Effects of Hospital Type and Distance on Lymph Node Assessment for Colon Cancer Among Metropolitan and Nonmetropolitan Patients in Appalachia

Barriers to Adherence to Standard of Care in Appalachia: A Qualitative Assessment in Gastrointestinal Cancers

Purpose

Appalachian Kentucky, a 32-county region in the eastern part of the state, has elevated colon cancer mortality rates. While recommended as the standard of care, access to adjuvant chemotherapy treatment is limited in this region due to scarce health services and significant social and geographical barriers. The purpose of this investigation was to improve understanding of barriers that cancer patients residing in rural areas not served directly by tertiary medical systems must overcome in completing adjuvant therapy.

Methods

Participants were recruited from two medical centers: A tertiary care NCI designated Cancer Center and a regional hospital. Participants underwent a 15-20 minute interview to assess factors associated with adherence to adjuvant treatment recommendations. Grounded theory identified themes related to patient behaviors and non-adherence to standard of care recommendations.

Results

Data were collected in 45 telephone and in-person patient interviews, 26 from an NCI-designated cancer center and 19 from a rural hospital. Statistically the two groups were equivalent in terms of age, subjective health status, and medical comorbidities. Six themes were identified from analysis of the transcribed interviews including: confidence in my care provider, communication, treatment issues, distrust, faith, and barriers to obtaining healthcare. Participants completing adjuvant therapy were more likely to express trust in their provider and describe fewer barriers to obtaining healthcare than those not completing adjuvant therapy.

Conclusion

Barriers to completing adjuvant therapy may differ between rural and urban healthcare systems which may yield opportunities for targeted interventions to improve rates of completion of colon cancer adjuvant chemotherapy.

Urban-Rural Variations in Quality of Care Among Patients With Cancer in California

INTRODUCTION

Previous research suggests cancer patients living in rural areas have lower quality of care, but population-based studies have yielded inconsistent results. This study examines the impact of rurality on care quality for 7 cancer types in California.

METHODS

Breast, ovarian, endometrial, cervix, colon, lung, and gastric cancer patients diagnosed from 2004 to 2017 were identified in the California Cancer Registry. Multivariable logistic regression and proportional hazards models were used to assess effects of residential location on quality of care and survival. Stratified models examined the impact of treatment at National Cancer Institute designated cancer centers (NCICCs). Quality of care was evaluated using Commission on Cancer measures. Medical Service Study Areas were used to assess urban/rural status. Data were collected in 2004-2019 and analyzed in 2020.

RESULTS

989,747 cancer patients were evaluated, with 14% living in rural areas. Rural patients had lower odds of receiving radiation after breast conserving surgery compared to urban residents. Colon and gastric cancer patients had 20% and 16% lower odds, respectively, of having optimal surgery. Rural patients treated at NCICCs had greater odds of recommended surgery for most cancer types. Survival was similar among urban and rural subgroups.

CONCLUSIONS

Rural residence was inversely associated with receipt of recommended surgery for gastric and colon cancer patients not treated at NCICCs, and for receiving recommended radiotherapy after breast conserving surgery regardless of treatment location. Further studies investigating the impact of care location and availability of supportive services on urban-rural differences in quality of care are warranted.

Rural Disparities in Hospital Patient Satisfaction: Multilevel Analysis of the Massachusetts AHA, SID, and HCAHPS Data

INTRODUCTION

Hospital patient satisfaction has been a salient policy concern. We examined rurality's impact on patient satisfaction measures.

METHODOLOGY

We examined patients (age 50 and up) from 65 rural and urban hospitals in Massachusetts, using the merged data from 2007 American Hospital Association Annual Survey, State Inpatient Database and Survey of Patients' Hospital Experiences, utilizing Hierarchical binary logistic regression analyses to examine the rural disparities in patient satisfaction measures.

RESULTS

Relative to the urban location, rurality reduced the likelihood of cleanliness of environment (odds ratio = 0.66, 95% confidence interval: [0.63-0.70]); but increased the likelihood of staff responsiveness and quietness. Compared to Caucasian counterparts, Hispanic patients were less likely to reside in a quiet hospital. Compared to other payments, Medicare or Medicaid coverage each reduced the likelihood of staff responsiveness and cleanliness. Compared to other diagnoses, depressive or psychosis disorders predicted smaller odds in responsiveness and cleanliness. Anxiety diagnosis reduced the likelihood of cleanness and quietness. At the facility level, higher registered nurse full-time equivalent (FTE)s or being a teaching hospital increased the likelihood of all measures.

CONCLUSION

Relative to the urban counterparts, rural patients experienced lower likelihood of staff responsiveness after adjusting for other factors. Compared to Caucasian patients, Hispanic patients were less likely to reside in quiet hospital environment. Research is needed to further explore the basis of these disparities. Mental health diagnoses in depressive and psychosis disorders also called upon further studies in special care needs.

Lost in the Black Belt South: health outcomes and transportation infrastructure

The importance of transportation infrastructure to health outcomes has been increasingly recognized. However, the relationship between transportation and health is underexplored in rural areas. This study fills the gap by investigating rural health outcomes in association with two transportation infrastructures-highways and airports-in the Black Belt counties of the USA, a region characterized as predominantly rural and black and as having high poverty and unemployment. Spatial regression models are applied to analyze the 2010 data. The results suggest Black Belt counties have poorer health outcomes than their non-Black Belt counterparts, and the difference increases as the percentage of blacks increases. The results also show that the higher accessibility to an airport a county has, the better its health outcomes. Highways, however, do not have a statistically significant association with health outcomes. The poor health outcomes in the Black Belt counties are also influenced by poverty, rurality, unemployment, and low educational attainment. This research was the first to study transportation, especially airports, in the rural US South with relation to health outcomes. Our findings shed new light on removing the health disadvantages accumulated in the Black Belt.

Use of instrumental variables for endogenous treatment at the provider level

Health economists are often interested in the effects of provider-level attributes (e.g., nonprofit status or quality rating) on patient outcomes, but estimation is subject to selection bias due to correlation with other omitted provider-level attributes that also affect patient outcomes. Recently, researchers have attempted to use patient-level instrumental variables, such as differential distance, to solve this problem of a provider-level endogenous treatment variable in settings where patients are nested within providers. However, to satisfy validity assumptions, an instrumental variable for a provider attribute must be at the provider level or a larger unit of aggregation, not at the patient level. A patient-level instrument cannot predict variation in a provider attribute separately from other, potentially unmeasured, provider attributes. In this paper, we explain this misapplication, review the extent of this problem in recent literature, and offer alternative approaches to avoid this misapplication of patient-level instrumental variables.

Spatially varying effects of predictors for the survival prediction of nonmetastatic colorectal Cancer

Background

An increasing number of studies have identified spatial differences in colorectal cancer survival. However, little is known about the spatially varying effects of predictors in survival prediction modeling studies of colorectal cancer that have focused on estimating the absolute survival risk for patients from a wide range of populations. This study aimed to demonstrate the spatially varying effects of predictors of survival for nonmetastatic colorectal cancer patients.

Methods

Patients diagnosed with nonmetastatic colorectal cancer from 2004 to 2013 who were followed up through the end of 2013 were extracted from the Surveillance Epidemiology End Results registry (Patients: 128061). The log-rank test and the restricted mean survival time were used to evaluate survival outcome differences among spatial clusters corresponding to a widely used clinical predictor: stage determined by AJCC 7th edition staging system. The heterogeneity test, which is used in meta-analyses, revealed the spatially varying effects of single predictors. Then, considering the above predictors in a standard survival prediction model based on spatially clustered data, the spatially varying coefficients of these models revealed that some covariate effects may not be constant across the geographic regions of the study. Then, two types of survival prediction models (a statistical model and a machine learning model) were built; these models considered the predictors and enabled survival prediction for patients from a wide range of geographic regions.

Results

Based on univariate and multivariate analysis, some prognostic factors, such as “TNM stage”, “tumor size” and “age at diagnosis,” have significant spatially varying effects among different regions. When considering these spatially varying effects, machine learning models have fewer assumption constraints (such as proportional hazard assumptions) and better predictive performance compared with statistical models. Upon comparing the concordance indexes of these two models, the machine learning model was found to be more accurate (0.898[0.895,0.902]) than the statistical model (0.732 [0.726, 0.738]).

Conclusions

Based on this study, it’s recommended that the spatially varying effect of predictors should be considered when building survival prediction models involving large-scale and multicenter research data. Machine learning models that are not limited by the requirement of a statistical hypothesis are promising alternative models.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4985-2) contains supplementary material, which is available to authorized users.