Rubella Epidemiology in Africa in the Prevaccine Era, 2002–2009

Goodson, James L.; Masresha, Balcha; Dosseh, Annick; Byabamazima, Charles; Nshimirimana, Deo; Cochi, Stephen L.; Reef, Susan E.

doi:10.1093/infdis/jir108

Cited by 68 publications

(102 citation statements)

References 8 publications

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“…S1 and S2) (15,(22)(23)(24)(25). Similar patterns have been reported in other countries in East Africa, suggesting regional consistency (26). In the majority of provinces, transmission peaks in September and February (except Northeastern province, where inference is weak given paucity of data; Fig.…”

Section: Resultssupporting

confidence: 81%

Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

Wesolowski

Metcalf

Eagle

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

130

116

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Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-income settings, are often static, relying on approximate travel times, road networks, or crosssectional surveys. Mobile phone data provide a unique source of information about human travel, but the power of these data to describe epidemiologically relevant changes in population density remains unclear. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates. Further, combining seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal fluctuations in risk across Kenya and produce dynamic importation risk maps for rubella. Mobile phone data therefore offer a valuable previously unidentified source of data for measuring key drivers of seasonal epidemics.rubella | mobile phones | population mobility | Kenya | seasonality S easonal variation in infectious disease incidence is a ubiquitous phenomenon observed for a range of pathogens such as malaria, measles, and influenza (1-7). Understanding and quantifying key mechanisms that drive seasonal variability such as climatic conditions (malaria and influenza) or patterns of human aggregation (measles and influenza) contribute to our fundamental understanding of epidemic dynamics; they also have important implications for evaluating public health measures that may reduce transmission such as vaccination and school closures (8-11).The effectiveness of any public health measure designed to reduce seasonal transmission by modifying patterns of human aggregation and travel will depend on the degree to which transmission depends on population density and movement. Direct measures of population travel are rare (2, 4, 12, 13). As a result, proxy measures such as school terms and rainfall patterns have been used (1,9,(13)(14)(15). Term time forcing, where school-driven aggregation leads to seasonal peaks of transmission for directly transmitted immunizing infections such as measles, mumps, and rubella, has been observed in many high-income countries (8,16,17) [England and Wales (8), Peru (15), and Denmark (17)]. On the other end of the spectrum in the low-income, predominantly agricultural context of Niger (13), analysis of night lights indicates that peaks in transmission reflect population changes resulting from annual mass migrations of individuals between agricultural areas to cities in the dry season (1). School terms and holidays versus agricultural movements likely represent the extremes in terms of density-related drivers of transmiss...

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Section: Resultssupporting

confidence: 81%

Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

Wesolowski

Metcalf

Eagle

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

130

116

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“…Other publications confirm more recent rubella virus transmission in DRC and report the 343 majority of cases being aged <15 years with some cases among WCBA [1,12]. Our finding of lower antibody 344 seroprevalence in Kinshasa and lower overall antibody seroprevalence than previously documented may be 345 explained by conducting the studies at different points in the epidemic cycle of rubella, differences in laboratory 346 methodologies (e.g., haemagglutination inhibition versus ELISA), or differences in the age distribution of the 347 populations [11,30].…”

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confidence: 68%

“…The sentence now reads "CRS can result in hearing impairment, blindness, congenital heart disease, mental retardation, and/or other manifestations [1][2]." *Response to Reviewers 2.…”

Section: Consider Changing Deafness For Hearing Impairmentmentioning

confidence: 99%

“…However, there are tentative plans for introduction into the7 childhood vaccination schedule before 2020 [10]. Documentation of DRC's historical burden of rubella virus 107 infection and CRS has been minimal [1,11,12]. Moreover, DRC has no surveillance system for either disease, 108 but rubella virus transmission within the country has been documented by a rubella antibody seroprevalence 109 assessment conducted in Kinshasa city in 1987-1988 and by measles case-based surveillance since 2005, with 110 serological testing for rubella-specific immunoglobulin type M (IgM) when suspected measles cases are negative 111 for measles IgM [1,11,12].…”

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confidence: 99%

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Estimating the burden of rubella virus infection and congenital rubella syndrome through a rubella immunity assessment among pregnant women in the Democratic Republic of the Congo: Potential impact on vaccination policy

Alleman

Wannemuehler

Hao

et al. 2016

Vaccine

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A rubella antibody seroprevalence assessment was conducted using serum collected during 2008-2009 from 1,605 pregnant women aged 15-46 years attending 7 antenatal care sites in 3 of DRC's provinces. Estimates of age-and site-specific rubella antibody seroprevalence, population, and fertility rates were used in catalytic models to estimate the incidence of CRS per 100,000 live births and the number of CRS cases born in 2013 in DRC.Overall 84% (95% CI 82, 86) of the women tested were estimated to be rubella antibody seropositive. The association between age and estimated antibody seroprevalence, adjusting for study site, was not significant (p=0.10). Differences in overall estimated seroprevalence by study site were observed indicating variation by geographical area (p<=0.03 for all). Estimated seroprevalence was similar for women declaring residence in urban (84%) versus rural (83%) settings (p=0.67). In 2013 for DRC nationally, the estimated incidence of CRS was 69/100,000 live births (95% CI 0, 186), corresponding to 2886 infants (95% CI 342, 6395) born with CRS.In the 3 provinces, rubella virus transmission is endemic, and most viral exposure and seroconversion occurs before age 15 years. However, approximately 10%-20% of the women were susceptible to rubella virus infection and thus at risk for having an infant with CRS. Per World Health Organization recommendations, introduction of RCV should be accompanied by a campaign targeting children 9 months to 14 years and vaccination of women of child bearing age through routine services. Please find below our responses (in italics in blue below each reviewer's comments) to the comments and the details describing how the manuscript was revised to address each.Reviewer #1: This manuscript provides estimates of Congenital Rubella Syndrome (CRS) for the Democratic Republic of Congo (DRC) using seroprevalence data obtained from existing blood specimens from antenatal clinics. It provides an idea of the occurrence of CRS in a country with little rubella data and that has not yet introduced a rubella-containing vaccine, thus shedding light on an issue that is relevant when discussing vaccine introduction and vaccination strategies. The paper is well written and the supplemental material useful to understand the models used.Specific comments: Abstract 1. What % of the population live in the selected provinces 193-199, lines 206-211, and lines 252-257). Additional details regarding a number of elements of DRC's population have been incorporated into the Methods section of the manuscript (Please see lines Clarify that the estimated CRS incidence and cases in 2013 is for all of DRCThe abstract has been revised to read "In 2013 for DRC nationally, the estimated incidence of CRS was 69/100,000 live births (95% CI 0, 186), corresponding to 2886 infants (95% CI 342, 6395) born with CRS."

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“…Along with age-specific immunity levels, general population dynamics including age distribution and birth rate affect rubella/CRS epidemiology. Until recently, estimates of rubella epidemiology and CRS were derived using mathematical models extrapolated from seroprevalence survey results [74]. However, with declining birth rates and increasing RCV use in many countries, research is needed to predict how varying birth rates affect the epidemiology of rubella/CRS.…”

Section: Panel 6 Rubella/ Crs Cont Rol and Eli Minaɵo N -Key Res Earmentioning

confidence: 99%

Research priorities for global measles and rubella control and eradication

Goodson

Chu

Rota

et al. 2012

Vaccine

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Rubella Epidemiology in Africa in the Prevaccine Era, 2002–2009

Cited by 68 publications

References 8 publications

Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

Estimating the burden of rubella virus infection and congenital rubella syndrome through a rubella immunity assessment among pregnant women in the Democratic Republic of the Congo: Potential impact on vaccination policy

Research priorities for global measles and rubella control and eradication

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