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Translating surveillance data into incidence estimates.

ABSTRACT: Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which we may estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence does not change between monitoring rounds, resulting in an underestimation of the disease incidence. In this paper, we develop an incidence estimation model accounting for epidemic growth with monitoring rounds that sample varying incidence. We also show how to accommodate the asymptomatic period that is the characteristic of most diseases. For practical use, we produce an approximation of the model, which is subsequently shown to be accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations. The estimation model is made available as an online application. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

SUBMITTER: Bourhis Y

PROVIDER: S-EPMC6558556 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Translating surveillance data into incidence estimates.

Bourhis Y Y Gottwald T T van den Bosch F F

Philosophical transactions of the Royal Society of London. Series B, Biological sciences 20190701 1776

Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which we may estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence does not change between monitoring rounds, resulting in an underestimation of the disease incidence. In this paper, we develop an incidence estimation model accounting for epidemic growth with monitoring rounds that sampl ...[more]

PMID: 31104599

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Project description:BackgroundKnowledge Translation (KT) and data visualization play a vital role in the dissemination of data and information to improve healthcare systems. A better understanding of KT and its utility requires examining its processes, and how these interact with available data tools and platforms and various users. In this context, the aim of this paper is to understand how relevant users interact with data visualization tools, in particular Global Burden of Disease (GBD) visualizations, while also examining KT processes related to data visualization.MethodsA qualitative case-study consisting of semi-structured interviews with eight policy officers. Interviewees were selected by the Institute for Health Metrics and Evaluation (IHME) from three countries: Canada, Kenya and New Zealand. Data were analyzed through framework coding, using qualitative analysis software.ResultsPolicy officers' responses indicated that data can prompt action by engaging users, and effective delivery and translation of data was enhanced by data visualization tools. GBD was considered valuable for use in policy (e.g., planning and prioritizing health policy; facilitating accountability; and tracking and monitoring progress and trends over time and between countries). Using GBD and data visualization tools, participants quickly and easily accessed key information and turned it into actionable messages; engaging visuals captured decision-makers' attention while providing information in a digestible, time-saving manner. However, participants emphasized an overall disconnect between research and public health. Functionality and technical issues, e.g., absence of tool guides and tool complexity, as well as lacking buy-in and awareness of certain tools from those less familiar with GBD, were named as major barriers. In order to address this "know-do" gap, user-friendly knowledge translation tools were stated as crucial, as was the importance of collaboration and leveraging different insights from data generators and users to inform health policy.ConclusionsPolicy officers aware of KT are willing to utilize data visualization tools as they value them as an engaging and important mechanism to foster the use of GBD data in policy-making. To further facilitate policy officers' efforts to effectively use GBD data in policy and practice, further research is required into how users perceive and interact with data visualization and other KT tools.

Project description:IntroductionThe incidence and prevalence of diabetes within a population are important public health metrics. Pharmaceutical administrative data may offer a resource that can contribute to quantifying these measures using the recorded signals derived from the drugs used to treat people with diabetes.ObjectiveTo estimate the longitudinal incidence and prevalence of drug treated (DT) diabetes in Australia utilising an Australian Pharmaceutical Benefits Scheme (PBS) dataset and compare estimates with community survey data for all diabetes reported in the Australian National Health Survey (NHS).MethodsPersons with DT diabetes were identified within the PBS dataset using assigned Anatomic Therapeutic Chemical codes for 'Drugs used in diabetes'. Prevalent persons with DT diabetes were determined by a single annual treatment, and incident cases from the earliest treatment with diabetes medications. Counts were aggregated by age group and utilised Australian national census data as a denominator to calculate diabetes disease frequencies for the period 2004-14. Comparison of PBS prevalence data was made with NHS surveys over equivalent years.ResultsThe age adjusted incidence of DT diabetes was 3.4/1000 in 2006 and increased to 3.8/1000 in 2011 and 5.1/1000 in 2014. Age adjusted prevalence of DT diabetes in Australia also rose from 26.7/1000 in 2006 to 32.1/1000 in 2011 and 42.1/1000 in 2014. DT diabetes prevalence estimates correlated with NHS estimates of self-reported diabetes prevalence across age groups and in 2014 was r = 0.987. However, PBS estimates of DT diabetes prevalence generally underestimated NHS values of self-reported diabetes in older age groups with mean percentage differences of -22% to -3%. In contrast, PBS data captured more younger persons with diabetes in comparison to NHS data. These differences were then used to adjust DT diabetes incidence rates to provide age specific estimates that could potentially reflect diabetes incidence estimates acquired by community survey.ConclusionsPBS data representing dispensed medications prescribed to persons with diabetes offers a perspective for the assessment of diabetes incidence and prevalence. PBS derived DT diabetes prevalence estimates correlate well with community survey estimates of self-reported diabetes, but underestimate NHS data in older age groups. Calibrated DT incidence estimates may potentially reflect community survey derived diabetes incidence estimates and may offer a method for longitudinal monitoring.

Project description:BackgroundAccurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviours are not well defined. This study's aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility.MethodsOutpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a 3-year period (2011-2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts.ResultsA total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi = 0.64, p < 0.001; correlation in Nagongera = 0.34, p = 0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the 3 years in Kihihi (0.5 cases per person-year (PPY) vs 1.7 cases PPY) and Nagongera (0.3 cases PPY vs 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera).ConclusionsAlthough malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.

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Translating surveillance data into incidence estimates.

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Translating surveillance data into incidence estimates.

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