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ObjectiveTo estimate the economic burden of systematic lupus erythematous (SLE), stratified by disease severity, in commercially- and Medicaid-insured US populations.MethodsAdults (≥18 years) with SLE treated with antimalarials, selected biologics, immunosuppressants, and systemic glucocorticoids (2010-2014) were identified within the commercial and Medicaid insurance IBM MarketScan® databases (index date = first SLE medication claim). Both cohorts were stratified into mild (receiving antimalarial or glucocorticoid monotherapy ≤5 mg/day) versus moderate/severe SLE (receiving glucocorticoids >5 mg/day, biologic, immunosuppressant, or combination therapy) during a 6-month exposure period. All-cause healthcare utilization and costs were evaluated during the 12 months following the exposure period.ResultsAmong 8231 commercially-insured patients, 32.6% had mild and 67.4% had moderate/severe SLE by our definition. Among 802 Medicaid-insured patients, 25.2% had mild and 74.8% had moderate/severe SLE. Adjusted mean total healthcare costs, excluding pharmacy, for moderate/severe SLE patients were higher than for mild SLE patients in the commercially-insured ($39,021 versus $23,519; p < 0.0001) and Medicaid-insured populations ($56,050 versus $44,932; p = 0.06). In both SLE severity populations total unadjusted costs were significantly higher among Medicaid-insured than commercially-insured patients.ConclusionCommercially-insured patients with treatment suggesting moderate/severe SLE incurred significantly higher adjusted mean healthcare costs, excluding pharmacy, compared with mild SLE patients. While not reaching statistical significance, moderate/severe Medicaid-insured patients had higher costs then mild SLE patients. Total unadjusted healthcare costs were significantly higher among Medicaid-insured than commercially-insured patients. These differential costs are important to consider and monitor when implementing interventions to improve health and reduce healthcare spending for SLE.

The cumulative advantage hypothesis suggests diverging socioeconomic status (SES) based gaps in health with age. However, previous studies yield inconsistent findings regarding the association between SES and health across the adult life span. Dealing with the issue of mortality selection bias, this study utilizes latent growth-curve modeling to comprehensively examine age trajectories of both physical and mental health by SES using panel data based on a national probability sample of 3617 US adults. We find that education-and income-based gaps in physical impairment and the education-based gap in depression diverge over time for all adult age groups, supporting the hypothesis of cumulative advantage. In contrast, we find that the income-based gap in depression converges in older age, supporting the hypothesis of age-as-leveler. Mortality selection bias is unlikely to be a major part of the explanation for the convergence. These results indicate that age-related patterns in health trajectories may differ by various dimensions of SES and health. Finally, we take into account persistence or change in income over time to examine the relationship between trajectories of income and health across adulthood, highlighting the importance of considering the temporal patterns of income in understanding age trajectories of health. Keywords: USA; Socioeconomic status (SES); Health disparities; Latent growth-curve modeling; Cumulative advantage; Mortality selection

Abstract Background A fever is an increase in the body's temperature above normal. This study examined how US pediatricians perceive and manage fever generally versus fever occurring after vaccination in infants up to six months of age. Methods A web-based survey of 400 US pediatricians subscribing to the Physician Desk Reference was conducted in December 2008. Data were collected on the respondents' socio-demographics, number of years in practice, type of practice, their definition of fever severity in infants, and their recommendations for managing fever. Generalized Estimating Equations were used to estimate the odds of a pediatrician recommending an emergency room visit (ER) or a hospital admission, office visits, or other treatment option, as a function of infant's age, temperature, whether the infant has recently received a vaccine, and whether the fever was reported during or after office hours, adjusting for practice type and socio-demographic variables. Results On average, the 400 responding pediatricians' (64% were female, average age of 49 years, years in practice = 20 years) threshold for extremely serious fever was ≥39.5°C and ≥ 40.0°C for infants 0-2 month and >2-6 month of age respectively. Infants were more likely to be referred to an ER or hospital admission if they were ≤ 2 months of age (Odds Ratio [OR], 29.13; 95% Confidence interval [95% CI], 23.69-35.82) or >2-4 months old (OR 3.37; 95% CI 2.99-3.81) versus > 4 to 6 months old or if they had a temperature ≥ 40.0°C (OR 21.06; 95% CI 17.20-25.79) versus a temperature of 38.0-38.5°C. Fever after vaccination (OR 0.29; 95% CI 0.25-0.33) or reported during office hours (OR 0.17; 95% CI 0.15-0.20) were less likely to result in referral to ER or hospital admission. Conclusion Within this sample of US pediatricians, perception of the severity of fever in infants, as well as the response to infant fever are likely to depend on the infant's age. Recommendations for the management of fever in infants are likely to depend on fever severity level, the infant age, timing in relation to recent vaccination, and the time of day fever is reported. Our results indicate that US pediatricians are more concerned about general fever than fever following vaccination.

OBJECTIVES: To characterize the prevalence of obesity and associated health care use within an integrated health care system in California. STUDY DESIGN: Cross-sectional study using electronic health records. METHODS: Primary care patients 18 years and older receiving care at Sutter Health between 2015 and 2020 were included in the study. Obesity was classified and health care utilization was ascertained at index and during the follow-up periods. Differences in prevalence by demographic and clinical characteristics among patients with and without obesity were assessed. Logistic regression was used to estimate the relationship between obesity class and health care utilization (outpatient encounters). RESULTS: Of the 1,094,790 primary care patients included in the analysis, 35% were classified as having obesity, defined as a body mass index of 30 kg/m2 or more or 25 kg/m2 or more for Asian individuals. Obesity prevalence was greater in Hispanic patients (46%) than in non-Hispanic White patients (30%). Patients without obesity had fewer outpatient visits (mean [SD], 3.7 [3.8]) than those with class 1 (4.1 [4.0]), class 2 (4.6 [4.4]), and class 3 (5.2 [4.8]) obesity. In the fully adjusted regression model, the odds of being a high utilizer among patients with obesity were 1.1 (class 1), 1.2 (class 2), and 1.3 (class 3) times that of patients without obesity (P < .001). CONCLUSION: Obesity prevalence is high among patients in the Sutter Health system, varying by race/ethnicity, and was associated with increased outpatient visit utilization. There is a need for greater awareness of the impact of obesity and the specific patient populations affected by the disease. [ABSTRACT FROM AUTHOR]