Travel Time and Costs for Abortion for Military Service Members After the Dobbs Decision.

This study quantifies the change in travel times for military service personnel to abortion facilities following the US Supreme Court Dobbs decision and estimates the cost of an abortion-related travel reimbursement policy.

Identifying COVID-19 Vaccine Deserts and Ways to Reduce Them: A Digital Tool to Support Public Health Decision-Making.

A private-academic partnership built the Vaccine Equity Planner (VEP) to help decision-makers improve geographic access to COVID-19 vaccinations across the United States by identifying vaccine deserts and facilities that could fill those deserts. The VEP presented complex, updated data in an intuitive form during a rapidly changing pandemic situation. The persistence of vaccine deserts in every state as COVID-19 booster recommendations develop suggests that vaccine delivery can be improved. Underresourced public health systems benefit from tools providing real-time, accurate, actionable data. (Am J Public Health. 2023;113(4):363-367. https://doi.org/10.2105/AJPH.2022.307198).

Emerging Socioeconomic Disparities in COVID-19 Vaccine Second-Dose Completion Rates in the United States.

Although COVID-19 vaccination plans acknowledge a need for equity, disparities in two-dose vaccine initiation have been observed in the United States. We aim to assess if disparity patterns are emerging in COVID-19 vaccination completion. We gathered (n = 843,985) responses between February and November 2021 from a web survey. Individuals self-reported demographics and COVID-19 vaccination status. Dose initiation and completion rates were calculated incorporating survey weights. A multi-variate logistic regression assessed the association between income and completing vaccination, accounting for other demographics. Overall, 57.4% initiated COVID-19 vaccination, with 84.5% completing vaccination. Initiation varied by income, and we observed disparities in completion by occupation, race, age, and insurance. Accounting for demographics, higher incomes are more likely to complete vaccination than lower incomes. We observe disparities in completion across annual income. Differences in COVID-19 vaccination completion may lead to two tiers of protection in the population, with certain sub-groups being better protected from future infection.

A 10-Year Social Media Analysis Exploring Hospital Online Support of Black Lives Matter and the Black Community.

Importance: Tensions around COVID-19 and systemic racism have raised the question: are hospitals advocating for equity for their Black patients? It is imperative for hospitals to be supportive of the Black community and acknowledge themselves as safe spaces, run by clinicians and staff who care about social justice issues that impact the health of the Black community; without the expression of support, Black patients may perceive hospitals as uncaring and unsafe, potentially delaying or avoiding treatment, which can result in serious complications and death for those with COVID-19. Objective: To explore how hospitals showed public-facing support for the Black community as measured through tweets about social equity or the Black Lives Matter (BLM) movement. Design, Setting, and Participants: Using a retrospective longitudinal cohort study design, tweets from the top 100 ranked hospitals were collected, starting with the most recent over a 10-year span, from May 3, 2009, to June 26, 2020. The date of the George Floyd killing, May 25, 2020, was investigated as a point of interest. Data were analyzed from June 11 to December 4, 2020. Main Outcomes and Measures: Tweets were manually identified based on 4 categories: BLM, associated with the BLM movement; Black support, expressed support for Black population within the hospital's community; Black health, pertained to health concerns specific to and the creation of health care for the Black community; or social justice, associated with general social justice terms that were too general to label as Black. If a tweet did not contain any hashtags from these categories, it remained unlabeled. Results: A total of 281 850 tweets from 90 unique social media accounts were collected. Each handle returned at least 1279 tweets, with 85 handles (94.4%) returning at least 3000 tweets. Tweet publication dates ranged from 2009 to 2020. A total of 274 tweets (0.097%) from 67 handles (74.4%) used a hashtag to support the BLM movement. Among the tweets labeled BLM, the first tweet was published in 2018 and only 4 tweets (1.5%) predated the killing of George Floyd. A similar trend of low signal observed was detected for the other categories (Black support: 244 tweets [0.086%] from 42 handles [46.7%] starting in 2013; Black health: 28 tweets [0.0099%] from 15 handles [16.7%] starting in 2018; social justice: 40 tweets [0.014%] from 21 handles [23.3%] starting in 2015). Conclusions and Relevance: These findings reflect the low signal of tweets regarding the Black community and social justice in a generalized way across approximately 10 years of tweets for all the hospital handles within the data set. From 2009 to 2020, hospitals rarely engaged in issues pertaining to the Black community and if so, only within the last half of this time period. These later entrances into these discussions indicate that these discussions are relatively recent.

Associations between changes in population mobility in response to the COVID-19 pandemic and socioeconomic factors at the city level in China and country level worldwide: a retrospective, observational study.

BACKGROUND: Until broad vaccination coverage is reached and effective therapeutics are available, controlling population mobility (ie, changes in the spatial location of a population that affect the spread and distribution of pathogens) is one of the major interventions used to reduce transmission of SARS-CoV-2. However, population mobility differs across locations, which could reduce the effectiveness of pandemic control measures. Here we assess the extent to which socioeconomic factors are associated with reductions in population mobility during the COVID-19 pandemic, at both the city level in China and at the country level worldwide. METHODS: In this retrospective, observational study, we obtained anonymised daily mobile phone location data for 358 Chinese cities from Baidu, and for 121 countries from Google COVID-19 Community Mobility Reports. We assessed the intra-city movement intensity, inflow intensity, and outflow intensity of each Chinese city between Jan 25 (when the national emergency response was implemented) and Feb 18, 2020 (when population mobility was lowest) and compared these data to the corresponding lunar calendar period from the previous year (Feb 5 to March 1, 2019). Chinese cities were classified into four socioeconomic index (SEI) groups (high SEI, high-middle SEI, middle SEI, and low SEI) and the association between socioeconomic factors and changes in population mobility were assessed using univariate and multivariable linear regression. At the country level, we compared six types of mobility (residential, transit stations, workplaces, retail and recreation, parks, and groceries and pharmacies) 35 days after the implementation of the national emergency response in each country and compared these to data from the same day of the week in the baseline period (Jan 3 to Feb 6, 2020). We assessed associations between changes in the six types of mobility and the country's sociodemographic index using univariate and multivariable linear regression. FINDINGS: The reduction in intra-city movement intensity in China was stronger in cities with a higher SEI than in those with a lower SEI (r=-0·47, p<0·0001). However, reductions in inter-city movement flow (both inflow and outflow intensity) were not associated with SEI and were only associated with government control measures. In the country-level analysis, countries with higher sociodemographic and Universal Health Coverage indexes had greater reductions in population mobility (ie, in transit stations, workplaces, and retail and recreation) following national emergency declarations than those with lower sociodemographic and Universal Health Coverage indexes. A higher sociodemographic index showed a greater reduction in mobility in transit stations (r=-0·27, p=0·0028), workplaces (r=-0·34, p=0·0002), and areas retail and recreation (rxs=-0·30, p=0·0012) than those with a lower sociodemographic index. INTERPRETATION: Although COVID-19 outbreaks are more frequently reported in larger cities, our analysis shows that future policies should prioritise the reduction of risks in areas with a low socioeconomic level-eg, by providing financial assistance and improving public health messaging. However, our study design only allows us to assess associations, and a long-term study is needed to decipher causality. FUNDING: Chinese Ministry of Science and Technology, Research Council of Norway, Beijing Municipal Science & Technology Commission, Beijing Natural Science Foundation, Beijing Advanced Innovation Program for Land Surface Science, National Natural Science Foundation of China, China Association for Science and Technology.

Early detection of COVID-19 in China and the USA: summary of the implementation of a digital decision-support and disease surveillance tool.

OBJECTIVES: Rapid detection and surveillance of COVID-19 is essential to reducing spread of the virus. Inadequate screening capacity has hampered COVID-19 detection, while traditional infectious disease response has been delayed due to significant demands for healthcare resources, time and personnel. This study investigated whether an online health decision-support tool could supplement COVID-19 surveillance and detection in China and the USA. SETTING: Daily website traffic to Thermia was collected from China and the USA, and cross-correlation analyses were used to assess the designated lag time between the daily time series of Thermia sessions and COVID-19 case counts from 22 January to 23 April 2020. PARTICIPANTS: Thermia is a validated health decision-support tool that was modified to include content aimed at educating users about Centers for Disease Control and Prevention recommendations on COVID-19 symptoms. An advertising campaign was released on Microsoft Advertising to refer searches for COVID-19 symptoms to Thermia. RESULTS: The lead times observed for Thermia sessions to COVID-19 case reports was 3 days in China and 19 days in the USA. We found negative cross-correlation between the number of Thermia sessions and rates of influenza A and B, possibly due to the decreasing prevalence of influenza and the lack of specificity of the system for identification of COVID-19. CONCLUSION: This study suggests that early deployment of an online campaign and modified health decision-support tool may support identification of emerging infectious diseases like COVID-19. Researchers and public health officials should deploy web campaigns as early as possible in an epidemic to detect, identify and engage those potentially at risk to help prevent transmission of the disease.

Investigation of Geographic and Macrolevel Variations in LGBTQ Patient Experiences: Longitudinal Social Media Analysis.

BACKGROUND: Discrimination in the health care system contributes to worse health outcomes among lesbian, gay, bisexual, transgender, and queer (LGBTQ) patients. OBJECTIVE: The aim of this study is to examine disparities in patient experience among LGBTQ persons using social media data. METHODS: We collected patient experience data from Twitter from February 2013 to February 2017 in the United States. We compared the sentiment of patient experience tweets between Twitter users who self-identified as LGBTQ and non-LGBTQ. The effect of state-level partisan identity on patient experience sentiment and differences between LGBTQ users and non-LGBTQ users were analyzed. RESULTS: We observed lower (more negative) patient experience sentiment among 13,689 LGBTQ users compared to 1,362,395 non-LGBTQ users. Increasing state-level liberal political identification was associated with higher patient experience sentiment among all users but had stronger effects for LGBTQ users. CONCLUSIONS: Our findings highlight that social media data can yield insights about patient experience for LGBTQ persons and suggest that a state-level sociopolitical environment influences patient experience for this group. Efforts are needed to reduce disparities in patient care for LGBTQ persons while taking into context the effect of the political climate on these inequities.

The relationship between Jim Crow laws and social capital from 1997-2014: A 3-level multilevel hierarchical analysis across time, county and state.

INTRODUCTION: Jim Crow laws in the United States promoted racial prejudice, which may have reduced social capital. Our study tests the relationship between Jim Crow laws and social capital. METHODS: We conducted 3-level multilevel hierarchical modeling to study differences in the stock of social capital for 1997, 2005, 2009 in Jim Crow states compared to states without Jim Crow laws. We examined the moderation effects of county level median income, percent Black and percent with high school education and Jim Crow laws on social capital. RESULTS: Jim Crow laws significantly reduced stock of social capital across 1997, 2005, 2009. The model was robust to the inclusion of random county, states, time and fixed county and state level covariates for median income, percent Black and percent with high school education. The largest percent of between state variations explained for fixed variables was from the addition of Jim Crow laws with 2.86%. These results demonstrate that although Jim Crow laws were abolished in 1965, the effects of racial segregation appear to persist through lower social connectiveness, community and trust. A positive moderation effect was seen for median income and percent Black with Jim Crow laws on social capital. DISCUSSION: Our study supports a negative association between Jim Crow laws and reduction in the stock of social capital. This may be attributed to the fracturing of trust, reciprocity and collective action produced by legal racial segregation. Findings from this study offer insight on the potential impacts of historical policies on the social structure of a community. Future research is necessary to further identify the mechanistic pathways and develop interventions to improve social capital.

Racial and Ethnic Disparities in Patient Experiences in the United States: 4-Year Content Analysis of Twitter.

BACKGROUND: Racial and ethnic minority groups often face worse patient experiences compared with the general population, which is directly related to poorer health outcomes within these minority populations. Evaluation of patient experience among racial and ethnic minority groups has been difficult due to lack of representation in traditional health care surveys. OBJECTIVE: This study aims to assess the feasibility of Twitter for identifying racial and ethnic disparities in patient experience across the United States from 2013 to 2016. METHODS: In total, 851,973 patient experience tweets with geographic location information from the United States were collected from 2013 to 2016. Patient experience tweets included discussions related to care received in a hospital, urgent care, or any other health institution. Ordinary least squares multiple regression was used to model patient experience sentiment and racial and ethnic groups over the 2013 to 2016 period and in relation to the implementation of the Patient Protection and Affordable Care Act (ACA) in 2014. RESULTS: Racial and ethnic distribution of users on Twitter was highly correlated with population estimates from the United States Census Bureau's 5-year survey from 2016 (r2=0.99; P<.001). From 2013 to 2016, the average patient experience sentiment was highest for White patients, followed by Asian/Pacific Islander, Hispanic/Latino, and American Indian/Alaska Native patients. A reduction in negative patient experience sentiment on Twitter for all racial and ethnic groups was seen from 2013 to 2016. Twitter users who identified as Hispanic/Latino showed the greatest improvement in patient experience, with a 1.5 times greater increase (P<.001) than Twitter users who identified as White. Twitter users who identified as Black had the highest increase in patient experience postimplementation of the ACA (2014-2016) compared with preimplementation of the ACA (2013), and this change was 2.2 times (P<.001) greater than Twitter users who identified as White. CONCLUSIONS: The ACA mandated the implementation of the measurement of patient experience of care delivery. Considering that quality assessment of care is required, Twitter may offer the ability to monitor patient experiences across diverse racial and ethnic groups and inform the evaluation of health policies like the ACA.

Evaluation of Volume of News Reporting and Opioid-Related Deaths in the United States: Comparative Analysis Study of Geographic and Socioeconomic Differences.

BACKGROUND: News media coverage is a powerful influence on public attitude and government action. The digitization of news media covering the current opioid epidemic has changed the landscape of coverage and may have implications for how to effectively respond to the opioid crisis. OBJECTIVE: This study aims to characterize the relationship between volume of online opioid news reporting and opioid-related deaths in the United States and how these measures differ across geographic and socioeconomic county-level factors. METHODS: Online news reports from February 2018 to April 2019 on opioid-related events in the United States were extracted from Google News. News data were aggregated at the county level and compared against opioid-related death counts. Ordinary least squares regression was used to model opioid-related death rate and opioid news coverage with the inclusion of socioeconomic and geographic explanatory variables. RESULTS: A total of 35,758 relevant news reports were collected representing 1789 counties. Regression analysis revealed that opioid-related death rate was positively associated with news reporting. However, opioid-related death rate and news reporting volume showed opposite correlations with educational attainment and rurality. When controlling for variation in death rate, counties in the Northeast were overrepresented by news coverage. CONCLUSIONS: Our results suggest that regional variation in the volume of opioid-related news reporting does not reflect regional variation in opioid-related death rate. Differences in the amount of media attention may influence perceptions of the severity of opioid epidemic. Future studies should investigate the influence of media reporting on public support and action on opioid issues.

Mapping global variation in human mobility.

The geographic variation of human movement is largely unknown, mainly due to a lack of accurate and scalable data. Here we describe global human mobility patterns, aggregated from over 300 million smartphone users. The data cover nearly all countries and 65% of Earth's populated surface, including cross-border movements and international migration. This scale and coverage enable us to develop a globally comprehensive human movement typology. We quantify how human movement patterns vary across sociodemographic and environmental contexts and present international movement patterns across national borders. Fitting statistical models, we validate our data and find that human movement laws apply at 10 times shorter distances and movement declines 40% more rapidly in low-income settings. These results and data are made available to further understanding of the role of human movement in response to rapid demographic, economic and environmental changes.

Leveraging black-market street buprenorphine pricing to increase capacity to treat opioid addiction, 2010-2018.

Increasing capacity to provide buprenorphine, a treatment for opioid addiction, can help mitigate the opioid epidemic in the United States. This study models black-market pricing of buprenorphine to better understand supply and demand for opioid addiction treatment. A mixed effects linear model was used to quantify the effect of county-level racial composition, health insurance coverage, and drug characteristics on price variation. From November 2010 to June 2018, there were 2481 submissions for street buprenorphine transactions in the StreetRx dataset. The mean price was $3.95/mg (SD = $23.12/mg). Price decreased 3.05% each year and was highest in the summer and spring. Brand name buprenorphine was on average 11.18% more expensive than generic buprenorphine. Buprenorphine/naloxone combinations were on average 19.75% less expensive than pure buprenorphine. Purchases in bulk were on average 10.51% cheaper than purchases not in bulk. Street buprenorphine in film form was on average 14.34% more expensive than in pill/tablet form. Buprenorphine street price was 17.12% higher in spring and 22.26% higher in summer compared to fall. For every percentage point increase in percent white, buprenorphine sold for 0.88% higher price. For every percentage point increase in health insurance coverage, street buprenorphine sold for 0.02% lower price. Findings demonstrate that geographic, demographic, and socioeconomic factors shape the diversion of opioid addiction treatment to the black-market. Buprenorphine street pricing can help estimate public need, gaps in care and emerging public health priorities.

Use of social media to assess the impact of equitable state policies on LGBTQ patient experiences: An exploratory study.

Limited research has evaluated these equitable policies because of the difficulty of capturing LGBTQ patient experience. Previous studies have shown that LGBTQ persons report increased rates of discrimination across a wide variety of healthcare settings which may prevent them from disclosing their LGBTQ status. The goal of this research was to use a social media big dataset to evaluate the impact of equitable policies on patient experiences for LGBTQ persons.

The distribution of antibiotic use and its association with antibiotic resistance.

Antibiotic use is a primary driver of antibiotic resistance. However, antibiotic use can be distributed in different ways in a population, and the association between the distribution of use and antibiotic resistance has not been explored. Here, we tested the hypothesis that repeated use of antibiotics has a stronger association with population-wide antibiotic resistance than broadly-distributed, low-intensity use. First, we characterized the distribution of outpatient antibiotic use across US states, finding that antibiotic use is uneven and that repeated use of antibiotics makes up a minority of antibiotic use. Second, we compared antibiotic use with resistance for 72 pathogen-antibiotic combinations across states. Finally, having partitioned total use into extensive and intensive margins, we found that intense use had a weaker association with resistance than extensive use. If the use-resistance relationship is causal, these results suggest that reducing total use and selection intensity will require reducing broadly distributed, low-intensity use.

Investigating inequities in hospital care among lesbian, gay, bisexual, and transgender (LGBT) individuals using social media.

RATIONALE: Persons who identify as lesbian, gay, bisexual, and transgender (LGBT) face health inequities due to unwarranted discrimination against their sexual orientation or identity. An important contributor to LGBT health disparities is the inequitable or substandard care that LGBT individuals receive from hospitals. OBJECTIVE: To investigate inequities in hospital care among LGBT patients using the popular social media platform Twitter. METHOD: This study examined a dataset of Twitter communications (tweets) collected from February 2015 to May 2017. The tweets mentioned Twitter handles for hospitals (i.e., usernames for hospitals) and LGBT related terms. The topics discussed were explored to develop an LGBT position index referring to whether the hospital appears supportive or not supportive of LGBT rights. Results for each hospital were then compared to the Healthcare Equality Index (HEI), an established index to evaluate equity of hospital care towards LGBT patients. RESULTS: In total, 1856 tweets mentioned LGBT terms representing 653 unique hospitals. Of these hospitals, 189 (28.9%) were identified as HEI leaders. Hospitals in the Northeast showed significantly greater support towards LGBT issues compared to hospitals in the Midwest. Hospitals deemed as HEI leaders had higher LGBT position scores compared to non-HEI leaders (p = 0.042), when controlling for hospital size and location. CONCLUSIONS: This exploratory study describes a novel approach to monitoring LGBT hospital care. While these initial findings should be interpreted cautiously, they can potentially inform practices to improve equity of care and efforts to address health disparities among gender minority groups.

County-level assessment of United States kindergarten vaccination rates for measles mumps rubella (MMR) for the 2014-2015 school year.

United States kindergarten measles-mumps-rubella (MMR) vaccination rates are typically reported at the state level by the Centers for Disease Control and Prevention (CDC). The lack of local MMR data prevents identification of areas with low vaccination rates that would be vulnerable to the spread of disease. We collected county-level vaccination rates for the 2014-2015 school year with the objective of identifying these regions. We requested county-level kindergarten vaccination data from state health departments, and mapped these data to visualize geographic patterns in achievement of the 95% MMR vaccination target. We aggregated the county-level data to the state level for comparison against CDC state estimates. We also analyzed the relationship of MMR vaccination level with county-level and state-level poverty (using U.S. census data), using both a national mixed model with state as a random effect, and individual linear regression models by state. We received county vaccination data from 43 states. The median kindergarten MMR vaccination rate was 96.0% (IQR 89-98) across all counties, however, we estimated that 48.4% of the represented counties had vaccination rates below 95%. Our state estimates closely reflected CDC values. Nationally, every 10% increase in under-18 county poverty was associated with a 0.24% increase in MMR vaccination rates (95% CI: -0.07%; 0.54%), but the direction of this relationship varied by state. We found that county data can reveal vaccination trends that are unobservable from state-level data, but we also discovered that the current availability of county-level data is inadequate. Our findings can be used by state health departments to identify target areas for vaccination programs.

Disparities in digital reporting of illness: A demographic and socioeconomic assessment.

Although digital reports of disease are currently used by public health officials for disease surveillance and decision making, little is known about environmental factors and compositional characteristics that may influence reporting patterns. The objective of this study is to quantify the association between climate, demographic and socio-economic factors on digital reporting of disease at the US county level. We reference approximately 1.5 million foodservice business reviews between 2004 and 2014, and use census data, machine learning methods and regression models to assess whether digital reporting of disease is associated with climate, socio-economic and demographic factors. The results show that reviews of foodservice businesses and digital reports of foodborne illness follow a clear seasonal pattern with higher reporting observed in the summer, when most foodborne outbreaks are reported and to a lesser extent in the winter months. Additionally, factors typically associated with affluence (such as, higher median income and fraction of the population with a bachelor's degrees) were positively correlated with foodborne illness reports. However, restaurants per capita and education were the most significant predictors of illness reporting at the US county level. These results suggest that well-known health disparities might also be reflected in the online environment. Although this is an observational study, it is an important step in understanding disparities in the online public health environment.