Dataset Information

Impact of COVID-19 on the Health of the General and More Vulnerable Population and Its Determinants: Health Care and Social Survey-ESSOC, Study Protocol.

ABSTRACT: This manuscript describes the rationale and protocol of a real-world data (RWD) study entitled Health Care and Social Survey (ESSOC, Encuesta Sanitaria y Social). The study's objective is to determine the magnitude, characteristics, and evolution of the COVID-19 impact on overall health as well as the socioeconomic, psychosocial, behavioural, occupational, environmental, and clinical determinants of both the general and more vulnerable population. The study integrates observational data collected through a survey using a probabilistic, overlapping panel design, and data from clinical, epidemiological, demographic, and environmental registries. The data will be analysed using advanced statistical, sampling, and machine learning techniques. The study is based on several measurements obtained from three random samples of the Andalusian (Spain) population: general population aged 16 years and over, residents in disadvantaged areas, and people over the age of 55. Given the current characteristics of this pandemic and its future repercussions, this project will generate relevant information on a regular basis, commencing from the beginning of the State of Alarm. It will also establish institutional alliances of great social value, explore and apply powerful and novel methodologies, and produce large, integrated, high-quality and open-access databases. The information described here will be vital for health systems in order to design tailor-made interventions aimed at improving the health care, health, and quality of life of the populations most affected by the COVID-19 pandemic.

SUBMITTER: Sanchez-Cantalejo C

PROVIDER: S-EPMC8345631 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThis 4-year research project focuses on 6 social community enterprises (SCEs) that operate in 5 neighborhoods in a Dutch city. Residents of these neighborhoods face problems such as poor average levels of physical and mental health, high unemployment rates, and weak social cohesion. SCEs offer residents social, cultural, and work-related activities and are therefore believed to help these persons develop themselves and strengthen the social ties in the community. Because of a lack of empirical evidence; however, it is unclear whether and how SCEs benefit the health and well-being of participants.ObjectiveThis paper outlines a protocol for an evaluation study on the impact of SCEs, aiming to determine (1) to what extent SCEs affect health and well-being of participating residents, (2) what underlying processes and mechanisms can explain such impact, and (3) what assets are available to SCEs and how they can successfully mobilize these assets.MethodsA mixed methods multiple-case study design including repeated measurements will be conducted. Six SCEs form the cases. An integrated model of SCE health intervention will be used as the theoretical basis. First, the impact of SCEs is measured on the individual and community level, using questionnaires and in-depth interviews conducted with participants. Second, the research focuses on the underlying processes and mechanisms and the organizational and sociopolitical factors that influence the success or failure of these enterprises in affecting the health and well-being of residents. At this organizational level, in-depth interviews are completed with SCE initiators and stakeholders, such as municipal district managers. Finally, structurally documented observations are made on the organizational and sociopolitical context of the SCEs.ResultsThis research project received funding from the Netherlands Organization for Health Research and Development in 2018. Data collection takes place from 2018 until 2022. Data analysis starts after the last round of data collection in 2022 and finalizes in 2024. Expected results will be published in 2023 and 2024.ConclusionsDespite the societal relevance of SCEs, little empirical research has been performed on their functioning and impact. This research applies a variety of methods and includes the perspectives of multiple stakeholders aiming to generate new empirical evidence. The results will enable us to describe how SCE activities influence intermediate and long-term health outcomes and how the organizational and sociopolitical context of SCEs may shape opportunities or barriers for health promotion. As the number of these initiatives in the Netherlands is increasing rapidly, this research can benefit many SCEs attempting to become more effective and increase their impact. The findings of this research will be shared directly with relevant stakeholders through local and national meetings and annual reports and disseminated among other researchers through scientific publications.International registered report identifier (irrid)DERR1-10.2196/37966.

Project description:BackgroundInterest in and funding for digital health interventions have rapidly grown in recent years. Despite the increasing familiarity with mobile health from regulatory bodies, providers, and patients, overarching research on digital health adoption has been primarily limited to morbidity-specific and non-US samples. Consequently, there is a limited understanding of what personal factors hold statistically significant relationships with digital health uptake. Moreover, this limits digital health communities' knowledge of equity along digital health use patterns.ObjectiveThis study aims to identify the social determinants of digital health tool adoption in Georgia.MethodsWeb-based survey respondents in Georgia 18 years or older were recruited from mTurk to answer primarily closed-ended questions within the following domains: participant demographics and health consumption background, telehealth, digital health education, prescription management tools, digital mental health services, and doctor finder tools. Participants spent around 15 to 20 minutes on a survey to provide demographic and personal health care consumption data. This data was analyzed with multivariate linear and logistic regressions to identify which of these determinants, if any, held statistically significant relationships with the total number of digital health tool categories adopted and which of these determinants had absolute relationships with specific categories.ResultsA total of 362 respondents completed the survey. Private insurance, residence in an urban area, having a primary care provider, fewer urgent emergency room (ER) visits, more ER visits leading to inpatient stays, and chronic condition presence were significantly associated with the number of digital health tool categories adopted. The separate logistic regressions exhibited substantial variability, with 3.5 statistically significant predictors per model, on average. Age, federal poverty level, number of primary care provider visits in the past 12 months, number of nonurgent ER visits in the past 12 months, number of urgent ER visits in the past 12 months, number of ER visits leading to inpatient stays in the past 12 months, race, gender, ethnicity, insurance, education, residential area, access to the internet, difficulty accessing health care, usual source of care, status of primary care provider, and status of chronic condition all had at least one statistically significant relationship with the use of a specific digital health category.ConclusionsThe results demonstrate that persons who are socioeconomically disadvantaged may not adopt digital health tools at disproportionately higher rates. Instead, digital health tools may be adopted along social determinants of health, providing strong evidence for the digital health divide. The variability of digital health adoption necessitates investing in and building a common framework to increase mobile health access. With a common framework and a paradigm shift in the design, evaluation, and implementation strategies around digital health, disparities can be further mitigated and addressed. This likely will begin with a coordinated effort to determine barriers to adopting digital health solutions.

Project description:ImportanceManaged care payment formulas commonly allocate more money for medically complex populations, but ignore most social determinants of health (SDH).ObjectiveTo add SDH variables to a diagnosis-based payment formula that allocates funds to managed care plans and accountable care organizations.Design, setting, and participantsUsing data from MassHealth, the Massachusetts Medicaid and Children's Health Insurance Program, we estimated regression models predicting Medicaid spending using a diagnosis-based and SDH-expanded model, and compared the accuracy of their cost predictions overall and for vulnerable populations. MassHealth members enrolled for at least 6 months in 2013 in fee-for-service (FFS) programs (n = 357 660) or managed care organizations (MCOs) (n = 524 607).ExposuresWe built cost prediction models from a fee-for-service program. Predictors in the diagnosis-based model are age, sex, and diagnoses from claims. The SDH model adds predictors describing housing instability, behavioral health issues, disability, and neighborhood-level stressors.Main outcomes and measuresOverall model explanatory power and overpayments and underpayments for subgroups of interest for all Medicaid-reimbursable expenditures excepting long-term support services (mean annual cost = $5590 per member).ResultsWe studied 357 660 people who were FFS participants and 524 607 enrolled in MCOs with a combined 806 889 person-years of experience. The FFS program experience included more men (49.6% vs 43.6%), older patients (mean age of 26.1 years vs 21.6 years), and sicker patients (mean morbidity score of 1.16 vs 0.89) than MCOs. Overall, the SDH model performed well, but only slightly better than the diagnosis-based model, explaining most of the spending variation in the managed care population (validated R2 = 62.4) and reducing underpayments for several vulnerable populations. For example, raw costs for the quintile of people living in the most stressed neighborhoods were 9.6% ($537 per member per year) higher than average. Since greater medical morbidity accounts for much of this difference, the diagnosis-based model underpredicts costs for the most stressed quintile by about 2.1% ($130 per member per year). The expanded model eliminates the neighborhood-based underpayment, as well as underpayments of 72% for clients of the Department of Mental Health (observed costs of about $30 000 per year) and of 7% for those with serious mental illness (observed costs of about $16 000 per year).Conclusions and relevanceSince October 2016, MassHealth has used an expanded model to allocate payments from a prespecified total budget to managed care organizations according to their enrollees' social and medical risk. Extra payments for socially vulnerable individuals could fund activities, such as housing assistance, that could improve health equity.

Project description:Background and purposeSocial determinants of health (SDOH) have been previously associated with incident stroke. Although SDOH often cluster within individuals, few studies have examined associations between incident stroke and multiple SDOH within the same individual. The objective was to determine the individual and cumulative effects of SDOH on incident stroke.MethodsThis study included 27 813 participants from the REGARDS (Reasons for Geographic and Racial Differences in Stroke) Study, a national, representative, prospective cohort of black and white adults aged ≥45 years. SDOH was the primary exposure. The main outcome was expert adjudicated incident stroke. Cox proportional hazards models examined associations between incident stroke and SDOH, individually and as a count of SDOH, adjusting for potential confounders.ResultsThe mean age was 64.7 years (SD 9.4) at baseline; 55.4% were women and 40.4% were blacks. Over a median follow-up of 9.5 years (IQR, 6.0-11.5), we observed 1470 incident stroke events. Of 10 candidate SDOH, 7 were associated with stroke (P<0.10): race, education, income, zip code poverty, health insurance, social isolation, and residence in one of the 10 lowest ranked states for public health infrastructure. A significant age interaction resulted in stratification at 75 years. In fully adjusted models, among individuals <75 years, risk of stroke rose as the number of SDOH increased (hazard ratio for one SDOH, 1.26 [95% CI, 1.02-1.55]; 2 SDOH hazard ratio, 1.38 [95% CI, 1.12-1.71]; and ≥3 SDOH hazard ratio, 1.51 [95% CI, 1.21-1.89]) compared with those without any SDOH. Among those ≥75 years, none of the observed effects reached statistical significance.ConclusionsIncremental increases in the number of SDOH were independently associated with higher incident stroke risk in adults aged <75 years, with no statistically significant effects observed in individuals ≥75 years. Targeting individuals with multiple SDOH may help reduce risk of stroke among vulnerable populations.

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Impact of COVID-19 on the Health of the General and More Vulnerable Population and Its Determinants: Health Care and Social Survey-ESSOC, Study Protocol.

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