Comparing the historical limits method with regression models for weekly monitoring of national notifiable diseases reports

Impact of Varicella Immunization and Public Health and Social Measures on Varicella Incidence: Insights from Surveillance Data in Shanghai, 2013-2022

To evaluate the impact of a two-dose VarV program on varicella incidence among the whole population, considering the influence of public health and social measures (PHSMs), we extracted surveillance data on varicella cases during 2013-2022 in Minhang, Shanghai. Then, we estimated the incidence trend of varicella through interrupted time-series analyses and quantified the impact of the immunization program and PHSMs using Serfling regression. We also explored the associations between PHSMs and varicella cases. The implementation of the two-dose VarV strategy was followed by a significant decrease in varicella incidence (-1.84% per month). After one year of the program, varicella incidence was estimated at a 45.25% reduction, which was higher in children (59.12% and 54.09%) than in adults (19.49%). The decrease attributed to PHSMs was 31.26% during 2020-2022, and school closing was identified as the most relevant PHSM (b = -8.03 cases, r = -0.67 with a 1-week lag). These findings indicate that the two-dose immunization program has more effectively reduced the varicella incidence compared with the one-dose vaccine, and interventions like school closings are also encouraged to serve as supplementary measures to prevent varicella epidemics.

Establishment of Outbreak Thresholds for Hepatitis A in South Africa Using Laboratory Surveillance, 2017–2020

As South Africa transitions from endemic to intermediate endemicity, hepatitis A surveillance needs strengthening to monitor trends in disease incidence and to identify outbreaks. We used passive laboratory-based surveillance data from the National Health Laboratory Services to calculate national hepatitis A incidence and to establish thresholds for outbreaks. Incidence was calculated by age and geographic location. The static threshold used two or three standard deviations (SDs) above the mean hepatitis A incidence in 2017–2019, and a cumulative summation (CuSum2) threshold used three SDs above the mean of the preceding seven months. These thresholds were applied to hepatitis A data for 2020. From 2017 to 2020, the mean incidence of hepatitis A IgM was 4.06/100,000 and ranged from 4.23 to 4.85/100,000 per year. Hepatitis A incidence was highest in the Western Cape province (WCP) (7.00–10.92/100,000 per year). The highest incidence was in the 1–9-year-olds. The incidence of hepatitis A in 2020 exceeded the static threshold in two districts of the WCP: Cape Winelands in January and Overberg district in August. The provincial incidence did not exceed the static and CuSum2 thresholds. District-level analysis using either threshold was sensitive enough to monitor trends and to alert district health authorities, allowing early outbreak responses.

Influence of atmospheric parameters on human mortality data at different geographical levels

Human mortality data are often modeled using a demographic approach as a function of time. This approach does not present an adequate fit model for the number of deaths with great variability. For this reason, additional information (social, economic and environmental) is required for complementing and improving demographic modelling. This article evaluated the association between human mortality data (segregated by age and sex) with meteorological and air pollutant covariates at three geographical levels: country, macro-climate regions and county. The modelling was based on a generalized linear modelling framework and takes into account the common characteristic of overdispersion in human mortality data through the application of negative binomial distribution. The proposed approach improved the dynamic behavior of the Farrington-like model (basic demographic model) and took into account the extreme meteorological and natural air pollution events. Notably, the proposed modelling worked well in cases where the amount of data was scarce.