PMI ITN Access and Use Reportversion of 9/22/18
PMI ITN Access and Use Report - 2016
Prepared by Hannah Koenker and Emily Ricotta
March 31, 2016
This report is made possible by the generous support of the American people through the United States Agency for International Development (USAID) under the terms of USAID/JHU Cooperative Agreement No: AID-OAA-A-14-00057. The contents do not necessarily reflect the views of USAID or the United States Government.
Abbreviations
2016 Update
Background
Definitions
Methods
Results
Angola
Observations
Implications for programming
Benin
Observations
Implications for programming
DRC
Observations
Implications for programming
Ethiopia
Ghana
Observations
Implications for programming
Guinea
Observations
Implications for programming
Kenya
Observations
Implications for programming
Liberia
Observations
Implications for programming
Madagascar
Observations
Implications for programming
Malawi
Observations
Implications for programming
Mali
Observations
Implications for programming
Mekong Region
Mozambique
Observations
Implications for programming
Nigeria
Observations
Implications for programming
Rwanda
Observations
Implications for programming
Senegal
Observations
Implications for programming
Tanzania
Observations
Implications for programming
Uganda
Observations
Implications for programming
Zambia
Observations
Implications for programming
Zimbabwe
Observations
Implications for programming
Abbreviations
DHSDemographic and Health Survey
MICSMultiple Cluster Indicator Survey
MISMalaria Indicator Survey
ITNInsecticide-treated net
IRSIndoor Residual Spraying
LLINLong-lasting insecticidal net
2016 Update
As of February 1, 2016, updated results are provided for Ghana, Kenya, Malawi, Senegal, and Uganda based on recently released surveys; in addition, all countries now include additional stratification for households sprayed with IRS in the previous 12 months.
Key findings across countries
Overall
- All but 7 PMI countries have the majority of their regional use:access ratios above 80%.
- Three countries, Mozambique, Senegal and Guinea, have a mix of regions at the red, yellow, and green categories, with specific regions showing that low use of available nets is likely due to dry season, higher altitude, and/or lower prevalence.
- Five countries appear to have below target use:access ratios over most of the country: Ghana, Nigeria, Senegal, Zambia, and Zimbabwe.
- Zimbabwe and Nigeria have the lowest use:access ratios, looking at the most recent datasets. Additional research in Nigeria indicates that zonal and seasonal influences contribute to larger net use variations in some areas of the country.
Wealth Quintiles
- Five countries (Ghana, Guinea, Nigeria, Senegal, and Zimbabwe) show below-target use:access ratios when viewed by wealth quintile.
- Of the 18 countries with available data, six demonstrate a mild pro-poor trend in use:access ratios, with poorest households having better use of available nets compared to richer households. Of these six, all are above the 80% targets. The remaining 12 countries show no observable differences in use:access among wealth quintiles.
- Nigeria shows a pro-rich trend in use:access in 2013, but pro-poor trends in the previous two surveys.
Urban/Rural
- There are no programmatic differences in urban/rural use:access ratios, apart from Ghana, where mean use:access is 0.44 in urban areas and 0.74 in rural areas, and in Mozambique, where urban areas have moderately better use:access (0.85) compared to rural areas (0.77).
Use of ITNs in IRS and non-IRS households
- Use:access ratiosare not programmatically different between sprayed and unsprayed households.
Background
National results for ownership, access, use, and the use:access ratio have been described in Koenker et al previously in detail[1]. However, national results conceal variations by region, which may result from differences in survey timing vis à vis rainy season (among other reasons). Variations in other subgroups such as wealth quintile or urban/rural residence may offer ways to identify target groups that do not use their available nets to the fullest degree.
Definitions
“Ownership”: household ownership of at least 1 ITN. Ownership indicator provides an estimate of the minimum threshold for ITN coverage – if the household has at least one. However, ownership does not take into account whether the household has enough nets for all family members.
“Access”: the proportion of the population with access to an ITN within their household. Also called “population access” or “ITN access”. This indicator is calculated based on the number of ITNs in the household and the number of household members. Over a large sample, it calculates the proportion of people who should have (in principle, based on the assumption that one ITN can be used by two people in the household) an ITN to sleep under. It cannot be calculated on an individual basis.
“Use”: the proportion of the population that slept under an ITN the night before the survey. Also called “population use” to distinguish it from use of ITNs by children under five or pregnant women.
“Use:access ratio”: the result when dividing access by use (i.e. use/access). Gives an estimate of the proportion of the population using nets, among those that have access to one within their household. As it is a ratio, it is not technically a percentage, although it can be interpreted as such. This indicator provides data on the behavioral gap for net use – rather than a use gap because not enough nets are available.
Methods
For each dataset three indicators were calculated: individual access to ITN within the household, individual use of ITN the previous night, and household ownership of at least one ITN. The ratio of population ITN use to population ITN access within the household was calculated and is referred to here as the use:access ratio.
ITN use was calculated in the household member file, as was access to an ITN. This appropriately weights the access ratio for each household according to the number of members in each household. (Running the access calculation and calculating the mean within the household file does not take into account the number of people in each household, making that result an unweighted mean.) However, ITN ownership is calculated within the household file. Data management and analysis was done using Stata version 12 (Stata Corporation, College Station, Texas, USA). All analyses accounted for survey design including sampling weights where applicable using the survey command family in STATA.
The survey indicator of access to ITN within the household was calculated from the datasets of individual household members as recommended by MERG[2]. First, an intermediate variable of “potential ITN users” was created by multiplying the number of ITN in each household by a factor of 2.0. In order to adjust for households with more than one net for every two people, the potential ITN users were set equal to the de-facto population in that household if the potential users exceeded the number of people in the household. Second, the population access indicator was calculated by dividing the potential ITN users by the number of de-facto members for each household and determining the overall sample mean of that fraction.
Use of an ITN the previous night was calculated for eachde factomember of the household, i.e. those present in the house the previous night, as recommended by MERG using the listings of net users from the net roster2. Household ownership of at least one ITN was also calculated for each dataset based on the number of ITN observed in the household and defining an ITN as a long-lasting insecticidal net (LLIN) identified by its label or a net that was treated with an insecticide within the last 12 months.
Access, use, and ownership were stratified by region, by rural/urban status (residence), wealth quintile, and where available, whether the household had received IRS in the last 12 months for each country. Cluster weighted univariate regressions were conducted to assess whether significant differences existed between strata. Results
National results for ownership, access, use, and the use:access ratio have been described in Koenker et al in detail (Koenker, 2014), and national results for PMI countries are presented below in Table 1 for quick reference. However, national results conceal variations by region, which may result from differences in survey timing in regards to rainy season. Regional or provincial results, indicators by wealth quintile and by urban or rural residence are presented below for each PMI country where data is available. Results for ratio of use:access are color-coded as follows:
Note 1: Color coding of use:access ratios and explanation
≥0.80 / Use:access ratio is good, with approximately 80% of those with access to an ITN using one the previous night≥0.60-<0.80 / Use:access ratio is below target level; improvements should be made
<0.60 / Use:access ratio is poor; explore reasons for non-use of available nets, such as dry season, low-transmission area, and IRS activities.
Table 1: National results for ITN ownership, access, use, and use:access ratio for PMI countries and those receiving malaria funds)
Country | Survey | Year / % of households owning ≥1 ITN / % of population with access to an ITN within their own household / % of population that used an ITN the previous night / Ratio of use:accessAngola MIS 2006-7 / 27.5% / 14.5% / 11.9% / 0.82
Angola MIS 2011 / 34.5% / 19.0% / 18.9% / 0.99
Benin DHS 2006 / 24.5% / 14.7% / 14.7% / 1.00
Benin DHS 2011-12 / 81.8% / 64.0% / 62.6% / 0.98
Burkina Faso DHS 2010 / 56.9% / 36.1% / 31.5% / 0.87
Burundi DHS 2010 / 52.0% / 39.1% / 37.8% / 0.97
Burundi MIS 2012 / 66.0% / 46.0% / 48.6% / 1.06
DRC DHS 2007 / 9.2% / 4.2% / 4.3% / 1.03
DRC MICS 2010 / 65.5% / 41.6% / 30.9 / 0.74
DRC DHS 2013-2014 / 70.0% / 46.5% / 50.2% / 1.08
Ghana DHS 2008 / 41.7% / 30.1% / 20.9% / 0.69
Ghana MICS 2011 / 43.0% / 37.7% / 28.1% / 0.74
Ghana DHS 2014 / 68.3% / 59.0% / 35.7% / 0.60
Guinea DHS 2005 / 3.5% / 1.5% / 1.1% / 0.77
Guinea DHS 2012 / 47.4% / 25.3% / 18.9% / 0.75
Kenya DHS 2008 / 55.7% / 42.3% / 35.1% / 0.83
Kenya DHS 2014 / 58.9% / 48.2% / 42.6% / 0.88
Liberia MIS 2009 / 47.2% / 25.4% / 22.8% / 0.90
Liberia MIS 2011 / 49.7% / 30.8% / 32.1% / 1.04
Liberia DHS 2013 / 54.6% / 37.0% / 31.7% / 0.86
Madagascar DHS 2008 / 57.0% / 34.7% / 36.6% / 1.05
Madagascar MIS 2011 / 80.5% / 57.3% / 68.4% / 1.19
Madagascar MIS 2013 / 69.2% / 47.8% / 55.0% / 1.15
Malawi DHS 2010 / 56.8% / 37.6% / 29.0% / 0.77
Malawi MIS 2012 / 55.0% / 37.2% / 40.9% / 1.10
Malawi DHS 2014 / 70.2% / 51.8% / 52.5% / 1.01
Mali DHS 2006 / 50.0% / 29.7% / 21.4% / 0.72
Mali AP 2010 / 85.9% / 61.6% / 56.2% / 0.91
Mali DHS 2013 / 84.4% / 65.1% / 60.4% / 0.93
Mozambique DHS 2011 / 54.7% / 37.0% / 29.4% / 0.80
Nigeria DHS 2008 / 8.0% / 4.8% / 3.2% / 0.68
Nigeria MIS 2010 / 41.5% / 28.7% / 23.3% / 0.81
Nigeria DHS 2013 / 49.5% / 36.1% / 12.9% / 0.36
Rwanda 2007-8 DHS / 55.6% / 38.1% / 39.7% / 1.04
Rwanda DHS 2010 / 82.0% / 64.2% / 57.7% / 0.90
Rwanda MIS 2013 / 82.6% / 65.9% / 60.9% / 0.92
Senegal MIS 2008 / 60.4% / 34.9% / 22.9% / 0.66
Senegal DHS 2010 / 66.2% / 38.1% / 28.9% / 0.76
Senegal cDHS 2012 / 72.8% / 57.4% / 40.7% / 0.71
Senegal cDHS 2014 / 74.4% / 58.4% / 40.4% / 0.69
Tanzania THMIS 2007-8 / 39.2% / 25.4% / 20.3% / 0.80
Tanzania DHS 2010 / 63.8% / 46.6% / 45.1% / 0.97
Tanzania THMIS 2011 / 90.9% / 74.5% / 68.4% / 0.92
Uganda MIS 2009 / 46.7% / 31.6% / 25.6% / 0.81
Uganda DHS 2011 / 59.8% / 44.7% / 35.0% / 0.78
Uganda MIS 2014-15 / 90.2% / 78.8% / 68.6% / 0.87
Zambia DHS 2007 / 53.3% / 33.9% / 23.0% / 0.68
Zambia DHS 2013-14 / 67.7% / 46.6% / 34.9% / 0.75
Zimbabwe DHS 2005-2006 / 9.1% / 4.8% / 2.4% / 0.50
Zimbabwe DHS 2010 / 28.8% / 20.2% / 8.7% / 0.43
Angola
Two surveys were available in Angola, the 2006-2007 MIS and the 2011 MIS. Rains in the south are from February through April. In the north, rain is from October to May. The northernmost parts of Angola experience rain throughout most of the year. The 2006-2007 survey took place from November, 2006 through March, 2007. Fieldwork for the 2011 MIS was completed January through May, 2011.
2006 / 2011 / 2006 / 2011 / 2006 / 2011 / 2006 / 2011Region / % of households owning ≥1 ITN / % of population with access to an ITN within their own household / % of population that used an ITN the previous night / Ratio of use:access
Stable Mesoendemic § / 20% / 36% / 11% / 19% / 10% / 21% / 0.91 / 1.11
Hyperendemic / 51%* / 30% / 30%* / 17% / 25%* / 17% / 0.83 / 1.00
Instable Mesoendemic / 23% / 37% / 12% / 20% / 8% / 19% / 0.67 / 0.95
Luanda / 23% / 35% / 11% / 19% / 9% / 17% / 0.82 / 0.89
WealthQuintile
Poorest§ / 26% / 15%* / 15% / 8%* / 13% / 9%* / 0.87 / 1.13
Poorer / 22% / 23%* / 12% / 11%* / 10% / 13%* / 0.83 / 1.18
Middle / 32% / 33%* / 16% / 17%* / 15% / 18%* / 0.94 / 1.06
Richer / 28% / 42%* / 14% / 23%* / 11% / 22%* / 0.79 / 1.00
Richest / 31% / 44%* / 16% / 24%* / 9% / 23%* / 0.56 / 0.96
Residence
Urban§ / 29% / 39% / 15% / 22% / 11% / 20% / 0.73 / 0.91
Rural / 26% / 32%* / 14% / 17%* / 13% / 18% / 0.93 / 1.06
IRS
No§ / 27% / 34% / 15% / 18% / 12% / 18% / 0.80 / 1.00
Yes / 34% / 46%* / 13% / 27%* / 6% / 28%* / 0.46 / 1.03
*p-value≤0.05 compared to reference group (denoted with §)
Observations
While the use:access ratio has been generally high in Angola, the 2011 survey saw all regions, wealth quintiles, and residences increase their ratio above .80. Access and use both increase significantly as wealth increases. While urban residences have higher net access, there is no significant difference in use between the categories. In the 2006 survey, there was no significant difference in net use or access between households who had reported IRS and those who had not. In 2011, households with IRS had significantly higher access and use than those without.
Implications for programming
Net use and access seem to be high across Angola. Net distribution and SBCC nationally should be continued throughout the country to maintain the very high use:access ratio here.
Benin
Available data for Benin include the 2006 DHS, conducted primarily in September 2006, at the end of the Northern Benin rainy season and just before rains started in the south, and the 2011-2012 DHS, conducted in Littoral in September 2011, just prior to rainy season there, with most fieldwork done in remaining regions in January-March 2012, during their cooler/drier season.
2006 DHS / 2011-12 DHS / 2006 DHS / 2011-12 DHS / 2006 DHS / 2011-12 DHS / 2006 DHS / 2011-12 DHS% of households owning ≥1 ITN / % of population with access to an ITN within their own household / % of population that used an ITN the previous night / Ratio of use:access
Province
Alibori§ / 8% / 89% / 3% / 68% / 3% / 68% / 0.96 / 0.96
Atacora / 26%* / 93%* / 14% / 77%* / 13% / 66% / 0.93 / 0.83
Atlantique / 13%* / 75%* / 9% / 60%* / 9% / 62% / 1.03 / 1.01
Borgou / 20%* / 81%* / 10% / 62%* / 10% / 59% / 0.93 / 0.93
Collines / 30%* / 78%* / 19% / 62%* / 19% / 60% / 0.99 / 0.93
Couffo / 29%* / 82%* / 15% / 62%* / 15% / 60% / 0.96 / 0.95
Donga / 30%* / 84% / 14% / 64% / 13% / 61% / 0.87 / 0.93
Littoral / 28%* / 78%* / 22% / 65% / 23% / 64% / 1.01 / 0.97
Mono / 25%* / 74%* / 16% / 60%* / 16% / 60% / 1.03 / 1.00
Oueme / 34%* / 74%* / 21% / 58%* / 22% / 59% / 1.06 / 1.00
Plateau / 21%* / 84% / 14% / 68% / 13% / 68% / 0.95 / 0.98
Zou / 27%* / 79%* / 16% / 62%* / 17% / 65% / 1.05 / 1.04
SES
Poorest§ / 11% / 79% / 6% / 62% / 7% / 61% / 1.08 / 0.98
Poorer / 17%* / 81% / 9% / 64% / 10% / 63% / 1.08 / 0.98
Middle / 24%* / 80% / 13% / 63% / 14% / 62% / 1.03 / 0.99
Richer / 31%* / 78% / 18% / 64% / 17% / 61% / 0.98 / 0.97
Richest / 39%* / 80% / 27% / 67%* / 26% / 65%* / 0.95 / 0.97
Residence
Urban§ / 29% / 78% / 19% / 64% / 19% / 62% / 0.96 / 0.97
Rural / 21%* / 81%* / 12% / 64% / 12% / 63% / 1.04 / 0.98
IRS
No§ / 79% / 63% / 62% / 0.98
Yes / 94%* / 79%* / 70%* / 0.89
*p-value≤0.05 compared to reference group (denoted with §)
Observations
Overall, the ratio between ITN access and ITN use is excellent in Benin, indicating that those who have nets available are using them. There was no significant change between 2006 and 2012 in the ratio, although there was a slight pro-poor trend when looking at wealth quintile in 2006 and the use:access ratio, which disappeared in 2012. In 2006 in general there was a pro-rich trend to ITN ownership, access, and use, although this hid the pro-poor trend in the use:access ratio. By 2012 access and use was relatively consistent among wealth quintiles. In the same vein, in 2006 and 2012 there were differences between urban and rural ownership of nets, but there was no significant difference in access and use. Again the ratio of use:access remained stable between the subgroups and surveys. There was no IRS data collected in the 2006 survey, but the 2012 survey demonstrated that those households with IRS were had significantly higher ownership, access, and use than those without. However, the use to access ratio was higher in the non-IRS group than the IRS group.
Implications for programming
Overall improvements in ownership and access appear to reduce disparities among wealth quintiles in Benin, and between urban and rural residents. There is no clear need for prioritizing SBCC messages in certain regions over others; all regions have a use:access ratio of over 0.80. Additional work may be helpful to determine whether dry-season net use and transmission patterns warrant increased SBCC during lower-net use seasons.
DRC
Available data for DRC include the 2013 DHS, the 2010 MICS, and the 2007 DHS. The 2013 DHS was conducted primarily in December-January 2013-2014, during the rainy season (excepting the provinces north of the equator, Oriental and Equatorial, where it was dry). The 2010 MICS was conducted in January-February 2010, also during rainy season in the southern part of the country. Due to time elapsed, the 2007 DHS is not included here. No IRS data was included in the 2010 MICS or 2013 DHS.
2010 MICS / 2013 DHS / 2010 MICS / 2013 DHS / 2010 MICS / 2013 DHS / 2010 MICS / 2013 DHS% of households owning ≥1 ITN / % of population with access to an ITN within their own household / % of population that used an ITN the previous night / Ratio of use:access
Province
Kinshasa§ / 85% / 59% / 53% / 37% / 10% / 39% / 0.76 / 1.06
Bandundu / 66% / 88%* / 48% / 63%* / 35% / 70%* / 0.73 / 1.11
Bas-Congo / 49% / 76%* / 35%* / 51%* / 24%* / 58%* / 0.69 / 1.13
Equateur / 79% / 83%* / 57% / 57%* / 41% / 63%* / 0.72 / 1.09
Kasai-Occidental / 34% / 58% / 20%* / 31% / 14%* / 31%* / 0.69 / 1.00
Kasai-Oriental / 24% / 64% / 14%* / 38% / 10%* / 43% / 0.68 / 1.11
Katanga / 46% / 80%* / 32%* / 55%* / 24%* / 57%* / 0.75 / 1.04
Maniema / 94% / 59% / 65% / 37% / 52%* / 40% / 0.80 / 1.08
Nord-Kivu / 63% / 60% / 39% / 39% / 27%* / 39% / 0.68 / 0.99
Orientale / 85% / 47%* / 54% / 33% / 45% / 35% / 0.83 / 1.06
Sud-Kivu / 62% / 70%* / 42% / 46%* / 32% / 51%* / 0.75 / 1.12
Wealth Quintile
Poorest§ / 54% / 59% / 34% / 39% / 25% / 42% / 0.74 / 1.09
Poorer / 62% / 72%* / 40%* / 48%* / 29% / 53%* / 0.73 / 1.12
Middle / 62% / 75%* / 40%* / 50%* / 29% / 55%* / 0.72 / 1.09
Richer / 66% / 77%* / 42%* / 52%* / 32%* / 55%* / 0.77 / 1.07
Richest / 82% / 68%* / 51%* / 44%* / 39%* / 46% / 0.76 / 1.04
Residence
Urban§ / 74% / 71% / 46% / 46% / 35% / 48% / 0.76 / 1.04
Rural / 62% / 70% / 40% / 47% / 29%* / 51% / 0.74 / 1.10
*p-value≤0.05 compared to reference group (denoted with §)
Observations
Based on the 2013-14 data, there are no particular provinces where ITN use among those with access is worrisome. Nor is ITN use:access ratio related to socio-economic status, or residence. Overall, rates of ITN use are extremely good throughout DRC, assuming nets are available. This is improved from the 2010 MICS survey across all residences and wealth quintiles, and across most provinces. The exceptions were Maniema and Orientale, whose ratios were at 0.80 or above prior to the 2013-2014 survey.
Implications for programming
There is a need for additional nets to fill gaps at the household level in population access to ITNs; SBCC programming for net use does not need to be targeted to certain areas over others.
Ethiopia
The most recent available survey, the 2011 DHS, does not contain the malaria module. The dataset for the 2011 MIS is not availablepublicly. An MIS was fielded in late 2015, with funding from PMI and Global Fund, but is not yet available.
2011 MIS / % of households owning ≥1 ITN / % of population with access to an ITN within their own household / % of population that used an ITN the previous night / Ratio of use:accessProvince
Amhara / 73.6
B. Gumuz & Gambella / 69.0
Diredawa / 78.9
Oromia / 43.7
SNNPR / 57.0
Somali & Afar / 45.0
Tigray / 65.8
Wealth Quintile
Poorest / 44.2
Poorer / 52.4
Middle / 54.6
Richer / 31.2
Richest / 66.4
Residence
Elev <2000m / 54.8
Elev >2000m / 37.6
Ghana
Available data for Ghana include the 2008 DHS, conducted primarily in September and October 2008, during rainy/high transmission season; the 2011 MICS, conducted in September-November 2011, also during rainy/high transmission season; and the 2014 DHS, conducted from early September through mid December 2014, in rainy/high transmission season. Questions on IRS were not included in the 2008 and 2011 surveys.
2008 DHS / 2011 MICS / 2014 DHS* / 2008 DHS / 2011 MICS / 2014 DHS* / 2008 DHS / 2011 MICS / 2014 DHS* / 2008 DHS / 2011 MICS / 2014 DHS*% of households owning ≥1 ITN / % of population with access to an ITN within their own household / % of population that used an ITN the previous night / Ratio of use:access
Region
Western§ / 41% / 44% / 67% / 30% / 29% / 59% / 20% / 23% / 38% / 0.66 / 0.79 / 0.65
Central / 42% / 33%* / 70% / 30% / 24% / 58% / 17% / 17% / 42% / 0.56 / 0.72 / 0.73
Greater Accra / 30%* / 30%* / 53%* / 24%* / 17%* / 49%* / 12%* / 11%* / 16%* / 0.51 / 0.65 / 0.32
Volta / 43% / 90%* / 76%* / 30% / 79%* / 70%* / 22% / 64%* / 54%* / 0.72 / 0.82 / 0.77
Eastern / 36% / 78%* / 73% / 26% / 60%* / 64% / 19% / 49%* / 38% / 0.72 / 0.81 / 0.60
Ashanti / 40% / 42% / 70% / 28% / 32% / 60% / 19% / 23% / 34% / 0.70 / 0.72 / 0.57
Brong Ahafo / 51%* / 56%* / 81%* / 36%* / 39% / 70%* / 32%* / 27% / 52%* / 0.87 / 0.71 / 0.75
Northern / 54%* / 69%* / 71% / 31% / 41%* / 55% / 23% / 27% / 36% / 0.72 / 0.67 / 0.66
Upper East / 53%* / 53%* / 73% / 37% / 40%* / 56% / 26%* / 28% / 31% / 0.71 / 0.70 / 0.56
Upper West / 71%* / 66%* / 77%* / 50%* / 48%* / 61% / 42%* / 33%* / 38% / 0.84 / 0.69 / 0.61
Wealth Quintile
Poorest§ / 50% / 69% / 80% / 32% / 54% / 60% / 28% / 39% / 46% / 0.86 / 0.72 / 0.77
Second / 46% / 61%* / 78% / 32% / 47%* / 64% / 25% / 36% / 50% / 0.78 / 0.77 / 0.77
Middle / 40%* / 55%* / 70%* / 29% / 40%* / 61% / 21%* / 30%* / 39%* / 0.70 / 0.76 / 0.64
Fourth / 36%* / 44%* / 63%* / 27%* / 29%* / 56%* / 17%* / 21%* / 25%* / 0.63 / 0.72 / 0.46
Richest / 39%* / 37%* / 58%* / 30% / 19%* / 54%* / 14%* / 14%* / 18%* / 0.47 / 0.74 / 0.33
Residence
Urban§ / 35% / 41% / 60% / 26% / 26% / 54% / 15% / 19% / 24% / 0.56 / 0.74 / 0.44
Rural / 48%* / 63%* / 78%* / 34%* / 49%* / 64%* / 26%* / 37%* / 47%* / 0.77 / 0.76 / 0.74
IRS
No IRS§ / 50% / 68% / 59% / 36% / 0.61
Sprayed within <12 months / 52% / 72% / 59% / 33% / 0.56
*p-value≤0.05 compared to reference group (denoted with §)
Figure 1: ITN use:access by region, Ghana