Data Source
Nationally representative data from the Demographic and Health Surveys (DHS) Program for SSA countries from 2010 to 2018 were acquired for analysis in this study. The DHS program provides a large secondary data gathered from surveys using probability sampling methods, following standard protocols that are internationally accepted. Different sets of questionnaires designed and pre-tested to ensure reliability and amenability for comparison of data gathered on various spatial and temporal scales are used in the survey. Some questionnaires the program uses include the “Children’s questionnaire” “Mother’s questionnaire”, “Men’s questionnaire” and “Household questionnaire”. These questionnaires cover a broad range of variables cutting across demographics and anthropometrics, water and sanitation, health, wealth, nutrition among others. The program recruits and trains field officers to collect accurate data and measurement of weight, height, anaemia using recommended guidelines and instruments. Data on other important variables such as household cooking fuel, urbanicity, wealth, water and sanitation are taken at the household level.
Study Countries
A sample of 95,056 was drawn from 29 countries in SSA (show in Fig. 1). For a country to be selected, it must meet the following criteria; should be found in SSA based on the United Nations regional groupings; it must have a DHS dataset with standardized questions and observations on anaemia level of children under five years as well as household cooking fuel type, urbanicity, source of drinking water and type of toilet facility. Where multiple datasets exist for a country, the most recent dataset is used. Detailed information on countries, together with years of survey are shown in Table 1.
Table 1
Selected countries and available dataset
Country | Available dataset | Country | Available dataset |
Angola | 2016 | Malawi | 2016 |
Benin | 2018 | Mali | 2018 |
Burkina Faso | 2010 | Mozambique | 2015 |
Burundi | 2017 | Namibia | 2013 |
Cameroon | 2019 | Niger | 2012 |
Congo | 2012 | Nigeria | 2018 |
Cote D'Ivoire | 2012 | Rwanda | 2015 |
DR Congo | 2014 | Sierra Leone | 2013 |
Ethiopia | 2016 | Senegal | 2017 |
Gabon | 2012 | Tanzania | 2016 |
Gambia | 2013 | Togo | 2014 |
Ghana | 2014 | Uganda | 2016 |
Guinea | 2018 | Zambia | 2019 |
Lesotho | 2014 | Zimbabwe | 2015 |
South Africa | 2016 | | |
Definition Of Important Variables
The dataset provided information on household cooking fuel type, source of drinking water and toilet facility type at the household level. The observations for household cooking fuel were classified into “Clean” and “Unclean” (polluting fuels) following the criterion some studies (Naz, Page and Agho, 2017; Sreeramareddy, Shidhaye & Sathiakumar, 2011) used (see Table 2). The weight of child at birth named as “Birth weight” was categorized as “Underweight” (< 2.5 kg) and “Normal” (≥ 2.5 kg) (see, Yaya et al., 2019). Also, the observations for household source of drinking water and type of toilet facility were classified into “improved” and “unimproved” using the revised definitions by the WHO/UNICEF Joint Monitoring Programme (JMP) report (World Health Organization & UNICEF, 2017). Table 2 summarises the descriptions of improved and unimproved sources of water and toilet facilities. Armah et al. (2018) further explicates the categorisation of these critical basic services.
Table 2
Classification of source of drinking water and toilet facilities under WHO/UNICEF Joint Monitoring Programme and cooking fuel.
Service | Improved/Clean | Unimproved/Unclean |
Drinking water sources | Piped water, boreholes or tube wells, protected dug wells, protected springs, rainwater, and packaged or delivered water. | Unprotected dug well, unprotected spring, river, dam, lake, pond, stream, canal and irrigation canal |
Type of toilet facilities | Flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, composting toilets or pit latrines with slabs. | Pit latrines without a slab or platform, hanging latrines or bucket latrines and open defecation. |
Cooking fuel type | Electricity, liquid petroleum gas (LPG), natural gas and biogas | Kerosene, coal/lignite, charcoal, wood, straw/shrubs/grass and animal dung |
Measures
Outcome variable
Anaemia status of children is the outcome variable considered in this study. According to DHS, the anaemia status of living children within the age bracket 0–4 years before the survey night was taken. It has its responses classified into four (4) categories according to the WHO recommendation as: (i) “Not anaemic” for children with hemoglobin count(g/dl) measuring above 11 g/dl; (ii) “Mild anaemia” for hemoglobin count of 10-10.9 g/dl; (iii) “Moderate anaemia” for hemoglobin count between 7.0-9.9 g/dl; and (iv) “Severe anaemia” for hemoglobin count less than 7.0 g/dl. Children with no observations for anaemia count (not tested), and those whose mothers were not listed in the household questionnaire were excluded. Observations under mild, moderate and severe were combined and recoded as “Anaemic (Yes)” and observations under not anaemic was recoded as “Not anaemic (No)”. The Anaemia status of children was represented as a dichotomous variable with “0” representing “No” and 1 representing “Yes”
Main Predictor Variable
The predictor chosen for this study is a composite variable formed from the interactive effect of household cooking fuel type and urbanicity. The selection of the predictor variable was based on parsimony, literature review, theoretical relevance as well as practical significance. Household cooking fuel type and Urbanicity both had two categories, since the former was classified into “Clean” and “Unclean” and the latter measured as “Rural” and “Urban” per the Demographic Health Survey (DHS). This therefore gave a four mutually exclusive groups: Unclean urban (households relying on “unclean” cooking fuel and found in urban areas); Unclean rural (households relying on “unclean” cooking fuel and found in rural areas); Clean urban (households using “clean” cooking fuels and found in urban areas); and clean rural (households using “clean” cooking fuels and found in rural areas).
Compositional And Contextual Variables
Variables which relate to an individual’s socio-demographic characteristics (biosocial and socio-cultural factors) together constitute compositional factors (Collins et al., 2017; Pol & Thomas, 2001). Biosocial factors refer to underlying biological and physical attributes present in an individual at birth and are not amenable to change. Socio-cultural factors on the other hand refers to customs, beliefs, lifestyles and values (Armah et al., 2018).
Biosocial variables considered in this study included: sex of child (male, female); current age of child in years with categories 0, 1, 2, 3, and 4; age of mother (15–19, 20–24, 25–29, 30–34, 35–39,40–44, 45–49); sex of household head (male, female); age of household head (“young adult” for those below 35years, middle-age adult for 35–55 years, and “old-age adult” for those above 55 years).
The study considered socio-cultural factors including: educational attainment of mother (no education, primary, secondary, tertiary); birth order number (1, 2, 3, 4, 5 and above); household size (small: 1–5, medium: 6–10, large: above 10). Also, the DHS collects data on wealth index of all interviewed households and place them into five wealth quintiles (poorer, poor, middle, rich, richer). Observations of wealth index under poorer and poor were combined and recoded as “poor”. Similarly, observations under “rich” and “richer” were combined and recoded as “rich”. Finally, household source of drinking water and type toilet facility which were both categorized into “improved” and “unimproved”.
According to studies (Collins et al., 2017; Ross & Mirowsky, 2008), contextual factors refers to those factors related to respondent’s neighbourhood attributes or opportunities and services that are space-bound (Armah et al., 2018; Collins et al., 2017; Ross & Mirowsky, 2008). Contextual factors considered in this study include country and geographic region (Western Africa, Eastern Africa, Southern Africa, and Central Africa).
Data Analysis
The Stata 14 MP software was used for the analysis of data. To understand the distribution of childhood anaemia and influence of predictive factors on anaemia, descriptive analysis was performed. We then determined the associations between anaemia status of children under five and the relevant predictors using inferential statistics. These relationships were further examined using multivariate techniques while controlling for theoretically relevant compositional and contextual factors. Statistical significance of 0.05 and 95% confidence interval (CI) were used in analysis and results presented as contingency tables.
Univariate Analysis
Pearson chi-square test of independence and Cramer’s V statistic were applied in the univariate analysis of predictors of child under five anaemia. The strength of associations between anaemia status and the predictors was tested using Cramer’s V statistics.
Multivariate Regression
The outcome variable (anaemia status of children under age 5) had 57% of responses in the non-affirmative and 43% were affirmative. The relationship between anaemia status and the interactive effect of household cooking fuel and urbanicity was analysed using negative log-log regression model. A negative log-log regression model is apt when the responses to a dichotomous response variable is asymmetric in the [0, 1] interval for which the non-affirmative is more than 55% as in the case of the response variable in this study (Aitkin et al., 2005; Armah et al., 2019; Fahrmeir & Tutz, 2013). The likelihood of a child been anaemic was estimated and reported as exponential coefficients - odds ratios (OR). An OR of 1 means that the predictor does not affect the odds of a child been anaemic; OR > 1 means that the predictor is associated with higher odds of been anaemic; and OR < 1 means that the predictor is associated with lower odds of been anaemic. Clustering of observations in units of households was controlled by imposing on the models a “cluster” variable, thus, the identification numbers of the respondents at the cluster level. This adjusted the SE leading to statistically robust estimation of parameters.
At the multivariate level, four (4) models were run: the joint effect of household cooking fuel type and urbanicity (Model 1); birth weight and biosocial factors (Model 2), sociocultural (Model 3), and the contextual (Model 4). Literature and parsimony informed the chosen groups of references for the predictor variables in the models. Respondents from urban settings who rely on unclean cooking fuel types “Unclean urban” was chosen for the key predictor. The selected reference group for sex of child and sex of household head was “male”. Studies show that males in households are often less worried about children under five years as well as water and sanitation issues (Armah et al., 2018; Mulenga et al., 2017). The selected reference group for current age of child was “0”. Young adults and no education were respective selected as reference groups for age of household head and educational attainment of mother. Unimproved was selected as the reference group for source of drinking water and type of toilet facility. The young adult group was selected as the reference group because this is a demographic group in transition and may be unable to provide better services for the family while those with no education has direct effect on ability to afford and capacity to spearhead decision-making of households with respect to better services and conditions. Small household size was selected as the reference group for household size.
Ethical Considerations
The DHS Program recognizes and adheres to established international and local ethical standards and protocols in its surveys. The ICF International’s Institutional Review Board (IRB) through The DHS Program’s reviewed and approved all survey procedures and instruments used before implementation. The board aside providing technical assistance to the program ensures that the survey complies with the United States Department of Health and Human Services regulations for the protection of human subjects CFR 46 as well as the laws of the individual countries.