Methodology

To augment existing literature with more diverse methodologies, in addition to a review of existing literature, we incorporated quantitative and qualitative analyses to inform our research on reduction of anemia among women of reproductive age (WRA) in Uganda.

Literature review

We performed a systematic search of published peer-reviewed literature to gather information on contextual factors, interventions, policies, strategies, programs, and initiatives that may have influenced anemia among WRA in Uganda during the study period.

The epidemiology and etiology of anemia is multifactorial and involves a complex interplay of distal, intermediate, and proximal causes.^1,2,3 As part of the larger Exemplars project on anemia reduction among WRA, we carried out a systematic review of peer-reviewed and gray literature of the determinants and drivers of WRA anemia reduction in low- and middle-income countries. ⁴ Our search identified several review articles, which we then used to create a conceptual framework to guide our analytical approach and assist in identifying and interpreting determinants of anemia among WRA.

Our initial search of indexed literature database returned 3,869 records, which we then reduced to 2,088 after deduplication. After applying the screening criteria to titles and abstracts, we had 231 records, which were then reduced to 31 after conducting a full-text review.

Additional searches of gray literature and manual searches for relevant reviews identified seven additional articles that met all inclusion criteria. As a result, we included a total number of 38 indexed and gray literature records in the review.

Quantitative analyses

The quantitative methods included in this analysis were (1) descriptive analyses to provide contextual understanding of the anemia decline across geographic, socioeconomic, gender, and age segments; and (2) hierarchical multivariable regression and regression-based decomposition analyses to understand the major predictors of the stunting decline, as well as their relative importance to Uganda’s progress.

Four rounds of Demographic and Health Surveys (DHS) were conducted in Uganda during the study period: 2000,⁵ 2006,⁶ 2011,⁷ and 2016.⁸ These were the primary quantitative data sets used to address the research aims and objectives of this study. After the study ended, Uganda conducted a Demographic and Health Survey in 2022. Relevant data from the 2022 survey have been incorporated into the report where applicable.

The DHS are nationally representative household surveys. During each round, data for a wide range of indicators in the areas of population, health, and nutrition are collected, using a comparable standardized method. Data on anemia testing for WRA were included in each of the four rounds of the Uganda DHS.

Determinants of WRA anemia can be grouped by hierarchical levels, in terms of their causal proximity to the outcome of interest, specifically in distal, intermediate, and proximal causes (see Figure 21). Each of these determinants lies on a causal pathway toward nutritional outcomes, with more proximal causes functioning as mediators of distal determinants.

Descriptive analyses

We created panel data sets from the DHS 2006 and DHS 2016 to assess factors associated with hemoglobin (Hb) increase/anemia prevalence decrease during the study period. Given the changes in Hb levels, which occur throughout pregnancy due to hemodilution and increase in blood volume, we restricted all multivariable analysis to non-pregnant women only.

Our conceptual framework (Figure 21) was used to select possible predictors of Hb and anemia (defined as Hb <12 g/dL in non-pregnant women).⁹ Univariate statistics were estimated using means and standard deviations for continuous variables and frequencies/proportions for categorical variables, as appropriate. All analyses account for survey design and weighting, as appropriate.

Equity analyses were designed to illustrate subnational variations in WRA anemia prevalence across household wealth, maternal education, area of residence, and geographic region. The slope index of inequality and the concentration index were also calculated to measure absolute and relative socioeconomic inequalities, respectively.

Step 1 was a series of bivariate regressions to determine crude associations between indicators in our conceptual framework and Hb/anemia outcome. Step 2 was to use all candidate variables for multivariable model building (i.e., with a P value ≤.20). Bivariate models estimated the absolute crude associations between the covariable and the outcome, and they highlighted the total (unadjusted) effect of the factor on Hb/anemia.

To examine the association between Hb and various indicators, we conducted a series of hierarchical models using distal, intermediate, and proximal variables to generate the final multivariable models. Variables within each level were selected from our general conceptual framework (Figure 21). The final multivariable regression coefficient was adjusted for child age, sex, and region (control variables) and all confounders in preceding levels.

Oaxaca-Blinder decomposition

We included only the survey years of 2006 and 2016 by design, since the Oaxaca-Blinder decomposition uses only two survey time points in a given analysis and thus “ignores” in-between survey rounds and any intermittent fluctuations in the predictors. As has been suggested in previous decomposition analyses, we operationalized Hb as the linear outcome due to its greater statistical efficiency relative to the dichotomous anemia outcome.

We undertook the commonly used Oaxaca-Blinder decomposition methods to assess determinants of change in WRA anemia over time in Uganda.¹¹ These methods based on individual-level data have increased statistical power and have been widely used to assess nutrition determinants over time in low- and middle-income settings.^11,12,13,14

Qualitative analysis

The qualitative element of the study was conducted at national, district, and community levels to investigate a diverse range of stakeholder perspectives. Based on the prevalence of maternal anemia in 2016, a high-performing district (17.7% anemia prevalence in Pallisa District) and a low-performing district (47.2% anemia prevalence in Buyende District) were explored to identify best practices and challenges across Uganda.

At the national level, a total of 19 informants were interviewed for the study, selected on the basis of their experience in areas or institutions that were instrumental in the design and implementation of interventions to reduce maternal anemia in Uganda. The national interviews involved key government ministries, departments, and agencies; development partners (i.e., US Agency for International Development, World Food Programme, United Nations Children’s Fund, World Health Organization, and international NGOs); and industries engaged in food fortification such as Mandela Millers and SMA Millers.

At the district level, interviews were conducted with 28 stakeholders: 15 key informants were interviewed in Pallisa and 13 in Buyende. These key informants represented nutrition service providers who have worked in their community for over five years, including health workers, village health team members, cultural leaders, community development officers, and subcounty women representatives.

At the community level, 23 focus group discussion were conducted (12 in Pallisa, 11 in Buyende), each with about seven women. Participants were WRA (15–49 years) who had the will and consented to articulate their views. Village health team volunteers helped recruit women from different geographical locations within the study districts.

Program, policy, and financing review

To understand the implementation of nutrition-sensitive and nutrition-specific policies, programs, and strategies, we conducted additional research and corroborated our findings with country experts. A similar multipronged data collection and corroboration exercise was undertaken to track financial data linked to the nutrition policy and program timeline.

Limitations

The main strength of our study is the mixed methods approach, which contextualizes the quantitative findings and situates them within a framework that clearly demonstrates how Uganda was able to achieve a reduction in anemia burden between 2006 and 2016. However, there are limitations to our analyses as well. The main data source for our analysis was the Uganda DHS, which is a cross-sectional survey conducted approximately every five years. Therefore, we cannot infer causality. However, we used data from two survey years to create a panel data set, which enabled us to assess the effect of changes in anemia determinants on anemia among WRA over time. Additionally, our multivariate statistical analysis controlled for time-invariant region of residence, which accounted for any trend effects. Second, as previously established, the etiology of anemia is complex and multifactorial, and includes nutrient deficiencies, infection, inflammation, and hemoglobinopathies, for which quantitative data—specifically dietary intake and iron deficiency—were not available. Finally, although our qualitative inquiry was used to address some of the gaps in our quantitative data, we collected subnational data in only two regions and community-level data in two districts. Therefore, our findings are not generalizable at the national level.