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AbstractObjectiveTo examine the distribution and patterns of opioid prescribing in the United States.DesignRetrospective, observational study.SettingNational private insurer covering all 50 US states and Washington DC.ParticipantsAn annual average of 669 495 providers prescribing 8.9 million opioid prescriptions to 3.9 million patients from 2003 through 2017.Main outcome measuresStandardized doses of opioids in morphine milligram equivalents (MMEs) and number of opioid prescriptions.ResultsIn 2017, the top 1% of providers accounted for 49% of all opioid doses and 27% of all opioid prescriptions. In absolute terms, the top 1% of providers prescribed an average of 748 000 MMEs—nearly 1000 times more than the middle 1%. At least half of all providers in the top 1% in one year were also in the top 1% in adjacent years. More than two fifths of all prescriptions written by the top 1% of providers were for more than 50 MMEs a day and over four fifths were for longer than seven days. In contrast, prescriptions written by the bottom 99% of providers were below these thresholds, with 86% of prescriptions for less than 50 MMEs a day and 71% for fewer than seven days. Providers prescribing high amounts of opioids and patients receiving high amounts of opioids persisted over time, with over half of both appearing in adjacent years.ConclusionsMost prescriptions written by the majority of providers are under the recommended thresholds, suggesting that most US providers are careful in their prescribing. Interventions focusing on this group of providers are unlikely to effect beneficial change and could induce unnecessary burden. A large proportion of providers have established relationships with their patients over multiple years. Interventions to reduce inappropriate opioid prescribing should be focused on improving patient care, management of patients with complex pain, and reducing comorbidities rather than seeking to enforce a threshold for prescribing.

We provide a graphical illustration of how standard consumer and producer theory can be used to quantify the welfare loss associated with inefficient pricing in insurance markets with selection. We then show how this welfare loss can be estimated empirically using identifying variation in the price of insurance. Such variation, together with quantity data, allows us to estimate the demand for insurance. The same variation, together with cost data, allows us to estimate how insurers' costs vary as market participants endogenously respond to price. The slope of this estimated cost curve provides a direct test for both the existence and the nature of selection, and the combination of demand and cost curves can be used to estimate welfare.We illustrate our approach by applying it to data on employer-provided health insurance from one specific company. We detect adverse selection but estimate that the quantitative welfare implications associated with inefficient pricing in our particular application are small, in both absolute and relative terms.

Our current understanding of Asian American mortality patterns has been distorted by the historical aggregation of diverse Asian subgroups on death certificates, masking important differences in the leading causes of death across subgroups. In this analysis, we aim to fill an important knowledge gap in Asian American health by reporting leading causes of mortality by disaggregated Asian American subgroups. We examined national mortality records for the six largest Asian subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese) and non-Hispanic Whites (NHWs) from 2003-2011, and ranked the leading causes of death. We calculated all-cause and cause-specific age-adjusted rates, temporal trends with annual percent changes, and rate ratios by race/ethnicity and sex. Rankings revealed that as an aggregated group, cancer was the leading cause of death for Asian Americans. When disaggregated, there was notable heterogeneity. Among women, cancer was the leading cause of death for every group except Asian Indians. In men, cancer was the leading cause of death among Chinese, Korean, and Vietnamese men, while heart disease was the leading cause of death among Asian Indians, Filipino and Japanese men. The proportion of death due to heart disease for Asian Indian males was nearly double that of cancer (31% vs. 18%). Temporal trends showed increased mortality of cancer and diabetes in Asian Indians and Vietnamese; increased stroke mortality in Asian Indians; increased suicide mortality in Koreans; and increased mortality from Alzheimer's disease for all racial/ethnic groups from 2003-2011. All-cause rate ratios revealed that overall mortality is lower in Asian Americans compared to NHWs. Our findings show heterogeneity in the leading causes of death among Asian American subgroups. Additional research should focus on culturally competent and cost-effective approaches to prevent and treat specific diseases among these growing diverse populations.

We analyze the extent to which individuals'choices over five employerprovided insurance coverage decisions and one 401 (k) investment decision exhibit systematic patterns, as would be implied by a general utility component of risk preferences. We provide evidence consistent with an important domain-general component that operates across all insurance choices. We find a considerably weaker relationship between one's insurance decisions and 401 (k) asset allocation, although this relationship appears larger for more if financially sophisticated\" individuals. Estimates from a stylized coverage choice model suggest that up to 30 percent of our sample makes choices that may be consistent across all 6 domains.

Despite global commitments to achieving gender equality and improving health and wellbeing for all, quantitative data and methods to precisely estimate the effect of gender norms on health inequities are underdeveloped. Nonetheless, existing global, national, and subnational data provide some key opportunities for testing associations between gender norms and health. Using innovative approaches to analysing proxies for gender norms, we generated evidence that gender norms impact the health of women and men across life stages, health sectors, and world regions. Six case studies showed that: (1) gender norms are complex and can intersect with other social factors to impact health over the life course; (2) early gender-normative influences by parents and peers can have multiple and differing health consequences for girls and boys; (3) non-conformity with, and transgression of, gender norms can be harmful to health, particularly when they trigger negative sanctions; and (4) the impact of gender norms on health can be context-specific, demanding care when designing effective gender-transformative health policies and programmes. Limitations of survey-based data are described that resulted in missed opportunities for investigating certain populations and domains. Recommendations for optimising and advancing research on the health impacts of gender norms are made.

The COVID-19 pandemic has affected every aspect of medical care, including surgical treatment. It is critical to understand the association of government policies and infection burden with surgical access across the United States. To describe the change in surgical procedure volume in the US after the government-suggested shutdown and subsequent peak surge in volume of patients with COVID-19. This retrospective cohort study was conducted using administrative claims from a nationwide health care technology clearinghouse. Claims from pediatric and adult patients undergoing surgical procedures in 49 US states within the Change Healthcare network of health care institutions were used. Surgical procedure volume during the 2020 initial COVID-19-related shutdown and subsequent fall and winter infection surge were compared with volume in 2019. Data were analyzed from November 2020 through July 2021. 2020 policies to curtail elective surgical procedures and the incidence rate of patients with COVID-19. Incidence rate ratios (IRRs) were estimated from a Poisson regression comparing total procedure counts during the initial shutdown (March 15 to May 2, 2020) and subsequent COVID-19 surge (October 22, 2020-January 31, 2021) with corresponding 2019 dates. Surgical procedures were analyzed by 11 major procedure categories, 25 subcategories, and 12 exemplar operative procedures along a spectrum of elective to emergency indications. A total of 13 108 567 surgical procedures were identified from January 1, 2019, through January 30, 2021, based on 3498 Current Procedural Terminology (CPT) codes. This included 6 651 921 procedures in 2019 (3 516 569 procedures among women [52.9%]; 613 192 procedures among children [9.2%]; and 1 987 397 procedures among patients aged ≥65 years [29.9%]) and 5 973 573 procedures in 2020 (3 156 240 procedures among women [52.8%]; 482 637 procedures among children [8.1%]; and 1 806 074 procedures among patients aged ≥65 years [30.2%]). The total number of procedures during the initial shutdown period and its corresponding period in 2019 (ie, epidemiological weeks 12-18) decreased from 905 444 procedures in 2019 to 458 469 procedures in 2020, for an IRR of 0.52 (95% CI, 0.44 to 0.60; P < .001) with a decrease of 48.0%. There was a decrease in surgical procedure volume across all major categories compared with corresponding weeks in 2019. During the initial shutdown, otolaryngology (ENT) procedures (IRR, 0.30; 95% CI, 0.13 to 0.46; P < .001) and cataract procedures (IRR, 0.11; 95% CI, -0.11 to 0.32; P = .03) decreased the most among major categories. Organ transplants and cesarean deliveries did not differ from the 2019 baseline. After the initial shutdown, during the ensuing COVID-19 surge, surgical procedure volumes rebounded to 2019 levels (IRR, 0.97; 95% CI, 0.95 to 1.00; P = .10) except for ENT procedures (IRR, 0.70; 95% CI, 0.65 to 0.75; P < .001). There was a correlation between state volumes of patients with COVID-19 and surgical procedure volume during the initial shutdown (r = -0.00025; 95% CI, -0.0042 to -0.0009; P = .003), but there was no correlation during the COVID-19 surge (r = -0.00034; 95% CI, -0.0075 to 0.00007; P = .11). This study found that the initial shutdown period in March through April 2020, was associated with a decrease in surgical procedure volume to nearly half of baseline rates. After the reopening, the rate of surgical procedures rebounded to 2019 levels, and this trend was maintained throughout the peak burden of patients with COVID-19 in fall and winter; these findings suggest that after initial adaptation, health systems appeared to be able to self-regulate and function at prepandemic capacity.

Background In this paper, we argue for Gender as a Sociocultural Variable (GASV) as a complement to Sex as a Biological Variable (SABV). Sex (biology) and gender (sociocultural behaviors and attitudes) interact to influence health and disease processes across the lifespan—which is currently playing out in the COVID-19 pandemic. This study develops a gender assessment tool—the Stanford Gender-Related Variables for Health Research—for use in clinical and population research, including large-scale health surveys involving diverse Western populations. While analyzing sex as a biological variable is widely mandated, gender as a sociocultural variable is not, largely because the field lacks quantitative tools for analyzing the influence of gender on health outcomes. Methods We conducted a comprehensive review of English-language measures of gender from 1975 to 2015 to identify variables across three domains: gender norms, gender-related traits, and gender relations. This yielded 11 variables tested with 44 items in three US cross-sectional survey populations: two internet-based ( N = 2051; N = 2135) and a patient-research registry ( N = 489), conducted between May 2017 and January 2018. Results Exploratory and confirmatory factor analyses reduced 11 constructs to 7 gender-related variables: caregiver strain, work strain, independence, risk-taking, emotional intelligence, social support, and discrimination. Regression analyses, adjusted for age, ethnicity, income, education, sex assigned at birth, and self-reported gender identity, identified associations between these gender-related variables and self-rated general health, physical and mental health, and health-risk behaviors. Conclusion Our new instrument represents an important step toward developing more comprehensive and precise survey-based measures of gender in relation to health. Our questionnaire is designed to shed light on how specific gender-related behaviors and attitudes contribute to health and disease processes, irrespective of—or in addition to—biological sex and self-reported gender identity. Use of these gender-related variables in experimental studies, such as clinical trials, may also help us understand if gender factors play an important role as treatment-effect modifiers and would thus need to be further considered in treatment decision-making.

Triclosan-a ubiquitous chemical in toothpastes, soaps, and household cleaning supplies-has the potential to alter both gut microbiota and endocrine function and thereby affect body weight. We investigated the relationship between triclosan and body mass index (BMI) using National Health and Nutrition Examination Surveys (NHANES) from 2003-2008. BMI and spot urinary triclosan levels were obtained from adults. Using two different exposure measures-either presence vs. absence or quartiles of triclosan-we assessed the association between triclosan and BMI. We also screened all NHANES serum and urine biomarkers to identify correlated factors that might confound observed associations. Compared with undetectable triclosan, a detectable level was associated with a 0.9-point increase in BMI (p<0.001). In analysis by quartile, compared to the lowest quartile, the 2nd, 3rd and 4th quartiles of urinary triclosan were associated with BMI increases of 1.5 (p<0.001), 1.0 (p = 0.002), and 0.3 (p = 0.33) respectively. The one strong correlate of triclosan identified in NHANES was its metabolite, 2,4-dichlorophenol (ρ = 0.4); its association with BMI, however, was weaker than that of triclosan. No other likely confounder was identified. Triclosan exposure is associated with increased BMI. Stronger effect at moderate than high levels suggests a complex mechanism of action.

Background Belief is that chronic disease prevalence is rising in Bangladesh since death from them has increased. We reviewed published cardiovascular (CVD) and Type 2 Diabetes Mellitus (T2DM) studies between 1995 and 2010 and conducted a meta-analysis of disease prevalence. Methods A systematic search of CVD and T2DM studies yielded 29 eligible studies (outcome: CVD only = 12, T2DM only = 9, both = 8). Hypertension (HTN) was the primary outcome of CVD studies. HTN and T2DM were defined with objective measures and standard cut-off values. We assessed the study quality based on sampling frame, sample size, and disease evaluation. Random effects models calculated pooled disease prevalence (95% confidence interval) in studies with general population samples (n = 22). Results The pooled HTN and T2DM prevalence were 13.7% (12.1%–15.3%) and 6.7% (4.9%–8.6%), respectively. Both diseases exhibited a secular trend by 5-year intervals between 1995 and 2010 (HTN = 11.0%, 12.8%, 15.3%, T2DM = 3.8%, 5.3%, 9.0%). HTN was higher in females (M vs. F: 12.8% vs.16.1%) but T2DM was higher in males (M vs. F: 7.0% vs. 6.2%) (non-significant). Both HTN and T2DM were higher in urban areas (urban vs. rural: 22.2% vs. 14.3% and 10.2% vs. 5.1% respectively) (non-significant). HTN was higher among elderly and among working professionals. Both HTN and T2DM were higher in ‘high- quality’ studies. Conclusions There is evidence of a rising secular trend of HTN and T2DM prevalence in Bangladesh. Future research should focus on the evolving root causes, incidence, and prognosis of HTN and T2DM.

Life expectancy at birth, estimated from United States period life tables, has been shown to vary systematically and widely by region and race. We use the same tables to estimate the probability of survival from birth to age 70 (S(70)), a measure of mortality more sensitive to disparities and more reliably calculated for small populations, to describe the variation and identify its sources in greater detail to assess the patterns of this variation. Examination of the unadjusted probability of S(70) for each US county with a sufficient population of whites and blacks reveals large geographic differences for each race-sex group. For example, white males born in the ten percent healthiest counties have a 77 percent probability of survival to age 70, but only a 61 percent chance if born in the ten percent least healthy counties. Similar geographical disparities face white women and blacks of each sex. Moreover, within each county, large differences in S(70) prevail between blacks and whites, on average 17 percentage points for men and 12 percentage points for women. In linear regressions for each race-sex group, nearly all of the geographic variation is accounted for by a common set of 22 socio-economic and environmental variables, selected for previously suspected impact on mortality; R(2) ranges from 0.86 for white males to 0.72 for black females. Analysis of black-white survival chances within each county reveals that the same variables account for most of the race gap in S(70) as well. When actual white male values for each explanatory variable are substituted for black in the black male prediction equation to assess the role explanatory variables play in the black-white survival difference, residual black-white differences at the county level shrink markedly to a mean of -2.4% (+/-2.4); for women the mean difference is -3.7% (+/-2.3).