Interventions
Interventions.Rmdsite_file$interventions
Interventions contains the historical intervention information for a site. It is also the section of the site file that you would modify with intervention information for future scenarios. Details, references and methods for individual interventions are shown below:
ITNs
ITN use
site_file$interventions$itn_use
Due to differences in the availability of data sources the approach for countries within sub-Saharan Africa differs to countries outside of sub-Saharan Africa:
Within sub-Saharan Africa
The population at risk weighted mean ITN use estimates for each site are taken from the malaria atlas project raster entitled: “Insecticide treated bednet (ITN) use version 2020”. This, and other elements of the netz R package are based on work by Bertozzi-Villa et al1
Outside of sub-Saharan Africa
ITN use is much more heterogeneous outside of SSA and data are less systematically collected. As a result, there are strong assumptions associated with the historical scale and magnitude of ITN distributions. We make the assumption that any reported ITN distributions (as detailed by the world malaria report2) are tarted to areas that have > 1% PfPr or > 1% PvPr at baseline. Annual distributions in these regions are then scaled so that that number of ITNs distributed is aligned with the world malaria report.
Missing years
Available data on ITN use (via MAP3 or the world malaria report @ WMR) will not extend to the present year. Missing ITN use estimates to present are filled assuming a constant, continuing level of coverage. To respect the multi-year cyclical nature of ITN distribution cycles any missing estimates are filled in assuming that coverage is constant with respect to 3 years prior. For example if years 2019, 2020 and 2021 are missing then 2019 == 2016, 2020 == 2017 and 2021 == 2018.
Net type
site_file$interventions$net_type
It is assumed that all nets distributed prior to 2025 are standard
pyrethroid (pyrethroid_only) bed nets. Current available
net types are: pyrethroid_only, pyrethroid_pbo and
pyrethroid_pyrrole.
Net input distribution
site_file$interventions$itn_input_dist
We need to estimate the annual ITN distributions that would
cumulatively result in our observed timeseries of ITN usage. For the we
use the fit_usage() function from the netz R package. We an impose
3 year cyclical limits to the number of nets that are distributed to
avoid unrealistic over-distribution.
Pyrethroid resistance
site_file$interventions$pyrethroid_resistance
For each site we include an estimated level of pyrethroid insecticide resistance. This has been estimated by Tom Churcher and colleagues using spatio-temporally distributed bioassay mortality data. This work is not yet published. Therefore for attribution/citation and further information on the methods used please contact Pete Winskill or Tom Churcher.
ITN efficacy parameters
site_file$interventions$dn0
site_file$interventions$rn0
site_file$interventions$gamman
site_file$interventions$rnm
Given an ITN type and level of pyrethroid insecticide resistance, we can link to the corresponding estimates of the key ITN efficacy parameters. These have been estimated by Ellie Sherrard-Smith et al4. Please note that gamman is provided in units of years here, these will need to be converted to days for use in malariasimulaiton.
IRS
IRS coverage
site_file$interventions$irs_cov
As with ITNs, due to differences in the availability of data sources the approach for countries within sub-Saharan Africa differs to countries outside of sub-Saharan Africa:
Within sub-Saharan Africa
The population at risk weighted mean IRS coverage estimates for each site are taken from the malaria atlas project raster entitled: “Indoor Residual Spraying (IRS) coverage version 2020”3. Coverage estimates are rescaled such that the country-level estimate of the number of persons protected by IRS matches the number reported in the world malaria report2.
Outside of sub-Saharan Africa
IRS coverage is much more heterogeneous outside of SSA and data are less systematically collected. As a result, there are strong assumptions associated with the historical scale and magnitude of IRS campaigns. We make the assumption that any reported IRS campaigns (as detailed by the world malaria report2) are tarted to areas that have > 1% PfPr or > 1% PvPr at baseline. Coverage in these regions are then scaled so that that number of persons protected by IRS is aligned with the world malaria report.
IRS insecticide
site_file$interventions$irs_insecticide
It is assumed that a DDT-type insecticide is used prior to 2017, after which there is a switch to an actellic-like insecticide.
Current available IRS insecticide options are: ddt, actellic, bendiocarb, sumishield.
Number of rounds of IRS per year
site_file$interventions$irs_spray_rounds
We assume a single IRS spray round per year.
IRS efficacy parameters
site_file$interventions$ls_theta
site_file$interventions$ls_gamma
site_file$interventions$ks_theta
site_file$interventions$ks_gamma
site_file$interventions$ms_theta
site_file$interventions$ms_gamma
Given an IRS insecticide type we can link to the corresponding estimates of the key IRS efficacy parameters. These have been estimated by Ellie Sherrard-Smith et al5.
IRS households sprayed
It is often helpful to convert the number of persons protected by IRS into an estimate of the number of households covered. To aid this conversion we have included country-levels estimates of the average household size obtained from the UN6.
site_file$interventions$hh_size
Treatment
Coverage
site_file$interventions$tx_cov
The population at risk weighted mean treatment coverage of an effective antimalarial for each site are taken from the malaria atlas project raster entitled: “Effective treatment with an Antimalarial drug version 2020”3.
Drug type
site_file$interventions$prop_act
We estimate the proportion of treatments that are with an ACT from DHS StatCompiler data7, using the indicator: “Children who took any ACT” (ID: ML_AMLD_C_ACT). For SSA estimates by year are expanded by linear interpolation between data points and an assumption of constant coverage after the most recent data point. We assume that ACT coverage is zero before 2006, when the WHO recommendation was first issued. For outside of SSA the DHS indicator is confounded by treatment for Plasmodium vivax, and we therefore assume the mean values by year from data within SSA.
Drug provider
site_file$interventions$prop_public
We include an estimate of the proportion of treatments that are from
the public sector prop_public. This is useful for costing.
We use the DHS StatCompiler7 indicator “Children with fever for
whom advice or treatment was sought, the source was a public sector
facility” (ID: ML_FEVA_C_PUB). We assume a constant proportion over time
by country, estimated as the mean from all country survey estimates
since 2010. For countries without survey data, we assume the median
across all estimates.
Seasonal malaria chemoprevention (SMC)
SMC coverage
site_file$interventions$smc_cov
Historical SMC implementation and coverage estimates are fragmented. We identify historical SMC implementation areas from maps presented by both Access SMC8 and more recently SMC alliance9. We assume a linear increase in coverage post implementation initiation up to a maximum of 80% to capture an increasing number of smaller sub-national units being targeted over time.
SMC drug
site_file$interventions$smc_drug
We assume that SP-AQ is used for SMC. This is currently the only available drug option.
RTS,S vaccine
site_file$interventions$rtss_cov
We include historical RTS,S coverage that has occurred as part of the MVIP implementation trial, sub-nationally in Malawi, Ghana and Kenya. The spatial distribution is informed from an MVIP briefing presentation10
Perennial malaria chemoprevention (PMC).
This intervention has been known in the past as intermittent preventative treatment of infants (IPTi).