This site needs JavaScript to work properly. Please enable it to take advantage of the complete set of features!
Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

NIH NLM Logo
Log in
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Jun 30;10(1):321.
doi: 10.1186/s13071-017-2256-8.

Identifying optimal threshold statistics for elimination of hookworm using a stochastic simulation model

Affiliations

Identifying optimal threshold statistics for elimination of hookworm using a stochastic simulation model

James E Truscott et al. Parasit Vectors. .

Abstract

Background: There is an increased focus on whether mass drug administration (MDA) programmes alone can interrupt the transmission of soil-transmitted helminths (STH). Mathematical models can be used to model these interventions and are increasingly being implemented to inform investigators about expected trial outcome and the choice of optimum study design. One key factor is the choice of threshold for detecting elimination. However, there are currently no thresholds defined for STH regarding breaking transmission.

Methods: We develop a simulation of an elimination study, based on the DeWorm3 project, using an individual-based stochastic disease transmission model in conjunction with models of MDA, sampling, diagnostics and the construction of study clusters. The simulation is then used to analyse the relationship between the study end-point elimination threshold and whether elimination is achieved in the long term within the model. We analyse the quality of a range of statistics in terms of the positive predictive values (PPV) and how they depend on a range of covariates, including threshold values, baseline prevalence, measurement time point and how clusters are constructed.

Results: End-point infection prevalence performs well in discriminating between villages that achieve interruption of transmission and those that do not, although the quality of the threshold is sensitive to baseline prevalence and threshold value. Optimal post-treatment prevalence threshold value for determining elimination is in the range 2% or less when the baseline prevalence range is broad. For multiple clusters of communities, both the probability of elimination and the ability of thresholds to detect it are strongly dependent on the size of the cluster and the size distribution of the constituent communities. Number of communities in a cluster is a key indicator of probability of elimination and PPV. Extending the time, post-study endpoint, at which the threshold statistic is measured improves PPV value in discriminating between eliminating clusters and those that bounce back.

Conclusions: The probability of elimination and PPV are very sensitive to baseline prevalence for individual communities. However, most studies and programmes are constructed on the basis of clusters. Since elimination occurs within smaller population sub-units, the construction of clusters introduces new sensitivities for elimination threshold values to cluster size and the underlying population structure. Study simulation offers an opportunity to investigate key sources of sensitivity for elimination studies and programme designs in advance and to tailor interventions to prevailing local or national conditions.

Keywords: Cluster randomized trials; Elimination of transmission; Mass drug administration; Positive/Negative predictive value; Soil-transmitted helminths; Stochastic models.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Distribution of village sizes in the Vellore study (a) and from the Indian census, 2001 (b). a Histogram of the Vellore data and the equivalent expectations for the fitted model (Parameters: mean = 263, aggregation parameter = 7.7. Labels give lower bounds of bins with width 50). b The Indian census distribution is an approximation from the number of communities in a range of size categories (mean = 2770, standard deviation = 1870)
Fig. 2
Fig. 2
Time series of measured prevalence in a selection of individual communities within the stochastic simulation. Vertical lines indicate the four distinct regions of the simulation; endemic behaviour, LF treatment period, the duration of the study and the post-study period without MDA treatment. Red and green lines indicate communities that ultimately bounce back or eliminate, respectively
Fig. 3
Fig. 3
Summary statistics for measured prevalences across communities with baseline prevalence in the range 10–20% going to elimination (green) and bouncing back (red). Solid lines represent mean values and broken lines the 95% prediction interval
Fig. 4
Fig. 4
Histograms for three possible post-study threshold statistics: a measured prevalence at 1 year post-study; b prevalence difference between 1 year and 3 months post-study; and c the ratio of prevalence at 1 year post-study to baseline prevalence. Values from eliminating and rebounding communities are green and red, respectively. Results represent 1000 model iterations
Fig. 5
Fig. 5
a Probability of elimination for communities with different baseline prevalence ranges and across a range of village population sizes. Error bars show 2 standard deviations for the R0 ranges of different baseline prevalence limits (indicated by circles). b PPV values for a range of elimination thresholds and baseline prevalences
Fig. 6
Fig. 6
Impact of cluster size and composition on probability of elimination (a and b) and threshold PPV (c and d). Probability of elimination and PPV are plotted against cluster size (a and c) and mean number of communities (b and d), respectively. Prevalence threshold is set at 2%, one year post study, with baseline prevalence range of 5–40% and sample size of 200 individuals
Fig. 7
Fig. 7
Sensitivity of PPV to time since end of study for communities (mean = 2770) and clusters of size (3–5000) individuals. Sample size is 200 individuals and the overall probability of elimination is approximately 27%

References

    1. WHO . Report of the third global meeting of the partners for parasite control: Deworming for Health and Development. Geneva: WHO; 2005.
    1. Pullan RL, Smith JL, Jasrasaria R, Brooker SJ. Global numbers of infection and disease burden of soil transmitted helminth infections in 2010. Parasit Vectors. 2014;7:37. doi: 10.1186/1756-3305-7-37. - DOI - PMC - PubMed
    1. WHO. Eliminating soil-transmitted helminthiases as a public health problem in children: progress report 2001–2010 and strategic plan 2011–2020. Geneva: WHO. p. 2012.
    1. WHO. Helminth control in school age children: a guide for managers of control programmes. A. Montresor ed. 2011. p. 75. ISBN: 978 92 4 150 312 9.
    1. Truscott JE, Turner HC, Anderson RM. What impact will the achievement of the current World Health Organisation targets for anthelmintic treatment coverage in children have on the intensity of soil transmitted helminth infections? Parasit Vectors. 2015;8:551. doi: 10.1186/s13071-015-1135-4. - DOI - PMC - PubMed
Full text links
BioMed Central full text link BioMed Central Free PMC article
Cite

AltStyle によって変換されたページ (->オリジナル) /