Effects of the COVID-19 early pandemic on delivery outcomes among women with and without COVID-19 at birth.

BACKGROUND: The COVID-19 pandemic may influence delivery outcomes through direct effects of infection or indirect effects of disruptions in prenatal care. We examined early pandemic-related changes in birth outcomes for pregnant women with and without a COVID-19 diagnosis at delivery. METHODS: We compared four delivery outcomes-preterm delivery (PTD), severe maternal morbidity (SMM), stillbirth, and cesarean birth-between 2017 and 2019 (prepandemic) and between April and December 2020 (early-pandemic) using interrupted time series models on 11.8 million deliveries, stratified by COVID-19 infection status at birth with entropy weighting for historical controls, from the Healthcare Cost and Utilization Project across 43 states and the District of Columbia. RESULTS: Relative to 2017-2019, women without COVID-19 at delivery in 2020 had lower odds of PTD (OR = 0.93; 95% CI = 0.92-0.94) and SMM (OR = 0.88; 95% CI = 0.85-0.91) but increased odds of stillbirth (OR = 1.04; 95% CI = 1.01-1.08). Absolute effects were small across race/ethnicity groups. Deliveries with COVID-19 had an excess of each outcome, by factors of 1.07-1.46 for outcomes except SMM at 4.21. The effect for SMM was more pronounced for Asian/Pacific Islander non-Hispanic (API; OR = 10.51; 95% CI = 5.49-20.14) and Hispanic (OR = 5.09; 95% CI = 4.29-6.03) pregnant women than for White non-Hispanic (OR = 3.28; 95% CI = 2.65-4.06) women. DISCUSSION: Decreasing rates of PTD and SMM and increasing rates of stillbirth among deliveries without COVID-19 were small but suggest indirect effects of the pandemic on maternal outcomes. Among pregnant women with COVID-19 at delivery, adverse effects, particularly SMM for API and Hispanic women, underscore the importance of addressing health disparities.

Contributors to disparities in postpartum readmission rates between safety-net and non-safety-net hospitals: A decomposition analysis.

BACKGROUND: Safety-net hospitals (SNHs) treat more maternal patients with risk factors for postpartum readmission. OBJECTIVE: To assess how patient, hospital, and community characteristics explain the SNH/non-SNH disparity in postpartum readmission rates. DESIGN: A linear probability model assessed covariates associated with postpartum readmissions. Oaxaca-Blinder decomposition estimates quantified the contribution of covariates to the SNH/non-SNH disparity in postpartum readmission rates. SETTING: Healthcare Cost and Utilization Project 2016-2018 State Inpatient Databases from 25 states. PARTICIPANTS: 3.5 million maternal delivery stays. MEASUREMENTS: The outcome was inpatient readmission within 42 days of delivery. SNHs had a share of Medicaid/uninsured stays in the top quartile. A range of patient, hospital, and community characteristics was considered as covariates. RESULTS: The unadjusted postpartum readmission rate was 4.2 per 1000 index deliveries higher at SNHs than at non-SNHs (19.1 vs. 14.9, p < .001). Adjustment reduced the risk difference to 0.65 per 1000 (95% confidence interval [CI]: -0.14, 1.44). Patient (66%), hospital (14%), and community (4%) characteristics explained 84% of the disparity. The single largest contributors to the disparity were race/ethnicity (20%), hypertension (12%), hospital preterm delivery rate (10%), and preterm delivery (7%). Collectively, patient comorbidities explained 31% of the disparity. CONCLUSION: Higher postpartum readmission rates at SNHs versus non-SNHs were largely due to differences in the patient mix rather than hospital factors. Hospital initiatives are needed to reduce the risk of postpartum readmissions among SNH patients. Improving factors that contribute to the disparity, including underlying health conditions and health inequities associated with race, requires enduring investments in public health.

Readmissions Following Inpatient Treatment for Opioid-Related Conditions.

BACKGROUND: Previous research suggests that relatively few hospitalized patients with opioid-related conditions receive substance use treatment during their inpatient stay. Without treatment, these individuals may be more likely to have subsequent hospitalizations for continued opioid use disorder. OBJECTIVE: To evaluate the relationship between receipt of inpatient drug detoxification and/or rehabilitation services and subsequent opioid-related readmission. METHODS: This study used combined hospital inpatient discharge and emergency department visit data from the Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project. Our sample consisted of 329,037 patients from seven states with an opioid-related index hospitalization occurring between March 2010 and September 2013. Multivariate analysis was conducted to examine the relationship between opioid-related readmission and the receipt of inpatient drug detoxification and/or rehabilitation during the index visit. RESULTS: A relatively small percentage (19.4%) of patients with identified opioid-related conditions received treatment for drug use during their hospital inpatient stay. Patients who received drug rehabilitation, but not drug detoxification, during an opioid-related index hospitalization had lower odds of an opioid-related readmission within 90 days of discharge (odds ratio = 0.60, 95% confidence interval = 0.54-0.67) compared with patients with no inpatient drug detoxification or rehabilitation. Conclusions/Importance: A low percentage of patients receive inpatient services for drug use during an index stay involving an opioid-related diagnosis. Our findings indicate that receipt of drug rehabilitation services in acute care hospitals is associated with a lower 90-day readmission rate. Further research is needed to understand factors associated with the receipt of inpatient services and readmissions.

State variation in opioid treatment policies and opioid-related hospital readmissions.

BACKGROUND: State policy approaches designed to provide opioid treatment options have received significant attention in addressing the opioid epidemic in the United States. In particular, expanded availability of naloxone to reverse overdose, Good Samaritan laws intended to protect individuals who attempt to provide or obtain emergency services for someone experiencing an opioid overdose, and expanded coverage of medication-assisted treatment (MAT) for individuals with opioid abuse or dependence may help curtail hospital readmissions from opioids. The objective of this retrospective cohort study was to evaluate the association between the presence of state opioid treatment policies-naloxone standing orders, Good Samaritan laws, and Medicaid medication-assisted treatment (MAT) coverage-and opioid-related hospital readmissions. METHODS: We used 2013-2015 hospital inpatient discharge data from 13 states from the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project. We examined the relationship between state opioid treatment policies and 90-day opioid-related readmissions after a stay involving an opioid diagnosis. RESULTS: Our sample included 383,334 opioid-related index hospitalizations. Patients treated in states with naloxone standing-order policies at the time of the index stay had higher adjusted odds of an opioid-related readmission than did those treated in states without such policies; however, this relationship was not present in states with Good Samaritan laws. Medicaid methadone coverage was associated with higher odds of readmission among all insurance groups except Medicaid. Medicaid MAT coverage generosity was associated with higher odds of readmission among the Medicaid group but lower odds of readmission among the Medicare and privately insured groups. More comprehensive Medicaid coverage of substance use disorder treatment and a greater number of opioid treatment programs were associated with lower odds of readmission. CONCLUSIONS: Differences in index hospitalization rates suggest that states with opioid treatment policies had a higher level of need for opioid-related intervention, which also may account for higher rates of readmission. More research is needed to understand how these policies can be most effective in influencing acute care use.

Impact of Race/Ethnicity and Socioeconomic Status on Risk-Adjusted Hospital Readmission Rates Following Hip and Knee Arthroplasty.

BACKGROUND: Readmission rates following total hip arthroplasty (THA) and total knee arthroplasty (TKA) are increasingly used to measure hospital performance. Readmission rates that are not adjusted for race/ethnicity and socioeconomic status, patient risk factors beyond a hospital's control, may not accurately reflect a hospital's performance. In this study, we examined the extent to which risk-adjusting for race/ethnicity and socioeconomic status affected hospital performance in terms of readmission rates following THA and TKA. METHODS: We calculated 2 sets of risk-adjusted readmission rates by (1) using the Centers for Medicare & Medicaid Services standard risk-adjustment algorithm that incorporates patient age, sex, comorbidities, and hospital effects and (2) adding race/ethnicity and socioeconomic status to the model. Using data from the Healthcare Cost and Utilization Project, 2011 State Inpatient Databases, we compared the relative performances of 1,194 hospitals across the 2 methods. RESULTS: Addition of race/ethnicity and socioeconomic status to the risk-adjustment algorithm resulted in (1) little or no change in the risk-adjusted readmission rates at nearly all hospitals; (2) no change in the designation of the readmission rate as better, worse, or not different from the population mean at >99% of the hospitals; and (3) no change in the excess readmission ratio at >97% of the hospitals. CONCLUSIONS: Inclusion of race/ethnicity and socioeconomic status in the risk-adjustment algorithm led to a relative-performance change in readmission rates following THA and TKA at <3% of the hospitals. We believe that policymakers and payers should consider this result when deciding whether to include race/ethnicity and socioeconomic status in risk-adjusted THA and TKA readmission rates used for hospital accountability, payment, and public reporting. LEVEL OF EVIDENCE: Prognostic Level III. See instructions for Authors for a complete description of levels of evidence.

Predictors of medication adherence in patients with type 2 diabetes mellitus.

OBJECTIVE: Medical professionals are often challenged by lack of patient compliance with pharmaceutical treatments. Research has shown that patients with diabetes have one of the lowest medication adherence rates at 65% to 85%. Some causes have been identified in the literature, but the influence of type of medication is unknown. This study assessed the impact of a broad range of factors on medication adherence and persistence among adult patients with type 2 diabetes mellitus. METHODS: Patients were selected from the Truven Health MarketScan Research Databases of healthcare administrative claims (2009 through 2012), assigned to mutually exclusive cohorts based on initiation of saxagliptin (a dipeptidyl peptidase-4 [DPP-4] inhibitor), or a glucagon-like peptide 1 (GLP-1) receptor agonist (daily or twice daily formulation), sulfonylurea (SU), or thiazolidinedione (TZD), and screened for continuous enrollment 1 year before and after drug initiation. Adherence and persistence were measured using proportion of days covered and time to discontinuation, respectively. Multivariate models were used to examine the impact of study drug and demographic and clinical factors. RESULTS: Overall, 45.1% of patients were adherent with their study drug over the 1 year follow-up period. Adherence was higher among patients who were male, older, or residing in non-Southern states. Adherence was better with mail-order use and lower levels of cost sharing. Patients taking a GLP-1 (OR = 0.40, 95% CI = 0.37, 0.42), SU (OR = 0.49, 95% CI = 0.46, 0.52), or TZD (OR = 0.54, 95% CI = 0.51, 0.57) were less likely to be adherent compared with those taking saxagliptin. Results were mixed regarding the impact of comorbidities and polypharmacy on medication adherence. Influencing factors may be the type of comorbidity, overall health level, number of drugs, and complexity of the drug regimen. KEY LIMITATIONS: Adherence was measured using data for prescriptions dispensed and it is not known whether patients actually took the medications, hence adherence may be overestimated. Whether patients who discontinued the study drugs switched to other diabetes medications or discontinued treatment completely was not measured. CONCLUSION: Identified risk factors can guide medical professionals in their attempts to increase the likelihood of patient adherence to drug treatment regimens.

Using a return-on-investment estimation model to evaluate outcomes from an obesity management worksite health promotion program.

OBJECTIVE: Certain modifiable risk factors lead to higher health care costs and reduced worker productivity. A predictive return-on-investment (ROI) model was applied to an obesity management intervention to demonstrate the use of econometric modeling in establishing financial justification for worksite health promotion. METHODS: Self-reported risk factors (n = 890) were analyzed using chi2 and t test methods. Changes in risk factors, demographics, and financial measures comprised the model inputs that determined medical and productivity savings. RESULTS: Over 1 year, 7 of 10 health risks decreased. Of total projected savings ($311,755), 59% were attributed to reduced health care expenditures ($184,582) and 41% resulted from productivity improvements ($127,173), a $1.17 to $1.00 ROI. CONCLUSIONS: Using an ROI model to project program savings is a practical way to provide financial justification for investment in worksite health promotion when risk reduction data are available.