· Web viewTwo 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 …

ITN Access and Use Report - 2016

Hannah Koenker and Emily RicottaDecember 2, 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.

Recommended citation:Koenker H, Ricotta E. November 2016. Insecticide-Treated Nets (ITN) Access and Use Report. Baltimore, MD. PMI | VectorWorks Project, Johns Hopkins Center for Communication Programs.

ITN Use and Access Report – January 31 20172

Table of ContentsAbbreviations..............................................................................................5Updates......................................................................................................5Key findings across PMI focus countries........................................................5Background.................................................................................................6

Definitions.....................................................................................................................6Methods......................................................................................................6Results........................................................................................................8

Key Global Findings:.....................................................................................................8PMI Focus Countries...................................................................................12

Angola.........................................................................................................................12Benin...........................................................................................................................14Burma.........................................................................................................................16Cambodia....................................................................................................................17DRC.............................................................................................................................19Ethiopia.......................................................................................................................20Ghana.........................................................................................................................22Guinea........................................................................................................................25Kenya..........................................................................................................................27Lao PDR......................................................................................................................30Liberia.........................................................................................................................32Madagascar................................................................................................................34Malawi.........................................................................................................................37Mali.............................................................................................................................39Mozambique...............................................................................................................41Nigeria........................................................................................................................43Rwanda.......................................................................................................................45Senegal.......................................................................................................................47Tanzania.....................................................................................................................51Uganda.......................................................................................................................55Vietnam......................................................................................................................58Zambia........................................................................................................................59Zimbabwe...................................................................................................................62

Appendix: Non PMI-Focus Countries............................................................65Burkina Faso...............................................................................................................65Burundi.......................................................................................................................67Cameroon...................................................................................................................69Central African Republic..............................................................................................71Chad...........................................................................................................................73Comoros......................................................................................................................75Republic of Congo (Brazzaville)...................................................................................78Cote d’Ivoire...............................................................................................................80Gabon.........................................................................................................................82Gambia.......................................................................................................................84Guyana.......................................................................................................................86Haiti............................................................................................................................88Mauritania...................................................................................................................90Namibia.......................................................................................................................92Niger...........................................................................................................................94Sao Tome....................................................................................................................96Sierra Leone................................................................................................................98Suriname..................................................................................................................100


Swaziland..................................................................................................................102Timor Leste...............................................................................................................104Togo..........................................................................................................................106


AbbreviationsDHS Demographic and Health SurveyMICS Multiple Cluster Indicator SurveyMIS Malaria Indicator SurveyITN Insecticide-treated netIRS Indoor Residual SprayingLLIN Long-lasting insecticidal netPMI President’s Malaria Initiative

UpdatesThis report was updated January 31, 2017. This report now contains results from 91 household surveys in 44 countries.

Questions and comments can be directed to [email protected] for inclusion in subsequent updates of this report.

Key findings across PMI focus countriesOverall

All but 6 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.

Four countries appear to have below target use:access ratios over most of the country: Ghana, Nigeria, Senegal, and Zimbabwe. Senegal’s use:access appears, however, to be highly seasonally driven. Nigeria’s use:access ratios have improved in many instances between the 2013 and 2015 surveys.

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.

ITN access remains well below target for the majority of countries, indicating more nets are needed to fill gaps within households.

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.


mailto:[email protected]

Nigeria shows a pro-poor trend in use:access in 2015, 2011, and 2008, but pro-rich trends in 2013.

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 ratios are not programmatically different between sprayed and

unsprayed households.

BackgroundNational results for ownership, access, use, and the use:access ratio have been described in Koenker et al previously in detail1. 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 use by access (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.

1 Koenker H, Kilian A (2014) Recalculating the Net Use Gap: A Multi-Country Comparison of ITN Use versus ITN Access. PLoS ONE 9(5): e97496. doi: 10.1371/journal.pone.0097496ITN Use and Access Report – January 31 2017

6

MethodsFor 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 14 (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 MERG2. 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 each de facto member 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.

2 MEASURE Evaluation, MEASURE DHS, President's Malaria Initiative, Roll Back Malaria Partnership, UNICEF, et al. (2013) Household survey indicators for malaria control.


ResultsNational 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 all 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 country where data is available.

Key Global Findings:

% 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:acce

ss

Median (2005-2015) 54.6% 37.2% 30.9% 0.87

Mean (2005-2015) 51.8% 36.8% 30.9% 0.80

Median pre-2010 40.0% 22.5% 19.8% 0.81

Median post-2010 59.4% 40.1% 35.0% 0.87

Mean pre-2010 35% 22% 18% 0.77Mean post-2010 58% 42% 36% 0.82Minimum 3.5% 1.5% 0.3% 0.11Maximum 93.0% 78.8% 68.6% 1.19

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 at least 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

<0.60Use: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 in PMI Focus Countries

Country | Survey | Year

% of households owning ≥1 ITN

% of population with

access to an

% of population

that used an

Ratio of use:acce

ssITN Use and Access Report – January 31 2017

8

ITN within their own household

ITN the previous

nightAngola MIS 2006-7 27.5% 14.5% 11.9% 0.82Angola MIS 2011 34.5% 19.0% 18.9% 0.99Benin DHS 2006 24.5% 14.7% 14.7% 1.00Benin DHS 2011-12 81.8% 64.0% 62.6% 0.98Cambodia DHS 2005 4.5% 3.1% 3.0% 0.96DRC DHS 2007 9.2% 4.2% 4.3% 1.03DRC MICS 2010 51.0% 30.1% 30.9% 1.03DRC DHS 2013-2014 70.0% 46.5% 50.2% 1.08Ghana DHS 2008 41.7% 30.1% 20.9% 0.69Ghana MICS 2011 49.3% 38.0% 27.8% 0.73Ghana DHS 2014 68.3% 59.0% 35.7% 0.60Guinea DHS 2005 3.5% 1.5% 1.1% 0.77Guinea DHS 2012 47.4% 25.3% 18.9% 0.75Kenya DHS 2008 55.7% 42.3% 35.1% 0.83Kenya DHS 2014 58.9% 48.2% 42.6% 0.88Kenya MIS 2015 62.5% 52.5% 47.6% 0.91Lao MICS 2012 47.9% 40.2% 40.9% 1.02Liberia MIS 2009 47.2% 25.4% 22.8% 0.90Liberia MIS 2011 49.7% 30.8% 32.1% 1.04Liberia DHS 2013 54.6% 37.0% 31.7% 0.86Madagascar DHS 2008 57.0% 34.7% 36.6% 1.05Madagascar MIS 2011 80.5% 57.3% 68.4% 1.19Madagascar MIS 2013 69.2% 47.8% 55.0% 1.15Malawi DHS 2010 56.8% 37.6% 29.0% 0.77Malawi MIS 2012 55.0% 37.2% 40.9% 1.10Malawi MICS 2013-14 78.0% 56.6% 53.9% 0.95Malawi DHS 2014 70.2% 51.8% 52.5% 1.01Mali DHS 2006 50.0% 29.7% 21.4% 0.72Mali A&P 2010 85.9% 61.6% 56.2% 0.91Mali DHS 2013 84.4% 65.1% 60.4% 0.93Mali MIS 2015 93.0% 69.5% 63.8% 0.92Mozambique DHS 2011 54.7% 37.0% 29.4% 0.80Nigeria DHS 2008 8.0% 4.8% 3.2% 0.68Nigeria MIS 2010 41.5% 28.7% 23.3% 0.81Nigeria MICS 2011 41.1% 28.6% 12.3% 0.29Nigeria DHS 2013 49.5% 36.1% 12.9% 0.36Nigeria MIS 2015 68.8% 54.7% 37.3% 0.68Rwanda 2007-8 DHS 55.6% 38.1% 39.7% 1.04Rwanda DHS 2010 82.0% 64.2% 57.7% 0.90


Rwanda MIS 2013 82.6% 65.9% 60.9% 0.92Rwanda DHS 2014-2015 80.6% 63.8% 61.4% 0.96

Senegal MIS 2006 36.3% 17.5% 12.2% 0.69Senegal MIS 2008 60.4% 34.9% 22.9% 0.66Senegal DHS 2010 66.2% 38.1% 28.9% 0.76Senegal cDHS 2012 72.8% 57.4% 40.7% 0.71Senegal cDHS 2014 74.4% 58.4% 40.4% 0.69Senegal cDHS 2015 76.8% 66.0% 51.0% 0.77Tanzania THMIS 2007-8 39.2% 25.4% 20.3% 0.80Tanzania DHS 2010 63.8% 46.6% 45.1% 0.97Tanzania THMIS 2011 90.9% 74.5% 68.4% 0.92Tanzania DHS 2015-16 65.6% 55.9% 49.0% 0.88Uganda MIS 2009 46.7% 31.6% 25.6% 0.81Uganda DHS 2011 59.8% 44.7% 35.0% 0.78Uganda MIS 2014-15 90.2% 78.8% 68.6% 0.87Zambia DHS 2007 53.3% 33.9% 23.0% 0.68Zambia DHS 2013-14 67.7% 46.6% 34.9% 0.75Zambia MIS 2015 79.8% 65.0% 56.9% 0.88Zimbabwe DHS 2005-2006 9.1% 4.8% 2.4% 0.50Zimbabwe DHS 2010 28.8% 20.2% 8.7% 0.43Zimbabwe MICS 2014 44.7% 34.0% 24.1% 0.71Zimbabwe DHS 2015 47.9% 37.2% 8.5% 0.23


Table 2: National results for ITN ownership, access, use, and use:access ratio in non PMI-Focus countries

Country | Survey | Year% of

households owning ≥1

ITN

% of population

with access to an ITN within

their own household

% of population


previous night

Ratio of use:acce

ss

Burkina Faso DHS 2010 56.9% 36.1% 31.5% 0.87Burkina Faso MIS 2014 89.8% 71.2% 67.0% 0.94Burundi DHS 2010 52.0% 39.1% 37.8% 0.97Burundi MIS 2012 66.0% 46.0% 48.6% 1.06Cameroon DHS 2011 18.3% 10.8% 7.6% 0.71Central African Republic MICS 2010 49.2% 31.1% 33.3% 1.07Chad MICS 2010 42.0% 27.7% 9.2% 0.33Chad DHS 2014-2015 51.0% 39.9% 21.7% 0.54Comoros DHS 2012 59.1% 41.2% 38.3% 0.93Congo (Brazzaville) DHS 2011-12 33.1% 22.6% 26.0% 1.15Cote d’Ivoire DHS 2012 67.3% 49.0% 33.2% 0.68Gabon DHS 2012 36.1% 26.9% 26.7% 0.99Gambia DHS 2013 68.9% 45.3% 36.9% 0.82Guyana DHS 2012 25.6% 22.3% 21.1% 0.95Haiti DHS 2012 18.8% 10.8% 7.1% 0.65Mauritania MICS 2011 43.5% 24.2% 15.1% 0.62Namibia DHS 2006 20.2% 12.8% 5.5% 0.43Namibia DHS 2013 24.4% 18.1% 3.9% 0.22Niger DHS 2006 43.0% 19.6% 4.4% 0.22Niger DHS 2012 61.3% 37.3% 13.8% 0.37Sao Tome DHS 2008 60.8% 51.0% 45.9% 0.90Sierra Leone DHS 2008 36.6% 19.2% 18.8% 1.02Sierra Leone MICS 2010 37.8% 18.2% 20.7% 1.13Sierra Leone DHS 2013 64.4% 37.7% 41.8% 1.11Suriname MICS 2010 6.6% 4.7% 4.1% 0.89Swaziland DHS 2006 4.4% 2.3% 0.3% 0.11Swaziland MICS 2010 10.1% 7.6% 1.1% 0.15Timor Leste DHS 2009 40.9% 25.5% 29.2% 1.14Togo MICS 2010 57.1% 38.0% 34.6% 0.91Togo DHS 2013 65.4% 48.8% 33.6% 0.69


PMI Focus Countries AngolaTwo 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. In 2006-7, 101 of 2,599 households reported being sprayed with IRS (4%); in 2011, 505 of 8,030 households reported spraying (6%). An integrated measles/ITN campaign for children under 5 was done in 2006. A subnational universal coverage campaign was done in 2011. Angola’s universal coverage campaign began in 2013 and continued into 2016. (Angola MOP FY15).

2006-7 MIS 2011 MIS 2006-7

MIS 2011 MIS 2006-7 MIS 2011 MIS 2006-7

MIS 2011 MIS


% of population with access to an


% of population that used an ITN the previous night

Ratio of use:access

RegionStable Mesoendemic § 20% 36% 11% 19% 10% 21% 0.91 1.11Hyperendemic 51%* 30% 30%* 17% 25%* 17% 0.83 1.00Instable Mesoendemic 23% 37% 12% 20% 8% 19% 0.67 0.95Luanda 23% 35% 11% 19% 9% 17% 0.82 0.89

Wealth QuintilePoorest§ 26% 15%* 15% 8%* 13% 9%* 0.87 1.13Poorer 22% 23%* 12% 11%* 10% 13%* 0.83 1.18

ITN Use and Access Report – January 31 2017 12

2006-7 MIS 2011 MIS 2006-7

MIS 2011 MIS 2006-7 MIS 2011 MIS 2006-7

MIS 2011 MIS


% of population with access to an



Ratio of use:access

Middle 32% 33%* 16% 17%* 15% 18%* 0.94 1.06Richer 28% 42%* 14% 23%* 11% 22%* 0.79 1.00Richest 31% 44%* 16% 24%* 9% 23%* 0.56 0.96

ResidenceUrban§ 29% 39% 15% 22% 11% 20% 0.73 0.91Rural 26% 32%* 14% 17%* 13% 18% 0.93 1.06

IRSNo§ 27% 34% 15% 18% 12% 18% 0.80 1.00Yes 34% 46%* 13% 27%* 6% 28%* 0.46 1.03*p-value≤0.05 compared to reference group (denoted with §)

ObservationsWhile 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. As of 2011, overall access to ITNs was very low.

Implications for programmingWhile access to ITNs is quite low, use of these ITNs is high across Angola. ITN distribution should be increased, and SBCC should be continued throughout the country to maintain the very high use:access ratio here.


BeninAvailable 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. In 2011-12, 1,289 households reported being sprayed with IRS, of 17,422 (7%). Benin implemented an under 5 campaign in 2007 and a universal coverage campaign in 2011 and 2014 (Benin MOP FY15).

2006 DHS 2011-12 DHS 2006 DHS 2011-12

DHS 2006 DHS 2011-12 DHS

2006 DHS

2011-12 DHS


% of population with access to an ITN within their own

household


Ratio of use:access

ProvinceAlibori§ 8% 89% 3% 68% 3% 68% 0.96 0.96Atacora 26%* 93%* 14% 77%* 13% 66% 0.93 0.83Atlantique 13%* 75%* 9% 60%* 9% 62% 1.03 1.01Borgou 20%* 81%* 10% 62%* 10% 59% 0.93 0.93Collines 30%* 78%* 19% 62%* 19% 60% 0.99 0.93Couffo 29%* 82%* 15% 62%* 15% 60% 0.96 0.95Donga 30%* 84% 14% 64% 13% 61% 0.87 0.93Littoral 28%* 78%* 22% 65% 23% 64% 1.01 0.97Mono 25%* 74%* 16% 60%* 16% 60% 1.03 1.00Oueme 34%* 74%* 21% 58%* 22% 59% 1.06 1.00Plateau 21%* 84% 14% 68% 13% 68% 0.95 0.98Zou 27%* 79%* 16% 62%* 17% 65% 1.05 1.04

SESPoorest§ 11% 79% 6% 62% 7% 61% 1.08 0.98Poorer 17%* 81% 9% 64% 10% 63% 1.08 0.98Middle 24%* 80% 13% 63% 14% 62% 1.03 0.99


2006 DHS 2011-12 DHS 2006 DHS 2011-12

DHS 2006 DHS 2011-12 DHS

2006 DHS

2011-12 DHS



household


Ratio of use:access

Richer 31%* 78% 18% 64% 17% 61% 0.98 0.97Richest 39%* 80% 27% 67%* 26% 65%* 0.95 0.97

ResidenceUrban§ 29% 78% 19% 64% 19% 62% 0.96 0.97Rural 21%* 81%* 12% 64% 12% 63% 1.04 0.98

IRSNo§ 79% 63% 62% 0.98Yes 94%* 79%* 70%* 0.89*p-value≤0.05 compared to reference group (denoted with §)

ObservationsOverall, 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 programmingOverall 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.

BurmaThe Myanmar 2015 DHS contains the malaria module but is not yet released.


CambodiaCambodia’s 2005 DHS contains information on malaria and ITNs; its 2010 and 2014 DHS surveys do not. Cambodia implemented mass ITN distribution in 2012.

2005 DHS



% of population that used an ITN

the previous nightRatio of

use:access

RegionBanteay Mean Chey§ 4.9% 3.1% 3.5% 1.10Kampong Cham 4.7% 3.8% 3.0% 0.78Kampong Chhnang 3.5% 2.3% 2.3% 0.97Kampong Speu 0.5%* 0.2%* 0.4%* 1.66Kampong Thom 6.4%* 4.1% 4.3% 1.04Kandal 0.4%* 0.2%* 0.1%* 0.94Kratie 18.8%* 13.4%* 11.6%* 0.87Phnom Penh 0.2%* 0.1%* 0.0%* 0.50Prey VengPursat 0.3%* 0.1%* 0.1%* 1.28Siem Reap 2.8% 1.9% 1.3% 0.69Svay RiengTakeo 0.3%* 0.2% 0.0%* 0.20Otdar Mean Chey 30.4%* 20.8%* 26.3%* 1.26Battambang & Krong Pailin 6.0% 3.7%* 3.6% 0.97Kampot & Krong Kep 3.6% 2.5% 2.4% 0.97


2005 DHS





use:access

Krong Preah Sihanouk & Kaoh Kong

11.4%* 6.7% 7.6% 1.13

Preah Vihear & Steung Treng 48.4%* 32.3%* 30.1%* 0.93Mondol Kiri & Rattanak Kiri 38.9%* 26.5%* 29.3%* 1.11

Wealth QuintilePoorest§ 9% 6% 6% 1.00Poorer 6%* 4%* 4% 0.96Middle 4%* 3%* 3%* 0.97Richer 2%* 2%* 1%* 0.85Richest 1%* 1%* 1%* 0.89

ResidenceUrban§ 2% 1% 1% 0.94Rural 5%* 3%* 3%* 0.97

ObservationsCambodia had an excellent ratio of use:access in 2005, with lower rates in specific regions.


Implications for programmingGiven the time elapsed since this survey, no programming implications are noted here.

DRCAvailable 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. The 2007 DHS was fielded from February to July. No IRS data was included in any of the surveys. DRC implemented universal coverage campaigns in 2011-2012 and in 2015-2016.

2007 DHS

2010 MICS

2013 DHS

2007 DHS

2010 MICS

2013 DHS

2007 DHS

2010 MICS

2013 DHS

2007 DHS

2010 MICS

2013 DHS


% of population with access to an ITN within

their own household% of population that used an ITN the previous night Ratio of use:access

ProvinceKinshasa§ 16% 52% 59% 8% 29% 37% 8% 35% 39% 1.10 1.19 1.06Bandundu 12% 42% 88%* 5% 23% 63%* 5%* 24%* 70%* 1.00 1.07 1.11Bas-Congo 35%* 66%* 76%* 18%* 37%* 51%* 18%* 41% 58%* 0.97 1.11 1.13Equateur 4%* 28%* 83%* 2%* 13%* 57%* 2%* 14%* 63%* 1.14 1.08 1.09Kasai-Occidental

7%* 19%* 58% 3%* 9%* 31% 3%* 9%* 31%* 0.99 1.08 1.00

Kasai-Oriental 6%* 36%* 64% 2%* 20%* 38% 2%* 24%* 43% 0.88 1.18 1.11Katanga 8%* 72%* 80%* 4%* 44%* 55%* 5%* 39% 57%* 1.11 0.90 1.04Maniem 13% 85%* 59% 6% 64%* 37% 6%* 52%* 40% 1.10 0.81 1.08


2007 DHS

2010 MICS

2013 DHS

2007 DHS

2010 MICS

2013 DHS

2007 DHS

2010 MICS

2013 DHS

2007 DHS

2010 MICS

2013 DHS



their own household% of population that used an ITN the previous night Ratio of use:access

aNord-Kivu 4%* 51% 60% 2%* 26% 39% 2%* 27% 39% 0.97 1.02 0.99Orientale 3%* 72%* 47%* 2%* 51%* 33% 2%* 45% 35% 0.90 0.88 1.06Sud-Kivu 6%* 50% 70%* 3%* 26% 46%* 3%* 32% 51%* 1.00 1.22 1.12

Wealth QuintilePoorest§ 3% 41% 59% 1% 24% 39% 1% 25% 42% 0.99 1.04 1.09Poorer 6%* 49%* 72%* 3%* 28%* 48%* 3%* 29% 53%* 1.05 1.04 1.12Middle 10%* 49%* 75%* 4%* 28%* 50%* 5%* 29% 55%* 1.04 1.04 1.09Richer 11%* 53%* 77%* 5%* 30%* 52%* 5%* 32%* 55%* 0.95 1.08 1.07Richest 16%* 67%* 68%* 7%* 41%* 44%* 8%* 39%* 46% 1.07 0.95 1.04

ResidenceUrban§ 12% 58% 71% 6% 35% 46% 6% 34% 48% 1.05 0.98 1.04Rural 7%* 48%* 70% 3%* 28%* 47% 3%* 29% 51% 1.00 1.05 1.10

*p-value≤0.05 compared to reference group (denoted with §)

ObservationsThe MICS and DHS data show that 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.


Implications for programmingThere 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 appear to need to be targeted to certain areas over others.

EthiopiaThe most recent available survey, the 2011 DHS, does not contain the malaria module. The dataset for the 2011 MIS is not available publicly, but ownership results are reported here from the survey report3. An MIS was fielded in late 2015, with funding from PMI and Global Fund, but is not yet available. Ethiopia implemented mass campaigns in 2007, 2010, 2013, but with ongoing large scale ITN distribution since 2005 in all years.

2011 DHS% of


ITN



Ratio of use:access

ProvinceAmhara 73.6B. Gumuz & Gambella 69.0Diredawa 78.9Oromia 43.7SNNPR 57.0Somali & Afar 45.0Tigray 65.8

Wealth QuintilePoorest 44.2Poorer 52.4Middle 54.6

3 Ethiopia National Malaria Indicator Survey, 2011. http://www.unicef.org/ethiopia/ET_MIS_2011_Report.pdf


http://www.unicef.org/ethiopia/ET_MIS_2011_Report.pdf

2011 DHS% of


ITN



Ratio of use:access

Richer 31.2Richest 66.4

ResidenceElev <2000m 54.8Elev >2000m 37.6


GhanaAvailable 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. In 2014, 2,157 households reported spraying with IRS, of 11,835 (18%). Ghana implemented mass distributions in 2006-2008, 2011-2012, and 2014-2015.

2008 DHS

2011 MICS

2014 DHS

2008 DHS

2011 MICS

2014 DHS

2008 DHS

2011 MICS

2014 DHS

2008 DHS

2011 MICS

2014 DHS



household


Ratio of use:access

RegionWestern§ 41% 43% 67% 30% 32% 59% 20% 23% 38% 0.66 0.72 0.65Central 42% 32%* 70% 30% 21%* 58% 17% 17%* 42% 0.56 0.79 0.73Greater Accra 30%* 26%* 53%* 24%* 20%* 49%* 12%* 11%* 16%* 0.51 0.53 0.32Volta 43% 86%* 76%* 30% 79%* 70%* 22% 65%* 54%* 0.72 0.82 0.77Eastern 36% 78%* 73% 26% 71%* 64% 19% 49%* 38% 0.72 0.69 0.60Ashanti 40% 40% 70% 28% 28% 60% 19% 22% 34% 0.70 0.80 0.57Brong Ahafo 51%* 54%* 81%* 36%* 34% 70%* 32%* 27% 52%* 0.87 0.78 0.75Northern 54%* 67%* 71% 31% 43%* 55% 23% 27% 36% 0.72 0.64 0.66Upper East 53%* 52% 73% 37% 34% 56% 26%* 28% 31% 0.71 0.84 0.56Upper West 71%* 61%* 77%* 50%* 39%* 61% 42%* 31%* 38% 0.84 0.79 0.61

Wealth


2008 DHS

2011 MICS

2014 DHS

2008 DHS

2011 MICS

2014 DHS

2008 DHS

2011 MICS

2014 DHS

2008 DHS

2011 MICS

2014 DHS



household


Ratio of use:access

QuintilePoorest§ 50% 66% 80% 32% 45% 60% 28% 38% 46% 0.86 0.86 0.77Second 46% 59%* 78% 32% 43%* 64% 25% 36% 50% 0.78 0.82 0.77Middle 40%* 52%* 70%* 29% 41%* 61% 21%* 30%* 39%* 0.70 0.73 0.64Fourth 36%* 42%* 63%* 27%* 32%* 56%* 17%* 21%* 25%* 0.63 0.66 0.46Richest 39%* 35%* 58%* 30% 29%* 54%* 14%* 14%* 18%* 0.47 0.49 0.33

ResidenceUrban§ 35% 40% 60% 26% 31% 54% 15% 19% 24% 0.56 0.62 0.44Rural 48%* 60%* 78%* 34%* 45% 64%* 26%* 36%* 47%* 0.77 0.81 0.74

IRSNo§ 49% 68% 38% 59% 36% 36% 0.74 0.61Yes 47% 72% 36% 59% 19% 33% 0.62 0.56


ObservationsUse given access is lowest in Greater Accra (0.32), and highest in Volta and Brong Ahafo in 2014 (0.75-0.77). These last two regions also had the highest rates of intra-household access to nets in the 2014 DHS (a pattern observed also in 2011). However, the range of use:access is fairly consistent across regions. Across the country, the ratio declined between 2011 and 2014, erasing gains made in the 2011 MICS. The largest drop was in Greater Accra. In 2008, there was a large discrepancy in urban vs rural use:access ratios (0.56 vs 0.74), and the urban rates caught up to the rural rates in 2011; in 2014 we see again that rural households are using their nets more than urban households. 2011 may have been an anomaly, but access within both types of households was relatively good (>50%) in 2014. Low rates of use:access among urban households may simply reflect SES differences.


A more dramatic change is seen in use:access ratios by wealth quintile. We observe a strong negative trend in 2008 and in 2014 from poorest to richest quintiles, indicating that the wealthiest households are not using nets they have. However, in 2011, the use:access ratio had evened out completely among all five quintiles, probably reflecting gains in use in wealthier urban and peri-urban areas, despite a linear negative trend of population access itself in 2011. It is again unclear whether this is a seasonal anomaly from 2011 (as with rural/urban trends), or reflects a decline in net culture among the wealthiest since 2011. Since each survey was done at generally the same time of year, it appears unlikely that year-to-year differences are due to seasonal changes, although more work is needed to confirm that rainfall and survey timing were equivalent in all years. There was no difference in net ownership, access, or use among households who had received IRS and those who had not; the use:access ratio is similarly low for both groups.

Implications for programmingThere is no indication from these data that certain regions should be prioritized for SBCC messaging due to lower use:access ratios. Rather, the country as a whole has a ‘use gap’ of roughly 30-40 percentage points.


GuineaAvailable data for Guinea include the 2005 DHS, fielded January to June during dry season and early rains, and the 2012 DHS, which was conducted between June and August during Guinea’s rainy season. Conakry fieldwork was done at the very beginning of rainy season. In 2012 119 households reported spraying with IRS, of 7,109 (1.7%). Guinea implemented an under-five campaign in 2009, and universal coverage campaigns in 2013 and in 2016.

2005 DHS

2012 DHS 2005 DHS 2012 DHS 2005 DHS 2012 DHS 2005

DHS2012 DHS



their own household


Ratio of use:access

RegionBoké§ 3.9% 42% 1.7% 25% 1.0% 21% 0.57 0.84Conakry 7.6% 36% 3.5% 19%* 3.2% 13%* 0.92 0.69Faranah 3.4% 64%* 1.4% 31% 0.9% 26% 0.68 0.85Kankan 4.6% 51% 1.5% 26% 0.9% 25% 0.58 0.94Kindia 2.4% 42% 0.9% 21% 0.8% 14%* 0.90 0.67Labé 0.4% 62%* 0.3% 38%* 0.1% 18% 0.39 0.49Mamou - 53%* - 33%* - 12%* 0.38Nzerekore 4.0% 45% 1.4% 23% 1.2% 22% 0.81 0.96

Wealth QuintilePoorest§ 1.8% 49% 0.6% 27% 0.3% 20% 0.48 0.74Poorer 1.9% 48% 0.8% 26% 0.5% 21% 0.61 0.81Middle 3.0% 51% 1.2% 27% 1.0% 19% 0.82 0.72Richer 4.6% 49% 1.8% 27% 1.2% 20% 0.65 0.75Richest 6.7% 39%* 3.0% 20%* 2.8% 14%* 0.93 0.70


2005 DHS

2012 DHS 2005 DHS 2012 DHS 2005 DHS 2012 DHS 2005

DHS2012 DHS



their own household


Ratio of use:access

ResidenceUrban§ 5.7% 42% 2.4% 22% 2.1% 17% 0.87 0.78Rural 2.7% 50%* 1.1% 27%* 0.7% 20% 0.69 0.73

IRSNo§ 47% 25% 19% 0.76Yes 55% 29% 26% 0.90


ObservationsRegional variations in household ownership were present in 2012, with Conakry having the lowest rates of ownership. However, DHS data was collected prior to the 2014 mass campaign, which undoubtedly increased coverage. Conakry, Kindia, and most especially Labe and Mamou regions had lower-than-targeted ratios of ITN use:access. Mamou and Labe are in the Fouta Djallon area, which have higher altitudes and cooler temperatures. These low ratios are contributing to the <0.80 ratios observed when looking at SES and at residence. As is observed in some other countries, there is a slight pro-poor trend towards better ITN use:access ratio. There was no difference in ITN ownership, access, or use among households having received IRS and those who did not, although households not receiving IRS had a lower use:access ratio (0.76 compared to 0.90). However, only 119 of 7,109 households reported being sprayed, mostly by private companies.

Implications for programmingGiven the very low ITN use:access ratio in Mamou and Labe, further investigation would be useful to determine whether this is a result of seasonality or altitude (it would have been raining in Mamou at the time of data collection, usually contributing to better use rates) and/or behavioral barriers to net use.



KenyaKenya has three national surveys and the 2013-14 Regional MICS to include in this analysis. The 2008-2009 DHS was completed between November 2008 and February 2009, during the short rains. The 2013-14 Regional MICS were completed between November and January, also during the short rains. The 2014 DHS was completed between April and October, during the long rains. Kenya has rain periodically throughout the year, with “short rains” in November and December, followed by a dry season from December to March. Long rains occur from April through June, and it is cool and dry again from June through October. The 2010 MIS, implemented by DOMC, is not publically available. The 2015 MIS was fielded in July-August 2015 (long rains), but does not include information on IRS. In 2008-9, 617 households reported being sprayed with IRS, of 9,057 (7%). In 2014, 905 of 36,369 households reported IRS (2%). Kenya implemented mass ITN distributions in 2011-12, and a rolling campaign in 2014-15.

2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS

2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS

2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS

2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS



household


Ratio of use:access

RegionNairobi§ 51% 44% 66% 47% 40% 62% 42% 38% 53% 0.89 0.95 0.85Central 33%

* 38% 41%*

27%*

33%*

35%*

23%*

27%*

28%* 0.85 0.82 0.81

Coast^ 66%*

69%* 72% 53% 58%

*58%

* 46% 55%* 57% 0.87 0.95 1.00

Eastern 60%*

56%*

62%* 44% 44% 46%

* 35% 37% 39%* 0.80 0.84 0.85

Nyanza^ 76%*

81%*

86%*

58%*

62%*

75%* 49% 61%

*71%

* 0.84 0.98 0.95


2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS

2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS

2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS

2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS



household


Ratio of use:access

Rift Valley^

41%*

56%*

54%*

29%* 44% 48%

*22%

* 35% 42%* 0.76 0.80 0.87

Western^

71%*

82%*

79%* 51% 64%

*57%

* 43% 58%* 55% 0.84 0.91 0.98

North-eastern

73%* 49% 49%

* 48% 34% 33%* 46% 30%

*34%

* 0.96 0.88 1.02

Regional MICS4

Bungoma 78% 61% 57%

0.94

Kakamega 78% 63% 62%

0.98

Nyanza5 92%Turkana 37% 22% 20% 0.90

Wealth quintile

4 Regional MICS in 2013-14 were conducted in four areas of Kenya. Results are presented only for the regional estimates, and not for subanalyses below (wealth quintile etc).5 The Nyanza regional MICS did not collect data on which household members stayed in the house the previous night, which is necessary for calculating ITN use and ITN access.


2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS

2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS

2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS

2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS



household


Ratio of use:access

Poorest§ 49% 51% 49% 30% 36% 37% 26% 31% 34% 0.87 0.86 0.94Poorer 58%

*61%

*63%

*41%

*46%

*52%

*32%

*41%

*49%

* 0.78 0.89 0.94

Middle 60%*

64%*

68%*

42%*

52%*

56%*

35%*

46%*

51%* 0.83 0.88 0.91

Richer 55% 57%*

62%*

46%*

51%*

55%*

37%*

45%*

49%* 0.80 0.88 0.90

Richest 56% 61%*

68%*

52%*

56%*

62%*

45%*

50%*

54%* 0.87 0.89 0.86

ResidenceUrban§ 58% 56% 62% 52% 49% 54% 47% 46% 49% 0.90 0.94 0.90Rural 55% 61%

* 63% 40%* 47% 52% 32%

*41%

* 47% 0.80 0.87 0.91

IRSNo§ 55% 58% 42% 48% 35% 42% 0.83 0.88Yes 65%

*77%

* 46% 62%* 37% 55%

* 0.80 0.89*p-value≤0.05 compared to reference group (denoted with §)


2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS

2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS

2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS

2008-

2009

DHS

2013-14

MICS

2014

DHS

2015

MIS



household


Ratio of use:access

^ region received LLINs in the 2011-2012 mass campaign.

ObservationsKenya has a relatively high use:access ratio throughout the country and across wealth quintiles and residences, with improvement over the last three available surveys which may be due in part to survey timing. Both access and use increase with increasing wealth, however, use:access ratio is similar across all wealth quintiles. Prior to 2015, urban residents had higher access and use than those in rural areas, but this has evened out in the 2015 survey. In both DHS surveys, households receiving IRS had a higher proportion of ITN ownership, but use:access ratio was the same in both groups.

Implications for programmingKenya is doing well in terms of the proportion of the population using nets when they have access to them. Continued net distributions and SBCC campaigns in areas targeted for ITNs will help in maintaining these high numbers.


Lao PDRThe 2011-12 MICS was fielded primarily from October to February, during the drier cooler season.

2011-12 MICS





use:access

RegionVientiane Capital§ 22% 18% 17% 0.94 Phongsaly 51%* 39%* 36%* 0.92 Luangnamtha 44%* 39%* 41%* 1.06 Oudomxay 86%* 74%* 80%* 1.08 Bokeo 50%* 38%* 41%* 1.06 Luangprabang 69%* 57%* 60%* 1.06 Huaphanh 38%* 32%* 30%* 0.93 Xayabury 49%* 45%* 48%* 1.06 Xiengkhuang 25% 21% 20% 0.94 Vientiane 29% 20% 16% 0.80 Borikhamxay 67%* 61%* 62%* 1.02 Khammuane 51%* 42%* 42%* 1.00 Savannakhet 48%* 38%* 37%* 0.99 Saravane 72%* 57%* 62%* 1.09 Sekong 75%* 61%* 64%* 1.05 Champasack 40%* 33%* 33%* 1.01 Attapeu 84%* 71%* 74%* 1.05


2011-12 MICS





use:access

Wealth QuintilePoorest§ 56% 42% 48% 1.13 Poorer 56% 46% 48% 1.05 Middle 54% 47% 47% 1.00 Richer 46%* 40%* 39%* 0.96 Richest 31%* 26%* 22%* 0.87

ResidenceUrban§ 35% 30% 29% 0.95 Rural with road 52%* 44%* 45%* 1.03 Rural without road 59%* 45%* 47%* 1.04

ObservationsLao PDR has excellent use:access ratios indicating a strong culture of net use. There is a marked decline in ownership, access, and use among wealthier quintiles, but use of nets by those with access remains high in all socioeconomic quintiles. Urban households have lower rates of ownership, use, and access to ITNs, but similar use:access ratios those in rural areas.

Implications for programmingMessaging around net use does not appear to need to be targeted to specific groups based on this analysis, but further analysis by high-risk group would be useful.


LiberiaThe dry season in Liberia lasts from December to April, with the rains occurring between May and November. There were three surveys available in Liberia: the 2008-2009 DHS, the 2011 MIS, and the 2013 DHS. The majority of fieldwork was completed from December 2008 to February of 2009 (dry season), from September to December 2011 (primarily rainy), and from March through July 2013 (both dry and rainy seasons), respectively. In the 2011 MIS, 439 of 4,162 households reported being sprayed with IRS (10%); in 2013, 943 of 9,333 (10%) reported IRS. Liberia conducted mass distribution on a rolling basis between 2008 and 2012, and a national campaign in 2015.

2008-2009MIS

2011MIS

2013DHS

2008-2009MIS

2011MIS

2013DHS

2008-2009MIS

2011MIS

2013DHS

2008-2009MIS

2011MIS

2013DHS




the previous nightRatio of use:access

RegionMonrovia§ 34% 53% 17% 34% 14% 35% 0.82 1.03North Western§§ 63%* 44% 69% 41%* 26%* 49% 34%* 29% 43% 0.83 1.12 0.88South Central 32% 36%* 47%* 16% 21%* 31%* 13% 22%* 26%* 0.81 1.05 0.84South Eastern A 60%* 61% 45%* 31%* 38% 28%* 30%* 41% 24%* 0.97 1.08 0.86South Eastern B 66%* 64%* 46%* 39%* 40% 27%* 31%* 36% 21%* 0.79 0.90 0.78North Central 57%* 51% 66% 30%* 31% 47% 28%* 34% 41% 0.93 1.10 0.87

Wealth QuintilePoorest§ 48% 41% 53% 26% 24% 36% 22% 27% 31% 0.85 1.13 0.86Poorer 54% 53%* 64%* 32% 32%* 44%* 30%* 35%* 38%* 0.94 1.09 0.86Middle 52% 49%* 61%* 28% 30% 42%* 25% 32% 38%* 0.89 1.07 0.90Richer 43% 52%* 52% 22% 33%* 34% 21% 36%* 30% 0.95 1.09 0.88


2008-2009MIS

2011MIS

2013DHS

2008-2009MIS

2011MIS

2013DHS

2008-2009MIS

2011MIS

2013DHS

2008-2009MIS

2011MIS

2013DHS





Richest 38% 54%* 43%* 19% 34%* 29%* 16% 30% 22%* 0.84 0.88 0.76

ResidenceUrban§ 42% 52% 50% 22% 34% 33% 20% 34% 28% 0.91 1.00 0.85Rural 52%* 47% 61%* 28%* 28%* 42%* 25% 30% 36%* 0.89 1.07 0.86

IRSNo§ 50% 54% 31% 36% 32% 31% 1.03 0.86Yes 48% 61%* 29% 44%* 29% 36% 1.00 0.82

*p-value≤0.05 compared to reference group (denoted with §); §§ As Monrovia was not sampled in 2013 survey, North Western region was used as reference group.

ObservationsLiberia has consistently had a very high net use:access ratio across the country. The only region in which this ratio is less than 0.80 is South Eastern B, whose ratio is 0.78. All wealth quintiles with the exception of the wealthiest in the 2013 survey (0.76) were above 0.80. Net access seems to be highest in the poorer wealth quintiles. Use is relatively similar among each category. In 2013, households receiving IRS had a higher proportion of ITN ownership and access. There was no difference in proportion using an ITN nor in the use:access ratio between those receiving IRS and those without.

Implications for programmingLiberia is doing a very good job both in terms of access and use when compared to other countries. Continued net distributions and SBCC campaigns nationwide will help in maintaining these high numbers.


MadagascarAvailable data for Madagascar include the 2008 DHS, the 2011 MIS, and the 2013 MIS. The 2008 DHS fieldwork was conducted between November 2008 and July 2009, mainly during dry season but stretching into rainy season in some areas, while the two MIS surveys were conducted primarily between February and April, prior to rainy season. In 2011, 3,710 households out of 9,094 (46%) reported IRS; in 2013, 3,014 of 8,574 (35%) reported IRS. The 2008 DHS sampled 22 regions that did not match up directly with the sampled regions in the later MIS surveys; as it is older data, the 2008 regional analysis is excluded in the table below. Madagascar implemented mass distributions in 2009-2010, 2012-2013, and 2015.

2008 DHS

2011 MIS

2013 MIS

2008 DHS

2011 MIS

2013 MIS

2008 DHS

2011 MIS

2013 MIS

2008 DHS

2011 MIS

2013 MIS



household


Ratio of use:access

Transmission ZoneEquatorial§ 78% 95% 93% 51% 69% 71% 57% 84% 83% 1.11 1.22 1.16Tropical 78% 94% 73%* 51% 70% 48%* 53% 81% 57%* 1.04 1.17 1.19Sub-desert 70%* 93% 55%* 40%* 63%* 31%* 37%* 84% 43%* 0.94 1.34 1.37High Plateaux 38%* 36%* 25%* 22%* 23%* 16%* 22%* 24%* 13%* 1.03 1.07 0.79

RegionAntananarivo§ 45% 37% 31% 24% 33% 20% 1.10 0.87Fianarantsoa 72%* 66%* 48%* 46%* 60%* 54%* 1.24 1.17Toamasina 95%* 89%* 72%* 69%* 84%* 76%* 1.16 1.11Mahajanga 97%* 81%* 75%* 55%* 89%* 69%* 1.18 1.24Toliary 94%* 63%* 63%* 39%* 80%* 51%* 1.26 1.30


2008 DHS

2011 MIS

2013 MIS

2008 DHS

2011 MIS

2013 MIS

2008 DHS

2011 MIS

2013 MIS

2008 DHS

2011 MIS

2013 MIS



household


Ratio of use:access

Antsiranana 91%* 85%* 69%* 69%* 79%* 77%* 1.14 1.11

Wealth QuintilePoorest§ 65% 92% 71% 35% 62% 45% 39% 81% 59% 1.10 1.31 1.29Poorer 61%* 87%* 71% 36% 62% 49% 40% 77%* 58% 1.12 1.25 1.20Middle 51%* 83%* 73% 30%* 59% 50% 32%* 71%* 59% 1.07 1.20 1.17Richer 52%* 73%* 69% 32% 52%* 50% 33%* 60%* 54% 1.02 1.15 1.10Richest 56%* 69%* 60%* 40%* 51%* 45% 39% 52%* 45%* 0.96 1.02 0.99

ResidenceUrban§ 60% 87% 80% 43% 67% 63% 44% 72% 67% 1.01 1.07 1.06Rural 56% 80%* 67%* 33%* 56%* 46%* 35%* 68% 54%* 1.06 1.21 1.16

IRSNo§ 90% 75% 66% 54% 80% 63% 1.21 1.17Yes 67%* 54%* 46%* 34%* 54%* 38%* 1.17 1.12


ObservationsMadagascar is the PMI country with the highest observed ITN use:access ratios, rarely dipping below 1.00 except for 0.87 observed in Antananarivo in the 2013 MIS, and 0.79 for the High-Plateaux transmission zone, also in 2013. Madagascar also has a high average number of users per net, driving up their ratio of ITN use:access, in part due to relatively small houses where it is difficult to hang multiple ITNs for a 1:2 net-to-user ratio. There is a consistent trend towards poorer


households having higher use:access ratios, perhaps due to smaller house size, as well as the general phenomenon of wealthier households having lower numbers of people per sleeping space. Since 2008 the trends in use:access ratios for the wealth quintiles have improved over time; this is not statistically significant for other subgroups (urban/rural or regionally), as ratios are already very high. Ratios tend to be higher in the rural areas than in urban areas, but are still excellent in urban areas. Households that did not receive IRS had better ITN ownership, access, and use; the use:access ratio of both those with and without IRS remains above 1.00 in both cases in all surveys.

Implications for programmingIn general, ITN use among those with access to a net within their household is excellent in Madagascar; further determinants of net use should become available through the HC3 Malaria Behavioral Survey very shortly.


MalawiMalawi has four surveys available for analysis. The 2010 DHS was completed between June and September 2010, during the cool dry season. The 2012 MIS was conducted on the edge of the hot wet season, in March and April (rains are variable in March and are mostly complete in April). The 2013-2014 MICS was conducted primarily from December to March, during the hot wet season. The 2014 MIS was conducted in May and June, during high transmission season (cool dry season). In 2010, 865 of 24,825 households reported IRS (3%); in 2012, 290 of 3,404 (8%) reported IRS; in the 2013-14 MICS, 3,353 households reported being sprayed with IRS, of 26,713 (13%). In 2014, 210 of 3,405 households reported IRS (6%). Malawi implemented mass distributions in 2012 (after the MIS), and 2015-2016.

2010 DHS

2012

MIS

2013-14

MICS

2014

MIS

2010

DHS

2012

MIS

2013-14

MICS

2014

MIS

2010

DHS

2012

MIS

2013-14

MICS

2014

MIS

2010

DHS

2012

MIS

2013-14

MICS

2014

MIS



household


Ratio of use:access

RegionNorthern§ 56% 64% 84% 78% 38% 44% 64% 60% 27% 43% 61% 54% 0.71 0.98 0.96 0.91

Central 54% 57% 79%*

70%* 35% 39% 58%

* 53% 27% 44% 59% 53% 0.77 1.13 1.01 1.01

Southern 59% 51%*

76%* 68% 40% 33%

*54%

*48%

*31%

* 37% 48%* 51% 0.78 1.12 0.89 1.07

Wealth QuintilePoorest§ 41% 47% 67% 61% 24% 31% 44% 42% 18% 36% 43% 45% 0.75 1.16 0.98 1.07

Poorer 51%* 54%*

77%* 66% 32%

* 34% 52%* 46% 24%

* 40% 52%* 48% 0.75 1.18 1.00 1.05

Middle 58%* 54% 80% 69% 37% 35% 56% 47% 29% 41% 54% 51% 0.78 1.17 0.96 1.10


2010 DHS

2012

MIS

2013-14

MICS

2014

MIS

2010

DHS

2012

MIS

2013-14

MICS

2014

MIS

2010

DHS

2012

MIS

2013-14

MICS

2014

MIS

2010

DHS

2012

MIS

2013-14

MICS

2014

MIS



household


Ratio of use:access

* * * * * *

Richer 61%* 57%*

82%*

78%*

40%* 37% 61%

*62%

*30%

* 41% 56%*

60%* 0.75 1.11 0.92 0.97

Richest 74%* 65%*

84%*

78%*

55%*

48%*

70%*

62%*

43%*

46%*

64%*

57%* 0.78 0.96 0.92 0.92

ResidenceUrban§ 64% 56% 79% 75% 47% 40% 65% 60% 38% 41% 61% 57% 0.81 1.03 0.93 0.94

Rural 55%* 55% 78% 69% 36%* 37% 55%

*50%

*27%

* 41% 53%* 52% 0.75 1.11 0.96 1.03

IRSNo§ 57% 54% 78% 71% 37% 36% 57% 52% 29% 40% 54% 52% 0.77 1.11 0.95 1.00

Yes 68%* 64% 79% 66% 46%*

45%* 57% 50% 42%

* 49% 56%* 54% 0.91 1.07 0.99 1.09


ObservationsMalawi continues to have a very high use to access ratio across all categories and regions. The 2012 and 2014 MIS was conducted during the rainy season, when people tend to report using their nets more consistently. Net ownership and access improve with higher wealth quintiles; however, use of nets, given access, is marginally better among the poorer quintiles. In earlier surveys, households having received IRS had higher ITN ownership, access, and use. By the 2014 survey, there was no significant difference between these groups.


Implications for programmingMalawi is doing a very good job both in terms of access and use when compared to other countries. Continued net distributions and SBCC campaigns nationwide will help in maintaining these high numbers.


MaliAvailable data for Mali include the 2006 DHS, the 2010 Anemia and Parasitemia survey, the 2012-2013 DHS, and the 2015 MIS. Fieldwork for the 2006 DHS was conducted between April and August, with Bamako region split between April and October. Fieldwork for the 2010 A&P was done in the high transmission period, primarily in September and October (wealth index information was not collected). Fieldwork for the 2012-2013 DHS was done beginning in late November and into February, during the end of the transmission period and then the cooler season. The 2015 MIS was fielded in September, October, and November, during transmission season. The northern zone of Timbuktu, Gao, and Kidal was not sampled during the 2012-2013 DHS or the 2015 MIS, for security reasons. In 2010, 82 households reported IRS (with no timeframe), of 1,617 (5%); in 2012-12, 842 of 10,105 (8%) reported IRS in the last 12 months; in 2015, 272 of 4240 (6%) households reported IRS in the last 12 months. Mali implemented a national under five campaign in 2008, and universal coverage campaigns in 2011-13 and 2015.

2006DHS

2010

A&P

2012-

2013

DHS

2015

MIS

2006

DHS

2010

A&P

2012-

2013

DHS

2015

MIS

2006

DHS

2010

A&P

2012-

2013

DHS

2015

MIS

2006

DHS

2010

A&P

2012-

2013DHS

2015

MIS



their own household


Ratio of use:access

RegionKayes§ 44

% 90% 79% 95% 27% 71% 58% 67% 20% 62% 53% 64% 0.73 0.88 0.91 0.95

Koulikoro

42% 85% 81% 94% 21% 57%

* 61% 71% 15% 50%* 56% 66% 0.69 0.88 0.92 0.93

Sikasso 47% 88% 88%* 98%* 25% 61% 67%

* 78%* 20% 59% 63%* 71%* 0.79 0.96 0.94 0.92

Segou 50% 87% 92%* 89%* 31% 63% 77%

* 63%* 23% 59% 73%* 58% 0.73 0.94 0.95 0.92

Mopti 65%* 87% 85%* 89%* 41%

* 65% 66%* 66% 28% 62% 61%

* 64% 0.68 0.95 0.92 0.97

Tombouctou

48% 85% 31% 64% 26% 55% 0.85 0.86


2006DHS

2010

A&P

2012-

2013

DHS

2015

MIS

2006

DHS

2010

A&P

2012-

2013

DHS

2015

MIS

2006

DHS

2010

A&P

2012-

2013

DHS

2015

MIS

2006

DHS

2010

A&P

2012-

2013DHS

2015

MIS



their own household


Ratio of use:access

Gao 45%

66%* 28% 44%

* 17% 47% 0.61 1.05

Kidal 29%*

55%*

15%*

28%* 1%* 16%

* 0.10 0.60

Bamako 54%* 86% 77% 94% 34% 60%

* 57% 71% 25% 51%* 51% 59% 0.74 0.85 0.91 0.83

Wealth QuintilePoorest§ 52

% 80% 94% 28% 60% 68% 20% 56% 63% 0.71 0.93 0.93

Poorer 49% 86%* 94% 28% 66%

* 70% 21% 61%* 66%* 0.73 0.92 0.94

Middle 44%* 87%* 90%* 26% 67%

* 68% 19% 63%* 62% 0.72 0.93 0.91

Richer 49% 86%* 92% 30% 67%

* 69% 21% 63%* 65%* 0.71 0.95 0.94

Richest 57%* 84%* 95% 36%

*65%

* 73% 26%* 59% 63% 0.73 0.91 0.87

ResidenceUrban§ 54

% 87% 82% 94% 33% 62% 63% 71% 23% 55% 58% 61% 0.71 0.88 0.92 0.86

Rural 48%* 86% 85%* 93% 28%

* 61% 65% 69% 21% 57% 61% 65% 0.73 0.92 0.93 0.93


2006DHS

2010

A&P

2012-

2013

DHS

2015

MIS

2006

DHS

2010

A&P

2012-

2013

DHS

2015

MIS

2006

DHS

2010

A&P

2012-

2013

DHS

2015

MIS

2006

DHS

2010

A&P

2012-

2013DHS

2015

MIS



their own household


Ratio of use:access

IRSNo§ 86% 84% 93% 61% 64% 70% 56% 60% 64% 0.92 0.94 0.92

Yes 91% 93%* 87%* 65% 74%* 66%* 60% 67%

* 60%* 0.92 0.90 0.90


ObservationsBetween 2006 and 2010, the use:access ratio for almost all regions increased to above .80, and this was maintained through the 2012-2013 survey. This pattern was the same for both wealth quintiles (when measured) as well as rural and urban sites. ITN ownership, use, and access tended to be higher as wealth increased, although this did not affect the use:access ratio, which was similar among all regions, wealth quintiles, and residencies. Households having received IRS had a marginally higher proportion of all three variables in the 2010 and 2012-2013 surveys, although this pattern reversed in 2015. The use:access ratio was very similar across IRS strata and surveys.

Implications for programmingMali continues to have a very strong culture of net use, and access has been sustained at relatively high levels since 2010. Net distribution and SBCC nationally should be continued throughout the country to maintain the very high use:access ratio here.

MozambiqueThe only available data for Mozambique was the 2011 DHS. Fieldwork was done from May to December 2011, during the dry season. The 2008 MICS did not include data on ITNs. In 2011, 2,930 households reported being sprayed with IRS, of 13,919 (21%). Mozambique implemented mass distribution in rolling campaigns between 2010 and 2014, and in 2016-17.


2011 DHS





use:access

RegionNiassa§ 47% 34% 34% 1.00Cabo Delgado 61%* 48%* 37% 0.77Nampula 60%* 48%* 44%* 0.92Zambezia 46% 31% 28% 0.90Tete 47% 33% 25%* 0.76Manica 54% 35% 31% 0.89Sofala 57% 39% 34% 0.87Inhambane 54% 44%* 20%* 0.45Gaza 45% 32% 8%* 0.25

Wealth QuintilePoorest§ 45% 32% 26% 0.81Poorer 50%* 35%* 28% 0.80Middle 52%* 36%* 30% 0.83Richer 55%* 41%* 30% 0.73Richest 56%* 41%* 33%* 0.80


IRS


2011 DHS





use:access

No§ 51% 36% 29% 0.80Yes 55%* 39% 32% 0.82*p-value≤0.05 compared to reference group (denoted with §)

ObservationsThe majority of regions in Mozambique have a very good use:access ratio, with the exception of Inhambane and Gaza, which had ratios of 0.45 and 0.25, respectively. The ratio was similar among wealth quintiles, averaging 0.79, among urban and rural clusters, averaging 0.81, and between IRS strata, also averaging 0.81.

Implications for programmingPopulation access in Inhambane and Gaza is not lower than that of the other regions, so additional, targeted net distribution is not necessary. However, because use is much lower in these two regions, SBCC campaigns encouraging net use may be needed. These areas experience lower transmission and have historically been targeted for IRS spraying; further investigation of the data may prove helpful.


NigeriaRains in Nigeria begin in the south between February and March and have reached the rest of the country by early summer. They last through September. Fieldwork for the 2008 DHS was conducted from June to October 2008, primarily during the rainy season. The 2010 MIS survey was completed from October to December 2010, during the high transmission season. The 2011 MICS was conducted in February and March, at beginning of rainy season in the south, and during dry season in the north. The 2013 DHS was conducted from February to June 2013, with rain in some areas and not in others. In 2010, 57 households reporting being sprayed with IRS, of 5,895 (1%); In 2013, 827 households of 38,522 reported IRS (2%). Nigeria implemented its first universal coverage campaign starting in 2009 and continuing through 2011, and again in 2014-15. The 2015 MIS was conducted in October and November of 2015 during high transmission season; 138 of 7,745 households reported IRS (2%).

2008

DHS

2010

MIS

2011

MICS

2013

DHS

2015

MIS

2008

DHS

2010

MIS

2011

MICS

2013

DHS

2015

MIS

2008

DHS

2010

MIS

2011

MICS

2013

DHS

2015

MIS

2008

DHS

2010

MIS

2011

MICS

2013

DHS

2015

MIS



household% of population that used an

ITN the previous night Ratio of use:access

RegionNorth Central§ 7% 32% 40% 50% 55% 4% 20% 26

% 37% 41% 3% 14% 12% 13% 30% 0.75 0.7

20.26 0.36 0.73

North East 7% 63%

*54%*

61%*

80%* 4% 45%

*35%*

43%*

60%* 3% 42

%* 12% 10% 45%* 0.75 0.9

50.35 0.24 0.75

North West 8% 58%

*63%* 49% 91%

* 4% 34%*

39%* 33% 68%

* 3% 31%*

17%* 11% 54%

* 0.75 0.91

0.39 0.33 0.80

South East

10%* 32% 32

%*57%

* 64% 7%* 23% 24%* 42% 51% 4%* 13

% 8%* 18%*

21%* 0.57 0.5

60.24 0.43 0.41

South South

10%* 44% 43% 43%

* 64% 6%* 27% 30% 33% 54% 5%* 21

%16%* 15% 29% 0.83 0.7

90.30 0.44 0.53

South West 6% 20% 21

%*42%

* 53% 4% 16% 14%* 33% 41% 2% 8% 7%* 15% 21%

* 0.50 0.51

0.14 0.44 0.51

Wealth QuintilePoorest§ 4% 49% 47% 55% 86% 2% 33% 31

% 37% 64% 2% 31% 10% 9% 53% 0.80 0.9

30.31 0.26 0.82


2008

DHS

2010

MIS

2011

MICS

2013

DHS

2015

MIS

2008

DHS

2010

MIS

2011

MICS

2013

DHS

2015

MIS

2008

DHS

2010

MIS

2011

MICS

2013

DHS

2015

MIS

2008

DHS

2010

MIS

2011

MICS

2013

DHS

2015

MIS



household% of population that used an

ITN the previous night Ratio of use:access

Poorer 6%* 44% 46% 54% 74%* 4%* 30% 32

% 39% 57%* 3%* 27

%14%*

15%*

45%* 0.77 0.8

90.32 0.38 0.79

Middle 8%* 46% 41%* 52% 69%

* 5%* 32% 28% 38% 57%

* 4%* 27%

14%*

16%*

40%* 0.74 0.8

50.28 0.42 0.70

Richer 10%*

36%*

37%*

46%*

64%* 6%* 23%

*26%* 34% 51%

* 4%* 17%*

13%*

13%*

28%* 0.68 0.7

20.26 0.37 0.54

Richest 11%*

34%*

36%*

42%*

58%* 7%* 23%

*25%*

33%*

45%* 4%* 13

%* 11% 12%*

22%* 0.55 0.5

90.25 0.37 0.49

ResidenceUrban§ 8% 33% 33% 42% 63% 5% 23% 23

% 31% 50% 3% 16% 11% 13% 29% 0.62 0

.710.23 0.41 0.59

Rural 8% 45%*

46%*

55%*

73%* 5% 30%

*32%*

39%*

58%* 3% 25

%* 13% 13% 42%* 0.71 0.8

40.32 0.33 0.73

No§ 41% 49% 69% 28% 36% 55% 23% 13% 37% 0.8

2 0.36 0.68

Yes 70%*

63%*

83%*

44%*

43%*

69%*

34%

23%* 44% 0.7

7 0.53 0.63


ObservationsWhile the use:access ratio started off relatively high in the 2008 and 2010 surveys in the majority of regions, the 2011 and 2013 results dropped below 0.50. In the 2015 survey, some regions and categories have again improved their use:access ratio. North West region had the highest ratio at 0.80, with North Central and North East both above 0.70. Poorer wealth quintiles had significantly higher proportions of net access during the 2015 survey than in previous surveys, and the poorest quintiles also had the highest proportion of net use overall, and better use:access ratios than richer households. ITN access has increased steadily from 2008 to 2015 in all geographic zones, and net use likewise reached its highest levels in 2015. In 2015, households in rural regions have improved their use:access ratio since the 2013 survey.


Implications for programmingThe surveys thus far indicate that Nigeria has an overall ITN ‘use gap’ of 20-60 percentage points, depending on the region and the season in which fieldwork was done. While net access should be improved by nation-wide distributions, SBCC is urgently needed to increase net use throughout the country, as recent studies indicate that multi-channel BCC efforts do improve net use in the country (Kilian et al 2016). Further analysis of recent data is crucial to identify specific reasons for non-use among certain demographics and geographic zones.


RwandaFour surveys were available for analysis in Rwanda. These included the 2007-2008 interim DHS, the 2010-2011 DHS, the 2013 MIS, and the 2014-2015 DHS. Fieldwork for these surveys occurred from December 2007-April 2008, August 2010 to March 2011, February to April 2013, and November 2014-March 2015. There are two rainy seasons in Rwanda, from March to May and October to November. June to September and December to February are dry. In 2007-8, 447 of 7,377 households (6%) reported being sprayed with IRS; in 2013, 513 of 4,766 households reported IRS (11%). Rwanda implemented mass distributions in 2010-2011, 2012-13, and plans one in 2016.

2007-

2008

DHS

2010-

2011

DHS

2013

MIS

2014-15

DHS

2007-

2008

DHS

2010-

2011

DHS

2013

MIS

2014-15 DHS

2007-

2008

DHS

2010-

2011

DHS

2013

MIS

2014-15 DHS

2007-

2008

DHS

2010-

2011

DHS

2013

MIS

2014-15 DHS



their own household


Ratio of use:access

Region

Kigali§ 67% 86% 84% 86% 51% 76% 72% 75% 46% 64% 68% 75% 0.90 0.84 0.94 1.00

South57%

* 83% 86% 85% 39%*

65%* 68% 67%

*41%

*58%

* 66% 66%* 1.05 0.89 0.97 0.9

8

West48%

*79%

* 80% 69%*

32%*

59%*

60%*

52%*

36%*

56%*

52%*

50%* 1.13 0.95 0.87 0.9

6

North49%

*70%

* 85% 79%*

32%*

50%* 69% 63%

*34%

*44%

* 64% 56%* 1.06 0.88 0.93 0.8

8

East 63% 90%* 80% 85% 44%

* 74% 65%*

66%* 44% 66% 59%

*65%

* 1.00 0.89 0.91 0.98

Wealth Quintile

Poorest§ 39% 73% 76% 65% 26% 56% 58% 48% 28% 49% 54% 47% 1.08 0.88 0.93 0.99


2007-

2008

DHS

2010-

2011

DHS

2013

MIS

2014-15

DHS

2007-

2008

DHS

2010-

2011

DHS

2013

MIS

2014-15 DHS

2007-

2008

DHS

2010-

2011

DHS

2013

MIS

2014-15 DHS

2007-

2008

DHS

2010-

2011

DHS

2013

MIS

2014-15 DHS



their own household


Ratio of use:access

Poorer51%

*79%

*80%

*78%

*33%

*59%

*62%

*58%

*36%

*53%

* 57% 56%* 1.09 0.90 0.92 0.9

6

Middle57%

*84%

*85%

*85%

*37%

*64%

*67%

*65%

*40%

*58%

*62%

*63%

* 1.08 0.91 0.93 0.96

Richer65%

*88%

*88%

*89%

*42%

*68%

*70%

*70%

*46%

*62%

*66%

*68%

* 1.10 0.91 0.94 0.97

Richest66%

*88%

*85%

*89%

*50%

*74%

*72%

*78%

*46%

*66%

*64%

*74%

* 0.92 0.89 0.89 0.95

Residence

Urban§ 65% 84% 83% 82% 49% 71% 70% 71% 46% 63% 65% 69% 0.94 0.89 0.93 0.97

Rural54%

* 82% 82% 80% 36%*

63%*

65%* 62% 38%

*57%

* 60% 60%* 1.06 0.90 0.92 0.9

6

IRSNo§ 63% 82% 52% 65% 43% 60% 0.83 0.92

Yes75%

* 89% 58% 73%* 53% 67% 0.91 0.92


ObservationsRwanda has consistently had a very high net use:access ratio across the country. All wealth quintiles and residence types were above 0.80. Both net access and use are extremely high, although lower in the poorest quintile. While households


receiving IRS had higher ITN ownership in the 2007-2008 survey, and higher ITN access in the 2013 survey, there was no difference in net use or the use:access ratio between groups.

Implications for programmingRwanda is doing an excellent job both in terms of net access and use when compared to other countries. Continued net distributions and SBCC campaigns nationwide will help in maintaining these high numbers.

SenegalThere are six surveys with available information in Senegal: 2006 MIS (not presented below), 2008-2009 MIS, 2010-2011 DHS, 2012-2013 continuous DHS, the 2014 continuous DHS, and the 2015 continuous DHS. The fieldwork was conducted from November – December 2006, November 2008 – February 2009, December 2010 – April 2011, September 2012 – June 2013, January – October 2014, and February-October 2015, respectively. Rainy season varies by region, but is generally from May to October. 2014 was a year of historically low rainfalls, particularly in the northern and center-west regions, with rainfalls 30% below the 20-year averages (WFP). For each survey, households reporting being sprayed with IRS in the last 12 months were: 2006 (73/3,063 – 2%); 2008-9 (934/9,291 – 10%); 2010 (973/7,902 – 12%); 2012-13 (644/4,175 – 15%); 2014 (471/4,231 – 11%); 2015 (311/4,188 – 7%). Senegal implemented an under-five campaign in 2007-2009, and universal coverage campaigns in 2010-13, and 2016.


2008-

2009

MIS

2010-

2011

DHS

2012-

2013

cDHS

2014

cDHS

2015

cDHS

2008-

2009

MIS

2010-

2011

DHS

2012-

2013

cDHS

2014

cDHS

2015

cDHS

2008-

2009

MIS

2010-

2011

DHS

2012-

2013

cDHS

2014

cDHS

2015

cDHS

2008-

2009

MIS

2010-

2011

DHS

2012-

2013

cDHS

2014

cDHS

2015

cDHS

% 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

RegionDakar§ 36% 37% 34% 56% 54% 18% 18% 19% 45% 46% 14% 14% 13% 27% 32% 0.78 0.78 0.68 0.59 0.70Zuguinchor

76%*

80%*

93%*

90%*

90%*

53%*

56%*

85%*

74%*

75%*

41%*

40%*

56%*

59%*

66%* 0.77 0.71 0.66 0.80 0.88

Diourbel 56%*

65%*

91%*

86%*

90%*

29%*

35%*

76%*

72%*

78%* 20% 25%

*59%

* 41% 70%* 0.69 0.71 0.78 0.57 0.90

Saint-Louis

57%*

79%*

91%* 76% 80%

*32%

*49%

*65%

* 57% 62%* 20% 47%

*63%

* 47% 62%* 0.63 0.96 0.97 0.83 1.00

Tambacounda

49%*

79%*

82%*

83%*

92%*

33%*

49%*

53%*

65%*

79%* 16% 35%

* 25% 47% 52%* 0.48 0.71 0.47 0.73 0.66

Kaolack 83%*

88%*

80%*

78%*

96%*

50%*

56%*

53%* 42% 85%

*25%

*41%

*35%

* 30% 66%* 0.50 0.73 0.66 0.71 0.78

Thies 79%*

53%*

82%* 73% 77%

*43%

*27%

*64%

*61%

*59%

*23%

* 20% 38%*

42%*

42%* 0.53 0.74 0.59 0.69 0.72

Louga 66%*

61%*

96%*

87%*

75%*

32%*

29%*

81%*

69%*

54%* 11% 22%

*60%

*47%

*43%

* 0.34 0.76 0.74 0.68 0.79

Fatick 90%*

77%*

94%*

88%*

95%*

59%*

46%*

77%*

67%*

85%*

51%*

26%*

56%*

56%*

69%* 0.86 0.57 0.73 0.83 0.81

Kolda 69%*

95%*

80%*

97%*

92%*

38%*

71%*

55%*

81%*

74%*

34%*

56%*

37%*

59%*

40%* 0.89 0.79 0.67 0.73 0.55

Matam 48% 70%*

93%*

85%*

78%*

29%*

37%*

65%*

62%*

57%* 20% 36%

*59%

*50%

*54%

* 0.69 0.97 0.91 0.82 0.94

Kaffrine 69%*

90%*

74%*

93%*

45%*

68%* 49% 81%

*31%

*34%

* 27% 45%* 0.69 0.50 0.56 0.55

Kedougou

94%*

84%*

82%*

90%*

75%*

58%*

70%*

73%*

46%*

32%*

49%*

41%* 0.61 0.55 0.69 0.56

Sedhiou 94%*

92%*

84%*

98%*

70%*

70%*

69%*

83%*

60%*

55%* 47% 73%

* 0.86 0.79 0.68 0.88

Wealth QuintilePoorest§ 67% 75% 88% 83% 88% 38% 48% 63% 61% 74% 22% 34% 35% 37% 49% 0.58 0.71 0.56 0.60 0.66

Poorer 70% 75% 91% 86% 88% 42%* 48% 70%

* 65% 75% 27%* 35% 46%

*45%

*57%

* 0.64 0.73 0.66 0.68 0.76


2008-

2009

MIS

2010-

2011

DHS

2012-

2013

cDHS

2014

cDHS

2015

cDHS

2008-

2009

MIS

2010-

2011

DHS

2012-

2013

cDHS

2014

cDHS

2015

cDHS

2008-

2009

MIS

2010-

2011

DHS

2012-

2013

cDHS

2014

cDHS

2015

cDHS

2008-

2009

MIS

2010-

2011

DHS

2012-

2013

cDHS

2014

cDHS

2015

cDHS

% 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

Middle 74%*

69%* 88% 81% 82% 41% 41%

* 66% 63% 68% 25% 34% 54%*

49%*

59%* 0.61 0.83 0.82 0.77 0.87

Richer 53%*

54%*

66%*

71%*

73%*

31%*

31%*

52%* 54% 64%

* 24% 25%* 41% 40% 55% 0.77 0.81 0.79 0.74 0.86

Richest 42%*

42%*

45%*

58%*

59%*

22%*

22%*

36%*

49%*

50%*

16%*

16%* 28% 32% 36%

* 0.73 0.73 0.78 0.65 0.73

ResidenceUrban§ 50% 52% 56% 66% 67% 29% 30% 43% 52% 58% 22% 25% 35% 38% 46% 0.76 0.83 0.81 0.73 0.80

Rural 70%*

73%*

89%*

84%*

88%*

39%*

44%*

67%*

64%*

72%* 24% 32%

*45%

* 42% 55%* 0.62 0.73 0.67 0.66 0.76

IRSNo§ 60% 62% 73% 74% 76% 35% 37% 58% 58% 65% 23% 28% 41% 40% 52% 0.66 0.76 0.72 0.69 0.79

Yes 72%*

73%* 77% 82%

*92%

* 40% 46%* 57% 61% 75%

* 22% 35%* 38% 41% 41%

* 0.55 0.76 0.66 0.68 0.54*p-value≤0.05 compared to reference group (denoted with §)

Stratification by season of interview in 2014 and 2015 continuous DHS. Dry season defined as January-June; early rains July-August; late rains September-October (no fieldwork was done in November or December).

2014 cDHS 2015 cDHSUse/

Access Dry

Season

Use/Access Early Rains

Use/Access Late Rains

Use/Access

Dry Season



Dakar 61% 46% 53% 0.61 0.77 0.90


2014 cDHS 2015 cDHSUse/

Access Dry

Season



Use/Access

Dry Season



Zuguinchor 64% 103% 97% 0.70 0.96 0.99Diourbel 51% 80% 0.78 1.00 1.03Saint-Louis 78% 85% 95% 0.96 0.97 1.05Tambacounda 54% 79% 100% 0.22 0.97 0.95Kaolack 66% 81% 0.59 0.95 0.99Thies 60% 66% 92% 0.60 0.78 0.90Louga 66% 97% 0.71 0.96Fatick 66% 73% 0.76 0.90Kolda 56% 75% 96% 0.39 1.03 1.03Matam 75% 91% 93% 0.75 1.10 1.12Kaffrine 40% 72% 96% 0.26 0.81 0.95Kedougou 41% 96% 92% 0.24 0.95 0.93Sedhiou 63% 100% 0.76 1.00 0.94

ObservationsThe continuous DHS, implemented since 2012, sheds additional light on what initially seems to be a low use:access ratio across surveys. While the mean use:access rates across the surveys are mediocre, especially for a Sahelian country with an entrenched culture of net use, analysis by rainy season (second table) shows a pattern of net use that increases during the early rains and peaks during the late rains (September/October), indicating that net use is likely driven by perceived nuisance biting, and diminishes when mosquitoes are less present. Ownership and access seem to be higher on the whole in the lower wealth quintiles than in the wealthier ones. As in many other countries, rural residents have higher access and use rates than urban residents. Households receiving IRS had significantly higher ITN ownership in all surveys; use of ITNs given access.


Implications for programmingEven before the 2016 mass campaign, ITN ownership and access were high in Senegal. While reduced rainfall in 2014 may have contributed to lower use of ITNs for those with access, this problem seems to have resolved in the 2015 data, where use:access ratios are nearly all above 0.90 by early rainy season. SBCC campaigns to increase net use in the dry season may still be needed.


TanzaniaAvailable data for Tanzania include the 2007-2008 THMIS, the 2010-2011 DHS, the 2011-2012 THMIS, and the 2015-2016 DHS. Fieldwork for the 2007-8 THMIS was done from October 2008 to February 2008, during the dry season. The 2010-2011 DHS and the 2011-2012 THMIS were conducted primarily from January-April, prior to rainy season. The 2015-2016 DHS was fielded from September to January. In 2007-8, 2,161 households reported being sprayed with IRS, of 8,497 (25%); in 2011-12, 2,309 of 10,040 were sprayed (23%); in 2015-16, 1,139 of 12,562 (9%) were sprayed. Tanzania implemented an under-five campaign in 2009-10, and a universal coverage campaign in 2010-2011 and in 2015-2016.

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS



household


Ratio of use:access

RegionDodoma§ 28% 72% 93% 39% 20% 49% 79% 26% 7% 54% 72% 17% 0.37 1.09 0.91 0.93Arusha 32% 5

1%* 85% 43% 21% 34%*

66%*

31%*

20%*

29%*

56%*

29%* 0.94 0.87 0.84 0.70

Kilimanjaro 30% 4

9%* 95% 64%* 19% 36%

* 81% 51%* 14% 32%

* 66% 36%* 0.71 0.90 0.82 0.93

Tanga 39% 60%* 90% 53%

* 25% 46% 70%*

36%*

19%*

41%* 70% 33%

* 0.77 0.89 1.00 1.05Morogoro

44%*

37%* 91% 55%

*33%

*26%

* 79% 45%*

31%*

27%* 76% 47%

* 0.94 1.04 0.96 0.95Pwani 4 69% 95% 65% 35% 54% 84% 51% 32% 52% 80% 48% 0.90 0.97 0.95 1.03


2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS



household


Ratio of use:access

8%* * * * * *Dar es Salaam

71%*

62%*

79%*

66%*

55%* 46% 71%

*53%

*56%

* 47% 66% 54%* 1.03 1.02 0.93 0.81

Lindi 40%* 64% 96% 70%

*32%

* 53% 91%*

59%*

22%*

44%*

83%*

48%* 0.67 0.83 0.91 0.82

Mtwara

43%*

64%* 93% 61%

*31%

* 51% 85% 54%*

17%*

45%* 74% 44%

* 0.57 0.88 0.87 0.86Ruvuma

39%* 70% 95% 66%

* 24% 50% 83% 56%*

20%*

46%* 74% 48%

* 0.82 0.91 0.89 0.79

Iringa 18% 53%* 92% 46% 12% 4

0%* 82% 35%* 6% 2

5%* 68% 28%* 0.47 0.64 0.83 0.69

Mbeya 30% 5

8%* 91% 50% 22% 42%* 79% 38%

*1

6%*3

1%* 60% 26%* 0.74 0.74 0.75 0.95

Singida 26% 3

4%* 95% 44% 14% 22%* 78% 31% 11% 1

8%* 74% 30%* 0.77 0.85 0.95 1.03

Tabora

40%* 73% 95% 91%

* 21% 49% 74% 74%* 8% 47% 73% 76%

* 0.36 0.96 0.99 0.85Rukwa 29% 67% 86% 29% 18% 44% 6

3%*18%

* 9% 50% 64% 15% 0.52 1.13 1.01 0.63Kigoma 31% 5

8%* 95% 94%* 17% 4

1%* 76% 86%* 12% 3

8%* 72% 54%* 0.74 0.93 0.96 0.70


2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS



household


Ratio of use:access

Shinyanga 38% 8

5%* 94% 79%* 19% 5

9%* 77% 67%*

19%*

65%* 73% 47%

* 1.02 1.10 0.95 0.87Kagera 30% 68% 92% 90%

* 18% 50% 75% 78%*

13%* 48% 68% 67%

* 0.75 0.97 0.91 0.86Mwanza

48%* 79% 96% 90%

* 29% 54% 73% 79%*

24%*

62%* 77% 68%

* 0.85 1.15 1.06 0.84

Mara 56%*

84%* 96% 91%

*3

1%*5

9%*7

6%*79%

*3

0%*6

2%* 73% 67%* 0.98 1.05 0.95 0.86

Manyara 22% 73% 89% 22%

* 12% 52% 67%*

15%* 11% 49% 5

3%* 13% 0.88 0.93 0.79 0.93Njombe 49% 40%

* 17% 0.42

Katavi95%

*79%

*85%

* 1.08Simiyu

98%*

83%*

84%* 1.00

Geita96%

*81%

*85%

* 1.05Zanz North

71%*

87%*

77%*

82%*

57%*

72%*

65%*

68%*

38%* 47% 4

9%*52%

* 0.67 0.65 0.75 0.77Zanz South

72%*

90%*

79%*

83%*

57%*

76%*

63%*

69%*

45%* 59% 4

8%*51%

* 0.80 0.78 0.76 0.74Zanz 6 6 6 64% 5 44% 5 47% 4 3 4 37% 0.82 0.85 0.79 0.79


2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS



household


Ratio of use:access

Town West 8%* 1%* 7%* * 1%* 2%* * 2%* 8%* 1%* *Pemba North

73%*

88%* 84% 80%

*6

1%*7

1%* 72% 63%*

42%* 54% 4

1%*55%

* 0.69 0.76 0.57 0.87

Pemba South

80%* 73% 8

3%*77%

*6

3%* 52% 62%*

58%*

54%*

44%*

54%*

56%* 0.86 0.83 0.86 0.96

Wealth QuintilePoorest§ 22% 57% 90% 57% 12% 40% 69% 50% 8% 40% 65% 44% 0.67 1.01 0.95 0.88Poorer

28%*

64%* 92% 64%

*1

6%*4

4%* 72% 56% 11%*

44%* 67% 48% 0.70 0.99 0.93 0.86

Middle

34%*

64%*

95%*

66%*

20%*

46%*

77%*

57%*

15%* 43% 7

0%* 48% 0.75 0.95 0.91 0.84

Richer 41%*

67%* 93% 71%

*2

7%*5

0%*7

8%*59%

*2

1%*4

7%*7

0%*52%

* 0.76 0.96 0.90 0.89Richest

67%*

68%*

86%*

68%*

51%*

54%*

77%*

58%*

46%*

52%*

70%*

53%* 0.91 0.95 0.90 0.92


2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS

2007-8 THMIS

2010-

2011

DHS

2011-

2012

THMIS

2015-

2016

DHS



household


Ratio of use:access

ResidenceUrban§ 59% 65% 87% 67% 45% 51% 77% 56% 43% 50% 72% 54% 0.94 0.99 0.94 0.95

Rural 33%* 63% 9

2%* 65% 20%*

45%*

74%* 56% 1

4%*4

4%*6

7%*47%

* 0.71 0.96 0.91 0.85

IRSNo§ 38% 91% 64% 24% 75% 55% 20% 69% 48% 0.83 0.92 0.88

Yes62%

* 91% 88%*

48%*

70%*

73%*

39%* 66% 64%

* 0.81 0.94 0.87*p-value≤0.05 compared to reference group (denoted with §)

ObservationsTanzania has some of the highest rates of ITN use among those with access to an ITN of all PMI countries, and the ratio of use:access has improved since 2007, averaging over 0.90 in 2010-11 and in 2011-12, and 0.88 in 2015-16. The regions with the lowest use:access ratios are Njombe (0.42), and Zanzibar, around 0.75-0.80 in the latest THMIS. Ratios are fairly consistent within a given region for the last two surveys.

As observed in other PMI countries, the earlier THMIS (2007-8) shows a trend in use:access ratios that is pro-rich, which then flattens out entirely in the latter two surveys, indicating that socio-economic status no longer contributes


significantly to use rates. Similarly, the difference in use:access ratio between urban and rural residence disappears after the 2007-8 THMIS.

Implications for programmingUse:access ratios are good in nearly all regions (between 0.80 and 1.00), and lower in low-transmission regions (Zanzibar and Pemba). Tanzania has implemented significant SBCC for ITN use over the years, in particular on mainland, which may be one of the reasons behind the high use:access ratios. However, additional SBCC appears to be needed in Unguja (less so in Pemba), where malaria risk is lower, to maintain net use behaviors in the face of low transmission.


UgandaThree surveys were available in Uganda, the 2009 MIS, the 2011 DHS, and the 2014-2015 MIS. Fieldwork for the 2009 survey was conducted in November and December of 2009. Fieldwork for the 2011 DHS was conducted from June to November 2011. Fieldwork for the MIS was conducted from December 2014 through February 2015. Uganda has two rainy seasons. It typically rains from March to May and September to November, leaving December to February and June to August dry. Different regions were sampled in both of these surveys, making comparison between years difficult. In 2009, 121 of 4,421 households reported being sprayed with IRS (3%). In 2011, 753 of 9,033 (8%) reported IRS. In 2014-15, 432 of 5,325 (8%) reported IRS. Uganda implemented under-five ITN distribution in 2010, and mass distributions in 2013-2014.

2009

MIS2011 DHS

2014-15 MIS

2009 MIS

2011 DHS

2014-15 MIS

2009 MIS

2011 DHS

2014-15 MIS

2009 MIS

2011 DHS

2014-15 MIS



household


Ratio of use:access

RegionCentral 1§ 35% 59% 81% 24% 49% 72% 17% 35% 59% 0.71 0.71 0.82Central 2 24% 60% 82% 14% 49% 71% 9%* 37% 59% 0.64 0.76 0.83Kampala 49%

* 57% 86% 45%* 52% 78% 38%* 44%* 71%* 0.84 0.85 0.90East Central 34% 38%* 82% 21% 25%* 67% 18% 19%* 62% 0.86 0.76 0.92Mid Eastern

59%* 95%* 36% 79% 31%* 71%* 0.86 0.90

North East 77%* 97%* 53%* 81%* 50%* 81%* 0.94 1.00

Eastern 56% 38%* 35% 0.92North 67% 46% 36% 0.78


2009

MIS2011 DHS

2014-15 MIS

2009 MIS

2011 DHS

2014-15 MIS

2009 MIS

2011 DHS

2014-15 MIS

2009 MIS

2011 DHS

2014-15 MIS



household


Ratio of use:access

Karamoja 57% 37%* 35% 0.95Mid Northern

64%* 94%* 43%* 84%* 31%* 75%* 0.72 0.90

West Nile 52% 82%* 96%* 32% 60%* 85%* 31%* 46%* 72%* 0.97 0.77 0.86Western 69%* 52% 41% 0.79Mid Western 34% 94%* 22% 81% 16% 76%* 0.73 0.94Southwest 58% 97%* 43% 90%* 30% 63% 0.70 0.70South 44% 31% 23% 0.74

Wealth QuintilePoorest§ 47% 55% 91% 30% 37% 77% 27% 33% 72% 0.90 0.89 0.94Poorer 44% 58% 94%* 30% 42%* 82%* 24% 33% 73% 0.80 0.79 0.89Middle 49% 60% 93% 33% 43%* 80% 26% 33% 70% 0.79 0.77 0.87Richer 45% 62%* 88% 29% 47%* 79% 21% 34% 64%* 0.72 0.72 0.81Richest 49% 63%* 85%* 36% 54%* 76% 29% 42%* 64%* 0.81 0.78 0.84

ResidenceUrban§ 46% 59% 84% 37% 51% 76% 30% 42% 65% 0.81 0.82 0.86Rural 47% 60% 92%* 31% 44%* 79% 25% 34%* 69% 0.81 0.77 0.87


2009

MIS2011 DHS

2014-15 MIS

2009 MIS

2011 DHS

2014-15 MIS

2009 MIS

2011 DHS

2014-15 MIS

2009 MIS

2011 DHS

2014-15 MIS



household


Ratio of use:access

IRSNo§ 46% 59% 90% 31% 44% 79% 26% 35% 68% 0.81 0.79 0.87Yes 54% 72%* 94% 37% 53%* 82% 28% 41%* 73% 0.76 0.77 0.88


ObservationsDue to the 2013 universal coverage campaign, the 2014 results are vastly improved in all indicators, and the use:access ratio increased from 2011 to 2014. The percent of the population with access to a net is one of the highest observed among PMI countries. The Southwest region continues to be an under-performer for net use, however. Net access and use both increased between surveys in the majority of regions, wealth quintiles, and residence types. Earlier trends of wealthier households having better ownership, access, and use of ITNs was reversed in 2014. Urban residences had lower ownership, likely reflecting challenges with campaign implementation in urban areas, but access and use were similar among residences. Ownership, access, and use of ITNs was similar whether or not households reported being sprayed with IRS in the previous 12 months.

Implications for programmingUganda is doing a good job both in terms of access and use when compared to other countries. Continued net distributions nationwide will help in maintaining the high proportion of households with access to nets. Additional focus on net use throughout the year may be useful to boost rates in the drier seasons.


VietnamVietnam’s last available DHS is from 2002 and does not contain a malaria module. Its MICS surveys from 2006, 2011, and 2013-14 do not contain the ITN module.


ZambiaZambia has two distinct seasons, with rain from November to April and dry weather from May to October. The 2007 DHS was conducted in May through September 2007, during the dry season. The 2013-2014 DHS was conducted between August 2013 and April 2014, with each province splitting data collection relatively evenly between late 2013 and early 2014, with the exception of Copperbelt, Lusaka, Northwestern, and Western, which conducted fieldwork primarily before the end of 2013. Comparing 2007 to 2013-14 should take into consideration dry season fieldwork in 2007, and rainy season fieldwork (primarily) in 2013-14. The 2015 MIS was fielded in April and May 2015, in the high transmission season. Muchinga province was created just prior to the 2013-14 DHS, and therefore does not have data available for 2007. In 2007, 1,002 of 7,164 households reported being sprayed with IRS (14%). In 2013-14, 5,315 of 15,902 reported IRS (33%). In 2015, 977 of 3574 households reported IRS (27.3%). Zambia implemented targeted distributions in 2007, and mass distributions in 2011 and 2013-2014, and in 2015.

2007 DHS

2013-14

DHS2015 MIS

2007 DHS

2013-14

DHS2015 MIS

2007 DHS

2013-14

DHS2015 MIS

2007 DHS

2013-14

DHS2015 MIS



household


Ratio of use:access

RegionCentral§ 56% 67% 73% 34% 45% 60% 22% 31% 53% 0.65 0.69 0.88Copperbelt 55% 75%* 77% 35% 53%* 53% 24% 38%* 44% 0.69 0.71 0.82Eastern 50% 77%* 95%* 33% 51%* 78%* 20% 38%* 82%* 0.61 0.75 1.05Luapula 81%* 63% 89%* 56%* 41% 76%* 52%* 37%* 67%* 0.93 0.90 0.89Muchinga 73% 61% 51% 53% 41%* 37% 0.80 0.70Lusaka 50% 49%* 82% 33% 34%* 65% 16% 19%* 60% 0.48 0.56 0.93Northern 48% 60%* 81% 30% 36%* 62% 24% 31% 55% 0.80 0.86 0.89Northwestern 54% 66% 81% 31% 41% 66% 23% 34% 51% 0.74 0.82 0.77Southern 40%* 79%* 81% 23%* 56%* 71% 12%* 41%* 60% 0.52 0.73 0.85


2007 DHS

2013-14

DHS2015 MIS

2007 DHS

2013-14

DHS2015 MIS

2007 DHS

2013-14

DHS2015 MIS

2007 DHS

2013-14

DHS2015 MIS



household


Ratio of use:access

Western 55% 77%* 72% 38% 58%* 54% 25% 55%* 50% 0.66 0.95 0.92

Wealth QuintilePoorest§ 48% 64% 79% 28% 41% 60% 17% 35% 55% 0.61 0.84 0.91Poorer 53%* 72%* 80% 35%* 46%* 62% 26%* 38%* 56%* 0.74 0.82 0.89Middle 55%* 72%* 79% 35%* 48%* 68% 27%* 36% 63% 0.77 0.75 0.93Richer 51% 64% 82% 33%* 46%* 66% 22%* 31%* 59% 0.67 0.67 0.90Richest 60%* 68%* 79% 39%* 51%* 67% 24%* 34% 52% 0.62 0.68 0.78

ResidenceUrban§ 52% 62% 77% 34% 45% 65% 22% 32% 54% 0.65 0.71 0.82Rural 54% 72%* 81% 34% 48% 65% 24% 37%* 58% 0.71 0.78 0.89

IRSNo§ 53% 65% 76% 34% 44% 62% 23% 33% 53% 0.68 0.75 0.86Yes 54% 75%* 89% 33% 52%* 72% 21% 39%* 65% 0.64 0.75 0.90


ObservationsOn the whole, the net use:access ratio in Zambia was average in 2007, with the exception of Luapula and Northern provinces who have a ratio ≥0.80, and Lusaka and Southern whose ratios are around 0.50. There was improvement in use:access ratios throughout the country in 2013-14, except in Central, Copperbelt, and Eastern provinces. The greatest


gains were seen in Lusaka, Southern, and Western. As in many other countries, the poorer wealth quintiles have higher use:access ratios than richer ones. There is no programmatic difference in use or access for urban vs rural households. Households receiving IRS reported significantly higher ITN ownership, access, and use; this did not affect the use:access ratio, which programmatically equivalent in all three surveys. We see an improvement in 2015 use:access ratios likely attributable to the survey timing in high transmission season, indicating that there may be strong seasonal patterns to ITN use in Zambia.

Implications for programmingNet distribution and SBCC campaigns should be targeted to the regions with the lowest proportions, in an effort to increase both net access and use. Specifically, SBCC campaigns would be useful to improve net use in times and areas of lower transmission and throughout the year.


ZimbabweFour surveys are available for analysis from Zimbabwe. Fieldwork for the 2005-2006 DHS was conducted from August 2005 to February 2006, which was partly during the rainy season (November to March), and partly during the dry season (April to October). The 2010-2011 DHS was conducted from October 2010 through March 2011, primarily during the dry season. The 2012 MIS is not publicly available. A MICS was fielded in 2014, from February to April, in high transmission season. The 2015 DHS was fielded from July to December, in both dry and early rainy seasons. An MIS was fielded in March-April 2016 but is not yet available. In 2005-06, 1,402 households reported being sprayed with IRS, of 9,285 (15%). In 2010, 1,892 of 9,756 (19%) reported spraying. In 2014, 3,731 of 15,686 households reporting being sprayed with IRS (24%); in 2015, 2,263 of 10,534 (22%) households reported IRS. Zimbabwe implemented targeted distributions in 2008-2010, and mass distributions in 2013-2014.

2005-6 DHS

2010

DHS

2014

MICS

2015

DHS

2005-6 DHS

2010

DHS

2014

MICS

2015

DHS

2005-6 DHS

2010

DHS

2014

MICS

2015

DHS

2005-6 DHS

2010

DHS

2014

MICS

2015

DHS



household


Ratio of use:access

Region† Manicaland§

9% 46% 61% 57% 5% 35% 49% 46% 2% 16% 36% 11% 0.40 0.46 0.73 0.24

† Mashonaland Central

11% 33% 55% 49% 7% 24% 40% 34% 2% 13% 37% 9% 0.29 0.54 0.93 0.27

† Mashonaland East

5% 26%*

44%* 53% 3% 18%

*34%* 43% 2% 7%* 27

%* 10% 0.67 0.39 0.81 0.22

Mashonala 9% 22% 49 67%* 6% 15% 36 52% 4% 6%* 25 12% 0.67 0.40 0.69 0.22


2005-6 DHS

2010

DHS

2014

MICS

2015

DHS

2005-6 DHS

2010

DHS

2014

MICS

2015

DHS

2005-6 DHS

2010

DHS

2014

MICS

2015

DHS

2005-6 DHS

2010

DHS

2014

MICS

2015

DHS



household


Ratio of use:access

nd West * %* * %* %*Matebeleland North 8% 41% 73

%* 71%* 4% 29% 61%* 58% 2% 13% 39% 14% 0.50 0.45 0.65 0.24

Matebeleland South 6% 7%* 11

%* 40%* 3% 3%* 5%* 32% 1% 2%* 3%* 6%* 0.33 0.67 0.57 0.20

Midlands 12% 36% 38%* 47% 6% 23%

*27%* 38% 2% 8%* 18

%* 8% 0.33 0.35 0.68 0.22

Masvingo 3%* 29%* 51% 55% 2%* 22%

*39%* 40% 1% 6%* 28

%* 8% 0.50 0.27 0.71 0.20

Harare 11% 15%*

28%* 17% 7% 10%

*18%* 11% 4% 4%* 11

%* 2%* 0.57 0.40 0.59 0.20

Bulawayo 6% 26%*

31%* 30%* 3% 17%

*20%* 20% 2% 10%

*12%* 8% 0.67 0.59 0.58 0.39

Wealth QuintilePoorest§ 5% 35% 59% 53% 3% 25% 44% 38% 1% 10% 32% 10% 0.33 0.40 0.72 0.26

Poorer 8%* 30%*

49%* 55% 4% 21%

*37%* 43% 1% 8% 28

%* 10% 0.25 0.38 0.77 0.24

Middle 6% 29%*

43%* 59%* 3% 20%

*33%* 47% 1% 8% 24

%* 10% 0.33 0.40 0.72 0.21

Richer 8% 24%*

40%* 38%* 5%* 17%

*29%* 30% 3%* 8% 22

%* 6%* 0.60 0.47 0.75 0.21

Richest 15%*

27%*

37%* 38%* 9%* 18%

*26%* 28% 5%* 8% 14

%* 7%* 0.56 0.44 0.54 0.23


2005-6 DHS

2010

DHS

2014

MICS

2015

DHS

2005-6 DHS

2010

DHS

2014

MICS

2015

DHS

2005-6 DHS

2010

DHS

2014

MICS

2015

DHS

2005-6 DHS

2010

DHS

2014

MICS

2015

DHS



household


Ratio of use:access

ResidenceUrban§ 11% 23% 35% 32% 7% 15% 24% 23% 4% 7% 15% 6% 0.57 0.47 0.60 0.27

Rural 7%* 32%*

49%* 56%* 4%* 22%

*38%* 43%* 2%* 9% 28

%* 10%* 0.50 0.41 0.74 0.22

IRSNo§ 7% 22% 35% 43% 4% 15% 25% 33% 2% 6% 16% 7% 0.50 0.40 0.67 0.21

Yes 15%*

57%*

76%* 66%* 9%* 42%

*60%

* 51%* 4%* 18%*

45%* 13%* 0.44 0.43 0.76 0.26

† Manicaland, Mashonaland East, and Mashonaland Central comprise 86% of all cases in Zimbabwe.*p-value≤0.05 compared to reference group (denoted with §)

ObservationsNet use and access is below target across the board, and there were only slight improvements in the proportion of households with access to nets between 2010 and 2015. This pattern of low use and access is seen in all wealth quintiles and residence types. ITN ownership, access, and use were significantly higher in households receiving IRS; however the use:access ratio was similar among both groups. The 2014 MICS shows improvements, likely related to being fielded during high transmission season, but use:access ratios are still under target for the most part – although highest in the higher transmission regions. The 2015 DHS was fielded in dry and early rainy season and shows low use generally, with higher use:access ratios in Bulewayo. While fieldwork was conducted in each region across several months, the % of ITNs used the previous night increased steadily over time (data not shown) from 17% in July to 32% in December.


Implications for programmingWith an overall use gap of 20-80 percentage points depending on the season, SBCC campaigns encouraging net use must accompany ITN distribution, as the proportion of people using nets across the country is below target. Given that the majority of cases occur in Manicaland (51%), Mashonaland East (16%), and Mashonaland Central (19%), special attention to boosting the ratio of use:access should be given in those provinces. Further work to assess trends in seasonality would be useful.


Appendix: Non PMI-Focus CountriesBurkina FasoBurkina Faso’s 2010 DHS was conducted primarily in July-December, although some fieldwork occurred in the first half of the year. The 2014 MIS was fielded from September to December, during high transmission season. Rainy season in Burkina Faso is from approximately May to September, with a shorter period in the north. In 2010 only 220 households reported being sprayed with IRS, out of 14,150 (1.6%); in 2014, 289 reported being sprayed with IRS, out of 38,873 (0.7%). Burkina Faso implemented a universal coverage campaign in 2010 and again in early 2014. A third universal campaign is planned for 2016.

2010 DHS

2014 MIS

2010 DHS

2014 MIS

2010 DHS

2014 MIS

2010 DHS

2014 MIS





RegionBoucle De Mouhon§ 49% 90% 27% 67% 26% 68% 0.95 1.02 Cascades 69%* 88% 49%* 68% 44%* 68% 0.91 1.00 Centre 55% 86% 35% 72% 21% 60%* 0.59 0.84 Centre-Est 46% 96%* 26% 77%* 23% 79%* 0.86 1.02 Centre-Nord 34%* 96%* 18%* 73%* 17%* 73%* 0.95 0.99 Centre-Ouest 62%* 90% 36%* 71% 33%* 61%* 0.91 0.87 Centre-Sud 49% 90% 24% 76% 24% 75%* 0.99 0.99 Est 66%* 78%* 39%* 59%* 32%* 63% 0.84 1.07 Hauts Basins 49% 93% 29% 78% 27% 70% 0.95 0.91 Nord 95%* 95%* 74%* 76%* 61%* 66% 0.82 0.86


2010 DHS

2014 MIS

2010 DHS

2014 MIS

2010 DHS

2014 MIS

2010 DHS

2014 MIS





Plateau Central 91%* 92% 71%* 71%* 67%* 64% 0.95 0.90 Sahel 47% 88% 24% 66% 26% 65% 1.06 0.98 Sud-Ouest 64%* 84%* 43% 67% 40%* 60%* 0.92 0.89

Wealth QuintilePoorest§ 49% 84% 29% 63% 26% 64% 0.88 1.01 Poorer 53%* 92%* 34%* 72%* 30%* 69%* 0.90 0.96 Middle 57%* 94%* 36%* 74%* 33%* 71%* 0.91 0.96 Richer 60%* 94%* 37%* 75%* 34%* 70%* 0.92 0.94 Richest 65%* 87% 44%* 72%* 34%* 61% 0.78 0.85

ResidenceUrban§ 60% 87% 40% 71% 31% 62% 0.78 0.87 Rural 56%* 91%* 35% 71% 32% 69%* 0.90 0.97

IRSNo§ 57% 90% 36% 71% 31% 67% 0.87 0.94 Yes 75% 99% 51%* 77% 46% 63% 0.91 0.82

ObservationsBurkina Faso has excellent use of available nets, reflected in use:access ratios that are nearly always above 0.80, with exceptions in 2010 for the richest wealth quintiles, urban areas, and the Centre region (which houses Ouagadougou).


However, by 2014 following the country’s second mass ITN campaign, use rates are uniformly good during the high transmission season, across the country. Ownership and access to ITNs are lower among the poorest wealth quintiles in both surveys, indicating a potential failure of ITN campaigns to reach these vulnerable groups.

Implications for programmingProgram planners should maintain the net use promotion activities currently underway, and explore methods to ensure that ITNs are accessible by the poorest wealth quintiles.

BurundiBurundi’s 2010 DHS was fielded from August to January 2010-2011, during the minor rainy and short dry season. The 2012 MIS was fielded from November to January 2012-13, during the short dry season. In 2010, 49 households reported being sprayed with IRS out of 8,596 (0.6%), and in 2014, 232 households reported being sprayed, of 4,866 (4.8%). Burundi implemented universal coverage campaigns in 2011 and 2014.

2010 DHS 2012 MIS 2010 DHS 2012 MIS 2010 DHS 2012 MIS 2010 DHS 2012 MIS



household


Ratio of use:access

RegionBujumbura§ 67% 78% 50% 63% 51% 68% 1.02 1.09North 41%* 56% 29%* 42% 29%* 43% 0.99 1.04Centre-east 54%* 56% 39%* 39% 38%* 41% 0.96 1.04West 71% 69% 59% 49% 58% 55% 0.98 1.10South 46%* 73% 33%* 54% 29%* 56% 0.90 1.03


2010 DHS 2012 MIS 2010 DHS 2012 MIS 2010 DHS 2012 MIS 2010 DHS 2012 MIS



household


Ratio of use:access

Wealth QuintilePoorest§ 37% 47% 29% 34% 27% 35% 0.95 1.04Poorer 50%* 60%* 37%* 43%* 35%* 47%* 0.95 1.08Middle 54%* 69%* 40%* 49%* 39%* 52%* 0.98 1.06Richer 59%* 71%* 41%* 49%* 40%* 52%* 0.98 1.05Richest 65%* 75%* 49%* 55%* 47%* 57%* 0.97 1.04

ResidenceUrban§ 68% 75% 51% 59% 52% 63% 1.01 1.08Rural 50%* 62%* 38%* 45%* 36%* 47%* 0.96 1.05

IRSNo§ 52% 62% 39% 45% 38% 47% 0.97 1.05Yes 53% 80% 46% 64% 40% 69% 0.87 1.08

ObservationsBurundi has excellent use of available nets. Ownership and access are significantly lower among the poorest wealth quintile, and among rural populations.

Implications for programmingAdditional focus on ensuring that ITNs reach the poorest wealth quintile is needed.



CameroonThe 2011 DHS was carried out from January to August, spanning dry and rainy seasons. Cameroon conducted mass ITN campaigns in 2011 after the DHS was fielded. Only 226 households reported being sprayed with IRS, out of 7,125 (3.2%). Cameroon conducted a mass distribution in 2011.

2011 DHS





use:access

RegionAdamaoua§ 19% 11% 11% 0.94Centre 20% 11% 9% 0.79Douala 19% 13% 12% 0.93Est 18% 11% 9% 0.81Extreme-Nord 13%* 7%* 2%* 0.30Littoral 21% 13% 11% 0.84Nord 26%* 15%* 7%* 0.43Nord-Ouest 23%* 12%* 11% 0.91Ouest 16% 8% 6%* 0.75Sud 16% 11% 9% 0.83Sud-Ouest 17% 10% 8% 0.80Yaoundé 16%* 10% 8% 0.81

Wealth QuintilePoorest§ 17% 9% 5% 0.49Poorer 18% 10% 7%* 0.68


2011 DHS





use:access

Middle 20%* 11%* 8%* 0.74Richer 19% 11%* 9%* 0.79Richest 18% 13% 10%* 0.79

ResidenceUrban§ 18% 11% 9% 0.77Rural 19% 10% 7%* 0.65

IRSNo§ 36% 20% 14% 0.71Yes 49% 33%* 24% 0.73

ObservationsUse:access ratios are lowest in the north and extreme-north of the country, potentially reflecting dry season net use habits, and among the poorest quintile, possibly correlated with residence in the north. The center and west of the country show room for improvement in net use:access, although with the very low overall ownership and access rates, this may improve following mass ITN distribution.

Implications for programmingUse of available ITNs is lowest in the Nord and Extreme-Nord regions; additional focus may be needed there to boost rates. Additional data and a seasonal analysis would help to further interpret these findings.


Central African RepublicThe 2010 MICS was fielded in CAR between June and December. The country’s first universal coverage campaign began in September 2010. Ombella Mpoku’s fieldwork was completed prior to the UCC. A second universal LLIN campaign began in December 2014 and continued into mid-20166.

2010 MICS





use:access

RegionOmbella Mpoku§ 20% 12% 14% 1.23Lobaye 43%* 27%* 29%* 1.08Mambere Kadei 46%* 30%* 29%* 0.95Nana Mambéré 36%* 23%* 22%* 0.99Sangha Mbaere 41%* 25%* 28%* 1.10Ouham Pende 66%* 40%* 42%* 1.05Ouham 75%* 45%* 49%* 1.09Kémo 33%* 20% 23%* 1.19Nana Grebizi 50%* 31%* 34%* 1.10Ouaka 47%* 34%* 37%* 1.08Haute-Kotto 33%* 22%* 24%* 1.10Baminigui Bangoran 36%* 27%* 28%* 1.03Vakaga 32%* 26%* 30%* 1.15Basse Kotto 70%* 47%* 49%* 1.03

6 http://www.ifrc.org/fr/nouvelles/nouvelles/africa/central-african-republic/la-distribution-de-moustiquaires-pour-sauver-des-vies-en-republique-centrafricaine/


2010 MICS





use:access

Mbomou 40%* 28%* 30%* 1.07Haut Mbomou 42%* 31%* 33%* 1.05Bangui 53%* 33%* 36%* 1.10

Wealth QuintilePoorest§ 47% 30% 33% 1.08Poorer 50% 32% 34% 1.08Middle 50% 31% 34% 1.09Richer 47% 28% 31% 1.10Richest 55%* 34% 34% 1.02

ResidenceUrban§ 47% 29% 32% 1.08Rural 50% 32%* 34% 1.06

ObservationsCAR shows high rates of use among those with access to a net, across the country. There are no major differences by socioeconomic quintile or by residence.

Implications for programmingIncreasing access to ITNs by the population as a whole will be important.


ChadThe 2004 DHS does not contain the updated ITN roster. The 2010 MICS was conducted from January to May, during the dry season. The 2014-2015 DHS was conducted primarily from November to March. Rains in Chad are minimal in the northern part of the country, and from May-November in the central and southern portions. Chad conducted mass distributions in 2010 and 2014. A total of 95 households reported being sprayed with IRS in the previous 12 months, of 11,347 households (0.8%).

2010 MICS

2015 DHS 2010 MICS

2015 DHS 2010 MICS

2015 DHS 2010 MICS

2015 DHS



household


Ratio of use:access

RegionBhata§ 42% 50% 25% 35% 9% 15% 0.38 0.43BET 20%* 13%* 14%* 10%* 3%* 3%* 0.22 0.28Chari Baguirmi 50% 48% 37%* 40% 4%* 33%* 0.10 0.82Guéra 46% 58%* 27% 41%* 5% 16% 0.17 0.40Hadjer Lamis 59%* 59%* 38%* 46%* 7% 8% 0.18 0.17Kanem 35% 22%* 21% 14%* 1%* 1%* 0.07 0.04Lac 36% 44% 24% 29%* 10% 10% 0.39 0.36Logone Occidental 18%* 58%* 10%* 48%* 1%* 14% 0.12 0.29Logone Oriental 32%* 57%* 19% 43%* 0%* 24%* 0.02 0.56Mandoul 12%* 56%* 7%* 42%* 2%* 27%* 0.25 0.65Mayo Kebbi Est 52%* 58%* 36%* 51%* 2%* 34%* 0.06 0.67


2010 MICS

2015 DHS 2010 MICS

2015 DHS 2010 MICS

2015 DHS 2010 MICS

2015 DHS



household


Ratio of use:access

Mayo Kebbi Ouest 25%* 63%* 11%* 52%* 1%* 29%* 0.11 0.56Moyen Chari 41% 58%* 25% 47%* 3%* 44%* 0.13 0.93Ouaddai 46% 37%* 31% 27%* 12% 15% 0.38 0.55Salamat 57%* 52% 37%* 40%* 10% 18% 0.27 0.45Tandjilé 44% 61%* 27% 50%* 1%* 15% 0.05 0.30Wad Fira 20%* 10%* 14%* 7%* 3%* 2%* 0.23 0.27Ndjaména 73%* 56%* 55%* 47%* 48%* 44%* 0.87 0.94Barh el Gazal 33% 35%* 22% 22%* 1%* 3%* 0.05 0.15Ennedi 12%* 9%* 2%* 0.27Sila 51% 31%* 28%* 20%* 6% 19% 0.21 0.94

Wealth QuintilePoorest§ 25% 50% 15% 38% 2% 21% 0.14 0.54Poorer 34%* 50% 21%* 39% 4%* 21%* 0.18 0.55Middle 39%* 49% 24%* 38% 5%* 18%* 0.19 0.49Richer 49%* 51% 31%* 39% 7%* 16%* 0.23 0.42Richest 67%* 55%* 48%* 46%* 29%* 32%* 0.60 0.69


2010 MICS

2015 DHS 2010 MICS

2015 DHS 2010 MICS

2015 DHS 2010 MICS

2015 DHS



household


Ratio of use:access

ResidenceUrban§ 61% 55% 45% 45% 28% 31% 0.62 0.68Rural 36%* 50%* 23%* 38%* 4%* 19%* 0.16 0.50*

IRSNo 77% 61% 33% 0.54Yes 92%* 75%* 50%* 0.68

ObservationsChad’s has very low use:access ratios across most of the country, with exceptions in the capital, Ndjamena, Sila, Chani Begiurmi, and Moyen Chani, for whom data collection occurred in November/December, during transmission season,. While ownership of any ITN remained fairly stable from 2010 to 2015, access to ITNs increased across much of the country. Disparities in ownership, access, and use in rural and urban areas decreased from 2010 to 2014-15. Use:access ratio is much higher in urban areas, particularly Ndjamena, and use is highest in that region. There is a strong pro-rich trend for ownership, access, use, and use:access ratio, which may reflect geographic conditions for these populations.

Implications for programmingAdditional data is needed to confirm whether use:access ratios systematically improve during rainy season. Increasing access to nets, particularly among poorer and rural populations, is key.

ComorosThe 2012 DHS was fielded between August and December, prior to and during the beginning of the rainy season. In 2012, 624 households reported being sprayed with IRS, of 4,482 surveyed households (14%).


2012 DHS





use:access

RegionMohéli§ 64% 44% 43% 0.98 Ndzouani 51%* 34%* 33%* 0.98 Ngazidja 67% 49% 43% 0.89

Wealth QuintilePoorest§ 54% 33% 34% 1.02 Poorer 54% 36%* 36% 0.98 Middle 62%* 45%* 42%* 0.93 Richer 64%* 45%* 40%* 0.90 Richest 62%* 46%* 39%* 0.85

ResidenceUrban§ 57% 40% 37% 0.91 Rural 60% 42% 39% 0.94

IRSNo§ 58% 41% 38% 0.93 Yes 67%* 47%* 44%* 0.94


ObservationsComoros has good use:access ratios, indicating a strong culture of net use. Ownership and access to ITNs is higher among wealthier socioeconomic quintiles.

Implications for programmingThere is no particular need for targeted BCC messaging to specific regions or groups based on these results.


Republic of Congo (Brazzaville)The Republic of Congo’s 2011-2012 DHS was fielded from September through February, including the rainy season in the southern part of the country, and covering the short dry season in the northern part. Congo implemented a mass distribution in 2011-12; the DHS captured the first wave of distribution in Pool province, but not in other areas.

2011-2012 DHS





use:access

RegionKouilou§ 30% 21% 26% 1.27 Niari 35% 23% 28% 1.20 Lekoumou 29% 20%* 22% 1.11 Bouenza 30% 19%* 22% 1.11 Pool 73%* 62%* 68%* 1.10 Plateaux 40%* 27%* 31%* 1.15 Cuvette 40%* 27%* 32%* 1.18 Cuvette-ouest 41%* 27%* 30% 1.09 Sangha 37% 23%* 26% 1.14 Likouala 57%* 39%* 51%* 1.30 Brazzaville 30% 20%* 22% 1.10 Pointe Noire 19%* 12%* 15%* 1.25

Wealth quintilePoorest§ 43% 31% 37% 1.19


2011-2012 DHS





use:access

Poorer 36%* 24%* 29%* 1.21 Middle 32%* 22%* 25%* 1.16 Richer 27%* 18%* 21%* 1.14 Richest 26%* 18%* 17%* 0.99

ResidenceUrban§ 27% 18% 20% 1.15 Rural 43%* 31%* 36%* 1.15

ObservationsCongo-Brazzaville’s use:access ratios are extremely high, reflecting an average number of users per net higher than 2. It is highest among the poorest wealth quintiles. Access to ITNs is higher in rural areas, and regional differences in ITN access reflect timing of the survey vs the mass campaign in 2011-12.

Implications for programmingAdditional efforts should be made to ensure access to ITNs, and to maintain BCC messaging for net use, but the use culture in Congo-Brazzaville appears excellent country-wide.


Cote d’IvoireThe 2012 DHS was fielded primarily from December 2011 to March 2012, during the late high transmission season/early dry season. There were 135 households reporting being sprayed with IRS, out of 9,686 (1.4%). Cote d’Ivoire implemented mass distribution in 2008 and 2010-2011, and 2014.

2011-2012 DHS





use:access

RegionCentre§ 71% 50% 32% 0.64 Centre-Est 65% 51% 37% 0.73 Centre-Nord 75% 58% 46%* 0.80 Centre-Ouest 77% 55% 47%* 0.85 Nord 69% 50% 36% 0.72 Nord-Est 85%* 61%* 46%* 0.76 Nord-Ouest 78% 51% 36% 0.69 Ouest 53%* 36%* 27% 0.75 Sud sans Abidjan 69% 52% 32% 0.61 Sud-Ouest 65% 50% 34% 0.69 Ville d'Abidjan 56%* 41%* 17%* 0.41

Wealth QuintilesPoorest§ 70% 52% 43% 0.82 Poorer 72% 54% 39%* 0.72 Middle 66% 47% 33%* 0.71


2011-2012 DHS





use:access

Richer 66% 48% 29%* 0.61 Richest 60%* 44%* 23%* 0.51

ResidenceUrban§ 60% 44% 25% 0.57 Rural 73%* 53%* 40%* 0.75

IRSNo§ 67% 49% 33% 0.68 Yes 66% 46% 27% 0.59

ObservationsUse:access ratios are below target in much of the country, and this may reflect survey fieldwork being conducted in the drier season. There is a pro-poor trend in use:access, also reflected in higher use:access among rural populations, and a very low use:access ratio in Abidjan itself.

Implications for programmingAdditional seasonal analysis would help to pinpoint BCC messaging to promote ITN use at specific times of year and among specific target groups.


GabonFieldwork for the 2012 DHS was conducted from January to May, during the short dry and early rainy season. In 2012, 304 of 9,755 households reported being sprayed with IRS (3%).

2012 DHS





use:access

RegionLibreville-Port Gentil§ 31% 23% 23% 1.00Estuaire 42%* 30%* 30%* 0.98Haut-Ogooué 45%* 31%* 30%* 0.97Moyen-Ogooué 40%* 30%* 30%* 1.00Ngounié 40%* 30%* 29%* 0.97Nyanga 47%* 36%* 37%* 1.02Ogooué Marit.. 54%* 44%* 45%* 1.03Ogooué-Ivindo 60%* 36%* 40%* 1.11Ogooué-Lolo 41%* 29%* 29%* 1.00Woleu-Ntem 38%* 27%* 25% 0.93

Wealth QuintilePoorest§ 42% 31% 32% 1.02Poorer 42% 33% 35% 1.04Middle 37%* 29% 30% 1.02Richer 36%* 26%* 26%* 0.98


2012 DHS





use:access

Richest 21%* 14%* 12%* 0.81


IRSNo§ 37% 27% 27% 1.00Yes 35% 23% 21% 0.92

ObservationsGabon has a strong culture of net use despite low levels of ITN access in 2012.

Implications for programmingIncreasing access among populations at risk should contribute directly to higher use of ITNs generally.


GambiaGambia’s 2013 DHS was fielded from February to April, during the dry season. A full 50% of the 52,691 households surveyed in the Gambia reported being sprayed with IRS (Banjul and Kanifing regions had far fewer households reporting spraying). Gambia conducted a mass ITN campaign in 2013.

2013 DHS





use:access

RegionBanjul§ 55% 35% 35% 1.02Kanifing 58% 34% 35% 1.00Brikama 64%* 40%* 35% 0.86Mansakonko 79%* 56%* 50%* 0.89Kerewan 74%* 52%* 40% 0.77Kuntaur 88%* 57%* 42% 0.73Janjanbureh 88%* 59%* 51%* 0.87Basse 88%* 52%* 27% 0.52

Wealth QuintilePoorest§ 77% 54% 44% 0.81Poorer 80% 53%* 39%* 0.74Middle 75% 47%* 35%* 0.75Richer 62%* 39%* 36%* 0.92Richest 55%* 33%* 30%* 0.92


2013 DHS





use:access

ResidenceUrban§ 61% 38% 37% 0.95Rural 80%* 52%* 37% 0.71

IRSNo§ 62% 40% 35% 0.87Yes 84%* 54%* 41%* 0.75

ObservationsDespite survey timing in full dry season, use:acess ratios are relatively high, reflecting the extensive net use culture in the Gambia. Access to ITNs shows a pro-poor trend, also demonstrated in the higher access among rural populations. We also observe higher access to ITNs but a lower use:access ratio among households reporting IRS. Basse region has a particularly poor use of available nets; fieldwork for Basse, furthest inland and drier than other regions, was done in March-April, which is hot season.

Implications for programmingWhile it is unlikely that Gambians would benefit from additional BCC messaging about ITN use, given the strong culture of net use in the country, additional seasonal analysis may illuminate trends in use of the course of the year. Further attention may need to be paid in Basse.


GuyanaThe 2009 DHS was fielded from February to August, during the rainy and rainier seasons.

2009 DHS

% of households

owning ≥1 ITN

% of population


their own household

% of population


previous night

Ratio of use:access

Region Region 1 – Barima-Waini§ 38% 29% 32% 1.12Region 2 – Pomeroon-Supenaam 62%* 56%* 56%* 1.01Region 3 – Essequibo Islands-West Demarara 23%* 19%* 19%* 1.02Region 4 – Demerara-Mahaica 14%* 12%* 11%* 0.89Region 5 – Mahica-Berbice 25%* 23%* 21%* 0.90Region 6 – East Berbice-Corentyne 45%* 41% 39% 0.95Region 7 – Cuyuni-Mazaruni 48%* 33% 33% 1.00Region 8 – Potaro-Siparuni 40% 31% 29% 0.93Region 9 – Upper Takutu-Upper Essequibo 23%* 17%* 8%* 0.47Region 10 – Upper Demarara-Berbice 5%* 3%* 2%* 0.62

Wealth QuintilePoorest§ 29% 22% 22% 1.00Poorer 28% 24% 24% 1.00


2009 DHS

% of households

owning ≥1 ITN

% of population


their own household

% of population


previous night

Ratio of use:access

Middle 29% 25% 24% 0.96Richer 25% 24% 21% 0.87Richest 17%* 16%* 15%* 0.91

ResidenceUrban§ 13% 11% 10% 0.92Rural 31%* 27%* 25% 0.95

ObservationsUse:access ratios are good throughout the country with the exception of regions 9 and 10. There are no major differences in use:access ratios among wealth quintiles or urban/rural residence, although there is a very slight pro-poor trend to ITN ownership and access.

Implications for programmingIncreased messaging on net use may be useful in regions 9 and 10, depending on seasonality analysis and malaria risk in these areas.


HaitiHaiti’s DHS was fielded in 2012 from January to June, during the drier season and beginning of rainy season. There were 1,119 households reporting being sprayed with IRS, of 58,548 (1.9%).

2012 DHS





use:access

RegionAire Metropolitaine/ Reste-Ouest§ 20% 11% 8% 0.69Sud-Est 26% 16%* 7% 0.46Nord 19% 12% 5% 0.43Nord-Est 19% 10% 8% 0.81Artibonite 9%* 6%* 5%* 0.79Centre 14%* 6%* 6% 0.91Sud 19% 10% 7% 0.67Grand'anse 18% 9% 5%* 0.55Nord-Ouest 17% 9% 6% 0.72Nippes 36%* 23%* 12%* 0.53Camps 29%* 20%* 14%* 0.71

Wealth QuintilePoorest§ 9% 5% 3% 0.55Poorer 10% 6% 3%* 0.57Middle 19%* 11%* 7%* 0.65


2012 DHS





use:access

Richer 25%* 15%* 10%* 0.69Richest 30%* 18%* 12%* 0.67


IRSNo§ 19% 11% 7% 0.65Yes 31%* 19%* 12%* 0.61

ObservationsWe observe quite low ITN ownership and access across Haiti, and mediocre culture of net use, although the available data was collected just prior to transmission season.

Implications for programmingFurther data will be helpful to interpret seasonal trends in the indicators.


MauritaniaThe 2011 MICS was fielded primarily from June through September, during high transmission season/rainy season, although still dry in many part of the country.

2011 MICS





use:access

RegionHodh Charghy§ 42% 21% 14% 0.66Hodh Gharby 35% 16% 8%* 0.51Assaba 54%* 26%* 17% 0.66Gorgol 57%* 33%* 20%* 0.61Brakna 76%* 47%* 28%* 0.59Trarza 64%* 39%* 25%* 0.66Adrar 79%* 40%* 2%* 0.05Dakhlett Nouadibou 1%* 0%* 0%* 0.51Tagant 18%* 9%* 1%* 0.15Guidimagha 72%* 38%* 27%* 0.71Tirs-ezemour 1%* 0%* (0%) -Nouakchott 22%* 11%* 9%* 0.77

Wealth QuintilePoorest§ 49% 24% 18% 0.75Poorer 55%* 30%* 18% 0.61Middle 48% 28% 16% 0.56


2011 MICS





use:access

Richer 35%* 21%* 12%* 0.57Richest 30%* 17%* 11%* 0.64


ObservationsLike other very dry countries (Niger; Chad), use:access ratios are poor to fair.

Implications for programmingAdditional data and a seasonal analysis would help to interpret these results. Improved access to ITNs is needed.


NamibiaThe 2006-7 DHS was fielded from November to March, during the dry and rainy seasons, and the 2013 DHS from January to September, during the rainy and dry seasons. Only the regions of Caprivi, Kavango, Kunene, Ohangwena, Omaheke, Omusati, Oshana, Oshikoto, and Otjozondjupa are considered malaria endemic. In 2013 respondents were asked whether their household was sprayed with IRS in the previous 12 months; 9,128 of 41,627 responded yes (22%). These households were primarily in Caprivi, Kavango, Kunene, Ohangwena, Omusati, Oshana, Oshikoto, and Otjozondjupa.

2006-7 DHS

2013 DHS

2006-7 DHS

2013 DHS

2006-7 DHS

2013 DHS

2006-7 DHS

2013 DHS



household


Ratio of use:access

RegionCaprivi§ 54% 59% 39% 46% 30% 19% 0.76 0.42Erongo 1%* 4%* 0%* 3%* 0%* 0%* 0.37 0.05Hardap 4%* 12%* 2%* 7%* 1%* 0%* 0.37 0.04Karas 3%* 4%* 1%* 2%* 1%* 1%* 0.42 0.32Kavango 32%* 41%* 16%* 25%* 11%* 10%* 0.71 0.39Khomas 4%* 7%* 3%* 4%* 1%* 1%* 0.51 0.18Kunene 11%* 24%* 6%* 16%* 2%* 3%* 0.37 0.16Ohangwena 38%* 37%* 20%* 28%* 4%* 5%* 0.22 0.17Omaheke 21%* 20%* 12%* 15%* 3%* 1%* 0.23 0.05Omusati 27%* 32%* 16%* 20%* 5%* 2%* 0.30 0.11Oshana 38%* 42%* 23%* 31%* 9%* 7%* 0.40 0.22Oshikoto 30%* 39%* 16%* 28%* 4%* 3%* 0.24 0.10Otjozondjupa 14%* 15%* 9%* 10%* 3%* 1%* 0.35 0.07


2006-7 DHS

2013 DHS

2006-7 DHS

2013 DHS

2006-7 DHS

2013 DHS

2006-7 DHS

2013 DHS



household


Ratio of use:access

Wealth QuintilePoorest§ 28% 33% 15% 23% 8% 6% 0.50 0.26Poorer 31% 30%* 17%* 22%* 6% 6% 0.37 0.26Middle 23%* 29%* 15% 21%* 6% 4%* 0.40 0.17Richer 16%* 21%* 11%* 16%* 5%* 3%* 0.49 0.19Richest 8%* 13%* 6%* 9%* 2%* 1%* 0.36 0.15


IRSNo§ 20% 15% 3% 0.20Yes 45%* 29%* 7%* 0.24

ObservationsNamibia has low ownership and access, with the exception of Caprivi region, near Angola and Zambia, and very low use:access ratios across the rest of the country. ITN distributions are targeted to particular regions.

Implications for programmingNamibia’s low culture of net use would benefit from additional SBCC activities and ITN distributions in malarious areas.


NigerThe 2006 DHS was fielded from January to June, during the dry season, and in 2012 from January to June, also during dry season. In 2012, 109 households reported being sprayed with IRS, out of 10,750 surveyed households. Niger implemented mass distributions in 2005, 2009, and 2014.

2006 DHS

2012 DHS

2006 DHS

2012 DHS

2006 DHS

2012 DHS

2006 DHS

2012 DHS



household


Ratio of use:access

RegionsAgadez§ 54% 48% 23% 28% 9% 14% 0.40 0.51Diffa 42%* 64%* 23% 46%* 5%* 12% 0.22 0.26Dosso 47% 74%* 21%* 41%* 5%* 16% 0.27 0.39Maradi 47% 67%* 22%* 39%* 3%* 11% 0.14 0.29Tahoua 42%* 60%* 19%* 36%* 3%* 8%* 0.17 0.23Tillabéri 48% 52% 19%* 32%* 6%* 21%* 0.32 0.65Zinder 42%* 56% 21%* 36%* 4%* 13% 0.20 0.35Niamey 17%* 68%* 9%* 43%* 4%* 28%* 0.52 0.66

Wealth QuintilePoorest§ 44% 46% 18% 25% 3% 6% 0.15 0.25Poorer 40%* 56%* 19% 33%* 3% 9%* 0.18 0.27Middle 45% 65%* 21% 39%* 4% 13%* 0.17 0.34Richer 43% 69%* 20% 42%* 4% 16%* 0.18 0.39


2006 DHS

2012 DHS

2006 DHS

2012 DHS

2006 DHS

2012 DHS

2006 DHS

2012 DHS



household


Ratio of use:access

Richest 42% 73%* 21% 47%* 9%* 24%* 0.41 0.50


IRSNo§ 61% 37% 14% 0.37Yes 67% 46% 27%* 0.59

ObservationsNiger has good levels of ITN ownership and fair levels of ITN access as of 2012, although dry season fieldwork reveals that nets are used very rarely in the hotter months when available. Additional data collected during rainy/high transmission season would be useful to confirm that households make use of nets at some point in the year.

Implications for programmingAdditional efforts to encourage ITN use throughout the year would help to reduce malaria transmission in drier seasons; more data is needed to assess net use culture during transmission season.


Sao TomeThe 2008 DHS was fielded from September to January 2008-2009 during the short rainy season.

2008 DHS





use:access

RegionRegiao Centre§ 63% 54% 47% 0.89Regiao Sul 57% 50% 48% 0.95Regiao Norte 56% 43% 41% 0.95Regiao de Principe 68% 59% 46% 0.77

Wealth QuintilePoorest§ 44% 38% 31% 0.81Poorer 58%* 45%* 40%* 0.90Middle 65%* 54%* 49%* 0.92Richer 70%* 57%* 53%* 0.93Richest 73%* 62%* 57%* 0.92



ObservationsSao Tome & Principe demonstrate good levels of use:access ratio, and fair levels of ITN ownership and access. There is a strong pro-rich and pro-urban trend to ownership, access, and use.

Implications for programmingThe poorest and rural households may need specific targeting with BCC messages to improve net use, particularly on Principe.


Sierra LeoneSierra Leone’s 2008 DHS was fielded in April, May and June, just prior to rainy season, and did not contain IRS information. The 2010 MICS was fielded in October, November, and December, in high transmission season, and the 2013 DHS was fielded mostly in June to September (with some data collection in October and November), during rainy season. The rainy season is from May to November. In the 2010 MICS the number of households reporting they had been sprayed in the last 12 months was 1,565 out of 66,571 (2.4%). In 2013, only 550 households reported being sprayed, out of 12,629 (4%). Sierra Leone had a targeted campaign in 2006, and universal coverage campaigns in 2010 and in 2014.

2008DHS

2010MICS

2013DHS

2008DHS

2010MICS

2013DHS

2008DHS

2010MICS

2013DHS

2008DHS

2010MICS

2013DHS



household


Ratio of use:access

RegionEastern§ 33% 35% 64% 18% 18% 38% 19% 21% 42% 1.04 1.15 1.12Northern 35% 39% 65% 16% 18% 36%* 17% 21% 41% 1.07 1.16 1.13Southern 45%* 40% 77%* 26%* 21% 48%* 27%* 24% 56%* 1.02 1.13 1.15Western 34% 25%* 47%* 18% 13%* 25%* 16% 13%* 23%* 0.89 0.99 0.90

Wealth QuintilePoorest§ 30% 29% 64% 16% 15% 38% 17% 17% 44% 1.08 1.16 1.17Poorer 34% 35%* 65% 17% 17%* 39% 18% 20%* 45% 1.04 1.15 1.15Middle 40%* 43%* 70%* 19%* 22%* 41%* 21%* 26%* 47%* 1.06 1.17 1.14Richer 42%* 40%* 69%* 21%* 19%* 40%* 22%* 22%* 44% 1.06 1.15 1.11Richest 38%* 34%* 54%* 20%* 18%* 31%* 17% 19% 29%* 0.87 1.04 0.93


2008DHS

2010MICS

2013DHS

2008DHS

2010MICS

2013DHS

2008DHS

2010MICS

2013DHS

2008DHS

2010MICS

2013DHS



household


Ratio of use:access

ResidenceUrban§ 36% 33% 58% 19% 16% 33% 18% 18% 33% 0.95 1.10 1.00Rural 37% 38%* 68%* 19% 19%* 40%* 20% 22%* 46%* 1.05 1.15 1.15

IRSNo§ 35% 64% 19% 37% 18% 42% 0.95 1.11Yes 36%* 74% 18%* 41% 21%* 44% 1.14 1.06

ObservationsWe observe excellent use:access ratios in Sierra Leone across the three surveys. The wealthiest quintile appear to use available nets to a lesser degree than poorer quintiles, and similarly, rural populations have higher rates of use and of use:access ratios than urban populations, with the gap widening over time. Sufficient access to ITNs within the household is still below target as of the 2013 DHS, but the mass campaign of 2014 would have brought these numbers up, in principle.

Implications for programmingMany use:access ratios are greater than 1.0, indicating that more than 2 people on average are using nets. Increasing access to ITNs in all households will be key.


SurinameThe 2010 MICS was fielded from July to September, during the wet season, one of which runs from April to August, and then from November to February. Only two regions, Brokopondo and Sipaliwini, owned bednets, and these regions are classified primarily as rural interior areas. Only 57 households out of 1864 reported being sprayed with IRS (3%). Suriname distributed ITNs in targeted areas between 2006-2008, and in 2010.

2010 MICS





use:access

RegionParamariboWanicaNickerieCoronieSaramaccaCommewijneMarowijneParaBrokopondo§ 61% 37% 34% 0.92Sipaliwini 60% 46% 41%* 0.88

Wealth QuintilePoorest§ 31% 21% 19% 0.90Poorer 3.2%* 2.1%* 1.7%* 0.82Middle 0.4%* 0.4%* 0.3%* 0.75


2010 MICS





use:access

RicherRichest 0.03%* 0.02%* 0.03%* 1.40

ResidenceRural interior 60% 44% 39% 0.89

IRSYes§ 84% 61% 66% 1.07No 60%* 44%* 38%* 0.88

ObservationsOnly two regions of Suriname own nets, with fair ITN ownership and access rates. Ownership and access are highest among the poorest wealth quintile, reflecting targeted distributions, but use of available nets is good for most subgroups.

Implications for programmingDistribution of nets to these targeted populations will result in their use.


Swaziland2006 DHS was fielded between July 2006 and February 2007, during the rainy season. The 2010 MICS was fielded in August to November, as the dry cooler weather turned to more humid summer. Swaziland distributed nets in 2009-2010 in targeted areas. A total of 621 households reported being sprayed with IRS, 4,834 (13%); 563 of these were in Lubombo region.

2006 DHS 2010 MICS

2006 DHS

2010 MICS

2006 DHS

2010 MICS

2006 DHS

2010 MICS





RegionHhohho§ 1.6% 5.4% 0.9% 5.0% 0.1% 0.4% 0.06 0.08Manzini 2.2% 4.6% 1.2% 2.3% 0.1% 0.6% 0.08 0.24Shiselweni 2.5% 1.0%* 1.2% 0.4%* 0.2% 0.1%* 0.15 0.34Lubombo 13.2%* 34.5%* 6.9%* 27.4%* 0.8%* 4.2%* 0.12 0.15

Wealth QuintilePoorest§ 7.3% 14.4% 3.5% 10.2% 0.3% 1.7% 0.08 0.17Poorer 4.2%* 12.0% 1.8%* 8.4% 0.1% 1.1% 0.07 0.13Middle 2.4%* 9.6%* 1.2%* 8.2% 0.2% 1.1% 0.13 0.14Richer 3.5%* 9.5%* 1.9%* 6.1%* 0.3% 0.7%* 0.17 0.12Richest 4.7% 7.0%* 2.9% 4.8%* 0.4% 1.0% 0.13 0.21

ResidenceUrban§ 3.2% 5.6% 2.1% 3.6% 0.5% 0.9% 0.26 0.26


2006 DHS 2010 MICS

2006 DHS

2010 MICS

2006 DHS

2010 MICS

2006 DHS

2010 MICS





Rural 5.0% 12.5%* 2.3% 8.7%* 0.2%* 1.2% 0.08 0.14

IRSYes§ 47.6% 40.1% 5.4% 0.13 No 5.1%* 3.0%* 0.5%* 0.18

ObservationsSwaziland has very low use:access ratios, reflecting low malaria risk across most of the country. The Lubombo region has higher ITN ownership and access due to malaria control interventions there, but net use and use:access ratio are still very low.

Implications for programmingNet use messages need to be carefully targeted to specific at-risk populations. These subnational analyses are not precise enough to illuminate habits of those at risk for malaria.


Timor LesteThe 2009-2010 DHS was fielded from August to January, spanning dry (May-Nov) and wet (Dec-April) seasons.

2009-10 DHS





use:access

RegionAileu§ 52% 32% 39% 1.22Ainaro 16%* 9%* 11%* 1.23Baucau 22%* 15%* 17%* 1.16Bobonaro 42%* 25%* 31%* 1.21Cova Lima 62%* 42% 47%* 1.13Dili 51% 34% 40% 1.15Ermera 27%* 14%* 17%* 1.26Liquica 38%* 20%* 24%* 1.19Lautem 53% 30% 33%* 1.09Manufahi 23%* 15%* 18%* 1.18Manatuto 55% 35% 43% 1.21Oecussi 54% 35% 34% 0.98Viqueque 43%* 30%* 30%* 1.00

Wealth QuintilePoorest§ 24% 13% 14% 1.08Poorer 31%* 18%* 21%* 1.16


2009-10 DHS





use:access

Middle 41%* 24%* 29%* 1.17Richer 55%* 34%* 40%* 1.17Richest 55%* 38%* 42%* 1.12

ResidenceUrban§ 51% 33% 38% 1.14Rural 38%* 23% 27%* 1.14

ObservationsDespite relatively low ownership and access of ITNs, Timor Leste has excellent net use culture. There is a strong pro-rich and pro-urban trend to ownership, access, and use, although use:access ratios do not vary programmatically.

Implications for programmingThere is no particular area or group that would benefit from increased SBCC on net use.


TogoTogo’s MICS was fielded in 2010 from September to November during high transmission season, and the DHS was fielded from November to March 2013-2014, during the dry season. Togo conducted mass distributions 2004, 2008, 2011, and 2014.

2010 MICS

2013-14 DHS

2010 MICS

2013-14 DHS

2010 MICS

2013-14 DHS

2010 MICS

2013-14 DHS





RegionLome§ 44% 52% 30% 39% 26% 24% 0.86 0.61Maritime (sans Lome) 51%* 64%* 33%* 48%* 26% 28%* 0.81 0.58Plateaux 57%* 72%* 39%* 52%* 37%* 40%* 0.95 0.78Centrale 64%* 77%* 41%* 57%* 38%* 43%* 0.92 0.75Kara 66%* 77%* 41%* 60%* 39%* 44%* 0.95 0.72Savanes 80%* 69%* 50%* 47%* 51%* 32%* 1.01 0.69

Wealth QuintilesPoorest§ 57% 75% 36% 50% 35% 36% 0.99 0.72Poorer 63%* 75% 41%* 54% 41%* 40% 0.99 0.73Middle 60% 67%* 42% 51%* 38% 35% 0.92 0.70Richer 52% 59%* 34% 44%* 29%* 29%* 0.85 0.67Richest 55% 58%* 38% 45%* 29%* 28%* 0.78 0.62

Residence


2010 MICS

2013-14 DHS

2010 MICS

2013-14 DHS

2010 MICS

2013-14 DHS

2010 MICS

2013-14 DHS





Urban§ 51% 58% 34% 44% 28% 28% 0.83 0.63Rural 62%* 71%* 41%* 51%* 39%* 37%* 0.95 0.72

ObservationsTogo’s two surveys reveal high use of available nets during high transmission season, and reductions in net use during dry season, typical for its geography. There is a pro-poor trend in ITN ownership, reflective of Togo’s repeated mass campaigns, and slight pro-poor trends in use:access ratio.

Implications for programmingAdditional BCC messaging to promote ITN use during drier times of the year would be helpful, but overall Togo has a relatively strong culture of net use.


· Web viewTwo 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 …

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