Treatment Coverage Estimation for Mass Drug Administration for Malaria with Dihydroartemisinin-Piperaquine in Southern Province, Zambia

Timothy P Finn, Joshua O Yukich, Adam Bennett, Travis R Porter, Christopher Lungu, Busiku Hamainza, Elizabeth Chizema Kawesha, Ruben O Conner, Kafula Silumbe, Richard W Steketee, John M Miller, Joseph Keating, Thomas P Eisele, Timothy P Finn, Joshua O Yukich, Adam Bennett, Travis R Porter, Christopher Lungu, Busiku Hamainza, Elizabeth Chizema Kawesha, Ruben O Conner, Kafula Silumbe, Richard W Steketee, John M Miller, Joseph Keating, Thomas P Eisele

Abstract

Mass drug administration (MDA) is currently being considered as an intervention in low-transmission areas to complement existing malaria control and elimination efforts. The effectiveness of any MDA strategy is dependent on achieving high epidemiologic coverage and participant adherence rates. A community-randomized controlled trial was conducted from November 2014 to March 2016 to evaluate the impact of four rounds of MDA or focal MDA (fMDA)-where treatment was given to all eligible household members if anyone in the household had a positive malaria rapid diagnostic test-on malaria outcomes in Southern Province, Zambia (population approximately 300,000). This study examined epidemiologic coverage and program reach using capture-recapture and satellite enumeration methods to estimate the degree to which the trial reached targeted individuals. Overall, it was found that the percentage of households visited by campaign teams ranged from 62.9% (95% CI: 60.0-65.8) to a high of 77.4% (95% CI: 73.8-81.0) across four rounds of treatment. When the maximum number of visited households across all campaign rounds was used as the numerator, program reach for at least one visit would have been 86.4% (95% CI: 80.8-92.0) in MDA and 83.5% (95% CI: 78.0-89.1) in fMDA trial arms. As per the protocol, the trial provided dihydroartemisinin-piperaquine treatment to an average of 58.8% and 13.3% of the estimated population based on capture-recapture in MDA and fMDA, respectively, across the four rounds.

Conflict of interest statement

Disclaimer: The funding source had no role in the conduct, analysis, or interpretation of results of the study. All authors had full access to all the data in the study.

Figures

Figure 1.
Figure 1.
Study timeline and matching of rounds to parasite survey. This figure appears in color at www.ajtmh.org.
Figure 2.
Figure 2.
Comparison of capture–recapture, Ministry of Health administrative and maximum campaign round households per catchment for the mass drug administration trial arm. This figure appears in color at www.ajtmh.org.
Figure 3.
Figure 3.
Comparison of capture–recapture, Ministry of Health administrative and maximum campaign round households per catchment for the focal MDA trial arm. This figure appears in color at www.ajtmh.org.

References

    1. Eisele TP, et al. 2016. Short-term impact of mass drug administration with dihydroartemisinin plus piperaquine on malaria in Southern Province Zambia: a cluster-randomized controlled trial. J Infect Dis 214: 1831–1839.
    1. Brady OJ, et al. 2017. Role of mass drug administration in elimination of Plasmodium falciparum malaria: a consensus modelling study. Lancet Glob Health 5: e680–e687.
    1. Stuckey EM, Miller JM, Littrell M, Chitnis N, Steketee R, 2016. Operational strategies of anti-malarial drug campaigns for malaria elimination in Zambia’s Southern Province: a simulation study. Malar J 15: 1–14.
    1. Gerardin J, Bever CA, Hamainza B, Miller JM, Eckhoff PA, Wenger EA, 2016. Optimal population-level infection detection strategies for malaria control and elimination in a spatial model of malaria transmission. PLoS Comput Biol 12: e1004707.
    1. Nikolov M, Bever CA, Upfill-Brown A, Hamainza B, Miller JM, Eckhoff PA, Wenger EA, Gerardin J, 2016. Malaria elimination campaigns in the Lake Kariba region of Zambia: a spatial dynamical model. PLoS Comput Biol 12: e1005192.
    1. Newby G, et al. 2015. Review of mass drug administration for malaria and its operational challenges. Am J Trop Med Hyg 93: 125–134.
    1. Eisele TP, et al. 2015. Assessing the effectiveness of household-level focal mass drug administration and community-wide mass drug administration for reducing malaria parasite infection prevalence and incidence in Southern Province, Zambia: study protocol for a community randomized controlled trial. Trials 16: 347.
    1. Astale T, et al. 2018. Population-based coverage survey results following the mass drug administration of azithromycin for the treatment of trachoma in Amhara, Ethiopia. PLoS Negl Trop Dis 12: e0006270–13.
    1. Griswold E, et al. 2018. Evaluation of treatment coverage and enhanced mass drug administration for onchocerciasis and lymphatic filariasis in five local government areas treating twice per year in Edo state, Nigeria. Am J Trop Med Hyg 99: 396–403.
    1. Baker MC, Krotki K, Sankara DP, Trofimovich L, Zoerhoff KL, Courtney L, Chowdhury D, Linehan M, 2013. Measuring treatment coverage for neglected tropical disease control programs: analysis of a survey design. Am J Epidemiol 178: 268–275.
    1. Cromwell EA, Ngondi J, McFarland D, King JD, Emerson PM, 2012. Methods for estimating population coverage of mass distribution programmes: a review of practices in relation to trachoma control. Trans R Soc Trop Med Hyg 106: 588–595.
    1. Shuford K, Were F, Awino N, Samuels A, Ouma P, Kariuki S, Desai M, Allen DR, 2016. Community perceptions of mass screening and treatment for malaria in Siaya County, western Kenya. Malaria Journal 15: 71.
    1. Adhikari RK, Sherchand JB, Mishra SR, Ranabhat K, Wagle RR, 2014. Awareness and coverage of mass drug administration for elimination of lymphatic filariasis: a community based cross sectional study in Nepal. J Community Health 40: 34–40.
    1. Poirot E, Skarbinski J, Sinclair D, Kachur SP, Slutsker L, Hwang J, 2013. Mass drug administration for malaria. Cochrane Database Syst Rev 12: CD008846. Available at: .
    1. Seidlein von L, Greenwood BM, 2003. Mass administrations of antimalarial drugs. Trends Parasitol 19: 452–460.
    1. Cromwell EA, Ngondi J, Gatpan G, Becknell S, Kur L, McFarland D, King JD, Emerson PM, 2009. Estimation of population coverage for antibiotic distribution for trachoma control: a comparison of methods. Int Health 1: 182–189.
    1. Mathieu E, Deming M, Lammie PJ, McLaughlin SI, Beach MJ, Deodat DJ, Addiss DG, 2003. Comparison of methods for estimating drug coverage for filariasis elimination, Leogane Commune, Haiti. Trans R Soc Trop Med Hyg 97: 501–505.
    1. Woodhall DM, Mkwanda S, Dembele M, Lwanga H, Drexler N, Dubray C, Harris J, Worrell C, Mathieu E, 2014. Exploring innovative ways to conduct coverage surveys for neglected tropical diseases in Malawi, Mali, and Uganda. Acta Trop 132: 119–124.
    1. Armstrup SC, McDonald TL, Manly BF, eds. 2005. Handbook of Capture-Recapture Analysis. Princeton, NJ: Princeton University Press.
    1. Seber GAF, 1982. The Estimation of Animal Abundance and Related Parameters, 2nd edition London, United Kingdom: C. Griffin & Co., Ltd.
    1. Chao A, Tsay PK, Lin SH, Shau WY, Chao DY, 2001. The applications of capture-recapture models to epidemiological data. Statist Med 20: 3123–3157.
    1. Carter KL, Williams G, Tallo V, Sanvictores D, Madera H, Riley I, 2011. Capture-recapture analysis of all-cause mortality data in Bohol, Philippines. Popul Health Metr 9: 9.
    1. Lewden C, et al. 2000 Study Group , 2000. Number of deaths among HIV-infected adults in France in 2000, three-source capture-recapture estimation. Epidemiol Infect 134: 1345–1352.
    1. Roberts B, Morgan OW, Sultani MG, Nyasulu P, Rwebangila S, Myatt M, Sondorp E, Chandramohan D, Checchi F, 2010. A new method to estimate mortality in crisis-affected and resource-poor settings: validation study. Int J Epidemiol 39: 1584–1596.
    1. Vong S, Goyet S, Ly S, Ngan C, Huy R, Duong V, Wichmann O, Letson GW, Margolis HS, Buchy P, 2012. Under-recognition and reporting of dengue in Cambodia: a capture-recapture analysis of the National Dengue Surveillance System. Epidemiol Infect 140: 491–499.
    1. Peters TR, Snively BM, Suerken CK, Bischoff W, Vannoy L, Blakeney E, Bischoff T, Palavecino E, Sherertz R, Poehling KA, 2016. Estimating the burden of pandemic infectious disease: the case of the second wave of pandemic influenza H1N1 in Forsyth county, North Carolina. N C Med J 77: 15–22.
    1. Héraud-Bousquet V, Lot F, Esvan M, Cazein F, Laurent C, Warszawski J, Gallay A, 2012. A three-source capture-recapture estimate of the number of new HIV diagnoses in children in France from 2003–2006 with multiple imputation of a variable of heterogeneous catchability. BMC Infect Dis 12: 251.
    1. Eisele TP, Lindblade KA, Rosen DH, Odhiambo F, Vulule JM, Slutsker L, 2003. Evaluating the completeness of demographic surveillance of children less than five years old in western Kenya: a capture-recapture approach. Am J Trop Med Hyg 69: 92–97.
    1. Wampler PJ, Rediske RR, Molla AR, 2013. Using ArcMap, google earth, and global positioning systems to select and locate random households in rural Haiti. Int J Health Geogr 12: 1.
    1. Chang AY, Parrales ME, Jimenez J, Sobieszczyk ME, Hammer SM, Copenhaver DJ, Kulkarni RP, 2009. Combining google earth and GIS mapping technologies in a dengue surveillance system for developing countries. Int J Health Geogr 8: 49.
    1. Escamilla V, Emch M, Dandalo L, Miller WC, Martinson F, Hoffman I, 2014. Sampling at community level by using satellite imagery and geographical analysis. Bull World Health Organ 92: 690–694.
    1. Haenssgen MJ, 2015. Satellite-aided survey sampling and implementation in low- and middle-income contexts: a low-cost/low-tech alternative. Emerg Themes Epidemiol 12: 20.
    1. Pearson AL, Rzotkiewicz A, Zwickle A, 2015. Using remote, spatial techniquesto select a random household sample in a dispersed, semi-nomadic pastoral community: utility for a longitudinal health and demographic surveillance system. Int J Health Geogr 14: 1–10. 10.1186/s12942-015-0026-4.
    1. Lowther SA, Curriero FC, Shields T, Ahmed S, Monze M, Moss WJ, 2009. Feasibility of satellite image-based sampling for a health survey among urban townships of Lusaka, Zambia. Trop Med Int Health 14: 70–78.
    1. Kamanga A, Renn S, Pollard D, Bridges DJ, Chirwa B, Pinchoff J, Larsen DA, Winters AM, 2015. Open-source satellite enumeration. Malar J 14: 11–7. 10.1186/s12936-015-0831-z.
    1. Pinchoff J, et al. 2016. Targeting indoor residual spraying. Malar J 15: 11–6. 10.1186/s12936-015-1073-9.
    1. Barau I, Zubairu M, Mwanza MN, Seaman VY, 2014. Improving polio vaccination coverage in Nigeria through the use of geographic information system technology. J Infect Dis 210 (Suppl 1): S102–S110.
    1. Finn TP, et al. 2020. Adherence to mass drug administration of dihydroartemisinin-piperaquine and Plasmodium falciparum clearance in Southern Province, Zambia. Am J Trop Med Hyg 103 Suppl 2): 37–45.
    1. World Health Organization , 2017. Coverage Evaluation Surveys for Preventive Chemotherapy, 1–54. Geneva, Switzerland: World Health Organization.
    1. Blakely T, Salmond C, 2002. Probabilistic record linkage and a method to calculate the positive predictive value. Int J Epidemiol 31: 1246–1252.
    1. Krebs CJ, 1998. Ecological Methodology, 2nd edition Menlo Park, CA: Benjamin Cumings.
    1. R Core Team , 2018. R: a Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; Available at: .
    1. Ogle DH, Wheeler P, Dinno A, 2020. FSA: Fisheries Stock Analysis. R package version 0.8.30.9000. Available at: .
    1. Larsen DA, Bennett A, Silumbe K, Hamainza B, Yukich JO, Keating JA, Littrell M, Miller JM, Steketee RW, Eisele TP, 2015. Population-wide malaria testing and treatment with rapid diagnostic tests and artemether-lumefantrine in Southern Zambia: a community randomized step-wedge control trial design. Am J Trop Med Hyg 92: 913–921.
    1. Bridges DJ, Pollard D, Winters AM, Winters B, Sikaala C, Renn S, Larsen DA, 2018. Accuracy and impact of spatial aids based upon satellite enumeration to improve indoor residual spraying spatial coverage. Malar J 17: 1–8. 10.1186/s12936-018-2236-2.
    1. Erbach-Schoenberg EZ, et al. 2016. Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates. Popul Health Metr 14: 1–10. 10.1186/s12963-016-0106-0.
    1. Tatem AJ, 2014. Mapping the denominator: spatial demography in the measurement of progress. Int Health 6: 153–155.
    1. Tatem AJ, 2014. Mapping population and pathogen movements. Int Health 6: 5–11.
    1. Gali E, et al. 2016. Revised household-based microplanning in polio supplemental immunization activities in Kano state, Nigeria. 2013–2014. J Infect Dis 213 (Suppl 3): S73–S78.
    1. Rosencrans LC, Sume GE, Kouontchou J-C, Voorman A, Anokwa Y, Fezeu M, Seaman VY, 2017. Mapping for health in Cameroon: polio legacy and beyond. J Infect Dis 216 (Suppl 1): S337–S342.
    1. Budge PJ, Sognikin E, Akosa A, Mathieu EM, Deming M, 2016. Accuracy of coverage survey recall following an integrated mass drug administration for lymphatic filariasis, schistosomiasis, and soil-transmitted helminthiasis. PLoS Negl Trop Dis 10: e0004358.
    1. Cromwell EA, King JD, McPherson S, Jip FN, Patterson AE, Mosher AW, Evans DS, Emerson PM, 2013. Monitoring of mass distribution interventions for trachoma in Plateau state, Nigeria. PLoS Negl Trop Dis 7: e1995.

Source: PubMed

Sponsorit ja yhteistyökumppanit

Sairaudet

Huumeiden interventiot

3
Tilaa