American Journal of Preventive Medicine最新文献

Introduction: The aim of this study was to determine the associations between type 2 diabetes or prediabetes and loneliness and related social experiences in young adults, a population at increasingly high risk of type 2 diabetes.

Methods: This was a cross-sectional analysis using data from adults aged 18-35 years enrolled in the All of Us Research Program. Exposures included loneliness, social support, discrimination, neighborhood social cohesion, and stress, measured by standardized surveys. The main outcome was type 2 diabetes or prediabetes by self-report or linked health record. Logistic regression determined the odds of type 2 diabetes/prediabetes for each survey measure, adjusting for age, sex, race or ethnicity, income, and family history. Latent class analysis evaluated clustering of social experiences. Data were collected from 2018 to 2022 and analyzed in May 2023-June 2024.

Results: The cohort included 14,217 young adults (aged 28.2 ± 4.4 years, 73.1% [n=10,391] women, 64.1% [n=9,111] White, 10.6% [n=1,506] Hispanic, 5.7% [n=806] Black, and 9.1% [n=1,299] multiracial). Overall, 5.5% (n=777) had either prediabetes or type 2 diabetes. The 2 highest loneliness quartiles were associated with increased odds of prediabetes/type 2 diabetes (Q3: OR=1.42 [95% CI=1.15, 1.76] and Q4: OR=1.78 [95% CI=1.45, 2.19]). Greater stress and discrimination and lower social support and neighborhood social cohesion were also associated with increased odds of prediabetes/type 2 diabetes. Latent class analysis revealed 3 distinct phenotypes, with elevated odds of prediabetes/type 2 diabetes in the 2 with the most adverse social profiles (OR=2.32 [95% CI=1.89, 2.84] and OR=1.28 [95% CI=1.04, 1.58]).

Conclusions: Loneliness and related experiences are strongly associated with type 2 diabetes and prediabetes in young adults. Whether these factors could be leveraged to reduce type 2 diabetes risk should be investigated.

Introduction: Social risk factors are associated with worse access to care. This study measured the prevalence of social risk factors among low-income adults, assessed the relationship between number of social risk factors and access to care, and examined heterogeneity by health insurance type.

Methods: Using 2022 Behavioral Risk Factor Surveillance Survey data from 39 states, the association between number of SRFs (0, 1, 2-3, 4, or more) and three access to care measures (having a personal doctor, having a routine checkup, and inability to see doctor because of cost) were measured using multivariable linear probability models. The analysis was stratified by health insurance coverage type (private, Medicare, Medicaid, or uninsured) to assess whether effects were differential. Analyses were conducted in 2024.

Results: Among 90,208 low-income adults, 46.6% reported at least one SRF. Compared to people who reported no SRFs, those who reported four or more were more likely to report being unable to afford care (28.21 percentage points [PP], p<0.001) and less likely to have a personal doctor (-4.98 PP, p<0.001) or routine checkup in the last two years (-4.29 PP, p<0.001). The magnitude of disparity by number of SRFs in inability to afford care was larger among privately insured and uninsured people compared to those with Medicare or Medicaid coverage.

Conclusions: Higher levels of SRFs were associated with worse access to care among low-income adults. Policies that minimize cost-related barriers to care, coupled with care delivery reforms and social policies that address SRFs, may improve access to care.

Introduction: A lung cancer screening program using low dose CT (LDCT) in a Federally Qualified Health Center (FQHC) in Central Texas was developed and assessed for equitable implementation.

Methods: From 11/2020-8/2023, patients aged 55-77 years who currently smoked or quit within 15 years with ≥20 pack-years of exposure were identified through EHR query and mailed outreach, or through direct provider referrals. A bilingual social worker confirmed eligibility, provided telecare shared decision-making (SDM), coordinated screening, and offered smoking cessation. To assess equity, LDCT completion across demographics was compared, in 2023.

Results: A total of 6,486 patients were mailed outreach materials; 479 patients responded, of whom 108 (22.5%) were eligible and 71 (65.7%) participated in SDM. 629 eligible patients were referred internally; 579 (92.0%) completed SDM. Of the 650 patients who completed SDM, 636 (97.8%) agreed to screening. Mean age was 61.7 years; 38.1% were female. The population was diverse: 35.8% identified as Latino, 17.8% as African-American, 26.8% had Medicare or Medicaid, 48.0% used the county medical assistance program, 14.2% were uninsured, and 76.7% currently smoked. Overall, 528 (83.0%) patients completed LDCT. There were no statistically significant differences in completion by age, gender, race/ethnicity, or insurance status. Spanish-speaking patients were more likely to complete the CT than English speakers (OR 2.22, 95% CI=1.22, 4.41) and those who formerly smoked were more likely to complete the CT than patients who currently smoked (OR 1.93, 95% CI=1.12, 3.51).

Conclusions: The navigator-centered program achieved equitable implementation of lung cancer screening in a diverse FQHC system.

Introduction: Healthcare providers should facilitate smoking cessation, and primary care is an ideal setting for delivering this evidence-based care. This study's objective was to describe readiness to quit smoking combustible cigarettes among adult Appalachian primary care patients and determine their providers' implementation of an established tobacco treatment model.

Methods: As part of a randomized clinical trial, 298 providers in 10 health systems across 4 states received tobacco treatment training. Periodically between January 2022 and June 2023, anonymous surveys were distributed to patients after primary care visits. The survey included questions about demographics, visit type, smoking status, readiness to quit, and provider behavior related to tobacco treatment implementation. In 2023, descriptive statistics, bivariate tests and logistic regression models were conducted.

Results: Among 1,242 survey respondents, 34.1% reported current smoking. Among those who reported current smoking, 13.9% expressed readiness to quit within the next 30 days and 15.3% reported readiness in 1-6 months while 36.7% indicated "never" wanting to quit. Nearly all providers asked respondents about smoking status (96.9%) and advised them to quit (89.8%); fewer providers engaged in cessation assistance by discussing behavioral counseling, discussing medication options, and/or prescribing medication (25.1%-64.6% across behaviors). Provider behavior was most consistently associated with patient age and visit type.

Conclusions: Nearly one third of Appalachian patients who smoke reported readiness to quit within 6 months and nearly all received advice to quit from their provider. Patients would benefit from more intensive tobacco treatment delivery in primary care settings, with a focus on assisting with quit attempts.

Introduction: Age-adjusted mortality rates (AAMR) for cardiovascular diseases (CVD) increased in 2020 and 2021, and provisional data indicated an increase in 2022, resulting in substantial excess CVD deaths during the COVID-19 pandemic. Updated estimates using final data for 2022 are needed.

Methods: The National Vital Statistics System's final Multiple Cause of Death files were analyzed in 2024 to calculate AAMR from 2010 to 2022 and excess deaths from 2020 to 2022 for U.S. adults aged ≥35 years, with CVD as the underlying cause of death.

Results: The CVD AAMR among adults aged ≥35 years in 2022 was 434.6 deaths per 100,000 (95% CI=433.8, 435.5), which was lower than in 2021 (451.8 deaths per 100,000; 95% CI=450.9, 452.7). The most recent year with a similarly high CVD AAMR as in 2022 was 2012 (434.7 deaths per 100,000 population, 95% CI=433.8, 435.7). The CVD AAMR for 2022 calculated using provisional data overestimated the AAMR calculated using final data by 4.6% (95% CI=4.3%, 4.9%) or 19.9 (95% CI=18.6, 21.2) deaths per 100,000 population. From 2020 to 2022, an estimated 190,661 (95% CI=158,139, 223,325) excess CVD deaths occurred.

Conclusions: In 2022, the CVD AAMR among adults aged ≥35 years did not increase, but rather declined from a peak in 2021, signaling improvements in adverse mortality trends that began in 2020, amid the COVID-19 pandemic. However, the 2022 CVD AAMR remains higher than observed before the COVID-19 pandemic, indicating an ongoing need for CVD prevention, detection, and management.

Introduction: Unexpected out-of-pocket (OOP) costs for preventive care reduce future uptake. Because adherence to service guidelines differs by patient populations, understanding the role of patient demographics and social determinants of health (SDOH) in the incidence and size of unexpected cost-sharing is necessary to address these disparities. This study examined the associations between patient demographics and cost-sharing for common preventive services.

Methods: This cross-sectional study used a national sample of insurance claims for recommended preventive services provided to privately insured adult patients between 2017 and 2020. The relationships between patient demographics and OOP costs were adjusted for service type, insurance type, geographic location, and time trends using regression analysis. Analyses were conducted in 2024.

Results: The sample included 1,736,063 unique preventive care encounters of 1,078,010 individuals. Among preventive encounters, 40.3% resulted in OOP costs. Lower-educated patients had 9.4% (OR=1.094; 95% CI=1.082, 1.106) higher odds of incurring OOP costs than patients with college degrees. Low-income patients (annual household income of $49,999 or less) had 10.7% (OR=0.893; 95% CI=0.880, 0.906) lower odds of incurring OOP costs than high-income patients. Conditional on incurring costs, lower educated patients paid $15.07 (95% CI= -$15.24, -$14.91) less than higher educated patients, and low-income patients paid $11.76 (95% CI=$11.58, $11.95) more than high-income patients. Significant differences across racial and ethnic groups were observed.

Conclusions: The likelihood and size of OOP costs for preventive care varied considerably by patient demographics; this may contribute to inequitable access to high-value care.

Introduction: Ignition interlock devices installed after conviction for driving under the influence of alcohol (DUI) have been shown to reduce subsequent DUI arrests (specific deterrence). However, there is little evidence on how interlock-device penalties might affect general deterrence, that is, deterring people from driving after consuming alcohol prior to a DUI conviction.

Methods: A discrete choice experiment was conducted and data were analyzed in 2023 with 583 U.S.-based adults who consume alcohol at least once in the past week to assess the deterrent effects of five different penalties (fine, jail time, interlock device, license suspension, alcohol treatment) for alcohol-impaired driving under randomized sequential scenarios of high (20% chance of being caught) and low (1%) police enforcement. Participants resided in 46 states.

Results: Deterrent effects of an interlock penalty, operationalized as having to install an interlock device for 1 year, are large and on par with a 20-fold increase in police enforcement activity (from 1% chance of being caught to 20%), or a $2,000 increase in the DUI fine under the status quo enforcement regime. On average, a 1-year interlock penalty had the same deterrent effect as a 10-day increase in jail time.

Conclusions: Wider use of interlock devices as a DUI penalty could have large deterrent effects, independent of their ability to physically prevent the motor vehicle of an intoxicated driver from starting. The deterrent effect documented here adds to evidence on interlock devices' overall effectiveness as well as their potential to shift DUI penalties away from criminalization (jail time) and toward immobilization and rehabilitation.

Introduction: Addressing the public health problem of physical inactivity, this study evaluates SNapp, a just-in-time adaptive app intervention to promote walking through dynamically tailored coaching content. It assesses SNapp's impact on daily steps and how users' perceptions regarding ease of use and usefulness moderated its effectiveness.

Methods: SNapp was evaluated in an RCT from February 2021 to May 2022.This trial was preregistered in the Dutch Trial Register (NL7064). Analyses were conducted in November 2022. A total of 176 adults (76% female, mean age of 56 years) were randomized to a control group receiving a step counter app (n=89) or an intervention group receiving the app plus coaching content (n=87). SNapp's coaching content encompasses individually tailored feedback on step counts and advice to engage in more walking, taking preferences regarding behavior change techniques into account. Additionally, SNapp provides contextualized content calling attention to suitable walking locations in the user's environment. The primary outcome was daily step count as recorded by the step counter app. User perceptions regarding ease of use and usefulness were assessed via survey at 3-month follow-up.

Results: Mixed models indicated that the intervention did not significantly impact step counts on average over time (B= -202.30, 95% CI= -889.7, 485.1), with the coefficient indicating that the intervention group walked fewer steps per day on average, though this difference was not statistically significant. Perceived ease of use did not moderate the intervention effect (B_{group × perceived ease of use}=38.60, 90% CI= -276.5, 353.7). Perceived usefulness significantly moderated the intervention effect (B_{group × perceived usefulness}=344.38, 90% CI=40.4, 648.3).

Conclusions: SNapp increased steps only in users who deemed the app useful, underscoring the importance of user perceptions in app-based interventions.

Introduction: This study examines the prospective association between financial strain and smoking cessation and smoking relapse among U.S. adults with established smoking.

Methods: Discrete-time survival models were fit to nationally representative data in Waves 1-5 (2013-2019) of the U.S. Population Assessment of Tobacco and Health Study for smoking cessation (n=6,972) and smoking relapse (n=1,195). Models were adjusted for demographics (age, sex, race, and ethnicity), socioeconomic positioning (education, income, health insurance status), and tobacco-related confounders (quit attempts, coupon receipt, and nicotine dependence). Data were collected between 2013 and 2019, and the analysis was conducted in 2023-2024.

Results: Among adults with established cigarette smoking, financial strain was associated with a reduced likelihood of cigarette smoking cessation (HR: 0.81, 95% CI: 0.72, 0.92) and an increased likelihood of cigarette smoking relapse (HR: 1.56, 95% CI: 1.24, 1.96) in multivariable models. Results were robust to sensitivity analyses varying confounder control, sample restrictions, and survey weights used.

Conclusions: The results from this study suggest that financial strain is a barrier to cigarette smoking without relapse, which may be due to stress and coping processes. Smoking cessation interventions would benefit from considering the role that financial strain plays in inhibiting smoking cessation without relapse.