COVID-19 Health Impact: A Use Case for Syndromic Surveillance System Monitoring Based on Primary Care Patient Registries in the Netherlands.

BACKGROUND

The COVID-19 pandemic challenged societies worldwide. The implementation of mitigation measures to limit the number of SARS-CoV-2 infections resulted in unintended health effects.

OBJECTIVE

The objective of this study is to demonstrate the use of an existing syndromic surveillance system in primary care during a first series of quarterly cross-sectional monitoring cycles, targeting health problems presented in primary care among Dutch youth since August 2021.

METHODS

Aggregated data from the surveillance system of Nivel Primary Care Database were analyzed quarterly to monitor 20 health problems often reported in the aftermath of disasters and environmental incidents. Results were stratified by age (ie, 0-4, 5-14, and 15-24 years), sex, and region (province). Weekly prevalence rates were calculated as the number of persons consulting their general practitioner in a particular week, using the number of enlisted persons as the denominator. Findings were compared to quarterly survey panel data, collected in the context of the Integrated Health Monitor COVID-19, and the Dutch stringency index values, indicative of the intensity of COVID-19 mitigation measures.

RESULTS

Over time, weekly rates pointed to an increased number of consultations for depressive feelings and suicide (attempts) among youth, during and after periods with intensified domestic restrictions.

CONCLUSIONS

The results illustrate how, from a disaster health research perspective based on the COVID-19 pandemic, health consequences of pandemics could be successfully followed over time using an existing infrastructure for syndromic surveillance and monitoring. Particular areas of health concern can be defined beforehand, and may be modified or expanded during the monitoring activities to track relevant developments. Although an association between patterns and changes in the strictness of mitigation measures might seem probable, claims about causality should be made with caution.