INTRODUCTION: The COVID-19 pandemic and its associated social distancing policies such as lockdowns and quarantine influenced people's lives and health behaviors. We comprehensively assessed national trends in sales of cigarettes, cigars, e-cigarettes, and over-the-counter nicotine replacement therapy (NRT) products before and during the pandemic, allowing for cross-product comparisons. Stockpiling behavior was also assessed. METHODS: We used US national tobacco and over-the-counter NRT retail store scanner data (excluding internet, specialty/vape store, and prescription sales) collected at 4-week intervals by NielsenIQ from December 2018 to June 2021. We applied an interrupted time-series model to assess differences in tobacco product and NRT unit sales before and during the pandemic. We defined the prepandemic period as December 16, 2018, through April 4, 2020, pandemic as starting on April 5, 2020, through June 26, 2021, and the stockpiling period as one 4-week period before the pandemic started. RESULTS: Four-week cigarette, e-cigarette, and cigar unit sales on average increased by 11.5% (P = .006), 37.1% (P < .001), and 26.1% (P < .001) respectively, while 4-week NRT unit sales decreased on average by 13.1% (P < .001), during the pandemic compared with the prepandemic period. Stockpiling was associated with increases in sales of all tobacco products and NRT products. CONCLUSION: Unit sales of assessed tobacco products increased while NRT unit sales decreased during the COVID-19 pandemic, compared with the prepandemic period. These changes may suggest an increase in the intensity of tobacco product use or stockpiling of tobacco products among people who use tobacco.

INTRODUCTION: Most e-cigarettes contain highly addictive nicotine. This study assessed trends in nicotine strength in e-cigarettes sold in the United States during January 2017-March 2022. AIMS AND METHODS: We obtained January 2017-March 2022 national retail e-cigarette sales data from NielsenIQ. We assessed monthly average nicotine strength overall, by e-cigarette product and flavor type, and manufacturer. A Joinpoint regression model assessed the magnitude and significance of changes in nicotine strength. RESULTS: During January 2017-March 2022, monthly average nicotine strength of e-cigarette products increased from 2.5% to 4.4%, an average of 0.8% per month (p < .001). Monthly average nicotine strength of disposable e-cigarettes increased the most (average monthly percentage change [AMPC] = 1.26%, p < .001) as compared to prefilled pods (AMPC = 0.6%, p < .001) and e-liquids (AMPC = 0.5%, p = .218). Monthly average nicotine strength for all flavors of e-cigarette products increased except for mint-flavored products. Increases were greatest for beverage-flavored products (AMPC = 2.1%, p < .001), followed by menthol-flavored products (AMPC = 1.2%, p < .001). Among the top 10 e-cigarette manufacturers assessed, monthly average nicotine strength decreased for Juul Labs products from 5% to 4.7% (AMPC = -0.1%, p < .001) but increased significantly for five manufacturers' products and remained unchanged at 5%-6% for four manufacturers' products. CONCLUSIONS: Monthly average nicotine strength of e-cigarette products increased overall, for most product and flavor types, and for some manufacturers in the United States during the study period. Imposing maximum limits on nicotine strength of e-cigarettes together with other evidence-based tobacco control strategies can help reduce the use of e-cigarettes among youth and increase tobacco product cessation among adults. IMPLICATIONS: From January 2017 to March 2022, the monthly average nicotine strength of disposable e-cigarettes increased substantially and exceeded prefilled pods since May 2020. E-cigarettes with menthol flavor and youth-appealing flavors, like fruit, also had sharp increases in monthly average nicotine strength. Among the top 10 e-cigarette manufacturers, monthly average nicotine strength increased or remained unchanged at a high nicotine level for all manufacturers' products, except Juul Lab's products. Comprehensive strategies including restricting sales of all flavored e-cigarettes, restricting youth tobacco product access, and imposing maximum limits on nicotine strength may help reduce youth e-cigarette use and increase tobacco cessation.

INTRODUCTION: In 2019, an outbreak of e-cigarette, or vaping, product use-associated lung injury (EVALI) occurred in the US. We used Nielsen retail sales data to assess trends in sales of e-cigarettes, cigarettes, and nicotine replacement therapy (NRT) products before, during, and after the EVALI outbreak. METHODS: Monthly unit sales of e-cigarettes, cigarettes, and NRT products overall and by product type were assessed during January 2019 through June 2020 by using an interrupted time series model. Two time points were specified at the period ending July 13, 2019, and the period ending February 22, 2020, to partition before, during, and after the outbreak period. Sales trends by aggregated state-level EVALI case prevalence (low, medium, and high) were assessed to investigate interstate variations in changes of sales coinciding with the EVALI outbreak. RESULTS: Monthly e-cigarette sales increased 3.5% (P < .001) before the outbreak and decreased 3.1% (P < .001) during the outbreak, with no significant changes after the outbreak. Monthly cigarette sales increased 1.6% (P < .001) before the outbreak, decreased 1.8% (P < .001) during the outbreak, and increased 2.7% (P < .001) after the outbreak. NRT sales did not change significantly before or during the outbreak but decreased (2.8%, P = .01) after the outbreak. Sales trends by state-level EVALI case prevalence were similar to national-level sales trends. CONCLUSION: Cigarette and e-cigarette sales decreased during the EVALI outbreak, but no changes in overall NRT sales were observed until after the outbreak. Continued monitoring of tobacco sales data can provide insight into potential changes in use patterns and inform tobacco prevention and control efforts.

INTRODUCTION: Information on morbidity-related productivity losses attributable to cigarette smoking, an important component of the economic burden of cigarette smoking, is limited. This study fills this gap by estimating these costs in the U.S. and by state. METHODS: A human capital approach was used to estimate the cost of the morbidity-related productivity losses (absenteeism, presenteeism, household productivity, and inability to work) attributable to cigarette smoking among adults aged 18 years in the U.S. and by state. A combination of data, including the 2014-2018 National Health Interview Survey, 2018 Current Population Survey Annual Social and Economic Supplement, 2018 Behavioral Risk Factor Surveillance System, 2018 value of daily housework, and literature-based estimate of lost productivity while at work (presenteeism), was used. Costs were estimated for 2018, and all analyses were conducted in 2021. RESULTS: Estimated total cost of morbidity-related productivity losses attributable to cigarette smoking in the U.S. in 2018 was $184.9 billion. Absenteeism, presenteeism, home productivity, and the inability to work accounted for $9.4 billion, $46.8 billion, $12.8 billion, and $116.0 billion, respectively. State-level total costs ranged from $291 million to $16.9 billion with a median cost of $2.7 billion. CONCLUSIONS: The cost of morbidity-related productivity losses attributable to cigarette smoking in the U.S. and in each state was substantial in 2018 and varied across the states. These estimates can guide public health policymakers and practitioners planning and evaluating interventions designed to alleviate the burden of cigarette smoking at the state and national levels.

OBJECTIVE: To assess the association of body mass index (BMI) with inpatient care cost, duration, and acute complications among patients hospitalized for COVID-19 at 273 U.S. hospitals. METHODS: Children (2-17 years) and adults (≥18 years) hospitalized for COVID-19 during March 2020-July 2021 and measured BMI in a large electronic administrative healthcare database were included. We used generalized linear models to assess the association of BMI categories with the cost and duration of inpatient care. RESULTS: Among 108,986 adults and 409 children hospitalized for COVID-19, obesity prevalence was 53.4% and 45.0%, respectively. Among adults, overweight and obesity were associated with higher costs of care, and obesity was associated with longer hospital stays. Children with severe obesity had a higher cost of care, but not significantly longer hospital stays, compared to those with healthy weights. Children with severe obesity were 3.7 times (95% CI: 1.5 to 9.5) as likely to have invasive mechanical ventilation and 62% more likely to have an acute complication (95% CI, 39-90), compared to children with healthy weight. CONCLUSIONS: These findings show that patients with high BMIs experience significant healthcare burden during inpatient COVID-19 care. This article is protected by copyright. All rights reserved.

BACKGROUND: Information on the costs of inpatient care for patients with coronavirus disease 2019 (COVID-19) is very limited. This study estimates the per-patient cost of inpatient care for adult COVID-19 patients seen at >800 US hospitals. METHODS: Patients aged ≥18 years with ≥1 hospitalization during March 2020-July 2021 with a COVID-19 diagnosis code in a large electronic administrative discharge database were included. We used validated costs when reported; otherwise, costs were calculated using charges multiplied by cost-to-charge ratios. We estimated costs of inpatient care per patient overall and by severity indicator, age, sex, underlying medical conditions, and acute complications of COVID-19 using a generalized linear model with log link function and gamma distribution. RESULTS: The overall cost among 654673 patients hospitalized with COVID-19 was $16.2 billion. Estimated per-patient hospitalization cost was $24 826. Among surviving patients, estimated per-patient cost was $13 090 without intensive care unit (ICU) admission or invasive mechanical ventilation (IMV), $21 222 with ICU admission alone, and $59 742 with IMV. Estimated per-patient cost among patients who died was $27 017. Adjusted cost differential was higher among patients with certain underlying conditions (eg, chronic kidney disease [$12 391], liver disease [$8878], cerebrovascular disease [$7267], and obesity [$5933]) and acute complications (eg, acute respiratory distress syndrome [$43 912], pneumothorax [$25 240], and intracranial hemorrhage [$22 280]). CONCLUSIONS: The cost of inpatient care for COVID-19 patients was substantial through the first 17 months of the pandemic. These estimates can be used to inform policy makers and planners and cost-effectiveness analysis of public health interventions to alleviate the burden of COVID-19.

INTRODUCTION: Cigarette smoking continues to be the leading cause of preventable disease and death in the U.S. Smoking also carries an economic burden, including smoking-attributable healthcare spending. This study assessed smoking-attributable fractions in healthcare spending between 2010 and 2014, overall and by insurance type (Medicaid, Medicare, private, out-of-pocket, other federal, other) and by medical service (inpatient, non-inpatient, prescriptions). METHODS: Data were obtained from the 2010-2014 Medical Expenditure Panel Survey linked to the 2008-2013 National Health Interview Survey. The final sample (n = 49,540) was restricted to non-pregnant adults aged 18 years or older. Estimates from two-part models (multivariable logistic regression and generalized linear models) and data from 2014 national health expenditures were combined to estimate the share of and total (in 2014 dollars) annual healthcare spending attributable to cigarette smoking among U.S. adults. All models controlled for socio-demographic characteristics, health-related behaviors, and attitudes. RESULTS: During 2010-2014, an estimated 11.7% (95% CI = 11.6%, 11.8%) of U.S. annual healthcare spending could be attributed to adult cigarette smoking, translating to annual healthcare spending of more than $225 billion dollars based on total personal healthcare expenditures reported in 2014. More than 50% of this smoking-attributable spending was funded by Medicare or Medicaid. For Medicaid, the estimated healthcare spending attributable fraction increased more than 30% between 2010 and 2014. CONCLUSIONS: Cigarette smoking exacts a substantial economic burden in the U.S. Continuing efforts to implement proven population-based interventions have been shown to reduce the health and economic burden of cigarette smoking nationally.

OBJECTIVE: Studies examining the use of smoking cessation treatment and related spending among enrollees with employer-sponsored health insurance are dated and limited in scope. We assessed changes in annual receipt of and spending on cessation medications approved by the US Food and Drug Administration (FDA) among tobacco users with employer-sponsored health insurance from 2010 to 2017. METHODS: We analyzed data on 439 865 adult tobacco users in 2010 and 344 567 adult tobacco users in 2017 from the IBM MarketScan Commercial Database. We used a negative binomial regression to estimate changes in receipt of cessation medication (number of fills and refills and days of supply). We used a generalized linear model to estimate spending (total, employers', and out of pocket). In both models, covariates included year, age, sex, residence, and type of health insurance plan. RESULTS: From 2010 to 2017, the percentage of adult tobacco users with employer-sponsored health insurance who received any cessation medication increased by 2.4%, from 15.7% to 16.1% (P < .001). Annual average number of fills and refills per user increased by 15.1%, from 2.5 to 2.9 (P < .001) and days of supply increased by 26.4%, from 81.9 to 103.5 (P < .001). The total annual average spending per user increased by 53.6%, from $286.40 to $440.00 (P < .001). Annual average out-of-pocket spending per user decreased by 70.9%, from $70.80 to $20.60 (P < .001). CONCLUSIONS: Use of smoking cessation medications is low among smokers covered by employer-sponsored health insurance. Opportunities exist to further increase the use of cessation medications by promoting the use of evidence-based cessation treatments and reducing barriers to coverage, including out-of-pocket costs.

OBJECTIVE: To estimate the excess maternal health services utilization and direct maternal medical expenditures associated with hypertensive disorders during pregnancy and one year postpartum among women with private insurance in the United States. STUDY DESIGN: We used 2008-2014 IBM MarketScan® Commercial Databases to identify women aged 15-44 who had a pregnancy resulting in live birth during 1/1/09-12/31/13 and were continuously enrolled with non-capitated or partially capitated coverage from 12 months before pregnancy through 12 months after delivery. Hypertensive disorders identified by diagnosis codes were categorized into three mutually exclusive types: preeclampsia and eclampsia, chronic hypertension, and gestational hypertension. Multivariate negative binomial and generalized linear models were used to estimate service utilization and expenditures, respectively. MAIN OUTCOME MEASURES: Per person excess health services utilization and medical expenditures during pregnancy and one year postpartum associated with hypertensive disorders (in 2014 US dollars). RESULTS: Women with preeclampsia and eclampsia, chronic hypertension, and gestational hypertension had $9,389, $6,041, and $2,237 higher mean medical expenditures compared to women without hypertensive disorders ($20,252), respectively (ps < 0.001). One-third (36%) of excess expenditure associated with hypertensive disorders during pregnancy was attributable to outpatient services. CONCLUSIONS: Hypertensive disorders during pregnancy were associated with significantly higher health services utilization and medical expenditures among privately insured women with hypertensive disorders. Medical expenditures varied by types of hypertensive disorders. Stakeholders can use this information to assess the potential economic benefits of interventions that prevent these conditions or their complications.

AIMS: To estimate the prevalence and medical expenditures of diabetes-related complications (DRCs) among adult Medicaid enrollees with diabetes. METHODS: We estimated the prevalence and medical expenditures for 12 diabetes-related complications by Medicaid eligibility category (disability-based vs. non-disability-based) in eight states. We used generalized linear models with log link and gamma distribution to estimate the total per-person annual medical expenditures for DRCs, controlling for demographics, and other comorbidities. RESULTS: Among non-disability-based enrollees (NDBEs), 40.1% (in California) to 47.5% (in Oklahoma) had one or more DRCs, compared to 53.6% (in Alabama) to 64.8% (in Florida) among disability-based enrollees (DBEs). The most prevalent complication was neuropathy (16.1%-27.1% for NDBEs; 20.2%-30.4% for DBEs). Lower extremity amputation (<1% for both eligibilities) was the least prevalent complication. The costliest per-person complication was dialysis (per-person excess annual expenditure of $22,481-$41,298 for NDBEs; $23,569-$51,470 for DBEs in 2012 USD). Combining prevalence and per-person excess expenditures, the three costliest complications were nephropathy, heart failure, and ischemic heart disease (IHD) for DBEs, compared to neuropathy, nephropathy, and IHD for NDBEs. CONCLUSIONS: Our study provides data that can be used for assessing the health care resources needed for managing DRCs and evaluating cost-effectiveness of interventions to prevent and management DRCs.

INTRODUCTION: Since 2012, the Centers for Disease Control and Prevention has conducted the national Tips From Former Smokers public education campaign, which motivates smokers to quit by featuring people living with the real-life health consequences of smoking. Cost effectiveness, from the healthcare sector perspective, of the Tips From Former Smokers campaign was compared over 2012-2018 with that of no campaign. METHODS: A combination of survey data from a nationally representative sample of U.S. adults that includes cigarette smokers and literature-based lifetime relapse rates were used to calculate the cumulative number of Tips From Former Smokers campaign‒associated lifetime quits during 2012-2018. Then, lifetime health benefits (premature deaths averted, life years saved, and quality-adjusted life years gained) and healthcare sector cost savings associated with these quits were assessed. All the costs were adjusted for inflation in 2018 U.S. dollars. The Tips From Former Smokers campaign was conducted and the survey data were collected during 2012-2018. Analyses were conducted in 2019. RESULTS: During 2012-2018, the Tips From Former Smokers campaign was associated with an estimated 129,100 premature deaths avoided, 803,800 life years gained, 1.38 million quality-adjusted life years gained, and $7.3 billion in healthcare sector cost savings on the basis of an estimated 642,200 campaign-associated lifetime quits. The Tips From Former Smokers campaign was associated with cost savings per lifetime quit of $11,400, per life year gained of $9,100, per premature deaths avoided of $56,800, and per quality-adjusted life year gained of $5,300. CONCLUSIONS: Mass-reach health education campaigns, such as Tips From Former Smokers, can help smokers quit, improve health outcomes, and potentially reduce healthcare sector costs.

OBJECTIVE: We examined changes in glucose-lowering medication spending and quantified the magnitude of factors that are contributing to these changes. RESEARCH DESIGN AND METHODS: Using the Medical Expenditure Panel Survey, we estimated the change in spending on glucose-lowering medications during 2005-2007 and 2015-2017 among adults aged ≥18 years with diabetes. We decomposed the increase in total spending by medication groups: for insulin, by human and analog; and for noninsulin, by metformin, older, newer, and combination medications. For each group, we quantified the contributions by the number of users and cost-per-user. Costs were in 2017 U.S. dollars. RESULTS: National spending on glucose-lowering medications increased by $40.6 billion (240%), of which insulin and noninsulin medications contributed $28.6 billion (169%) and $12.0 billion (71%), respectively. For insulin, the increase was mainly associated with higher expenditures from analogs (156%). For noninsulin, the increase was a net effect of higher cost for newer medications (+88%) and decreased cost for older medications (-34%). Most of the increase in insulin spending came from the increase in cost-per-user. However, the increase in the number of users contributed more than cost-per-user in the rise of most noninsulin groups. CONCLUSIONS: The increase in national spending on glucose-lowering medications during the past decade was mostly associated with the increased costs for insulin, analogs in particular, and newer noninsulin medicines; and cost-per-user had a larger effect than the number of users. Understanding the factors contributing to the increase helps identify ways to curb the growth in costs.

OBJECTIVE: While diabetic ketoacidosis (DKA) is common in youth at the onset of the diabetes, the excess costs associated with DKA are unknown. We aimed to quantify the health care services use and medical care costs related to the presence of DKA at diagnosis of diabetes. RESEARCH DESIGN AND METHODS: We analyzed data from the U.S. MarketScan claims database for 4,988 enrollees aged 3-19 years insured in private fee-for-service plans and newly diagnosed with diabetes during 2010-2016. Youth with and without DKA at diabetes diagnosis were compared for mean health care service use (outpatient, office, emergency room, and inpatient visits) and medical costs (outpatient, inpatient, prescription drugs, and total) for 60 days prior to and 60 days after diabetes diagnosis. A two-part model using generalized linear regression and logistic regression was used to estimate medical costs, controlling for age, sex, rurality, health plan, year, presence of hypoglycemia, and chronic pulmonary condition. All costs were adjusted to 2016 dollars. RESULTS: At diabetes diagnosis, 42% of youth had DKA. In the 60 days prior to diabetes diagnosis, youth with DKA at diagnosis had less health services usage (e.g., number of outpatient visits: -1.17; P < 0.001) and lower total medical costs (-$635; P < 0.001) compared with youth without DKA at diagnosis. In the 60 days after diagnosis, youth with DKA had significantly greater health care services use and health care costs ($6,522) compared with those without DKA. CONCLUSIONS: Among youth with newly diagnosed diabetes, DKA at diagnosis is associated with significantly higher use of health care services and medical costs.

The significant increase in the complexity of diabetes care over the last two decades is associated with a high economic burden (1), with almost one-quarter of adults with diabetes burdened with significant out of pocket expenses (OOPEs) (2). Little is known about the OOPEs for families of adolescents and young adults with type 1 diabetes. We therefore report the diabetes-related OOPEs for these families from the SEARCH for Diabetes in Youth (SEARCH) study and their association with demographic, socioeconomic, clinical, and health care characteristics.

OBJECTIVE: We assessed the excess medical expenditures for adults newly diagnosed with diabetes, for up to 10 years before and after diabetes diagnosis. RESEARCH DESIGN AND METHODS: Using the 2001-2013 MarketScan data, we identified people with newly diagnosed diabetes among adults aged 25-64 years (diabetes cohort) and matched them with people who did not have diagnosed diabetes (control cohort) using 1:1 propensity score matching. We followed these two cohorts up to +/-10 years from the index date, with annual matched cohort sizes ranging from 3,922 to 39,726 individuals. We estimated the yearly and cumulative excess medical expenditures of the diabetes cohorts before and after the diagnosis of diabetes. RESULTS: The per-capita annual total excess medical expenditures for the diabetes cohort was higher for the entire 10 years prior to their index date, ranging between $1,043 in year -10 and $4,492 in year -1. Excess expenditure spiked in the index year, year 1 ($8,109), declined in year 2, and then increased steadily, ranging from $4,261 to $6,162 in years 2-10. The cumulative excess expenditure for the diabetes cohort during the entire 20 years of follow-up was $69,177 ($18,732 before and $50,445 after diagnosis). CONCLUSIONS: People diagnosed with diabetes had higher medical expenditures compared with their counterparts, not only after diagnosis, but also up to 10 years prior to diagnosis. Managing risk factors for type 2 diabetes and cardiovascular disease before diagnosis, and for diabetes-related complications after diagnosis, could alleviate medical expenditure in people with diabetes.

OBJECTIVE: To examine changes in diabetes-related preventable hospitalization costs and to determine the contribution of each underlying factor to these changes. RESEARCH DESIGN AND METHODS: We used data from the 2001-2014 U.S. National Inpatient Sample for adults (>/=18 years old) to estimate the trends in hospitalization costs (2014 USD) in total and by condition (short-term complications, long-term complications, uncontrolled diabetes, and lower-extremity amputation). Using regression and growth models, we estimated the relative contribution of following underlying factors: total number of hospitalizations, rate of hospitalization, the number of people with diabetes, mean cost per admission, length of stay, and cost per day. RESULTS: During 2001-2014, the estimated total cost of diabetes-related preventable hospitalizations increased annually by 1.6% (92.9 million USD; P < 0.001). Of this 1.6% increase, 75% (1.2%) was due to the increase in the number of hospitalizations, which is a result of a 3.8% increase in diabetes population and a 2.6% decrease in the hospitalization rate, and 25% (0.4%) was due to the increase in cost per admission, for a net result of a 1.6% increase in cost per day and a 1.3% decline in mean length of stay. By component, the cost of short-term complications, lower-extremity amputations, and long-term complications increased annually by 4.2, 1.9, and 1.5%, respectively, while the cost of uncontrolled diabetes declined annually by 2.6%. CONCLUSIONS: The total cost of diabetes-related preventable hospitalizations had been increasing during 2001-2014, mainly resulting from increases in number of people with diabetes and cost per hospitalization day. The underlying factors identified in our study could lead to efforts that may lower future hospitalization costs.

RATIONALE & OBJECTIVE: As the prevalence of obesity continues to rise in the United States, it is important to understand its impact on the lifetime risk of chronic kidney disease (CKD). STUDY DESIGN: The CKD Health Policy Model was used to simulate the lifetime risk of CKD for those with and without obesity at baseline. Model structure was updated for glomerular filtration rate (GFR) decline to incorporate new longitudinal data from the Chronic Renal Insufficiency Cohort (CRIC) study. SETTING AND POPULATION: The updated model was populated with a nationally representative cohort from National Health and Nutrition Examination Survey (NHANES). OUTCOMES: Lifetime risk of CKD, highest stage and any stage. MODEL, PERSPECTIVE, & TIMEFRAME: Simulation model following up individuals from current age through death or age 90 years. RESULTS: Lifetime risk of any CKD stage was 32.5% (95% CI 28.6%-36.3%) for persons with normal weight, 37.6% (95% CI 33.5%-41.7%) for persons who were overweight, and 41.0% (95% CI 36.7%-45.3%) for persons with obesity at baseline. The difference between persons with normal weight and persons with obesity at baseline was statistically significant (p<0.01). Lifetime risk of CKD stages 4 and 5 was higher for persons with obesity at baseline (Stage 4: 2.1%, 95% CI 0.9%-3.3%; stage 5: 0.6%, 95% CI 0.0%-1.1%), but the differences were not statistically significant (stage 4: p = 0.08; stage 5: p = 0.23). LIMITATIONS: Due to limited data, our simulation model estimates are based on assumptions about the causal pathways from obesity to CKD, diabetes, and hypertension. CONCLUSIONS: The results of this study indicate that obesity may have a large impact on the lifetime risk of CKD. This is important information for policymakers seeking to set priorities and targets for CKD prevention and treatment.

OBJECTIVE: To estimate direct medical and indirect costs attributable to diabetes in each U.S. state in total and per person with diabetes. RESEARCH DESIGN AND METHODS: We used an attributable fraction approach to estimate direct medical costs using data from the 2013 State Health Expenditure Accounts, 2013 Behavioral Risk Factor Surveillance System, and the Centers for Medicare & Medicaid Services' 2013-2014 Minimum Data Set. We used a human capital approach to estimate indirect costs measured by lost productivity from morbidity (absenteeism, presenteeism, lost household productivity, and inability to work) and premature mortality, using the 2008-2013 National Health Interview Survey, 2013 daily housework value data, 2013 mortality data from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research, and mean wages from the 2014 Bureau of Labor Statistics. Costs were adjusted to 2017 U.S. dollars. RESULTS: The estimated median state economic cost was $5.9 billion, ranging from $694 million to $55.5 billion, in total and $18,248, ranging from $15,418 to $30,915, per person with diabetes. The corresponding estimates for direct medical costs were $2.8 billion (range: $0.3-22.9) and $8,544 (range: $6,591-12,953) and for indirect costs were $3.0 billion (range: $0.4-32.6) and $9,672 (range: $7,133-17,962). In general, the estimated state median indirect costs resulting from morbidity were larger than costs from mortality both in total and per person with diabetes. CONCLUSIONS: Economic costs attributable to diabetes were large and varied widely across states. Our comprehensive state-specific estimates provide essential information needed by state policy makers to monitor the economic burden of the disease and to better plan and evaluate interventions for preventing type 2 diabetes and managing diabetes in their states.

INTRODUCTION: Little information is available on state-specific financial burdens of diabetes in the Medicaid population, yet such information is essential for state Medicaid programs to plan diabetes care and evaluate the benefits of diabetes prevention. We estimated medical expenditures associated with diabetes among adult Medicaid enrollees in 8 states. METHODS: We analyzed the latest available 2012 CMS Medicaid claims data for 1,193,811 adult enrollees aged 19-64 years in 8 states: Alabama, California, Connecticut, Florida, Illinois, Iowa, New York, and Oklahoma. For each state, we stratified the study population by Medicaid eligibility criteria: disability and nondisability. For each group, we estimated per capita annual medical expenditures on outpatient care, inpatient care, and prescription drugs by using a 2-part model, adjusted for age, sex, race/ethnicity, and comorbidities. We calculated the expenditures associated with diabetes as the difference in predicted expenditures for enrollees with and without diabetes. Analyses were done in 2017. RESULTS: For disability-based enrollees, the estimated total per capita annual diabetes expenditures ranged from $6,183 in Alabama to $15,319 in New York (all P < .001). For nondisability-based enrollees, the corresponding estimates ranged from $4,985 in Alabama to $15,366 in New York (all P < .001). The proportion of individual components varied by state and eligibility criteria. CONCLUSION: Medical expenditures associated with diabetes among adults on Medicaid were substantial and varied across studied states. Our estimates can be used by the 8 state Medicaid programs to prepare health care resources needed for diabetes care and assess the financial benefits of diabetes prevention programs.

OBJECTIVE: To estimate the diabetes-attributable nursing home costs for each state. RESEARCH DESIGN AND METHODS: We used a diabetes-attributable fraction (AF) approach to estimate nursing home costs attributable to diabetes (in 2013 $) in aggregate and per person with diabetes in each state. We calculated the AFs as the difference in diabetes prevalence between nursing homes and the community. We used the Centers for Medicare and Medicaid's 2013-2015 Minimum Data Set to estimate the prevalence of diabetes in nursing homes and to adjust for the intensity of care among people with diabetes in nursing homes. Community prevalence was estimated using the Behavioral Risk Factor Surveillance System (BRFSS). State nursing home expenditures were from the 2013 State Health Expenditure Accounts. RESULTS: The fraction of total nursing home expenditures attributable to diabetes ranged from 12.3% (Illinois) to 22.5% (Washington, DC; median AF of 15.6%, New Jersey). The median AF was highest in the 19-64 years age-group and lowest in the 85 years or older age-group. Nationally, diabetes-attributable nursing home costs were $18.6 billion. State-level diabetes-attributable costs ranged from $21 million in Alaska to $2.0 billion in California. Diabetes-attributable nursing home costs per person ranged from $374 in New Mexico to $1,610 in Washington, DC (median of $799 in Maine). CONCLUSIONS: Our estimates provide state policymakers with an improved understanding of the economic burden of diabetes in each state's nursing homes. These estimates could serve as critical inputs for planning and evaluating diabetes prevention and management interventions that can keep people healthier and living longer in their communities.

INTRODUCTION: The coexistence of diabetes among people with acute myocardial infarction (AMI) or acute ischemic stroke (AIS) is common. However, little is known about the extent of excess medical expenditures associated with having diabetes among AMI and AIS patients. METHODS: Data on 3,307 AMI patients and 2,460 AIS patients aged ≥18 years from the 2008 to 2014 Medical Expenditure Panel Survey were analyzed. Per capita annual medical expenditures associated with diabetes were separately estimated by healthcare components with generalized linear models and two-part models. Excess expenditure associated with diabetes is the difference between estimated expenditure conditional on having both diabetes and AMI (or AIS) and the estimated expenditure conditional on having AMI (or AIS) but not diabetes. All expenditures were adjusted to 2014 U.S. dollars. The analysis was conducted in 2017. RESULTS: Per capita annual total excess expenditures associated with diabetes were $5,117 (95% CI=$4,989, $5,243) for AMI patients and $5,734 (95% CI=$5,579, $5,887) for AIS patients. Of the total excess expenditures, prescription drugs accounted for 40% among AMI patients and 42% among AIS patients. Higher expenditures associated with diabetes were explained more by higher volume of utilization than higher per unit expenditures. CONCLUSIONS: Excess expenditures associated with diabetes were substantial among both AMI and AIS patients. These results highlight the needs for both prevention and better management of diabetes among AMI and AIS patients, which in turn may lower the financial burden of treating these conditions.

BACKGROUND: Information on diabetes-related excess medical expenditures for youth is important to understand the magnitude of financial burden and to plan the health care resources needed for managing diabetes. However, diabetes-related excess medical expenditures for youth covered by Medicaid program have not been investigated recently. OBJECTIVE: To estimate excess diabetes-related medical expenditures among youth aged below 20 years enrolled in Medicaid programs in the United States. METHODS: We analyzed data from 2008 to 2012 MarketScan multistate Medicaid database for 6502 youths with diagnosed diabetes and 6502 propensity score matched youths without diabetes, enrolled in fee-for-service payment plans. We stratified analysis by Medicaid eligibility criteria (poverty or disability). We used 2-part regression models to estimate diabetes-related excess medical expenditures, adjusted for age, sex, race/ethnicity, year of claims, depression status, asthma status, and interaction terms. RESULTS: For poverty-based Medicaid enrollees, estimated annual diabetes-related total medical expenditure was $9046 per person [$3681 (no diabetes) vs. $12,727 (diabetes); P<0001], of which 41.7%, 34.0%, and 24.3% were accounted for by prescription drugs, outpatient, and inpatient care, respectively. For disability-based Medicaid enrollees, the estimated annual diabetes-related total medical expenditure was $9944 per person ($14,149 vs. $24,093; P<0001), of which 41.5% was accounted for by prescription drugs, 31.3% by inpatient, and 27.3% by outpatient care. CONCLUSIONS: The per capita annual diabetes-related medical expenditures in youth covered by publicly financed Medicaid programs are substantial, which is larger among those with disabilities than without disabilities. Identifying cost-effective ways of managing diabetes in this vulnerable segment of the youth population is needed.

BACKGROUND: Better treatment during early stages of chronic kidney disease (CKD) may slow progression to end-stage renal disease and decrease associated complications and medical costs. Achieving early treatment of CKD is challenging, however, because a large fraction of persons with CKD are unaware of having this disease. Screening for CKD is one important method for increasing awareness. We examined the cost-effectiveness of identifying persons for early-stage CKD screening (i.e., screening for moderate albuminuria) using published CKD risk scores. METHODS: We used the CKD Health Policy Model, a micro-simulation model, to simulate the cost-effectiveness of using CKD two published risk scores by Bang et al. and Kshirsagar et al. to identify persons in the US for CKD screening with testing for albuminuria. Alternative risk score thresholds were tested (0.20, 0.15, 0.10, 0.05, and 0.02) above which persons were assigned to receive screening at alternative intervals (1-, 2-, and 5-year) for follow-up screening if the first screening was negative. We examined incremental cost-effectiveness ratios (ICERs), incremental lifetime costs divided by incremental lifetime QALYs, relative to the next higher screening threshold to assess cost-effectiveness. Cost-effective scenarios were determined as those with ICERs less than $50,000 per QALY. Among the cost-effective scenarios, the optimal scenario was determined as the one that resulted in the highest lifetime QALYs. RESULTS: ICERs ranged from $8,823 per QALY to $124,626 per QALY for the Bang et al. risk score and $6,342 per QALY to $405,861 per QALY for the Kshirsagar et al. risk score. The Bang et al. risk score with a threshold of 0.02 and 2-year follow-up screening was found to be optimal because it had an ICER less than $50,000 per QALY and resulted in the highest lifetime QALYs. CONCLUSIONS: This study indicates that using these CKD risk scores may allow clinicians to cost-effectively identify a broader population for CKD screening with testing for albuminuria and potentially detect people with CKD at earlier stages of the disease than current approaches of screening only persons with diabetes or hypertension.

BACKGROUND: In recent decades, the United States experienced increasing prevalence and incidence of diabetes, accompanied by large disparities in county-level diabetes prevalence and incidence. However, whether these disparities are widening, narrowing, or staying the same has not been studied. We examined changes in disparity among U.S. counties in diagnosed diabetes prevalence and incidence between 2004 and 2012. METHODS: We used 2004 and 2012 county-level diabetes (type 1 and type 2) prevalence and incidence data, along with demographic, socio-economic, and risk factor data from various sources. To determine whether disparities widened or narrowed over the time period, we used a regression-based beta-convergence approach, accounting for spatial autocorrelation. We calculated diabetes prevalence/incidence percentage point (ppt) changes between 2004 and 2012 and modeled these changes as a function of baseline diabetes prevalence/incidence in 2004. Covariates included county-level demographic and, socio-economic data, and known type 2 diabetes risk factors (obesity and leisure-time physical inactivity). RESULTS: For each county-level ppt increase in diabetes prevalence in 2004 there was an annual average increase of 0.02 ppt (p<0.001) in diabetes prevalence between 2004 and 2012, indicating a widening of disparities. However, after accounting for covariates, diabetes prevalence decreased by an annual average of 0.04 ppt (p<0.001). In contrast, changes in diabetes incidence decreased by an average of 0.04 ppt (unadjusted) and 0.09 ppt (adjusted) for each ppt increase in diabetes incidence in 2004, indicating a narrowing of county-level disparities. CONCLUSIONS: County-level disparities in diagnosed diabetes prevalence in the United States widened between 2004 and 2012, while disparities in incidence narrowed. Accounting for demographic and, socio-economic characteristics and risk factors for type 2 diabetes narrowed the disparities, suggesting that these factors are strongly associated with changes in disparities. Public health interventions that target modifiable risk factors, such as obesity and physical inactivity, in high burden counties might further reduce disparities in incidence and, over time, in prevalence.

BACKGROUND: Although major guidelines uniformly recommend iron supplementation and erythropoietin stimulating agents (ESAs) for managing chronic anemia in persons with chronic kidney disease (CKD), there are differences in the recommended hemoglobin (Hb) treatment target and no guidelines consider the costs or cost-effectiveness of treatment. In this study, we explored the most cost-effective Hb target for anemia treatment in persons with CKD stages 3-4. METHODS AND FINDINGS: The CKD Health Policy Model was populated with a synthetic cohort of persons over age 30 with prevalent CKD stages 3-4 (i.e., not on dialysis) and anemia created from the 1999-2010 National Health and Nutrition Examination Survey. Incremental cost-effectiveness ratios (ICERs), computed as incremental cost divided by incremental quality adjusted life years (QALYs), were assessed for Hb targets of 10 g/dl to 13 g/dl at 0.5 g/dl increments. Targeting a Hb of 10 g/dl resulted in an ICER of $32,111 compared with no treatment and targeting a Hb of 10.5 g/dl resulted in an ICER of $32,475 compared with a Hb target of 10 g/dl. QALYs increased to 4.63 for a Hb target of 10 g/dl and to 4.75 for a target of 10.5 g/dl or 11 g/dl. Any treatment target above 11 g/dl increased medical costs and decreased QALYs. CONCLUSIONS: In persons over age 30 with CKD stages 3-4, anemia treatment is most cost-effective when targeting a Hb level of 10.5 g/dl. This study provides important information for framing guidelines related to treatment of anemia in persons with CKD.

IMPORTANCE: Little is known regarding eye care use among low-income persons with diabetes mellitus, especially African Americans. OBJECTIVE: To investigate eye care use among patients with diabetes who were seen in a county hospital clinic that primarily serves high-risk, low-income, non-Hispanic African American patients. DESIGN, SETTING, AND PARTICIPANTS: A retrospective cohort study with 2 years of follow-up examined eye care use among adult patients with diabetes seen in 2007 in an outpatient medical clinic of a large, urban county hospital that primarily serves low-income, non-Hispanic African American patients. Patients with a history of retinopathy and macular edema or a current diagnosis indicating ophthalmic complications were excluded. Eye care use was defined dichotomously as whether or not patients had a visit to the eye clinic for any eye care examination or procedure. We estimated crude and adjusted rate ratios (aRRs) and 95% CIs for the association between eye care use and selected clinical and demographic characteristics. RESULTS: There were 867 patients with diabetes identified: 61.9% were women, 76.2% were non-Hispanic African American, and 61.4% were indigent, with a mean age of 51.8 years. Eye care utilization rates were 33.2% within 1 and 45.0% within 2 years. For patients aged 19 to 39 years compared with those aged 65 years or older, significantly decreased eye care utilization rates were observed within 1 year (aRR, 0.48; 95% CI, 0.27-0.84) and within 2 years (aRR, 0.61; 95% CI, 0.38-0.99). CONCLUSIONS AND RELEVANCE: Overall eye care utilization rates were low. Additional education efforts to increase the perception of need among urban minority populations may be enhanced if focused on younger persons with diabetes.

AIM: We aimed at estimating excess medical expenditures associated with major depressive disorder (MDD) among working-age adults diagnosed with diabetes, disaggregated by treatment mode: insulin-treated diabetes (ITDM) or non-insulin-treated diabetes (NITDM). METHODS: We analyzed data for over 500,000 individuals with diagnosed diabetes from the 2008 U.S. MarketScan claims database. We grouped diabetic patients first by treatment mode (ITDM or NITDM), then by MDD status (with or without MDD), and finally by whether those with MDD used antidepressant medication. We estimated annual mean excess outpatient, inpatient, prescription drug, and total expenditures using regression models, controlling for demographics, types of health coverage, and comorbidities. RESULTS: Among persons having ITDM, the estimated annual total mean expenditure for those with no MDD (the comparison group) was $19,625. For those with MDD, the expenditures were $12,406 (63%) larger if using antidepressant medication and $7322 (37%) larger if not using antidepressant medication. Among persons having NITDM, the corresponding estimated expenditure for the comparison group was $10,746, the excess expenditures were $10,432 (97%) larger if using antidepressant medication and $5579 (52%) larger if not using antidepressant medication, respectively. Inpatient excess expenditures were the largest of total excess expenditure for those with ITDM and MDD treated with antidepressant medication; for all others with diabetes and MDD, outpatient expenditures were the largest excess expenditure. CONCLUSIONS: Among working-age adults with diabetes, MDD was associated with substantial excess medical expenditures. Implementing the effective interventions demonstrated in clinical trials and treatment guidelines recommended by professional organizations might reduce the economic burden of MDD in this population.

In April 2009, the United States began a response to the emergence of a pandemic influenza virus strain: A(H1N1)pdm09. Vaccination began in October 2009. By using US surveillance data (April 12, 2009-April 10, 2010) and vaccine coverage estimates (October 3, 2009-April 18, 2010), we estimated that the A(H1N1)pdm09 virus vaccination program prevented 700,000-1,500,000 clinical cases, 4,000-10,000 hospitalizations, and 200-500 deaths. We found that the national health effects were greatly influenced by the timing of vaccine administration and the effectiveness of the vaccine. We estimated that recommendations for priority vaccination of targeted priority groups were not inferior to other vaccination prioritization strategies. These results emphasize the need for relevant surveillance data to facilitate a rapid evaluation of vaccine recommendations and effects.

OBJECTIVE: To build a tool to assist disaster response planning and estimate the numbers of displaced persons that will require special medical care during a disaster. METHODS: We developed a tool, titled MedCon:PreEvent, which incorporates data from the 2006 National Health Interview Survey, 2005 National Hospital Discharge Survey, and 2004 National Nursing Home Survey to calculate numbers of emergency room/emergency department (ER/ED) visits, surgeries, health care home visits, overnight hospital stays, office visits, and self-rated health status. We then used thresholds of more than 12 office visits or 6 or more ER/ED visits or 6 or more surgeries or more than 4 home visits or more than 6 overnight hospital stays within the past 12 months to calculate rates per million evacuees requiring special medical care, including daily bed hospital and nursing home bed occupancy. RESULTS: We calculated that 79,428 (95% CI = 76,940-81,770) per million evacuees would need special medical care. The daily occupation of hospital beds would be 1710 beds (95% CI = 1328-2160) per million. The occupation of nursing home beds would be 5094 beds (95% CI = 5040-5148) per million. Changing the threshold to just those who self-rated health as "poor," the demand for special medical care would be 24,348 (95% CI = 23,087-25,535) per million. Using threshold utilization values at half the original level would increase the estimate to 226,988 (95% CI = 224,444-229,384) per million. CONCLUSIONS: A substantial number of persons with preexisting conditions will need suitable medical care following a disaster. The MedCon:PreEvent tool can assist disaster planners to prepare for medical care needs of large numbers of evacuees and consider re-evaluating the approach to utilizing and augmenting medical care services.

To calculate the burden of 2009 pandemic influenza A (pH1N1) in the United States, we extrapolated from the Centers for Disease Control and Prevention's Emerging Infections Program laboratory-confirmed hospitalizations across the entire United States, and then corrected for underreporting. From 12 April 2009 to 10 April 2010, we estimate that approximately 60.8 million cases (range: 43.3-89.3 million), 274,304 hospitalizations (195,086-402,719), and 12,469 deaths (8,868-18,306) occurred in the United States due to pH1N1. Eighty-seven percent of deaths occurred in those under 65 years of age with children and working adults having risks of hospitalization and death 4 to 7 times and 8 to 12 times greater, respectively, than estimates of impact due to seasonal influenza covering the years 1976-2001. In our study, adults 65 years of age or older were found to have rates of hospitalization and death that were up to 75% and 81%, respectively, lower than seasonal influenza. These results confirm the necessity of a concerted public health response to pH1N1.