Changes in State Laws Related to Coverage for Substance Use Disorder Treatment Across Insurance Sectors, 2006-2020.

OBJECTIVE: The authors assessed changes in state insurance laws related to coverage for substance use disorder treatment across public and private insurance sectors from 2006 through 2020 in all 50 U.S. states. METHODS: Structured policy surveillance methods, including a coding protocol with duplicate coding and quality controls, were used to track changes in state laws during the 2006-2020 period. The legal database Westlaw was used to identify relevant statutes within each state's commercial insurance (large group, small group, and individual), state employee health benefits, and Medicaid codes. The legal coding instrument included six questions across four themes: parity, mandated coverage, definition of substance use disorders, and enforcement and compliance. Scores were calculated to reflect the comprehensiveness of states' laws and to interpret changes in scores over time. RESULTS: Comprehensiveness scores across all sectors (on a 0-9 scale) increased, on average, from 1.47 in 2006 to 2.84 in 2020. In 2006, mean scores ranged from 0.47 (state employee sector) to 2.80 (large-group sector) and in 2020, from 1.22 (state employee) to 4.26 (large group). CONCLUSIONS: Comprehensiveness of state insurance laws in relation to substance use disorder treatment improved across all insurance sectors in 2006-2020. The State Substance Use Disorder Insurance Laws Database created in this study will aid future legal epidemiology studies in assessing the cumulative effects of parity-related insurance laws on outcomes of substance use disorder treatments.

Redlining-associated methylation in breast tumors: the impact of contemporary structural racism on the tumor epigenome.

Purpose: Place-based measures of structural racism have been associated with breast cancer mortality, which may be driven, in part, by epigenetic perturbations. We examined the association between contemporary redlining, a measure of structural racism at the neighborhood level, and DNA methylation in breast tumor tissue. Methods: We identified 80 Black and White women diagnosed and treated for a first-primary breast cancer at Emory University Hospitals (2008-2017). Contemporary redlining was derived for census tracts using the Home Mortgage Disclosure Act database. Linear regression models were used to calculate the association between contemporary redlining and methylation in breast tumor tissue. We also examined epigenetic age acceleration for two different metrics, regressing ß values for each cytosine-phosphate-guanine dinucleotide (CpG) site on redlining while adjusting for covariates. We employed multivariable Cox-proportional hazards models and 95% confidence intervals (CI) to estimate the association between aberrant methylation and mortality. Results: Contemporary redlining was associated with 5 CpG sites after adjustment for multiple comparisons (FDR<0.10). All genes were implicated in breast carcinogenesis, including genes related to inflammation, immune function and stress response (ANGPT1, PRG4 and PRG4). Further exploration of the top 25 CpG sites, identified interaction of 2 sites (MRPS28 and cg11092048) by ER status and 1 site (GDP1) was associated with all-cause mortality. Contemporary redlining was associated with epigenetic age acceleration by the Hannum metric (ß=5.35; CI 95%=0.30,10.4) and showed positive but non-significant correlation with the other clock. Conclusion: We identified novel associations between neighborhood contemporary redlining and the breast tumor DNA methylome, suggesting that racist policies leading to inequitable social and environmental exposures, may impact the breast tumor epigenome. Additional research on the potential implications for prognosis is needed.

Opportunities and Challenges to Advance Health Equity Using Digital Health Tools in Underserved Communities in Southeast US: A Mixed Methods Study.

INTRODUCTION: Over the last 30 years, the adoption of health information technology and digital health tools (DHTs) into the US health system has been instrumental to improving access to care, especially for people living in rural, underserved, and underrepresented communities. Despite widespread adoption of DHTs by primary care clinicians, documented challenges have contributed to inequitable use and benefit. The COVID-19 pandemic necessitated rapid adoption of DHTs, accelerated by state and federal policy changes, in order to meet patient needs and ensure access to care. METHODS: The Digital Health Tools Study employed a mixed methods approach to assess adoption and use of DHTs by primary care clinicians in southeastern states and identify individual- and practice-level barriers and facilitators to DHT implementation. A survey was conducted using a multi-modal recruitment strategy: newsletters, meeting/conference presentations, social media, and emails/calls. Focus groups were conducted to assess priorities, barriers, and facilitators and were recorded/transcribed verbatim. Descriptive statistics were calculated for survey results, produced for the whole sample, and stratified by state. Thematic analysis was conducted of focus group transcripts. RESULTS: There were 1215 survey respondents. About 55 participants who had missing demographic information were excluded from the analysis. About 99% of clinicians used DHTs in the last 5 years, modalities included: telehealth (66%), electronic health records (EHRs; 66%), patient portals (49%), health information exchange (HIE; 41%), prescription drug monitoring programs (39%), remote/home monitoring (27%), and wearable devices (22%). Time (53%) and cost (51%) were identified as barriers. About 61% and 75% of clinicians reported being "satisfied" to "very satisfied" with telemedicine and EHRs, respectively. Seven focus groups with 25 clinicians were conducted and indicated COVID-19 and the use of supplemental tools/apps to connect patients to resources as major motivators for adopting DHTs. Challenges included incomplete and difficult-to-utilize HIE interfaces for providers and internet/broadband access and poor connectivity for patients. CONCLUSIONS: This study describes the impact adopting DHTs by primary care clinicians has on expanded access to healthcare and reducing health disparities in regions with longstanding health and social inequities. The findings identify opportunities to leverage DHTs to advance health equity and highlight opportunities for policy improvement.

Population-Level Disparities in Exposure to COVID-19 Mitigation Policies, April 2020-April 2021.

CONTEXT: Studies have found that COVID-19 stay-at-home orders (SHOs) and face mask policies (FMPs) were associated with reduced COVID-19 transmission and deaths. But it is unknown whether exposure to these policies varied by sociodemographic characteristics across the US population. OBJECTIVE: The goal of this study was to quantify and characterize the sociodemographic characteristics and geographic distribution of populations exposed to evidence-based COVID-19 mitigation policies. DESIGN: We obtained statewide SHOs and FMPs for all US counties from April 10, 2020, to April 10, 2021, calculated median policy lengths, and categorized counties into 4 groups based on length of policy exposure: low SHO-low FMP, high SHO-low FMP, low SHO-high FMP, and high SHO-high FMP. We described exposure groups by COVID-19 cumulative case/death and vaccination rates and county sociodemographic characteristics. SETTING: In total, 3142 counties from all 50 states and Washington, District of Columbia, were included in the analysis. MAIN OUTCOME MEASURES: County-level sociodemographic factors and county cumulative rates for COVID-19 cases, deaths, and vaccinations. RESULTS: The largest percentage of the US population lived in counties with high exposure to SHOs and FMPs. However, populations living in high SHO-high FMP counties had the lowest percent non-Hispanic Black (NHB) and highest percent non-Hispanic White (NHW) populations. Populations living in high SHO-low FMP counties had the highest percent NHB and Hispanic populations and the lowest percent NHW population. CONCLUSION: This study identified county-level racial, ethnic, and sociodemographic disparities in exposure to evidence-based statewide COVID-19 mitigation policies. POLICY IMPLICATIONS: Exposure to evidence-based policies is an important consideration for studies evaluating the root causes of health inequities.

Interpersonal Primary Care Continuity for Chronic Conditions Is Associated with Fewer Hospitalizations and Emergency Department Visits Among Medicaid Enrollees.

BACKGROUND: Interpersonal primary care continuity or chronic condition continuity (CCC) is associated with improved health outcomes. Ambulatory care-sensitive conditions (ACSC) are best managed in a primary care setting, and chronic ACSC (CACSC) require management over time. However, current measures do not measure continuity for specific conditions or the impact of continuity for chronic conditions on health outcomes. The purpose of this study was to design a novel measure of CCC for CACSC in primary care and determine its association with health care utilization. METHODS: We conducted a cross-sectional analysis of continuously enrolled, nondual eligible adult Medicaid enrollees with a diagnosis of a CACSC using 2009 Medicaid Analytic eXtract files from 26 states. We conducted adjusted and unadjusted logistic regression models of the relationship between patient continuity status and emergency department (ED) visits and hospitalizations. Models were adjusted for age, sex, race/ethnicity, comorbidity, and rurality. We defined CCC for CACSC as at least 2 outpatient visits with any primary care physician for a CACSC in the year, and (2) more than 50% of outpatient CACSC visits with a single PCP. RESULTS: There were 2,674,587 enrollees with CACSC and 36.3% had CCC for CACSC visits. In fully adjusted models, enrollees with CCC were 28% less likely to have ED visits compared with those without CCC (aOR = 0.71, 95% CI = 0.71 - 0.72) and were 67% less likely to have hospitalization than those without CCC (aOR = 0.33, 95% CI = 0.32-0.33). CONCLUSIONS: CCC for CACSCs was associated with fewer ED visits and hospitalizations in a nationally representative sample of Medicaid enrollees.

Access and Utilization of Asthma Medications Among Patients Who Receive Care in Federally Qualified Health Centers.

OBJECTIVES: To describe access to and use of prescription asthma medications, and to assess factors associated with asthma exacerbation, healthcare utilization, and health status among asthma patients treated at Federally Qualified Health Centers. METHODS: This is a retrospective cross-sectional study. We analyzed data from the 2014 National Health Center Patient Survey. This data is publicly available from the Health Resources and Services Administration. Data was collected from patients receiving face-to-face care from health centers funded under Section 330 of the Public Health Service Act. Data from patients was collected between October 8, 2014, and April 17, 2015. We included adult participants who reported having a diagnosis of asthma and confirmed that they still have asthma. Association between explanatory variables (access to prescription medications and use of asthma controller medications) and outcome variables (asthma exacerbations, asthma hospitalizations or emergency department visits, and self-rated health) was assessed using multivariable regression analyses while adjusting for demographics. RESULTS: A total of 919 participants with asthma were included. Approximately 32% of the participants experienced delays in getting prescription medications, 26% were unable to get them, 60% experienced an asthma exacerbation last year, 48% rated their health as fair/poor, and 19% visited a hospital or an emergency department last year. Multivariable results showed that participants who were currently taking controller medications were more likely to have experienced an asthma exacerbation (OR = 4.02; 95% CI 1.91 to 8.45; P < .01), or visited a hospital or an emergency department (OR = 3.07; 95% CI 1.39 to 6.73; P < .01) in the last year compared with those who had never taken controller medications. Experiencing difficulties in accessing asthma medications was associated with lower self-rated health (ß = -.51; 95% CI -0.94 to -0.08; P = .02). CONCLUSIONS: Future interventions should seek to improve asthma patient care and health outcomes using innovative strategies that act at multiple levels of the healthcare system (eg, individual, interpersonal, community levels).

Individual and Neighborhood Influences on the Relationship Between Waist Circumference and Coronary Heart Disease in the REasons for Geographic and Racial Differences in Stroke Study.

INTRODUCTION: The objective of this study was to describe how the relationship between waist circumference and incident coronary heart disease (CHD) is influenced by individual and neighborhood factors in the REasons for Geographic and Racial Differences in Stroke (REGARDS) Study. METHODS: REGARDS is a cohort study of 30,239 US adults. The primary exposure was sex-specific quartiles of waist circumference. Individual covariates included sociodemographic characteristics, health status, health behavior, and usual source of care. Neighborhood (ie, zip code-level) covariates included access to primary care, poverty, rurality, and racial segregation. The main outcome was incident CHD from baseline (2003) through 2017. We used descriptive statistics, Kaplan-Meier curves, and Cox proportional hazard models to analyze the overall sample and race-sex subgroups. RESULTS: During the study period, 23,042 study participants had 1,499 CHD events. We found a higher risk of incident CHD in the upper quartile of waist circumference compared with the first quartile in all 4 race-sex subgroups except African American men, among whom we found no relationship between waist circumference and incident CHD. Covariates did not attenuate these relationships. CONCLUSION: In all groups except African American men, waist circumference in the highest quartile was associated with increased risk of incident CHD. Individual and neighborhood factors did not influence the relationship between waist circumference and development of CHD but differentially influenced incident CHD among race-sex subgroups.

Creation of a Longitudinal Legal Data Set to Support Legal Epidemiology Studies of Mental Health Insurance Legislation.

OBJECTIVE: This article describes policy surveillance methodology used to track changes in the comprehensiveness of state mental health insurance laws over 23 years, resulting in a data set that supports legal epidemiology studies measuring effects of these laws on mental health outcomes. METHODS: Structured policy surveillance methods, including a coding protocol, blind coding of laws in 10% of states, and consensus meetings, were used to track changes in state laws from 1997 through 2019-2020. The legal database Westlaw was used to identify relevant statutes. The legal coding instrument included six questions across four themes: parity, mandated coverage, definitions of mental health conditions, and enforcement-compliance. Points (range 0-7) were assigned to reflect the laws' comprehensiveness and aid interpretation of changes over time. RESULTS: The search resulted in 147 coding time periods across 51 jurisdictions (50 states, District of Columbia). Intercoder consensus rates increased from 89% to 100% in the final round of blinded duplicate coding. Since 1997, average comprehensiveness scores increased from 1.31 to 3.82. In 1997, 41% of jurisdictions had a parity law, 28% mandated coverage, 31% defined mental health conditions, and 8% required state agency enforcement. In 2019-2020, 94% of jurisdictions had a parity law, 63% mandated coverage, 75% defined mental health conditions, and 29% required state enforcement efforts. CONCLUSIONS: Comprehensiveness of state mental health insurance laws increased from 1997 through 2019-2020. The State Mental Health Insurance Laws Dataset will enable evaluation research on effects of comprehensive legislation and cumulative impact.

Variation in Reporting of the Race and Ethnicity of COVID-19 Cases and Deaths Across US States: April 12, 2020, and November 9, 2020.

Despite growing evidence that COVID-19 is disproportionately affecting communities of color, state-reported racial/ethnic data are insufficient to measure the true impact.We found that between April 12, 2020, and November 9, 2020, the number of US states reporting COVID-19 confirmed cases by race and ethnicity increased from 25 to 50 and 15 to 46, respectively. However, the percentage of confirmed cases reported with missing race remained high at both time points (29% on April 12; 23% on November 9). Our analysis demonstrates improvements in reporting race/ethnicity related to COVID-19 cases and deaths and highlights significant problems with the quality and contextualization of the data being reported.We discuss challenges for improving race/ethnicity data collection and reporting, along with opportunities to advance health equity through more robust data collection and contextualization. To mitigate the impact of COVID-19 on racial/ethnic minorities, accurate and high-quality demographic data are needed and should be analyzed in the context of the social and political determinants of health.

Population-Level Disparities in COVID-19: Measuring the Independent Association of the Proportion of Black Population on COVID-19 Cases and Deaths in US Counties.

CONTEXT: There is a need to understand population race and ethnicity disparities in the context of sociodemographic risk factors in the US experience of the COVID-19 pandemic. OBJECTIVE: Determine the association between county-level proportion of non-Hispanic Black (NHB) on county COVID-19 case and death rates and observe how this association was influenced by county sociodemographic and health care infrastructure characteristics. DESIGN AND SETTING: This was an ecologic analysis of US counties as of September 20, 2020, that employed stepwise construction of linear and negative binomial regression models. The primary independent variable was the proportion of NHB population in the county. Covariates included county demographic composition, proportion uninsured, proportion living in crowded households, proportion living in poverty, population density, state testing rate, Primary Care Health Professional Shortage Area status, and hospital beds per 1000 population. MAIN OUTCOME MEASURES: Outcomes were exponentiated COVID-19 cases per 100 000 population and COVID-19 deaths per 100 000 population. We produced county-level maps of the measures of interest. RESULTS: In total, 3044 of 3142 US counties were included. Bivariate relationships between the proportion of NHB in a county and county COVID-19 case (Exp ß = 1.026; 95% confidence interval [CI], 1.024-1.028; P < .001) and death rates (rate ratio [RR] = 1.032; 95% CI, 1.029-1.035; P < .001) were not attenuated in fully adjusted models. The adjusted association between the proportion of NHB population in a county and county COVID-19 case was Exp ß = 1.025 (95% CI, 1.023-1.027; P < .001) and the association with county death rates was RR = 1.034 (95% CI, 1.031-1.038; P < .001). CONCLUSIONS: The proportion of NHB people in a county was positively associated with county COVID-19 case and death rates and did not change in models that accounted for other socioecologic and health care infrastructure characteristics that have been hypothesized to account for the disproportionate impact of COVID-19 on racial and ethnic minority populations. Results can inform efforts to mitigate the impact of structural racism of COVID-19.

Nonmetropolitan COVID-19 Incidence and Mortality Rates Surpassed Metropolitan Rates Within the First 24 Weeks of the Pandemic Declaration: United States, March 1-October 18, 2020.

PURPOSE: This report compares COVID-19 incidence and mortality rates in the nonmetropolitan areas of the United States with the metropolitan areas across three 11-week periods from March 1 to October 18, 2020. METHODS: County-level COVID-19 case, death, and population counts were downloaded from USAFacts.org. The 2013 NCHS Urban-Rural Classification Scheme was collapsed into two categories called metropolitan (large central, large fringe, medium, and small metropolitans) and nonmetropolitan (micropolitan/noncore). Daily COVID-19 incidence and mortality rates were computed to show temporal trends for each of these two categories. Maps showing the ratio of nonmetropolitan to metropolitan COVID-19 incidence and mortality rates by state identify states with higher rates in nonmetropolitan areas than in metropolitan areas in each of the three 11-week periods. FINDINGS: In the period between March 1 and October 18, 2020, 13.8% of the 8,085,214 confirmed COVID-19 cases and 10.7% of the 217,510 deaths occurred among people residing in nonmetropolitan counties. The nonmetropolitan incidence and mortality trends steadily increased and surpassed those in metropolitan areas, beginning in early August. CONCLUSIONS: Despite the relatively small size of the US population living in nonmetropolitan areas, these areas have an equal need for testing, health care personnel, and mitigation resources. Having state-specific rural data allow the development of prevention messages that are tailored to the sociocultural context of rural locations.

Percentage of Black Population and Primary Care Shortage Areas Associated with Higher COVID-19 Case and Death Rates in Georgia Counties.

OBJECTIVES: We hypothesized that the proportion of Black individuals in a county would be associated with higher rates of coronavirus disease 2019 (COVID-19) cases and deaths, even after accounting for other high-risk socioecologic factors such as poverty, population density, and household crowding, and uninsured rates. We also expected that counties designated as primary care health professional shortage areas (PCHPSAs) would be associated with higher COVID-19 death rates, and the lack of primary care access would exacerbate racial disparities in death rates. We undertook this study to test these hypotheses and discern the independent effects of racial composition, socioecologic characteristics, and healthcare system factors on COVID-19 cases and deaths in Georgia counties. METHODS: We used county-level COVID-19 cases and deaths on April 23, 2020 from the Johns Hopkins Coronavirus Resource Center and estimates of 2019 county-level populations from the US Census Bureau to calculate the cumulative event rates for the state of Georgia. We used multiple regression models to examine crude and adjusted associations of socioecologic and health system variables with county-level COVID-19 case and mortality rates. RESULTS: After adjustment, a 1% increase in the proportion of Black people in the county resulted in a 2.3% increase in the county COVID-19 confirmed case rate and a 3.0% increase in the death rate (relative risk 1.03, 95% confidence interval 1.01-1.05, P < 0.001). Primary care shortage areas had a 74% higher death rate (relative risk 1.74, 95% confidence interval 1.00-3.00, P = 0.049). CONCLUSIONS: These results highlight the impact of racial disparities on the spatial patterns of COVID-19 disease burden in Georgia, which can guide interventions to mitigate racial disparities. The results also support the need for robust primary care infrastructure throughout the state.

County-Level Concentration of Selected Chronic Conditions Among Medicare Fee-for-Service Beneficiaries and Its Association with Medicare Spending in the United States, 2017.

Multiple chronic conditions (MCC) reduce quality of life and are associated with high per capita health care spending. One potential way to reduce Medicare spending for MCC is to identify counties whose populations have high levels of spending compared to level of disease burden. Using a nationally representative sample of Medicare Fee-for-Service beneficiaries, this paper presents a method to measure the collective burden of several chronic conditions in a population, which the authors have termed the concentration of chronic conditions (CCC). The authors observed a significantly positive linear relationship between the CCC measure and county-level per capita Medicare spending. This area-level measure can be operationalized to identify counties that might benefit from targeted efforts designed to optimally manage and prevent chronic illness.

Neighborhood-Level Redlining and Lending Bias Are Associated with Breast Cancer Mortality in a Large and Diverse Metropolitan Area.

BACKGROUND: Structural inequities have important implications for the health of marginalized groups. Neighborhood-level redlining and lending bias represent state-sponsored systems of segregation, potential drivers of adverse health outcomes. We sought to estimate the effect of redlining and lending bias on breast cancer mortality and explore differences by race. METHODS: Using Georgia Cancer Registry data, we included 4,943 non-Hispanic White (NHW) and 3,580 non-Hispanic Black (NHB) women with a first primary invasive breast cancer diagnosis in metro-Atlanta (2010-2014). Redlining and lending bias were derived for census tracts using the Home Mortgage Disclosure Act database. We calculated hazard ratios and 95% confidence intervals (CI) for the associations of redlining, lending bias on breast cancer mortality and estimated race-stratified associations. RESULTS: Overall, 20% of NHW and 80% of NHB women lived in redlined census tracts, and 60% of NHW and 26% of NHB women lived in census tracts with pronounced lending bias. Living in redlined census tracts was associated with a nearly 1.60-fold increase in breast cancer mortality (hazard ratio = 1.58; 95% CI, 1.37-1.82) while residing in areas with substantial lending bias reduced the hazard of breast cancer mortality (hazard ratio = 0.86; 95% CI, 0.75-0.99). Among NHB women living in redlined census tracts, we observed a slight increase in breast cancer mortality (hazard ratio = 1.13; 95% CI, 0.90-1.42); among NHW women the association was more pronounced (hazard ratio = 1.39; 95% CI, 1.09-1.78). CONCLUSIONS: These findings underscore the role of ecologic measures of structural racism on cancer outcomes. IMPACT: Place-based measures are important contributors to health outcomes, an important unexplored area that offers potential interventions to address disparities.

Estimating health service utilization potential using the supply-concentric demand-accumulation spatial availability index: a pulmonary rehabilitation case study.

The potential for a population at a given location to utilize a health service can be estimated using a newly developed measure called the supply-concentric demand accumulation (SCDA) spatial availability index. Spatial availability is the amount of demand at the given location that can be satisfied by the supply of services at a facility, after discounting the intervening demand among other populations that are located nearer to a facility location than the given population location. This differs from spatial accessibility measures which treat absolute distance or travel time as the factor that impedes utilization. The SCDA is illustrated using pulmonary rehabilitation (PR), which is a treatment for people with chronic obstructive pulmonary disease (COPD). The spatial availability of PR was estimated for each Census block group in Georgia using the 1105 residents who utilized one of 45 PR facilities located in or around Georgia. Data was provided by the Centers for Medicare & Medicaid Services. The geographic patterns of the SCDA spatial availability index and the two-step floating catchment area (2SFCA) spatial accessibility index were compared with the observed PR utilization rate using bivariate local indicators of spatial association. The SCDA index was more associated with PR utilization (Morans I = 0.607, P < 0.001) than was the 2SFCA (Morans I = 0.321, P < 0.001). These results suggest that the measures of spatial availability may be a better way to estimate the health care utilization potential than measures of spatial accessibility.

Primary Care Comprehensiveness and Care Coordination in Robust Specialist Networks Results in Lower Emergency Department Utilization: A Network Analysis of Medicaid Physician Networks.

Background: Care coordination is an essential and difficult to measure function of primary care. Objective: Our objective was to assess the impact of network characteristics in primary/specialty physician networks on emergency department (ED) visits for patients with chronic ambulatory care sensitive conditions (ACSCs). Subjects and Measures: This cross-sectional social network analysis of primary care and specialty physicians caring for adult Medicaid beneficiaries with ACSCs was conducted using 2009 Texas Medicaid Analytic eXtract (MAX) files. Network characteristic measures were the main exposure variables. A negative binomial regression model analyzed the impact of network characteristics on the ED visits per patient in the panel. Results: There were 42 493 ACSC patients assigned to 5687 primary care physicians (PCPs) connected to 11 660 specialist physicians. PCPs whose continuity patients did not visit a specialist had 86% fewer ED visits per patient in their panel, compared with PCPs whose patients saw specialists. Among PCPs connected to specialists in the network, those with a higher number of specialist collaborators and those with a high degree of centrality had lower patient panel ED rates. Conclusions: PCPs providing comprehensive care (ie, without specialist consultation) for their patients with chronic ACSCs had lower ED utilization rates than those coordinating care with specialists. PCPs with robust specialty networks and a high degree of centrality in the network also had lower ED utilization. The right fit between comprehensiveness of primary care, care coordination, and adequate capacity of specialty availability in physician networks is needed to drive outcomes.

Differences in Travel Time to Cancer Surgery for Colon versus Rectal Cancer in a Rural State: A New Method for Analyzing Time-to-Place Data Using Survival Analysis.

PURPOSE: Rectal cancer is rarer than colon cancer and is a technically more difficult tumor for surgeons to remove, thus rectal cancer patients may travel longer for specialized treatment compared to colon cancer patients. The purpose of this study was to evaluate whether travel time for surgery was different for colon versus rectal cancer patients. METHODS: A secondary data analysis of colorectal cancer (CRC) incidence data from the Iowa Cancer Registry data was conducted. Travel times along a street network from all residential ZIP Codes to all cancer surgery facilities were calculated using a geographic information system. A new method for analyzing "time-to-place" data using the same type of survival analysis method commonly used to analyze "time-to-event" data is introduced. Cox proportional hazard model was used to analyze travel time differences for colon versus rectal cancer patients. RESULTS: A total of 5,844 CRC patients met inclusion criteria. Median travel time to the nearest surgical facility was 9 minutes, median travel time to the actual cancer surgery facilities was 22 minutes, and the median number of facilities bypassed was 3. Although travel times to the nearest surgery facilities were not significantly different for colon versus rectal cancer patients, rectal cancer patients on average traveled 15 minutes longer to their actual surgery facility and bypassed 2 more facilities to obtain surgery. DISCUSSION: In general, the survival analysis method used to analyze the time-to-place data as described here could be applied to a wide variety of health services and used to compare travel patterns among different groups.

A Practical Approach to Establishing a Practice-Based Research Network Stakeholder Engagement Infrastructure.

INTRODUCTION: While there have been several articles detailing the importance of stakeholder engagement in research broadly and in practice-based research networks (PBRNs) specifically, few of these articles offer a replicable engagement approach that is detailed enough to translate to another setting. The goal of this article is to offer a detailed example of building stakeholder engagement infrastructure that could be replicated or translated to other settings. APPROACH: We offer a review of 1 regional PBRN's approach to building a stakeholder engagement infrastructure over a 2-year period by describing engagement activities deployed across a large, regional PBRN including a needs assessment around research and training conducted in each state of the network and a centralized conference where themes from that needs assessment were leveraged to produce a stakeholder-defined research agenda and elect a steering committee. RESULTS: Products from this process include the stakeholder-defined research agenda as well as a multi-level organizational framework for assessing facilitators and barriers in a large PBRN and an example of a framework of individualized stakeholder group preferences for engagement modalities. CONCLUSIONS: This article presents a detailed timeline and replicable approach to building a stakeholder engagement infrastructure in a regional PBRN. This article details a practical process that is embedded in the lived values of practice-based research.

Identifying County-Level All-Cause Mortality Rate Trajectories and Their Spatial Distribution Across the United States.

INTRODUCTION: All-cause mortality in the United States declined from 1935 through 2014, with a recent uptick in 2015. This national trend is composed of disparate local trends. We identified distinct groups of all-cause mortality rate trajectories by grouping US counties with similar temporal trajectories. METHODS: We used all-cause mortality rates in all US counties for 1999 through 2016 and estimated discrete mixture models by using county level mortality rates. Proc Traj in SAS was used to detect how county trajectories clustered into groups on the basis of similar intercepts, slopes, and higher order terms. Models with increasing numbers of groups were assessed on the basis of model fit. We created county-level maps of mortality trajectory groups by using ArcGIS. RESULTS: Eight unique trajectory groups were detected among 3,091 counties. The average mortality rate in the most favorable trajectory group declined 29.4%, from 592.3 deaths per 100,000 in 1999 to 418.2 in 2016. The least favorable mortality trajectory group declined 3.4% over the period, from 1,280.3 deaths per 100,000 to 1,236.9. We saw significant differences in the demographic and socioeconomic profiles and geographic patterns across the trajectory categories, with favorable mortality trajectories in the Northeast, Midwest, and on the West Coast and unfavorable trajectories concentrated in the Southeast. CONCLUSIONS: County-level disparities in all-cause mortality rates widened over the past 18 years. Further investigation of the determinants of the trajectory groupings and the geographic outliers identified by our research could inform interventions to achieve equitable distribution of county mortality rates.

Using spatially adaptive floating catchments to measure the geographic availability of a health care service: Pulmonary rehabilitation in the southeastern United States.

A spatially adaptive floating catchment is a circular area that expands outward from a provider location until the estimated demand for services in the nearest population locations exceeds the observed number of health care services performed at the provider location. This new way of creating floating catchments was developed to address the change of spatial support problem (COSP) by upscaling the availability of the service observed at a provider location to the county-level so that its geographic association with utilization could be measured using the same spatial support. Medicare Fee-for-Service claims data were used to identify beneficiaries aged ≥ 65 years who received outpatient pulmonary rehabilitation (PR) in the Southeastern United States in 2014 (n = 8798), the number of PR treatments these beneficiaries received (n = 132,508), and the PR providers they chose (n = 426). The positive correlation between PR availability and utilization was relatively low, but statistically significant (r = 0.619, p < 0.001) indicating that most people use the nearest available PR services, but some travel long distances. SAFCs can be created using data from health care systems that collect claim-level utilization data that identifies the locations of providers chosen by beneficiaries of a specific health care procedure.