Contemporary burden and trends of opioid-overdose mortality in New York State.

BACKGROUND: Limited research exists on contemporary opioid overdose mortality burden and trends in New York State, with most studies focusing on New York City. This study aimed to assess opioid overdose burden and death trends in New York State by age, sex, race/ethnicity, geographic area, opioid type, and overdose intent from 1999 to 2020. METHODS: Mortality data were obtained from the Centers for Disease Control and Prevention's WONDER database. Opioid overdose decedents were identified using relevant International Classification of Diseases, 10th Revision codes. Joinpoint regression analyzed trends, estimating annual and average annual percentage changes in age-adjusted mortality rates (AAMR). 95% confidence intervals were derived using the Parametric Method. RESULTS: From 1999 to 2020, New York State recorded 34,109 opioid overdose deaths (AAMR = 7.9 per 100,000 persons; 95% CI: 7.8-7.9). The overall trend increased by 12.6% per year (95% CI: 10.8, 14.4) from 2004 to 2020. Subgroups exhibited varying trends, with an 11.1% yearly increase among Non-Hispanic White persons from 2007 to 2020 (95% CI: 9.0, 13.2), a 24.6% annual rise among Non-Hispanic Black persons from 2012 to 2020 (95% CI: 17.7, 31.8), and an 18.3% increase yearly among Hispanic individuals from 2011 to 2020 (95% CI: 14.0, 22.9). Recent trends have worsened in both males and females, across all age groups, in both New York City (NYC) and areas outside NYC, and for heroin, natural and semisynthetic opioids, and synthetic opioids. CONCLUSIONS: Opioid overdose mortality in New York State has worsened significantly in the last two decades. Further research is essential to identify driving factors for targeted public health interventions.

Relationship between Patient Experience Scores and Health Insurance.

(1) Background: the patient experience may be a performance indicator in value-based reimbursement. Accordingly, providers have an incentive to understand factors that affect their patients' experiences. This study evaluated the relationship between health insurance type and patient experience ratings. (2) Methods: individual-level demographic, health/healthcare, and patient experience data were extracted from the Full-Year Consolidated Data File of the 2019 Medical Expenditure Panel Surveys. A logistic regression was used to evaluate whether how persons­included in this study's analytic sample (aged 18 and over with complete covariate information)­rated the healthcare they received from all their providers was associated with their health insurance types controlling for covariates. (3) Results: relative to people 18−64 years of age with private health insurance, people 18−64 years of age without health insurance were less likely to rank their healthcare as a 9 or 10­where a 10 indicates the best possible care­(OR: 0.69; p = 0.015) while people aged 65 years or over with Medicare (OR: 1.34; p = 0.002) or with Medicare/private health insurance (OR: 1.48; p < 0.001) were more likely to rank their healthcare as a 9 or 10. (4) Conclusions: Select health insurance types were associated with how patients rate their healthcare. Stakeholders could use this information to create programs aimed to improve patient experience.

Values-Based Foundation for a U.S. Single Payer Health System Model.

A universal, single payer model for the American health system aligns with and should emanate from commonly held values contained within the country's foundational religious teachings, morals, ethics and democratic heritage. The Affordable Care Act in its attempt to create expanded health access has met with significant challenges. The conservative Supreme Court decreases the likelihood of a federal mandated single payer model. As uncertainty of the structure of the healthcare system increases, this paper supports its transformation to a single payer model. Healthcare should be considered a duty within the framework of a Kantian approach to ethics and a social good. Evidently ignoring this duty, the American health system perpetuates a healthcare underclass, with underserved portions of the population, with unequal access to quality care and persistent health status and outcome disparities. The COVID-19 pandemic demonstrated the effect of social determinants on optimal health outcome. A health insurance system based on the nation's commonly held values has the potential to eliminate these disparities.

Predictors of Food Insecurity for Hospitals' Patients and Communities: Implications for Establishing Effective Population Health Initiatives.

As food insecurity interventions are incorporated into hospitals' population health initiatives, addressing the needs of hospitals' patients and communities through the same interventions may be ineffective if the groups vary and have different needs. This study examined whether food insecurity predictors were different in the general population compared to individuals with hospital discharges, and also whether food-insecure hospital patients differed from food-insecure community members. National data were extracted from the 2016 Medical Expenditures Panel Survey. Summary statistics were compared to test for differences between food security status groups. Logistic regressions were estimated for the general population and for individuals with hospital discharges to identify associations between food insecurity and demographic, socioeconomic, and health characteristics. Food-insecure individuals with and without hospital discharges differed statistically across multiple variables, including 15 of 16 health-related variables. However, compared to food-secure individuals with hospital discharges, food-insecure individuals with hospital discharges differed on only half of the health variables. Food insecurity predictors also differed among the general population and hospital discharge samples; for instance, age and race were only associated with higher likelihoods of food insecurity in the population sample. Furthermore, 9 health-related variables were associated with food insecurity in the population sample relative to only 2 in the hospital discharge sample. Food insecurity predictors differed between the general population and individuals with hospital discharges; food-insecure individuals with and without hospital discharges also differed statistically. Therefore, hospitals should carefully consider their target populations when constructing population health initiatives.