Relationship between production diversity of Ethiopian smallholder farming households and anemia status of pregnant women in USAID-ENGINE project areas

Krista Zillmer, Ashish Pokharel, Robert Houser AbstractIntroduction: Anemia among pregnant women is associated with higher risk of mortality and low birth weight of infants. The causes of anemia are multifaceted and the policymakers have recognized the role of multisectoral programs in correcting anemia among other nutritional problems. Evidence of the positive association between increased farm production diversity and improved diet quality justifies the implementation of multisectoral programs in countries like Ethiopia, which has high prevalence of anemia with 22% of pregnant women and 17% of women of reproductive age being anemic. Cross cutting programs like ENGINE are implemented along with other agricultural programs to improve nutritional outcomes.

Objective: The objective of this study was to examine the association between household production diversity and the prevalence of anemia among pregnant women. The study explored the relationship of seasonal crop production and livestock production with anemia outcomes and examined the hypothesis that increased production diversity is positively associated with reduced anemia prevalence among pregnant women.

Methods: This cross-sectional study used survey data from 4680 pregnant women (aged 14-50 years) in Oromia region of Ethiopia, where ENGINE program is implemented. Two logistic regression models were used to examine the relationship between household production diversity and anemia. The first model used total production diversity for both seasons. The second model separated production diversity into crop production score by season and livestock production score. Anemia was defined as having hemoglobin levels below <11 mg/DL. Important variables such as education, age, number of antenatal care visits, iron supplementation, and weight status were covariates for in the model.

Results: Increased total production diversity was significantly associated with anemia status during the second season (Belg), but not during season 1 (Meher). The results from the second model using disaggregated scores indicated that livestock production diversity score was significantly correlated with the decreased odds of anemia status. Other factors such as being underweight and multiple pregnancies also had significant association with increased odds of being anemic. Years of education seemed to have a positive effect on decreased odds of being anemic.

Discussion: While household production diversity during the Belg season and household animal production were associated with reduced odds of anemia among pregnant women, further evidence is needed before recommending intervention strategies to increase households’ production of animal source foods. On its own, animal production is likely insufficient to make significant reductions in anemia prevalence. Other factors, including maternal workload, presence of disease, market access, and utilization of health services must also be taken into consideration.

Introduction Anemia is a major public health concern, as it is one of the largest nutritional deficiencies in the world and the prevalence of anemia remains high in many countries of South Asia and sub-Saharan Africa [1]. High rates of child and maternal mortality due to severe anemia is a critical public health issue in pregnant women; and loss of productivity and fatigue in adults and poor cognitive and physical growth of children are the adverse economic and social impacts of anemia [1]. The implications of anemia reach beyond health concerns to social and economic sectors of society as anemia adversely affects productivity in adults [2]. From an economic point of view, data from developing countries estimate that the economic losses attributed to physical and cognitive effects of anemia amounts to 4.05% of the total GDP [2]. Importantly, anemia is of particular concern for pregnant women because of its association with high prevalence and increased risks of maternal and perinatal mortality, low birth weight, and disease prevalence later in life [3].

Dietary practices, behavioral practices, educational attainment, women empowerment, body weight, trimester of pregnancy, and wealth status are associated with anemia status [4]. Often an integrated approach addressing all of these facets is needed to reduce the prevalence of anemia. The stakeholders, both nationally and internationally, have recognized the multifaceted causes of anemia and are committed to reducing the prevalence of anemia globally, especially among women and children in developing countries [5]. In recent years, there has been an increased focus on multisectoral programs to tackle this problem. Key areas of focus for improving overall nutrition of women and children are through programs and interventions in agricultural production, health services for women, education, targeted supplementation of micronutrients, empowerment of women, and other related interventions. Agriculture can improve nutrition through multiple pathways. Such pathways may improve nutritional status through direct food consumption from household production or increased income through market sales.

Historically the major focus of agricultural interventions has been to increase yields of major staple crops [6]. However, there has been recent interest in addressing nutritional problems like anemia through nutrition sensitive agriculture [7]. Jones et. al state that increased diversity of production in subsistence farming households has positive associations with increased dietary diversity and thus better diet quality. However, most farming households in developing countries are a combination of subsistence and market oriented which may add complexity to farm diversity and diet diversity relationship [6]. Regardless of the assumptions, it is clear that better dietary diversity either through increased production diversity or by increased market and economic access is likely to improve anemia status of women. This is supported by DeClerck et. al. who mention that increased functional agrobiodiversity can alleviate anemia and have direct and indirect effects on human health and nutrition [8]. Similarly, evidence from four countries (Bangladesh, Cambodia, Nepal and the Philippines) demonstrated increased consumption of nutrient rich foods and a reduction in anemia prevalence among young children in households that took part in homestead food production programs [9].

Normally, one of the main strategies for anemia treatment is through iron and folic acid supplementation. Widespread supplementation of iron to pregnant women has been recommended for decades although coverage is usually variable. The problem with this strategy is the potential harmful effects of iron supplementation that may occur in malaria-endemic areas. Evidence from a large randomized controlled trial in Pemba, Zanzibar suggested that iron supplementation to iron replete children may increase the risk of malaria mortality [10]. This has highlighted the need for a diet based approach to treat anemia. Among many small farm households, the mechanism through which anemia status may be improved is household agricultural production. Diversification of household production has shown to increase dietary diversity of the household [6]. Presumably, smallholder farmers would consume some portion of their produced goods aside from that which is sold in the market. In a recent paper, Ruel et. al stated that so far there is little evidence of homestead food production programs having an effect on maternal nutritional status. However, the evidence of women empowerment programs and behavior change programs on maternal nutritional status has been shown [11].

In Ethiopia, anemia is a significant contributor to morbidities in women and children. According to the WHO global database on Anemia, in 2006 anemia was classified as a severe public health problem with 75.2 % (40.7%-93.1%) of the entire population estimated to have hemoglobin levels <110gm/L [1]. More recently, among women of reproductive age, 17% were anemic, with 13% having mild anemia, 3% with moderate anemia and 1% with severe anemia. Among pregnant women, the prevalence is even higher at 22% [12]. Furthermore, anemia prevalence varied by area of residence. There was a higher prevalence of anemia in rural women (18%) than urban (11%) and geographically, the prevalence of anemia among pregnant women ranged from 44% in the Somali region to 9% in Addis Ababa. This was a significant improvement from 2006, where 62.7 % of the pregnant women and 52.7% of non-pregnant women were estimated to have anemia [1].

ENGINE (Empowering New Generations to Improve Nutrition and Economic Opportunities) is a USAID-funded program which aims to improve nutritional status in Ethiopia through a multi-sectoral approach including health, agriculture, and education. Interventions such as USAID’s ENGINE are implemented in agriculturally productive areas coupled with existing agriculture interventions such as AGP (Agricultural Growth Program) to improve service delivery and utilization of services through training and strengthening of the health force and increasing awareness, removing perceptions and improving knowledge and practices around nutrition and health of mothers and primary caregivers in Amhara, Oromia, SNNPR and Tigray regions of Ethiopia. In order to assess the effectiveness of intervention messages and activities, it is necessary to assess baseline characteristics and determine possible associations that exist between these characteristics and anemia status.

So far, there is limited evidence on what is the most effective set of approaches to address stunting and anemia in Ethiopia. The implementation of ENGINE provides a unique opportunity to understand the effectiveness of such approaches and strategies. This cross sectional study examines the relationship of agricultural practices, namely crop production diversity, and prevalence of anemia in pregnant women in ENGINE project areas.

Through the ENGINE birth cohort study, it is expected that increased utilization of direct nutrition interventions has a positive effect on anemia in mothers and their infants and that improvement of livelihoods through agriculture, coupled with access to direct nutrition interventions, will have positive effects on maternal health outcomes (e.g. anemia). The present study aimed to investigate the relationship between household agricultural production diversity, which includes both food crop production and livestock product production, with anemia status among pregnant women in three different woredas of the Oromia region in Ethiopia. The study hypothesized that the production diversity has a positive association in reducing anemia prevalence among pregnant women. Furthermore, the study will examine the relationship of seasonal food crop production diversity on anemia status.

MethodsDataThe present study analyzed baseline data from the USAID-ENGINE Ethiopia quasi-experimental observational birth cohort study. Pregnant women, ages 14-50 were recruited from three woredas from the Oromia region in Ethiopia; and, for each woreda 40 women were recruited from 39 kebeles (N=4680). Data will be collected twice during pregnancy, at the birth, and then every 3 months until the child is 24 months. The baseline data used in this study were obtained through surveys and assessments administered to the pregnant women during the time of recruitment. All eligible women were enrolled at the kebele level (which is equivalent to the municipalities) from the three woredas (which is equivalent to districts). In addition, the present study used data collected from baseline survey administered to the household head. Data was collected electronically through a tablet using a Gather Data software application. Measurement AnemiaA binary variable for anemia status was used as the outcome variable. Anemia prevalence was determined using the World Health Organization (WHO) established cutoff points for hemoglobin measures in pregnant women. The subject is classified as anemic if the measure of hemoglobin is below 11mg/dL. Mild anemia is between 10-10.9 mg/dL, moderate anemia is between 7-9.9 mg/dL, and severe anemia is below 7 mg/dL [3].

Production DiversityProduction diversity scores were calculated using a simple count variable that ranged from 0-12. This includes cereals, roots/tubers, legumes, cash crops, vegetables, fruits, oilseeds, spices, meat, dairy, poultry, and eggs. Production diversity was calculated separately for the 2 seasons. Crop production was reported by each season, but livestock production was not. Season 1 included the period from July 2012 to December 2012 and season 2 included the period from January 2013 to June 2013. The assumption was made that if a household was involved in livestock production, it was continuous throughout the year. The meat, dairy, poultry, and eggs scores were added to crop scores for season 1 to give the household production diversity of season 1. Likewise, the same animal source scores were added to the crop scores for season 2 to produce a season 2 household production diversity score.

Demographic characteristicsYears of education were recoded into 4 categories: no formal education (0 years), primary school-some or completed (1-5), secondary school-some or completed (6-9), and high school education or beyond (10+). Marital status was categorized into 3 groups: married and monogamous, married and polygamous, and not married. The not married group included single, cohabitating, separated, divorced, widowed as a single category. Religion was categorized as either Muslim, Christian (Catholic, Orthodox, Protestant), or other (pagan, traditional religions, or other).

Health characteristicsA binary variable was constructed for underweight as an indicator of nutritional status. Measures of weight or BMI are not appropriate for use in identifying undernourished pregnant women, so mid-upper arm circumference (MUAC) was used instead. The average of three MUAC measurements was used in this study. Underweight for pregnant women was defined as having a MUAC of less than 23 cm. The antenatal care variable, which refers to the number of antenatal visits during pregnancy, was classified as 0, 1, 2, 3, and 4 or more appointments attended. Iron supplementation was coded as a binary variable on whether the woman received or bought iron supplements during the current pregnancy. First pregnancy is a binary variable of whether a woman is pregnant with her first child or has had previous pregnancies.

Regression ModelLogistic regression was performed using StataCorp 2013, StataIC Release 13 software to predict anemia status from household production diversity scores from each season. The model was run separately for the production diversity scores corresponding with the two growing seasons. Only the households that completed both the crop and livestock surveys were included in the model (n=4628). Additional covariates were added to the model, which include maternal age, education, number of previous pregnancies, antenatal care attendance, nutritional status, and iron supplementation. The regression model was adjusted for clustering at the kebele level. A second model was performed using disaggregated production diversity scores which separated crop production from animal production. The models were tested for misspecification problems and goodness of fit using the Stata linktest and Hosmer’s and Lemeshow goodness-of-fit.

Results

Demographic and Health CharacteristicsThe majority of the women were between the ages of 20-29 (57%) and most of the women were in a monogamous marriage (96%) (Table 1). The women were primarily Muslim (67%) and had low educational attainment (mean: 2.3 years). The demographic and health data from the sample also suggests that health services and health care knowledge was poor among the pregnant women in the sample. This assumption is based on the findings that a large proportion of women are underweight (41%) and did not attend any antenatal care appointments during pregnancy (49%). Moreover, a significant proportion of the women did not take antenatal iron supplementation tablets during pregnancy (83%).

Anemia StatusOverall, the prevalence of anemia among the pregnant women in the sample was 12.39%. Among the women with anemia, the majority of anemic women had mild anemia at 68.91%, whereas a smaller percentage having moderate or severe anemia at 29.88% and 1.21% respectively. The prevalence of anemia increased as the age group increased whereas anemia prevalence decreased with increase in education (Table 2). The prevalence of anemia was higher in Muslim women compared to those practicing other religions. The prevalence of anemia was higher in women in polygamous marriage, although the number of women in this group was low in the sample. Similarly, the prevalence of anemia was higher in underweight women and women who have had previous pregnancies. Surprisingly, the prevalence of anemia was higher in women who had iron supplementation. Perhaps iron supplements were only given to women screened for low iron or hemoglobin concentration

Table 1:Demographic and Health Characteristics

Mean +/- SE Percent

Age 26.4 +/- 0.11

Age Group14-19 9.8720-29 56.630-39 31.8440-50 1.69

Marital StatusMarried-

monogamous 96.2

Married-polygamous 1.5

Not married1 2.3Religion

Muslim 67.26Christian 32.67Other 0.06

Years of Education 2.28 +/- 0.14

Educational Attainment0 yrs 55.241-5 yrs 27.126-9 yrs 13.710+ yrs 3.96

WoredaWoliso 33.33Goma 33.35Tiro Afeta 33.31

Underweight3

Yes 40.8No 59.2

First pregnancyYes 16.94No 83.06

Antenatal Appointments0 49.381 21.42 17.793 8.574+ 2.86

Received Iron Supplements

Yes 17.01No 82.99

Note: 1 Includes responses for single, cohabitating, separated, divorced, and widowed. 2 Religion: Christian includes Orthodox, Catholic, and Protestant; other includes traditional and other religions. 3Underweight classified as a MUAC score of <23 cm

and therefore, this result would be expected. However, little was known about the policies and practices in the study communities for distributing iron supplements to pregnant women.

Table 2: Anemia status across demographic and health characteristics

(n=) Hemoglobin (g/dL) mean +/- SE Anemia Prevalence (%)

Age Group14-19 (461) 12.67 +/- .07 9.9820-29 (2644) 12.49 +/- .05 11.8130-39 (1489) 12.35 +/- .06 13.6540-50 (78) 12.08 +/- .27 20.51

Marital Status

Married-monogamous (4495) 12.46 +/- 0.5 12.27Married-polygamous (69) 12.34 +/- .18 18.84Not married (108) 12.43 +/- .14 12.04

ReligionMuslim (3146) 12.33 +/- 0.6 15Christian (1523) 12.73 +/- .05 6.96Other (3) 12.70 +/- .39 0

Years of Education0 (2577) 12.34 +/-.06 14.491-5 (1270) 12.57 +/-.05 10.336-9 (640) 12.63 +/-.06 9.0610+ (185) 12.75 +/- .10 8.1

WoredaWoliso (1556) 12.56 +/-.04 10.73Goma (1561) 12.15 +/-.09 18.24Tiro Afeta (1555) 12.66 +/-.06 8.11

Underweight(MUAC)Yes (1905) 12.31 +/-.06 14.98No (2767) 12.56+/-.05 10.56

First time pregnantYes (791) 12.76+/-.06 8.09No (3881) 12.40 +/-.05 13.24

Antenatal appointments0 (2305) 12.49+/-.06 12.291 (1000) 12.40 +/-.05 12.32 (831) 12.40+/-.07 12.533 (402) 12.47 +/-.07 13.974+ (132) 12.70+/-.13 7.58

Received Iron SupplementsYes (797) 12.35 +/- .06 13.84

No (3875) 12.48 +/- .05 12.06

Household ProductionThe percentage of households in production of cereals, roots and tubers, legumes, vegetables, and oil seeds (rapeseed, flax, safflower, etc.) were higher for season 2 than season 1 (Table 3). This means more households participate in production of cereals, roots, legumes, vegetables, and oilseeds in season 2 compared to season 1. On the other hand, the percentage of households in production for fruits and spices were higher in season 1 than season 2, which means that households produce fewer fruits and spices in season 2 when compared to season 1.The production of cash crops remained similar for both the seasons. Equal livestock production diversity scores for meat, dairy, poultry, and eggs were assigned to both seasons as livestock production is not directly dependent upon season.

Table 3: Percentage of households in production

n= 4391 Season 1 Season 2

Cash Crops 63.56 63.43Eggs 33.4 33.4Poultry 31.51 31.51Cereals 31.22 56.62Fruits 26.58 18.65Vegetables 19.27 24.05Dairy 14.34 14.34Roots/Tubers 9.52 31.41Legume 6.15 15.08Oil Seeds 3.35 10.73Spices 1.66 0.27Meat 0.17 0.17

Logistic RegressionThe results from the first model indicates that in the second season for every one point increase in production diversity the odds of being anemia decreases by 7%, all else equal (Table 4a). In both seasons being underweight increases the odds of being anemic. For season 1 the odds of being anemic increases by 44% and for season 2 the odds increases by 42%. Similarly, the results indicate that women who are not in their first pregnancy have higher odds of being anemic than the women in their first pregnancy. The model predicts that the odds of having anemia decreases with increasing years of education with a 28% decrease in 1-5 years of education and 32% decrease in 6-9 years of education when compared to 0 years of education, all else equal.

Table 4a: Logistic Regression predicting anemia from production diversity and several covariates

Season 1 Season 2

n=4628 Adjusted Odds Ratio

95% Confidence

Interval

Adjusted Odds Ratio

95% Confidence

Interval

Production Diversity Score

1.02 0.95,1.09 0.93* 0.88, 0.98

Iron Supplementation

No reference

Yes 1.13 0.82,1.55 1.09 0.80, 1.48

Antenatal Appointments

0 reference

1 1.00 0.79, 1.27 1.01 0.80, 1.28

2 1.00 0.78, 1.27 1.03 0.81, 1.30

3 1.14 0.81, 1.60 1.15 0.82, 1.60

4+ 0.59 0.31, 1.13 0.58 0.30, 1.10

Underweight

No reference

Yes 1.44*** 1.19, 1.75 1.42*** 1.17, 1.73

First time pregnant

Yes reference

No 1.46* 1.01, 2.13 1.52* 1.04, 2.21

Age 1.00 0.98, 1.03 1.01 0.98, 1.03

Years of Education

0 reference

1-5 0.72* 0.56, 0.93 0.74* 0.58, 0.95

6-9 0.68* 0.47,0.99 0.70 0.48, 1.01

10+ 0.63 0.35, 1.12 0.63 0.35, 1.13

Significance at the level of 0.05*, 0.01**, 0.001***

The individual effect of independent variables on the likelihood of being anemic can be examined through crude odds ratio (shown in Table 4b). Crude odds ratio of the “underweight” variable is higher when compared to the adjusted odds ratio of the same variable. Similarly, the crude OR of the “first time pregnancy” variable is higher and statistically significant at a higher level when compared to the adjusted OR. Likewise, the crude odds ratio of the “years of education” variables are also different and statistically significant at a much higher level.

Table 4b: Crude Odds ratios of variables predicting anemia

n=4628 Crude Odds Ratio

95% Confidence

Interval

Season 1 Production Diversity Score

1.03 0.96,1.09

Season 2 Production Diversity Score

0.93* 0.88, 0.99

Iron Supplementation

No reference

Yes 1.16 0.87,1.55

Antenatal Appointments

0 reference

1 0.99 0.78, 1.27

2 1.01 0.81, 1.27

3 1.15 0.86, 1.53

4+ 0.58 0.30, 1.14

Underweight

No reference

Yes 1.49*** 1.22, 1.81

First time pregnant

Yes reference

No 1.73*** 1.32, 2.28

Age 1.02** 1.00, 1.04

Years of Education

0 reference

1-5 0.68*** 0.53, 0.85

6-9 0.59** 0.41,0.84

10+ 0.52* 0.31, 0.88

Significance at the level of 0.05*, 0.01**, 0.001***

The second model examined the individual effects of crop production diversity scores of the two seasons and animal production diversity scores (Table 5). The second model predicts that for one unit increase in Season 1 crop production diversity score the odds of being anemic increases by 11%. Not surprisingly, one unit increase in the animal production diversity reduces the odds of being anemic by 14% all else equal. Similar to the first model underweight, first time pregnancy, and education years are significant predictors of anemia. The odds of being anemic increased by 44% if the woman was underweight, all else equal. Likewise, the odds of being anemic increased by 46% if the woman was underweight, all else equal. Finally, the odds of being anemic decreases by 24% if the education was between 1-5 years compared to 0 years of education, all else equal.

Table 5: Logistic Regression predicting anemia using disaggregated scores

n=4628 Adjusted Odds Ratio

95% Confidence Interval

Crop Production Score (season 1) 1.11* 1.02, 1.20

Crop Production Score (season 2) 0.97 0.90, 1.05

Animal Production Score 0.86* 0.76, 0.97

Iron Supplementation

No reference

Yes 1.08 0.78, 1.47

Antenatal Appointments

0 reference

1 1.02 0.81, 1.29

2 1.03 0.82, 1.31

3 1.14 0.82, 1.59

4+ 0.59 0.31, 1.12

Underweight

No reference

Yes 1.44*** 1.19, 1.74

First time pregnant

Yes reference

No 1.46* 1.00, 2.14

Age 1.00 0.98, 1.03

Years of Education

0 reference

1-5 0.76* 0.59, 0.97

6-9 0.72 0.49, 1.04

10+ 0.66 0.37, 1.19

Significance at the level of 0.05*, 0.01**, 0.001***

Discussion

Model 1Season 1 in the survey refers to household production during the months of July 2012 to December 2012 and season 2 refers to January 2013 to June 2013. In Ethiopia, there are two main crop seasons: Meher and Belg. Season 1 most closely corresponds with the months of the Meher (September-November) and season 2 corresponds to the Belg (March–May). In the study woredas, September through November is time for harvesting and March through May is time for land preparation. However this is applicable for crops only. There is no specific harvesting time for fruits and vegetables. The Meher is the main season where 90-95% of the total cereal output in Ethiopia is produced [13]. While the Meher is the most important season in terms of the quantity of cereals produced, smallholder farmers are the only producers cultivating crops during the Belg and so it has relevance and importance to the study population [14].

Results from logistic regression showed that the relationship between production diversity and the odds of anemia were significant for season 2 (i.e. Belg) but not for season 1 (i.e. Meher). The higher

percentage of population involved in production of cereals, roots/tubers, and legumes in season 2 may be explained by a large number of smallholder farming households producing crops in Belg season, but with a lower total quantity of output.

During the Meher season, farmers may choose to specialize in a particular type of crop and produce a large volume of this crop to sell in the market. Household production may lead to improved nutritional status through multiple pathways, the primary pathways being direct consumption of foods produced by the household or by increased income through market sales, which allow the household to purchase other foods [15]. Production diversity at the household level is one way to increase dietary diversity and adequate micronutrient intake, including iron-rich foods. However, household production cannot replace the abundance of agricultural goods in the market place. Perhaps during the Meher season, there is an abundance of diverse foods available at the market and it is more beneficial for specialize in a particular crop and sell a large quantity of it to generate income for purchase of other goods. During the smaller harvest season, perhaps it is beneficial to then diversify the types of crops and livestock produced. Previous studies support this pattern as they show that poor households aim to fulfill their caloric requirements first through production and greater dietary diversity is achieved only through purchasing power from greater income [16].

Model 2 When total production diversity is disaggregated into separate crop production diversity and animal production diversity score, the results suggest that production of animal source foods is the main driver that reduces odds of women having anemia. The model showed that household animal production significantly reduced the odds of the anemia in the pregnant woman, but the same effect was not observed for the crop scores (Table 5). Therefore, animal production more than crop diversity drives the reduction in odds of anemia. This makes sense because animal products are richer sources of iron and contain the most bioavailable form. While household production of animal source foods is one strategy to improve iron intake, it may not be sufficient on its own to meet the high iron needs of a pregnant women. Thus, interventions should also incorporate strategies to improve use of antenatal care, coverage of iron supplementation, and promote female education, which was also a predictor of anemia status. As previously shown, utilization of antenatal care and iron-supplementation coverage are low among the study population (Table 1).

It has been noted that in areas where cereals are the predominant source of energy in the diet and where disease burden is high, even strategies to promote production of animal source foods is likely to be insufficient for improving anemia status in pregnant women [17]. Furthermore, there is insufficient evidence to determine whether promotion of animal source foods is an effective means to alleviate problems of undernutrition as there is little known about potential negative impacts on factors like maternal time and workload, maternal income, and the risk of concomitantly promoting zoonosis [18].

Other determinants of iron statusPrenatal iron supplementation helps to reduce iron deficiency anemia among women. However, in our models the effects of iron supplementation was non-significant and it increased the odds of being

anemic, all else equal. The possible explanation for this observation might be that supplementation might only be given to pregnant women who were extremely anemic. Thus, supplementation may have changed their status from severely or moderately deficient to mildly deficient. Since this study used anemia status as a binary “yes” or “no” variable, it was not possible to examine these subtleties.

Furthermore, in malaria endemic areas like Ethiopia iron supplementation can increase the risk of infection. Introduction of high iron quantities in the body causes a spike in circulating iron in the blood, which can be used by the malarial parasite. Therefore, it is important to minimize the amount of free iron in the blood so that it cannot be utilized by parasites. Consumption of iron in smaller doses through food based approach is perhaps a safer method to improve iron status in places like Ethiopia. In Ethiopia, anemia supplementation might be used for the treatment of severe anemia.

The number of antenatal visits also failed to show any statistical significance in both of the models. This might be explained by the fact that only 2.86 percent of the total women included in the model had the recommended 4 or more antenatal visits and only 8.6% of the total women had 3 antenatal visits. About 50% of the total women in the sample didn’t have any antenatal visits. Hence, poor or non-existing antenatal care might explain non-significant association with anemia status.

Both models showed that underweight was strongly correlated with anemia. This is intuitive as energy and nutrient deprivation often leads to underweight. Often, when a person is underweight their consumption of micronutrients like iron are very low. In this sample, only about 15 % of all underweight women were anemic, but the odds of being anemic increased significantly when the mothers were underweight when compared to not being underweight. If mothers have adequate sources of iron in their diet they may not be anemic even if they are underweight because of caloric deprivation. This might explain the findings in this sample that despite being underweight, about 85% of mothers were not anemic. The low prevalence of anemia among underweight women could be explained by common dietary practices in Ethiopia. Unlike most staple foods, Ethiopia’s major grain, teff, is a good source of iron. Further studies using dietary intake data can examine these consumption patterns in order to better explain the relationship between underweight and anemia.

The study showed a significant association of likelihood of being anemic and multigravida (i.e. pregnancy more than one time). Pregnancy increases the requirements for iron in the body and often multiple pregnancies deplete the iron stores in the women’s body, especially if they are closely spaced. Hence, the study naturally finds that women who are not in their first pregnancy are more likely to be anemic.

Study Limitations and Future WorkTo perform this analysis the assumption was made that producing households consumed some portion of the food they produced. Determining the relationship household production diversity and diversity of the diet would have established a more direct link between production and dietary habits and the effect on nutritional status, but survey data were not sufficient to calculate dietary diversity scores. However, in a study of 4 countries including Indonesia, Kenya, Malawi, and Ethiopia, household production diversity was associated with an increase in household dietary diversity [19]. And, dietary diversity is a

good indicator of micronutrient status of both children and women in a diverse sampling of developing countries [20, 21]. Therefore, it is plausible for household production diversity to impact anemia status.

The study did not control for purchasing power of the households due to limitations of the income data. Usually, households with sufficient economic access are able to purchase higher quality and diverse nutritious foods are purchased from the market. Furthermore, the distance to the local market might play an important role in the purchasing of higher value food items like meat products, fruits and vegetables. Hence, this study recommends controlling for these factors in future.

There is limited evidence on the role of household production diversity in health of pregnant women. This study contributes to the body of knowledge of agriculture to nutrition pathways. The results from our study show that further investigation needs to be carried out in order to examine the effect of agricultural outputs on health outcomes like anemia. This study, along with future studies on household production habits can help explain the pathways and mechanism of how agriculture and nutrition are related.

The results of this study can also be used by policy makers for programmatic purposes. Results indicate that a focus on improving the production of livestock and their products is likely to improve health outcomes of the mothers. The study suggests that the improved production of items like eggs, milk, and meat may translate into consumption by the household and consequently improve health of the mothers. Furthermore, these results support the need for improvements in complementary health interventions such as female education and access to basic health care facilities. Community health outreach programs that provide counseling and services to pregnant women can be initiated to improve coverage of services such as antenatal care appointments and distribution of iron and folic acid supplements. Promotion of healthy birth spacing can help reduce anemia in women by providing enough time to replenish iron stores before her needs are increased again during pregnancy. Finally, investments in education of adolescent girls and women is likely to improve health and nutrition of mothers and their families. Improved education, especially among females, has external benefits to society such as poverty reduction, income, and higher standard of living.

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