Jiang, David H.

ImportancePreventing diabetes complications requires monitoring and control of hyperglycemia and cardiovascular risk factors. Switching to high-deductible health plans (HDHPs) has been shown to hinder aspects of diabetes care; however, the association of HDHP enrollment with microvascular and macrovascular diabetes complications is unknown.ObjectiveTo examine the association between an employer-required switch to an HDHP and incident complications of diabetes.Design, Setting, and ParticipantsThis retrospective cohort study used deidentified administrative claims data for US adults with diabetes enrolled in employer-sponsored health plans between January 1, 2010, and December 31, 2019. Data analysis was performed from May 26, 2022, to January 2, 2024.ExposuresAdults with a baseline year of non-HDHP enrollment who had to switch to an HDHP because their employer offered no non-HDHP alternative in that year were compared with adults who were continuously enrolled in a non-HDHP.Main Outcomes and MeasuresMixed-effects logistic regression models examined the association between switching to an HDHP and, individually, the odds of myocardial infarction, stroke, hospitalization for heart failure, lower-extremity complication, end-stage kidney disease, proliferative retinopathy, treatment for retinopathy, and blindness. Models were adjusted for demographics, comorbidities, and medications, with inverse propensity score weighting used to account for potential selection bias.ResultsThe study included 42 326 adults who switched to an HDHP (mean [SD] age, 52 [10] years; 19 752 [46.7%] female) and 202 729 adults who did not switch (mean [SD] age, 53 [10] years; 89 828 [44.3%] female). Those who switched to an HDHP had greater odds of experiencing all diabetes complications (odds ratio [OR], 1.11; 95% CI, 1.06-1.16 for myocardial infarction; OR, 1.15; 95% CI, 1.09-1.21 for stroke; OR, 1.35; 95% CI, 1.30-1.41 for hospitalization for heart failure; OR, 2.53; 95% CI, 2.38-2.70 for end-stage kidney disease; OR, 2.23; 95% CI, 2.17-2.29 for lower-extremity complication; OR, 1.17; 95% CI, 1.13-1.21 for proliferative retinopathy; OR, 2.35; 95% CI, 2.18-2.54 for blindness; and OR, 2.28; 95% CI, 2.15-2.41 for retinopathy treatment).Conclusions and RelevanceThis study found that an employer-driven switch to an HDHP was associated with increased odds of experiencing all diabetes complications. These findings reinforce the potential harm associated with HDHPs for people with diabetes and the importance of affordable and accessible chronic disease management, which is hindered by high out-of-pocket costs incurred by HDHPs.

ImportanceOptimal diabetes care requires regular monitoring and care to maintain glycemic control. How high-deductible health plans (HDHPs), which reduce overall spending but may impede care by increasing out-of-pocket expenses, are associated with risks of severe hypoglycemia and hyperglycemia is unknown.ObjectiveTo examine the association between an employer-forced switch to HDHP and severe hypoglycemia and hyperglycemia.Design, Setting, and ParticipantsThis retrospective cohort study used deidentified administrative claims data for privately insured adults with diabetes from a single insurance carrier with multiple plans across the US between January 1, 2010, and December 31, 2018. Analyses were conducted between May 15, 2020, and November 3, 2022.ExposuresPatients with 1 baseline year of enrollment in a non-HDHP whose employers subsequently forced a switch to an HDHP were compared with patients who did not switch.Main Outcomes and MeasuresMixed-effects logistic regression models were used to examine the association between switching to an HDHP and the odds of severe hypoglycemia and hyperglycemia (ascertained using diagnosis codes in emergency department [ED] visits and hospitalizations), adjusting for patient age, sex, race and ethnicity, region, income, comorbidities, glucose-lowering medications, baseline ED and hospital visits for hypoglycemia and hyperglycemia, and baseline deductible amount, and applying inverse propensity score weighting to account for potential treatment selection bias.ResultsThe study population was composed of 42 326 patients who switched to an HDHP (mean [SD] age: 52 [10] years, 19 752 [46.7%] women, 7375 [17.4%] Black, 5740 [13.6%] Hispanic, 26 572 [62.8%] non-Hispanic White) and 202 729 patients who did not switch (mean [SD] age, 53 [10] years, 89 828 [44.3%] women, 29 551 [14.6%] Black, 26 689 [13.2%] Hispanic, 130 843 [64.5%] non-Hispanic White). When comparing all study years, switching to an HDHP was not associated with increased odds of experiencing at least 1 hypoglycemia-related ED visit or hospitalization (OR, 1.01 [95% CI, 0.95-1.06]; P = .85), but each year of HDHP enrollment did increase these odds by 2% (OR, 1.02 [95% CI, 1.00-1.04]; P = .04). In contrast, switching to an HDHP did significantly increase the odds of experiencing at least 1 hyperglycemia-related ED visit or hospitalization (OR, 1.25 [95% CI, 1.11-1.42]; P < .001), with each year of HDHP enrollment increasing the odds by 5% (OR, 1.05 [95% CI, 1.01-1.09]; P = .02).Conclusions and RelevanceIn this cohort study, employer-forced switching to an HDHP was associated with increased odds of potentially preventable acute diabetes complications, potentially because of delayed or deferred care. These findings suggest that employers should be more judicious in their health plan offerings, and health plans and policy makers should consider allowing preventive and high-value services to be exempt from deductible requirements.

Objective We examined the association between stay-at-home order implementation and the incidence of COVID-19 infections and deaths in rural versus urban counties of the United States. Design We used an interrupted time-series analysis using a mixed effects zero-inflated Poisson model with random intercept by county and standardised by population to examine the associations between stay-at-home orders and county-level counts of daily new COVID-19 cases and deaths in rural versus urban counties between 22 January 2020 and 10 June 2020. We secondarily examined the association between stay-at-home orders and mobility in rural versus urban counties using Google Community Mobility Reports. Interventions Issuance of stay-at-home orders. Primary and secondary outcome measures Co-primary outcomes were COVID-19 daily incidence of cases (14-day lagged) and mortality (26-day lagged). Secondary outcome was mobility. Results Stay-at-home orders were implemented later (median 30 March 2020 vs 28 March 2020) and were shorter in duration (median 35 vs 54 days) in rural compared with urban counties. Indoor mobility was, on average, 2.6%–6.9% higher in rural than urban counties both during and after stay-at-home orders. Compared with the baseline (pre-stay-at-home) period, the number of new COVID-19 cases increased under stay-at-home by incidence risk ratio (IRR) 1.60 (95% CI, 1.57 to 1.64) in rural and 1.36 (95% CI, 1.30 to 1.42) in urban counties, while the number of new COVID-19 deaths increased by IRR 14.21 (95% CI, 11.02 to 18.34) in rural and IRR 2.93 in urban counties (95% CI, 1.82 to 4.73). For each day under stay-at-home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981 to 0.982) in rural and 0.952 (95% CI, 0.951 to 0.953) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.977 (95% CI, 0.976 to 0.977) in rural counties and 0.935 (95% CI, 0.933 to 0.936) in urban counties. Each day after stay-at-home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994 to 0.995) in rural and 0.997 (95% CI, 0.995 to 0.999) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.969 (95% CI, 0.968 to 0.970) in rural counties and 0.928 (95% CI, 0.926 to 0.929) in urban counties. Conclusion Stay-at-home orders decreased mobility, slowed the spread of COVID-19 and mitigated COVID-19 mortality, but did so less effectively in rural than in urban counties. This necessitates a critical re-evaluation of how stay-at-home orders are designed, communicated and implemented in rural areas.

Research ObjectiveThroughout the spring of 2020, stay‐at‐home orders were imposed to curb the spread of the coronavirus disease 2019 (COVID‐19) in the United States. However, there is limited data and evidence on their effectiveness, especially in rural areas as compared to urban areas. Therefore, we examined the association between stay‐at‐home order implementation and the incidence of COVID‐19 cases in rural and urban counties.Study DesignWe conducted an interrupted time series analysis using a mixed effects zero‐inflated Poisson model with random intercept by county and standardized by population, which examined the association between stay‐at‐home orders and 14‐day lagged county‐level counts of daily new COVID‐19 cases in rural vs. urban counties between 1/22/2020 and 6/10/2020. To further bolster our findings, the association between stay‐at‐home orders and mobility in rural vs. urban counties was assessed using Google Community Mobility Reports.Population StudiedWe examined the COVID‐19 daily incident and stay‐at‐home orders for all 3142 United States counties. Each county was categorized as urban or rural using the 2013 county‐level Rural–Urban Continuum Codes per federal standards. There are 1976 rural and 1166 urban counties in the United States.Principal FindingsAs of June 10, 2020, there were 1,786,886 cases of COVID‐19 in the U.S., of which 9.0% (N = 161,452) were in rural counties. During the study period, 1854 (93.8%) rural counties and 1075 (92.2%) urban counties were covered by a stay‐at‐home order. Stay‐at‐home orders were implemented later (median March 30 vs. March 28) and were shorter (median 35 vs. 54 days) in rural than urban counties. Indoor mobility was, on average, 2.6–6.9% higher in rural than urban counties during and after stay‐at‐home orders. Compared to the pre‐stay‐at‐home baseline period, the number of new COVID‐19 cases increased under stay‐at‐home by IRR 1.60 (95% CI, 1.57–1.64) in rural and 1.36 (95% CI, 1.30–1.42) in urban counties. For each day under stay‐at‐home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981–0.982) in rural and 0.952 (95% CI, 0.951–0.953) in urban counties. Each day after stay‐at‐home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994–0.995) in rural and 0.997 (95% CI, 0.995–0.999) in urban counties.ConclusionsStay‐at‐home orders decreased mobility and slowed the spread of COVID‐19 less effectively in rural than urban counties.Implications for Policy or PracticeAs states are considering implementing a new round of stay‐at‐home orders, our findings reveal several ways to improve the implementation, enforcement, and adherence. First, they should be implemented earlier and maintained longer for optimal effectiveness. There was marked heterogeneity in the timing and duration of stay‐at‐home orders and our data reinforces the need for multi‐jurisdictional, ideally federal, infection control mandates. Any new stay‐at‐home orders should be gradually implemented to avoid the pre‐stay‐at‐home surge of mobility and subsequent spike in COVID‐19 cases. To better encourage and facilitate compliance, leaders at all levels need to use scientific evidence to advocate for the importance of stay‐at‐home orders, set personal examples, and develop employment, housing, educational, and healthcare assistance for the most vulnerable.Primary Funding SourceNational Institutes of Health.