Public Health Reports最新文献

Objectives: Fetal alcohol syndrome (FAS) is frequently undiagnosed or misdiagnosed, particularly among populations at elevated risk of the condition. We examined the prevalence of FAS among children aged 0 to 5 years enrolled in Medicaid, described characteristics of affected children, and evaluated diagnostic timing between children in foster care and children not in foster care.

Methods: We conducted a retrospective analysis of Medicaid Transformed Analytic Files for children born from 2015 through 2017 with FAS diagnoses (n = 771), following each birth cohort for 5 years. We used descriptive statistics to examine prevalence rates and demographic characteristics. Multivariate linear regression models assessed differences in diagnostic timing between children in foster care and children not in foster care, controlling for demographic factors.

Results: The overall FAS prevalence per 100 000 children aged 0 to 5 years enrolled in Medicaid was 7.7, increasing from 5.1 in 2015 to 11.4 in 2017. Children in foster care represented 60.2% (n = 464) of the FAS cohort. Although behavioral assessments occurred at similar ages for both groups, children in foster care received FAS diagnoses 4.6 to 5.4 months later than children not in foster care (P < .001). The time between the first behavioral assessment and FAS diagnosis was 2.1 to 3.9 months longer for children in foster care than for children not in foster case.

Conclusions: Children in foster care had substantial delays in diagnosis compared with children not in foster care. Initial access to behavioral assessment appears equitable; however, barriers exist in the progression from assessment to diagnosis for children in foster care. Implementing targeted screening protocols, improving cross-system information sharing, and enhancing health care provider training could reduce diagnostic delays and improve outcomes for this population.

Objective: Most people in the United States are unaware that alcohol causes cancer and increases heart disease risk. Warning labels on alcohol containers can increase knowledge about these harms. Yet, it is unclear if Americans support this policy. We assessed attitudes toward cancer and heart disease warning labels on alcohol containers.

Methods: In May 2024, we surveyed 1095 adults (aged ≥18 y) participating in AmeriSpeak, a nationally representative panel of the US noninstitutionalized civilian adult population. We analyzed support for warning labels overall and by sociodemographic characteristics by using descriptive statistics weighted to represent the US population.

Results: Overall, 4 in 10 respondents supported cancer or heart disease warning labels on alcohol containers. Fewer than 10% opposed warning labels; many were neutral. More young adults (aged 18-29 y; 57.9% [95% CI, 44.6%-70.0%]) than older adults (aged ≥60 y; 36.0% [95% CI, 30.3%-42.1%]) supported cancer warning labels (P = .04). More women (50.8%; 95% CI, 45.0%-56.6%) than men (40.5%; 95% CI, 34.1%-47.2%) supported heart disease warning labels (P = .03).

Conclusions: Most people in the United States are neutral about or support cancer and heart disease warning labels on alcohol containers. Leveraging warning labels is a cost-effective way to inform the public about alcohol's link to cancer and heart disease.

Objectives: During the past century, the age of menarche (onset of the first menstrual cycle) has dropped worldwide, with a decline of 2 or 3 months every 10 years in the United States. The adverse health effects of this change are just starting to be understood. Little is known about how the age of menarche affects the future health of African American women. Our objective was to examine the association between early menarche and cardiovascular disease in African American women.

Methods: The study sample included 2397 African American women in the Jackson Heart Study (64% of the total original cohort). We examined the association of early menarche (at age <12 y) with prevalent coronary heart disease, myocardial infarction, stroke, hypertension, diabetes, general obesity, and abdominal obesity.

Results: The mean (SD) baseline age of participants was 55.2 (12.5) years, with a mean (SD) age of menarche of 12.8 (1.8) years; 20.6% of these women experienced early menarche. After adjustment for relevant covariates, early menarche was significantly associated with increased odds of coronary heart disease (odds ratio [OR] = 2.16; 95% CI, 1.44-3.22), myocardial infarction (OR = 1.90; 95% CI, 1.17-3.08), hypertension (prevalence ratio [PR] = 1.15; 95% CI, 1.07-1.24), general obesity (PR = 1.22; 95% CI, 1.14-1.31), and abdominal obesity (PR = 1.14; 95% CI, 1.08-1.20).

Conclusions: Among this cohort, early menarche was a risk factor for the development of cardiovascular disease among African American women. Addressing this risk requires policies and clinical guidelines that recognize early menarche as a marker for early intervention.

Objectives: Concern is growing about rising opioid use disorder (OUD) rates and limited knowledge of how socioeconomic status (SES) and health factors interact. We examined whether health status moderates the relationship between SES and OUD among individuals with nonmedical opioid use.

Methods: We analyzed data from 10 984 adults aged ≥18 years in the 2015-2019 National Survey on Drug Use and Health. Logistic regression estimated odds of OUD using self-reported health (good/very good/excellent vs fair/poor) and SES indicators (education, income, employment, and marital status). Interaction terms tested whether health status modified SES-OUD associations.

Results: Fair/poor health increased OUD odds, whereas college graduation and employment were linked to lower odds. Interaction analyses showed that among those with fair/poor health, higher SES corresponded to increased OUD odds. Those with fair/poor health and a college degree had substantially higher odds of OUD (odds ratio [OR] = 3.35; P < .001) than less educated peers. Among those with fair/poor health, individuals with annual family incomes ≥$75 000 also had higher OUD odds (OR = 1.84; P = .03) than those with incomes <$20 000, and employment was associated with increased OUD odds (OR = 1.61; P = .008). Individuals who were widowed/divorced/separated (OR = 0.36; P < .001) and never married (OR = 0.48; P = .001) had lower OUD odds than married individuals.

Conclusions: Health status significantly moderated SES-OUD associations. Among those in poor health, higher SES was linked to greater OUD odds. Prevention and treatment efforts should consider how SES and health jointly shape OUD vulnerability.

Objectives: It is unclear how the incidence of each pediatric diabetes subtype changed during the COVID-19 pandemic. We assessed changes in the incidence of pediatric type 1 diabetes, pediatric type 2 diabetes, and prediabetes during the COVID-19 pandemic.

Methods: We analyzed data from young people aged <19 years without prior diabetes in a large integrated US health care system from March 2017 through February 2023 using interrupted time-series analyses to compare prepandemic and pandemic rates and trends in the incidence of type 1 diabetes, type 2 diabetes, and prediabetes.

Results: Of 288 574 pediatric patients, 242 were diagnosed with type 1 diabetes, 239 with type 2 diabetes, and 4438 with prediabetes. The incidence of type 1 diabetes did not change after pandemic onset in the interrupted time-series analysis. Although the incidence of type 2 diabetes and prediabetes changed with a single interruption analysis, an exploratory model of 2 interruptions revealed a better fit for each: for type 2 diabetes, the model showed a significant rise (128%; P < .001) and subsequent decrease after the early pandemic period; for prediabetes, it revealed a 142% increase (P = .005) with a rising trend (12% per month) early in the pandemic, followed by a 9% monthly downward trend.

Conclusion: Among this population, the COVID-19 pandemic did not affect the incidence of pediatric type 1 diabetes but was associated with significant increases in the incidence of type 2 diabetes and prediabetes, particularly early in the pandemic. Future work can build on these results to better understand how pandemic-related factors, including activity restrictions and COVID-19 disease, affected rates of pediatric type 2 diabetes and prediabetes and inform future public health practices.

Objectives: Nonprobability sampling, commonly used in disaster research, can lead to incorrect estimates or limit the generalizability of results. We collected data through the Texas Flood Registry (TFR) and used raking and propensity score weighting to provide insight into the effect of Hurricane Harvey (hereinafter, Harvey) on Harris County, Texas.

Methods: From April 2018 through October 2020, residents of areas affected by Harvey enrolled in the TFR completed a survey on their storm-related experiences (n = 20 653). Using logistic regression, we assessed the relationship between Harvey-related exposures and distress among Harris County residents (n = 12 279). We used raking to adjust the sample distribution to reflect demographic characteristics of Harris County and propensity scores to address confounding.

Results: Of respondents, 56% and 43% reported home damage and income loss due to Harvey, respectively. From April 2018 through April 2020, respondents completed the Impact of Event Scale questionnaire (n = 10 631), with 23% reporting symptoms consistent with severe distress related to Harvey. The raking-adjusted odds ratio of greater Harvey-related distress was 6.21 (95% CI, 5.44-7.09) times higher among residents who had home damage than among those who did not and 2.92 (95% CI, 2.59-3.30) times higher among those who had economic loss than among those who did not.

Conclusions: We found consistent associations between adverse storm experiences and Harvey-related distress across unweighted and weighted approaches. We recommend using raking to adjust a nonprobability sample to better reflect population demographic characteristics and obtain general trends of postdisaster exposures and outcomes. We recommend using propensity scores when outcomes may be related to unmeasured confounding.

Lifestyle medicine is an evidence-based approach that uses therapeutic lifestyle interventions as the primary method for preventing, treating, and reversing chronic diseases. According to the Centers for Disease Control and Prevention, 6 in 10 adults have at least 1 chronic condition and 4 in 10 adults have 2 or more chronic conditions, contributing to substantial morbidity, mortality, and escalating health care costs. Chronic conditions are responsible for 8 of the 10 leading causes of death in the United States, with lifestyle factors playing a critical role in their prevalence. This topical review synthesizes current literature on the integration of lifestyle medicine into primary care, emphasizing its 6 core pillars: optimal nutrition, physical activity, restorative sleep, risky substance avoidance, connectedness, and stress management. This review discusses evidence for the effectiveness of lifestyle medicine in chronic disease prevention and management, outlines barriers to its widespread adoption, and explores implementation strategies. This review also discusses the public health implications of scaling lifestyle medicine within primary care settings, underscoring its potential to reduce the risk of noncommunicable diseases and enhance quality of life. Although lifestyle medicine presents a promising strategy to address the growing chronic disease epidemic, achieving its full potential requires policy support, interdisciplinary collaboration, and improved training for health care professionals. By addressing the root causes of chronic diseases, lifestyle medicine offers a pathway toward improved population health and health equity.

Programs in the United States aimed at improving maternal and infant health vary in effectiveness in reaching eligible participants. Administrative burdens such as learning, psychological, and compliance costs increase barriers to initial and sustained enrollment in these programs. This case study explored the effectiveness of Rx Kids-the United States' first community-wide unconditional cash prescription program for mothers and infants launched in Flint, Michigan-in achieving exceptionally high program uptake. We compared total births among program participants with total births in Flint based on birth certificate records to estimate aggregate and prenatal uptake rates. We calculated an aggregate uptake rate in the range of 100% and a prenatal uptake rate of 93%. A limitation of our methodology was our inability to match program participants to birth certificate data. We discuss this and other limitations and offer recommendations for further refining estimates of program uptake using a data match process. The high uptake rate of Rx Kids underscores the importance of program design in reducing administrative burdens, with broad implications for improving maternal and infant health and public health at large.