The purpose of the study is to reduce rural-urban disparities and improve the quality of cancer care for these patients:~(Aim 1): By using mixed methods, investigators will adapt the stepped collaborative care intervention to address the contextual needs of patients diagnosed with comorbid cancer and depression living in rural, low-income communities. Using face-to-face qualitative interviews with 30 patients diagnosed with cancer and quantitative data from our past trial, investigators will adapt the stepped collaborative care intervention for patients diagnosed with cancer from rural, low-income communities. For all quantitative data, the frequency distributions of study variables will be examined prior to statistical analyses to provide descriptive data and to identify departures from normality. If nonlinearity is detected, a transformation will be performed using the Box-Cox transformation, which finds the optimal relationship between variables. All measures have established reliability and validity similar to that from which the study sample will be drawn; however, the reliabilities of scales will be assessed using Cronbach's. Thorough exploratory data analyses will be performed to ascertain data characteristics and to screen for anomalies (i.e., outliers). The preliminary exploration of the data will be used to: (1) examine univariate and bivariate distributions; (2) identify any imbalances between treatment groups on baseline characteristics; (3) investigate the magnitude of the associations between the dependent variables and the potential covariates; (4) determine the internal consistency reliabilities of the measurement scales using Cronbach's alpha; and (5) verify the statistical assumptions of the planned primary analyses. If assumptions are violated, then alternative procedures such as data transformations or more robust statistical methods will be considered. Missing data will be examined using available data on subject characteristics. Logistic regression models will be created with SAS PROC LOGISTIC to compare the characteristics of subjects who remained in the study versus those who dropped out. If data are determined to be missing at random, likelihood estimation procedures available in SAS PROC MIXED will produce unbiased estimates and allow the retention of subjects with missing data on outcomes. If data are determined to be not missing at random, pattern mixture or selection modeling will be used to investigate attrition. If necessary, multiple imputation (SAS PROC MI) will be used to impute missing values for covariates.~(Aim 2): To test, in a randomized controlled trial, the adapted stepped collaborative care intervention for 242 patients diagnosed with comorbid cancer and depression living in rural, low-income communities. Primary hypotheses are that patients randomized to the stepped collaborative care intervention will report clinically meaningful (0.40 or greater effect size) improvements in depression and Quality of Life (QoL) at 6- and 12-months when compared to patients randomized to the screening and referral arm. The secondary hypotheses are that patients randomized to the stepped collaborative care intervention will report clinically meaningful (0.30 or greater effect size) improvements in sleep quality, perceived stress, and social support when compared to the screening and referral arm. The expectation is that caregivers of patients randomized to the intervention arm will have reductions in stress and depression when compared to caregivers of patients randomized to the screening and referral arm. Further exploration will be done of the beneficial effects of the stepped collaborative care intervention on survival.~(Aim 3): To examine the cost-effectiveness of the stepped collaborative care intervention for 242 patients diagnosed with cancer and depression. The stepped collaborative care intervention is expected to be cost-effective compared to the screening and referral arm from the health system perspective in terms of cost per quality-adjusted life year based on established benchmarks for cost-effectiveness. Given the low cost of the intervention and our preliminary data showing reductions in complications and healthcare utilization, there is a high chance that the intervention is, on average, cost saving.~(Aim 4): Guided by the Consolidated Framework for Implementation Science (CFIR), The acceptability, feasibility and actionable factors within the rural context that influence the delivery of the stepped collaborative care intervention will be assessed.. Using mixed methods, guided by the CFIR framework, Surveys will be administered and qualitative interviews performed with patients, caregivers, providers, clinical and research staff after testing the screening and adapted intervention to further refine and inform future implementation in rural, low-income communities.~In terms of relevant prior knowledge and gaps in current knowledge, over 60 million people live in rural communities in the United States. Those who develop cancer are diagnosed later, at more advanced stages, with greater physical comorbidities, poorer quality of life (QoL), and have fewer options for treatment and resources to cope with a diagnosis of cancer when compared to those living in urban communities. As a result, they suffer from greater cancer-related distress and depression. Patients diagnosed with cancer from rural Appalachia were more likely to meet the criteria for clinical levels of distress compared to 27.4% of non-rural cancer patients. Investigators have also observed higher rates of depression in patients from rural, low-income areas when compared to their urban counterparts in Appalachia. Despite the high rates of depression, people who live in rural communities are 47% less likely to receive mental health treatment and 72% less likely to receive specialized mental health treatment when compared to those living in urban communities. Depression results in poorer QoL, decreased adherence to cancer treatments, and increased risk of mortality. Pharmacological and non-pharmacological treatments that have been effective in people from urban communities have not been effective in those living in rural, low-income communities. A stepped collaborative care intervention was designed and tested for socioeconomically disadvantaged patients with comorbid cancer and depression. While the intervention was effective for patients from urban, low-income communities, subgroup analyses revealed the intervention was not effective in patients from rural, low-income communities. While preliminary, there is the belief that cognitive-behavioral therapy (CBT) may not be culturally sensitive in rural, low-income patients. In addition, predictors of depression for these patients included poor sleep, high levels of perceived stress, and lack of social support. The proposal is to adapt the intervention using problem solving therapy, rather than CBT; provide resources to reduce financial stress; and incorporate stress management, support groups, and treatment of sleep problems in the novel intervention. In effort to reduce rural-urban disparities and improve the quality of cancer care, the objectives of this study will be to: (1) adapt the stepped collaborative care intervention to maximize effectiveness for rural, low-income patients, (2) test the effectiveness of the adapted intervention, and (3) prepare for implementation of the intervention in rural, low-income communities.
In an effort to reduce rural-urban disparities and improve the quality of cancer care for these patients, the objectives of this study will be to: (1) adapt the intervention to maximize effectiveness for rural, low-income patients, (2) test the effectiveness of the adapted intervention, and (3) prepare for implementation of the intervention in rural, low-income communities.