Findings from the First Year of a Federally Funded, Direct-to-Consumer HIV Self-Test Distribution Program - United States, March 2023-March 2024.

In September 2022, CDC funded a nationwide program, Together TakeMeHome (TTMH), to expand distribution of HIV self-tests (HIVSTs) directly to consumers by mail through an online ordering portal. To publicize the availability of HIVSTs to priority audiences, particularly those disproportionately affected by HIV, CDC promoted this program through established partnerships and tailored resources from its Let's Stop HIV Together social marketing campaign. The online portal launched March 14, 2023, and through March 13, 2024, distributed 443,813 tests to 219,360 persons. Among 169,623 persons who answered at least one question on a postorder questionnaire, 67.9% of respondents were from priority audiences, 24.1% had never previously received testing for HIV, and 24.8% had not received testing in the past year. Among the subset of participants who initiated a follow-up survey, 88.3% used an HIVST themselves, 27.1% gave away an HIVST, 11.7% accessed additional preventive services, and 1.9% reported a new positive HIVST result. Mailed HIVST distribution can quickly reach large numbers of persons who have never received testing for HIV or have not received testing as often as is recommended. TTMH can help to achieve the goal of diagnosing HIV as early as possible and provides a path to other HIV prevention and care services. Clinicians, community organizations, and public health officials should be aware of HIVST programs, initiate discussions about HIV testing conducted outside their clinics or offices, and initiate follow-up services for persons who report a positive or negative HIVST result.

Dark citations to Federal resources and their contribution to the public health literature.

The term "dark citations," which has been previously used to refer to citations of information products outside of traditional peer-reviewed journal articles, is adapted here to refer to those that are not linked to a known indexed identifier and are effectively invisible to traditional bibliometric analysis. We investigate an unexplored source of citations in the biomedical and public health literature by surveying the extent of dark citations across the U.S. government. We systematically focus on public health, quantify their occurrences across the government, and provide a comprehensive dataset for all dark citations within PubMed.

HIV Criminalization Laws and Enforcement: Assessing the Relationship Between HIV Criminalization at the State Level, Policing at the County Level, and County-level HIV Incidence Rates.

The U.S. HIV epidemic disproportionately affects Black and Hispanic communities via ecosocial determinants of excess HIV risk, including HIV criminalization laws and overpolicing. This study used multilevel modeling to test the hypothesis that HIV criminalization laws are associated with higher county HIV incidence, and that this effect is modified by heavier county-level policing. County-level HIV incidence data from 2010 to 2019 were merged with county-level demographic, socioeconomic, and jailed population rate data for counties with stable HIV incidence rates (rates generated from a numerator of at least 12) for > 5 years. Multivariable multilevel (hierarchical) models for count-rate data were fitted, with years nested inside counties, and counties nested within states. An HIV criminalization law was associated with higher countywide HIV incidence rate for the general, Black, and Hispanic populations (aRR = 1.14, 1.30, and 1.32, respectively). This association was modified by an increased county jailed population rate for the general and Black populations.

A Comparison of the Mortality Experience of U.S. Adults Estimated With the 2006-2018 National Health Interview Survey Linked Mortality Files and the Annual U.S. Life Tables.

Objective-Linking data is a powerful mechanism to provide policy-relevant information. The National Center for Health Statistics' Data Linkage Program produces linked mortality files (LMFs) for research by linking data from the National Center for Health Statistics' surveys, including the National Health Interview Survey (NHIS), to mortality data from the National Death Index. Assessing the accuracy of the linked data is an important step in ensuring its analytic use. This report compares the cumulative survival probabilities estimated with the 2006-2018 NHIS LMFs to those from the annual U.S. life tables.

Google Street View Images as Predictors of Patient Health Outcomes, 2017-2019.

Collecting neighborhood data can both be time- and resource-intensive, especially across broad geographies. In this study, we leveraged 1.4 million publicly available Google Street View (GSV) images from Utah to construct indicators of the neighborhood built environment and evaluate their associations with 2017-2019 health outcomes of approximately one-third of the population living in Utah. The use of electronic medical records allows for the assessment of associations between neighborhood characteristics and individual-level health outcomes while controlling for predisposing factors, which distinguishes this study from previous GSV studies that were ecological in nature. Among 938,085 adult patients, we found that individuals living in communities in the highest tertiles of green streets and non-single-family homes have 10-27% lower diabetes, uncontrolled diabetes, hypertension, and obesity, but higher substance use disorders-controlling for age, White race, Hispanic ethnicity, religion, marital status, health insurance, and area deprivation index. Conversely, the presence of visible utility wires overhead was associated with 5-10% more diabetes, uncontrolled diabetes, hypertension, obesity, and substance use disorders. Our study found that non-single-family and green streets were related to a lower prevalence of chronic conditions, while visible utility wires and single-lane roads were connected with a higher burden of chronic conditions. These contextual characteristics can better help healthcare organizations understand the drivers of their patients' health by further considering patients' residential environments, which present both risks and resources.

Pain and Poverty: Disparities by Poverty Level in the Experience of Pain-Related Interference.

OBJECTIVE: To assess the relationship between poverty and pain-related interference. SUBJECTS: Data on a sample of 108,259 adults aged 18 and older from the Household Component of the Medical Expenditure Panel Survey (MEPS) from 2013 to 2017 were analyzed. METHODS: I assess the odds of reporting any pain-related interference, as well as increasing levels of pain-related interference, using binary and ordinal logistic regression, respectively. RESULTS: After controlling for covariates, the analysis showed a significant association between poverty and pain-related interference, with more severe levels of poverty associated with increased odds of reporting any pain-related interference as well as increased levels of pain-related interference. However, Hispanics were less likely to report any pain-related interference overall, and more severe levels of poverty were associated with decreased odds of reporting pain among Hispanics. CONCLUSIONS: Policy makers should regard poverty as a social determinant of health, taking poverty and socioeconomic status into consideration when designing health policies.

Leveraging 31 Million Google Street View Images to Characterize Built Environments and Examine County Health Outcomes.

OBJECTIVES: Built environments can affect health, but data in many geographic areas are limited. We used a big data source to create national indicators of neighborhood quality and assess their associations with health. METHODS: We leveraged computer vision and Google Street View images accessed from December 15, 2017, through July 17, 2018, to detect features of the built environment (presence of a crosswalk, non-single-family home, single-lane roads, and visible utility wires) for 2916 US counties. We used multivariate linear regression models to determine associations between features of the built environment and county-level health outcomes (prevalence of adult obesity, prevalence of diabetes, physical inactivity, frequent physical and mental distress, poor or fair self-rated health, and premature death [in years of potential life lost]). RESULTS: Compared with counties with the least number of crosswalks, counties with the most crosswalks were associated with decreases of 1.3%, 2.7%, and 1.3% of adult obesity, physical inactivity, and fair or poor self-rated health, respectively, and 477 fewer years of potential life lost before age 75 (per 100 000 population). The presence of non-single-family homes was associated with lower levels of all health outcomes except for premature death. The presence of single-lane roads was associated with an increase in physical inactivity, frequent physical distress, and fair or poor self-rated health. Visible utility wires were associated with increases in adult obesity, diabetes, physical and mental distress, and fair or poor self-rated health. CONCLUSIONS: The use of computer vision and big data image sources makes possible national studies of the built environment's effects on health, producing data and results that may inform national and local decision-making.

Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19.

Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter's Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019-June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases.

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents' risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.

Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States.

Previous studies have demonstrated that there is a high possibility that the presence of certain built environment characteristics can influence health outcomes, especially those related to obesity and physical activity. We examined the associations between select neighborhood built environment indicators (crosswalks, non-single family home buildings, single-lane roads, and visible wires), and health outcomes, including obesity, diabetes, cardiovascular disease, and premature mortality, at the state level. We utilized 31,247,167 images collected from Google Street View to create indicators for neighborhood built environment characteristics using deep learning techniques. Adjusted linear regression models were used to estimate the associations between aggregated built environment indicators and state-level health outcomes. Our results indicated that the presence of a crosswalk was associated with reductions in obesity and premature mortality. Visible wires were associated with increased obesity, decreased physical activity, and increases in premature mortality, diabetes mortality, and cardiovascular mortality (however, these results were not significant). Non-single family homes were associated with decreased diabetes and premature mortality, as well as increased physical activity and park and recreational access. Single-lane roads were associated with increased obesity and decreased park access. The findings of our study demonstrated that built environment features may be associated with a variety of adverse health outcomes.

Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment.

BACKGROUND: The built environment is a structural determinant of health and has been shown to influence health expenditures, behaviors, and outcomes. Traditional methods of assessing built environment characteristics are time-consuming and difficult to combine or compare. Google Street View (GSV) images represent a large, publicly available data source that can be used to create indicators of characteristics of the physical environment with machine learning techniques. The aim of this study is to use GSV images to measure the association of built environment features with health-related behaviors and outcomes at the census tract level. METHODS: We used computer vision techniques to derive built environment indicators from approximately 31 million GSV images at 7.8 million intersections. Associations between derived indicators and health-related behaviors and outcomes on the census-tract level were assessed using multivariate regression models, controlling for demographic factors and socioeconomic position. Statistical significance was assessed at the α = 0.05 level. RESULTS: Single lane roads were associated with increased diabetes and obesity, while non-single-family home buildings were associated with decreased obesity, diabetes and inactivity. Street greenness was associated with decreased prevalence of physical and mental distress, as well as decreased binge drinking, but with increased obesity. Socioeconomic disadvantage was negatively associated with binge drinking prevalence and positively associated with all other health-related behaviors and outcomes. CONCLUSIONS: Structural determinants of health such as the built environment can influence population health. Our study suggests that higher levels of urban development have mixed effects on health and adds further evidence that socioeconomic distress has adverse impacts on multiple physical and mental health outcomes.

Evaluating Survey Report of Social Security Disability Benefit Receipt Using Linked National Health Interview Survey and Social Security Administration Data.

Linking nationally representative population health survey data with Social Security Administration (SSA) disability program data provides a rich source of information on program recipients. Survey participant data from the 1998-2005 National Health Interview Survey (NHIS) were linked to SSA administrative records from 1997 through 2005. The goal of this study was to assess agreement between the actual benefit receipt based on the SSA administrative records and the survey report of benefit receipt in the linked NHIS and SSA file for the U.S. civilian noninstitutionalized population. This evaluation provides information on the expected accuracy of survey report of Social Security Disability Insurance (SSDI) and Supplemental Security Income (SSI) benefit receipt, including how participant characteristics may be associated with reporting misclassification. The results indicate that there is some underreporting of SSA disability benefit receipt based on the NHIS responses compared with the SSA administrative records. The analysis identified some differences between the concordant and discordant groups for selected characteristics, but there were no clear patterns among the different survey questions or the different survey participant characteristics.

Mapping the global health employment market: an analysis of global health jobs.

BACKGROUND: The number of university global health training programs has grown in recent years. However, there is little research on the needs of the global health profession. We therefore set out to characterize the global health employment market by analyzing global health job vacancies. METHODS: We collected data from advertised, paid positions posted to web-based job boards, email listservs, and global health organization websites from November 2015 to May 2016. Data on requirements for education, language proficiency, technical expertise, physical location, and experience level were analyzed for all vacancies. Descriptive statistics were calculated for the aforementioned job characteristics. Associations between technical specialty area and requirements for non-English language proficiency and overseas experience were calculated using Chi-square statistics. A qualitative thematic analysis was performed on a subset of vacancies. RESULTS: We analyzed the data from 1007 global health job vacancies from 127 employers. Among private and non-profit sector vacancies, 40% (n = 354) were for technical or subject matter experts, 20% (n = 177) for program directors, and 16% (n = 139) for managers, compared to 9.8% (n = 87) for entry-level and 13.6% (n = 120) for mid-level positions. The most common technical focus area was program or project management, followed by HIV/AIDS and quantitative analysis. Thematic analysis demonstrated a common emphasis on program operations, relations, design and planning, communication, and management. CONCLUSIONS: Our analysis shows a demand for candidates with several years of experience with global health programs, particularly program managers/directors and technical experts, with very few entry-level positions accessible to recent graduates of global health training programs. It is unlikely that global health training programs equip graduates to be competitive for the majority of positions that are currently available in this field.

At the Nexus: How HIV-Related Immigration Policies Affect Foreign Nationals and Citizens in South Korea.

Effective HIV prevention requires the protection and empowerment of marginalized groups at high risk of infection. However, many policies persist that stigmatize these groups and hinder HIV prevention efforts, including HIV-related travel restrictions. In the Republic of Korea, which requires HIV tests for certain visa categories, these restrictions negatively affect the national HIV response and access to accurate information on effective HIV prevention. In addition, they violate migrants' human rights to confidentiality and informed consent to testing and the rights of persons living with HIV (PLHIV) to privacy, work, medical care, bodily integrity, and freedom from discrimination. Furthermore, the discrimination and misconceptions perpetuated by this policy may be driving South Korea's burgeoning infection rates.

Gender differences in acute pesticide-related illnesses and injuries among farmworkers in the United States, 1998-2007.

BACKGROUND: Farmworkers have a high risk for acute pesticide-related illness and injury, and the rate among female farmworkers is approximately twice as high as that among males. Surveillance data were used to identify reasons for this gender difference. METHODS: We identified acute pesticide-related illness and injury cases among farmworkers from the Sentinel Event Notification System for Occupational Risks (SENSOR)-Pesticides Program and the California Department of Pesticide Regulation. Gender-specific associations with acute pesticide-related illness and injury were assessed using chi-square tests. National Agricultural Workers Survey data were also examined. RESULTS: The over-representation of females among farmworker illness and injury cases was confined to females who did not handle pesticides (non-handlers). Female non-handler farmworkers who were affected were more likely to be working on fruit and nut crops, to be exposed to off-target pesticide drift, and to be exposed to fungicides and fumigants compared to males. CONCLUSIONS: Although there is an increased risk for acute pesticide-related illness and injury among female farmworkers, the absolute number of farmworkers with acute pesticide-related illness and injury is far higher among males than females. Furthermore, farmworkers have little or no control over many of the identified contributing factors that led to illness and injury. Stringent enforcement of existing regulations and enhanced regulatory efforts to protect against off-target drift exposures may have the highest impact in reducing acute pesticide-related illness and injury among farmworkers.