Engaging community health workers to increase cervical and colorectal cancer screenings is cost effective on the basis of estimated incremental cost-effectiveness ratios that were less than the conservative $50,000 per quality-adjusted life year threshold. In addition, quality-adjusted life years saved from colorectal screening with colonoscopy were associated with net healthcare cost savings.
 Engaging community health workers to increase cervical and colorectal cancer screenings is cost effective on the basis of estimated incremental cost-effectiveness ratios that were less than the conservative $50,000 per quality-adjusted life year threshold. In addition, quality-adjusted life years saved from colorectal screening with colonoscopy were associated with net healthcare cost savings.
  Previous studies have demonstrated cross-sectional associations between social media use and depression, but their temporal and directional associations have not been reported.
 
  In 2018, participants aged 18-30 years were recruited in proportion to U.S. Census characteristics, including age, sex, race, education, household income, and geographic region. Participants self-reported social media use on the basis of a list of the top 10 social media networks, which represent >95% of social media use. Depression was assessed using the 9-Item Patient Health Questionnaire. A total of 9 relevant sociodemographic covariates were assessed. All measures were assessed at both baseline and 6-month follow-up.
 
  Among 990 participants who were not depressed at baseline, 95 (9.6%) developed depression by follow-up. In multivariable analyses conducted in 2020 that controlled for all covariates and included survey weights, there was a significant linear association (p<0.001) between baseline social media use and the d in social media use at follow-up.  https://www.selleckchem.com/CDK.html  This pattern suggests temporal associations between social media use and depression, an important criterion for causality.
  Community health centers often screen for and address patients' unmet social needs. This study examines the degree to which community health center patients report receiving social needs assistance and compares measures of access and quality between patients who received assistance versus similar patients who did not.
 
  A nationally representative sample of 4,699 nonelderly adults receiving care at community health centers from the 2014-2015 Health Resources and Services Administration Health Center Patient Survey was used, representing 12.6 million patients. The exposure-having "received social needs assistance"-was based on whether a patient received any community health center assistance accessing social programs (e.g., applying for government benefits) or basic needs (e.g., obtaining transportation, housing, food). Using logistic regression models with inverse probability of treatment weights, outcomes for patients who received social needs assistance with similar patients who did not were compared. Stuider providing social needs assistance to patients, these results suggest that doing so may be associated with improved access to and quality of care.
 As community health centers and other providers consider providing social needs assistance to patients, these results suggest that doing so may be associated with improved access to and quality of care.
  Previously estimated effects of social distancing do not account for changes in individual behavior before the implementation of stay-at-home policies or model this behavior in relation to the burden of disease. This study aims to assess the asynchrony between individual behavior and government stay-at-home orders, quantify the true impact of social distancing using mobility data, and explore the sociodemographic variables linked to variation in social distancing practices.
 
  This study was a retrospective investigation that leveraged mobility data to quantify the time to behavioral change in relation to the initial presence of COVID-19 and the implementation of government stay-at-home orders. The impact of social distancing that accounts for both individual behavior and testing data was calculated using generalized mixed models. The role of sociodemographics in accounting for variation in social distancing behavior was modeled using a 10-fold cross-validated elastic net (linear machine learning model). Anag, with delays corresponding to an increase in a county's proportion of people without a high school diploma and proportion of racial and ethnic minorities.
 
  This retrospective analysis of mobility patterns found that social distancing behavior occurred well before the onset of government stay-at-home dates. This asynchrony leads to the underestimation of the impact of social distancing. Sociodemographic characteristics associated with delays in social distancing can help explain the disproportionate case burden and mortality among vulnerable communities.
 This retrospective analysis of mobility patterns found that social distancing behavior occurred well before the onset of government stay-at-home dates. This asynchrony leads to the underestimation of the impact of social distancing. Sociodemographic characteristics associated with delays in social distancing can help explain the disproportionate case burden and mortality among vulnerable communities.
  In March 2016, the Centers for Disease Control and Prevention issued opioid prescribing guidelines for chronic noncancer pain. In response, in April 2016, the North Carolina Medical Board launched the Safe Opioid Prescribing Initiative, an investigative program intended to limit the overprescribing of opioids. This study focuses on the association of the Safe Opioid Prescribing Initiative with immediate and sustained changes in opioid prescribing among all patients who received opioid and opioid discontinuation and tapering among patients who received high-dose (>90 milligrams of morphine equivalents), long-term (>90 days) opioid therapy.
 
  Controlled and single interrupted time series analysis of opioid prescribing outcomes before and after the implementation of Safe Opioid Prescribing Initiative was conducted using deidentified data from the North Carolina Controlled Substances Reporting System from January 2010 through March 2017. Analysis was conducted in 2019-2020.
 
  In an average study month, 513,717 patients, including patients who received 47,842 high-dose, long-term opioid therapy, received 660,912 opioid prescriptions at 1.