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To investigate factors associated with engagement of U.S. Federal Health Agencies via Twitter. Our specific goals are to study factors related to a) numbers of retweets, b) time between the agency tweet and first retweet and c) time between the agency tweet and last retweet. We collect 164,104 tweets from 25 Federal Health Agencies and their 130 accounts. We use negative binomial hurdle regression models and Cox proportional hazards models to explore the influence of 26 factors on agency engagement. Account features include network centrality, tweet count, numbers of friends, followers, and favorites. Tweet features include age, the use of hashtags, user-mentions, URLs, sentiment measured using Sentistrength, and tweet content represented by fifteen semantic groups. A third of the tweets (53,556) had zero retweets. Less than 1% (613) had more than 100 retweets (mean  = 284). The hurdle analysis shows that hashtags, URLs and user-mentions are positively associated with retweets; sentiment has no association with retweets; and tweet count has a negative association with retweets. Almost all semantic groups, except for geographic areas, occupations and organizations, are positively associated with retweeting. The survival analyses indicate that engagement is positively associated with tweet age and the follower count. Some of the factors associated with higher levels of Twitter engagement cannot be changed by the agencies, but others can be modified (e.g., use of hashtags, URLs). Our findings provide the background for future controlled experiments to increase public health engagement via Twitter.

Head Start (HS) is the largest federally funded preschool program for disadvantaged children. Research has shown relatively small impacts on cognitive and social skills; therefore, some have questioned its effectiveness. Using data from the Head Start Impact Study (3-year-old cohort; N = 2,449), latent class analysis was used to (a) identify subgroups of children defined by baseline characteristics of their home environment and caregiver and (b) test whether the effects of HS on cognitive, and behavioral and relationship skills over 2 years differed across subgroups. The results suggest that the effectiveness of HS varies quite substantially. For some children there appears to be a significant, and in some cases, long-term, positive impact. For others there is little to no effect.

Background Anecdotal evidence suggests that US practice patterns for ART differ by geographical region. The purpose of this study was to determine whether use of ICSI differs by region and to evaluate whether these rates are correlated with differences in live birth rates. Methods Public data for 2012 were obtained from the Centers for Disease Control and Prevention. Clinics with ≥100 fresh, non-donor cycles were grouped by 10 nationally recognized Department of Health & Human Services regions and 11 metropolitan Megaregions and were compared for use of ICSI, frequency of male factor infertility, and live birth rate in women <35 years. Results There were 274 clinics in the Health & Human Services regions and 247 in the Megaregions. ICSI utilization rates in Health & Human Services groups ranged between 52.5–78.2% ( P  < 0.0001). Live birth rates per cycle in women <35 years differed (34.1–47.6%; P  < 0.0001) but did not correlate with rates of ICSI (R 2  = 0.2096; P  = 0.18) per cycle. For Megaregions, rates of ICSI per cycle differed (63.4%–93.5%, P  < 0.0001) as did live birth rates per cycle for women <35 (36.0%–59.0%, P  = 0.001) but there was only minimal correlation between them (R 2  = 0.5347; P  = 0.01). Highest rates of ICSI occurred in Front Range (93.5%) and Gulf Coast (83.1%) Megaregions. Lowest rates occurred in the Northeast (63.4%) and Florida (64.8%) Megaregions. Male factor infertility rates did not differ across regions. Conclusions ICSI utilization and live birth rates per cycle for each clinic group were significantly different across geographical regions of the U.S. However, higher ICSI utilization rate was not associated with higher rates of male factor infertility nor were they strongly correlated with higher live birth rates per cycle. Studies are needed to understand factors that may influence ICSI overutilization in the U.S.

Risk adjustment has broad general application and is a key part of the Patient Protection and Affordable Care Act (ACA). Yet, little has been written on how data required to support risk adjustment should be collected. This paper offers analytical support for a distributed approach, in which insurers retain possession of claims but pass on summary statistics to the risk adjustment authority as needed. It shows that distributed approaches function as well as or better than centralized ones—where insurers submit raw claims data to the risk adjustment authority—in terms of the goals of risk adjustment. In particular, it shows how distributed data analysis can be used to calibrate risk adjustment models and calculate payments, both in theory and in practice—drawing on the experience of distributed models in other contexts. In addition, it explains how distributed methods support other goals of the ACA, and can support projects requiring data aggregation more generally. It concludes that states should seriously consider distributed methods to implement their risk adjustment programs.

This study provides new estimates of demand for employer-sponsored health insurance, using the 1997–2001 linked Household Component-Insurance Component of the Medical Expenditure Panel Survey (MEPS). Our focus is on households' decisions to take up coverage through a worker's employer. We found a significant inverse relationship between the out-of-pocket premium and the probability of taking up coverage, with the price effect considerably larger when we used instrumental variables methods to account for endogenous out-of-pocket premiums. Additionally, workers in families with more children eligible for Medicaid were less likely to take up coverage.

National levels of personal health-care access and quality can be approximated by measuring mortality rates from causes that should not be fatal in the presence of effective medical care (ie, amenable mortality). Previous analyses of mortality amenable to health care only focused on high-income countries and faced several methodological challenges. In the present analysis, we use the highly standardised cause of death and risk factor estimates generated through the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) to improve and expand the quantification of personal health-care access and quality for 195 countries and territories from 1990 to 2015. We mapped the most widely used list of causes amenable to personal health care developed by Nolte and McKee to 32 GBD causes. We accounted for variations in cause of death certification and misclassifications through the extensive data standardisation processes and redistribution algorithms developed for GBD. To isolate the effects of personal health-care access and quality, we risk-standardised cause-specific mortality rates for each geography-year by removing the joint effects of local environmental and behavioural risks, and adding back the global levels of risk exposure as estimated for GBD 2015. We employed principal component analysis to create a single, interpretable summary measure–the Healthcare Quality and Access (HAQ) Index–on a scale of 0 to 100. The HAQ Index showed strong convergence validity as compared with other health-system indicators, including health expenditure per capita (r=0·88), an index of 11 universal health coverage interventions (r=0·83), and human resources for health per 1000 (r=0·77). We used free disposal hull analysis with bootstrapping to produce a frontier based on the relationship between the HAQ Index and the Socio-demographic Index (SDI), a measure of overall development consisting of income per capita, average years of education, and total fertility rates. This frontier allowed us to better quantify the maximum levels of personal health-care access and quality achieved across the development spectrum, and pinpoint geographies where gaps between observed and potential levels have narrowed or widened over time. Between 1990 and 2015, nearly all countries and territories saw their HAQ Index values improve; nonetheless, the difference between the highest and lowest observed HAQ Index was larger in 2015 than in 1990, ranging from 28·6 to 94·6. Of 195 geographies, 167 had statistically significant increases in HAQ Index levels since 1990, with South Korea, Turkey, Peru, China, and the Maldives recording among the largest gains by 2015. Performance on the HAQ Index and individual causes showed distinct patterns by region and level of development, yet substantial heterogeneities emerged for several causes, including cancers in highest-SDI countries; chronic kidney disease, diabetes, diarrhoeal diseases, and lower respiratory infections among middle-SDI countries; and measles and tetanus among lowest-SDI countries. While the global HAQ Index average rose from 40·7 (95% uncertainty interval, 39·0–42·8) in 1990 to 53·7 (52·2–55·4) in 2015, far less progress occurred in narrowing the gap between observed HAQ Index values and maximum levels achieved; at the global level, the difference between the observed and frontier HAQ Index only decreased from 21·2 in 1990 to 20·1 in 2015. If every country and territory had achieved the highest observed HAQ Index by their corresponding level of SDI, the global average would have been 73·8 in 2015. Several countries, particularly in eastern and western sub-Saharan Africa, reached HAQ Index values similar to or beyond their development levels, whereas others, namely in southern sub-Saharan Africa, the Middle East, and south Asia, lagged behind what geographies of similar development attained between 1990 and 2015. This novel extension of the GBD Study shows the untapped potential for personal health-care access and quality improvement across the development spectrum. Amid substantive advances in personal health care at the national level, heterogeneous patterns for individual causes in given countries or territories suggest that few places have consistently achieved optimal health-care access and quality across health-system functions and therapeutic areas. This is especially evident in middle-SDI countries, many of which have recently undergone or are currently experiencing epidemiological transitions. The HAQ Index, if paired with other measures of health-system characteristics such as intervention coverage, could provide a robust avenue for tracking progress on universal health coverage and identifying local priorities for strengthening personal health-care quality and access throughout the world. Bill & Melinda Gates Foundation.

Racial and ethnic disparities are observed in the health status and health outcomes of Medicare beneficiaries. Reducing these disparities is a national priority, and having high-quality data on individuals' race and ethnicity is critical for researchers working to do so. However, using Medicare data to identify race and ethnicity is not straightforward. Currently, Medicare largely relies on Social Security Administration data for information about Medicare beneficiary race and ethnicity. Directly self-reported race and ethnicity information is collected for subsets of Medicare beneficiaries but is not explicitly collected for the purpose of populating race/ethnicity information in the Medicare administrative record. As a consequence of historical data collection practices, the quality of Medicare's administrative data on race and ethnicity varies substantially by racial/ethnic group; the data are generally much more accurate for whites and blacks than for other racial/ethnic groups. Identification of Hispanic and Asian/Pacific Islander beneficiaries has improved through use of an imputation algorithm recently applied to the Medicare administrative database. To improve the accuracy of race/ethnicity data for Medicare beneficiaries, researchers have developed techniques such as geocoding and surname analysis that indirectly assign Medicare beneficiary race and ethnicity. However, these techniques are relatively new and data may not be widely available. Understanding the strengths and limitations of different approaches to identifying race and ethnicity will help researchers choose the best method for their particular purpose, and help policymakers interpret studies using these measures.

The US Department of Health and Human Services (HHS) is committed to advancing the oral health and general well-being of all populations across the lifespan. The HHS Oral Health Strategic Framework 2014-2017 (hereinafter, the Framework) reflects the collective deliberations and next steps proposed by HHS and other federal partners to realize the department's oral health vision and eliminate oral health disparities. The Framework builds upon and outlines a strategic alignment of HHS operating and staff divisions' resources, programs, and leadership commitments to improve oral health with activities of other federal partners. Here, the goals of HHS Oral Health Strategic Framework are detailed.

In his State of the Union Address on February 5, 2019, President Donald J. Trump announced his administration’s goal to end the domestic HIV epidemic. Following the announcement of the Ending the HIV Epidemic: A Plan for America initiative, the president proposed $291 million in new funding for the fiscal year 2020 Department of Health and Human Services (HHS) budget to implement a new initiative to reduce the number of new HIV infections by 75% in the next five years (2025) and by 90% in the next 10 years (2030). This is in addition to the $20 billion the US government already spends each year, domestically, for HIV prevention and care. With this initiative, HHS recognizes that the time to end the HIV epidemic is now: we have the right data, the right biomedical and behavioral tools, and the right leadership. With the new resources, the goal is achievable. This article outlines how this initiative will be accomplished through the implementation of four fundamental strategies that will be tailored by local communities on the basis of their own needs and strengths.

Objectives. To evaluate trends in funding over the past 16 years for key federal public health preparedness and response programs at the US Department of Health and Human Services, to improve understanding of federal funding history in this area, and to provide context for future resource allocation decisions for public health preparedness. Methods. In this 2017 analysis, we examined the funding history of key federal programs critical to public health preparedness by reviewing program budget data collected for our annual examination of federal funding for biodefense and health security programs since fiscal year (FY) 2001. Results. State and local preparedness at the Centers for Disease Control and Prevention initially received $940 million in FY2002 and resulted in significant preparedness gains, but funding levels have since decreased by 31%. Similarly, the Hospital Preparedness Program within the Office of the Assistant Secretary for Preparedness and Response was funded at a high of $515 million in FY2003, but funding was reduced by 50%. Investments in medical countermeasure development and stockpiling remained relatively stable. Conclusions. The United States has made significant progress in preparing for disasters and advancing public health infrastructure. To enable continued advancement, federal funding commitments must be sustained.