Publications by authors named "Mirjam Kretzschmar"

160 Publications

Should I stay or should I go?

Elife 2021 03 16;10. Epub 2021 Mar 16.

Department of Mathematics, Technical University of Munich, Garching, Germany.

Analysing the characteristics of the SARS-CoV-2 virus makes it possible to estimate the length of quarantine that reduces the impact on society and the economy, while minimising infections.
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http://dx.doi.org/10.7554/eLife.67417DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7963471PMC
March 2021

Model-based evaluation of school- and non-school-related measures to control the COVID-19 pandemic.

Nat Commun 2021 03 12;12(1):1614. Epub 2021 Mar 12.

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.

The role of school-based contacts in the epidemiology of SARS-CoV-2 is incompletely understood. We use an age-structured transmission model fitted to age-specific seroprevalence and hospital admission data to assess the effects of school-based measures at different time points during the COVID-19 pandemic in the Netherlands. Our analyses suggest that the impact of measures reducing school-based contacts depends on the remaining opportunities to reduce non-school-based contacts. If opportunities to reduce the effective reproduction number (R) with non-school-based measures are exhausted or undesired and R is still close to 1, the additional benefit of school-based measures may be considerable, particularly among older school children. As two examples, we demonstrate that keeping schools closed after the summer holidays in 2020, in the absence of other measures, would not have prevented the second pandemic wave in autumn 2020 but closing schools in November 2020 could have reduced R below 1, with unchanged non-school-based contacts.
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http://dx.doi.org/10.1038/s41467-021-21899-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955041PMC
March 2021

Relevance of intra-hospital patient movements for the spread of healthcare-associated infections within hospitals - a mathematical modeling study.

PLoS Comput Biol 2021 Feb 3;17(2):e1008600. Epub 2021 Feb 3.

Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.

The aim of this study is to analyze patient movement patterns between hospital departments to derive the underlying intra-hospital movement network, and to assess if movement patterns differ between patients at high or low risk of colonization. For that purpose, we analyzed patient electronic medical record data from five hospitals to extract information on risk stratification and patient intra-hospital movements. Movement patterns were visualized as networks, and network centrality measures were calculated. Next, using an agent-based model where agents represent patients and intra-hospital patient movements were explicitly modeled, we simulated the spread of multidrug resistant enterobacteriacae (MDR-E) inside a hospital. Risk stratification of patients according to certain ICD-10 codes revealed that length of stay, patient age, and mean number of movements per admission were higher in the high-risk groups. Movement networks in all hospitals displayed a high variability among departments concerning their network centrality and connectedness with a few highly connected departments and many weakly connected peripheral departments. Simulating the spread of a pathogen in one hospital network showed positive correlation between department prevalence and network centrality measures. This study highlights the importance of intra-hospital patient movements and their possible impact on pathogen spread. Targeting interventions to departments of higher (weighted) degree may help to control the spread of MDR-E. Moreover, when the colonization status of patients coming from different departments is unknown, a ranking system based on department centralities may be used to design more effective interventions that mitigate pathogen spread.
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http://dx.doi.org/10.1371/journal.pcbi.1008600DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857595PMC
February 2021

Contact tracing - Old models and new challenges.

Infect Dis Model 2021 30;6:222-231. Epub 2020 Dec 30.

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584CX, Utrecht, the Netherlands.

Contact tracing is an effective method to control emerging infectious diseases. Since the 1980's, modellers are developing a consistent theory for contact tracing, with the aim to find effective and efficient implementations, and to assess the effects of contact tracing on the spread of an infectious disease. Despite the progress made in the area, there remain important open questions. In addition, technological developments, especially in the field of molecular biology (genetic sequencing of pathogens) and modern communication (digital contact tracing), have posed new challenges for the modelling community. In the present paper, we discuss modelling approaches for contact tracing and identify some of the current challenges for the field.
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http://dx.doi.org/10.1016/j.idm.2020.12.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806945PMC
December 2020

Modelling the impact of tailored behavioural interventions on chlamydia transmission.

Sci Rep 2021 Jan 25;11(1):2148. Epub 2021 Jan 25.

Center for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA, Bilthoven, The Netherlands.

Behavioural interventions tailored to psychological characteristics of an individual can effectively achieve risk-reducing behaviour. The impact of tailored interventions on population-level chlamydia prevalence is unknown. We aimed to assess the impact on overall chlamydia prevalence five years after the introduction of an intervention aimed at increasing self-efficacy, social norms, attitudes and intentions towards condom use (i.e., condom intervention), and an intervention aimed at increasing health goals and decreasing impulsiveness (i.e., impulsiveness intervention). A pair model, informed by longitudinal psychological and behavioural data of young heterosexuals visiting sexual health centers, with susceptible-infected-susceptible structure was developed. The intervention effect was defined as an increased proportion of each subgroup moving to the desired subgroup (i.e., lower risk subgroup). Interventions tailored to subgroup-specific characteristics, assuming differential intervention effects in each subgroup, more effectively reduced overall chlamydia prevalence compared to non-tailored interventions. The most effective intervention was the tailored condom intervention, which was assumed to result in a relative reduction in chlamydia prevalence of 18% versus 12% in the non-tailored scenario. Thus, it is important to assess multiple psychological and behavioural characteristics of individuals. Tailored interventions may be more successful in achieving risk-reducing behaviour, and consequently, reduce chlamydia prevalence more effectively.
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http://dx.doi.org/10.1038/s41598-021-81675-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7835240PMC
January 2021

Applications and Recruitment Performance of Web-Based Respondent-Driven Sampling: Scoping Review.

J Med Internet Res 2021 01 15;23(1):e17564. Epub 2021 Jan 15.

National Coordination Centre for Communicable Disease Control, Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands.

Background: Web-based respondent-driven sampling is a novel sampling method for the recruitment of participants for generating population estimates, studying social network characteristics, and delivering health interventions. However, the application, barriers and facilitators, and recruitment performance of web-based respondent-driven sampling have not yet been systematically investigated.

Objective: Our objectives were to provide an overview of published research using web-based respondent-driven sampling and to investigate factors related to the recruitment performance of web-based respondent-driven sampling.

Methods: We conducted a scoping review on web-based respondent-driven sampling studies published between 2000 and 2019. We used the process evaluation of complex interventions framework to gain insights into how web-based respondent-driven sampling was implemented, what mechanisms of impact drove recruitment, what the role of context was in the study, and how these components together influenced the recruitment performance of web-based respondent-driven sampling.

Results: We included 18 studies from 8 countries (high- and low-middle income countries), in which web-based respondent-driven sampling was used for making population estimates (n=12), studying social network characteristics (n=3), and delivering health-related interventions (n=3). Studies used web-based respondent-driven sampling to recruit between 19 and 3448 participants from a variety of target populations. Studies differed greatly in the number of seeds recruited, the proportion of successfully recruiting participants, the number of recruitment waves, the type of incentives offered to participants, and the duration of data collection. Studies that recruited relatively more seeds, through online platforms, and with less rigorous selection procedures reported relatively low percentages of successfully recruiting seeds. Studies that did not offer at least one guaranteed material incentive reported relatively fewer waves and lower percentages of successfully recruiting participants. The time of data collection was shortest in studies with university students.

Conclusions: Web-based respondent-driven sampling can be successfully applied to recruit individuals for making population estimates, studying social network characteristics, and delivering health interventions. In general, seed and peer recruitment may be enhanced by rigorously selecting and motivating seeds, offering at least one guaranteed material incentive, and facilitating adequate recruitment options regarding the target population's online connectedness and communication behavior. Potential trade-offs should be taken into account when implementing web-based respondent-driven sampling, such as having less opportunities to implement rigorous seed selection procedures when recruiting many seeds, as well as issues around online rather than physical participation, such as the risk of cheaters participating repeatedly.
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http://dx.doi.org/10.2196/17564DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7846441PMC
January 2021

The Rhythm of Risk: Sexual Behaviour, PrEP Use and HIV Risk Perception Between 1999 and 2018 Among Men Who Have Sex with Men in Amsterdam, The Netherlands.

AIDS Behav 2020 Dec 2. Epub 2020 Dec 2.

Department of Infectious Diseases, Research and Prevention, Public Health Service of Amsterdam, Amsterdam, The Netherlands.

HIV risk perception plays a crucial role in the uptake of preventive strategies. We investigated how risk perception and its determinants changed between 1999 and 2018 in an open, prospective cohort of 1323 HIV-negative men who have sex with men (MSM). Risk perception, defined as the perceived likelihood of acquiring HIV in the past 6 months, changed over time: being relatively lower in 2008-2011, higher in 2012-2016, and again lower in 2017-2018. Irrespective of calendar year, condomless anal intercourse (AI) with casual partners and high numbers of partners were associated with higher risk perception. In 2017-2018, condomless receptive AI with a partner living with HIV was no longer associated with risk perception, while PrEP use and condomless AI with a steady partner were associated with lower risk perception. We showed that risk perception has fluctuated among MSM in the past 20 years. The Undetectable equals Untransmittable statement and PrEP coincided with lower perceived risk.
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http://dx.doi.org/10.1007/s10461-020-03109-4DOI Listing
December 2020

Modelling pathogen spread in a healthcare network: Indirect patient movements.

PLoS Comput Biol 2020 11 30;16(11):e1008442. Epub 2020 Nov 30.

Institute for Medical Epidemiology, Biometrics and Informatics, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.

Inter-hospital patient transfers (direct transfers) between healthcare facilities have been shown to contribute to the spread of pathogens in a healthcare network. However, the impact of indirect transfers (patients re-admitted from the community to the same or different hospital) is not well studied. This work aims to study the contribution of indirect transfers to the spread of pathogens in a healthcare network. To address this aim, a hybrid network-deterministic model to simulate the spread of multiresistant pathogens in a healthcare system was developed for the region of Lower Saxony (Germany). The model accounts for both, direct and indirect transfers of patients. Intra-hospital pathogen transmission is governed by a SIS model expressed by a system of ordinary differential equations. Our results show that the proposed model reproduces the basic properties of healthcare-associated pathogen spread. They also show the importance of indirect transfers: restricting the pathogen spread to direct transfers only leads to 4.2% system wide prevalence. However, adding indirect transfers leads to an increase in the overall prevalence by a factor of 4 (18%). In addition, we demonstrated that the final prevalence in the individual healthcare facilities depends on average length of stay in a way described by a non-linear concave function. Moreover, we demonstrate that the network parameters of the model may be derived from administrative admission/discharge records. In particular, they are sufficient to obtain inter-hospital transfer probabilities, and to express the patients' transfers as a Markov process. Using the proposed model, we show that indirect transfers of patients are equally or even more important as direct transfers for the spread of pathogens in a healthcare network.
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http://dx.doi.org/10.1371/journal.pcbi.1008442DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728397PMC
November 2020

Key questions for modelling COVID-19 exit strategies.

Proc Biol Sci 2020 08 12;287(1932):20201405. Epub 2020 Aug 12.

Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge CB3 0ES, UK.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.
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http://dx.doi.org/10.1098/rspb.2020.1405DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575516PMC
August 2020

Impact of self-imposed prevention measures and short-term government-imposed social distancing on mitigating and delaying a COVID-19 epidemic: A modelling study.

PLoS Med 2020 07 21;17(7):e1003166. Epub 2020 Jul 21.

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.

Background: The coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread to nearly every country in the world since it first emerged in China in December 2019. Many countries have implemented social distancing as a measure to "flatten the curve" of the ongoing epidemics. Evaluation of the impact of government-imposed social distancing and of other measures to control further spread of COVID-19 is urgent, especially because of the large societal and economic impact of the former. The aim of this study was to compare the individual and combined effectiveness of self-imposed prevention measures and of short-term government-imposed social distancing in mitigating, delaying, or preventing a COVID-19 epidemic.

Methods And Findings: We developed a deterministic compartmental transmission model of SARS-CoV-2 in a population stratified by disease status (susceptible, exposed, infectious with mild or severe disease, diagnosed, and recovered) and disease awareness status (aware and unaware) due to the spread of COVID-19. Self-imposed measures were assumed to be taken by disease-aware individuals and included handwashing, mask-wearing, and social distancing. Government-imposed social distancing reduced the contact rate of individuals irrespective of their disease or awareness status. The model was parameterized using current best estimates of key epidemiological parameters from COVID-19 clinical studies. The model outcomes included the peak number of diagnoses, attack rate, and time until the peak number of diagnoses. For fast awareness spread in the population, self-imposed measures can significantly reduce the attack rate and diminish and postpone the peak number of diagnoses. We estimate that a large epidemic can be prevented if the efficacy of these measures exceeds 50%. For slow awareness spread, self-imposed measures reduce the peak number of diagnoses and attack rate but do not affect the timing of the peak. Early implementation of short-term government-imposed social distancing alone is estimated to delay (by at most 7 months for a 3-month intervention) but not to reduce the peak. The delay can be even longer and the height of the peak can be additionally reduced if this intervention is combined with self-imposed measures that are continued after government-imposed social distancing has been lifted. Our analyses are limited in that they do not account for stochasticity, demographics, heterogeneities in contact patterns or mixing, spatial effects, imperfect isolation of individuals with severe disease, and reinfection with COVID-19.

Conclusions: Our results suggest that information dissemination about COVID-19, which causes individual adoption of handwashing, mask-wearing, and social distancing, can be an effective strategy to mitigate and delay the epidemic. Early initiated short-term government-imposed social distancing can buy time for healthcare systems to prepare for an increasing COVID-19 burden. We stress the importance of disease awareness in controlling the ongoing epidemic and recommend that, in addition to policies on social distancing, governments and public health institutions mobilize people to adopt self-imposed measures with proven efficacy in order to successfully tackle COVID-19.
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http://dx.doi.org/10.1371/journal.pmed.1003166DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7373263PMC
July 2020

Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study.

Lancet Public Health 2020 08 16;5(8):e452-e459. Epub 2020 Jul 16.

Julius Center for Health Sciences and Primary Care, Utrecht University, Utrecht, Netherlands; Department of Medical Microbiology, Utrecht University, Utrecht, Netherlands.

Background: In countries with declining numbers of confirmed cases of COVID-19, lockdown measures are gradually being lifted. However, even if most physical distancing measures are continued, other public health measures will be needed to control the epidemic. Contact tracing via conventional methods or mobile app technology is central to control strategies during de-escalation of physical distancing. We aimed to identify key factors for a contact tracing strategy to be successful.

Methods: We evaluated the impact of timeliness and completeness in various steps of a contact tracing strategy using a stochastic mathematical model with explicit time delays between time of infection and symptom onset, and between symptom onset, diagnosis by testing, and isolation (testing delay). The model also includes tracing of close contacts (eg, household members) and casual contacts, followed by testing regardless of symptoms and isolation if testing positive, with different tracing delays and coverages. We computed effective reproduction numbers of a contact tracing strategy (R) for a population with physical distancing measures and various scenarios for isolation of index cases and tracing and quarantine of their contacts.

Findings: For the most optimistic scenario (testing and tracing delays of 0 days and tracing coverage of 100%), and assuming that around 40% of transmissions occur before symptom onset, the model predicts that the estimated effective reproduction number of 1·2 (with physical distancing only) will be reduced to 0·8 (95% CI 0·7-0·9) by adding contact tracing. The model also shows that a similar reduction can be achieved when testing and tracing coverage is reduced to 80% (R 0·8, 95% CI 0·7-1·0). A testing delay of more than 1 day requires the tracing delay to be at most 1 day or tracing coverage to be at least 80% to keep R below 1. With a testing delay of 3 days or longer, even the most efficient strategy cannot reach R values below 1. The effect of minimising tracing delay (eg, with app-based technology) declines with decreasing coverage of app use, but app-based tracing alone remains more effective than conventional tracing alone even with 20% coverage, reducing the reproduction number by 17·6% compared with 2·5%. The proportion of onward transmissions per index case that can be prevented depends on testing and tracing delays, and given a 0-day tracing delay, ranges from up to 79·9% with a 0-day testing delay to 41·8% with a 3-day testing delay and 4·9% with a 7-day testing delay.

Interpretation: In our model, minimising testing delay had the largest impact on reducing onward transmissions. Optimising testing and tracing coverage and minimising tracing delays, for instance with app-based technology, further enhanced contact tracing effectiveness, with the potential to prevent up to 80% of all transmissions. Access to testing should therefore be optimised, and mobile app technology might reduce delays in the contact tracing process and optimise contact tracing coverage.

Funding: ZonMw, Fundação para a Ciência e a Tecnologia, and EU Horizon 2020 RECOVER.
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http://dx.doi.org/10.1016/S2468-2667(20)30157-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7365652PMC
August 2020

The impact of STI test results and face-to-face consultations on subsequent behavior and psychological characteristics.

Prev Med 2020 10 11;139:106200. Epub 2020 Jul 11.

Center for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands.

Sexually transmitted infection (STI) testing without face-to-face counselling is increasingly offered at sexual health centers (SHC), and ordering self-sampling tests online is becoming more popular. However, the impact of testing without counselling on behavior is unknown. We examine the impact of STI testing with and without consultation and the combined effect of a positive test result and treatment consultation, on behavioral and psychological characteristics over time. Data from a longitudinal study among heterosexual SHC visitors aged 18-24 years was used. The impact of a test consultation (participants who tested chlamydia negative with vs. without consultation) and treatment consultation/positive test result (participants who tested chlamydia positive vs. negative), was assessed by comparing behavioral and psychological characteristics before testing (baseline), and at three-week and six-month follow-up, using generalized estimating equation models. Changes after testing were similar between participants who tested chlamydia negative with and without test consultation, namely decreased risk perception, shame, number of partners, and increased knowledge. However, participants who tested chlamydia positive reported stronger increases in health goals and intentions towards condom use, and stronger decreases in the number of partners and stigma, compared to participants who tested negative. Furthermore, condom use increased in chlamydia positive, and decreased in chlamydia negative participants. A treatment consultation/positive test result had a risk-reducing impact on behavioral and psychological characteristics, whereas the impact of a test consultation was limited. Since the majority of young heterosexuals test chlamydia negative, alternative interventions (e.g., online) achieving risk-reducing behavior change targeted to individuals who tested negative are needed.
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http://dx.doi.org/10.1016/j.ypmed.2020.106200DOI Listing
October 2020

Relationship between Coxiella burnetii (Q fever) antibody serology and time spent outdoors.

J Infect 2020 07 21;81(1):90-97. Epub 2020 Apr 21.

Institute for Risk Assessment Sciences (IRAS), Division Environmental Epidemiology and Veterinary Public Health, Utrecht University, Utrecht, the Netherlands.

Background/aim: From 2007 through 2010, the Netherlands experienced the largest recorded Q fever outbreak to date. People living closer to Coxiella burnetii infected goat farms were at increased risk for acute Q fever. Time spent outdoors near infected farms may have contributed to exposure to C. burnetii. The aim of this study was to retrospectively evaluate whether hours/week spent outdoors, in the vicinity of previously C. burnetii infected goat farms, was associated with presence of antibodies against C. burnetii in residents of a rural area in the Netherlands.

Methods: Between 2014-2015, we collected C. burnetii antibody serology and self-reported data about habitual hours/week spent outdoors near the home from 2494 adults. From a subgroup we collected 941 GPS tracks, enabling analyses of active mobility in the outbreak region. Participants were categorised as exposed if they spent time within specified distances (500m, 1000m, 2000m, or 4000m) of C. burnetii infected goat farms. We evaluated whether time spent near these farms was associated with positive C. burnetii serology using spline analyses and logistic regression.

Results: People that spent more hours/week outdoors near infected farms had a significantly increased risk for positive C. burnetii serology (time spent within 2000m of a C. burnetii abortion-wave positive farm, OR 3.6 (1.2-10.6)), compared to people spending less hours/week outdoors.

Conclusions: Outdoor exposure contributed to the risk of becoming C. burnetii serology positive. These associations were stronger if people spent more time near C. burnetii infected farms. Outdoor exposure should, if feasible, be included in outbreak investigations.
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http://dx.doi.org/10.1016/j.jinf.2020.04.013DOI Listing
July 2020

Hepatitis B prevention: Can we learn from the response to HIV/AIDS?

PLoS Med 2020 04 21;17(4):e1003109. Epub 2020 Apr 21.

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.

Mirjam Kretzschmar and Marianne van der Sande discuss the accompanying research study by Anna McNaughton and colleagues on strategies to reduce the burden of hepatitis B in African countries.
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http://dx.doi.org/10.1371/journal.pmed.1003109DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7173715PMC
April 2020

Clustering of chronic hepatitis B screening intentions in social networks of Moroccan immigrants in the Netherlands.

BMC Public Health 2020 Mar 17;20(1):344. Epub 2020 Mar 17.

National Coordination Centre for Communicable Disease Control, Centre for Infectious Disease Control, National Institute for Public Health and the Environment, RIVM/LCI, Postbus 1 (Postbak 13), 3720, BA, Bilthoven, The Netherlands.

Background: Early detection, identification, and treatment of chronic hepatitis B through screening is vital for those at increased risk, e.g. born in hepatitis B endemic countries. In the Netherlands, Moroccan immigrants show low participation rates in health-related screening programmes. Since social networks influence health behaviour, we investigated whether similar screening intentions for chronic hepatitis B cluster within social networks of Moroccan immigrants.

Methods: We used respondent-driven sampling (RDS) where each participant ("recruiter") was asked to complete a questionnaire and to recruit three Moroccans ("recruitees") from their social network. Logistic regression analyses were used to analyse whether the recruiters' intention to request a screening test was similar to the intention of their recruitees.

Results: We sampled 354 recruiter-recruitee pairs: for 154 pairs both participants had a positive screening intention, for 68 pairs both had a negative screening intention, and the remaining 132 pairs had a discordant intention to request a screening test. A tie between a recruiter and recruitee was associated with having the same screening intention, after correction for sociodemographic variables (OR 1.70 [1.15-2.51]).

Conclusions: The findings of our pilot study show clustering of screening intention among individuals in the same network. This provides opportunities for social network interventions to encourage participation in hepatitis B screening initiatives.
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http://dx.doi.org/10.1186/s12889-020-8438-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7077096PMC
March 2020

Longitudinal Patterns of Sexually Transmitted Infection Risk Based on Psychological Characteristics and Sexual Behavior in Heterosexual Sexually Transmitted Infection Clinic Visitors.

Sex Transm Dis 2020 03;47(3):171-176

Department of Interdisciplinary Social Science, Faculty of Social and Behavioral Sciences, Utrecht University, Utrecht, The Netherlands.

Background: Great heterogeneity in sexually transmitted infections (STI) risk exists, and investigating individual-level characteristics related to changes in STI risk over time might facilitate the development and implementation of effective evidence-based behavior change interventions. The aim of this study was to identify longitudinal patterns of STI risk based on psychological and behavioral characteristics.

Methods: A longitudinal study was conducted among heterosexual STI clinic visitors aged 18 to 24 years. Latent classes based on behavioral and psychological characteristics at baseline, and transitions from 1 latent class to another at 3-week, 6-month, and 1-year follow-up, were identified using latent transition analysis.

Results: Four latent classes were identified that could be differentiated by psychological and behavioral characteristics and STI risk: overall low-risk (10%), insecure high-risk (21%), condom-users (38%), and confident high-risk (31%). Although the majority of the total study population did not move to another latent class over time, the size of the overall low-risk group increased from 10% at baseline to 30% after 1 year. This was mainly due to transitions from the insecure high-risk, condom-users, and confident high-risk class at 3-week follow-up to the overall low-risk class at 6-month follow-up.

Conclusions: Distinct subgroups among heterosexual STI clinic visitors can be differentiated from each other by multiple psychological and behavioral characteristics, and these characteristics reflecting the risk of acquiring STI are consistent over the course of 1 year in most individuals. An integral approach, adapting behavioral interventions to match multiple psychological and behavioral characteristics of high-risk subgroups, might be more effective in controlling STI transmission.
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http://dx.doi.org/10.1097/OLQ.0000000000001110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7012346PMC
March 2020

Timeliness of infectious disease reporting, the Netherlands, 2003 to 2017: law change reduced reporting delay, disease identification delay is next.

Euro Surveill 2019 Dec;24(49)

Centre for Infectious Diseases, Leiden University Medical Centre, Leiden, the Netherlands.

BackgroundTimely notification of infectious diseases is essential for effective disease control and needs regular evaluation.AimOur objective was to evaluate the effects that statutory adjustments in the Netherlands in 2008 and raising awareness during outbreaks had on notification timeliness.MethodsIn a retrospective analyses of routine surveillance data obtained between July 2003 and November 2017, delays between disease onset and laboratory confirmation (disease identification delay), between laboratory confirmation and notification to Municipal Health Services (notification delay) and between notification and reporting to the National Institute for Public Health and the Environment (reporting delay) were analysed for 28 notifiable diseases. Delays before (period 1) and after the law change (periods 2 and 3) were compared with legal timeframes. We studied the effect of outbreak awareness in 10 outbreaks and the effect of specific guidance messages on disease identification delay for two diseases.ResultsWe included 144,066 notifications. Average notification delay decreased from 1.4 to 0.4 days across the three periods (six diseases; p < 0.05), reporting delay decreased mainly in period 2 (from 0.5 to 0.1 days, six diseases; p < 0.05). In 2016-2017, legal timeframes were met overall. Awareness resulted in decreased disease identification delay for three diseases: measles and rubella (outbreaks) and psittacosis (specific guidance messages).ConclusionsLegal adjustments decreased notification and reporting delays, increased awareness reduced identification delays. As disease identification delay dominates the notification chain, insight in patient, doctor and laboratory delay is necessary to further improve timeliness and monitor the impact of control measures during outbreaks.
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http://dx.doi.org/10.2807/1560-7917.ES.2019.24.49.1900237DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6905299PMC
December 2019

Use of viral load to improve survey estimates of known HIV-positive status and antiretroviral treatment coverage.

AIDS 2020 03;34(4):631-636

Division of Global HIV & TB, U.S. Centers for Disease Control and Prevention, Nairobi.

Objective: To compare alternative methods of adjusting self-reported knowledge of HIV-positive status and antiretroviral (ARV) therapy use based on undetectable viral load (UVL) and ARV detection in blood.

Design: Post hoc analysis of nationally representative household survey to compare alternative biomarker-based adjustments to population HIV indicators.

Methods: We reclassified HIV-positive participants aged 15-64 years in the 2012 Kenya AIDS Indicator Survey (KAIS) who were unaware of their HIV-positive status by self-report as aware and on antiretroviral treatment if either ARVs were detected or viral load was undetectable (<550 copies/ml) on dried blood spots. We compared self-report to adjustments for ARV measurement, UVL, or both.

Results: Treatment coverage among all HIV-positive respondents increased from 31.8% for self-report to 42.5% [95% confidence interval (CI) 37.4-47.8] based on ARV detection alone, to 42.8% (95% CI 37.9-47.8) when ARV-adjusted, 46.2% (95% CI 41.3-51.1) when UVL-adjusted and 48.8% (95% CI 43.9-53.8) when adjusted for either ARV or UVL. Awareness of positive status increased from 46.9% for self-report to 56.2% (95% CI 50.7-61.6) when ARV-adjusted, 57.5% (95% CI 51.9-63.0) when UVL-adjusted, and 59.8% (95% CI 54.2-65.1) when adjusted for either ARV or UVL.

Conclusion: Undetectable viral load, which is routinely measured in surveys, may be a useful adjunct or alternative to ARV detection for adjusting survey estimates of knowledge of HIV status and antiretroviral treatment coverage.
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http://dx.doi.org/10.1097/QAD.0000000000002453DOI Listing
March 2020

Disease modeling for public health: added value, challenges, and institutional constraints.

J Public Health Policy 2020 Mar;41(1):39-51

Center for Infectious Disease Control, National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands.

Public health policymakers face increasingly complex questions and decisions and need to deal with an increasing quantity of data and information. For policy advisors to make use of scientific evidence and to assess available intervention options effectively and therefore indirectly for those deciding on and implementing public health policies, mathematical modeling has proven to be a useful tool. In some areas, the use of mathematical modeling for public health policy support has become standard practice at various levels of decision-making. To make use of this tool effectively within public health organizations, it is necessary to provide good infrastructure and ensure close collaboration between modelers and policymakers. Based on experience from a national public health institute, we discuss the strategic requirements for good modeling practice for public health. For modeling to be of maximal value for a public health institute, the organization and budgeting of mathematical modeling should be transparent, and a long-term strategy for how to position and develop mathematical modeling should be in place.
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http://dx.doi.org/10.1057/s41271-019-00206-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7041603PMC
March 2020

Prediction of human active mobility in rural areas: development and validity tests of three different approaches.

J Expo Sci Environ Epidemiol 2020 11 26;30(6):1023-1031. Epub 2019 Nov 26.

Institute for Risk Assessment Sciences, Division Environmental Epidemiology and Veterinary Public Health, Utrecht University, Utrecht, The Netherlands.

Background/aim: Active mobility may play a relevant role in the assessment of environmental exposures (e.g. traffic-related air pollution, livestock emissions), but data about actual mobility patterns are work intensive to collect, especially in large study populations, therefore estimation methods for active mobility may be relevant for exposure assessment in different types of studies. We previously collected mobility patterns in a group of 941 participants in a rural setting in the Netherlands, using week-long GPS tracking. We had information regarding personal characteristics, self-reported data regarding weekly mobility patterns and spatial characteristics. The goal of this study was to develop versatile estimates of active mobility, test their accuracy using GPS measurements and explore the implications for exposure assessment studies.

Methods: We estimated hours/week spent on active mobility based on personal characteristics (e.g. age, sex, pre-existing conditions), self-reported data (e.g. hours spent commuting per bike) or spatial predictors such as home and work address. Estimated hours/week spent on active mobility were compared with GPS measured hours/week, using linear regression and kappa statistics.

Results: Estimated and measured hours/week spent on active mobility had low correspondence, even the best predicting estimation method based on self-reported data, resulted in a R of 0.09 and Cohen's kappa of 0.07. A visual check indicated that, although predicted routes to work appeared to match GPS measured tracks, only a small proportion of active mobility was captured in this way, thus resulting in a low validity of overall predicted active mobility.

Conclusions: We were unable to develop a method that could accurately estimate active mobility, the best performing method was based on detailed self-reported information but still resulted in low correspondence. For future studies aiming to evaluate the contribution of home-work traffic to exposure, applying spatial predictors may be appropriate. Measurements still represent the best possible tool to evaluate mobility patterns.
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http://dx.doi.org/10.1038/s41370-019-0194-6DOI Listing
November 2020

Tracking Pseudomonas aeruginosa transmissions due to environmental contamination after discharge in ICUs using mathematical models.

PLoS Comput Biol 2019 08 28;15(8):e1006697. Epub 2019 Aug 28.

Julius Center for Health Sciences and Primary Care of the UMC Utrecht, Utrecht University, Utrecht, The Netherlands.

Pseudomonas aeruginosa (P. aeruginosa) is an important cause of healthcare-associated infections, particularly in immunocompromised patients. Understanding how this multi-drug resistant pathogen is transmitted within intensive care units (ICUs) is crucial for devising and evaluating successful control strategies. While it is known that moist environments serve as natural reservoirs for P. aeruginosa, there is little quantitative evidence regarding the contribution of environmental contamination to its transmission within ICUs. Previous studies on other nosocomial pathogens rely on deploying specific values for environmental parameters derived from costly and laborious genotyping. Using solely longitudinal surveillance data, we estimated the relative importance of P. aeruginosa transmission routes by exploiting the fact that different routes cause different pattern of fluctuations in the prevalence. We developed a mathematical model including background transmission, cross-transmission and environmental contamination. Patients contribute to a pool of pathogens by shedding bacteria to the environment. Natural decay and cleaning of the environment lead to a reduction of that pool. By assigning the bacterial load shed during an ICU stay to cross-transmission, we were able to disentangle environmental contamination during and after a patient's stay. Based on a data-augmented Markov Chain Monte Carlo method the relative importance of the considered acquisition routes is determined for two ICUs of the University hospital in Besançon (France). We used information about the admission and discharge days, screening days and screening results of the ICU patients. Both background and cross-transmission play a significant role in the transmission process in both ICUs. In contrast, only about 1% of the total transmissions were due to environmental contamination after discharge. Based on longitudinal surveillance data, we conclude that cleaning improvement of the environment after discharge might have only a limited impact regarding the prevention of P.A. infections in the two considered ICUs of the University hospital in Besançon. Our model was developed for P. aeruginosa but can be easily applied to other pathogens as well.
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http://dx.doi.org/10.1371/journal.pcbi.1006697DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6736315PMC
August 2019

Who drops out and when? Predictors of non-response and loss to follow-up in a longitudinal cohort study among STI clinic visitors.

PLoS One 2019 19;14(6):e0218658. Epub 2019 Jun 19.

Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.

Introduction: Response rates in health research are declining, and low response rates could result in biased outcomes when population characteristics of participants systematically differ from the non-respondents. Few studies have examined key factors of non-response beyond demographic characteristics, such as behavioral and psychological factors. The aim of the current study was to identify predictors of non-response and loss to follow-up in a longitudinal sexual health study.

Materials And Methods: A longitudinal cohort study (iMPaCT) was conducted from November 2016 to July 2018 among heterosexual STI clinic visitors aged 18-24 years. At four different time points in one year, data was collected on sexual behavior, psychological determinants and chlamydia infections. The national STI surveillance database provided data on demographic, behavioral and sexual health-related characteristics for non-respondents. Predictors of non-response at baseline and of loss to follow-up were identified using multivariable logistic regression analyses.

Results: In total, 13,658 STI clinic visitors were eligible to participate, of which 1,063 (8%) participated. Male gender, low/medium education level, young age (≤ 20 years) and having a non-Dutch migration background were significant predictors of non-response at baseline. Furthermore, non-respondents at baseline were more likely to report STI-related symptoms, to have been notified by a partner, to have had condomless sex, and to have had ≤ 2 partners in the past six months, compared to participants. Psychological predictors of loss to follow-up differed between STI clinic regions, but low perceived importance of health at baseline was associated with loss to follow-up in all regions. The baseline chlamydia positivity rate was significantly higher in the non-respondents (17%) compared to the participants (14%), but was not a predictor of loss to follow-up.

Discussion: Targeted recruitment aimed at underrepresented groups in the population based on demographic, behavioral and psychological characteristics, might be necessary to decrease loss to follow-up, and to prevent non-response bias in health research.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218658PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6583983PMC
February 2020

A Multidimensional Approach to Assessing Infectious Disease Risk: Identifying Risk Classes Based on Psychological Characteristics.

Am J Epidemiol 2019 09;188(9):1705-1712

Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands.

Prevention of infectious diseases depends on health-related behavior, which is often influenced by psychological characteristics. However, few studies assessing health-related behavior have examined psychological characteristics to identify risk groups, and this multidimensional approach might improve disease risk assessment. We aimed to characterize subgroups based on psychological characteristics and examine their influence on behavior and disease risk, using chlamydia as a case study. Selected participants (heterosexuals aged 18-24 years and females aged 18-24 years who had sex with both men and women) in a Dutch longitudinal cohort study (the Mathematical Models Incorporating Psychological Determinants: Control of Chlamydia Transmission (iMPaCT) Study) filled out a questionnaire and were tested for chlamydia (2016-2017). Latent class analysis was performed to identify risk classes using psychological predictors of chlamydia diagnosis. Two classes were identified: class 1 (n = 488; 9% chlamydia diagnosis) and class 2 (n = 325; 13% chlamydia diagnosis). The proportion of participants with high shame, high impulsiveness, and lower perceived importance of health was higher in class 2 than in class 1. Furthermore, persons in class 2 were more likely to be male and to report condomless sex compared with class 1, but the number of recent partners was comparable. Thus, risk classes might be distinguished from each other by psychological characteristics beyond sexual behavior. Therefore, the impact of the same intervention could differ, and tailoring interventions based on psychological characteristics might be necessary to reduce chlamydia prevalence most effectively.
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http://dx.doi.org/10.1093/aje/kwz140DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6736114PMC
September 2019

Impact of sexual trajectories of men who have sex with men on the reduction in HIV transmission by pre-exposure prophylaxis.

Epidemics 2019 09 14;28:100337. Epub 2019 Mar 14.

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; Center for Infectious Disease Control, National Institute of Public Health and the Environment, Bilthoven, The Netherlands.

Changes in sexual risk behavior over the life course in men who have sex with men (MSM) can influence population-level intervention efficacy. Our objective was to investigate the impact of incorporating sexual trajectories describing long-term changes in risk levels on the reduction in HIV prevalence by pre-exposure prophylaxis (PrEP) among MSM. Based on the Amsterdam Cohort Study data, we developed two models of HIV transmission in a population stratified by sexual behavior. In the first model, individuals were stratified into low, medium and high risk levels and did not change their risk levels. The second model had the same stratification but incorporated additionally three types of sexual behavior trajectories. The models assumed universal antiretroviral treatment of HIV MSM, and PrEP use by high risk HIV MSM. We computed the relative reduction in HIV prevalence in both models for annual PrEP uptakes of 10% to 80% at different time points after PrEP introduction. We then investigated the impact of sexual trajectories on the effectiveness of PrEP intervention. The impact of sexual trajectories on the overall prevalence and prevalence in individuals at low, medium and high risk levels varied with PrEP uptake and time after PrEP introduction. Compared to the model without sexual trajectories, the model with trajectories predicted a higher impact of PrEP on the overall prevalence, and on the prevalence among the medium and high risk individuals. In low risk individuals, there was more reduction in prevalence during the first 15 years of PrEP intervention if sexual trajectories were not incorporated in the model. After that point, at low risk level there was more reduction in the model with trajectories. In conclusion, our study predicts that sexual trajectories increase the estimated impact of PrEP on reducing HIV prevalence when compared to a population where risk levels do not change.
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http://dx.doi.org/10.1016/j.epidem.2019.03.003DOI Listing
September 2019

Disease burden of varicella versus other vaccine-preventable diseases before introduction of vaccination into the national immunisation programme in the Netherlands.

Euro Surveill 2019 May;24(18)

Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands.

IntroductionEstimating burden of disease (BoD) is an essential first step in the decision-making process on introducing new vaccines into national immunisation programmes (NIPs). For varicella, a common vaccine-preventable disease, BoD in the Netherlands was unknown.AimTo assess national varicella BoD and compare it to BoD of other vaccine-preventable diseases before their introduction in the NIP.MethodsIn this health estimates reporting study, BoD was expressed in disability-adjusted life years (DALYs) using methodology from the Burden of Communicable Diseases in Europe (BCoDE)-project. As no parameters/disease model for varicella (including herpes zoster) were available in the BCoDE toolkit, incidence, disease progression model and parameters were derived from seroprevalence, healthcare registries and published data. For most other diseases, BoD was estimated with existing BCoDE-parameters, adapted to the Netherlands if needed.ResultsIn 2017, the estimated BoD of varicella in the Netherlands was 1,800 (95% uncertainty interval (UI): 1,800-1,900) DALYs. Herpes zoster mainly contributed to this BoD (1,600 DALYs; 91%), which was generally lower than the BoD of most current NIP diseases in the year before their introduction into the NIP. However, BoD for varicella was higher than for rotavirus gastroenteritis (1,100; 95%UI: 440-2,200 DALYs) and meningococcal B disease (620; 95%UI: 490-770 DALYs), two other potential NIP candidates.ConclusionsWhen considering the introduction of a new vaccine in the NIP, BoD is usually estimated in isolation. The current approach assesses BoD in relation to other vaccine-preventable diseases' BoD, which may help national advisory committees on immunisation and policymakers to set vaccination priorities.
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http://dx.doi.org/10.2807/1560-7917.ES.2019.24.18.1800363DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505181PMC
May 2019

Modelling the dynamics of population viral load measures under HIV treatment as prevention.

Infect Dis Model 2018 21;3:160-170. Epub 2018 Sep 21.

Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, the Netherlands.

In 2011 the Centers for Disease Control and Prevention (CDC) published guidelines for the use of population viral load (PVL), community viral load (CVL) and monitored viral load (MVL), defined as the average viral load (VL) of all HIV infected individuals in a population, of all diagnosed individuals, and of all individuals on antiretroviral treatment (ART), respectively. Since then, CVL has been used to assess the effectiveness of ART on HIV transmission and as a proxy for HIV incidence. The first objective of this study was to investigate how aggregate VL measures change with the HIV epidemic phase and the drivers behind these changes using a mathematical transmission model. Secondly, we aimed to give some insight into how well CVL correlates with HIV incidence during the course of the epidemic and roll out of ART. We developed a compartmental model for disease progression and HIV transmission with disease stages that differ in viral loads for epidemiological scenarios relevant to a concentrated epidemic in a population of men who have sex with men (MSM) in Western Europe (WE) and to a generalized epidemic in a heterosexual population in Sub-Saharan Africa (SSA). The model predicts that PVL and CVL change with the epidemic phase, while MVL stays constant. These dynamics are linked to the dynamics of infected subgroups (undiagnosed, diagnosed untreated and treated) in different disease stages (primary, chronic and AIDS). In particular, CVL decreases through all epidemic stages: before ART, since chronic population builds up faster than AIDS population and after ART, due to the build-up of treated population with low VL. The trends in CVL and incidence can be both opposing and coinciding depending on the epidemic phase. Before ART is scaled up to sufficiently high levels, incidence increases while CVL decreases. After this point, CVL is a useful indicator of changes in HIV incidence. The model predicts that during the ART scale-up HIV transmission is driven by undiagnosed and diagnosed untreated individuals, and that new infections decline due to the increase in the number of treated. Although CVL is not able to capture the contribution of undiagnosed population to HIV transmission, it declines due to the increase of people on ART too. In the scenarios described by our model, the present epidemic phase corresponds to declining trends in CVL and incidence.
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http://dx.doi.org/10.1016/j.idm.2018.09.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326229PMC
September 2018

A stochastic simulation model to study respondent-driven recruitment.

PLoS One 2018 15;13(11):e0207507. Epub 2018 Nov 15.

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.

Respondent-driven detection is a chain recruitment method used to sample contact persons of infected persons in order to enhance case finding. It starts with initial individuals, so-called seeds, who are invited for participation. Afterwards, seeds receive a fixed number of coupons to invite individuals with whom they had contact during a specific time period. Recruitees are then asked to do the same, resulting in successive waves of contact persons who are connected in one recruitment tree. However, often the majority of participants fail to invite others, or invitees do not accept an invitation, and recruitment stops after several waves. A mathematical model can help to analyse how various factors influence peer recruitment and to understand under which circumstances sustainable recruitment is possible. We implemented a stochastic simulation model, where parameters were suggested by empirical data from an online survey, to determine the thresholds for obtaining large recruitment trees and the number of waves needed to reach a steady state in the sample composition for individual characteristics. We also examined the relationship between mean and variance of the number of invitations sent out by participants and the probability of obtaining a large recruitment tree. Our main finding is that a situation where participants send out any number of coupons between one and the maximum number is more effective in reaching large recruitment trees, compared to a situation where the majority of participants does not send out any invitations and a smaller group sends out the maximum number of invitations. The presented model is a helpful tool that can assist public health professionals in preparing research and contact tracing using online respondent-driven detection. In particular, it can provide information on the required minimum number of successfully sent invitations to reach large recruitment trees, a certain sample composition or certain number of waves.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0207507PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6237413PMC
April 2019

Study protocol of the iMPaCT project: a longitudinal cohort study assessing psychological determinants, sexual behaviour and chlamydia (re)infections in heterosexual STI clinic visitors.

BMC Infect Dis 2018 Nov 13;18(1):559. Epub 2018 Nov 13.

Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.

Background: Chlamydia trachomatis (chlamydia), the most commonly reported sexually transmitted infection (STI) in the Netherlands, can lead to severe reproductive complications. Reasons for the sustained chlamydia prevalence in young individuals, even in countries with chlamydia screening programs, might be the asymptomatic nature of chlamydia infections, and high reinfection rates after treatment. When individuals are unaware of their infection, preventive behaviour or health-care seeking behaviour mostly depends on psychological determinants, such as risk perception. Furthermore, behaviour change after a diagnosis might be vital to reduce reinfection rates. This makes the incorporation of psychological determinants and behaviour change in mathematical models estimating the impact of interventions on chlamydia transmission especially important. Therefore, quantitative real-life data to inform these models is needed.

Methods: A longitudinal cohort study will be conducted to explore the link between psychological and behavioural determinants and chlamydia (re)infection among heterosexual STI clinic visitors aged 18-24 years. Participants will be recruited at the STI clinics of the public health services of Amsterdam, Hollands Noorden, Kennemerland, and Twente. Participants are enrolled for a year, and questionnaires are administrated at four time points: baseline (before an STI consultation), three-week, six-month and at one-year follow-up. To be able to link psychological and behavioural determinants to (re)infections, participants will be tested for chlamydia at enrolment and at six-month follow-up. Data from the longitudinal cohort study will be used to develop mathematical models for curable STI incorporating these determinants to be able to better estimate the impact of interventions.

Discussion: This study will provide insights into the link between psychological and behavioural determinants, including short-term and long-term changes after diagnosis, and chlamydia (re)infections. Our mathematical model, informed by data from the longitudinal cohort study, will be able to estimate the impact of interventions on chlamydia prevalence, and identify and prioritise successful interventions for the future. These interventions could be implemented at STI clinics tailored to psychological and behavioural characteristics of individuals.

Trial Registration: Dutch Trial Register NTR-6307 . Retrospectively registered 11-nov-2016.
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http://dx.doi.org/10.1186/s12879-018-3498-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6234675PMC
November 2018

Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis.

Lancet Infect Dis 2019 01 5;19(1):56-66. Epub 2018 Nov 5.

European Centre for Disease Prevention and Control, Solna, Sweden.

Background: Infections due to antibiotic-resistant bacteria are threatening modern health care. However, estimating their incidence, complications, and attributable mortality is challenging. We aimed to estimate the burden of infections caused by antibiotic-resistant bacteria of public health concern in countries of the EU and European Economic Area (EEA) in 2015, measured in number of cases, attributable deaths, and disability-adjusted life-years (DALYs).

Methods: We estimated the incidence of infections with 16 antibiotic resistance-bacterium combinations from European Antimicrobial Resistance Surveillance Network (EARS-Net) 2015 data that was country-corrected for population coverage. We multiplied the number of bloodstream infections (BSIs) by a conversion factor derived from the European Centre for Disease Prevention and Control point prevalence survey of health-care-associated infections in European acute care hospitals in 2011-12 to estimate the number of non-BSIs. We developed disease outcome models for five types of infection on the basis of systematic reviews of the literature.

Findings: From EARS-Net data collected between Jan 1, 2015, and Dec 31, 2015, we estimated 671 689 (95% uncertainty interval [UI] 583 148-763 966) infections with antibiotic-resistant bacteria, of which 63·5% (426 277 of 671 689) were associated with health care. These infections accounted for an estimated 33 110 (28 480-38 430) attributable deaths and 874 541 (768 837-989 068) DALYs. The burden for the EU and EEA was highest in infants (aged <1 year) and people aged 65 years or older, had increased since 2007, and was highest in Italy and Greece.

Interpretation: Our results present the health burden of five types of infection with antibiotic-resistant bacteria expressed, for the first time, in DALYs. The estimated burden of infections with antibiotic-resistant bacteria in the EU and EEA is substantial compared with that of other infectious diseases, and has increased since 2007. Our burden estimates provide useful information for public health decision-makers prioritising interventions for infectious diseases.

Funding: European Centre for Disease Prevention and Control.
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http://dx.doi.org/10.1016/S1473-3099(18)30605-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6300481PMC
January 2019