Dataset Information

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

ACCESS DATA

Publications

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

Similar Datasets

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: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.

Project description:BackgroundThe World Health Organization initiative to eliminate mother-to-child transmission of syphilis aims for ? 90% of pregnant women to be tested for syphilis and ? 90% to receive treatment by 2015. We calculated global and regional estimates of syphilis in pregnancy and associated adverse outcomes for 2008, as well as antenatal care (ANC) coverage for women with syphilis.Methods and findingsEstimates were based upon a health service delivery model. National syphilis seropositivity data from 97 of 193 countries and ANC coverage from 147 countries were obtained from World Health Organization databases. Proportions of adverse outcomes and effectiveness of screening and treatment were from published literature. Regional estimates of ANC syphilis testing and treatment were examined through sensitivity analysis. In 2008, approximately 1.36 million (range: 1.16 to 1.56 million) pregnant women globally were estimated to have probable active syphilis; of these, 80% had attended ANC. Globally, 520,905 (best case: 425,847; worst case: 615,963) adverse outcomes were estimated to be caused by maternal syphilis, including approximately 212,327 (174,938; 249,716) stillbirths (>28 wk) or early fetal deaths (22 to 28 wk), 91,764 (76,141; 107,397) neonatal deaths, 65,267 (56,929; 73,605) preterm or low birth weight infants, and 151,547 (117,848; 185,245) infected newborns. Approximately 66% of adverse outcomes occurred in ANC attendees who were not tested or were not treated for syphilis. In 2008, based on the middle case scenario, clinical services likely averted 26% of all adverse outcomes. Limitations include missing syphilis seropositivity data for many countries in Europe, the Mediterranean, and North America, and use of estimates for the proportion of syphilis that was "probable active," and for testing and treatment coverage.ConclusionsSyphilis continues to affect large numbers of pregnant women, causing substantial perinatal morbidity and mortality that could be prevented by early testing and treatment. In this analysis, most adverse outcomes occurred among women who attended ANC but were not tested or treated for syphilis, highlighting the need to improve the quality of ANC as well as ANC coverage. In addition, improved ANC data on syphilis testing coverage, positivity, and treatment are needed. Please see later in the article for the Editors' Summary.

Project description:Accelerometer-based activity monitors are widely used in research and surveillance applications for quantifying sedentary behavior (SB) and physical activity (PA). Considerable research has been done to refine methods for assessing PA, but relatively little attention has been given to operationalizing SB parameters (i.e., sedentary time and breaks) from accelerometer data - particularly in relation to health outcomes. This study investigated: (a) the accrued patterns of sedentary time and breaks; and (b) the associations of sedentary time and breaks in different bout durations with cardiovascular risk factors.Accelerometer data on 5,917 adults from the National Health Examination and Nutrition Survey (NHANES) 2003-2006 were used. Sedentary time and breaks at different bout durations (i.e., 1, 2-4, 5-9, 10-14, 15-19, 20-24, 25-29, and ≥ 30-min) were obtained using a threshold of < 100 counts per minute. Sedentary time and breaks were regressed on cardiovascular risk factors (waist circumference, triglyceride, and high-density lipoprotein cholesterol) and body mass index across bout durations.The results revealed that the majority of sedentary time occurred within relatively short bout durations (≈ 70% and ≈ 85% for < 5-min and < 10-min, respectively). The associations of sedentary time and breaks with health outcomes varied depending on how bout time was defined. Estimates of SB parameters based on bout durations of 5 min or shorter were associated with reduced cardiovascular risk factors while durations longer than 10-min were generally associated with increased risk factors.The present study demonstrates that the duration of sedentary bouts should be further considered when operationalizing the SB parameters from accelerometer data. The threshold of 5 minutes to define a bout is defensible, but a 10 minute threshold would provide a more conservative estimate to clearly capture the prolonged nature of sedentary behavior. Additional research is needed to determine the relative sensitivity and specificity of these thresholds.

Dataset Information

Translating surveillance data into incidence estimates.

Publications

Translating surveillance data into incidence estimates.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure