Active social engagement and health among older adults: assessing differences by cancer survivorship status.

INTRODUCTION: The number of older adults who are cancer survivors is rapidly growing. Evidence is needed to inform interventions to support successful aging among older adults (including older adult cancer survivors). Active engagement with life, that is, spending time with family and/or close friends, may be related to health outcomes, but this concept remains understudied. METHODS: We used survey data to assess active engagement among older adults (ages 50 + years) from seven mid-Atlantic US states (n = 2,914), and geocoded their residence to collect collected measures of community availability of social interaction. Outcomes were physical and mental health-related quality of life (HRQoL), assessed with the SF-12. We used multivariable, multilevel linear regression to evaluate relationships between social interactions (i.e., "active engagement with life," or visiting with family and/or friends at least once per week and having at least three close friends, and community-level availability, measured with census tract-level park land and walkability and with county-level availability of social associations) and HRQoL. Finally, we explored differences in these relationships by recent cancer survivorship. RESULTS: Overall, 1,518 (52.3%) participants were actively engaged. Active engagement was associated with higher physical HRQoL (estimate = 0.94, standard error [SE] = 0.46, p = .04) and mental HRQoL (estimate = 2.10, SE = 0.46, p < .001). The relationship between active engagement and physical HRQoL was stronger for recent cancer survivors (estimate = 4.95, SE = 1.84, p < .01) than for the general population (estimate = 1.10, SE = 0.43, p = .01). Community-level availability of social interaction was not associated with HRQoL. CONCLUSION: Our analysis demonstrated promising associations between active engagement with life and HRQoL among older adults, with large benefits for older cancer survivors. Additional research is needed on how active engagement is associated with better HRQoL, which can inform future policies and programs to optimize the aging process in the US.

Rural/Urban differences in uptake of preventive healthcare services: Variability in observed relationships across measures of rurality.

Rural residents are generally less likely to receive preventive healthcare than are urban residents, but variable measurement of rurality introduces inconsistency to these findings. We assessed the relationships between perceived and objective measures of rurality and uptake of preventive healthcare. In our sample, rural participants generally had equal or higher uptake of healthcare (i.e. private health insurance, check-up in the past year, being up-to-date on colorectal and cervical cancer screening) than urban participants. Importantly, the perceived measure of rurality performed similarly to the objective measures, suggesting that participant report could be a valid way to assess rurality in health studies. Significance for Public Health The ability to access routine preventive healthcare is a key component of public health. Comparing uptake of cancer screening in rural versus urban areas is one way to assess equity of healthcare access. Generally, rural areas have a higher burden of cancer than urban areas. The built environment, socioeconomic status, and patient perceptions can impact an individual's access to routine cancer screening. Preventive healthcare is of great importance to public health as a whole because screening can facilitate earlier diagnosis and more successful treatment for many preventable cancers, which may ultimately increase the quality and quantity of life.

Patterns of Cancer-Related Healthcare Access across Pennsylvania: Analysis of Novel Census Tract-Level Indicators of Persistent Poverty.

BACKGROUND: Persistent poverty census tracts have had ≥20% of the population living below the federal poverty line for 30+ years. We assessed the relationship between persistent poverty and cancer-related healthcare access across census tracts in Pennsylvania. METHODS: We gathered publicly available census tract-level data on persistent poverty, rurality, and sociodemographic variables, as well as potential access to healthcare (i.e., prevalence of health insurance, last-year check-up), realized access to healthcare (i.e., prevalence of screening for cervical, breast, and colorectal cancers), and self-reported cancer diagnosis. We used multivariable spatial regression models to assess the relationships between persistent poverty and each healthcare access indicator. RESULTS: Among Pennsylvania's census tracts, 2,789 (89.8%) were classified as non-persistent poverty, and 316 (10.2%) were classified as persistent poverty (113 did not have valid data on persistent poverty). Persistent poverty tracts had lower prevalence of health insurance [estimate = -1.70, standard error (SE) = 0.10], screening for cervical cancer (estimate = -4.00, SE = 0.17) and colorectal cancer (estimate = -3.13, SE = 0.20), and cancer diagnosis (estimate = -0.34, SE = 0.05), compared with non-persistent poverty tracts (all P < 0.001). However, persistent poverty tracts had higher prevalence of last-year check-up (estimate = 0.22, SE = 0.08) and screening for breast cancer (estimate = 0.56, SE = 0.15; both P < 0.01). CONCLUSIONS: Relationships between persistent poverty and cancer-related healthcare access outcomes differed in direction and magnitude. Health promotion interventions should leverage data at fine-grained geographic units (e.g., census tracts) to motivate focus on communities or outcomes. IMPACT: Future studies should extend these analyses to other states and outcomes to inform public health research and interventions to reduce geographic disparities.

Evaluating the Connection Between Rural Travel Time and Health: A Cross-Sectional Analysis of Older Adults Living in the Northeast United States.

INTRODUCTION: To characterize the impact of rural patients' travel time to obtain healthcare on their reported utilization of preventive healthcare services and personal health outcomes. METHODS: Online survey data from rural adults ages 50+ years living in the Northeastern United States were collected from February to August 2021. Study measures included self-reported travel time to obtain healthcare, use of preventive healthcare, and health outcomes. The associations between travel time with use of preventive care and health outcomes were assessed using linear, Poisson, and logistic regression analyses controlling for demographic variables. RESULTS: Our study population included 1052 rural adults, with a mean travel time of 18.5 min (range: 0-60). Travel time was greater for racial/ethnic minority participants and for higher-income participants (both P < .05), but it was not associated with use of preventive healthcare. Greater travel time was associated with poorer mental health and more comorbidities, including cancer and diabetes (all P < .05). CONCLUSIONS: Travel time varied by patient demographic factors, and it was associated with mental health and comorbidities. There was no association between travel time and preventive care use, suggesting that other barriers likely contribute to suboptimal use of these services within rural communities. Further research is needed to elucidate the causal pathways linking travel time to mental health and comorbidities within rural communities, as increased travel may exacerbate intrarural health disparities.

Examining the roles of travel distance, medical mistrust, and cancer fatalism in the uptake of clinical cancer prevention among women in rural and urban US communities: A secondary data analysis.

Introduction: Rural adults are less likely to receive cancer screening than urban adults, likely due to systematic differences in community- and individual-level factors. The purpose of this study was to analyze the relative contributions of rurality, travel time, medical mistrust, and cancer fatalism in explaining uptake of clinical cancer prevention services. Methods: We conducted a secondary data analysis of 2019-2020 survey data from women, ages 45-65, in rural and urban counties in central Pennsylvania, examining rurality, travel time to a primary care provider, medical mistrust, and cancer fatalism, as well as uptake of guideline-recommended colorectal cancer screening, cervical cancer screening, and preventive check-up. Final models used multivariable logistic regression to assess the relationships among study variables, controlling for participant demographics. Results: Among 474 participants, 48.9 % resided in rural counties. Most participants had received clinical cancer prevention services (colorectal cancer screening: 55.4 %; cervical cancer screening: 82.8 %; preventive check-up in the last year: 75.4 %). Uptake of services was less common among participants with higher medical mistrust (colorectal cancer screening: adjusted odds ratio [aOR] = 0.87, 95 % confidence interval [CI] = 0.76-1.00; cervical cancer screening: aOR = 0.79, 95 % CI = 0.63-1.00; last-year check-up: aOR = 0.74, 95 % CI = 0.63-0.88). Conclusions: Patient attitudes, particularly medical mistrust, may contribute to rural/urban disparities in clinical cancer prevention among women. Community- and individual-level interventions are needed to improve cancer outcomes in rural areas.

Hospital- and county-level characteristics explain geographic variability in prices of cancer-related procedures: Implications for policy and interventions.

BACKGROUND: Healthcare costs in the U.S. are high and variable, which can hinder access and impact health outcomes across communities. This study examined hospital- and county-level characteristics to identify factors that explain geographic variation in prices for four cancer-related procedures. METHODS: Data sources included Turquoise Health, which compiles publicly-available price data from U.S. hospitals. We examined list prices for four procedures: abdominal ultrasound, diagnostic colonoscopy, brain MRI, and pelvis CT scan, which we linked to characteristics of hospitals (e.g., number of beds) and counties (e.g., metropolitan status). We used multilevel linear regression models to assess multivariable relationships between prices and hospital- and county-level characteristics. Supplementary analyses repeated these models using procedures prices for commercial insurance plans. RESULTS: For each procedure, list prices varied across counties (intraclass correlation: abdominal ultrasound = 23.2%; colonoscopy = 17.1%; brain MRI = 37.2%; pelvis CT = 50.9%). List prices for each procedure were associated with hospital ownership (all p < 0.001) and percent of population without health insurance (all p < 0.05). For example, list prices for abdominal ultrasound were higher for proprietary versus Government-owned hospitals (ß = 539.10, 95% confidence interval [CI]: 256.12, 822.08, p < 0.001) and for hospitals in counties with more uninsured residents (ß = 23.44, 95% CI: 2.55, 44.33, p = 0.03). Commercial insurance prices were negatively associated with metropolitan status. CONCLUSIONS: Prices for cancer-related healthcare procedures varied substantially, with considerable heterogeneity associated with county location as well as county-level social determinants of health (e.g., health insurance coverage). Interventions and policy changes are needed to alleviate the financial burden of cancer care among patients, including geographic variation in prices for cancer-related procedures.

Exploratory Analysis of Concordance Between Clinician-Collected and Self-Sampled Human Papillomavirus Tests in a Small Cohort of Average- and High-Risk Patients.

Objectives: Cervical cancer screening rates have stagnated, but self-sampling modalities have the potential to increase uptake. This study compares the test characteristics of self-sampled high-risk human papillomavirus (hrHPV) tests with clinician-collected hrHPV tests in average-risk (i.e., undergoing routine screening) and high-risk patients (i.e., receiving follow-up after abnormal screening results). Methods: In this cross-sectional study, a relatively small cohort of average-risk (n = 35) and high-risk (n = 12) participants completed both clinician-collected and self-sampled hrHPV testing, along with a brief phone survey. We assessed hrHPV positivity, concordance, positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity across both methods (for types 16, 18, or other hrHPV). We also explored the relationship between test concordance and sociodemographic/behavioral factors. Results: Among average-risk participants, hrHPV positivity was 6% for both test methods (i.e., hrHPV-positive cases: n = 2), resulting in reported concordance, PPV, NPV, sensitivity, and specificity of 100%. Among high-risk participants, hrHPV positivity was 100% for clinician-collected tests but only 67% for self-sampled tests, showing varied concordance and sensitivity. Concordance was not associated with sociodemographic or behavioral factors. Conclusions: Self-sampled hrHPV testing demonstrated high accuracy for average-risk patients in this exploratory study. However, its performance was less consistent in high-risk patients who had already received an abnormal screening result, which could be attributed to spontaneous viral clearance over time. The limited number of participants, particularly HPV-positive cases, suggests caution in interpreting these results. Further research with larger cohorts is necessary to validate these findings and to explore the integration of self-sampled hrHPV testing into routine clinical care, particularly for patients with a history of cervical abnormalities. Clinical Trial Registration: NCT04591977, NCT04585243.

Addressing Social Risks to Accelerate Health Equity in Cancer Prevention and Control.

Addressing social risks in cancer prevention and control presents a new opportunity for accelerating cancer health equity. As members of the American Society of Preventive Oncology (ASPO) Cancer Health Disparities Special Interest Group, we describe the current state of science on social risks in oncology research and practice. To reduce and eliminate the unjust burden of cancer, we also provide recommendations for multilevel research examining social risks as contributors to inequities and the development of social risks-focused interventions. Suggestions for research and practice are provided within levels of the socio-ecological model, including the interpersonal, organizational, community, and policy levels.