Are COVID-19 models blind to the social determinants of health? A systematic review protocol

Do COVID-19 Infectious Disease Models Incorporate the Social Determinants of Health? A Systematic Review

Objectives

To identify COVID-19 infectious disease models that accounted for social determinants of health (SDH).

Methods

We searched MEDLINE, EMBASE, Cochrane Library, medRxiv, and the Web of Science from December 2019 to August 2020. We included mathematical modelling studies focused on humans investigating COVID-19 impact and including at least one SDH. We abstracted study characteristics (e.g., country, model type, social determinants of health) and appraised study quality using best practices guidelines.

Results

83 studies were included. Most pertained to multiple countries (n = 15), the United States (n = 12), or China (n = 7). Most models were compartmental (n = 45) and agent-based (n = 7). Age was the most incorporated SDH (n = 74), followed by gender (n = 15), race/ethnicity (n = 7) and remote/rural location (n = 6). Most models reflected the dynamic nature of infectious disease spread (n = 51, 61%) but few reported on internal (n = 10, 12%) or external (n = 31, 37%) model validation.

Conclusion

Few models published early in the pandemic accounted for SDH other than age. Neglect of SDH in mathematical models of disease spread may result in foregone opportunities to understand differential impacts of the pandemic and to assess targeted interventions.

Systematic Review Registration

[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020207706], PROSPERO, CRD42020207706.

Incorporating Social Determinants of Health in Infectious Disease Models: A Systematic Review of Guidelines

BACKGROUND

Infectious disease (ID) models have been the backbone of policy decisions during the COVID-19 pandemic. However, models often overlook variation in disease risk, health burden, and policy impact across social groups. Nonetheless, social determinants are becoming increasingly recognized as fundamental to the success of control strategies overall and to the mitigation of disparities.

METHODS

To underscore the importance of considering social heterogeneity in epidemiological modeling, we systematically reviewed ID modeling guidelines to identify reasons and recommendations for incorporating social determinants of health into models in relation to the conceptualization, implementation, and interpretations of models.

RESULTS

After identifying 1,372 citations, we found 19 guidelines, of which 14 directly referenced at least 1 social determinant. Age (n = 11), sex and gender (n = 5), and socioeconomic status (n = 5) were the most commonly discussed social determinants. Specific recommendations were identified to consider social determinants to 1) improve the predictive accuracy of models, 2) understand heterogeneity of disease burden and policy impact, 3) contextualize decision making, 4) address inequalities, and 5) assess implementation challenges.

CONCLUSION

This study can support modelers and policy makers in taking into account social heterogeneity, to consider the distributional impact of infectious disease outbreaks across social groups as well as to tailor approaches to improve equitable access to prevention, diagnostics, and therapeutics.

HIGHLIGHTS

Infectious disease (ID) models often overlook the role of social determinants of health (SDH) in understanding variation in disease risk, health burden, and policy impact across social groups.In this study, we systematically review ID guidelines and identify key areas to consider SDH in relation to the conceptualization, implementation, and interpretations of models.We identify specific recommendations to consider SDH to improve model accuracy, understand heterogeneity, estimate policy impact, address inequalities, and assess implementation challenges.

Impact of social and cultural factors on incidence, transmission and control of Coronavirus disease in Iran: a qualitative study

INTRODUCTION

COVID-19 pandemic has had mixed reactions from nations, people and governments about ways to cope with, prevent and control the disease. The current study identifies social, cultural and policy factors affecting the incidence and control of Coronavirus disease in Iran.

METHODS

A qualitative study consists of content analysis as well as the views of 20 experienced and knowledgeable subjects specialized in social and cultural health management. The data were gathered using three semi-structured interviews and then continued by 17 semi-structured interviews. Data analysis was done using Graneheim approach. After each interview, the recorded audio files transcript and reviewed. Then codes extracted and divided to categories and sub-categories.

RESULTS

There are distinct social and cultural factors in coping with Coronavirus disease. These consisted of three categories of governance, individual and community related factors. A total of 17 subcategories and 215 primary codes that were extracted from the text of interviews as variables of the study and in relation to the research question. Ten subdomains of governance including vaccination, political issues, knowledge, support services, administrative services, transportation, health and treatment, culturalization, legislation and, managerial and financial policies impacted the spread and mitigation of the pandemic at various levels.

CONCLUSION

The management of pandemics requires a comprehensive capacity for identifying and determining social and cultural criteria. A healthy partnership between governments and the community may be required to remove unnecessary obstacles that hinder public health attempt to alleviate the risk. The obtained criteria and indicators from this study may be utilized by policy makers in an attempt to strengthen protocols for mitigating pandemics. Further studies may be warranted to confirm these findings.

[Social determinants associated with self-reporting of symptoms and access to COVID-19 testing and diagnosis in the Plurinational State of BoliviaDeterminantes sociais associados ao autorrelato de sintomas, acesso a testagem e diagnóstico de COVID-19 no Estado Plurinacional da Bolívia]

Objective

To identify the prevalence of self-reporting of symptoms and access to testing and diagnosis of coronavirus-19 disease (COVID-19), as well as its association with social determinants of health (SDH).

Methods

Cross-sectional study with a sample of 11 728 men and 12 612 women over the age of 17, based on the National Household Survey 2020. The dependent variables were the self-reporting of symptoms, access to testing, and a positive COVID-19 test. The independent variables were age, educational level, area of residence and geographic area, ethnicity, type of household, income per capita, occupation, and health insurance. Prevalences, bivariate associations, and binomial logistical regression models (odds ratio (OR), and 95% confidence interval (CI95%) were calculated.

Results

Of the total individuals included, 16% reported symptoms, 10% a test, and 4.2% a positive COVID-19 test. Inequalities were observed in the reporting of COVID-19 symptoms, with a higher probability in women whose income had fallen (OR: 1.7; CI95%: 1.2-2.4) and unemployed persons (OR: 1.2; CI95%: 1.1-1.4 for men and OR: 1.3; CI95%: 1.5-1.5 for women). In contrast, with respect to access to diagnostic tests, the highest probability was observed in people with higher education (OR: 2.4; CI95%: 1.9-2.9 for men and OR: 2.7; CI95%: 2.2-3.4 for women), whose income was maintained (OR: 1.5; CI95%: 1.3-1.9 for men and OR: 1.7; CI95%: 1.4-2.0 for women) and those in the highest quartile of per capita household income (OR: 2.0; CI95%: 1.6-2.5 for men and OR: 1.6; CI95%: 1.3-2.0 for women). The probability of reporting symptoms and getting tested, and being diagnosed with COVID-19 increased with age for people with health insurance and those living in the llanos region; however, it decreased for residents of rural areas.

Conclusions

There are inequalities in access to testing and the reporting of COVID-19 symptoms.

Using a Social Determinants of Health Summary Measure to Predict General Health Status in the BRFSS

PURPOSE

This study aims to assess the relationship between social determinants of health (SDoH) burden and overall health.

DESIGN

Three years of Behavioral Risk Factor Surveillance System (BRFSS) data (2017-2019) were combined for this cross-sectional study.

SETTING

Massachusetts.

SUBJECTS

Out of a possible 21,312 respondents, 16,929 (79%) were eligible for inclusion.

MEASURES

To create the SDoH summary measure, items assessing social risk experiences including financial instability (1 item), housing instability (2 items), perceptions of neighborhood crime (1 item), and food insecurity (2 items) were summed to create a count of risk experiences. Outcome measures included self-rated general health, days of poor physical health, and days of poor mental health.

ANALYSIS

Multivariable logistic regression was used to evaluate the association between each outcome and the SDoH summary measure, adjusting for demographic confounders.

RESULTS

In adjusted analyses, respondents who reported experiencing 1, 2, 3, or 4+ SDoH had a 1.6 (95% CI: 1.3-2.0), 2.9 (95% CI: 2.3-3.7), 3.2 (95% CI: 2.4-4.3), or 5.3 (95% CI: 4.0-7.0) increased odds (respectively) of self-rated fair/poor health, compared to those who reported zero SDoH. The adjusted relationship between the SDoH summary measure and physical health and mental health was similar in magnitude and statistically significant.

CONCLUSIONS

These results demonstrate that the overall burden of risk due to SDoH is an important predictor of health.