality demographics. We computed the distance from at-risk women to physicians. Relative access scores were computed by a spatial model for each contiguous county. Access scores were compared across urban or rural status and racial and ethnic groups. RESULTS: Between 2001 and 2020, the gynecologic oncologist workforce increased. By 2020, there were 1,178 active physicians and 98.3% practiced in urban areas (37.3% of all counties). Geographic disparities were identified, with 1.09 physicians per 100,000 women in urban areas compared with 0.1 physicians per 100,000 women in rural areas. In total, 2,862 counties (57.4 million at-risk women) lacked an active physician. Additionally, there was no increase in rural physicians, with only 1.7% practicing in rural areas in 2016-2020 relative to 2.2% in 2001-2005 ( P =.35). Women in racial and ethnic minority populations, such as American Indian or Alaska Native and Hispanic women, exhibited the lowest level of access to physicians across all time periods. For example, 23.7% of American Indian or Alaska Native women did not have access to a physician within 100 miles between 2016 and 2020, which did not improve over time. Non-Hispanic Black women experienced an increase in relative accessibility, with a 26.2% increase by 2016-2020. However, Asian or Pacific Islander women exhibited significantly better access than non-Hispanic White, non-Hispanic Black, Hispanic, and American Indian or Alaska Native women across all time periods. CONCLUSION: Although the U.S. gynecologic oncologist workforce increased steadily over 20 years, this has not translated into evidence of improved access for many women from rural and underrepresented areas. However, health care utilization and cancer outcomes may not be influenced only by distance and availability. Policies and pipeline programs are needed to address these inequities in gynecologic cancer care.

Using Smartphone Survey and GPS Data to Inform Smoking Cessation Intervention Delivery: Case Study.

BACKGROUND: Interest in quitting smoking is common among young adults who smoke, but it can prove challenging. Although evidence-based smoking cessation interventions exist and are effective, a lack of access to these interventions specifically designed for young adults remains a major barrier for this population to successfully quit smoking. Therefore, researchers have begun to develop modern, smartphone-based interventions to deliver smoking cessation messages at the appropriate place and time for an individual. A promising approach is the delivery of interventions using geofences-spatial buffers around high-risk locations for smoking that trigger intervention messages when an individual's phone enters the perimeter. Despite growth in personalized and ubiquitous smoking cessation interventions, few studies have incorporated spatial methods to optimize intervention delivery using place and time information. OBJECTIVE: This study demonstrates an exploratory method of generating person-specific geofences around high-risk areas for smoking by presenting 4 case studies using a combination of self-reported smartphone-based surveys and passively tracked location data. The study also examines which geofence construction method could inform a subsequent study design that will automate the process of deploying coping messages when young adults enter geofence boundaries. METHODS: Data came from an ecological momentary assessment study with young adult smokers conducted from 2016 to 2017 in the San Francisco Bay area. Participants reported smoking and nonsmoking events through a smartphone app for 30 days, and GPS data was recorded by the app. We sampled 4 cases along ecological momentary assessment compliance quartiles and constructed person-specific geofences around locations with self-reported smoking events for each 3-hour time interval using zones with normalized mean kernel density estimates exceeding 0.7. We assessed the percentage of smoking events captured within geofences constructed for 3 types of zones (census blocks, 500 ft2 fishnet grids, and 1000 ft2 fishnet grids). Descriptive comparisons were made across the 4 cases to better understand the strengths and limitations of each geofence construction method. RESULTS: The number of reported past 30-day smoking events ranged from 12 to 177 for the 4 cases. Each 3-hour geofence for 3 of the 4 cases captured over 50% of smoking events. The 1000 ft2 fishnet grid captured the highest percentage of smoking events compared to census blocks across the 4 cases. Across 3-hour periods except for 3:00 AM-5:59 AM for 1 case, geofences contained an average of 36.4%-100% of smoking events. Findings showed that fishnet grid geofences may capture more smoking events compared to census blocks. CONCLUSIONS: Our findings suggest that this geofence construction method can identify high-risk smoking situations by time and place and has potential for generating individually tailored geofences for smoking cessation intervention delivery. In a subsequent smartphone-based smoking cessation intervention study, we plan to use fishnet grid geofences to inform the delivery of intervention messages.

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.

Improving longitudinal research in geospatial health: An agenda.

All aspects of public health research require longitudinal analyses to fully capture the dynamics of outcomes and risk factors such as ageing, human mobility, non-communicable diseases (NCDs), climate change, and endemic, emerging, and re-emerging infectious diseases. Studies in geospatial health are often limited to spatial and temporal cross sections. This generates uncertainty in the exposures and behavior of study populations. We discuss a research agenda, including key challenges and opportunities of working with longitudinal geospatial health data. Examples include accounting for residential and human mobility, recruiting new birth cohorts, geoimputation, international and interdisciplinary collaborations, spatial lifecourse studies, and qualitative and mixed-methods approaches.

Exploring Place-Based Differences in Suicide and Suicide-Related Outcomes Among North Carolina Adolescents and Young Adults.

PURPOSE: Suicide is an ongoing public health crisis among youth and adolescents, and few studies have investigated the spatial patterning in the United States among this subpopulation. Potential precursors to suicide in this vulnerable group are also on the rise, including nonfatal self-injury. METHODS: This study uses emergency department data, death certificates, and violent death reporting system data for North Carolina from 2009 to 2018 to investigate spatial clusters of self-injury and suicide. RESULTS: Findings show that the demographic characteristics of individuals committing fatal and nonfatal self-injury are quite different. Self-injury and completed suicides exhibited different geographical patterns. Area-level measures like micropolitan status and measures of racial and income segregation predicted the presence of high-risk suicide clusters. Suicides among Native Americans and veteran status/military personnel also were associated with higher risk suicide clusters. DISCUSSION: Future interventions should target these specific high-risk locations for immediate reductions in adolescent and youth suicides.

Uncertainty in geospatial health: challenges and opportunities ahead.

PURPOSE: Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few. METHODS: We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis. RESULTS: We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g., HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high-performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health. CONCLUSIONS: Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health.

A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020.

COVID-19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID-19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID-19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID-19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID-19 transmission and the associated risk factors.

A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States.

Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.

Space-Time Conditional Autoregressive Modeling to Estimate Neighborhood-Level Risks for Dengue Fever in Cali, Colombia.

Vector-borne diseases affect more than 1 billion people a year worldwide, causing more than 1 million deaths, and cost hundreds of billions of dollars in societal costs. Mosquitoes are the most common vectors responsible for transmitting a variety of arboviruses. Dengue fever (DENF) has been responsible for nearly 400 million infections annually. Dengue fever is primarily transmitted by female Aedes aegypti and Aedes albopictus mosquitoes. Because both Aedes species are peri-domestic and container-breeding mosquitoes, dengue surveillance should begin at the local level-where a variety of local factors may increase the risk of transmission. Dengue has been endemic in Colombia for decades and is notably hyperendemic in the city of Cali. For this study, we use weekly cases of DENF in Cali, Colombia, from 2015 to 2016 and develop space-time conditional autoregressive models to quantify how DENF risk is influenced by socioeconomic, environmental, and accessibility risk factors, and lagged weather variables. Our models identify high-risk neighborhoods for DENF throughout Cali. Statistical inference is drawn under Bayesian paradigm using Markov chain Monte Carlo techniques. The results provide detailed insight about the spatial heterogeneity of DENF risk and the associated risk factors (such as weather, proximity to Aedes habitats, and socioeconomic classification) at a fine level, informing public health officials to motivate at-risk neighborhoods to take an active role in vector surveillance and control, and improving educational and surveillance resources throughout the city of Cali.

Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first discovered in late 2019 in Wuhan City, China. The virus may cause novel coronavirus disease 2019 (COVID-19) in symptomatic individuals. Since December of 2019, there have been over 7,000,000 confirmed cases and over 400,000 confirmed deaths worldwide. In the United States (U.S.), there have been over 2,000,000 confirmed cases and over 110,000 confirmed deaths. COVID-19 case data in the United States has been updated daily at the county level since the first case was reported in January of 2020. There currently lacks a study that showcases the novelty of daily COVID-19 surveillance using space-time cluster detection techniques. In this paper, we utilize a prospective Poisson space-time scan statistic to detect daily clusters of COVID-19 at the county level in the contiguous 48 U.S. and Washington D.C. As the pandemic progresses, we generally find an increase of smaller clusters of remarkably steady relative risk. Daily tracking of significant space-time clusters can facilitate decision-making and public health resource allocation by evaluating and visualizing the size, relative risk, and locations that are identified as COVID-19 hotspots.

Residential mobility impacts relative risk estimates of space-time clusters of chlamydia in Kalamazoo County, Michigan.

We determine the impact of residential mobility in the prevalence and transmission dynamics of sexually transmitted infections. We illustrate our approach on reported chlamydia infections obtained from the Michigan Disease Surveillance System for Kalamazoo County, USA, from 2006 to 2014. We develop two scenarios, one with fixed residential addresses and one considering residential mobility. We then compare the resulting space-time clusters and relative risk (RR) of infection. The space-time scan statistics showed increased RR in an area with previously low risk of sexually transmitted infections. In addition, even though the spatial extent of the three clusters identified did not change significantly at the scale we conducted our analysis at, the temporal extent (duration) did exhibit significant changes and could be considered for unique interventions. The results indicate that residential mobility has some dependency on the prevalence and transmission dynamics of sexually transmitted infections to new areas. We suggest that strategies adopted to reduce the burden of sexually transmitted infections take into consideration the relatively high residential mobility of at-risk populations to reduce spreading the infections to new areas.

Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data.

Long term surveillance of vectors and arboviruses is an integral aspect of disease prevention and control systems in countries affected by increasing risk. Yet, little effort has been made to adjust space-time risk estimation by integrating disease case counts with vector surveillance data, which may result in inaccurate risk projection when several vector species are present, and when little is known about their likely role in local transmission. Here, we integrate 13 years of dengue case surveillance and associated Aedes occurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of Panama. Our exploratory space-time modelling approach detected the presence of five clusters, which varied by duration, relative risk, and spatial extent after incorporating vector species as covariates. The Ae. aegypti model contained the highest number of districts with more dengue cases than would be expected given baseline population levels, followed by the model accounting for both Ae. aegypti and Ae. albopictus. This implies that arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk estimation, potentially improving efforts to understand outbreak dynamics at national scales.