"Urban-Satellite" estimates in the ABCD Study: Linking Neuroimaging and Mental Health to Satellite Imagery Measurements of Macro Environmental Factors.

While numerous studies over the last decade have highlighted the important influence of environmental factors on mental health, globally applicable data on physical surroundings are still limited. Access to such data and the possibility to link them to epidemiological studies is critical to unlocking the relationship of environment, brain and behaviour and promoting positive future mental health outcomes. The Adolescent Brain Cognitive Development (ABCD) Study is the largest ongoing longitudinal and observational study exploring brain development and child health among children from 21 sites across the United States. Here we describe the linking of the ABCD study data with satellite-based "Urban-Satellite" (UrbanSat) variables consisting of 11 satellite-data derived environmental indicators associated with each subject's residential address at their baseline visit, including land cover and land use, nighttime lights, and population characteristics. We present these UrbanSat variables and provide a review of the current literature that links environmental indicators with mental health, as well as key aspects that must be considered when using satellite data for mental health research. We also highlight and discuss significant links of the satellite data variables to the default mode network clustering coefficient and cognition. This comprehensive dataset provides the foundation for large-scale environmental epidemiology research.

Use of Assistive Technology for Persons with Psychosocial Disability: Systematic Review.

BACKGROUND: Assistive technology (AT) refers to assistive products (AP) and associated systems and services that are relevant for function, independence, well-being, and quality of life for individuals with disabilities. There is a high unmet need for AT for persons with disabilities and this is worse for persons with cognitive and mental or psychosocial disabilities (PDs). Further, information and knowledge on AT for PDs is limited. OBJECTIVE: The aim of this review was to explore the pattern of AT use among persons with PDs and its associated socioeconomic and health benefits. METHODS: The review was reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), and we conducted systematic searches in the 4 databases: PubMed, Embase.com, APA PsycInfo (Ebsco), and Web of Science (Core Collection) with the following index terms: "Assistive Technology," "Self-Help Devices," "Quality of Life," "Activities of Daily Living," "Mental Disorders." We included only AT individuals with PDs can independently use without reliance on a provider. Identified papers were exported to EndNote (Clarivate) and we undertook a narrative synthesis of the included studies. RESULTS: In total, 5 studies were included in the review which reported use of different AT for schizophrenia, bipolar disorder, depression and anxiety disorders. The APs described in the included studies are Palm tungsten T3 handheld computer, MOBUS, personal digital assistant, automated pill cap, weighted chain blankets, and smartphone function. All the AT products identified in the studies were found to be easily usable by individuals with PDs. The APs reported in the included studies have broad impact and influence on social function, productivity, and treatment or management. The studies were heterogeneous and were all conducted in high-income countries. CONCLUSIONS: Our study contributes to and strengthens existing evidence on the relevance of AT for PDs and its potential to support socioeconomic participation and health. Although AT has the potential to improve function and participation for individuals with PDs; this review highlights that research on the subject is limited. Further research and health policy changes are needed to improve research and AT service provision for individuals with PDs especially in low-income settings. TRIAL REGISTRATION: PROSPERO CRD42022343735; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=343735.

A systematic review for the impacts of global approaches to regulating electronic nicotine products.

Background: The rapid increase in electronic nicotine product (ENP) use among young people has been a global public health challenge, given the potential harm of ENPs and nicotine dependence. Many countries have recently introduced legislations to regulate ENPs, but the impacts of these policies are poorly understood. This systematic review aims to critically synthesise empirical studies on the effects of global regulations regarding ENPs on the prevalence of use, health outcomes and their determinants, using the 4A marketing mix framework (acceptability, affordability, accessibility and awareness). Methods: Following the PRISMA guideline, we searched PubMed, Embase, Scopus, Web of Science, Academic Search Complete, Business Source Complete, and APA PsycINFO databases from inception until June 14, 2022 and performed citation searches on the included studies. Reviewed literature was restricted to peer-reviewed, English-language articles. We included all pre-post and quasi-experimental studies that evaluated the impacts of e-cigarette policies on the prevalence of ENP use and other health outcomes. A modified Joanna Briggs Institute (JBI) Critical Appraisal checklist for quasi-experimental studies was used for quality assessment. Due to heterogeneity of the included studies, we conducted a narrative synthesis of evidence. Results: Of 3991 unduplicated records screened, 48 (1.2%) met the inclusion criteria, most were from high-income countries in North America and Europe and 26 studies measured self-reported ENPs use. Flavour restrictions significantly decreased youth ENP use and taxation reduced adult use; mixed results were found for the impacts of age restrictions. Indoor vaping restrictions and the European Tobacco Products Directive (TPD) did not seem to reduce ENP use based on existing studies. Changes in determinants such as sales and perceptions corroborated our conclusions. Few studies assessed the impacts of other regulations such as advertising restrictions and retail licensing requirements. Conclusions: Flavour restrictions and taxes have the strongest evidence to support effective control of ENPs, while others need powerful enforcement and meaningful penalties to ensure their effectiveness. Future research should focus on under-examined policies and differential impacts across sociodemographic characteristics and countries. Registration: PROSPERO CRD42022337361.

Association of death or illness from COVID-19 among family and friends on vaccine uptake within four months of the Emergency Use Authorization. Findings from a national survey in the United States.

OBJECTIVE: To examine the relationship between knowing that a friend or family member became ill with, or died from, COVID-19 and receiving a vaccine dose within four months of the FDA's Emergency Use Authorization. METHODS: A national sample of 1,517 respondents were surveyed from April 7 to April 12, 2021, 1,193 of whom were eligible for the vaccine when the data were collected. RESULTS: Respondents who knew someone who became ill with COVID-19 (AOR = 2.32, 95 % CI 1.74-3.09) or knew someone who died (AOR = 2.29, 95 % CI 1.32-3.99) from COVID-19 were more likely to receive at least a single COVID-19 vaccine dose. CONCLUSION: Encouraging people to share their COVID-19 illness and bereavement experiences with their local network such as friends, families, social-networks and via social media might help increase vaccine uptake.

Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data at a tertiary hospital in North Tanzania.

BACKGROUND: Prediction of low Apgar score for vaginal deliveries following labor induction intervention is critical for improving neonatal health outcomes. We set out to investigate important attributes and train popular machine learning (ML) algorithms to correctly classify neonates with a low Apgar scores from an imbalanced learning perspective. METHODS: We analyzed 7716 induced vaginal deliveries from the electronic birth registry of the Kilimanjaro Christian Medical Centre (KCMC). 733 (9.5%) of which constituted of low (< 7) Apgar score neonates. The 'extra-tree classifier' was used to assess features' importance. We used Area Under Curve (AUC), recall, precision, F-score, Matthews Correlation Coefficient (MCC), balanced accuracy (BA), bookmaker informedness (BM), and markedness (MK) to evaluate the performance of the selected six (6) machine learning classifiers. To address class imbalances, we examined three widely used resampling techniques: the Synthetic Minority Oversampling Technique (SMOTE) and Random Oversampling Examples (ROS) and Random undersampling techniques (RUS). We applied Decision Curve Analysis (DCA) to evaluate the net benefit of the selected classifiers. RESULTS: Birth weight, maternal age, and gestational age were found to be important predictors for the low Apgar score following induced vaginal delivery. SMOTE, ROS and and RUS techniques were more effective at improving "recalls" among other metrics in all the models under investigation. A slight improvement was observed in the F1 score, BA, and BM. DCA revealed potential benefits of applying Boosting method for predicting low Apgar scores among the tested models. CONCLUSION: There is an opportunity for more algorithms to be tested to come up with theoretical guidance on more effective rebalancing techniques suitable for this particular imbalanced ratio. Future research should prioritize a debate on which performance indicators to look up to when dealing with imbalanced or skewed data.

Validating machine learning models for the prediction of labour induction intervention using routine data: a registry-based retrospective cohort study at a tertiary hospital in northern Tanzania.

OBJECTIVES: We aimed at identifying the important variables for labour induction intervention and assessing the predictive performance of machine learning algorithms. SETTING: We analysed the birth registry data from a referral hospital in northern Tanzania. Since July 2000, every birth at this facility has been recorded in a specific database. PARTICIPANTS: 21 578 deliveries between 2000 and 2015 were included. Deliveries that lacked information regarding the labour induction status were excluded. PRIMARY OUTCOME: Deliveries involving labour induction intervention. RESULTS: Parity, maternal age, body mass index, gestational age and birth weight were all found to be important predictors of labour induction. Boosting method demonstrated the best discriminative performance (area under curve, AUC=0.75: 95% CI (0.73 to 0.76)) while logistic regression presented the least (AUC=0.71: 95% CI (0.70 to 0.73)). Random forest and boosting algorithms showed the highest net-benefits as per the decision curve analysis. CONCLUSION: All of the machine learning algorithms performed well in predicting the likelihood of labour induction intervention. Further optimisation of these classifiers through hyperparameter tuning may result in an improved performance. Extensive research into the performance of other classifier algorithms is warranted.

Combining Resampling Strategies and Ensemble Machine Learning Methods to Enhance Prediction of Neonates with a Low Apgar Score After Induction of Labor in Northern Tanzania.

OBJECTIVE: The goal of this study was to establish the most efficient boosting method in predicting neonatal low Apgar scores following labor induction intervention and to assess whether resampling strategies would improve the predictive performance of the selected boosting algorithms. METHODS: A total of 7716 singleton births delivered from 2000 to 2015 were analyzed. Cesarean deliveries following labor induction, deliveries with abnormal presentation, and deliveries with missing Apgar score or delivery mode information were excluded. We examined the effect of resampling approaches or data preprocessing on predicting low Apgar scores, specifically the synthetic minority oversampling technique (SMOTE), borderline-SMOTE, and the random undersampling (RUS) technique. Sensitivity, specificity, precision, area under receiver operating curve (AUROC), F-score, positive predicted values (PPV), negative predicted values (NPV) and accuracy of the three (3) boosting-based ensemble methods were used to evaluate their discriminative ability. The ensemble learning models tested include adoptive boosting (AdaBoost), gradient boosting (GB) and extreme gradient boosting method (XGBoost). RESULTS: The prevalence of low (<7) Apgar scores was 9.5% (n = 733). The prediction models performed nearly similar in their baseline mode. Following the application of resampling techniques, borderline-SMOTE significantly improved the predictive performance of all the boosting-based ensemble methods under observation in terms of sensitivity, F1-score, AUROC and PPV. CONCLUSION: Policymakers, healthcare informaticians and neonatologists should consider implementing data preprocessing strategies when predicting a neonatal outcome with imbalanced data to enhance efficiency. The process may be more effective when borderline-SMOTE technique is deployed on the selected ensemble classifiers. However, future research may focus on testing additional resampling techniques, performing feature engineering, variable selection and optimizing further the ensemble learning hyperparameters.

Active commuting and the risk of obesity, hypertension and diabetes: a systematic review and meta-analysis of observational studies.

Active commuting may hold a potential for preventing adverse health outcomes. However, evidence of the association of active commuting and the risk of health outcomes remains debatable. The current study systematically and quantitatively summarised research findings on the association between active commuting and the risk of the mentioned health outcomes. We comprehensively searched four databases (PubMed, EMBASE, Web of Science and Open Grey) from inception to 2 August 2020 for observational studies investigating the associations among adult population. Summary relative risks (RRs) and 95% CIs were estimated for the association. Heterogeneity was investigated using Cochran's Q test and the I 2 statistic. Restricted cubic splines were used to evaluate linear and nonlinear relations. The search yielded 7581 initial references. We included 28 articles in the meta-analysis. Compared with inactive commuting, active commuting reduced the risk of obesity (RR=0.88, 95% CI 0.83 to 0.94, I2=69.1%), hypertension (RR=0.95, 95% CI 0.87 to 1.04, I2=82.2%) and diabetes (RR=0.82, 95% CI 0.76 to 0.90, I2=44.5%). Restricted cubic splines showed linear associations between active commuting and obesity, hypertension and diabetes (Pnonlinearity=0.640; Pnonlinearity=0.886; Pnonlinearity=0.099). As compared with the lowest active commuting group, the risk of obesity, hypertension and diabetes in the highest active commuting group were reduced by 13% (95% CI 0.82 to 0.93, I2=65.2%); 6% (95% CI 0.86 to 1.02, I2=75.2%) and 19% (95% CI 0.73 to 0.91, I2=49.8%) respectively. Active commuting seemed to be associated with lower risk of obesity, hypertension and diabetes. However, the results should be interpreted cautiously because this meta-analysis was based solely on observational studies.PROSPERO registration numberCRD42020202723.

The Association Between Substance Use and Violence: Results from a Nationally Representative Sample of High School Students in the United States.

With over 52% of high school students reporting that they have tried alcohol or illicit drugs, 16% carrying a weapon, and 23% engaging in a physical fight, substance use and youth violence remain critical public health challenges in the United States. Using data from the 2017 Youth Risk Behavior Survey, study results revealed that youth who reported heavy use of either alcohol, marijuana, or illicit drugs were three to ten times more likely to report carrying a weapon or engaging in a physical fight. Similarly, youth with heavy substance use were one and half times to 14 times more likely to be a victim of violence or sexual or dating violence. The SEM analysis indicated that substance use had a significant effect on all aspects of violence. School-based behavioral health specialists and community-based pediatricians may need to develop targeted messages to address the potential for violence among youth who use alcohol and/or illicit drugs.

Quality of Novel Coronavirus Related Health Information over the Internet: An Evaluation Study.

BACKGROUND: The novel coronavirus disease (COVID-19) has spread globally from its epicenter in Hubei, China, and was declared a pandemic by the World Health Organization (WHO) on March 11, 2020. The most popular search engine worldwide is Google, and since March 2020, COVID-19 has been a global trending search term. Misinformation related to COVID-19 from these searches is a problem, and hence, it is of high importance to assess the quality of health information over the internet related to COVID-19. The objective of our study is to examine the quality of COVID-19 related health information over the internet using the DISCERN tool. METHODS: The keywords included in assessment of COVID-19 related information using Google's search engine were "Coronavirus," "Coronavirus causes," "Coronavirus diagnosis," "Coronavirus prevention," and "Coronavirus management". The first 20 websites from each search term were gathered to generate a list of 100 URLs. Duplicate sites were excluded from this search, allowing analysis of unique sites only. Additional exclusion criteria included scientific journals, nonoperational links, nonfunctional websites (where the page was not loading, was not found, or was inactive), and websites in languages other than English. This resulted in a unique list of 48 websites. Four independent raters evaluated the websites using a 16-item DISCERN tool to assess the quality of novel coronavirus related information available on the internet. The interrater reliability agreement was calculated using the intracluster correlation coefficient. RESULTS: Results showed variation in how the raters assigned scores to different website categories. The .com websites received the lowest scores. Results showed that .edu and .org website category sites were excellent in communicating coronavirus related health information; however, they received lower scores for treatment effect and treatment choices. CONCLUSION: This study highlights the gaps in the quality of information that is available on the websites related to COVID-19 and study emphasizes the need for verified websites that provide evidence-based health information related to the novel coronavirus pandemic.

Trends and best practices in health care cybersecurity insurance policy.

Health care organizations are a major target for cyberattacks. This is primarily due to their peculiar vulnerabilities and attractiveness to nefarious cyber actors. Data breaches from these attacks present a significant threat to the viability of health care organizations, ranging from financial losses to compromised patient safety. Cybersecurity insurance has become an essential tool for mitigating financial liabilities that may arise from breaches for many organizations. This paper reviews the current state of cybersecurity insurance adoption in the health care sector. It highlights best practices in cybersecurity insurance policy for health care organizations and recommends future directions to strengthen cybersecurity and improve cybersecurity insurance.

Self-Check-In Kiosks Utilization and Their Association With Wait Times in Emergency Departments in the United States.

BACKGROUND: Delayed care in emergency departments (EDs) is a serious problem in the United States. Patient wait time is considered a critical measure of delayed care in EDs. Several strategies have been employed by EDs to reduce wait time, including implementation of self-check-in kiosks. However, the effect of kiosks on wait time in EDs is understudied. OBJECTIVES: To assess the association between patient wait time and utilization of self-check-in kiosks in EDs. To investigate a series of other patient-, ED-, and hospital-level predictors of wait time in EDs. METHODS: Using data from the 2015 and 2016 National Hospital Ambulatory Medical Care Survey, we analyzed 40,528 ED visits by constructing a multivariable linear regression model of the log-transformed wait time data as an outcome, then computing percent changes in wait times. RESULTS: During the study period, about 9% of EDs in the United States implemented kiosks. In our linear regression model, the wait time in EDs with kiosk self-check-in services was 56.8% shorter (95% confidence interval ̶ 130% to ̶ 6.4%, p < 0.05) compared with EDs without kiosk services. In addition to kiosks, patients' day of visit, arrival time, triage assessment, arrival by ambulance, chronic medical conditions, ED boarding, hospitals' full-capacity protocol, and hospitals' location were significant predictors of wait time. CONCLUSIONS: Self-check-in kiosks are associated with shorter ED wait time in the United States. However, prolonged ED wait time continues to be a system-wide problem, and warrants multilayered interventions to address this challenge for those who are in acute need of immediate care.

Effects of the 2013 American College of Cardiology/American Heart Association guidelines on racial and ethnic disparities in statin treatment among diabetics.

BACKGROUND: Significant racial and ethnic disparities in statin prescribing and utilization have been constantly documented. OBJECTIVE: To examine whether racial/ethnic disparities in statin treatment have decreased among the diabetic population after the release of the 2013 American College of Cardiology/American Heart Association (ACC/AHA) guidelines. METHODS: This retrospective study analyzed patients with diabetes aged 40-75 years old in the Medicare Expenditure Panel Survey (2011-2012 and 2014-2015). Racial and ethnic disparities in the likelihood of statin use and number of statin prescriptions were compared before and after the guideline release. Logistic and negative binomial regressions were used to adjust for patient characteristics. A difference-in-difference model (DID) was used to examine disparity changes. RESULTS: This study included 2584 patients from 2011 to 2012 and 2610 from 2014 to 2015. During 2011-2012, racial/ethnic disparities were significant for the likelihood of statin use. For the number of statin prescriptions, racial disparity was significant, but not for the ethnic disparity. During 2014-2015, racial/ethnic disparities were significant for the likelihood of statin use but were not significant for the number of statin prescriptions. The DID model found that the 2013 guidelines were not associated with a reduction in racial and ethnic disparities in statin treatment. CONCLUSIONS: This study found persistent disparities in the likelihood of statin use. The 2013 ACC/AHA guidelines were not associated with a reduction in racial and ethnic disparities in statin treatment.

Use of mobile health applications for health-promoting behavior among individuals with chronic medical conditions.

BACKGROUND: Chronic medical conditions (CCs) are leading causes of morbidity and mortality in the United States. Strategies to control CCs include targeting unhealthy behaviors, often through the use of patient empowerment tools, such as mobile health (mHealth) technology. However, no conclusive evidence exists that mHealth applications (apps) are effective among individuals with CCs for chronic disease self-management. METHODS: We used data from the Health Information National Trends Survey (HINTS 5, Cycle 1, 2017). A sample of 1864 non-institutionalized US adults (≥18 years) who had a smartphone and/or a tablet computer and at least one CC was analyzed. Using multivariable logistic regressions, we assessed predisposing, enabling, and need predictors of three health-promoting behaviors (HPBs): tracking progress on a health-related goal, making a health-related decision, and health-related discussions with a care provider among smart device and mHealth apps owners. RESULTS: Compared to those without mHealth apps, individuals with mHealth apps had significantly higher odds of using their smart devices to track progress on a health-related goal (adjusted odds ratio (aOR) 8.74, 95% confidence interval (CI): 5.66-13.50, P < .001), to make a health-related decision (aOR 1.77, 95% CI: 1.16-2.71, P < .01) and in health-related discussions with care providers (aOR 2.0, 95% CI: 1.26-3.19, P < .01). Other significant factors of at least one type of HPB among smart device and mHealth apps users were age, gender, education, occupational status, having a regular provider, and self-rated general health. CONCLUSION: mHealth apps are associated with increased rates of HPB among individuals with CCs. However, certain groups, like older adults, are most affected by a digital divide where they have lower access to mHealth apps and thus are not able to take advantage of these tools. Rigorous randomized clinical trials among various segments of the population and different health conditions are needed to establish the effectiveness of these mHealth apps. Healthcare providers should encourage validated mHealth apps for patients with CCs.

Association between Having a Regular Health Provider and Access to Services Linked to Electronic Health Records.

OBJECTIVE: A regular care provider is an important measure of access to health services, but little is known about the association between having a regular provider and patients' access to services associated with electronic health records (EHR). Furthermore, the composition of the additional electronic services made available to patients is not well studied. METHODS: We analyzed the most recent data from the Health Information National Trends Survey (HINTS5-Cycle1, 2017, n = 3,285). We estimated a weighted multivariable logistic regression model to assess the association between having a regular provider (65.3%) and access to EHR (29%). Control variables were selected based on Andersen's Behavioral Model. RESULTS: In the adjusted model, participants with a regular provider had significantly greater access to an EHR (aOR 2.91, p < .001) compared to participants without a regular provider. Participants were more likely to have access to an EHR if they were females (aOR 1.56, p < .01), had a tablet computer (aOR 1.55, p < .05), smartphone (aOR 2.27, p < .01), a former smoker (aOR 1.67, p < .05) or had two or more chronic medical conditions (aOR 1.79, p < .01). DISCUSSION: Individuals who have a regular provider are roughly three times as likely to have access to services linked to an EHR. Access to an EHR enhances both potential and realized access to many healthcare services. CONCLUSION: The availability of a regular care provider impacts the "digital divide." The expansion of electronic health services intensifies the importance of a regular care provider.

Mobile Health Application and e-Health Literacy: Opportunities and Concerns for Cancer Patients and Caregivers.

Health literacy is critical for cancer patients as they must understand complex procedures or treatment options. Caregivers' health literacy also plays a crucial role in caring for cancer patients. Low health literacy is associated with low adherence to medications, poor health status, and increased health care costs. There is a growing interest in the use of mobile health applications (apps) to improve health literacy. Mobile health apps can empower underserved cancer patients and their caregivers by providing features or functionalities to enhance interactive patient-provider communication and to understand medical information more readily. Despite the potentiality of improving health literacy through mobile health apps, there exist several related concerns: no equal access to mobile technology, no familiarity or knowledge of using mobile health apps, and privacy and security concerns. These elements should be taken into account for health policy making and mobile apps design and development. Importantly, mobile apps should be developed with the goal of achieving a high range of user access by considering all health literacy level and various cultural and linguistic needs.

The Role of Gender in Cost-Related Medication Nonadherence Among Patients with Diabetes.

OBJECTIVE: Under 50% of type 2 diabetic patients achieve the recommended glycemic control. One barrier to glycemic control is patients' cost-related nonadherence to medications. We hypothesize gender differences in medication nonadherence due to costs among diabetic patients. METHODS: US National Health Interview Survey (2011 to 2014) data yielded 5260 males and 6188 females with diabetes for over a year. We applied 2 analytic methods (A and B below) across multiple outcome measures (1 to 4) of medication nonadherence due to cost. The key independent variable was participant's gender. RESULTS: Across methods and measure, females consistently report significantly higher rates of medication nonadherence due to costs. Pearson's χ2 showed that female patients were more likely to (1) skip medication (13.5%-10.2%; P < .001), take less than prescribed medication (13.9%-10.5%; P < .001), delay filling prescriptions (16.8%-12.5%; P < .001), and ask doctors to prescribe lower-cost alternative medications (31.8%-28.0%; P < .001). Controlling for covariates, logistic regression models found females more likely to skip medication (OR, 1.30; 95% CI, 1.09-1.55), take less than prescribed medication (OR, 1.26; 95%, CI, 1.06-1.50), delay filling prescriptions, (OR, 1.29; 95% CI, 1.11-1.50), and request lower-cost medication (OR, 1.17; 95% CI, 1.04-1.32). Our results report other factors that influence medication adherence, including socioeconomic and health status variables. CONCLUSIONS: A significant gender-based disparity exists on cost-related nonadherence of medication among diabetic patients. Health care providers and policy-makers should pay close attention to find ways to address cost-related nonadherence of medication among patients with chronic illness, especially among female patients.

Access to Employee Wellness Programs and Use of Preventive Care Services Among U.S. Adults.

INTRODUCTION: There is little research at the national level on access to employee wellness programs and the use of preventive care services. This study examined the use of seven preventive care services among U.S working adults with access to employee wellness programs. METHODS: The study population comprised 17,699 working adults aged ≥18 years, obtained from the 2015 National Health Interview Survey. Multivariate logistic regression models examined the relationship between access to employee wellness programs and use of seven preventive care services: influenza vaccination, blood pressure check, diabetes check, cholesterol check, Pap smear test, mammogram, and colon cancer screening. Data analysis began in Fall 2016. RESULTS: Overall, 46.6% of working adults reported having access to employee wellness programs in 2015. Working adults with access to employee wellness programs had higher odds of receiving influenza vaccination (OR=1.57, 95% CI=1.43, 1.72, p<0.001), blood pressure check (OR=2.46, 95% CI=2.17, 2.78, p<0.001), diabetes check (OR=1.30, 95% CI=1.12, 1.50, p<0.001), cholesterol check (OR=1.48, 95% CI=1.33, 1.67, p<0.001), and mammogram (OR=1.57, 95% CI=1.24, 1.98, p<0.001). However, there was no significant difference between access to employee wellness programs and the use of Pap smear test and colon cancer screening services. CONCLUSIONS: Using a nationally representative sample of individuals, this study found a positive association between access to employee wellness programs and the use of preventive care services. The results support favorable policies to encourage implementing wellness programs in all worksites, especially those with <50 employees.