Comparing insecticide-treated nets access-use based on universal household and population indicators vis-a-vis measures adapted to sleeping spaces in Ethiopia

Abstract

Background Insecticide-treated nets (ITNs) access-use has been pivotal monitoring indicator for malaria prevention and control, particularly in resource limited settings. The objective of the study was to compare ITN access-use based on universal household and population indicators and measures adapted to sleeping spaces. Methods A cross-sectional study was conducted in five districts of Jimma Zone, Ethiopia, March, 2019. 762 HHs were sampled for the survey. Multi-stage followed by simple random sampling used. Monitoring and evaluation reference group’s (MERG’s) indicators were used for measuring ITN access-use. MERG’s indicators are each adapted ITN access-use to sleeping spaces. Household (ownership, saturation and sufficiency) and population access and household members’ status of last night sleeping under ITN compared based on the two models. Differences of estimates of ITN access-use based on the two methods reported as magnitude of over/under estimations, at p-value < 0.05. Results Based on MERG’s approach, the study revealed household (HH) based indicators as such: HH ownership of at least 1 ITN (92.6%), sufficiency of ITN for every two people in HH (50.3%), and saturation of ITN for every 2 people in HHs with any ITN (54.6%). Moreover, population based indicators were: population with ITN access (P3 = 78.6%), people who slept under ITN previous night (63.0%), people who slept under ITN among who accessed it (73.1%), ITN use-gap (26.9%). Equivalent indicators of HH ownership, sufficiency, saturation, and people accessed at where they actually slept, and people slept under ITN among those accessed at where they slept estimated at 71.3%, 49.4%, 69.3%, 66.3%, and 92.1%, respectively. MERG’s approach over-estimated ownership, people’s access, and behaviour-failures by 21.3%, 12.3%, 19.0%, respectively. Over-estimation occurred for reasons such as many sleeping spaces lack ITN and > 2 people actually slept per sleeping space. Conclusions MERG’s universal indicators over estimated households and populations ITN access-use as a result of absence of measures capturing access-use values at spaces where people actually slept. Consequently, measures adapted to sleeping contexts revealed potential misdistributions practiced when the existing indicators are in use. Insertion of sleeping spaces into existing approach will be worthwhile and needs to be promoted as it improves curiosity in ITN distribution, produces closer estimates and prevents malaria prevention and control programmes from overlooking access-use challenges.

Gaps in long lasting insecticide-treated net coverage 2 years after a mass distribution campaign in central Côte d’Ivoire

“…Household and population LLIN access based on 1 net per 2 persons was 37.6% and 59.3% respectively. Household and population access in other countries 1 to 4 years after a mass distribution campaign were within range of those found in our study; Nigeria (25% and 43% respectively) [45], Benin (55.9% and 79.5% respectively) [48], Ethiopia (50.3% and 78.6% respectively) [22], Cameroon (41% and 59% respectively) [49] and Tanzania (41% household LLIN access) [53]. It is worth noting that during the 2021 DHS survey in Côte d'Ivoire, household LLIN access was 51% [3].…”

“…This difference was not unexpected as the DHS survey was done 6 months following the mass LLIN distribution campaign, while the present study was done 24 months after. Other studies done between 1 to 4 years post mass distribution campaigns had various household LLIN ownership; Tanzania (74.5%) [44], Nigeria (56%) [45], Burkina Faso (33%) [46] and Guinea (44%) [47], Benin (95.8%) [48], Ethiopia (92.6%) [22] and Cameroon (73%) [49]. These differences could be explained by differences in proximity of coverage surveys to mass distributions, the frequencies of and strategies used for mass net distribution across countries (central point versus door-to-door distribution), the use of school-based top-up campaigns (not implemented in Cote d'Ivoire), as well as differences in LLIN durability.…”

“…The operational de nitions used to determine access based on household indicators were from RBM MERG 2018 [17] whereas, the indicators adapted to sleeping units were adapted from an Ethiopian study, for easy comparability [22] and are summarized in Table 1. 2.…”

“…Some studies recommend the consideration of sleeping units, as an addition to the aforementioned RBM MERG indicators, to improve the accuracy of access estimates [18][19][20]. A study conducted in Tanzania found that RBM MERG indicators under-estimated LLIN access especially in lower socio-economic households which tend to be overcrowded [21], whereas another study from Ethiopia found that the RBM MERG indicators over-estimated LLIN coverage because it did not take into account units where people actually slept [22]. The Ethiopian study suggested the following access indicators for sleeping units: 1) household LLIN access de ned as the proportion of households with at least one LLIN for every sleeping unit within the household; 2) intra-sleeping space ownership gap de ned as the proportion of households with insu cient LLINs for every sleeping unit amongst those reporting at least 1 LLIN and 3) population access de ned as the proportion of population with access to LLIN within their households based on one LLIN per sleeping unit.…”

Factors associated with the ownership and use of insecticide-treated nets in Guinea: an analysis of the 2018 Demographic and Health Survey

“…Larger households may be more likely to have a member present at the time of distribution, which could explain larger households being more likely to own any net. Large households have a higher probability of having a child under 5 years of age or a pregnant woman in the household, which increases the likelihood of obtaining ITNs during antenatal care visits or at birth, but this mechanism appears insufficient to compensate for the lower per capita nets given to large households during campaigns [ 21 ]. The results suggest that the distribution allowance may contribute substantially to inadequate access if the implementation does not take household size sufficiently into account.…”