Risk factors of anemia among Women of Reproductive Age in Rwanda: implications for designing better interventions

Background Anemia among Women of Reproductive Age (WRA) continues to be among the major public health problems in many developing Rwanda where It was increased comparing 2015 to 2010 Demographic and Health Survey (DHS) reports. A thorough understanding of the its risk factors is necessary to design new better approaches. However, to the best of our knowledge, no study assessing factors associated with anemia among WRA has been conducted. Therefore, this study aims to identify anemia risk factors among WRA in Rwanda. Methods This was a quantitative, cross-sectional study using secondary data from the Rwanda Demographic and Health Survey (RDHS) 2014-2015. The study population consisted of 6680 WRA who were tested for anemia during the survey. Anemia was defined as having equal or below to 10.9 g/dl for a pregnant woman, and hemoglobin level equal or below to 11.9 g/ for a non-pregnant woman. Pearson’s chi-squared test and multiple logistic regression were conducted for bivariate and multivariable analysis respectively. We reported Odds Ratio (OR), 95% Confidence Intervals (CI) and p-values. We used Stata version 14.2 for all analyses. was CI: - After controlling - - sleeping under a mosquito CI: - odds (OR: CI: - 95% CI: - province (OR: 1.45, 95% CI: 1.11 - 1.89) the Eastern (OR: 95%

interventions should consider geographic variations, improve women economic status, and strengthen iron supplementation especially for IUD users. Additionally, given the association between anemia and malaria, interventions to prevent malaria should be enhanced.

Background
Anemia is a significant public health problem affecting around 1.93 billion people worldwide (1). It affects 29.4% of WRA and 38.2% of pregnant women (2). Anemia among pregnant women is associated with increased risk of maternal mortality, low birth weight, preterm birth, perinatal mortality and neonatal mortality as well as occurrence of anemia for the child (3,4) (5,6). It also affects cognitive development in children, reduces work abilities for affected women, and is ultimately associated with increased healthcare expenditures (2,7,8).
Anemia disproportionally affects developing countries, with Asian and Sub-Saharan African countries bear 89% of the anemia burden (1). Over 38% of all WRA in the African Region are anemic (2), with significant variations between countries; in some African countries, anemia among WRA is above 50% (9).
Rwanda has made progress to improve maternal health. However, anemia among WRA is still a burden. The recent Demographic and Health Survey (DHS) report showed that anemia among WRA in Rwanda is estimated to be 19.2% (10). Although it is still considered as a mild public health problem according to WHO criteria (11), the prevalence was on the rise from 17% in 2010 (10). Moreover, in 11 of Rwanda's 30 districts, the prevalence of anemia among WRA is much higher than the national average, with some districts even reached over 30% (10,12,13).
Such increase in prevalence and disparity across subgroups and regions suggested that, if unaddressed, the problem will grow despite current efforts made to improve the health of the population. Understanding the risk factors for anemia is crucial for the development of innovative and evidence-based interventions to reduce its prevalence nationwide.
However, the few studies that have been conducted in Rwanda to identify risk factors for anemia were restricted to children or only a subset of WRA such as women living with Human Immunodeficiency Virus infection or young women (18-27 years old); such studies lack national representation and also had inconsistent and conflicting results (13)(14)(15)(16)(17)(18). This study was carried out to identify the risk factors of anemia among WRA in Rwanda using nationally representative RDHS data in order help identify specific interventions to reduce the prevalence of anemia among WRA.

Study setting
Rwanda is a land-locked African country with an estimated population of 10

Sampling and Data Collection Method
The 2014-15 RDHS data were collected using standard DHS questionnaires, which were adapted by stakeholders from the Rwandan government and its partners to include questions on specific issues related to the Rwandan social and cultural context. All questionnaires were translated from English to Kinyarwanda and were pre-tested prior to actual data collection. The data collection was conducted by qualified and trained professionals with rigorous supervision. Sampling was based on the 2012 Rwanda Population and Housing Census (RPHC) which consists of a list of villages, considered enumeration areas (EAs). They were stratified by type of residence ( rural or urban) in each district, and 60 sampling strata were created from all districts. A two-stage cluster sampling design was used to ensure that estimates were representative at national level.
At the first stage, 492 EAs were selected from all sampling strata (113 from urban areas and 379 from rural areas), and then, systematic sampling strategy was carried out by first listing all households in selected EAs. At the second stage, 26 households were randomly selected from each EA, resulting in 12,792 households total. Anemia was tested for in half of the households selected for the general survey, and this resulted in a representative sample of 6680 eligible women tested (10). On-site hemoglobin analysis was conducted to test for anemia using a battery-operated portable HemoCue analyzer, and results were adjusted for altitude and for smoking status (if known) using the recommended Centers for Disease Control and Prevention (CDC) formula (10). Our study analyzed all WRA in RDHS with anemia test results.

Dependent variables
Our dependent variable was anemia status at the time of the survey. Pregnant women with hemoglobin level equal to or below 10.9 g/dl and non-pregnant women with hemoglobin level equal to or below 11.9 g/dl were considered anemic. Moreover, the other anemia operational definitions were considered where pregnant women and non-pregnant women with hemoglobin level between 10.0-10.9 g/dl and 10.0-11.9 g/dl respectively were considered to have mild anemia; women with hemoglobin level between 7.0-9.9 g/dl and less than 7.0 g/dl were considered to have moderate and severe anemia, respectively.

Independent variables
Based on previous literature and biological knowledge, the independent variables included: social and demographic characteristics (age of the respondent, province of residence, type of residence-rural or urban, educational attainment, economic status, union/marital status), variables related to reproductive health and mother health status (pregnancy status, number of children ever born from the women, breastfeeding status, body mass index, use of family planning method). We also include variables related to access to information (frequency of reading newspaper or magazine, listening to radio, and watching television), and variables related to living conditions (including having the toilet facility in the household, main source of drinking water for the household, having mosquito bed net for sleeping, respondent sleeping under mosquito bed net the night before the survey, and respondent considering distance to health facility as a problem).

Data access and analysis
The RDHS data were downloaded from the DHS program website in STATA format after the approval from the DHS program. We conducted descriptive analyses to summarize the anemia status according to the independent variables. Pearson's chi-squared test was used to assess the association between anemia status and other independent variables.
To identify factors associated with anemia among WRA in Rwanda, all significant variables in bivariate analysis were further analyzed using multivariate analysis with backward stepwise logistic regression, after checking collinearity. A variable was removed from the full model when it was not statistically significant at p = 0.05. Marital status and age were maintained in the model to adjust for given their influence on other variables. Sampling weights were included in all analyses to adjust for the effects of the stratification and cluster sampling approaches used in RDHS. Odds Ratios (OR), 95% confidence intervals (CI) and p-values were reported. Data were analyzed using STATA version 14.2 (StataCorp Lakeway Drive College Station, Texas).

Socio-demographic characteristics
A total of 6680 women were included in the analysis. The mean age was 28.6 years (SD:  Anemia prevalence was relatively low among those who completed secondary school  Table 1)..

Factors associated with anemia
Six risk factors for anemia among WRA adjusted for age and marital status were found in the multivariate analysis, they were: 1) the nutrition status, 2) economic status, 3) type of contraceptive method used, 4) use of a mosquito net, 5) marriage status, and 6) province of residence (Table 2).
Compared to WRA with normal BMI, underweight women were more likely to have anemia Compared to women in poor category, women in the rich category were less likely to have anemia (OR: 0.74, 95% CI: 0.63 -0.87, p value <0.001). There was a marginally significant difference in the likelihood of having anemia between women in the poor category and women in the middle category (OR: 0.83, 95% CI: 0.69-1.00, p value = 0.044).
Women who were using hormonal contraceptives were less likely to have anemia (OR: 0.60, 95% CI: 0.50 -0.72, p value: <0.001) while those who were using Intrauterine Devices were more likely to have anemia (OR: 1.97, 95% CI: 1.04 -3.73, p value = 0.037) compared to those who were not using any contraceptive method or who were using natural barriers or permanent contraceptive methods.
Compared to women who were not married, women who were separated or widowed were

Discussion
According to the analysis, the prevalence of anemia among WRA in Rwanda was 19.2%.
Although lower than many other countries in the Sub-Saharan Africa region (2,9), it is still a public health problem according to WHO criteria (22). In addition, the prevalence had increased from previous years and varied across population subgroups (10,23).
Similar to study results in other settings including Ethiopia and Pakistan, our analysis found that poor and undernourished women are more likely to have anemia (24,25).
Anemia is a multifaceted problem where nutrition and economic status work in synergy.
Evidence suggested that improved economic status was associated with appropriate nutrition conditions (26), lower infection morbidity (25), increased access to health services as well as other favourable living conditions (26,27), all of which influence anemia. Malnourished women have greater risk of iron deficiency -the most common proximate cause of anemia (1) and they are mostly associated with poor socio-economic status (28). Interventions that aim to empower women economically should be considered in order to positively impact anemia. Moreover, malnutrition management programs should ensure sustained effort for supplementation of iron.
In this study, the use of hormonal contraceptives was associated with lower risk of anemia among WRA, while the use of IUDs was associated with higher risk. Using hormonal contraceptive can be resulted in less bleeding during the menstruation, which ultimately reduces blood loss over time (29,30); similar findings were seen in other studies conducted in 14 different low-and middle-income countries including Tanzania and Ethiopia (31) (24)(32). Another study conducted in seven countries also found that hormonal contraceptive users had higher haemoglobin and ferritin levels compared to non-users (33).
A study conducted in Pakistan also observed higher anemia risk among IUD users (34). IUD may increase uterine blood flow, menstruation bleeding and the duration of menstruation periods especially during the first months of usage, which in turn increase the likelihood of anemia (35) (36). In addition, some research has found that IUD users have a reduction in hemoglobin content and iron saturation/ ferritin levels, which may trigger or worsen existing anemia (33,37). While more investigations are needed to understand the real physiological mechanisms, our study findings supported existing evidence that IUD use is among the risk factors of anemia in WRA. Clinical guidelines should take into consideration bleeding treatments (38) as well as iron supplementation as an additional option for IUD users especially during the first months of usage.
Geographic area of residence was found to be associated with anemia, with women in the Eastern and Southern provinces being more likely to have anemia. The Eastern and Southern provinces in Rwanda are considered to be high malaria endemic provinces, and thus people living in those areas are likely to be affected by malaria, which is itself considered to be among anemia risk factors (39). Similar associations between anemia and geographic location were found in another study in Tanzania (31). In Rwanda, iron supplementation during pregnancy is less commonly used in the Eastern province than in other provinces (10). While further investigations are needed to better understand why, interventions that aim to address anemia, including iron supplementation and promotion of foods rich in iron and other micronutrients, as well as interventions to prevent malaria (12,40), should consider geographic variations and prioritized the most affected areas.
Consistent to the results from other studies, sleeping under mosquito nets was associated with lower likelihood of anemia in our study, as malaria is a risk factor of anemia (41) Castelli et al., 2014). As mosquito nets coverage and usage remain challenges in many developing countries (42,43) (44), malaria prevention strategies including, efforts to ensure the availability as well as proper use of mosquito nets in the community should be integrated in anemia programs.
Marital status was found to be a risk factor for anemia, with widowers or women separated with husband being more likely to have anemia. Traditionally men are breadwinners in most developing societies (45). Many widows and women separated from their husbands face social and economic challenges, and those challenges may worsen when they lack support to sustain their families, predisposing them to economic deprivation, poverty, malnutrition and poor access to health services (26)(27)(28). In addition, our analysis showed a correlation between marital status and age (r = 0.63). Older age was found to be associated with anemia in some studies (24). While further investigations are needed to better understand the possible associations between marital status, age and anemia status, our findings suggest that old women, especially widows, may face many other health problems that are understudied. Special attention and priority should be provided to this vulnerable group.
Our study found samples with lower education levels had slightly higher prevalence of anemia, although statistical significance was not found. Other studies have found education level as a risk factor of anemia (24,31). This study successfully identified some risk factors among WRA in Rwanda and proposed some recommendation. However, the results must be seen in light of some limitations.
This study could only variables that were in the DHS, due to the nature of secondary data analysis. Qualitative information could provide more insights into the risk factors of anemia in Rwandan community, leading to improved understanding of the attitudes and practices related to variations in food consumption patterns. However, the DHS survey used a national representative sample and was conducted with standardized quality assurance measures in both data collection and management to ensure reliability and validity of the results (48,49), that could improve the generalizability of the results of our analysis.

Conclusions
In order to address anemia among WRA in Rwanda, programs that improve women's economic livelihoods, and malnutrition. Furthermore, clinical guidelines should ensure that women using IUDs as contraceptive methods have access to bleeding treatments as well as iron supplementation. Special attention should be provided based on geographic variations. Integrating malaria prevention strategies into anemia program should also be considered.

Availability of data and materials
The data used for this study are from the Rwanda Demographic and Health surveys (DHS) and are publicly available here: https://dhsprogram.com/data/available-datasets.cfm . Ethics approval and consent to participate Since this study was a secondary analysis of the Rwanda Demographic and Health surveys (RDHS) data, which are publicly available, the study did not require any ethics approval.
Only DHS program authorization was requested to download the dataset.

Consent for publication
Being a secondary analysis, no consent to publish was needed for this study. There was no identifiable data.