Scoping review of the methodology of large health surveys conducted in Spain early on in the COVID-19 pandemic

Comparing temporal changes and predictors of different types of mental health and socio-emotional wellbeing outcomes during COVID-19: an overlapping panel study of Spanish residents

OBJECTIVES

Few panel studies have investigated how different types of mental health (MH) and socio-emotional wellbeing (SEW) outcomes have changed during the pandemic and if their burden has been equally distributed at the population-level. We aimed to examine temporal changes in these outcomes and their socio-ecological predictors using panel data.

STUDY DESIGN

Longitudinal population-based survey with overlapping panels.

METHODS

Analyses were carried out using four measurements of data from the Health and Social Survey (April 2020 to April 2021). Participants included Andalusian (Spanish) residents aged 16 years or older who participated in all four measurements (n = 1223). Seven dichotomous MH and SEW outcomes, as well as several socio-ecological predictors informed by a conceptual model, were examined in descriptive and multivariate analyses.

RESULTS

Unadjusted odds of regular/bad perceived mental health (vs. excellent/very good/good), low socio-emotional wellbeing (vs. regular), low happiness (vs. regular), and feeling anxious (vs. not feeling anxious) decreased significantly from the first to the second measurement; however, in the fourth, low socio-emotional wellbeing significantly increased while low optimism decreased. Considering varying coefficients, objectively measured COVID-19 status and self-reported severity levels of the infection were statistically significant. Health status, social support, and household financial difficulty predicted higher adjusted odds in most of the seven assessed outcomes.

CONCLUSIONS

Significant temporal variations in MH and SEW outcomes, along with their predictors, were observed during the first year of the pandemic. Some of these outcomes worsened as the pandemic progressed, whereas others improved. Findings also suggest that some individuals such as those experiencing poor health, limited social support, and low socioeconomic status are disproportionately impacted.

Calibration and XGBoost reweighting to reduce coverage and non-response biases in overlapping panel surveys: application to the Healthcare and Social Survey

BACKGROUND

Surveys have been used worldwide to provide information on the COVID-19 pandemic impact so as to prepare and deliver an effective Public Health response. Overlapping panel surveys allow longitudinal estimates and more accurate cross-sectional estimates to be obtained thanks to the larger sample size. However, the problem of non-response is particularly aggravated in the case of panel surveys due to population fatigue with repeated surveys.

OBJECTIVE

To develop a new reweighting method for overlapping panel surveys affected by non-response.

METHODS

We chose the Healthcare and Social Survey which has an overlapping panel survey design with measurements throughout 2020 and 2021, and random samplings stratified by province and degree of urbanization. Each measurement comprises two samples: a longitudinal sample taken from previous measurements and a new sample taken at each measurement.

RESULTS

Our reweighting methodological approach is the result of a two-step process: the original sampling design weights are corrected by modelling non-response with respect to the longitudinal sample obtained in a previous measurement using machine learning techniques, followed by calibration using the auxiliary information available at the population level. It is applied to the estimation of totals, proportions, ratios, and differences between measurements, and to gender gaps in the variable of self-perceived general health.

CONCLUSION

The proposed method produces suitable estimators for both cross-sectional and longitudinal samples. For addressing future health crises such as COVID-19, it is therefore necessary to reduce potential coverage and non-response biases in surveys by means of utilizing reweighting techniques as proposed in this study.