Malaria Clinical Trial
Official title:
Targeting Malaria High-risk Populations With Tailored Intervention Packages: A Study to Assess Feasibility and Effectiveness in Northern Namibia
Verified date | November 2020 |
Source | University of California, San Francisco |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Interventional |
This study aims to determine the effectiveness, cost-effectiveness, acceptability, and feasibility of targeted delivery of a package of malaria interventions for improving effective coverage and reducing Plasmodium falciparum malaria transmission among malaria high-risk populations in Northern Namibia. Previous research identified cattle herders and agricultural workers as populations at higher risk of infection. The investigators hypothesize that targeted delivery of interventions will lead improve coverage in these groups and lead to a reduction in P. falciparum transmission.
Status | Completed |
Enrollment | 3302 |
Est. completion date | June 30, 2020 |
Est. primary completion date | June 30, 2020 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 6 Months and older |
Eligibility | Inclusion Criteria: HRP study populations (all) - Study participants include those in the 8 selected health facility catchment areas within Zambezi and Ohangwena Regions. - Identify primary occupation as a agricultural worker or cattle herder - Zambezi Region: Have slept or worked outside at a farm or cattle post in the past 7 days or will do over the next 3 weeks (sleeping outside, working outside ploughing or guarding crops/cattle, or sleeping in any type of structure located at a farm or cattle post site) - Ohangwena Region: Report overnight travel to Angola for grazing cattle during the malaria transmission season (November to May) Be willing and able to provide consent (ie mentally fit) Inclusion Criteria: Presumptive AL treatment - In addition to the above, subjects must report travel outside of Namibia within the past 60 days to be eligible to receive AL. Inclusion Criteria: Enhanced vector control - In addition to the above, participants must not sleep in a structure sprayed with insecticide to be eligible to receive an LLIN or sprayed tent/tarp. Inclusion Criteria: Focus group discussions and key informant interviews - Meet eligibility criteria as a member of an HRP, health facility staff or health extension worker involved in the diagnosis and treatment of HRP populations. - Individuals must be 18 years and older and willing and able to provide consent to be included in the GPS logger, focus group discussions or key informant interviews Exclusion Criteria: - Per national guidelines in Namibia, presumptive treatment with AL will not be given to women who are pregnant in the first trimester, individuals weighing less than 5kg, those with a known AL allergy or suspected severe malaria. - Individuals under the age of 18 will be excluded from the GPS logger study, focus group discussions and key informant interviews. |
Country | Name | City | State |
---|---|---|---|
Namibia | University of Namibia, Multidisciplinary Research Centre | Windhoek |
Lead Sponsor | Collaborator |
---|---|
University of California, San Francisco | London School of Hygiene and Tropical Medicine, Ministry of Health and Social Services, Namibia, University of Namibia, University of Notre Dame, University of Texas Southwestern Medical Center |
Namibia,
Hsiang MS, Ntuku H, Roberts KW, Dufour MK, Whittemore B, Tambo M, McCreesh P, Medzihradsky OF, Prach LM, Siloka G, Siame N, Gueye CS, Schrubbe L, Wu L, Scott V, Tessema S, Greenhouse B, Erlank E, Koekemoer LL, Sturrock HJW, Mwilima A, Katokele S, Uusiku P, Bennett A, Smith JL, Kleinschmidt I, Mumbengegwi D, Gosling R. Effectiveness of reactive focal mass drug administration and reactive focal vector control to reduce malaria transmission in the low malaria-endemic setting of Namibia: a cluster-randomised controlled, open-label, two-by-two factorial design trial. Lancet. 2020 Apr 25;395(10233):1361-1373. doi: 10.1016/S0140-6736(20)30470-0. — View Citation
Initiative ME. Planning for targeted malaria interventions in high-risk populations (HRPs) in Northern Namibia: Findings from a rapid formative assessment in Zambezi Region. University of California, San Francisco, 2019.
IOM (International Organization for Migration). A global report on population mobility and malaria: moving towards elimination with migration in mind. 2013.
Jacobson JO, Cueto C, Smith JL, Hwang J, Gosling R, Bennett A. Surveillance and response for high-risk populations: what can malaria elimination programmes learn from the experience of HIV? Malar J. 2017 Jan 18;16(1):33. doi: 10.1186/s12936-017-1679-1. Review. — View Citation
Kern SE, Tiono AB, Makanga M, Gbadoé AD, Premji Z, Gaye O, Sagara I, Ubben D, Cousin M, Oladiran F, Sander O, Ogutu B. Community screening and treatment of asymptomatic carriers of Plasmodium falciparum with artemether-lumefantrine to reduce malaria disease burden: a modelling and simulation analysis. Malar J. 2011 Jul 29;10:210. doi: 10.1186/1475-2875-10-210. — View Citation
Malaria Elimination Initiative. Planning for targeted malaria interventions in high-risk populations (HRPs) in Northern Namibia: Findings from a rapid formative assessment in Ohangwena Region. University of California, San Francisco, 2019.
Naing C, Whittaker MA, Tanner M. Inter-sectoral approaches for the prevention and control of malaria among the mobile and migrant populations: a scoping review. Malar J. 2018 Nov 16;17(1):430. doi: 10.1186/s12936-018-2562-4. Review. — View Citation
Smith JL, Auala J, Haindongo E, Uusiku P, Gosling R, Kleinschmidt I, Mumbengegwi D, Sturrock HJ. Malaria risk in young male travellers but local transmission persists: a case-control study in low transmission Namibia. Malar J. 2017 Feb 10;16(1):70. doi: 10.1186/s12936-017-1719-x. — View Citation
Smith JL, Ghimire P, Rijal KR, Maglior A, Hollis S, Andrade-Pacheco R, Das Thakur G, Adhikari N, Thapa Shrestha U, Banjara MR, Lal BK, Jacobson JO, Bennett A. Designing malaria surveillance strategies for mobile and migrant populations in Nepal: a mixed-methods study. Malar J. 2019 May 3;18(1):158. doi: 10.1186/s12936-019-2791-1. — View Citation
The Global Health Group. Screen and treat strategies for malaria elimination: a review of evidence. 2018.
Wu L, van den Hoogen LL, Slater H, Walker PG, Ghani AC, Drakeley CJ, Okell LC. Comparison of diagnostics for the detection of asymptomatic Plasmodium falciparum infections to inform control and elimination strategies. Nature. 2015 Dec 3;528(7580):S86-93. doi: 10.1038/nature16039. — View Citation
* Note: There are 11 references in all — Click here to view all references
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Effective coverage of AL (presumptive treatment) | This is defined as the proportion of eligible HRPs who report receiving a full course of AL (presumptive treatment) at any time over the study period. The difference in intervention coverage between arms at end-line will be assessed using generalized linear models adjusted for potential confounders and a fixed effect to capture clustering at the health facility level. | 6 months, measured post-intervention only | |
Primary | Effective coverage of IRS | This is defined as the proportion of eligible HRPs who report sleeping at a worksite in a structure sprayed with insecticide during the past 6 months (IRS), at any time over the study period. The difference in intervention coverage between arms will be assessed using a difference-in-difference approach using generalized linear models adjusted for potential confounders and a fixed effect to capture clustering at the health facility level. | 6 months, measured pre-and post-intervention | |
Primary | Effective coverage of LLIN | This is defined as the proportion of eligible HRPs who report sleeping under a bednet (LLIN) the last night staying at a worksite, at any time over the study period. The difference in intervention coverage between arms will be assessed using a difference-in-difference approach using generalized linear models adjusted for potential confounders and a fixed effect to capture clustering at the health facility level. | 6 months, measured pre-and post-intervention | |
Primary | Prevalence of infection measured by polymerase chain reaction (PCR) | Species-specific prevalence of malaria infection will be calculated as the proportion of people testing positive for each species malaria by PCR out of all tested HRPs. The reduction in malaria prevalence will be assessed using a difference-in-difference approach, comparing change (pre- versus post-intervention) in all-species infection between intervention and control groups. | 6 months | |
Secondary | Odds of symptomatic malaria associated with receiving each intervention | The odds of clinical malaria associated with receiving each intervention will be measured by comparing the risk of exposure in cases (RDT-positive) to the risk in controls (RDT-negative) in HRPs within study areas. | 6 months | |
Secondary | Total confirmed outpatient (OPD) malaria case incidence by health facility, stratified by HRP status | The total confirmed outpatient malaria case incidence will be estimated for each of the study health catchment areas and stratified by HRP status. RDTs used for routine malaria diagnosis will be collected and a short questionnaire affixed to the back to allow collection of key indicators to stratify incidence estimates. Denominators will be based on health catchment populations and population size estimates obtained through this study. A reduction in malaria incidence in HRPs and the overall catchment population will be evaluated using a difference-in-difference approach, comparing change (pre- versus post-intervention) between intervention and control groups. | 6 months | |
Secondary | Malaria seroprevalence in HRPs | Malaria exposure in HRPs will be measured as the proportion of people with antimalarial antibodies present in their blood serum. ELISA assays will be used to detect biomarkers of Pf exposure, using collected dried blood spots during baseline and endline cross-sectional surveys. The reduction in malaria exposure will be assessed using a difference-in-difference approach, comparing change (pre- versus post-intervention) between intervention and control groups. | 6 months | |
Secondary | Test positivity rates from RACD | Test positivity rates will be calculated as the proportion of individuals screened during routine RACD who test positive for malaria by RDT and PCR. Rates will be stratified by HRP status and location of event (either within villages or at an HRP worksite).Differences in prevalence measures between HRP and non-HRP populations will be assessed using a ?2 test, as well as logistic regression models to account for potential confounding factors. | 6 months | |
Secondary | Entomological Indicators | Entomological measurements including descriptions of vector occurrence, estimates of human biting rates, measures of indoor and outdoor densities, and insecticide susceptibility status and frequency. Measures will be compared between intervention and control study areas, for each type of intervention. | 6 months | |
Secondary | Intervention acceptability as evaluated by participation rate | The acceptability of a targeted delivery of presumptive treatment and vector control interventions to HRPs will be assessed as the proportion of targeted individuals refusing each intervention, among those eligible for inclusion. Acceptability of IRS will be assessed as the proportion of targeted farms refusing LLIN, among those with at least one sprayable structure missed during the spray campaign. | 6 months | |
Secondary | Intervention acceptability as evaluated by qualitative assessment | Qualitative data collection methods such as focus groups and key informant interviews with HRPs, health extension workers, employers and other health sector staff will be implemented to assess intervention acceptability. | 6 months | |
Secondary | Cost effectiveness | The costs and cost-effectiveness of the intervention package will be assessed as an incremental cost effectiveness ratio (ICER), as well as cost per population and case averted as measured through the difference in infections identified subsequent to the intervention. Program data on costs/expenditure for each intervention will be collected and combined with estimates of program effectiveness to estimate these measures. | 6 months | |
Secondary | Adherence to presumptive treatment intervention as evaluated by pill count | Participant adherence to presumptive treatment will be assessed within a sub-sample of people distributed the drug and measured as the proportion of people who complete all pills in the pill pack after receiving the first dose of AL by DOT. Changes in proportions over time will be quantified between the two distribution rounds. | 6 months | |
Secondary | Self-reported compliance to LLIN and topical repellents. | Self-reported user compliance to LLINs and topical repellents will be assessed through the quantitative baseline and endline cross-sectional surveys, as well as through a use tracking log tracking compliance over a 30-day period (Appendix 16 and 17) in the cohort included in the pill count. LLIN use and condition will be empirically assessed during the endline cross-sectional survey and at 30 days, as well as repellent containers weighed after 30 in the cohort. Qualitative information on use of these interventions will be collected during key informant interviews and focus group discussions. | 6 months | |
Secondary | HRP population size | The population size of target HRPs will be estimated in intervention areas using a combination of multiplier and multiple capture-recapture (CR) methods. In Ohangwena Region, the population is assumed fixed, although accessibility of cattle herders in Namibia may vary throughout the year. In Zambezi Region, the population is estimated at two distinct time periods in order to capture turnover between agricultural seasons. | 6 months | |
Secondary | HRP movement patterns | The proportion of individual's time spent in different locations will be measured from aggregating GPS readings (located within pre-defined space-time windows) which are spatially located within a given type of area (farm, cattle post, grazing location, village etc). Where possible, common locations will be tagged and classified by field workers to distinguish individual farms, fields and other points of interests (such as bars, homes, etc). Aggregated GPS readings (located within pre-defined space-time windows) will be plotted to distinguish individual trips and measure specific characteristics of each trip, including distance of travel and frequency of trips. | 6 months |
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