Towards elimination of childhood and adolescent tuberculosis in the Netherlands: an epidemiological time-series analysis of national surveillance data

BackgroundTuberculosis (TB) in children and adolescents is a sentinel event for ongoing transmission. In the Netherlands, epidemiological characteristics of childhood and adolescent TB have not been fully evaluated. Therefore, we aimed to assess TB epidemiology within this population to provide guidance for TB elimination.MethodsA retrospective time-series analysis using national surveillance data from 1993–2018 was performed in children (aged <15 years) and adolescents (aged 15–19 years) with TB. Poisson regression models offset with log-population size were used to estimate notification rates and rate ratios. Trends in notification rates were estimated using average annual percentage changes (AAPC) based on the segmented linear regression analysis.ResultsAmong 3899 children and adolescents with TB notified during 1993–2018, 2418 (62%) were foreign-born (725 (41.3%) out of 1755 children and 1693 (78.9%) out of 2144 adolescents). The overall notification rate in children was 2.3 per 100 000 person-years, declining steadily during the study period (AAPC −10.9%, 95% CI −12.6–−9.1). In adolescents, the overall notification rate was 8.4 per 100 000 person-years, strongly increasing during 1993–2001 and 2012–2018. Compared to Dutch-born children and adolescents, substantially higher notification rates were observed among African-born children and adolescents (116.8 and 316.6 per 100 000 person-years, respectively). Additionally, an increasing trend was observed in African-born adolescents (AAPC 18.5%, 95% CI 11.9–25.5). Among the foreign-born population, those from countries in the horn of Africa contributed most to the TB caseload.ConclusionTB notification rate among children was low and constantly declining across different demographic groups. However, heterogeneities were shown in adolescents, with an increasing trend in the foreign-born, particularly those from Africa.

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An Early Detection of Decline in Rotavirus Cases during the 2013/2014 Season in Japan as Revealed by Time-series Analysis of National Surveillance Data

Tropical Medicine and Health ◽

10.2149/tmh.2015-23 ◽

2015 ◽

Vol 43 (3) ◽

pp. 177-181 ◽

Cited By ~ 9

Author(s):

Masahiro Hashizume ◽

Toyoko Nakagomi ◽

Osamu Nakagomi

Keyword(s):

Time Series ◽

Early Detection ◽

Time Series Analysis ◽

Surveillance Data ◽

National Surveillance ◽

Series Analysis

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Spectrum Parameter Estimation in Time Series Analysis† †Part of this work was supported by the Netherlands Organization for the Advancement of Pure Research (Z.W.O.) under Grant B62-165.

Developments in Statistics ◽

10.1016/b978-0-12-426604-9.50007-0 ◽

1983 ◽

pp. 1-96 ◽

Cited By ~ 7

Author(s):

K.O. DZHAPARIDZE ◽

A.M. YAGLOM

Keyword(s):

Time Series ◽

Parameter Estimation ◽

The Netherlands ◽

Time Series Analysis ◽

Spectrum Parameter ◽

Series Analysis ◽

Pure Research

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Variable impact of an antimicrobial stewardship programme in three intensive care units: time-series analysis of 2012–2017 surveillance data

Journal of Hospital Infection ◽

10.1016/j.jhin.2019.10.002 ◽

2020 ◽

Vol 104 (2) ◽

pp. 150-157 ◽

Cited By ~ 1

Author(s):

S. Abbara ◽

M. Domenech de Cellès ◽

R. Batista ◽

J.P. Mira ◽

C. Poyart ◽

...

Keyword(s):

Time Series ◽

Intensive Care ◽

Time Series Analysis ◽

Intensive Care Units ◽

Antimicrobial Stewardship ◽

Surveillance Data ◽

Series Analysis ◽

Variable Impact

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A Time-Series Analysis of Contextual-Level Effects on Hate Crime in The Netherlands

Social Forces ◽

10.1093/sf/soaa102 ◽

2020 ◽

Author(s):

Laura Jacobs ◽

Joost van Spanje

Keyword(s):

Time Series ◽

The Netherlands ◽

Time Series Analysis ◽

Hate Crime ◽

Hate Speech ◽

Contextual Factors ◽

Hate Crimes ◽

Party Leader ◽

Terrorist Attacks ◽

Series Analysis

Abstract Nowadays, registered hate crimes are on the rise in many Western societies. What explains temporal variation in the incidence of hate crimes? Combining insights from the grievance model and the opportunity model, we study the role of three types of contextual factors: security (terrorism), media (news about terrorism and immigration), and political factors (speech by anti-immigration actors, hate speech prosecution, and high-profile anti-immigration victories). We apply time-series analysis to our original dataset of registered hate crimes in the Netherlands, 2015–2017 (N = 7,219). Findings indicate that terrorist attacks, (both print and online) news on refugees, immigration, and terrorism boost nonviolent hate crime. Similarly, news of the hate speech prosecution of Freedom Party leader Geert Wilders increases nonviolent crime as well. Tentative evidence points to a contagion effect of speech by anti-immigration actors. With regard to violent hate crime, only terrorist attacks had an effect. This effect was modest and only found in one of our models. Hence, the grievance and the opportunities model each partially explain nonviolent hate crime, although the security and media context seem most influential. Our findings help to identify the contextual factors contributing to a climate for hate and suggest that perceived threats play a key role.

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