Primary Care Physician Density Research Articles

Factors contributing to differences in cervical cancer screening in rural and urban community health centers.

Community health centers (CHCs) provide historically marginalized populations with primary care, including cancer screening. Previous studies have reported that women living in rural areas are less likely to be up to date with cervical cancer screening than women living in urban areas. However, little is known about rural-urban differences in cervical cancer screening in CHCs and the contributing factors, and whether such differences changed during the COVID-19 pandemic. Using 8-year pooled Uniform Data System (2014-2021) data and Oaxaca-Blinder decomposition, the extent to which CHC- and catchment area-level characteristics explained rural-urban differences in up-to-date cervical cancer screening was estimated. Up-to-date cervical cancer screening was lower in rural CHCs than urban CHCs (38.2% vs 43.0% during 2014-2019), and this difference increased during the pandemic (43.5% vs 49.0%). The rural-urban difference in cervical cancer screening in 2014-2019 was mostly explained by differences in CHC-level proportions of patients with limited English proficiency (55.9%) or income below the poverty level (12.3%) and females aged 21 to 64years (9.8%), and catchment area-level's unemployment (3.4%) and primary care physician density (3.2%). However, Medicaid (-48.5%) or no insurance (-19.6%) counterbalanced the differences between rural-urban CHCs. The contribution of these factors to rural-urban differences in cervical cancer screening generally increased in 2020-2021. Rural-urban differences in cervical cancer screening were mostly explained by multiple CHC-level and catchment area-level characteristics. The findings call for tailored interventions, such as providing resources and language services, to improve cancer screening utilization among uninsured, Medicaid, and patients with limited English proficiencyin rural CHCs.

Association of Primary Care Physician Supply with Population Mortality in South Korea: A Pooled Cross-Sectional Analysis.

Primary care physicians perform a comprehensive role by providing continuous, patient-centered, and accessible healthcare and establishing connections with specialized care. However, the association between the supply of primary care physicians and mortality rates in South Korea has not been thoroughly investigated. This study utilized data from 229 si-gun-gu in South Korea from 2016 to 2020. The densities of primary care physicians, physicians in functional primary clinics, specialists in primary care facilities, and active physicians per 100,000 people were independent variables. Age-adjusted all-cause mortality and cause-specific mortality rates per 100,000 individuals were the dependent variables. Negative binomial regression, negative binomial regression with a pseudo-panel approach, and geographically weighted regression were used to analyze the data. Our study revealed a significant negative association between the density of primary care physicians and all-cause mortality. An increase in a primary care physician per 100,000 population was significantly linked to a 0.11% reduction in all-cause mortality (incidence rate ratio, 0.9989; 95% confidence interval, 0.9983-0.9995). Similar associations have been observed between mortality rates owing to cardiovascular diseases, respiratory tract diseases, and traffic accidents. This study provides evidence that having a higher number of primary care physicians in South Korea is associated with lower mortality rates. Future research should consider better indicators that reflect the quality of primary care to better understand its impact on population health outcomes. These findings emphasize the significance of strengthening primary care in the South Korean healthcare system to improve the overall health and wellbeing.

Relationship Between Availability of Urologists and Primary Care Providers and Stage of Diagnosis for Invasive Urinary Bladder Cancer.

With no recommended screening approach, urinary bladder cancer patients rely on referral to urologists to ensure timely diagnosis of bladder cancer. This requires coordination between primary and specialty care. We provide estimates of the relative association between primary care physician and urologist density on stage of urinary bladder cancer diagnosis. We used 2010 to 2016 Pennsylvania Cancer Registry data to identify all adult patients diagnosed with bladder cancer. Our primary outcome was locoregional stage of diagnosis, since treatment modality changes and prognosis worsens beyond this stage. Based on patient's residential location at the time of diagnosis we defined both density of urologists and number of primary care providers (defined as providers per population) within the patient's county. We used univariate and multivariate logistic regression to estimate the association between provider density and likelihood of locoregional stage of diagnosis. We also controlled for age, sex, race/ethnicity, insurance type, and year. Our sample included 11,771 urinary bladder cancer patients with 10,607 diagnosed at locoregional stage and 1164 at distant stage. Multivariate regression results show primary care density was associated with significantly higher odds of locoregional stage of diagnosis (odds ratio of 1.05 [95% CI: 1.02-1.08]) while urologist density was associated with significantly lower odds of locoregional stage (odds ratio of 0.65 [95% CI: 0.48-0.89]). We found primary care density but not urologist density was associated with earlier stage of diagnosis, highlighting the importance of access to primary care and need for timely referral to urologic care.

Effect of area-level socioeconomic deprivation on mental and physical health: A longitudinal natural experiment among refugees in Germany

Existing studies on contextual health effects struggle to account for compositional bias, limiting causal interpretation. We use refugee dispersal in Germany as a natural experiment to study the effect of area-level socioeconomic deprivation on mental and physical health, while considering the potential mediating role of neighbourhood characteristics. Refugees subject to dispersal (n = 1466) are selected from a nation-wide longitudinal refugee study (IAB-SOEP-BAMF Panel; 2016–2018). Multi-level linear regression models, adjusted for age, sex, education, country of origin, federal state, asylum status and length of residence in Germany, are fitted to the change in mental and physical health subscales of the SF-12 depending on quintiles (Q1 – Q5) of commune-level socioeconomic deprivation (German Index of Socioeconomic Deprivation, GISD). This is followed by sensitivity analyses and mediation analyses for housing, social cohesion, proportion of non-citizens in the neighbourhood, access to green space, population density and primary care physician density. Residency in districts with moderate-high deprivation (Q4) has a negative impact on physical health (coef.: -2.2, 95%CI: -4.1;-0.2) compared to lowest deprivation (Q1). Moderate-high deprivation (Q4) has a positive impact on mental health, but the effect is statistically insignificant (coef.: 1.6, 95%CI: -0.7; 3.9). Comparisons with other deprivation quintiles are statistically insignificant. Sensitivity analyses confirm results of the final models, while no mediating factors show a substantial impact on the observed relationship. The results point to gaps in health and social service provision for refugees living in the most deprived regions, but further research is required to understand the precise mechanisms behind the observed relationships. Further research using longer timeframes and larger sample sizes are required to confirm results.

Bridging geographical disparities across 368 townships with healthcare system and socioeconomic factors in Taiwan

A universal health insurance program such as the National Health Insurance in Taiwan offers a wide coverage and increased access to healthcare services. Despite its ongoing efforts to enhance healthcare accessibility, differences in health for people living in urban and resource-deprived areas remain substantial. To investigate the longitudinal impact of the healthcare system and other potential structural drivers such as education and economic development on geographical disparities in health, we designed a panel study with longitudinal open secondary data, covering all 368 townships in Taiwan between 2013 and 2017. Our findings indicated higher mortality rates in the mountainous and rural areas near the east and south regions of the island in both years. Multivariate analyses showed an increase in the density of primary care physicians (PCP) was associated with lower all-cause mortality (β = − 0.72, p < 0.0001) and cardiovascular disease mortality (β = − 0.41, p < 0.0001). Effect of PCP is evident, but merely focusing on access to healthcare is still not enough. Additional measures are warranted to address the health disparities existing between urban and underprivileged areas.

Presence of Primary Care Physicians and Patients' Ability to Register: A Simulated-Patient Survey in the Paris Region.

Using the health care system fully in some countries requires patients to register with a primary care physician (PCP). Public health policies measure PCP density to maintain satisfactory local PCP supplies and limit geographic inequalities. In an exhaustive simulated-patient survey in the Paris, France region, we analyzed how well presence of PCPs was associated with patients' ability to register for care. Of 5,188 census blocks, 55.4% had at least 1 PCP; however, only 38.6% had at least 1 PCP accepting registration for office visits, and only 19.4% had at least 1 PCP accepting registration for home visits (P <.001 across the 3 indicators). Cross-block inequalities in accepting registration were steeper than those related to PCP density, indicating that this density metric offers false reassurance and is inadequate to support policy decisions.

Trends in Alzheimer Disease Mortality Among American Indian and Alaska Native People Between 2011 and 2019.

The number of American Indian and Alaska Native (AI/AN) people living with dementia is expected to increase 5-fold by 2060. Social determinants of health may explain disparities in the incidence of Alzheimer disease (AD) but remain largely overlooked. We examined the time trend of AD mortality rates and associations of the percentage of AI/ANs, density of primary care physicians and neurologists, area deprivation index, rurality, and Indian Health Service region with AD mortality in 646 purchased/referred care delivery area counties. AD mortality rates significantly increased over time. Counties with higher concentrations of AI/AN people had lower AD mortality. More deprived counties had 34% higher AD mortality compared with less deprived counties. AD mortality was 20% lower in nonmetro counties than in metro counties. Findings have implications for prioritizing areas where more resources for AD care, education, or outreach are needed.

Association of Physician Densities and Gynecologic Cancer Outcomes in the United States

ABSTRACT Gynecologic cancers account for approximately 94,000 new cancer diagnosis yearly in the United States, with prognosis largely dependent on stage at diagnosis, sociodemographic, geographic, and other biological factors. Proximity to gynecologic oncologists has been shown to improve cancer outcomes; however, it remains unclear whether physician density of nonsubspecialists such as obstetrician-gynecologists (OBGYNs) or primary care physicians (PCPs) has a similar benefit. The aim of this study was to determine whether there is a correlation between the density of OBGYNs and PCPs with outcomes of patients with gynecologic cancer at the county level in the United States. County-level oncologic and sociodemographic data on cases between 2005 and 2018 were obtained from the Surveillance, Epidemiology, and End Results (SEER) Research Plus database. Female population by county was collected from the United States Census Bureau and physician density information from the Area Health Resources File. Backward stepwise linear regression was performed for both AJCC cancer stage at diagnosis and 5-year survival rate while accounting for county-level sociodemographic factors. A total of 113,938 patients were included in the analysis for AJCC stage at diagnosis and 98,573 for 5-year survival rate. Across all cancer types, the average density was 18 and 114 OBGYNs and PCPs per 100,000 females in metropolitan areas, respectively, whereas the average density was 8 and 89 OBGYNs and PCPs per 100,000 females in nonmetropolitan areas, respectively. Increasing PCP density was associated with an earlier stage at diagnosis for cervical cancer (95% CI, −6.27 to −0.05; P &lt; 0.05), even after adjusting for sociodemographic factors. No correlation between OBGYN density and cervical cancer stage at diagnosis was found. After adjusting for county-level sociodemographic factors, increasing median household income was associated with an earlier stage at diagnosis for cervical cancer (P = 0.01) and increasing percentage of Black females was associated with a later stage at diagnosis for uterine cancer (P &lt; 0.01). Increasing PCP density was associated with an increased 5-year survival rate for cervical cancer (95% CI, 0.03–0.09; P &lt; 0.05). After adjustment for sociodemographic factors, no correlation between OBGYN density and cervical cancer survival was observed, nor was any correlation between PCP or OBGYN density and 5-year survival rate by stage for either endometrial or ovarian cancer. After adjustment for county-level sociodemographic factors, an increasing percentage of those with a bachelor’s degree was associated with an increased 5-year survival in cervical cancer (P &lt; 0.01), an increasing percentage of Black females was associated with a decreased 5-year survival rate for uterine cancer (P &lt; 0.01), and increasing median household income was associated with an increased 5-year survival rate for ovarian cancer (P &lt; 0.01). The results of this study suggest that only cervical cancer outcomes are significantly correlated with PCP density at the county level, possibly because cervical cancer is the only gynecologic cancer with recommended routine screening guidelines.

Retail Health Clinic Growth as a Potential Primary Care Disruptor.

Retail health clinics (RHCs) have been described as a disruptive model of care delivery. We describe RHC market presence in the United States from 2008 to 2016 with a focus on the characteristics of counties where new clinics open. We merge national data on RHC openings and closings from Merchant Medicine with the Area Health Resources File. We examined county-level counts and ownership of RHCs over time. From 2008 to 2016, we found increasing ownership of RHCs by retail pharmacies, and, contrary to earlier predictions, RHCs continue to be located in affluent counties and did not open in underserved or provider shortage areas. Most new clinics opened in counties where RHCs already had a presence, and these counties also had greater primary care physician, nurse practitioner, and physician assistant density per capita (100,000). As RHCs expand and offer more services, they may place new competitive pressures on nearby primary care providers and practices.

Association of Physician Densities and Gynecologic Cancer Outcomes in the United States.

To evaluate whether there is an association between county-level obstetrician-gynecologist (ob-gyn) and primary care physician (PCP) densities and gynecologic cancer outcomes in the United States. A retrospective cohort study of gynecologic cancers (uterine, ovarian, and cervical) in the Surveillance, Epidemiology, and End Results (SEER) database was performed from 2005 to 2018. County-level demographics were abstracted from the SEER database, population density from the United States Census Bureau, and physician density (ob-gyns and PCPs/100,000 females) from the Area Health Resources File. Backward stepwise regression models were used. Final analysis included 113,938 patients for stage at diagnosis analysis and 98,573 patients for 5-year survival analysis. Uterine, ovarian, and cervical cancers represented 60.0%, 25.0%, and 15.0% of patients, respectively. Most counties (57%) were nonmetropolitan and had a mean ob-gyn density of 8 per 100,000 females and a mean PCP density of 89 per 100,000 females. Multivariate analysis showed that increasing PCP density was associated with earlier stage at diagnosis (95% CI -6.27 to -0.05; P &lt;.05) and increased 5-year survival rates in cervical cancer (95% CI 0.03-0.09; P &lt;.05). Obstetrician-gynecologist density was not found to affect stage or survival outcomes for uterine or ovarian cancer. Analysis of sociodemographic factors for cervical cancer showed that median household income was negatively correlated with stage ( P =.01) and that the percentage of those with bachelor's degrees and metropolitan status were positively correlated with 5-year survival rates ( P &lt;.01). For uterine cancer, the percentage of Black females was positively correlated with stage ( P &lt;.01) and negatively correlated with 5-year survival rates ( P &lt;.01). Increasing PCP density, but not ob-gyn density, is associated with earlier stage at diagnosis and improved 5-year survival rates in cervical cancer. County-level sociodemographic factors, including population diversity, metropolitan status, educational attainment, and household income, were also correlated with outcomes across all cancer types. Targeting PCP supply and education in lower density counties may improve population-based care for cervical cancer.

Social vulnerability, medical care access and asthma related emergency department visits and hospitalization: An observational study

BackgroundThe Social Vulnerability Index (SVI) is a publicly available dataset to identify communities in greatest need of resources. ObjectiveTo examine the utility of using the county-level SVI as predictors of asthma-related outcomes. MethodsWe used the American Community Survey-derived SVI and the National Environmental Public Health Tracking Network – Query Tool to retrieve data for all counties with available SVI data and at least one matched outcome of interest. Then, we tested SVI as a predictor for emergency department visits (EDV) and hospitalizations, with investigating disparities in primary care physician (PCP) density and emergency department physicians (EDP) density. Linear and logistic regression models were used. ResultsCompared to counties of the lowest SVI quartile, counties of mid-low, mid-high, and highest SVI quartiles had 1%, 4%, and 5% higher odds of asthma-related EDV per 10,000 population, respectively, and 4%, 21%, and 24% higher odds of asthma-related hospitalization per 10,000 population, respectively. Moreover, the data showed an apparent resources mismatch between the EDP densities per 10,000 populations and the SVI quartiles, and the effect of the county level SVI on the asthma-related EDV and hospitalization is not strongly affected by PCP or EDP densities. ConclusionThe counties with the highest SVI -and the most vulnerable to asthma hazards- have a lower coverage of PCP and EDP. Interventions directed to address persistent social vulnerability would offer the opportunity of primary prevention with less exhaustion for the medical resources.

Associations Between Obesity, Physical Inactivity, Healthcare Capacity, and the Built Environment: Geographic Information System Analysis.

BackgroundObesity is one of the major critical health conditions affecting many people across the world. One of the major causes of obesity is identified to be sedentary lifestyles and physical inactivity, which may be associated with environmental factors.ObjectiveThe study analyzes variations in obesity and physical inactivity in the State of South Carolina, US, and their association with healthcare capacity and the built environment.MethodsData were obtained from different secondary sources and surveys, 2012, and then linked on the county-level using ArcGIS. Global Moran’s I was used to examine the spatial distribution at the state level, and Anselin’s local Moran’s I was used to detect any significant clusters at the county level. Ordinary least squares regression models were calculated for obesity and physical inactivity separately.ResultsMore than 70% of SC counties had high levels of obesity and physical inactivity. Spatial analysis showed statistical clusters of high obesity, high physical inactivity, and low access to exercise opportunities in rural areas compared to urban areas. Conversely, clusters of high density of health-care facilities appeared in urban areas. Through the regression models, the density of primary care physicians (p = 0.025) and access to exercise opportunities (p = 0.075) were negatively associated with obesity, while the low perception of own health (p = 0.001) and obesity rate (0.011) were positively associated with physical inactivity.ConclusionGIS was useful to illustrate and identify significant geographic variations and high clusters of obesity and physical inactivity in rural areas, compared with high clusters of access to exercise opportunities and health-care facilities in urban areas. The international health community is encouraged to utilize spatial information systems to examine variations and recommend evidence-based recommendations to redistribute equitable public health efforts. The development of strategies and initiatives toward reducing variation in health and sustainable development is key to promote the population wellbeing.

The impact of primary care physician density on perinatal health: Evidence from a natural experiment.

We examined the impact of primary care physician density on perinatal health outcomes in Norway. From 1992 and onwards, primary care physicians who chose to work in selected remote municipalities were given an annual reduction in their student loan. This reduction, combined with increased supply of physicians, led to an increase in the density of primary care physicians in these selected municipalities. Our register-based population study showed that this increase in physician density significantly improved perinatal health in terms of fewer fetal deaths and increased birth weight. The richness of the data allowed us to perform several robustness tests.

Association of healthcare system factors with childhood leukemia mortality in Peru, 2017–2019: A population-based analysis

BackgroundChildhood leukemia mortality is expected to increase in certain developing Latin American countries by 2030. The development of tailored public health interventions could benefit by understanding the role of healthcare system factors. Therefore, we aimed to evaluate the area-level association of healthcare system factors with leukemia mortality among Peruvian children aged 0–19. MethodsWe abstracted province-level death counts from the National Death Registry Information System database between 2017 and 2019. Continuous factors were dichotomized by their median. We fitted multivariate quasi-Poisson regression models for all leukemia and stratifying by subtypes of leukemia. ResultsFrom 2017 to 2019, 755 leukemia deaths were reported. Healthcare system factors associated with mortality varied by leukemia subtype. In lymphoid leukemia, a prolonged travel time to a healthcare facility (RR: 1.26, 95 % CI: 1.01−1.58), a high density of primary care physicians (RR: 1.57, 95 % CI: 1.25−1.97), and a high density of hospitals (RR: 1.64, 95 % CI: 1.23−2.20) were related to mortality. In myeloid leukemia, only a high density of hospitals (RR: 1.76, 95 % CI: 1.06−3.01) was associated with mortality. ConclusionThe province-level healthcare system factors related to mortality varied by leukemia subtype on children aged 0–19. The association found in provinces with a high density of primary care physicians or hospitals suggests a disparity in case identification between the areas with better and worse healthcare capacity. Higher mortality rates in provinces with prolonged travel time to a healthcare facility indicate a centralization of cancer-directed treatment and difficulties with its distribution. Policy summaryTo prevent childhood leukemia deaths, we highlight the need to strengthen the public health system through policies that enhance the distribution of leukemia treatment to locations far away from medical centers. Strengthening the supply, distribution, and adherence to leukemia cancer-directed treatment should be a public health priority for Peru.

Estimated Effect on Life Expectancy of Alleviating Primary Care Shortages in the United States.

Prior studies have reported that greater numbers of primary care physicians (PCPs) per population are associated with reduced population mortality, but the effect of increasing PCP density in areas of low density is poorly understood. To estimate how alleviating PCP shortages might change life expectancy and mortality. Generalized additive models, mixed-effects models, and generalized estimating equations. 3104 U.S. counties from 2010 to 2017. Children and adults. Age-adjusted life expectancy; all-cause mortality; and mortality due to cardiovascular disease, cancer, infectious disease, respiratory disease, and substance use or injury. Persons living in counties with less than 1 physician per 3500 persons in 2017 had a mean life expectancy that was 310.9 days shorter than for persons living in counties above that threshold. In the low-density counties (n = 1218), increasing the density of PCPs above the 1:3500 threshold would be expected to increase mean life expectancy by 22.4 days (median, 19.4 days [95% CI, 0.9 to 45.6 days]), and all such counties would require 17 651 more physicians, or about 14.5 more physicians per shortage county. If counties with less than 1 physician per 1500 persons (n = 2636) were to reach the 1:1500 threshold, life expectancy would be expected to increase by 56.3 days (median, 55.6 days [CI, 4.2 to 105.6 days]), and all such counties would require 95754 more physicians, or about 36.3 more physicians per shortage county. Some projections are based on extrapolations of the actual data. In counties with fewer PCPs per population, increases in PCP density would be expected to substantially improve life expectancy. None.

Hospitalization Trajectories in Home- and Community-Based Services Recipients: The Influence of Physician and Social Care Density.

Repeated hospitalizations among older adults receiving Home- and Community-Based Services (HCBS) may indicate unmet medical and social needs. This study examined all-cause hospitalization trajectories and the association between area-level resource density for medical and social care and the trajectory group membership. The study participants included 11,223 adults aged 60 years or older who were enrolled in public HCBS programs in Michigan between 2008 and 2012. Data sources included the Michigan interRAI-Home Care, Dartmouth Atlas of Health Care Data, the American Community Survey, and the County Business Patterns from the Census Bureau. The group-based trajectory modeling was used to identify trajectories of hospitalization over 15 months. Correlates of the trajectories were examined using multinomial logistic regression. Four distinct hospitalization trajectory groups emerged: "never" (43.1%)-individuals who were rarely hospitalized during the study period, "increasing" (19.9%)-individuals who experienced an increased risk of hospitalization, "decreasing" (21.6%)-individuals with a decreased risk, and "frequent" (15.8%)-individuals with frequent hospitalizations. Older adults living in areas with a higher number of social service organizations for older adults and persons with disability were less likely to be on the "frequent" trajectory relative to the "decreasing" trajectory. The density of primary care physicians was not associated with the trajectory group membership. Area-level social care resource density contributes to changes in 15-month hospitalization risks among older adult recipients of HCBS.

Assessment of Changes in Rural and Urban Primary Care Workforce in the United States From 2009 to 2017

Access to primary care clinicians, including primary care physicians and nonphysician clinicians (nurse practitioners and physician assistants) is necessary to improving population health. However, rural-urban trends in primary care access in the US are not well studied. To assess the rural-urban trends in the primary care workforce from 2009 to 2017 across all counties in the US. In this cross-sectional study of US counties, county rural-urban status was defined according to the national rural-urban classification scheme for counties used by the National Center for Health Statistics at the Centers for Disease Control and Prevention. Trends in the county-level distribution of primary care clinicians from 2009 to 2017 were examined. Data were analyzed from November 12, 2019, to February 10, 2020. Density of primary care clinicians measured as the number of primary care physicians, nurse practitioners, and physician assistants per 3500 population in each county. The average annual percentage change (APC) of the means of the density of primary care clinicians over time was calculated, and generalized estimating equations were used to adjust for county-level sociodemographic variables obtained from the American Community Survey. The study included data from 3143 US counties (1167 [37%] urban and 1976 [63%] rural). The number of primary care clinicians per 3500 people increased significantly in rural counties (2009 median density: 2.04; interquartile range [IQR], 1.43-2.76; and 2017 median density: 2.29; IQR, 1.57-3.23; P < .001) and urban counties (2009 median density: 2.26; IQR. 1.52-3.23; and 2017 median density: 2.66; IQR, 1.72-4.02; P < .001). The APC of the mean density of primary care physicians in rural counties was 1.70% (95% CI, 0.84%-2.57%), nurse practitioners was 8.37% (95% CI, 7.11%-9.63%), and physician assistants was 5.14% (95% CI, 3.91%-6.37%); the APC of the mean density of primary care physicians in urban counties was 2.40% (95% CI, 1.19%-3.61%), nurse practitioners was 8.64% (95% CI, 7.72%-9.55%), and physician assistants was 6.42% (95% CI, 5.34%-7.50%). Results from the generalized estimating equations model showed that the density of primary care clinicians in urban counties increased faster than in rural counties (β = 0.04; 95% CI, 0.03 to 0.05; P < .001). Although the density of primary care clinicians increased in both rural and urban counties during the 2009-2017 period, the increase was more pronounced in urban than in rural counties. Closing rural-urban gaps in access to primary care clinicians may require increasingly intensive efforts targeting rural areas.

Role of physician density in predicting stage and survival for head and neck squamous cell carcinoma.

Identifying and linking barriers to access to head and neck cancer care, specifically provider density, to stage of diagnosis and survival outcomes is important to serve as a foundation for policy interventions. Retrospective cohort study using patients with head and neck squamous cell (HNSCC) in the Surveillance, Epidemiology, and End Results (SEER) database from 2007 to 2016 and Area Resource File. Primary outcomes included stage of presentation and cancer-specific 5-year survival and relation to provider density. The initial cohort consisted of 18 342 patients with oral cavity, 21 809 oropharyngeal, 15 860 laryngeal, and 2887 patients with hypopharyngeal malignancy. Non-Hispanic Black race and being uninsured increased the odds of presenting with advanced stage HNSCC and increased hazard of death. There was no significant and consistent association identified between Health Service Areas provider density and advanced stage at diagnosis or cancer-specific 5-year mortality. Provider density of otolaryngologists and primary care physicians and dentists was not significantly associated with stage of presentation or cancer-specific survival for HNSCC while race and insurance status remained independent predictors for worse outcomes.

Changes in Disparities in Stage of Breast Cancer Diagnosis in Pennsylvania After the Affordable Care Act.

Background: This study sought to determine if increased access to health insurance following the Affordable Care Act (ACA) resulted in an increased proportion of early-stage breast cancer diagnosis among women in Pennsylvania, particularly minorities, rural residents, and those of lower socioeconomic status. Materials and Methods: Data on 35,735 breast cancer cases among women 50-64 and 68-74 years of age in Pennsylvania between 2010 and 2016 were extracted from the Pennsylvania Cancer Registry and analyzed in 2019. Women 50-64 years of age were subdivided by race/ethnicity, area of residence, and socioeconomic status as measured by area deprivation index (ADI). We compared the proportions of early-stage breast cancer diagnosis pre-ACA (2010-2013) and post-ACA (2014-2016) for all women 50-64 years of age to all women 68-74 years of age. This comparison was also made between paired sociodemographic subgroups for women 50-64 years of age. Multivariable logistic regression models were constructed to assess how race, area of residence, ADI, and primary care physician (PCP) density interacted to impact breast cancer diagnosis post-ACA. Results: The proportion of early-stage breast cancer diagnosis increased by 1.71% post-ACA among women 50-64 years of age (p < 0.01), whereas women 68-74 years of age saw no change. Multivariable logistic regression analysis demonstrated that minority women had lower odds of early-stage breast cancer diagnosis pre-ACA, but not post-ACA, when controlling for ADI. Meanwhile, increased area-level socioeconomic advantage was associated with higher odds of being diagnosed with early-stage breast cancer pre- and post-ACA irrespective of controlling for race, area of residence, or PCP density. Conclusions: Enhanced access to health insurance under the ACA was associated with an increased proportion of early-stage breast cancer diagnosis in Pennsylvanian women 50-64 years of age and may have reduced racial, but not socioeconomic, disparities in breast cancer diagnosis.

Abstract D002: Associations between multilevel health factors and cancer mortality according to rural residence

Abstract Surveillance reports consistently observe that cancer mortality rates are higher in rural than urban areas, yet data on the multi-level factors that impact rural disparities have not been fully leveraged to identify the areas of greatest need for research and policy changes. To address gaps in cancer data for rural communities, we adapted the County Health Rankings model of the multiple determinants of health to cancer. Using publicly available data, we compared health factors and cancer mortality for rural versus urban counties in Wisconsin. Counties were defined as rural (N=19) or non-rural (“urban”, N=53) based on Rural Urban Continuum Codes 7-9 and 1-6, respectively. Age-adjusted county-specific cancer mortality rates for all cancer sites combined were obtained from the state cancer registry. Health factor data were obtained from multiple sources in 4 categories: health behaviors (smoking, drinking alcohol, obesity, physical activity); clinical care (HPV vaccination; breast, cervical, and colorectal cancer screening; density of primary care physicians); socioeconomic factors (Area Deprivation Index based on 17 census items); and physical environment (access to grocery stores and alcohol outlets, air quality, pesticide use). Items were ranked for the 72 counties with lower-risk values having better ranks, e.g., higher values for screening and lower values for obesity ranked closer to 1. A composite health factor ranking was defined using County Health Rankings weights, equal to 0.3*(behavioral factors) + 0.2*(clinical factors) + 0.4*(socioeconomic factors) + 0.1*(physical environment). Cancer death rates were higher in rural than in urban counties (181 vs 164 per 100,000). The composite health ranking was positively associated with cancer mortality rates (Pearson correlation coefficient 0.38, 95% CI 0.17-0.57), with worse rankings for rural (average 44, interquartile range, IQR 39-51) than for urban counties (average 34, IQR 25-42). The difference in health factor category rankings between rural and urban counties was greatest for socioeconomic factors (rural average rank 50 vs urban average rank 32) followed by clinical care (rural average rank 43 vs urban average rank 34) and behavioral factors (rural average rank 40 vs urban average rank 35). Physical environment factor rankings were slightly better for rural (average 33) than urban (average 37) counties. In conclusion, we confirmed that cancer mortality in Wisconsin is higher in rural as compared with urban areas. Future analyses will (a) refine the set of health factors used to construct the composite health factor ranking (e.g., account more fully for distance to care) and (b) optimize the weights applied to the categories to calculate the composite ranking. These initial findings suggest that, to increase the impact of future research and policy efforts, clinical and behavioral interventions targeting cancer health disparities in rural counties should include strategies to address socioeconomic factors. Citation Format: Amy Trentham-Dietz, Noelle K LoConte, Betsy Rolland, Lisa Cadmus-Bertram, Tracy M Downs, John M Eason, Cody M Fredrick, John M Hampton, Xiao Zhang, Ronald E Gangnon. Associations between multilevel health factors and cancer mortality according to rural residence [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr D002.