Individual, maternal and household risk factors for anaemia among young children in sub-Saharan Africa: A cross-sectional study

Objective Anaemia affects the majority of children in sub-Saharan Africa (SSA). Previous studies of risk factors for anaemia have been limited by sample size, geography and the association of many risk factors with poverty. In order to measure the relative impact of individual, maternal and household risk factors for anaemia in young children, we analysed data from all SSA countries that performed haemoglobin (Hb) testing in the Demographic and Health Surveys. Design and setting This cross-sectional study pooled household-level data from the most recent Demographic and Health Surveys conducted in 27 SSA between 2008 and 2014. Participants 96 804 children age 6-59 months. Results The prevalence of childhood anaemia (defined as Hb <11 g/dL) across the region was 59.9%, ranging from 23.7% in Rwanda to 87.9% in Burkina Faso. In multivariable regression models, older age, female sex, greater wealth, fewer household members, greater height-for-age, older maternal age, higher maternal body mass index, current maternal pregnancy and higher maternal Hb, and absence of recent fever were associated with higher Hb in tested children. Demographic, socioeconomic factors, family structure, water/sanitation, growth, maternal health and recent illnesses were significantly associated with the presence of childhood anaemia. These risk factor groups explain a significant fraction of anaemia (ranging from 1.0% to 16.7%) at the population level. Conclusions The findings from our analysis of risk factors for anaemia in SSA underscore the importance of family and socioeconomic context in childhood anaemia. These data highlight the need for integrated programmes that address the multifactorial nature of childhood anaemia. The DHS provides a unique perspective on child health in low-and-middle-income countries. These are nationally representative, probability-weighted, community-based household surveys, funded by the U.S. Agency for International Development with support from donors and host countries. The DHS programme enables countries to measure a wide variety of demographic and health indicators, including fertility, child mortality, nutrition, growth and access to healthcare. Since 1984, household surveys have been supported by DHS in >90 countries.26 27 Participating households are selected using a stratified two-stage cluster design. First, enumeration areas are selected using stratified random sampling from national census regions (strata); within these areas, households are randomly selected for survey administration. The household questionnaire is administered to women and men of reproductive age (typically age 15–49 years); the women’s questionnaire includes questions about child health. We included data from children age 6–59 months in the 27 SSA countries participating in the DHS that performed anaemia testing (see figures 1 and 2). We analysed the Children’s Recode using data from the most recent surveys available (2008–2014). In most cases, we used data from DHS-VI; for Ghana we used data from DHS-VII; for Sao Tome and Principe and Swaziland we used data from DHS-V. Madagascar was excluded from the analysis because of missing data on children’s weight. Responses were recoded to harmonise questionnaires that varied between countries and survey phases. Map of 27 sub-Saharan African countries included in analysis. Selection of study population. Note that some children were excluded for multiple reasons. A questionnaire was administered to an eligible adult respondent, and anthropometry and Hb testing were conducted on children age 6–59 months and their mothers during the study visit. In all countries but Tanzania and Zimbabwe, where universal testing was performed, only a subset of households were selected for anaemia testing. Capillary Hb testing was performed with the HemoCue Photometer, which is commonly used in screening for anaemia in low-resource settings.28 Children found to have severe anaemia were referred to local health facilities for treatment.29 Anaemia severity was classified according to the WHO’s ‘Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity’ as mild, moderate or severe based on blood Hb,30 and the relevant thresholds for anaemia severity were used for children, pregnant and non-pregnant women. We performed bivariate analyses and multivariable logistic and linear regression using survey procedures in Stata V.14. The svy procedures are a set of commands that account for sampling weights, clustering and stratification in complex survey data. For the purposes of this analysis, the levels of clustering that were considered in the variance estimates include country-based primary sampling unit, the household and the mother. The original individual sample weight from each dataset was used for each respondent. We selected variables from the DHS questionnaire based on potential association with risk of anaemia. We grouped risk factors as follows: demographic (child’s age, sex), environmental (urban vs rural location, altitude, floor type in home, biomass fuel used for cooking), socioeconomic (wealth index (a standardised variable constructed by the DHS using permanent income indicators),31 maternal years of education, maternal literacy), family structure (number of household members, number of children, birth order, multiple births), water/sanitation (use of shared toilet facilities, unimproved toilets, unimproved water source, water source located off premises, unsafe stool disposal), nutrition and growth (height-for-age Z score (HAZ), weight-for-age Z score (WAZ), weight-for-height Z score (WHZ), ever breast fed, meat consumption in the last 24 hours, consumption of high-iron foods in the last 24 hours), maternal health (maternal age, height, weight, body mass index (BMI), Hb, current pregnancy, iron supplementation and deworming during pregnancy), recent illnesses (diarrhoea or fever in the past two weeks) and prophylactic measures (iron supplementation in the last week, deworming in the last six months, bednet usage last night). Following WHO guidelines,32 unimproved toilet facilities were defined as pit latrines without slabs or platforms, open pit, hanging latrines, bucket latrines or open defecation. Improved toilet facilities were defined as a flush toilet, ventilated improved pit latrine, pit latrine with a slab, composting toilet or Ecosan. Unsafe stool disposal was defined as a child’s stool put or rinsed into drain or ditch, thrown into garbage, rinsed away or left in the open/not disposed of. Safe stool disposal was defined as a child’s use of toilet or latrine, faecal matter put or rinsed into a toilet or latrine, faecal matter buried, use of disposable diapers or use of washable diapers. An improved water source was defined as the main source of drinking water of piped connection to water supply, private and public tap, borehole, protected/dug well, protected spring, rainwater or bottled water. All other sources were considered unimproved. Unimproved floor was defined as natural, earth, sand, dung or rudimentary floor in the home. Cooking fuels were classified as biomass/high-polluting (kerosene, coal, lignite, charcoal, wood, straw, shrub, grass, agricultural crop, animal dung, gasoline or other) or non-biomass/low-polluting (electricity, liquefied petroleum gas, natural gas or biogas). Having a high-iron diet was defined as reporting one or more iron-rich foods in the past 24 hours, which includes infant formula, grains, meat or meat organs, leafy greens or other foods such as beans, peas, lentils and nuts. For maternal iron supplementation and maternal deworming during pregnancy, in children >12 months these variables were coded as ‘not applicable’. While the DHS reports altitude-adjusted Hb values in its publicly available data, in order to allow estimation of the effect of altitude on Hb and because altitude was missing for 26.4% of the sample, our analyses used unadjusted Hb rather than altitude-adjusted Hb values. A pairwise correlation was performed to determine the relationship between highly correlated variables. For this test, anything >0.6 was considered to be highly correlated. When choosing among highly correlated variables (eg, HAZ/WAZ/WHZ, maternal height/weight/BMI, number of household members/number of children, maternal iron supplementation/deworming during pregnancy), we selected the single variable that when added to the multivariable model improved the predictive value of the model most (greatest contribution to overall R2). For the bivariate analysis, we determined significance using ordered logistic regression to reflect natural ordering in multilevel categorical variables. All multivariable models included country as a fixed effect. Because several predictor variables were missing in a substantial number of respondents (online Supplementary table A1), we constructed three multivariable linear regression models: (1) model 1, which included only variables present in >90% of respondents; (2) model 2, which included variables present in >80% of respondents; and (3) model 3, which included all potentially relevant variables. For the anthropometric variables, which were missing in 4.7% of respondents, we performed a sensitivity analysis in which we assigned extreme values to all missing cases (HAZ =+2 or HAZ = −2). bmjopen-2017-019654supp001.pdf With the risk factors used in model 1, we constructed a multivariable logistic regression model to measure the association between the risk factors of interest and anaemia (as a dichotomous variable). To facilitate ease of interpretation, we converted continuous variables to categorical and standardised the reference group to ensure ORs were >1. We used the OR estimates to calculate population-attributable fraction (PAF), the proportion of anaemia in children age 6–59 months that can be attributed to the risk factor in question. This was calculated using the punaf command in Stata,33 which measures the proportion of respondents who would no longer be anaemic if the risk factor in question were removed (or at its lowest risk category) and all other risk factors held constant. Respondents provided informed consent prior to participation and provided separate consent for blood testing.