Predicting congenital rubella syndrome in Japan, 2018–2019

Abstract

A B S T R A C TObjectives: A rubella epidemic has been ongoing in Japan since August 2018. In the present study, we aimed to predict the likely size of a congenital rubella syndrome (CRS) epidemic during 2018-19. Methods: The expected number of CRS cases was estimated using an integral equation based on agespecific incidence of rubella among adult women, the time delay from gestational age of infection to diagnosis of CRS, and distribution of the mothers' age at delivery. We used epidemic data during 2012-14 to parameterize the model and applied this in the prediction for 2018-19. Results: In analyzing the 2012-14 epidemic data, the mean delay from the mother's infection to diagnosis was estimated at 24.2 weeks (95% confidence interval (CI): 20.7, 28.1). Applying the parameterized model, together with the more than 480 rubella cases in women in 2018 as well as delayed mother's age at delivery in 2017, we determined that the expected number of CRS cases would be 9.7 (95% CI: 6.5, 12.5) cases. As the epidemic is ongoing, the cumulative number of CRS cases could potentially reach 96.8 (95% CI: 65.3, 125.5) cases, if rubella cases in adult women rose to 10 times the number by week 49 in 2018. Conclusions: CRS is expected to occur an average of 24 weeks following the mother's infection with rubella virus. Accounting for an increase to 650 cases in women by week 5 in 2019, the expected number of CRS cases during 2018-19 has already exceeded 13 cases, as of week 5 in 2019.

Congenital Rubella Infections among the Pregnant Women at a Tertiary Care Hospital in Karachi, Pakistan

“…A study in Japan in 2018 showed that the prevalence of congenital rubella infection was 9.5% [17]. Where as our study showed 4.9% of CRS in the three years period with estimated 7 confirmed cases of congenital rubella syndrome.…”

Identifying geographic areas at risk of rubella epidemics in Japan using seroepidemiological data

“…The present study investigated the risk of a rubella epidemic by geographic region in Japan. On the basis of the spatial datasets of age-specific seroepidemiological surveys and the number of foreign travelers, the effective reproduction number was computed with data from 10 different geographic regions (Figure 1), using the nationwide estimate of the age-and sexdependent next-generation matrix parameterized and quantified in a previous study (Kayano et J o u r n a l P r e -p r o o f al., 2019). The populations of susceptible adult males were concentrated in urban regions ( Figure 4B), and those regions also had a greater number of foreign travelers ( Figure 4A), which contributes to a synergized effect resulting in a greater risk of exposure to untraced cases of rubella.…”

“…There was an epidemic from 2012 to 2014 involving more than 12,000 confirmed cases that were dominated by adult males aged 30–59, and there were as many as 45 confirmed CRS cases during the epidemic ( Kinoshita and Nishiura, 2016 , Kayano et al, 2019 ). A similar outbreak recurred in 2018–2019, also involving CRS cases ( Lee et al, 2019 ), which finally compelled the Japanese government to implement a supplementary immunization program for adult males ( Ministry of Health, Labour and Welfare, 2019 , Ujiie, 2019 ). As a result of the 2018–2019 epidemic, more than 5,000 confirmed cases were reported to the National Institute of Infectious diseases, with the majority of cases observed in urban areas and especially among adult male aged 35-64 years, a slightly elevated ages of cases compared with 2012-14 epidemic ( Ministry of Health, Labour and Welfare, 2020 ).…”

Modelling a Supplementary Vaccination Program of Rubella Using the 2012–2013 Epidemic Data in Japan

“…In addition to modelling the epidemiological dynamics of rubella, we have also devised a model to anticipate CRS from rubella in pregnant mothers. To do this, we used rubella data in adult females, demographic data of livebirths and the distribution of mothers’ ages at delivery, and a precise version of the predictive model can be found elsewhere [17]. In the present study, we used the CRS prediction model to anticipate the cumulative incidence of CRS under various SIP scenarios, and the quantified model predicted the same count (i.e., 45 cases) of CRS from 2012–2014.…”

“…That is, our subject ages were grouped as 0–4, 5–9, 10–14, 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–69 and ≥70 years old, that is, in total, there were 15 age groups ( n a = 15). In addition to the analysis of rubella case data, we predicted the number of CRS cases using the expected number of rubella cases in adult females [17]. To do so, we analyzed not only rubella case data, but also the vital statistics associated with child delivery.…”