BACKGROUND: Native Hawaiian and Pacific Islander (NHPI) adults have historically been grouped with Asian adults in U.S. mortality surveillance. Starting in 2018, the 1997 race and ethnicity standards from the U.S. Office of Management and Budget were adopted by all states on death certificates, enabling national-level estimates of cardiovascular disease (CVD) mortality for NHPI adults independent of Asian adults. OBJECTIVE: To describe CVD mortality among NHPI adults. DESIGN: Race-stratified age-standardized mortality rates (ASMRs) and rate ratios were calculated using final mortality data from the National Vital Statistics System for 2018 to 2022. SETTING: Fifty states and the District of Columbia. PARTICIPANTS: Adults aged 35 years or older at the time of death. MEASUREMENTS: CVD deaths were identified from International Classification of Diseases, 10th Revision codes indicating CVD (I00 to I99) as the underlying cause of death. RESULTS: From 2018 to 2022, 10 870 CVD deaths (72.6% from heart disease; 19.0% from cerebrovascular disease) occurred among NHPI adults. The CVD ASMR for NHPI adults (369.6 deaths per 100 000 persons [95% CI, 362.4 to 376.7]) was 1.5 times higher than for Asian adults (243.9 deaths per 100 000 persons [CI, 242.6 to 245.2]). The CVD ASMR for NHPI adults was the third highest in the country, after Black adults (558.8 deaths per 100 000 persons [CI, 557.4 to 560.3]) and White adults (423.6 deaths per 100 000 persons [CI, 423.2 to 424.1]). LIMITATION: Potential misclassification of underlying cause of death or race group. CONCLUSION: NHPI adults have a high rate of CVD mortality, which was previously masked by aggregation of the NHPI population with the Asian population. The results of this study support the need for continued disaggregation of the NHPI population in public health research and surveillance to identify opportunities for intervention. PRIMARY FUNDING SOURCE: National Institute of General Medical Sciences, National Institutes of Health.

BACKGROUND: There are no recent estimates for hypertension-associated medical expenditures. This study aims to estimate hypertension-associated incremental medical expenditures among privately insured US adults. METHODS: We conducted a retrospective cohort study using IQVIA's Ambulatory Electronic Medical Records-US data set linked with PharMetrics Plus claims data. Among privately insured adults aged 18 to 64 years, hypertension was identified as having ≥1 diagnosis code or ≥2 blood pressure measurements of ≥140/90 mm Hg, or ≥1 antihypertensive medication in 2021. Annual total expenditures (in 2021 $US) were estimated using a generalized linear model with gamma distribution and log-link function adjusting for demographic characteristics and cooccurring conditions. Out-of-pocket expenditures were estimated using a 2-part model that included logistic and generalized linear model regression. Overlap propensity score weights from logistic regression were used to obtain a balanced sample on hypertension status. RESULTS: Among the 393 018 adults, 156 556 (40%) were identified with hypertension. Compared with individuals without hypertension, those with hypertension had $2926 (95% CI, $2681-$3170) higher total expenditures and $328 (95% CI, $300-$355) higher out-of-pocket expenditures. Adults with hypertension had higher total inpatient ($3272 [95% CI, $1458-$5086]) and outpatient ($2189 [95% CI, $2009-$2369]) expenditures when compared with those without hypertension. Hypertension-associated incremental total expenditures were higher for women ($3242 [95% CI, $2915-$3569]) than for men ($2521 [95% CI, $2139-$2904]). CONCLUSIONS: Among privately insured US adults, hypertension was associated with higher medical expenditures, including higher inpatient and out-of-pocket expenditures. These findings may help assess the economic value of interventions effective in preventing hypertension.

Stroke was the fifth leading cause of death in the United States in 2021, and cost U.S. residents approximately $56.2 billion during 2019-2020. During 2006-2010, self-reported stroke prevalence among noninstitutionalized adults had a relative decrease of 3.7%. Data from the Behavioral Risk Factor Surveillance System were used to analyze age-standardized stroke prevalence during 2011-2022 among adults aged ≥18 years. From 2011-2013 to 2020-2022, overall self-reported stroke prevalence increased by 7.8% nationwide. Increases occurred among adults aged 18-64 years; females and males; non-Hispanic Black or African American (Black), non-Hispanic White (White), and Hispanic or Latino (Hispanic) persons; and adults with less than a college degree. Stroke prevalence was higher among adults aged ≥65 years than among younger adults; among non-Hispanic American Indian or Alaska Native, non-Hispanic Native Hawaiian or Pacific Islander, and Black adults than among White adults; and among adults with less than a high school education than among those with higher levels of education. Stroke prevalence decreased in the District of Columbia and increased in 10 states. Initiatives to promote knowledge of the signs and symptoms of stroke, and the identification of disparities in stroke prevalence, might help to focus clinical and programmatic interventions, such as the Million Hearts 2027 initiative or the Paul Coverdell National Acute Stroke Program, to improve prevention and treatment of stroke.

OBJECTIVE: The Mind, Exercise, Nutrition, Do It! 7-13 (MEND 7-13) program was adapted in 2016 by five Denver Health federally qualified health centers (DH FQHC) into MEND+, integrating clinician medical visits into the curriculum and tracking health measures within an electronic health record (EHR). We examined trajectories of body mass index (BMI, kg/m(2)) percentile, and systolic and diastolic blood pressures (SBP & DBP) among MEND+ attendees in an expanded age range of 4-17 years, and comparable non-attendees. METHODS: Data from April 2015 to May 2018 were extracted from DH FQHC EHR for children eligible for MEND+ referral (BMI ≥85(th) percentile). The sample included 347 MEND+ attendees and 21,061 non-attendees. Mixed-effects models examined average rate of change for BMI percent of the 95(th) percentile (%BMIp95), SBP, and DBP, after completion of the study period. RESULTS: Most children were ages 7-13 years, half were male, and most were Hispanic. An average of 4.2 MEND+ clinical sessions were attended. Before MEND+, %BMIp95 increased by 0.247 units/month among MEND+ attendees. After attending, %BMIp95 decreased by 0.087 units/month (p<0.001). Eligible non-attendees had an increase of 0.084/month in %BMIp95. Before MEND+ attendance, SBP and DBP increased by 0.041 and 0.022/month, respectively. After MEND+ attendance, SBP and DBP decreased by 0.254 /month (p<0.001) and 0.114/month (p<0.01), respectively. SBP and DBP increased by 0.032 and 0.033/month in eligible non-attendees, respectively. CONCLUSIONS: %BMIp95, SBP, and DBP significantly decreased among MEND+ attendees when implemented in community-based clinical practice settings at DH FQHC.

PURPOSE: Determine prevalence of overweight and obesity as reported in Head Start Program Information Reports. DESIGN: Serial cross-sectional census reports from 2012-2018. SETTING: Head Start programs countrywide, aggregated from program level to state and national level. SUBJECTS: Population of children enrolled in Head Start with reported weight status data. MEASURES: Prevalence of overweight (body mass index [BMI] ≥85th percentile to <95th percentile) and obesity (BMI ≥95th percentile). ANALYSIS: Used descriptive statistics to present the prevalence of overweight and obesity by state. Performed unadjusted regression analysis to examine annual trends or average annual changes in prevalence. RESULTS: In 2018, the prevalence of overweight was 13.7% (range: 8.9% in Alabama to 20.4% in Alaska). The prevalence of obesity was 16.6% (range: 12.5% in South Carolina to 27.1% in Alaska). In the unadjusted regression model, 34 states and the District of Columbia did not have a linear trend significantly different from zero. There was a statistically significant positive trend in obesity prevalence for 13 states and a negative trend for 3 states. CONCLUSION: The prevalence of obesity and overweight in Head Start children remained stable but continues to be high. Head Start reports may be an additional source of surveillance data to understand obesity prevalence in low-income young children.

The objective of this study was to describe the prevalence of sugar-sweetened beverage (SSB) intake among US adults (n = 68,896) residing in metropolitan and nonmetropolitan counties, by state, using data from the Behavioral Risk Factor Surveillance System. We used multinomial logistic regression to calculate adjusted prevalence ratios for daily (>/=1 time per day) SSB intake. Overall, 26.0% of respondents reported daily SSB intake, with significantly higher prevalence in nonmetropolitan counties (30.9%) than in metropolitan counties (24.8%) (adjusted prevalence ratio = 1.32, 95% confidence interval, 1.26-1.39). This same pattern was significant in 5 of 11 states with metropolitan and nonmetropolitan counties. These findings could inform efforts to reduce frequent SSB intake in nonmetropolitan areas.

PURPOSE: To examine associations of adolescent sugar-sweetened beverage (SSB) intake with parent SSB intake and parent and adolescent attitudes about limiting SSB and junk food (SSB/JF) intake. DESIGN: Quantitative, cross-sectional study. SETTING: The 2014 Family Life, Activity, Sun, Health, and Eating study. SAMPLE: Parent-adolescent dyads (N = 1555). MEASURES: The outcome was adolescent SSB intake. Exposure variables were parent SSB intake, sociodemographics, and parent and adolescent attitudes about SSB/JF intake (responses: agree, neither, or disagree). ANALYSIS: Multinomial logistic regressions estimated adjusted odds ratios (aOR) and 95% confidence intervals (CIs). RESULTS: Half (49.5%) of adolescents and 33.7% of parents consumed SSB >/=1 time/day. Parent daily SSB intake was associated with adolescent daily SSB intake (aOR = 8.9; CI = 4.6-17.3) [referent: no consumption]. Adolescents who disagreed on having confidence to limit SSB/JF intake had higher odds of daily SSB intake (aOR = 3.5; CI = 1.8-6.8), as did those who disagreed they felt bad about themselves if they did not limit SSB/JF intake (aOR = 1.9; CI=1.1-3.3), compared to adolescents who agreed with these attitudes. No parental attitudes were significant. CONCLUSION: Higher odds of daily SSB intake among adolescents was associated with parent SSB intake and adolescent attitudes about confidence in, and feeling bad about, limiting SSB/JF intake. Parent attitudes were not associated with daily adolescent SSB intake. Efforts to reduce adolescent SSB intake could consider strategies geared toward improving adolescent attitudes and dietary behaviors and parental SSB intake.

Background: Childhood obesity care management options can be delivered in community-, clinic-, and hospital-settings. The referral practices of clinicians to these various settings have not previously been characterized beyond the local level. This study describes the management strategies and referral practices of clinicians caring for pediatric patients with obesity and associated clinician characteristics in a geographically diverse sample. Methods: This cross-sectional study used data from the DocStyles 2017 panel-based survey of 891 clinicians who see pediatric patients. We used multivariable logistic regression to estimate associations between the demographic and practice characteristics of clinicians and types of referrals for the purposes of pediatric weight management. Results: About half of surveyed clinicians (54%) referred <25% of their pediatric patients with obesity for the purposes of weight management. Only 15% referred most (>/=75%) of their pediatric patients with obesity for weight management. Referral types included clinical referrals, behavioral referrals, and weight management program (WMP) referrals. Within these categories, the percentage referrals ranged from 19% for behavioral/mental health professionals to 72% for registered dieticians. Among the significant associations, female clinicians had higher odds of referral to community and clinical WMP; practices in the Northeast had higher odds of referral to subspecialists, dieticians, mental health professionals, and clinical WMP; and clinics having >/=15 well child visits per week were associated with higher odds of referral to subspecialists, mental health professionals, and health educators. Not having an affiliation with teaching hospitals and serving low-income patients were associated with lower odds of referral to mental health professionals, and community and clinical WMP. Compared to pediatricians, family practitioners, internists, and nurse practitioners had higher odds of providing referrals to mental health professionals and to health educators. Conclusion: This study helps characterize the current landscape of referral practices and management strategies of clinicians who care for pediatric patients with obesity. Our data provide insight into the clinician, clinical practice, and reported patient characteristics associated with childhood obesity referral types. Understanding referral patterns and management strategies may help improve care for children with obesity and their families.

INTRODUCTION: The U.S. Preventive Services Task Force recommends clinicians screen children aged 6 years or older for obesity and offer or refer children with obesity to intensive weight management programs. This study explores clinician awareness of weight management programs meeting the recommendation, adherence to the recommendation of screening and referral, and associations between provider and practice characteristics and weight management program referrals. METHODS: This cross-sectional study used data from the DocStyles survey 2017, a web-based panel survey, analyzed in 2017. Among 1,023 clinicians who see pediatric patients, this study examined clinician awareness of weight management programs in their communities that met the recommendation, practice of screening for childhood obesity, and referral to weight management programs. Multivariable logistic regression estimated associations between the demographic and practice characteristics of clinicians and weight management program referrals. RESULTS: Only 24.6% of surveyed clinicians were aware of a weight management program that met the U.S. Preventive Services Task Force recommendation in their community; of those aware, 88.9% referred patients to these weight management programs. Most (83.6%) clinicians screened children for obesity in >/=75% of visits. Overall, 53.5% of clinicians provided referrals to weight management programs. Referral was higher among female clinicians and clinicians serving mostly middle-income patients. Providers without teaching hospital privileges had lower odds of referral. CONCLUSIONS: Adherence to clinical recommendations is essential to curbing the childhood obesity epidemic. Only one in four surveyed clinicians were aware of weight management programs in their community meeting U.S. Preventive Services Task Force criteria. Half of clinicians referred pediatric patients with obesity to a weight management program. Results suggest efforts are needed to increase awareness of, and referral to, weight management programs meeting the recommendation.