Wealth, mother's education and physical access as determinants of retail sector net use in rural Kenya
© Noor et al; licensee BioMed Central Ltd. 2006
Received: 24 October 2005
Accepted: 26 January 2006
Published: 26 January 2006
Insecticide-treated bed nets (ITN) provide real hope for the reduction of the malaria burden across Africa. Understanding factors that determine access to ITN is crucial to debates surrounding the optimal delivery systems. The influence of homestead wealth on use of nets purchased from the retail sector is well documented, however, the competing influence of mother's education and physical access to net providers is less well understood.
Between December 2004 and January 2005, a random sample of 72 rural communities was selected across four Kenyan districts. Demographic, assets, education and net use data were collected at homestead, mother and child (aged < 5 years) levels. An assets-based wealth index was developed using principal components analysis, travel time to net sources was modelled using geographic information systems, and factors influencing the use of retail sector nets explored using a multivariable logistic regression model.
Homestead heads and guardians of 3,755 children < 5 years of age were interviewed. Approximately 15% (562) of children slept under a net the night before the interview; 58% (327) of the nets used were purchased from the retail sector. Homestead wealth (adjusted OR = 10.17, 95% CI = 5.45–18.98), travel time to nearest market centres (adjusted OR = 0.51, 95% CI = 0.37–0.72) and mother's education (adjusted OR = 2.92, 95% CI = 1.93–4.41) were significantly associated with use of retail sector nets by children aged less than 5 years.
Approaches to promoting access to nets through the retail sector disadvantage poor and remote communities where mothers are less well educated.
Insecticide treated bed nets (ITN) have been shown to provide significant protection against early childhood mortality under a range of malaria settings in Africa , reduce the incidence of clinical malaria and anaemia in young children [1, 2] and are regarded as a cost-effective public health intervention for low income countries . African Heads of State, as part of the Roll Back Malaria (RBM) initiative, agreed that they would ensure that 60% of at-risk populations in their countries would have access to ITN by 2005 . Since the Abuja summit, donor support to country national malaria control programmes or their non-governmental organisations (NGO) partners has substantially increased to support the adoption of ITN policies and programme implementation . Despite the overwhelming scientific evidence in support of the public health impact likely to result from widespread use of ITN, the political commitment and the increasing financial resources to support delivery, coverage among vulnerable young children remained poor in 2003 .
Factors that determine a community's use of nets should provide insight into why net coverage remains low even in countries where significant financial investments have been committed to expand coverage. Studies have shown that in most communities, net use is lowest among the poorest [6, 7]. The role of education and distance to net providers has been less well documented. In this paper, the combined effects of proximate determinants of wealth, mother's education and physical access to markets on the use of nets purchased from the retail sector among rural children under five years of age in four districts in Kenya are examined.
The Kenya context
The Kenyan Ministry of Health (MoH) developed an ITN strategy paper in 2000, which spelt out the government's intentions for scaling up the use of ITN nationwide . This was incorporated into the Kenyan National Malaria Strategy (NMS) as one of the four principal pillars of malaria prevention and control . In 2001, Population Services International (PSI) launched a nationwide ITN social marketing programme with funding from the British Department for International Development (DFID). The aim of the project was to establish a "net culture" in the country through extensive marketing and education. Information campaigns were undertaken to increase basic knowledge on the causes of malaria and means of prevention, emphasizing the vulnerability of pregnant women and children under five years of age . A rural kiosk-based distribution system was established to increase subsidised net availability run in parallel with joint programs to sell nets through other NGOs.
The study was carried out in four districts purposively sampled by the MoH to provide detailed longitudinal information on coverage of interventions proposed as part of the NMS . The study districts have been described elsewhere in detail [12, 13] and represent the broad categories of malaria transmission across Kenya . They include Kwale district on the coast with seasonal, high intensity malaria transmission; Bondo on the shores of Lake Victoria with high intensity perennial transmission; Kisii Central and Gucha districts (Greater Kisii) representing the low seasonal transmission conditions of the Western highlands; and Makueni district, a semi-arid area with acutely seasonal, low malaria transmission.
A geographic information system (GIS) was developed for each district reflecting high spatial resolution enumeration area (EA) population data, roads and footpaths, rivers, forests, gazetted areas and a 100 m Digital Elevation Model (DEM). In addition all market centres were mapped using Global Positioning Systems (GPS) [13, 15]. An EA was defined by the Central Bureau of Statistics (CBS) during the 1999 national census as an area of about 500 people or 100 homesteads . The GIS data were all stored in ArcView GIS 3.2 (ESRI Inc., New York, USA) raster format.
Sampling and homestead surveys
Summary of variables used in the computation homestead socio-economic status asset index from the first principal component
Homestead wealth asset index
Ownership of livestock
1. Number of cows
2. Number of shoats
3. Number of donkeys
4. Number of chickens
5. Number of ducks
Homestead head (HH) education level
6. No education
7. Primary incomplete
8. Primary complete
9. Secondary incomplete
10. Secondary complete
11. More than secondary
HH main source of income
13. Works for pay
14. Receives income from spouse/other members
15. Unpaid on family business/farm
17. Economically inactive
18. Persons per room
19. Owns land
20. Pays rent
21. No rent with owners consent
23. Owns bicycle
24. Owns motorcycle
25. Owns car or truck
26. Owns radio
27. Owns TV
28. Owns video
29. Owns refrigerator
30. Uses electricity
31. Uses solar power
32. Uses flush toilets
33. Uses pit latrines
34. Owns a phone
35. House has stone walls
36. House has clay walls
37. House has other type wall
38. House has stone floor
39. House has earth floor
40. House has other type of floor
41. House has tiled roof
42. House has iron sheets roof
43. House has grass roof
44. Uses electricity, gas or kerosene
45. Uses charcoal
46. Uses wood
Data entry and storage was undertaken using MS Access (Microsoft, Redmond, USA), through customised data entry screens with in-built range and consistency checks. Descriptive summaries of all information were generated using STATA version 8.2 (Statacorp 2003, College Station, USA) and MS Excel 2002 (Microsoft, Redmond, USA). All information specific to the EA, homestead, mother or guardian were linked to the relevant child through the use of a primary homestead identifier that was consistent across all data sets.
Creating a homestead assets-based wealth index
Principal component analysis (PCA) was performed, using STATA, to construct a homestead wealth assets index from information on the broad range of indicators collected during the survey. PCA is a data reduction technique that provides a method of identifying, from a large set of variables, those that contain most of the information common to all. The first principal component often represents the linear index of these important variables with most information [6, 18]. Quintile distributions were derived for each district separately based on this assets index. Quintile descriptions based on district-specific homestead wealth assets index were used to allow for aggregated wealth assets to be defined at the district level rather than across districts. The district effects were then adjusted for during the regression analysis.
Travel time to market centres
GPS coordinates for homesteads and market centres were used to develop physical accessibility models. Most studies of physical access to health interventions use straight-line (Euclidean) distances between population and service locations [19, 20]. Previous work has shown that this approach does not accurately reflect actual distances travelled and overestimates the extent of physical access to interventions especially in rural settings . A travel time model was developed from the high-resolution spatial raster data on market centres, transport network, rivers, permanent water bodies, topography and land cover for four the study districts. Using a GIS algorithm based on the specifications of the Naismith-Langmuir rule for pedestrian movement [21, 22] the model simulated the ease or difficulty of physical access to markets centres based on the presence of a road or footpath, changes in slope along the path and restriction caused by the presence of barriers such as rivers, forests or gazetted areas. This rule states that a walker can maintain a speed of 5 km/h on flat road; 2.5 km/h on level ground off-road; 1 hour added for every 600 m of ascent; 10 minutes subtracted for each 300 m moderate descents (-5° to -12°); and 10 minutes added for each 300 m steep descent (steeper than -12°). The algorithm used an iterative region-growing approach in which each pixel containing a market centre was taken as a 'seed' pixel around which regions of travel time pixels were grown. Only the fastest route to a given pixel was used to calculate travel time to the market centre. Only pedestrian motion was modelled as majority of people in rural homesteads of the study districts walked to market centres. Travel time to the nearest market centre was assigned to each homestead.
Statistical analyses of predictors of use of nets purchased from the retail sector
The study set out to look at predictors of use of nets by children <5 years of age obtained from the retail sector as these represent the principal sources of nets during the periods leading up to the time of the survey. Therefore, children who used nets purchased from the retail sector were compared to those who did not use nets. Children who used nets from sources other than the retail sector were excluded from the regression analysis. First, a univariate regression analysis was performed to identify which of the predictor variables were significant to the outcome measure, i.e. use of nets purchased from retail sector by children. These predictors defined homestead, mother and child characteristics. In the univariate analysis the odds ratio (OR), p-value and 95% confidence interval (CI) for each factor's association with nets purchased from the retail sector were computed. Any factor with a p-value < 0.15 was considered to be a potentially important covariate of retail-net use. The factors examined were: homestead wealth assets index; gender of homestead head; homestead demographics; travel time to the nearest market centre; homestead use of insecticide residual spraying (IRS); whether any homestead member attended public malaria awareness meeting (baraza) or owned printed education materials on malaria; whether mother could read; mother's education level (no education, primary incomplete, primary complete and secondary and above); mother's main source of income; mother's marital status; whether mother was pregnant; age of the child; sex of the child; and child's ownership of a health card.
All variables meeting the entrance criteria were used to estimate a multivariable logistic regression model to identify their combined effect on the use of nets purchased from the retail sector among children. The model was fitted using the STATA xtgee command with an exchangeable working correlation matrix. This procedure uses generalized estimating equations (GEE) to account for the potential correlation of use of nets purchased from the retail sector among children seen in the same EA while accounting for the variability between clusters. Results for all districts were combined and clustering was defined at the primary sampling unit, the EA. All results were weighted for unequal probability of selection of EA within each district (weight = 1/probability of selecting an EA). Parameter estimates: OR; 95% CI; and P-values; were recorded for each predictor. All predictors were adjusted for the effect of the variation between districts in the multivariable analysis.
Nets use and net sources among rural children in four districts in Kenya, 2004
Number of children seen (a)
Children who slept under a net (%)
Source of nets
Retail outlets (%)
Other (%) (b)*
NGO/MOH Community pharmacy
MoH ANC health facility
Mission health facility
Net given as a gift
Number of children included in the analysis (%) (a-b)
Total nets treated (%) †
Total retail sector nets treated (%)‡
Types of retail sector nets
PSI brand (SUPANET®) (%)
Other brands (%) **
Output of the PCA
Variation of retail sector net use with homestead wealth assets index, mother's education and travel time (hours) to nearest market centres among rural children < 5 years of age in four districts in Kenya, 2004
No nets (3193) n(%)
Retail sector nets (327) n(%)
Odds ratio (OR) (95% CI)†
Adjusted OR (95% CI)‡
Homestead wealth assets index quintile
Secondary and above
Travel time to nearest market centre (minutes)
Mean (min, max)
34.8 (1.2, 158.4)
24.0 (1.2, 138.0)
Mother's level of education
Children's use of nets purchased from the retail sector was shown to be closely correlated with mother's education, with only 14.4% of children of uneducated mothers using nets compared with 32.7% of those whose mother's had education up to secondary level and above (χ2 = 37.91, p < 0.0001). The univariate logistic regression of use of nets purchased from the retail sector with mother's education level (with and without adjusting for district effects) showed that children whose mothers were educated to secondary level or above were up to three times more likely to use nets purchased from the retail sector than were those whose mothers were not educated (OR = 2.42, 95% CI = 1.64–3.57; adjusted OR = 2.92, a95% CI = 1.93–4.41) (Table 2).
Travel time to nearest market centres
Modelled pedestrian travel time to the nearest market centre was used as an indicator of access to sources of retail sector nets. The mean travel time to market centres for users of nets purchased from the retail sector was 24 minutes compared to 35 minutes for those who were without these nets (Table 2). Most of the children, (54.7% of those who did not use nets purchased from the retail sector and 67.3% of those who did), were within 24 minutes of the nearest market centre. There was a significant difference in use of nets purchased from the retail sector with travel time between children within 24 minutes of the nearest market centre and those 48 minutes or more (χ2 = 37.85, p < 0.0001). The univariate logistic regression showed that each log unit increase in travel time resulted in a 75–82% decreased probability of using a net purchased from the retail sector (OR = 0.25, 95% CI = 0.13–0.44; adjusted OR = 0.18, 95% CI = 0.09–0.38) (Table 2).
Predictors of net use
Univariate statistical associations of factors with use of retail sector nets among rural children < 5 years of age in four districts of Kenya, 2004. These factors were excluded from the multivariable analysis
Number (%) in each category
No nets ( n = 3193)
Retail sector nets ( n = 327)
Homestead level predictors
Homestead used insecticide residual spraying*
Population 15–44 yrs of age
Mean (min, max
6.0 (0, 43)
4.9 (0, 43)
Mother level predictors
Mean (min, max)
29.1 (13, 103)
28.8 (16, 95)
Mother can read†
Mother's main source of income*
Unpaid working on family business/farm
Works for pay/receives money from spouse or homestead members
Child level predictors
Homestead, mother and child factors* associated with use of retail sector nets among rural children < 5 years of age in four districts in Kenya, 2004: multivariable analysis results
Number (%) in each category
Retail sector nets
Homestead level predictors
Homestead wealth assets index quintile
Travel time to nearest market centre (hours)
Mean (min, max)
24.0 (1.2, 138.0)
Homestead head sex
Population <5 yrs of age (mean (min, max))
3.2 (0, 16)
2.6 (1, 16)
Population 5–14 yrs of age (mean (min, max))
3.8 (0, 25)
2.8 (0, 21)
Population >44 yrs of age (mean (min, max))
1.6 (0, 8)
1.2 (0, 7)
Homestead member attended baraza /owns printed materials
Mother level predictors
Mother is pregnant
Mother's marital status
Child level predictors
Ownership of immunisation card
The predominant national delivery model for nets between 2001 and 2004 in Kenya was the adoption of a social marketing through the retail sector. PSI was awarded over 22 million UK pounds for a five year programme beginning in 2001 aiming to achieve at least 60% ITN coverage within 5 years [10, 23]. Our data suggest that this model of delivery had fallen a long way short of its proposed programme target and those set by the international RBM community in rural areas of four districts in Kenya. Only 15% of children in 72 randomly selected communities were protected by a net, of which 48% were treated with an insecticide during the previous six months. Comparisons with 2001 data from these same communities suggests that ITN coverage among children increased from 3% in 2000/1 (unpublished data) to 7% in 2004/5. In 2004/5 approximately 70% of nets obtained from the retail sector were those marketed by PSI. However, there is a clear unmet need. The present study explored reasons why children might not be protected by a net obtained from a heavily funded programme to promote access to nets in greater depth.
A number of traditional determinants of health service use (wealth and education) and a novel, but important variable (distance to retail sector providers) were used within a combined model to examine the independent contribution of these competing factors. Results show that the use of nets purchased from the retail sector was lowest among children from the poorest homesteads, with retail sector net use increasing monotonically with homestead wealth. Children of mothers with the highest education (secondary level and above) were twice as likely to use nets purchased from the retail sector compared to mothers who had no formal education. In addition, a child who lived in a homestead closer to a market centre was more likely to use nets purchased from the retail sector than those who lived at greater distances, with a unit increase in travel time reducing chances of using nets from the retail sector by almost half. Only 1.5% of children born to mothers with no formal education living in the poorest homesteads over 48 minutes from a market centre used a net socially marketed through the retail sector. This extreme example demonstrates the polarising nature of compounding risks of retail sector net distribution.
Other factors seem to influence the use of nets purchased from the retail sector. The presence of older children in the homestead seemed to potentially reduce the chances of children under the age of five using nets. A higher proportion of children of married mothers or those living with a partner used nets from the retail sector than those of single or widowed mothers. Ownership of an immunisation card also seemed to increase the child's chances of using a net from the retail sector. One could speculate that the demands put on already strained homestead resources by older children, e.g. for education, may reduce the chances of a homestead owning a net. However, more formative research is required to establish whether this is true and whether in the face of a newly established policy for universal free primary education in Kenya this might change. The majority of mother's looking after their children without the help of partners were self-employed (64%) and the time and economic constraints on single parents probably diminishes their capacity to procure nets from the retail sector for their children. The reasons why ownership of immunization card is associated with use of nets purchased from the retail sector are not intuitively obvious but might be a proxy for general health awareness. Interestingly, however, attendance at a malaria awareness meeting during the preceding 12 months or ownership of printed malaria materials was not significantly associated with use of nets purchased from the retail sector.
Despite a large financial investment in social marketing through the retail sector, net coverage in rural, Kenyan communities remains low and unlikely to meet RBM or national targets set for 2005/6. The most disadvantaged are those children both economically and geographically vulnerable. Ensuring equitable access and coverage must remain a priority. Toward the end of 2004 PSI adapted their strategy of net promotion to include heavily subsidised nets delivered through routine antenatal service and immunization clinics. This should improve spatial accessibility and affordability of nets to a larger sector of the rural poor. The vulnerability analysis undertaken as part of the present study should provide a model of analysis in 2006 to determine whether geographical and wealth barriers are overcome through this new approach to net delivery.
This study received financial support from the UK Department for International Development Kenya Program (# KEN/2004/085), The Wellcome Trust, UK (#058922) and the Kenya Medical Research Institute. RWS is supported by the Wellcome Trust as a Senior Research Fellow (#058992). The authors are grateful to Priscilla Gikandi, Lydiah Mwangi and Lucy Muhunyo for checking and cleaning the community survey data. We wish to express our gratitude to Dr Sam Ochola (DOMC, Ministry for Health) for his continued support for the monitoring and evaluation work. We are also grateful to Greg Fegan, Bruce Larson, Jane Chuma and Sassy Molyneuax for their comments on the manuscript. This paper is published with the permission of the director KEMRI.
- Lengeler C: Insecticide treated bednets and curtains for preventing malaria. Cochrane Database Systematic Reviews: Oxford, UK. 2005, Oxford, UKGoogle Scholar
- Korenromp EL, Armstrong-Schellenberg JRM, Williams BG, Nahlen BL, Snow RW: Impact of malaria control on childhood anaemia in Africa - a quantitative review. Trop Med Int Health. 2004, 9: 1050-1065. 10.1111/j.1365-3156.2004.01317.x.View ArticlePubMedGoogle Scholar
- Goodman CA, Coleman PG, Mills AJ: Cost-effectiveness of malaria control in sub-Saharan Africa. Lancet. 1999, 354: 378-385. 10.1016/S0140-6736(99)02141-8.View ArticlePubMedGoogle Scholar
- www.rbm.who.int/docs/abuja_declaration.pdf .
- Armstrong-Schellenberg JA, Victora CG, Mushi A, de Savigny D, Schellenberg D, Mshinda H, Bryce J, Tanzanian IMCI MCE baseline household survey study group: Inequities among the poor: health care for children in rural southern Tanzania. Lancet. 2003, 361: 561-566. 10.1016/S0140-6736(03)12515-9.View ArticleGoogle Scholar
- Howard N, Chandramohan D, Freedman T, Shafi A, Rafi M, Enayatullah S, Rowland M: Socio-economic factors associated with the purchasing of insecticide-treated nets in Afghanistan and their implications for social marketing. Trop Med Int Health. 2003, 8: 1043-1050. 10.1046/j.1365-3156.2003.01163.x.View ArticlePubMedGoogle Scholar
- Ministry of Health: Insecticide Treated Nets Strategy: 2001-2006. 2001, Ministry of Health, Government of KenyaGoogle Scholar
- Ministry of Health: National Malaria Strategy: 2001-2010. 2001, Division of Malaria Control, Ministry of Health, Government of KenyaGoogle Scholar
- Division of Malaria Control: Analysis of community-based, baseline survey of Roll Back Malaria indicators in four sentinel districts. 2002, Report prepared for AFRO/WHO and Ministry of Health, Government of KenyaGoogle Scholar
- Amin A, Marsh V, Noor A, Ochola S, Snow R: The use of formal and informal curative services in the management of paediatric fever in four districts in Kenya. Trop Med Int Health. 2003, 8: 1143-1152. 10.1046/j.1360-2276.2003.01140.x.View ArticlePubMedGoogle Scholar
- Noor AM, Zuvorac D, Hay SI, Ochola SA, Snow RW: Defining equity in physical access to clinical services using geographical information systems as part of malaria planning and monitoring in Kenya. Trop Med Int Health. 2003, 8: 917-926. 10.1046/j.1365-3156.2003.01112.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Omumbo J, Hay S, Snow R, Tatem A, Rogers D: Mapping malaria risk in East Africa at high spatial resolution. Trop Med Int Health. 2005, 10: 554-566. 10.1111/j.1365-3156.2005.01424.x.View ArticleGoogle Scholar
- Amin AA, Hughes DA, Marsh V, Abuya TO, Kokwaro GO, Winstanley PA, Ochola SA, Snow RW: The difference between effectiveness and efficacy of antimalarial drugs in Kenya. Trop Med Int Health. 2004, 9 (9): 967-974. 10.1111/j.1365-3156.2004.01291.x.View ArticlePubMedGoogle Scholar
- Central Bureau of Statistics: 1999 population and housing census: counting our people for development. Volume 1: population distribution by administrative areas and urban centres. 2001, Nairobi , Ministry of Finance & Planning, Government of Kenya, 415-Google Scholar
- Guyatt HL, Noor AM, Ochola SA, Snow RW: Use of intermittent presumptive treatment and insecticide treated bednets by pregnant women in four Kenyan districts. Trop Med Int Health. 2004, 9: 255-261. 10.1046/j.1365-3156.2003.01193.x.View ArticlePubMedGoogle Scholar
- Filmer D, Pritchett LH: Estimating wealth effects without expenditure data-or tears: and application to educational enrolments in states of India. Demography. 2001, 38: 115-132.PubMedGoogle Scholar
- Guagliardo MF, Ronzio CR, Cheung I, Chacko E, Joseph JG: Physician accessibility: an urban case study of pediatric providers. Health Place. 2004, 10: 273-283. 10.1016/j.healthplace.2003.01.001.View ArticlePubMedGoogle Scholar
- Wang F, Luo W: Assessing spatial and aspatial factors for health care access: towards an integrated approach to defining health professional shortage areas. Health & Place. 2004, 11: 131-146. 10.1016/j.healthplace.2004.02.003.View ArticleGoogle Scholar
- Noor A, Amin A, Gething P, Atkinson P, Hay S, Snow R: Modelling distances travelled to government health services in Kenya. Trop Med Int Health. 2006, 11: 1-9. 10.1111/j.1365-3156.2005.01544.x.View ArticleGoogle Scholar
- Langmuir E: Mountain craft and leadership. 1984, Cordee, Leicester , The Scottish Sport Council/MLTBGoogle Scholar
- www.dfid.gov.uk/news/files/pressreleases/bednets-info.asp .
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