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Research examining the mediating role of depressive symptoms in the association between resilience and antiretroviral therapy (ART) adherence is lacking. Therefore, this study aims to examine the mediating role of depression between resilience and ART adherence, as well as to explore whether this relationship varies across racial background. A total of 327 people living with HIV and on ART in South Carolina were surveyed about their ART adherence and mental health, including resilience and depressive symptoms. Path analyses were conducted to determine the direct and indirect effects between resilience, depressive symptoms, and ART adherence. After adjusting for age, gender, income, education, employment and time since diagnosis, among Black individuals, resilience was negatively associated with depressive symptoms (

Existing research and national surveillance data suggested an increase of the prevalence of mental disorders during the coronavirus disease 2019 (COVID-19) pandemic. Social media, such as Twitter, could be a source of data for estimation due to its real-time nature, high availability, and large geographical coverage. However, there is a dearth of studies validating the accuracy of Twitter-based prevalence for mental disorders through the comparison with CDC-reported prevalence.This study aims to verify the feasibility of Twitter-based prevalence for mental disorders symptoms being an instrument for prevalence estimation, where the feasibility is gauged via the correlations between Twitter-based prevalence of mental disorder symptoms (i.e., anxiety and depressive symptoms) and the one based on national surveillance data. In addition, this study aims to identify how the correlations changed over time (i.e., the temporal trend).State-level prevalence of anxiety and depressive symptoms were retrieved from the National Household Pulse Survey (HPS) through the Centers for Disease Control and Prevention (CDC) from April 2020 to July 2021. Tweets were retrieved from the Twitter streaming API during the same period and used to estimate the prevalence of mental disorder symptoms for each state using keyword analysis. Stratified linear mixed models were employed to evaluate the correlations between the Twitter-based prevalence of mental disorder symptoms and those reported by the CDC. The magnitude and significance of model parameters were used to evaluate the correlations. Temporal trends of correlations were tested after adding the time variable to the model. Geospatial differences were compared based on random effects.The Pearson correlations between the overall prevalence based on CDC and Twitter for anxiety and depressive symptoms were 0.587 (P.001) and 0.368 (P.001), respectively. Stratified by four phases (i.e., April 2020, August 2020, October 2020, and April 2021) defined by HPS, linear mixed models showed that Twitter-based prevalence for anxiety symptoms had a positive and significant correlation with CDC-reported prevalence in phases 2 and 3 while a significant correlation for depressive symptoms was identified in phases 1 and 3.Positive correlations are identified between Twitter-based and CDC-reported prevalence, and temporal trends of these correlations were found. Geospatial differences in the prevalence of mental disorder symptoms were found between the northern and southern U.S. Findings from this study could inform the future investigation on leveraging social media platforms to estimate mental disorder symptoms and the provision of immediate prevention measures to improve health outcomes.

During the COVID-19 pandemic, HIV-related services have been unavoidably disrupted and impacted. However, the nature and scope of HIV service disruptions due to COVID-19 has rarely been characterized in China. A cross-sectional online survey was conducted among 1029 HIV healthcare providers in Guangxi, China, from April to May 2020. Latent class analysis (LCA) was first used to identify HIV service disruption levels, then hierarchical multilevel logistic regression was conducted to analyze the relationships of COVID-19 challenges, institutional responses, and HIV service disruption levels adjusting for the clustering effect of institutional ownership levels. Four classes of HIV service disruption were identified, with 22.0% complete disruption, 15.4% moderate disruption, 21.9% minor disruption, and 40.7% almost no disruption. COVID-19 challenges were positively associated with the probabilities of service disruption levels. Institutional responses were negatively associated with the probabilities of being classified as "minor disruption" and moderated the association of COVID-19 challenges with complete and moderate disruptions compared with no disruption group. To maintain continuity of core HIV services in face of a pandemic, building a resilient health care system with adequate preparedness is necessary.

Previous studies have suggested that many HIV/AIDS healthcare providers (HCPs), especially those in resource-poor settings, suffer from various work-related stress because of the complexity of HIV/AIDS patients' medical conditions, occupational exposure, HIV-related stigma, and challenges with patients' physical, mental, and social conditions. However, data are limited regarding how HIV/AIDS HCPs cope with the stress and their perceived effectiveness of these coping strategies. This qualitative study was designed to explore HCPs' coping strategies and their perceptions of effectiveness of these strategies. We conducted in-depth face-to-face interviews with 46 HIV/AIDS HCPs in Guangxi, China. The interviews were audio-recorded, transcribed and imported into NVivo V.11. Data were analyzed using a thematic approach. The analysis of the data revealed six general coping strategies: seeking social support, applying problem-solving strategies, adopting healthy lifestyle, developing self-compassion, using mindfulness-based stress reduction methods, and avoidance and escaping. It is imperative to increase institutional support and develop training programs to improve problem-solving skills, healthy lifestyle, and self-compassion among HIV/AIDS HCPs in China.

Population mobility and aging at local areas contributed to the geospatial disparities in the coronavirus disease 2019 (COVID-19) transmission among 418 counties in the Deep South. In predicting the incidence of COVID-19, a significant interaction was found between mobility and the proportion of older adults. Effective disease control measures should be tailored to vulnerable communities.

Direct human physical contact accelerates COVID-19 transmission. Smartphone mobility data has been an emerging data source to reveal fine-grained human mobility, which can be used to estimate the intensity of physical contact surrounding different locations. Our study applied smartphone mobility data to simulate the second wave spreading of COVID-19 in January 2021 in three major metropolitan statistical areas (Columbia, Greenville, and Charleston) in South Carolina, United States. Based on the simulation, the number of historical county-level COVID-19 cases was allocated to neighborhoods (Census blockgroups) and points of interest (POIs), and the transmission rate of each allocated place was estimated. The result reveals that the COVID-19 infections during the study period mainly occurred in neighborhoods (86%), and the number is approximately proportional to the neighborhood’s population. Restaurants and elementary and secondary schools contributed more COVID-19 infections than other POI categories. The simulation results for the coastal tourism Charleston area show high transmission rates in POIs related to travel and leisure activities. The results suggest that the neighborhood-level infectious controlling measures are critical in reducing COVID-19 infections. We also found that the households of lower socioeconomic status may be an umbrella against infection due to fewer visits to places such as malls and restaurants associated with their low financial status. Control measures should be tailored to different geographic locations since transmission rates and infection counts of POI categories vary among metropolitan areas.

Background: Tuberculosis, like COVID-19, is most often a pulmonary disease. The COVID-19 pandemic has severely disrupted tuberculosis services in myriad ways: health facility closures, lockdowns, travel bans, overwhelmed healthcare systems, restricted export of antituberculous drugs, etc. The effects of the shared risk on outcomes of the two diseases is not known, particularly for the first year of the pandemic, during the period before COVID-19 vaccines became widely available. Objective: We embarked on a systematic review to elucidate the consequences of tuberculosis on COVID-19 outcomes and of COVID-19 on tuberculosis outcomes during the pre-vaccination period of the pandemic. Methods: The systematic review protocol is registered in PROSPERO. We conducted an initial search of PubMed, Embase, Web of Science, WHO coronavirus database, medRxiv, bioRxiv, preprints.org, and Google Scholar using terms relating to COVID-19 and tuberculosis. We selected cohort and case–control studies for extraction and assessed quality with the Newcastle-Ottawa scale. Results and Conclusion: We identified 2108 unique abstracts published between December 2019 and January 2021. We extracted data from 18 studies from 8 countries. A total of 650,317 persons had a diagnosis of COVID-19, and 4179 had a diagnosis of current or prior tuberculosis. We explored links between tuberculosis and COVID-19 incidence, mortality, and other adverse outcomes. Nine studies reported on mortality and 13 on other adverse outcomes; results on the association between tuberculosis and COVID-19 mortality/adverse outcomes were heterogenous. Tuberculosis outcomes were not fully available in any studies, due to short follow-up (maximum of 3 months after COVID-19 diagnosis), so the effects of COVID-19 on tuberculosis outcomes could not be assessed. Much of the rapid influx of literature on tuberculosis and COVID-19 during this period was published on preprint servers, and therefore not peer-reviewed. It offered limited examination of the effect of tuberculosis on COVID-19 outcomes and even less on the effect of COVID-19 on tuberculosis treatment outcomes.

Background Existing literature mostly consider HIV disclosure as a static event and investigate its relationship with stress using a cross-sectional design. It is unclear about the dynamic changes of HIV disclosure levels (defined as the number of disclosure targets) and how stress may influence these changes. This study explored different disclosure levels using a person-centered longitudinal approach, examined whether stress could predict these disclosure levels, and investigated if this relationship differed by gender among people living with HIV (PLWH). Methods Data were derived from a prospective cohort study conducted from November 2016 to January 2018 in Guangxi, China. Four hundred forty-four PLWH were included. Participants were assessed on perceived stress, sociodemographic characteristics, and number of HIV disclosure targets at baseline, 6-month, and 12-month follow-ups. Growth mixture modeling was used to characterize disclosure levels based on the changes of disclosure target number. Multinomial logistic regression was used to predict disclosure levels with baseline stress after adjusting for covariates. The interaction effect of stress by gender was examined. Adjusted odds ratio (AOR) with its 95% confidence interval were reported to show the strength of association. Results Three levels of disclosure were characterized as “Low levels of disclosure” (Level One), “Increased levels of disclosure” (Level Two), and “High levels of disclosure” (Level Three). Accordingly, 355 (81.2%), 28 (6.4%), and 64 (12.4%) of PLWH were categorized respectively under low, increased, and high levels of disclosure. The interaction of baseline stress by gender was significant in differentiating Level One from Three (AOR = 0.85 [0.74 ~ 0.99]) while it was not significant between Level One and Two (AOR = 0.96 [0.81 ~ 1.15]). Compared to female, male PLWH with higher baseline stress had lower probability to have consistent high disclosure levels over time. PLWH who were married/cohabited had lower probability of being classified into consistent high levels of disclosure than low level (AOR = 0.43 [0.19 ~ 0.94]). Conclusions There was gender difference in the relationship between stress and levels of HIV disclosure. To promote HIV disclosure, gender tailored interventions should be employed to help PLWH cope with stress.

This study aimed to examine the relationship between anticipated stigma and medication adherence as well as the mechanistic roles of medication support and ART self-efficacy. Data were derived from the baseline assessment of a prospective cohort study in Guangxi, China. A total of 1,198 PLWH were recruited and assessed on their sociodemographic characteristics, medication adherence, anticipated stigma, medication support, and ART self-efficacy. Path analysis was used to examine the direct effect from anticipated stigma to medication adherence and indirect effects through medication support and ART self-efficacy. Path model revealed that the indirect effect from anticipated stigma to medication adherence was statistically significant while the direct effect was not significant. Anticipated stigma could influence medication adherence through ART self-efficacy but not through medication support. The serial mediating effect of medication support and ART self-efficacy on the relationship between anticipated stigma and medication adherence was significant. Anticipated stigma affects medication adherence among PLWH through its adverse impacts on medication support and ART self-efficacy. Tailored interventions promoting medication support and ART self-efficacy may alleviate the negative influence of anticipated stigma on medication adherence among PLWH. Additionally, policy efforts aiming to reduce stigma against PLWH and increasing medication support are warranted to improve medication adherence among PLWH.

Despite a strong evidence on the negative association between HIV-related stigma and quality of life (QoL), few studies have examined the indirect effects of this relationship. This study aimed to examine the association between HIV-related stigma and QoL and the indirect effects of positive coping and perceived stress in people living with HIV and depressive symptoms (PLWHD). This study used baseline date from a randomized controlled trial. Structural equation model was used to examine the association between HIV-related stigma and QoL as well as the indirect effects of positive coping and perceived stress. Perceived and internalized stigma had negative direct (

BACKGROUND: Previous studies showed that food insecurity could adversely affect clinical outcomes of people living with HIV (PLWH). The mental health pathways of such effects are suggested in existing literature, but empirical data are limited in resource-limited settings. METHODS: This cross-sectional study aims to explore the role of depressive symptoms and anxiety on the association between food insecurity and CD4 counts among a sample of 2,987 PLWH in Guangxi, China. Path analysis was used to examine a hypothetical model and delta z test was used to assess the indirect effects of food insecurity on CD4 counts through depressive symptoms and anxiety. RESULTS: The prevalence of food insecurity in this sample was 25.3%, and the median CD4 counts was 318 cells/mm(3). In correlation analyses, food insecurity was not directly associated with LogCD4 but was associated with depressive symptoms and anxiety. Path analysis indicated a significant indirect effect from food insecurity to LogCD4 through depressive symptoms, but not anxiety. CONCLUSION: Improving mental health among PLWH with food insecurity may be beneficial for treatment outcomes. Besides intervening food insecurity, an intervention targeting depressive symptoms could improve the immunologic outcomes of PLWH.

Previous studies have documented the adverse effect of stigma on suicidal behaviors. Existing studies also indicated that depression and substance use were positively related to suicidal behaviors. However, data regarding the mechanism among these concepts are still limited. Therefore, this study aims to examine the relationship between experiences of stigma and suicidal behaviors as well as the mechanisms of how experiences of stigma affect suicidal behaviors through depression and substance use among migrant workers in China. A sample of 641 young rural-to-urban migrants was recruited through a venue-based sampling approach in Beijing, China. Participants were assessed on their sociodemographic characteristics, suicidal behaviors, experiences of stigma, depression, and substance use in their urban communities. The sum score of how often migrants had suicide ideation or suicidal attempt in the past six months was used as the indicators of suicidal behaviors. Path analysis was used to examine the relationship between experiences of stigma and suicidal behaviors as well as the mechanistic roles of depression and substance use. Among the participants, average score of suicidal behaviors in the last six months was 2.3 (±1.1). After adjusting for monthly income and health status, the final model showed an adequate goodness of fit. While the direct path from experiences of stigma to suicidal behaviors was not statistically significant (β=.01, p=.81), the indirect paths between these two concepts were statistically significant. Experiences of stigma were positively associated with both depression and substance use, which in turn were positively related to suicidal behaviors (Depression: Delta z=4.82, p

Work-related stress can negatively impact health care providers’ (HCPs) ability to provide care. We examined the sources of work-related stress experienced by HCPs who provided medical care for people living with HIV/AIDS and the impact of the stress on HCPs’ well-being and work performance. We conducted in-depth interviews with 46 HIV/AIDS HCPs in Guangxi, China. The interviews were audio-recorded, transcribed, and imported into NVivo V.11 for data management and data analysis using a thematic approach. We found that the key sources of stress at work included general work-related sources and HIV/AIDS-related sources. All stress was seen to have a substantial impact on the HCPs’ individual well-being, family and social life, and quality of care they provided. We recommended that government and health care facilities should take measures to improve institutional culture and professional development for HIV/AIDS HCPs. More professional training schemes should be provided to strengthen HCPs’ competence, improve universal protection from occupational exposure, and reduce the stigma toward HIV/AIDS patients and their care providers.

Few studies have examined the relationship between inconsistent condom use and sexual partnership characteristics among people living with HIV (PLWH). The current study focused on such association and its gender differences. The study was conducted in a large hospital in South China in 2013. A total of 320 dyads (PLWH indexes and their sexual partners) were recruited from an outpatient clinic using convenience sampling. The proportion of inconsistent condom use in the last six months among female indexes was higher than that among male indexes (52.4% vs. 43.6%). Of sexual partnership characteristics, HIV seropositive status was a risk factor for inconsistent condom use for both male and female indexes (aOR = 2.32, 95%CI = 1.15∼4.66, aOR = 3.09, 95%CI = 1.10∼8.67, respectively). For male indexes, lower educational level was also a risk factor (aOR = 2.39, 95%CI = 1.23∼4.67); while having had emotionally intimate relationships was a protective factor (aOR = 0.40, 95%CI = 0.21∼0.77). For female indexes, receiving material support was a risk factor (aOR = 10.17, 95%CI = 2.13∼48.61) and receiving health-related advice was a protective factor (aOR = 0.11, 95%CI = 0.02∼0.55). Future HIV interventions for PLWH need to be gender-sensitive and include their sexual partners.

Objective The aim of this study was to examine the geospatial variation of retention in care (RIC) across the counties in South Carolina (SC) from 2010 to 2016 and identify the relevant county-level predictors. Design Aggregated data on county-level RIC among HIV patients from 2010 to 2016 were retrieved from an electronic HIV/AIDS reporting system in SC Department of Health and Environmental Control. Sociological framework of health was used to select potential county-level predictors from multiple public datasets. Methods Geospatial mapping was used to display the spatial heterogeneity of county-level RIC rate in SC. Generalized linear mixed effect regression with least absolute shrinkage and selection operator (LASSO) was employed to identify county-level predictors related to the change of RIC status over time. Confusion matrix and area under the curve statistics were used to evaluate model performance. Results More than half of the counties had their RIC rates lower than the national average. The change of county-level RIC rate from 2010 to 2016 was not significant, and spatial heterogeneity in RIC rate was identified. A total of 22 of the 31 county-level predictors were selected by LASSO for predicting county-level RIC status. Counties with lower collective efficacy, larger proportions of men and/or persons with high education were more likely to have their RIC rates lower than the national average. In contrast, numbers of accessible mental health centres were positively related to county-level RIC status. Conclusion Spatial variation in RIC could be identified, and county-level factors associated with accessible healthcare facilities and social capital significantly contributed to these variations. Structural and individual interventions targeting these factors are needed to improve the county-level RIC and reduce the spatial variation in HIV care.

BackgroundThe healthcare system in China was largely overwhelmed during the unprecedented pandemic of coronavirus disease (COVID-19). HIV-related services have been unavoidably interrupted and impacted. However, the nature and scope of HIV service interruptions due to COVID-19 has rarely been characterized in China and how HIV service challenges affect the service interruptions are also unclear. The current study aimed at characterize HIV service interruption levels and analyzed its associated factors related to service challenges and institutional response from HIV healthcare providers’ viewpoint.MethodsA cross-sectional online survey was conducted among 1,029 HIV healthcare providers in Guangxi, China, from April to May 2020. Latent class analysis (LCA) was first used to identify HIV service interruption levels. Then hierarchical multinomial logistic regression was conducted to analyze the relationships of HIV care service challenges and institutional response with HIV service interruption levels. Simple slope analysis was employed to examine interaction effects between HIV service challenges and institutional response to COVID-19.ResultsFour classes of HIV service interruption were identified using LCA, with 22.0% complete interruption (class 1), 15.4% moderate interruption (class 2), 21.9% minor interruption (class 3) and 40.7% almost no interruption (class 4). Using class 4 as a reference group, HIV care service challenges were positively associated with the probabilities of service interruptions (Class 1: AOR=1.23, 95%CI: 1.19∼1.26; Class 2: AOR= 1.10, 95%CI: 1.08∼1.13; Class 3: AOR= 1.10, 95%CI: 1.08∼1.12). Institutional response to HIV healthcare delivery was negatively associated with the probabilities of being classified into Class 1 (“Complete interruption”) (AOR=0.97, 95%CI: 0.93∼1.00) and Class 3 (“minor interruption [Outreach service]”) (AOR=0.96, 95%CI: 0.93∼0.99) as compared to Class 4 (“almost no interruption”). Institutional response to HIV healthcare delivery moderated the association of HIV service challenges with complete interruption, but not with the moderate or minor interruption when comparing with no interruption group.ConclusionsA substantial HIV service interruptions occurs due to the COVID-19 pandemic, particularly services that require face-to-face interactions, such as VCT counselling, follow up and outreach services. HIV service challenges largely hinder the HIV service delivery. Institutional response to HIV healthcare delivery could marginally buffer the negative effect of service challenges on complete HIV service interruptions. To maintain continuity of core HIV services in face of a pandemic, build a resilient health care system with adequate preparedness is necessary.

BACKGROUND Mobile health (mHealth) interventions have been shown to effectively improve the quality of life (QOL) among people living with HIV. However, little is known about the long-term effects of mHealth interventions. OBJECTIVE This study aims to explore the intervention mechanisms of a social media–based intervention, Run4Love, on the QOL of people with HIV over across a 9-month follow-up period. METHODS We recruited people living with HIV who were concurrently experiencing elevated depressive symptoms from an HIV outpatient clinic in South China. A total of 300 eligible participants were randomized either to the intervention group or the control group in a 1:1 ratio after they provided informed consent and completed a baseline survey. The intervention group received a 3-month WeChat-based intervention, comprising cognitive-behavioral stress management (CBSM) courses and physical activity promotion. The control group received a printed brochure on nutrition guidelines in addition to the usual care for HIV treatment. Neither participants nor the research staff were blinded to group assignment. All patients were followed at 3, 6, and 9 months. The primary outcome was depressive symptoms. Structural equation model (SEM) with longitudinal data was conducted to examine the sequential mediating effects of HIV-related stigma and depressive symptoms on the long-term intervention effects on participants’ QOL. RESULTS About 91.3% (274/300), 88.3% (265/300), and 86.7% (260/300) of all participants completed follow-up surveys at 3, 6, and 9 months, respectively. Results showed that the intervention had significantly improved participants' QOL at 9 months, via complete mediating effects of reduced HIV-related stigma at 3 months and decreased depressive symptoms at 6 months. No adverse events were reported. CONCLUSIONS These findings underscore the critical roles of HIV-related stigma and depressive symptoms in an mHealth intervention with long-term effects on QOL improvements. We call for targeted mHealth interventions to improve QOL among people living with HIV, especially social media–based interventions that can address HIV-related stigma and alleviate depressive symptoms. CLINICALTRIAL Chinese Clinical Trial Registry ChiCTR-IPR-17012606; https://www.chictr.org.cn/showproj.aspx?proj=21019

Previous studies have documented that depression is positively associated with sexual risk behaviors (SRB) among rural-to-urban migrants. Existing literature has also suggested that acculturation and social capital might moderate this positive relationship. However, data regarding the moderating effects of acculturation and social capital have been inconsistent. The current study aims to examine the relationship between depression and SRB, as well as the moderating roles of acculturation and social capital in this relationship. A sample of 641 young rural-to-urban migrants was recruited through a venue-based sampling approach in Beijing, China. Results indicated that depression was positively associated with SRB. Both acculturation and social capital moderated this relationship, but they showed different moderating effects. Specifically, the level of acculturation was protective against SRB among migrants with a higher level of depression but not among migrants with a lower level of depression. Social capital played a protective role among migrants with a lower level of depression but became a risk factor for those with a higher level of depression. These findings suggested that targeted interventions aiming to reduce depression, improve acculturation stress management skills, and utilize social capital are needed to reduce SRB among rural-to-urban migrants.

BACKGROUND Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. OBJECTIVE The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina. METHODS This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. RESULTS Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%. CONCLUSIONS Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

Background COVID-19 pandemic has resulted in considerable morbidity and mortality. New York State (NY) is the hotspot with most coronavirus cases, while there are spatial/temporal variations. Yet, few examined county-level factors of mortality in COVID-19 patients in NY. Based on the sociological framework in health, this study links large and representative public data to understand COVID-19 mortality in NY over different stages of pandemic. Methods Mortality cases were from Mar 17 (state of emergency; 0.1 per 100,000), Apr 18 (coronavirus peak; 87.4), Apr 25 (expand testing; 108.7), and May 11 (daily reduced to original; 137.6). Three domains (compositional, contextual, and collective) and 28 county-level predictors of mortality were extracted from American Community Survey, Area Health Resources, US Crime Data, and Religious Data systems for each county. Compositional domain covered socio-demographic characteristics in local areas (e.g., age, sex, race/ethnicity, housing). Contextual domain covered include social and physical opportunities (e.g., health insurance coverage, transportation, mental health providers). Collective domain covered neighborhood safety and religious adherents. Mixed effect regression with the least absolute shrinkage selection operator (LASSO) was used to select the predictors and estimate the parameters after adjusting the time effect and cumulative prevalence of COVID-19. 有道词典 ; 0.1 per 100,000 people 详细X ;每100000人0.1 Results NYC and the nearby boroughs (i.e., Bronx, Kings, Manhattan, Queens) had the highest cumulative mortality (231.69 per 100,000 people). Counties far from New York Cities (e.g., Allegany, Cortland, Delaware) had the lowest cumulative mortality. Spatial variation showed counties with larger population density (β=.01, p=.022) and/or higher proportion of people with at least high school education (β=227.24, p=.03) were at risk of higher cumulative mortality in COVID-19. Conclusion Unique spatial clustering mortality risk of COVID-19s was detected, highlighting important but understudied roles of contextual and collective factors. Tailored policy efforts shall be designed to support counties with large population density and high levels of education to prevent the mortality related to COVID-19 infection in NY. Disclosures All Authors: No reported disclosures

Background More than 360,000 people infected with COVID-19 in New York State (NYS) by the end of May 2020. Although expanded testing could effectively control statewide COVID-19 outbreak, the county-level factors predicting the number of testing are unknown. Accurately identifying the county-level predictors of testing may contribute to more effective testing allocation across counties in NYS. This study leveraged multiple public datasets and machine learning algorithms to construct and compare county-level prediction models of COVID-19 testing in NYS. Methods Testing data by May 15th was extracted from the Department of Health in NYS. A total of 28 county-level predictors derived from multiple public datasets (e.g., American Community Survey and US Health Data) were used to construct the prediction models. Three machine learning algorithms, including generalized linear regression with the least absolute shrinkage and selection operator(LASSO), ridge regression, and regression tree, were used to identify the most important county-level predictors, adjusting for prevalence and incidence. Model performances were assessed using the mean square error (MSE), with smaller MSE indicating a better model performance. Results The testing rate was 70.3 per 1,000 people in NYS. Counties (Rockland and Westchester) closed to the epicenter had high testing rates while counties (Chautauqua and Clinton) located at the boundary of NYS and were far away from the epicenter had low testing rates. The MSEs of linear regression with the LASSO penalty, ridge regression, and regression tree was 123.60, 40.59, and 298.0, respectively. Ridge regression was selected as the final model and revealed that the mental health provider rate was positively associated with testing (β=5.11, p=.04) while the proportion of religious adherents (β=-3.91, p=.05) was inversely related to the variation of testing rate across counties. Conclusion This study identified healthcare resources and religious environment as the strongest predictor of spatial variations of COVID-19 testing across NYS. Structural or policy efforts should address the spatial variations and target the relevant county-level predictors to promote statewide testing. Disclosures All Authors: No reported disclosures

BackgroundTo control the COVID-19 pandemic, governments need to ensure a successful large-scale administration of COVID-19 vaccines when safe and efficacious vaccines become available. Vaccine acceptance could be a critical factor influencing vaccine uptake. Health information has been associated with vaccine acceptance. For college students who are embracing a digital era and being exposed to multimedia, the sources of COVID-19 vaccine information and their trust in these sources may play an important role in shaping their acceptance of vaccine uptake.MethodsIn September 2020, we conducted an online survey among 1062 college students in South Carolina to understand their perceptions and attitudes toward COVID-19 vaccination. Descriptive analysis and linear regression analysis were used to investigate vaccine information sources among college students and examine how COVID-19 vaccine acceptance was associated with information source and trust level in each source.ResultsThe top three sources of COVID-19 vaccine information were health agencies (57.7%), mass media (49.5%), and personal social networks (40.5%). About 83.1% of the participants largely or always trusted scientists, 73.9% trusted healthcare providers, and 70.2% trusted health agencies. After controlling for key demographics, vaccine acceptance was positively associated with scientists as information sources but negatively associated with pharmaceutical companies as sources. Higher trust levels in mass media, health agencies, scientists, and pharmaceutical companies was significantly associated with higher COVID-19 vaccine acceptance. However, trust in social media was negatively associated with vaccine acceptance.DiscussionCollege students use multiple sources to learn about upcoming COVID-19 vaccines including health agencies, personal networks, and social media. The level of trust in these information sources play a critical role in predicting vaccine acceptance. Trust in health authorities and scientists rather than social media is related to higher level vaccine acceptance. Our findings echo the call for restoring trust in government, healthcare system, scientists, and pharmaceutical industries in the COVID-19 era and highlight the urgency to dispel misinformation in social media. Effective strategies are needed to disseminate accurate information about COVID-19 vaccine from health authorities and scientific research to improve vaccine communication to the public and promote COVID-19 vaccine uptake.

Children exposed to violence are at risk of a range of adverse outcomes. Given the unique cultural and social context, understanding what fosters children's resilience in resource-limited areas such as sub-Saharan Africa, is vital.Using data from the International Survey of Children's Well-Being (ISCWeB), this paper explores individual-, family-, and school-level factors associated with positive outcomes for children who have experienced some form of violence in Khomas region, Namibia.Using a cross-sectional survey design, the ISCWeB questionnaire, assessing cognitive, affective, and psychological dimensions of well-being, child protection factors and violence exposure was administered to 2124 Grade four and six children.Our sample had a mean age of 11.2 years. Overall, 56.8 % of children reported at least one incident of violence from an adult caregiver at home and 86.0 % of children reported some form of peer violence at school. Materially deprived children experienced higher incidence of both family and peer violence. The quality of children's relationships at home (β = 0.17, p0.001), and school (β = 0.07, p0.001) emerged as important protective factors for children's well-being for both types of violence, suggesting that supportive family and school relationships may be more important to the subjective well-being of children who experienced violence than material wellbeing, violence severity, and individual child factors.Providing positive social interaction and emotional security in contextually and culturally appropriate ways within children's proximal systems should be prioritized while challenging norms that support violence in Namibian families and schools.

Background The effectiveness of psychosocial interventions on quality of life (QOL) among people living with HIV has been validated, including mobile health (mHealth) interventions. However, it is unclear which components of such interventions account for these effects. Objective This study aims to examine positive coping as a potential mediator of the effects of an mHealth intervention on QOL among people living with HIV. Methods For this secondary analysis, we used data from an mHealth-based randomized controlled trial, Run4Love, which was conducted to improve QOL and mental health outcomes of people living with HIV. A total of 300 participants were randomly assigned to the intervention group to receive the adapted cognitive-behavioral stress management courses and regular physical activity promotion or the waitlist control group in a 1:1 ratio. Our analysis focused on positive coping and QOL, which were repeatedly measured at baseline and at 3-, 6-, and 9-month follow-ups. Latent growth curve models were constructed to explore the mediating role of positive coping in the effects of the mHealth intervention on QOL. Results Positive coping served as a mediator in the effect of the mHealth intervention on QOL for up to 9 months. The mHealth intervention had a significant and positive indirect effect on the slope of QOL via the slope of positive coping (b=2.592×1.620=4.198, 95% CI 1.189-7.207, P=.006). The direct effect of the intervention was not significant (b=0.552, 95% CI −2.154 to 3.258, P=.69) when controlling for the mediator. Conclusions The longitudinal findings suggest that positive coping could be a crucial mediator of the mHealth intervention in enhancing QOL among people living with HIV. These findings underscore the importance of improving positive coping skills in mHealth interventions to improve QOL among people living with HIV.

Background The dose–response relationship between patient engagement and long-term intervention effects in mobile health (mHealth) interventions are understudied. Studies exploring long-term and potentially changing relationships between patient engagement and health outcomes in mHealth interventions are needed. Objective This study aims to examine dose–response relationships between patient engagement and 3 psychosocial outcomes in an mHealth intervention, Run4Love, using repeated measurements of outcomes at baseline and 3, 6, and 9 months. Methods This study is a secondary analysis using longitudinal data from the Run4Love trial, a randomized controlled trial with 300 people living with HIV and elevated depressive symptoms to examine the effects of a 3-month mHealth intervention on reducing depressive symptoms and improving quality of life (QOL). We examined the relationships between patient engagement and depressive symptoms, QOL, and perceived stress in the intervention group (N=150) using 4–time-point outcome measurements. Patient engagement was assessed using the completion rate of course assignments and frequency of items completed. Cluster analysis was used to categorize patients into high- and low-engagement groups. Generalized linear mixed effects models were conducted to investigate the dose–response relationships between patient engagement and outcomes. Results The cluster analysis identified 2 clusters that were distinctively different from each other. The first cluster comprised 72 participants with good compliance to the intervention, completing an average of 74% (53/72) of intervention items (IQR 0.22). The second cluster comprised 78 participants with low compliance to the intervention, completing an average of 15% (11/72) of intervention items (IQR 0.23). Results of the generalized linear mixed effects models showed that, compared with the low-engagement group, the high-engagement group had a significant reduction in more depressive symptoms (β=−1.93; P=.008) and perceived stress (β=−1.72; P Conclusions This study revealed a positive long-term dose–response relationship between patient engagement and 3 psychosocial outcomes among people living with HIV and elevated depressive symptoms in an mHealth intervention over 9 months using 4 time-point repeat measurement data. The high- and low-engagement groups showed significant and widening differences in depressive symptoms, QOL, and perceived stress at the 3-, 6-, and 9-month follow-ups. Future mHealth interventions should improve patient engagement to achieve long-term and sustained intervention effects. Trial Registration Chinese Clinical Trial Registry ChiCTR-IPR-17012606; https://www.chictr.org.cn/showproj.aspx?proj=21019

BACKGROUND The dose–response relationship between patient engagement and long-term intervention effects in mobile health (mHealth) interventions are understudied. Studies exploring long-term and potentially changing relationships between patient engagement and health outcomes in mHealth interventions are needed. OBJECTIVE This study aims to examine dose–response relationships between patient engagement and 3 psychosocial outcomes in an mHealth intervention, Run4Love, using repeated measurements of outcomes at baseline and 3, 6, and 9 months. METHODS This study is a secondary analysis using longitudinal data from the Run4Love trial, a randomized controlled trial with 300 people living with HIV and elevated depressive symptoms to examine the effects of a 3-month mHealth intervention on reducing depressive symptoms and improving quality of life (QOL). We examined the relationships between patient engagement and depressive symptoms, QOL, and perceived stress in the intervention group (N=150) using 4–time-point outcome measurements. Patient engagement was assessed using the completion rate of course assignments and frequency of items completed. Cluster analysis was used to categorize patients into high- and low-engagement groups. Generalized linear mixed effects models were conducted to investigate the dose–response relationships between patient engagement and outcomes. RESULTS The cluster analysis identified 2 clusters that were distinctively different from each other. The first cluster comprised 72 participants with good compliance to the intervention, completing an average of 74% (53/72) of intervention items (IQR 0.22). The second cluster comprised 78 participants with low compliance to the intervention, completing an average of 15% (11/72) of intervention items (IQR 0.23). Results of the generalized linear mixed effects models showed that, compared with the low-engagement group, the high-engagement group had a significant reduction in more depressive symptoms (β=−1.93; P=.008) and perceived stress (β=−1.72; Pβ=2.41; P=.01) over 9 months. From baseline to 3, 6, and 9 months, the differences in depressive symptoms between the 2 engagement groups were 0.8, 1.6, 2.3, and 3.7 points, respectively, indicating widening between-group differences over time. Similarly, between-group differences in QOL and perceived stress increased over time (group differences in QOL: 0.9, 1.9, 4.7, and 5.1 points, respectively; group differences in the Perceived Stress Scale: 0.9, 1.4, 2.3, and 3.0 points, respectively). CONCLUSIONS This study revealed a positive long-term dose–response relationship between patient engagement and 3 psychosocial outcomes among people living with HIV and elevated depressive symptoms in an mHealth intervention over 9 months using 4 time-point repeat measurement data. The high- and low-engagement groups showed significant and widening differences in depressive symptoms, QOL, and perceived stress at the 3-, 6-, and 9-month follow-ups. Future mHealth interventions should improve patient engagement to achieve long-term and sustained intervention effects. CLINICALTRIAL Chinese Clinical Trial Registry ChiCTR-IPR-17012606; https://www.chictr.org.cn/showproj.aspx?proj=21019

Transgender women (TGW) worldwide report disproportionate violence victimization, depression and condomless anal intercourse (CAI), but the mechanism of the coexistence remains unclear. Using snowball sampling, we recruited 198 self-identified TGW in Shenyang, China between April 2017 and July 2017, and conducted confidential questionnaire survey among them. Positive HIV status was reported by 49 (24.7%) participants. 117 (59.1%) reported at least one type of lifetime violence victimization, with transactional sexual partners being the main violence perpetrators. 108 (54.5%) reported mild to severe depression. 105 (53.0%) reported having CAI with their sexual partners during the last 6 months. Path analysis showed that the association between participants’ violence victimization and CAI was fully mediated by their depression (indirect effect: 0.083, p = 0.014; direct effect: 0.137, p = 0.121). We suggest to incorporate violence screening and prevention and mental health services into intervention strategies to prevent CAI among Chinese TGW.

Purpose: Exergaming play is associated with positive health benefits and increased engagement in physical activity outcomes in players. Research also indicates that factors such as enjoyment and en...

Previous studies revealed that experiences of stigma may negatively affect health service utilization (HSU) among young rural-to-urban migrants. Existing literature also suggested social factors including social capital may mediate such negative effect. However, data are limited regarding the mediation role of social capital among this vulnerable population. Therefore, the current study aimed to examine the associations among experiences of stigma, social capital, and HSU among young rural-to-urban migrants in China. A sample of 641 young rural-to-urban migrants was recruited through a venue-based sampling approach in Beijing, China. Participants were assessed on sociodemographic characteristics, experiences of stigma, and social capital in their urban communities. Self-reported frequency of physical examinations (regularly, irregularly, none) was used as an indicator of HSU. Structural equation modeling (SEM) was performed to examine the direct effect of stigma on HSU as well as the mediation effect of social capital. Among the 641 young rural-to-urban migrants, 32.3% (195/603) reported never having physical examinations while 50.6% (305/603) reported having them irregularly. The final model showed a goodness of fit (χ(2)/df=1.7, CFI=0.98, RMSEA=0.03, WRMR=0.74). Results of SEM revealed that both of the direct and indirect paths from experiences of stigma on HSU were statistically significant. There was a partial mediation effect of social capital on the association between experiences of stigma and HSU (delta z score=−5.49, p