Early warning system using primary healthcare data in the post-COVID-19-pandemic era: Brazil nationwide case-study (preprint)

BackgroundSyndromic surveillance utilising primary health care (PHC) data is a valuable tool for early outbreak detection, as demonstrated in the potential to identify COVID-19 outbreaks. However, the potential of such an early warning system in the post-COVID-19 era remains largely unexplored. MethodsWe analysed PHC encounter counts due to respiratory complaints registered in the Brazilian database of the Universal Health System between January and July 2023. We applied EARS (variation C1-C2-C3) and EVI to estimate the weekly thresholds. An alarm was determined when the number of encounters exceeded the week-specific threshold. We used data on hospitalisation due to respiratory disease to classify weeks in which the number of cases surpassed predetermined thresholds as anomalies. We compared EARS and EVIs efficacy in anticipating anomalies. FindingsA total of 119 anomalies were identified across 116 immediate regions during the study period. The EARS-C2 presented the highest early alarm rate, with 81/119 (68%) early alarms, and C1 the lowest, with 71 (60%) early alarms. The lowest true positivity was the EARS-C1 118/1354 (8.7%) and the highest EARS-C3 99/856 (11.6%). ConclusionRoutinely collected PHC data can be successfully used to detect respiratory disease outbreaks in Brazil. Syndromic surveillance enhances timeliness in surveillance strategies, albeit with lower specificity. A combined approach with other strategies is essential to strengthen accuracy, offering a proactive and effective public health response against future outbreaks.

Social and racial inequalities in COVID-19 risk of hospitalisation and death across Sao Paulo state, Brazil (preprint)

BackgroundLittle evidence exists on the differential health effects of COVID-19 on disadvantaged population groups. Here we characterise the differential risk of hospitalisation and death in Sao Paulo state, Brazil and show how vulnerability to COVID-19 is shaped by socioeconomic inequalities. MethodsWe conducted a cross-sectional study using hospitalised severe acute respiratory infections (SARI) notified from March to August 2020, in the Sistema de Monitoramento Inteligente de Sao Paulo (SIMI-SP) database. We examined the risk of hospitalisation and death by race and socioeconomic status using multiple datasets for individual-level and spatio-temporal analyses. We explained these inequalities according to differences in daily mobility from mobile phone data, teleworking behaviour, and comorbidities. FindingsThroughout the study period, patients living in the 40% poorest areas were more likely to die when compared to patients living in the 5% wealthiest areas (OR: 1{middle dot}60, 95% CI: 1{middle dot}48 - 1{middle dot}74) and were more likely to be hospitalised between April and July, 2020 (OR: 1{middle dot}08, 95% CI: 1{middle dot}04 - 1{middle dot}12). Black and Pardo individuals were more likely to be hospitalised when compared to White individuals (OR: 1{middle dot}37, 95% CI: 1{middle dot}32 - 1{middle dot}41; OR: 1{middle dot}23, 95% CI: 1{middle dot}21 - 1{middle dot}25, respectively), and were more likely to die (OR: 1{middle dot}14, 95% CI: 1{middle dot}07 - 1{middle dot}21; 1{middle dot}09, 95% CI: 1{middle dot}05 - 1{middle dot}13, respectively). InterpretationLow-income and Black and Pardo communities are more likely to die with COVID-19. This is associated with differential access to healthcare, adherence to social distancing, and the higher prevalence of comorbidities. FundingThis project was supported by a Medical Research Council-Sao Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0) (http://caddecentre.org/). This work received funding from the U.K. Medical Research Council under a concordat with the U.K. Department for International Development.