To examine the relationship between chronic health conditions and out-of-pocket costs (OOPC) and medical debt. Secondary data from the 2013 Panel Study of Income Dynamics (PSID) was used. Households whose head of household and spouse (for married households) were 18 to 64 years old were included. Households with 1 to 3 chronic conditions had higher odds of having any OOPC compared to households with no chronic conditions (AOR 1.74, 95% CI 1.39, 2.17) (p < .01). Households with 1 to 3 and 4 or more chronic health conditions were associated with higher odds of having any medical debt (AOR 2.24, 95% CI 1.75 to 2.87; AOR 5.04, 95% CI 3.04 to 8.34) compared to those with no chronic conditions (p < 0.01). Similarly, 1 to 3 and 4 or more chronic health conditions was associated with higher amounts of OOPC (Exponentiated Coefficient 1.18, 95% CI 1.03 to 1.36; Exponentiated Coefficient 1.56, 95% CI 1.17 to 2.07) and medical debt (Exponentiated Coefficient 1.69, 95% CI 1.23 to 2.34; Exponentiated Coefficient 2.73, 95% CI 1.19 to 6.25) compared to households with no chronic conditions (p < 0.05). Findings from this study show that the presence of chronic health conditions impose a large financial burden on some households.

Background: The American family structure has changed in the past few decades due to a rise in the divorce rate and unmarried women with children. Research suggests a salary disparity between men and women, especially for those women after pregnancy. However, these studies were confined to individuals within traditional families, and there is a lack of information of income disparity and poverty status between single mothers and fathers. The current study explored the disparities in single-parent families based on the household income and the poverty status using a set of nationwide censor data. Methods: The current study used data from the 2011 and 2013 Panel Study of Income Dynamics (N = 1135). Multivariate regression models were used in the analysis. Results: The demographic characteristics of the weighted population showed that taxable income, total income, and poverty status were higher for single fathers than mothers, while non-work income was higher for single mothers than fathers. Single mothers were much more likely to be at the crisis category than single fathers. Multivariate analyses showed that gender, age, marital status, years of experience, and geographic region had effects on taxable income, and only gender, marital status, and region had effects on poverty status. Conclusions: The results suggest that vulnerable group of single mothers was acknowledged according to income and poverty status. Age, marital status, years of experience, and region would be the critical factors for predicting the income and poverty status for single parenthood.

This study aimed to examine variations in patient-physician communication by obesity status. We pooled data from the 2005-2007 Medical Expenditure Panel Survey (MEPS),_included only individuals who completed the self-administered questionnaire themselves, and restricted the sample to patients who received care from primary care physicians. We included a total of 6,628 unique individuals between the ages of 18 and 65 who had at least one office or hospital outpatient visit during the past 12 months. There are six outcomes of interest in this study. The patient-physician communication composite score is based on five questions that the MEPS adapted from the Consumer Assessment of Healthcare Providers and Systems Survey. The other five variables were: respect from providers, providers’ listening skills, explanations from providers, time spent with patients, and patient involvement in treatment decisions. The key independent variable was obesity. Bivariate and multivariate models such as ordinary least squares (OLS) and logistic regression were used to examine the relationship between patient-physician communication and obesity status. Multivariate models showed that obese patients had a reduced physician-patient communication composite score of 0.19 (95% CI 0.03-0.34, p=0.02), physicians’ show of respect OR 0.77 (95% CI 0.61-0.98, p=0.04), listening ability OR 0.82 (95% CI 0.65-1.02, p=0.07), and spending enough time OR 0.80 (95% CI 0.62-0.99, p=0.04) compared to non-obese patients. We found a negative association between physician-patient communication and patients’ obesity status. These findings may inform public health practitioners in the design of effective initiatives that account for the needs and circumstances of obese individuals.

Acute low back pain is one of the most common reasons for individuals to seek medical care in the United States. The US Military Health System provides medical care to approximately 9.4 million beneficiaries annually. These patients also routinely suffer from acute low back pain. Within this health system, patients can receive care and treatment from physicians, or physician extenders including physician assistants and nurse practitioners. Given the diversity of provider types and their respective training programs, it would be informative to evaluate variation in care delivery, adherence to clinical guidelines, and differences within the MHS among a complex mix of provider types. This study was a retrospective, cross-sectional quantitative analysis that examined variations in treatment between provider types within the Military Health System in 2015 for treatment of acute low back pain using administrative data. In addition to descriptive and summary statistics, binomial logistic regression models were used to assess variation in practice patterns among physicians and mid-level practitioners for prescribing of non-steroidal anti-inflammatory, opioids, plain radiography, computed tomography, and magnetic resonance imaging. With regard to prescribing practices, results indicated that the odds of receiving non-steroidal anti-inflammatory prescriptions increased significantly for both physician assistants and nurse practitioners when compared to physicians. For basic radiological referrals, odds increased significantly for ordering plain radiography for physician assistants and nurse practitioners when compared to physicians. For more advanced imaging, odds significantly decreased for ordering computed tomography (CT) and slightly decreased for magnetic resonance for physician assistants, nurse practitioners and physician residents compared to the physician group. Additionally this study discovered differences in the prescribing patterns between provider categories. Both contractors and civilians had higher odds of prescribing opioids compared to active duty providers. As physician assistants and nurse practitioners continue to gain popularity as physician extenders in the US and in addressing provider shortages for the Military Health System, further research should be conducted to determine what impact, if any, the differences found in this study have on patient outcomes. In addition, provider type warrants further investigation to determine if labor mix and outsourcing decisions within a single payer system impacts health delivery and value based care.

Employing three years of inpatient discharge data from 11 states and inpatient and patient safety quality indicators from the Agency for Healthcare Research and Quality (AHRQ), this paper explored whether minority (black, Hispanic, and Asian) patients used lower quality hospitals. We found that the association between the share of minority patients and hospital quality depended on how quality was measured and varied by race and ethnicity. Hospitals serving Hispanics performed well on most patient safety measures. Higher percentages of all three minority patient groups corresponded to lower quality for only one measure, postoperative sepsis. Our analysis indicates that it is incorrect to generalize that minorities use lower quality hospitals. Analysts and policymakers should be cautious when making generalizations about the overall service quality of hospitals that treat minority patients.

This study examined the relationship between a diagnosis of cancer and the likelihood of having any out-of-pocket costs (OOPC) and medical debt, and the amounts of OOPC and medical debt, at the household level. We used the 2013 Panel Study of Income Dynamics, a continuous, representative panel survey that collects demographic, economic, and social data in the United States. The analytic sample included head of households and their spouse (if married), 18–64 years old. Two-part models were used. The first part consisted of logistic regression models and the second part consisted of generalized linear models with logarithmic link and a gamma distribution. Logistic regression results showed odds of 2.13 (CI: 1.27, 3.57, p < 0.01) for any OOPC and odds of 1.55 (CI: 0.93, 2.58, p < 0.1) for any medical debt for households in which either the head or spouse (if married) reported a diagnosis of cancer compared to those that did not report a diagnosis of cancer. Likewise, results from the second part of the model for households with a positive amount of OOPC showed an exponentiated coefficient of 1.73 (CI: 1.33, 2.25, p < 0.01) for households in which either the head or spouse (if married) reported a diagnosis of cancer compared to households without a diagnosis of cancer. This study shows that a diagnosis of cancer places a financial burden on families, particularly with all types of debt, in the United States even after controlling for differences between households with a diagnosis of cancer and those without a diagnosis of cancer.