Measuring and modelling the effects of systematic non-adherence to mass drug administration.
It is well understood that the success or failure of a mass drug administration campaign critically depends on the level of coverage achieved. To that end coverage levels are often closely scrutinised during campaigns and the response to underperforming campaigns is to attempt to improve coverage. M...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2017
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| _version_ | 1797060476391129088 |
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| author | Dyson, L Stolk, W Farrell, S Hollingsworth, T |
| author_facet | Dyson, L Stolk, W Farrell, S Hollingsworth, T |
| author_sort | Dyson, L |
| collection | OXFORD |
| description | It is well understood that the success or failure of a mass drug administration campaign critically depends on the level of coverage achieved. To that end coverage levels are often closely scrutinised during campaigns and the response to underperforming campaigns is to attempt to improve coverage. Modelling work has indicated, however, that the quality of the coverage achieved may also have a significant impact on the outcome. If the coverage achieved is likely to miss similar people every round then this can have a serious detrimental effect on the campaign outcome. We begin by reviewing the current modelling descriptions of this effect and introduce a new modelling framework that can be used to simulate a given level of systematic non-adherence. We formalise the likelihood that people may miss several rounds of treatment using the correlation in the attendance of different rounds. Using two very simplified models of the infection of helminths and non-helminths, respectively, we demonstrate that the modelling description used and the correlation included between treatment rounds can have a profound effect on the time to elimination of disease in a population. It is therefore clear that more detailed coverage data is required to accurately predict the time to disease elimination. We review published coverage data in which individuals are asked how many previous rounds they have attended, and show how this information may be used to assess the level of systematic non-adherence. We note that while the coverages in the data found range from 40.5% to 95.5%, still the correlations found lie in a fairly narrow range (between 0.2806 and 0.5351). This indicates that the level of systematic non-adherence may be similar even in data from different years, countries, diseases and administered drugs. |
| first_indexed | 2024-03-06T20:17:41Z |
| format | Journal article |
| id | oxford-uuid:2cb13f50-a4d4-4738-9937-e1bf4cf9c80b |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:17:41Z |
| publishDate | 2017 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:2cb13f50-a4d4-4738-9937-e1bf4cf9c80b2022-03-26T12:38:42ZMeasuring and modelling the effects of systematic non-adherence to mass drug administration.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:2cb13f50-a4d4-4738-9937-e1bf4cf9c80bEnglishSymplectic Elements at OxfordElsevier2017Dyson, LStolk, WFarrell, SHollingsworth, TIt is well understood that the success or failure of a mass drug administration campaign critically depends on the level of coverage achieved. To that end coverage levels are often closely scrutinised during campaigns and the response to underperforming campaigns is to attempt to improve coverage. Modelling work has indicated, however, that the quality of the coverage achieved may also have a significant impact on the outcome. If the coverage achieved is likely to miss similar people every round then this can have a serious detrimental effect on the campaign outcome. We begin by reviewing the current modelling descriptions of this effect and introduce a new modelling framework that can be used to simulate a given level of systematic non-adherence. We formalise the likelihood that people may miss several rounds of treatment using the correlation in the attendance of different rounds. Using two very simplified models of the infection of helminths and non-helminths, respectively, we demonstrate that the modelling description used and the correlation included between treatment rounds can have a profound effect on the time to elimination of disease in a population. It is therefore clear that more detailed coverage data is required to accurately predict the time to disease elimination. We review published coverage data in which individuals are asked how many previous rounds they have attended, and show how this information may be used to assess the level of systematic non-adherence. We note that while the coverages in the data found range from 40.5% to 95.5%, still the correlations found lie in a fairly narrow range (between 0.2806 and 0.5351). This indicates that the level of systematic non-adherence may be similar even in data from different years, countries, diseases and administered drugs. |
| spellingShingle | Dyson, L Stolk, W Farrell, S Hollingsworth, T Measuring and modelling the effects of systematic non-adherence to mass drug administration. |
| title | Measuring and modelling the effects of systematic non-adherence to mass drug administration. |
| title_full | Measuring and modelling the effects of systematic non-adherence to mass drug administration. |
| title_fullStr | Measuring and modelling the effects of systematic non-adherence to mass drug administration. |
| title_full_unstemmed | Measuring and modelling the effects of systematic non-adherence to mass drug administration. |
| title_short | Measuring and modelling the effects of systematic non-adherence to mass drug administration. |
| title_sort | measuring and modelling the effects of systematic non adherence to mass drug administration |
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