Association between 1-year patient outcomes and opioid-prescribing group of emergency department clinicians: A cohort study with Army active-duty soldiers.

OBJECTIVE: The objective was to examine the association between clinicians' opioid prescribing group and patients' outcomes among patients treated in the emergency department (ED). METHODS: This was a retrospective cohort study. The setting was the EDs of the U.S. Military Health System (MHS). Patients were 181,557 Army active-duty opioid-naïve (no fill in past 180 days) patients with an index encounter to the ED between October 2010 and September 2016. Exposure was patients classified by opioid prescribing tier of the treating ED clinician: top, middle, or bottom third relative to the clinician's peers in the same ED. Follow-up measurement was from 31 to 365 days after the index encounter. The primary outcome was long-term opioid prescriptions (LTOPs) defined as 180 (or more) days' supply within the follow-up window. We also computed the total morphine milligram equivalents (MME) and total opioid days' supply. Secondary measures were any repeat ED encounter, any hospitalization, any sick leave, and any military-duty restriction. RESULTS: We found a 2.5-fold variation in opioid prescribing rates among clinicians in the same MHS ED. Controlling for sample demographics, reason for encounter, and military background, in multivariate analyses the odds of receiving a 180-day opioid supply during follow-up were 1.19 (95% confidence interval [CI] = 1.01 to 1.40, p < 0.05) for the top opioid exposure group and 1.37 (95% CI = 1.19 to 1.57, p < 0.001) for the middle opioid exposure group compared to the bottom exposure group, and there were significant increases in total opioid days' supply and total MME. There were no differences in secondary outcome measures. CONCLUSION: In a relatively healthy sample of Army soldiers, variation in opioid exposure defined by clinician's prescribing history was associated with increased odds of LTOP and increase in opioid volume, but not in functional outcomes.

The Financial Burden of Cancer on Families in the United States.

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.

Opioid prescribing for surgical dental procedures in dental clinics of military treatment facilities.

BACKGROUND: Variation in opioid prescribing rates among geographic regions is well known and, to the authors' knowledge, there have been no studies of variation from 1 dental clinic to another, and such variation might suggest an excess of opioid prescriptions. METHODS: The authors used a retrospective cohort design study of all dental encounter records for 819,453 soldiers in the dental clinics (n = 250) of the US Military Health System during the period from 2008 through 2017. RESULTS: There were 743,459 dental surgical encounters. Opioid prescriptions were filled for 36.7% of these encounters. Multinomial multilevel regression found statistically significant between-facility variance in opioid prescribing, which was partially explained via facility-level characteristics (region, type, and percentage of surgeries for young patients), and practice variables (mean percentage extractions, percentage periodontic surgery, and percentage specialists). CONCLUSIONS: The authors found a substantial variation in opioid prescribing among dental clinics in the US Military Health System. Dentists at 11 of the 30 largest military treatment facilities prescribed at a rate 4 percentage points higher than expected, and dentists at 9 of these military treatment facilities had a rate of 4 percentage points lower than expected. Additional study of the factors allowing the low-prescribing facilities to achieve these rates might lead to an overall decrease in opioid prescribing. PRACTICAL IMPLICATIONS: The authors' findings of dental opioid prescribing in the military can lead to appreciation of the guidelines from the American Dental Association and the American Association of Oral and Maxillofacial Surgeons, which suggest alternatives to opioid prescribing for surgical procedures, particularly for tooth extractions.

Inequalities in Poverty and Income between Single Mothers and Fathers.

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.

The burden of out of pocket costs and medical debt faced by households with chronic health conditions in the United States.

INTRODUCTION: To examine the relationship between chronic health conditions and out-of-pocket costs (OOPC) and medical debt. METHODS: 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. RESULTS: 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). CONCLUSIONS: Findings from this study show that the presence of chronic health conditions impose a large financial burden on some households.