Bayesian kernel machine regression for count data: modelling the association between social vulnerability and COVID-19 deaths in South Carolina.

The COVID-19 pandemic created an unprecedented global health crisis. Recent studies suggest that socially vulnerable communities were disproportionately impacted, although findings are mixed. To quantify social vulnerability in the US, many studies rely on the Social Vulnerability Index (SVI), a county-level measure comprising 15 census variables. Typically, the SVI is modelled in an additive manner, which may obscure non-linear or interactive associations, further contributing to inconsistent findings. As a more robust alternative, we propose a negative binomial Bayesian kernel machine regression (BKMR) model to investigate dynamic associations between social vulnerability and COVID-19 death rates, thus extending BKMR to the count data setting. The model produces a 'vulnerability effect' that quantifies the impact of vulnerability on COVID-19 death rates in each county. The method can also identify the relative importance of various SVI variables and make future predictions as county vulnerability profiles evolve. To capture spatio-temporal heterogeneity, the model incorporates spatial effects, county-level covariates, and smooth temporal functions. For Bayesian computation, we propose a tractable data-augmented Gibbs sampler. We conduct a simulation study to highlight the approach and apply the method to a study of COVID-19 deaths in the US state of South Carolina during the 2021 calendar year.

Acceptability and uptake of oral HIV self-testing among rural community members in Tanzania: a pilot study.

New strategies are needed to improve HIV testing rates in Tanzania, particularly among adult men. We sought to investigate if HIV oral self-testing would increase HIV testing uptake in Tanzanian rural community homes. The study design was a prospective community-randomized pilot study, in two matched villages with similar characteristics (intervention and control villages) Before data collection, we trained village health workers and research assistants for one week. We recruited male and female adults from 50 representative households in each of two villages in eastern Tanzania. We collected data at baseline and we followed-up the enrolled households after a one-month period. There was a high interest in testing for HIV, with all participants from both arms (100%; n = 259) reporting that they would like to test for HIV. After the one-month follow-up, overall, 66.1% (162/245) of study participants reported to have tested for HIV in both arms. In the intervention arm, 97.6% (124/127) reported that they tested for HIV versus in the control arm, 32.2% (38/118) tested for HIV, p-value < 0.001. In Tanzania, we found that availability of HIV self-testing was associated with an enormous increase in HIV testing uptake in a rural population.

Bayesian negative binomial regression with spatially varying dispersion: Modeling COVID-19 incidence in Georgia.

Overdispersed count data arise commonly in disease mapping and infectious disease studies. Typically, the level of overdispersion is assumed to be constant over time and space. In some applications, however, this assumption is violated, and in such cases, it is necessary to model the dispersion as a function of time and space in order to obtain valid inferences. Motivated by a study examining spatiotemporal patterns in COVID-19 incidence, we develop a Bayesian negative binomial model that accounts for heterogeneity in both the incidence rate and degree of overdispersion. To fully capture the heterogeneity in the data, we introduce region-level covariates, smooth temporal effects, and spatially correlated random effects in both the mean and dispersion components of the model. The random effects are assigned bivariate intrinsic conditionally autoregressive priors that promote spatial smoothing and permit the model components to borrow information, which is appealing when the mean and dispersion are spatially correlated. Through simulation studies, we show that ignoring heterogeneity in the dispersion can lead to biased and imprecise estimates. For estimation, we adopt a Bayesian approach that combines full-conditional Gibbs sampling and Metropolis-Hastings steps. We apply the model to a study of COVID-19 incidence in the state of Georgia, USA from March 15 to December 31, 2020.

Associations Between Governor Political Affiliation and COVID-19 Cases, Deaths, and Testing in the U.S.

INTRODUCTION: The response to the COVID-19 pandemic became increasingly politicized in the U.S., and the political affiliation of state leaders may contribute to policies affecting the spread of the disease. This study examines the differences in COVID-19 infection, death, and testing by governor party affiliation across the 50 U.S. states and the District of Columbia. METHODS: A longitudinal analysis was conducted in December 2020 examining COVID-19 incidence, death, testing, and test positivity rates from March 15, 2020 through December 15, 2020. A Bayesian negative binomial model was fit to estimate the daily risk ratios and posterior intervals comparing rates by gubernatorial party affiliation. The analyses adjusted for state population density, rurality, Census region, age, race, ethnicity, poverty, number of physicians, obesity, cardiovascular disease, asthma, smoking, and presidential voting in 2020. RESULTS: From March 2020 to early June 2020, Republican-led states had lower COVID-19 incidence rates than Democratic-led states. On June 3, 2020, the association reversed, and Republican-led states had a higher incidence (risk ratio=1.10, 95% posterior interval=1.01, 1.18). This trend persisted through early December 2020. For death rates, Republican-led states had lower rates early in the pandemic but higher rates from July 4, 2020 (risk ratio=1.18, 95% posterior interval=1.02, 1.31) through mid-December 2020. Republican-led states had higher test positivity rates starting on May 30, 2020 (risk ratio=1.70, 95% posterior interval=1.66, 1.73) and lower testing rates by September 30, 2020 (risk ratio=0.95, 95% posterior interval=0.90, 0.98). CONCLUSIONS: Gubernatorial party affiliation may drive policy decisions that impact COVID-19 infections and deaths across the U.S. Future policy decisions should be guided by public health considerations rather than by political ideology.

Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States.

BACKGROUND: Socially vulnerable communities may be at higher risk for COVID-19 outbreaks in the US. However, no prior studies examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. Therefore, we examined temporal trends among counties with high and low social vulnerability to quantify disparities in trends over time. METHODS: We conducted a longitudinal analysis examining COVID-19 incidence and death rates from March 15 to December 31, 2020, for each US county using data from USAFacts. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention, with higher values indicating more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles, adjusting for rurality, percentage in poor or fair health, percentage female, percentage of smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, daily temperature and precipitation, and proportion tested for COVID-19. RESULTS: At the outset of the pandemic, the most vulnerable counties had, on average, fewer cases per 100,000 than least vulnerable SVI quartile. However, on March 28, we observed a crossover effect in which the most vulnerable counties experienced higher COVID-19 incidence rates compared to the least vulnerable counties (RR = 1.05, 95% PI: 0.98, 1.12). Vulnerable counties had higher death rates starting on May 21 (RR = 1.08, 95% PI: 1.00,1.16). However, by October, this trend reversed and the most vulnerable counties had lower death rates compared to least vulnerable counties. CONCLUSIONS: The impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties and back again over time.

Joint hypothesis testing of the area under the receiver operating characteristic curve and the Youden index.

In the receiver operating characteristic (ROC) analysis, the area under the ROC curve (AUC) serves as an overall measure of diagnostic accuracy. Another popular ROC index is the Youden index (J), which corresponds to the maximum sum of sensitivity and specificity minus one. Since the AUC and J describe different aspects of diagnostic performance, we propose to test if a biomarker beats the pre-specified targeting values of AUC0 and J0 simultaneously with H0 : AUC ≤ AUC0 or J ≤ J0 against Ha : AUC > AUC0 and J > J0 . This is a multivariate order restrictive hypothesis with a non-convex space in Ha , and traditional likelihood ratio-based tests cannot apply. The intersection-union test (IUT) and the joint test are proposed for such test. While the IUT test independently tests for the AUC and the Youden index, the joint test is constructed based on the joint confidence region. Findings from the simulation suggest both tests yield similar power estimates. We also illustrated the tests using a real data example and the results of both tests are consistent. In conclusion, testing jointly on AUC and J gives more reliable results than using a single index, and the IUT is easy to apply and have similar power as the joint test.

Associations between governor political affiliation and COVID-19 cases, deaths, and testing in the United States.

INTRODUCTION: The response to the COVID-19 pandemic became increasingly politicized in the United States (US) and political affiliation of state leaders may contribute to policies affecting the spread of the disease. This study examined differences in COVID-19 infection, death, and testing by governor party affiliation across 50 US states and the District of Columbia. METHODS: A longitudinal analysis was conducted in December 2020 examining COVID-19 incidence, death, testing, and test positivity rates from March 15 through December 15, 2020. A Bayesian negative binomial model was fit to estimate daily risk ratios (RRs) and posterior intervals (PIs) comparing rates by gubernatorial party affiliation. The analyses adjusted for state population density, rurality, census region, age, race, ethnicity, poverty, number of physicians, obesity, cardiovascular disease, asthma, smoking, and presidential voting in 2020. RESULTS: From March to early June, Republican-led states had lower COVID-19 incidence rates compared to Democratic-led states. On June 3, the association reversed, and Republican-led states had higher incidence (RR=1.10, 95% PI=1.01, 1.18). This trend persisted through early December. For death rates, Republican-led states had lower rates early in the pandemic, but higher rates from July 4 (RR=1.18, 95% PI=1.02, 1.31) through mid-December. Republican-led states had higher test positivity rates starting on May 30 (RR=1.70, 95% PI=1.66, 1.73) and lower testing rates by September 30 (RR=0.95, 95% PI=0.90, 0.98). CONCLUSION: Gubernatorial party affiliation may drive policy decisions that impact COVID-19 infections and deaths across the US. Future policy decisions should be guided by public health considerations rather than political ideology.

Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States.

BACKGROUND: Emerging evidence suggests that socially vulnerable communities are at higher risk for coronavirus disease 2019 (COVID-19) outbreaks in the United States. However, no prior studies have examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. The purpose of this study was to examine temporal trends among counties with high and low social vulnerability and to quantify disparities in these trends over time. We hypothesized that highly vulnerable counties would have higher incidence and death rates compared to less vulnerable counties and that this disparity would widen as the pandemic progressed. METHODS: We conducted a retrospective longitudinal analysis examining COVID-19 incidence and death rates from March 1 to August 31, 2020 for each county in the US. We obtained daily COVID-19 incident case and death data from USAFacts and the Johns Hopkins Center for Systems Science and Engineering. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention in which higher scores represent more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles. We adjusted for percentage of the county designated as rural, percentage in poor or fair health, percentage of adult smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, and the proportion tested for COVID-19 in the state. RESULTS: In unadjusted analyses, we found that for most of March 2020, counties in the upper SVI quartile had significantly fewer cases per 100,000 than lower SVI quartile counties. However, on March 30, we observed a crossover effect in which the RR became significantly greater than 1.00 (RR = 1.10, 95% PI: 1.03, 1.18), indicating that the most vulnerable counties had, on average, higher COVID-19 incidence rates compared to least vulnerable counties. Upper SVI quartile counties had higher death rates on average starting on March 30 (RR = 1.17, 95% PI: 1.01,1.36). The death rate RR achieved a maximum value on July 29 (RR = 3.22, 95% PI: 2.91, 3.58), indicating that most vulnerable counties had, on average, 3.22 times more deaths per million than the least vulnerable counties. However, by late August, the lower quartile started to catch up to the upper quartile. In adjusted models, the RRs were attenuated for both incidence cases and deaths, indicating that the adjustment variables partially explained the associations. We also found positive associations between COVID-19 cases and deaths and percentage of the county designated as rural, percentage of resident in fair or poor health, and average daily PM2.5. CONCLUSIONS: Results indicate that the impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties over time. This highlights the importance of protecting vulnerable populations as the pandemic unfolds.

Unraveling the risk factors for spontaneous intracerebral hemorrhage among West Africans.

OBJECTIVE: To characterize risk factors for spontaneous intracerebral hemorrhage (sICH) occurrence and severity among West Africans. METHODS: The Stroke Investigative Research and Educational Network (SIREN) study is a multicenter case-control study involving 15 sites in Ghana and Nigeria. Patients were adults ≥18 years old with CT-confirmed sICH with age-, sex-, and ethnicity-matched stroke-free community controls. Standard instruments were used to assess vascular, lifestyle, and psychosocial factors. Factors associated with sICH and its severity were assessed using conditional logistic regression to estimate odds ratios (ORs) and population-attributable risks (PARs) with 95% confidence intervals (CIs) for factors. RESULTS: Of 2,944 adjudicated stroke cases, 854 were intracerebral hemorrhage (ICH). Mean age of patients with ICH was 54.7 ± 13.9 years, with a male preponderance (63.1%), and 77.3% were nonlobar. Etiologic subtypes of sICH included hypertension (80.9%), structural vascular anomalies (4.0%), cerebral amyloid angiopathy (0.7%), systemic illnesses (0.5%), medication-related (0.4%), and undetermined (13.7%). Eight factors independently associated with sICH occurrence by decreasing order of PAR with their adjusted OR (95% CI) were hypertension, 66.63 (20.78-213.72); dyslipidemia, 2.95 (1.84-4.74); meat consumption, 1.55 (1.01-2.38); family history of CVD, 2.22 (1.41-3.50); nonconsumption of green vegetables, 3.61 (2.07-6.31); diabetes mellitus, 2.11 (1.29-3.46); stress, 1.68 (1.03-2.77); and current tobacco use, 14.27 (2.09-97.47). Factors associated with severe sICH using an NIH Stroke Scale score >15 with adjusted OR (95% CI) were nonconsumption of leafy green vegetables, 2.03 (1.43-2.88); systolic blood pressure for each mm Hg rise, 1.01 (1.00-1.01); presence of midline shift, 1.54 (1.11-2.13); lobar ICH, 1.72 (1.16-2.55); and supratentorial bleeds, 2.17 (1.06-4.46). CONCLUSIONS: Population-level control of the dominant factors will substantially mitigate the burden of sICH in West Africa.

Examining the relationship between social media engagement and hospital revenue.

Effective use of social media by hospitals has the potential to improve hospitals' financial performance by facilitating customer service and providing hospitals with a low-cost marketing platform. This cross-sectional study explored the relationship between hospital Facebook engagement and patient revenue in a simple random sample of United States short-term acute care hospitals. There was a positive relationship between Facebook engagement and hospital patient revenue for rural hospitals, but not for urban hospitals. Additional research is needed to identify the mechanisms through which hospitals' social media presence influences consumer health purchasing behavior and profitability.