Determinants of moderate-to-severe anaemia among women of reproductive age in Tanzania: analysis of data from the 2010 Tanzania demographic and health survey




To identify determinants of moderate-to-severe anaemia among women of reproductive age in Tanzania.


We included participants from the 2010 Tanzania Demographic and Health Survey, which collected data on socio-demographic and maternal health and determined haemoglobin levels from blood samples. We performed logistic regression to calculate adjusted odds ratios for associations between socio-demographic, contextual, reproductive and lifestyle factors, and moderate-to-severe anaemia and investigated interactions between certain risk factors.


Of 9477 women, 20.1% were anaemic. Pregnancy was significantly associated with anaemia (adjusted OR 1.75, 95% CI 1.43–2.15), but the effect varied significantly by urban/rural residence, wealth and education. The effect of pregnancy was stronger in women without education and those who were in lower wealth groups, with significant interactions observed for each of these factors. Education was associated with a lower anaemia risk, particularly in the poorest group (OR 0.58, 95% CI 0.43–0.80), and in pregnant women. The risk of anaemia fell with rising iron supplementation coverage. Lack of toilet facilities increased anaemia risk (OR 1.26, 95% CI 1.00–1.60), whereas using hormonal contraception reduced it. There was no association with age, urban/rural residence, wealth or type of cooking fuel in adjusted analysis.


Pregnant women in Tanzania are particularly at risk of moderate-to-severe anaemia, with the effect modified by urban/rural residence, education and wealth. Prevention interventions should target women with lower education or without proper sanitation facilities, and women who are pregnant, particularly if they are uneducated or in lower wealth groups.



Identifier les déterminants de l'anémie modérée à sévère chez les femmes en âge de reproduction en Tanzanie.


Nous avons inclus des participantes de la Surveillance Démographique et de Santé de 2010 en Tanzanie, qui a recueilli des données sociodémographiques et de la santé maternelle et déterminé le taux d'hémoglobine à partir d’échantillons sanguins. Nous avons effectué une régression logistique pour calculer les rapports de cote (odds ratios) ajustés pour les associations entre les caractéristiques sociodémographiques, contextuelles, reproductives et du style de vie avec l'anémie modérée à sévère, et avons investigué les interactions entre certains facteurs de risque.


Sur 9477 femmes, 20,1% étaient anémiques. La grossesse était significativement associée à l'anémie (OR ajusté = 1.75; IC 95%: 1.43 à 2.15), mais l'effet variait considérablement selon la résidence urbaine/rurale, la richesse et l’éducation. L'effet de la grossesse était plus fort chez les femmes sans éducation et celles qui étaient dans les groupes de richesse inférieure, avec des interactions significatives observées pour chacun de ces facteurs. L’éducation a été associée à un risque plus faible de l'anémie, en particulier dans le groupe le plus pauvre (OR = 0.58; IC 95%: 0.43 à 0.80) et chez les femmes enceintes. Le risque d'anémie chutait avec l'augmentation de la couverture en supplémentation en fer. Le manque d'installations sanitaires augmentait le risque de l'anémie (OR = 1.26; IC 95%: 1.00 à 1.60) tandis que l'utilisation de contraception hormonale la réduisait. Il n'y avait pas d'association avec l’âge, la résidence urbaine/rurale, la richesse ou le type de combustible pour la cuisine dans l'analyse ajustée.


Les femmes enceintes en Tanzanie sont particulièrement à risque d'anémie modérée à sévère, avec un effet modifié par la résidence urbaine/rurale, l’éducation et la richesse. Les Interventions de prévention devraient cibler les femmes avec un niveau d’éducation faible ou sans installations sanitaires adéquates et les femmes enceintes, en particulier si celles-ci n'ont pas reçu une scolarisation ou appartiennent aux groupes de richesse inférieure.



Identificar los determinantes de anemia moderada-a-severa entre mujeres en edad reproductiva en Tanzania.


Hemos incluido participantes del Censo Demográfico y de Salud del 2010 de Tanzania, en el que se recogieron datos sociodemográficos y de salud materna, y se determinaron los niveles de hemoglobina en muestras de sangre. Hemos realizado una regresión logística para calcular el cociente de probabilidades ajustado para asociaciones entre factores socio-demográficos, contextuales, reproductivos y de estilo de vida, así como el nivel de anemia – de moderada a severa, e investigando la interacción entre ciertos factores de riesgo.


De 9477 mujeres, un 20.1% estaban anémicas. El embarazo estaba significativamente asociado con la anemia (OR ajustado 1.75, IC 95% 1.43–2.15), pero el efecto variaba de forma significativa según la residencia urbana/rural, el nivel de riqueza y la educación. El efecto del embarazo era mayor en mujeres sin educación y aquellas que estaban en grupos de menor riqueza, con interacciones significativas para cada uno de estos factores. La educación estaba asociada con un menor riesgo de anemia, particularmente en el grupo más pobre (OR = 0.58, IC 95% 0.43–0.80), y en mujeres embarazadas. El riesgo de anemia decaía con el aumento de cobertura de la suplementación con hierro. La falta de instalación sanitaria aumentaba el riesgo de anemia (OR = 1.26, IC 95% 1.00–1.60), mientras que el uso de anticonceptivos hormonales lo reducía. No existía asociación entre la edad, el lugar de residencia rural/urbana, el nivel de riqueza o el tipo de combustible para cocinar en el análisis ajustado.


En Tanzania, las mujeres embarazadas están particularmente a riesgo de padecer anemia moderada a severa, con el efecto modificado según el lugar de residencia, y los niveles de educación y riqueza. Las intervenciones de prevención deberían centrarse en mujeres con menor nivel educativo o sin unas instalaciones sanitarias adecuadas, y en mujeres embarazadas, particularmente si son de grupos sin educación o con menor nivel de riqueza.


Anaemia is a widespread public health problem that affects 25% of the global population (WHO 2008). It reduces physical work capacity (Haas & Brownlie 2001) and is a major contributor to death and disability worldwide (Ezzati et al. 2004). Women of childbearing age are at an increased risk of anaemia: the global prevalence is 42% in pregnant women and 30% in non-pregnant women of reproductive age. The prevalence varies by geographical region and is highest in Africa, where 57% of pregnant and 48% of non-pregnant women are anaemic (WHO 2008). Anaemia in women is associated with maternal mortality, preterm delivery, stillbirths and low birth weight (Allen 2000; Kidanto et al. 2009; Zhang et al. 2009; Ali et al. 2011). Additionally, maternal anaemia increases the risk of iron deficiency in infants (Kilbride et al. 1999; Kalaivani 2009).

Anaemia is a major public health problem in Tanzania, and understanding more about its determinants will inform how best to direct new and existing prevention measures (WHO 2008) and contribute towards achieving the millennium development goal 5 of reducing maternal mortality (United Nations 2006). However, previous studies conducted in Tanzania to assess determinants of anaemia among women have found conflicting results, and most have been carried out in geographically restricted regions of the country or among subgroups of women, and hence are not generalizable to the entire country (Matteelli et al. 1994; Shulman et al. 1996; Hinderaker et al. 2001, 2002; Marchant et al. 2002b; Massawe et al. 2002b; Msuya et al. 2011; Saathoff et al. 2011; Finkelstein et al. 2012). Low income, older maternal age, hookworm infestation, malaria infection and micronutrient deficiencies are some of the risk factors for anaemia among pregnant women in Tanzania (Antelman et al. 2000; Hinderaker et al. 2001; Marchant et al. 2002b; Massawe et al. 2002b; Msuya et al. 2011; Finkelstein et al. 2012). Additionally, Iron deficiency has been shown to be a risk factor for anaemia in adolescent girls (Massawe et al. 2002a).

Also, although some factors are known to increase anaemia risk, other potential risk factors, such as type of cooking fuel and body mass index (BMI), have been less well investigated. Information on the modifying effects of education and wealth, and contextual factors such as geographical area and place of residence on the impact of key risk factors is lacking. A better understanding of the relationship between different risk factors will allow identification of high-risk groups and facilitate improved targeted prevention.

Iron supplementation for pregnant women is a key national strategy for anaemia prevention in Tanzania, but coverage varies by education, wealth and place of residence (NBS & ICF Macro 2011). The effect of pregnancy on anaemia may therefore vary by these factors. The effect of education on anaemia may be weaker among the wealthiest women, who are more likely to have better access to iron-rich foods such as meat and eggs irrespective of their education (NBS & ICF Macro 2011).

We therefore sought to identify determinants of moderate-to-severe anaemia and to explore a priori interactions between key risk factors, among women of reproductive age in Tanzania using a recent large national health survey.

Materials and methods

We analysed data from the 2010 Tanzania Demographic and Health Survey (TDHS) conducted between December 2009 and May 2010. The TDHS used a stratified multistage cluster sampling design to collect data from a nationally representative sample of women aged 15–49 years, which included information on socio-demographics and maternal health (NBS & ICF Macro 2011).

Blood samples were collected from a finger prick using a microcuvette, and haemoglobin (Hb) analysis was performed on-site using a battery-operated portable HemoCue® analyser, which is valid when compared to standard laboratory methods (Nkrumah et al. 2011).

Outcome and exposures

Women were classified as: (i) mildly or not anaemic or (ii) moderately or severely anaemic, with the latter defined by Hb <10.0 g/dl and <11 g/dl among pregnant and non-pregnant women, respectively (WHO 2011).

The exposure variables were socio-demographic factors: age in years (15–24, 25–34, 35–49); education level (none, primary, secondary and higher); marital status (never, currently or formerly married); number of household members; geographical zone (Western, Northern, Central, Southern Highlands, Lake, Eastern, Southern and Zanzibar); regional coverage of iron supplementation during pregnancy (<45%, 45–54.9%, 55–64.9%, 65–74.9%, >74.9%); area of residence (urban, rural); and wealth quintile [determined by principal components analysis of ownership of household assets, access to utilities and type of housing material (Rutstein & Rojas 2006)]. Reproductive health information included as follows: number of children ever born (0, 1–2, 3–4, >4); births in last 3 years (0, 1, >1); pregnancy (pregnant, not sure/not pregnant); ever terminated a pregnancy; and current contraceptive method (none, pill/injectable/Norplant, intra-uterine device [IUD], non-hormonal). Other potential risk factors for anaemia included in the analyses were as follows: BMI in kg/m2 (<18.5, 18.5–24.9, ≥25); current tobacco use; use of an insecticide-treated net (ITN) the night before the survey; type of toilet facility (flush/pit latrine, none/bush); and type of cooking fuel (electricity/gas/kerosene, charcoal, wood/grass/straw).

Statistical methods

Data were analysed using Stata v11, using survey commands to account for the study design and sample weights by specifying the stratifying, weighting and clustering variables available in the dataset. The weight for a particular individual is the inverse of the individual's selection probability multiplied by the inverse of the individual response rate of the individual's response rate group. Further details on weighting in the TDHS are available elsewhere (Rutstein & Rojas 2006). Adjusting for clustering in the sample prevents underestimation of variability in the estimates by adjusting standard errors, and weighting the data adjusts for undersampling and oversampling within strata.

All women aged 15–49 years who were usual residents in the households were included in the analysis. Those with outlying Hb values were excluded. Univariable analyses were performed using all available data but women with missing data were excluded from multivariable analysis. Unadjusted and adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for the associations between exposures and moderate-to-severe anaemia (hereafter referred to as anaemia) were computed using logistic regression. Exposures associated with anaemia with a P-value of <0.1 in unadjusted analyses were included in the multivariable analysis. The significance of each variable in the model was assessed using adjusted Wald tests. Age was treated as an a priori risk factor and retained in multivariable models. The final model was assessed for interaction between pregnancy and wealth quintile, education level and residence, and between education level and wealth quintile. As the use of contraception does not apply to pregnant women, we performed a subgroup analysis to assess the effect of contraception on anaemia in non-pregnant women only. The reported frequencies are unweighted, while the percentages are weighted.


Characteristics of women and unadjusted associations

A total of 9477 women were included in unadjusted analyses after excluding two women with outlying Hb values of 63.5 g/dl and 68.0 g/dl. Eight (0.1%) women were excluded from multivariable analyses due to missing data. The values for Hb concentration were normally distributed with a mean of 12.1 g/dl (standard error 0.03). The proportion of women with mild, moderate and severe anaemia was 19.5% (95% CI 18.4–20.7), 18.2% (95% CI 17.1–19.3) and 1.9% (95% CI 1.6–2.2), respectively.

Women with and without anaemia had similar distributions for age, marital status, number of children ever born and number of births in the last 3 years. There was also little difference in BMI, tobacco use, having terminated a pregnancy and use of an ITN (Table 1). Anaemia status varied by geographical area with all areas other than the Eastern and Zanzibar areas having a reduced risk of anaemia compared with Western areas. Women in regions with higher iron supplementation during pregnancy were less likely to be anaemic. The risk of anaemia increased with increasing number of household members. Having an education and residing in rural areas were associated with a significantly reduced risk of anaemia. In unadjusted analyses, women in the highest wealth quintile had an increased risk of anaemia compared with those in the lowest quintile. Being pregnant, using cooking fuel other than wood, straw or grass and having no toilet facilities were also associated with a significantly increased anaemia risk (Table 1).

Table 1. Characteristics of women of reproductive age in Tanzania and unadjusted associations of these characteristics with anaemia
VariableAnaemia status na (%)Unadjusted odds ratio (95% CI)P-value
None/mild (n = 7381)Moderate/severe (n = 2096)
  1. CI, confidence interval.

  2. The frequencies are unweighted, whereas the percentages are weighted.

  3. a

    n = 9465 for body mass index and n = 9473 for cooking fuel; type of toilet and use of tobacco.

Age in years
15–242935 (39.4)812 (40.3)10.709
25–342180 (30.2)603 (30.4)0.98 (0.86–1.13)
35–492266 (30.4)681 (29.3)0.94 (0.82–1.08)
Woman's education level
No education1321 (18.5)484 (22.6)1<0.001
Primary4453 (66.4)1090 (60.6)0.75 (0.63–0.89)
Secondary and above1607 (15.1)522 (16.8)0.91 (0.72–1.15)
Marital status
Never married1951 (24.4)536 (24.3)10.668
Currently married4637 (64.1)1319 (63.3)0.99 (0.85–1.15)
Formerly married793 (11.5)241 (12.4)1.08 (0.86–1.35)
Household members, mean (±SE)6.6 (0.15)7.0 (0.21)1.02 (1.00–1.04)0.021
Geographical zone
Western923 (15.8)346 (22.1)1<0.001
Northern1068 (15.9)196 (12.1)0.54 (0.42–0.70)
Central587 (8.9)100 (6.1)0.49 (0.34–0.69)
Southern Highlands851 (15.1)98 (7.5)0.36 (0.24–0.52)
Lake963 (18.3)226 (17.7)0.69 (0.54–0.89)
Eastern701 (13.5)271 (20.9)1.10 (0.87–1.41)
Southern754 (9.8)189 (8.9)0.64 (0.51–0.81)
Zanzibar1534 (2.8)670 (4.9)1.28 (1.07–1.52)
Area of residence
Urban1768 (26.0)588 (32.4)1<0.001
Rural5613 (74.0)1508 (67.6)0.73 (0.62–0.87)
Iron supplementation coverage
<45%672 (15.1)263 (22.3)1<0.001
45–54.9%1464 (21.5)335 (17.9)0.57 (0.45–0.71)
55–64.9%1344 (21.9)281 (19.0)0.59 (0.45–0.76)
65–74.9%2119 (29.5)591 (29.5)0.69 (0.57–0.84)
> = 75%1782 (12.0)626 (11.8)0.61 (0.48–0.77)
Wealth quintile
Poorest1187 (16.9)340 (16.7)10.018
Poorer1425 (20.1)373 (17.9)0.90 (0.74–1.10)
Middle1442 (20.4)365 (18.7)0.93 (0.75–1.16)
Richer1674 (21.2)474 (20.1)0.96 (0.79–1.18)
Richest1653 (21.4)544 (26.6)1.26 (1.01–1.56)
Children ever born
01990 (24.2)586 (25.6)10.190
1–21815 (26.9)501 (28.6)1.01 (0.87–1.16)
3–41560 (22.4)409 (20.5)0.86 (0.73–1.03)
>42016 (26.4)600 (25.3)0.91 (0.78–1.06)
Births in last 3 years
04336 (57.2)1223 (56.6)10.380
12610 (36.8)722 (36.2)0.99 (0.87–1.14)
>1435 (6.0)151 (7.1)1.20 (0.92–1.56)
Pregnant629 (8.6)257 (13.8)1.71 (1.41–2.08)<0.001
Ever terminated pregnancy1217 (16.9)399 (18.3)1.10 (0.96–1.27)0.177
Body mass index (Kg/m2)
<18.5870 (10.9)245 (10.8)10.448
18.5–244914 (68.2)1424 (69.8)1.04 (0.85–1.26)
>251588 (20.9)424 (19.4)0.93 (0.75–1.16)
Uses tobacco98 (1.4)30 (1.1)0.82 (0.48–1.43)0.493
Type of cooking fuel
Wood/straw/grass5798 (75.5)1586 (67.9)1<0.001
Charcoal1404 (21.5)459 (28.2)1.46 (1.23–1.73)
Electricity/gas/kerosene176 (3.0)50 (3.9)1.42 (1.03–1.97)
No toilet facility/bush1177 (13.4)443 (16.6)1.28 (1.05–1.57)0.014
Slept under insecticide-treated net3709 (51.3)1092 (54.0)1.12 (0.97–1.28)0.117

Adjusted associations

In the adjusted model, pregnancy, education level, geographical zone, iron supplementation coverage and toilet facility remained statistically significantly associated with anaemia but age, area of residence, wealth, type of cooking fuel and number of household members did not (Table 2).

Table 2. Adjusted odds ratios (ORs) for the associations between various factors and anaemia among women in Tanzania
VariableCategoriesAdjusteda ORs (95% CI)t-test P-valueP-valueb
  1. a

    All variables are adjusted for each other.

  2. b

    Adjusted Wald test P-value for the overall significance of the variable in the model.

Age15–241 0.746
25–341.01 (0.88–1.16)0.887
>341.06 (0.92–1.22)0.445
ZoneWestern1 <0.001
Northern0.75 (0.56–1.02)0.064
Central0.84 (0.57–1.24)0.381
Southern Highlands0.45 (0.30–0.66)<0.001
Lake0.72 (0.56–0 .92)0.009
Eastern1.48 (1.06–2.07)0.023
Southern1.11 (0.79–1.56)0.557
Zanzibar2.05 (1.42–2.96)<0.001
Iron supplementation coverage<45%1 0.005
45–54.9%0.72 (0.55–0.94)0.016
55–64.9%0.57 (0.38–0.85)0.006
65–74.9%0.58 (0.42–0.79)0.001
> = 75%0.47 (0.31–0.71)<0.001
ResidenceUrban1 0.745
Rural0.96 (0.74–1.24)0.745
Wealth quintilePoorest1 0.684
Poorer0.91 (0.74–1.12)0.374
Middle1.06 (0.85–1.32)0.605
Richer1.04 (0.83–1.32)0.712
Richest1.05 (0.78–1.41)0.764
PregnantNo1 <0.001
Yes1.75 (1.43–2.15)<0.001
EducationNo education1 0.013
Primary0.77 (0.64–0.92)0.004
Secondary or higher0.82 (0.62–1.07)0.137
Number of household members 1.01 (1.00–1.03)0.1190.119
Type of toilet facilityFlush/pit1 0.046
No facility/bush1.26 (1.004–1.59)0.046
Cooking fuelWood/straw/grass1 0.118
Charcoal1.36 (1.01–1.83)0.043
Electricity/gas/kerosene1.22 (0.78–1.91)0.385

The odds of anaemia varied by geographical zone with women in the Southern Highlands and Lake areas having lower odds of anaemia than women in the Western zone (OR 0.45, 95% CI 0.30–0.66 and OR 0.72, 95% CI 0.56–0.92, respectively). There was, however, an increased risk of anaemia in the Eastern zone (OR 1.48, 95% CI 1.06–2.07) and in Zanzibar (OR 2.05, 95% CI 1.42–2.96). The risk of anaemia fell with increasing regional prevalence of iron supplementation during pregnancy. Women from households without toilets had 26% higher odds of being anaemic than those with a toilet (OR 1.26, 95% CI 1.00–1.59).

Pregnancy and anaemia: interactions with, wealth, education and area of residence

Pregnancy was associated with a 75% increase in the odds of anaemia (OR 1.75, 95% CI 1.43–2.15, Table 2). There were significant interactions between pregnancy and each of wealth, place of residence and education (P < 0.001 for all). The effect of pregnancy on anaemia was strongest in women in the poorest and middle quintiles of wealth, and lowest among women in the wealthiest quintiles (Figure 1). There was a strong interaction with education, with pregnancy increasing the risk of anaemia by more than two-fold in non-educated women (OR 2.44, 95% CI 1.67–3.56) but not in those with secondary or higher education (Figure 1). The effect of pregnancy was slightly stronger in urban than in rural areas (OR 1.91, 95% CI 1.19–3.05 and OR 1.73, 95% CI 1.38–2.17, respectively).

Figure 1.

Adjusted odds ratios for the effect of pregnancy on anaemia, stratified by education, wealth and area of residence, and for the effect of education on anaemia, stratified by pregnancy and wealth. *Adjusted for age, area of residence, number of household members, geographical zone, iron supplementation coverage, type of toilet facility and cooking fuel.

Education and anaemia: interactions with pregnancy and wealth

Compared with having no education, primary education reduced the risk of anaemia by about 20% (OR 0.77, 95% CI 0.64–0.92). Having a secondary or higher education was associated with a similarly reduced, although not statistically significant, risk of anaemia (Table 2). There were significant interactions between education and wealth quintile (P = 0.025) and pregnancy (P < 0.001). Education was associated with a lower risk of anaemia in the poorest quintile (Figure 1). In contrast, there was no clear effect of education on anaemia in the other wealth quintiles. Primary education was associated with a greater reduced risk of anaemia among pregnant women than among non-pregnant women (OR 0.52, 95% CI 0.34–0.80 and OR 0.82, 95% CI 0.68–1.00, respectively; Figure 1).

Contraception use and anaemia

In the subgroup analysis of the effect of contraception on anaemia among 8561 non-pregnant women, use of hormonal contraception was associated with a 47% reduced risk of anaemia (adjusted OR 0.53, 95% CI 0.42–0.66). Use of other contraceptive methods was not associated with anaemia risk, but the number of IUD users in particular was very small (Table 3).

Table 3. Association between current use of contraception and anaemia among non-pregnant women
Current contraceptive typeAnaemia statusUnadjusted odds ratio (95% CI)Adjusted odds ratio (95% CI)at-test P-value
None/mild (n = 6752)Moderate/Severe (n = 1839)
  1. CI, confidence interval.

  2. The frequencies are unweighted, whereas the percentages are weighted.

  3. a

    Adjusted for age, wealth index, geographical zone, toilet facility, type of cooking fuel, education, place of residence and iron supplementation coverage.

None4717 (66.4)1467 (73.9) 1 
Oral pill/injection/Norplant1209 (1901)156 (10.7)0.51 (0.41–0.63)0.53 (0.42–0.66)<0.001
Intra-uterine device31 (0.5)6 (0.4)0.64 (0.25–1.63)0.76 (0.30–1.92)0.558
Other non-hormonal795 (14.1)210 (15.1)0.96 (0.79–1.16)0.97 (0.80–1.17)0.731


Moderate-to-severe anaemia among Tanzanian women of reproductive age was associated with pregnancy, education, geographical zone, regional prevalence of iron supplementation, use of contraceptives and type of toilet facility. The effect of pregnancy on anaemia varied significantly by place of residence, wealth and education level. Pregnancy increased the risk of anaemia to a greater extent among non-educated women. The effect of education on anaemia was also modified by wealth and pregnancy status, with education reducing the risk of anaemia more among the poorest women, and among pregnant women. Women without a toilet were at an increased risk of anaemia. Among non-pregnant women, hormonal contraception use was associated with a decreased risk of anaemia.

Our finding that education is associated with a decreased risk of anaemia is in keeping with the results of other studies which observed similar associations between education and moderate-to-severe anaemia (Ngnie-Teta et al. 2008; Ghosh 2009). In our study, the effect of education differed according to wealth and was particularly strong and significant among the poorest women. This could be explained by educated women being more likely to have better access to health information (Tanzania Commission for AIDS (TACAIDS), Zanzibar AIDS Commission (ZAC), National Bureau of Statistics (NBS), Office of the Chief Government Statistician (OCGS) & ICF International 2013) and to have better healthcare seeking behaviour than non-educated women (Ghosh et al. 2013). Our results suggest that educating women may help reduce the risk of anaemia, particularly among those in the lowest wealth strata.

Our finding that the risk of anaemia does not vary by urban/rural area of residence is consistent with that of a study conducted in Mali which similarly found no association between area of residence and moderate-to-severe anaemia (Ngnie-Teta et al. 2008). In contrast, a study conducted in India found an increased risk of anaemia among rural women, especially poorer rural women (Bentley & Griffiths 2003).

Pregnancy is an established risk factor for anaemia (Balarajan et al. 2011). The strength of association in our study is lower than that reported from analysis of the Mali Demographic and Health Survey data (Ngnie-Teta et al. 2008). This is probably because, unlike in the Mali study, we used different cut-off points for defining anaemia in pregnant and non-pregnant women. Pregnant women usually receive routine doses of iron supplements to prevent anaemia and coverage of iron supplementation varies throughout Tanzania. By showing that the risk of anaemia fell with increasing regional iron supplementation coverage, our study provides further evidence on the protective effect of iron supplementation on anaemia (Yakoob & Bhutta 2011). The variation in the effect of pregnancy on anaemia by wealth quintile could be due to differences in individual use of iron supplements, perhaps as a result of differences in healthcare access and health-seeking behaviour (NBS & ICF Macro 2011). Education may be associated with reduced anaemia risk in pregnant women but in not non-pregnant women because iron supplements are usually distributed during pregnancy and educated women are more likely to access the supplements than the uneducated (NBS & ICF Macro 2011).

Use of a copper IUD has been associated with increased risk of anaemia (Kivijarvi et al. 1986; Hassan et al. 1999b) through the side effect of heavy menstrual blood loss (Hassan et al. 1999a). However, some hormone-coated IUDs may reduce excessive menstrual blood loss and may be more appropriate to use, to limit risk of anaemia. We did not find an increased risk of anaemia among IUD users, although this was based on small numbers. Further studies that are adequately powered to investigate the effect of IUDs on anaemia are needed, as if an association does exist, women using IUDs may benefit from iron supplementation.

As expected, we found that hormonal contraception use was associated with a reduced risk of anaemia. Prevention of iron deficiency anaemia is known to be one of the non-contraceptive benefits of oral contraceptives (Mishell 1993).

The type of toilet facility is a measure of the general level of sanitation. In Tanzania, 15.9% of households have no toilet (NBS & ICF Macro 2011), and schistosomiasis and soil-transmitted helminths are found countrywide. In a study in northern Tanzania, 65.3% of pregnant women were infected with S. mansoni and 56.3% with hookworms (Ajanga et al. 2006). Women in households without toilets are at increased risk of infection by hookworms and other parasites, which may explain our findings that lack of toilet facilities increases anaemia risk. Hookworms increase the risk of anaemia in poor communities where sanitation standards are low (Brooker et al. 2008; Finkelstein et al. 2012), and treatment of S. mansoni or hookworm infections with chemotherapy improved Hb levels among anaemic children in Uganda (Koukounari et al. 2006). Parasite control through deworming and improved sanitation might therefore reduce the burden of anaemia.

Surprisingly, we did not find that ITNs reduced anaemia risk, but our results are in agreement with those of other studies, including a multicountry study which found that use of ITNs was not associated with moderate-to-severe anaemia in most nationally representative household surveys (Ngnie-Teta et al. 2008; Florey 2012). In contrast, in a study of pregnant women, Marchant et al. (2002a) found that ITNs did reduce anaemia. However, this study used a different Hb threshold for anaemia, included pregnant women only, and was undertaken in the context of social marketing of ITNs. In the TDHS, women are asked whether they slept under an ITN the night before the survey, which may not accurately reflect the usual pattern of mosquito net usage.

Finally, although use of biofuels for cooking increases the risk of anaemia in children (Mishra & Retherford 2007), we did not find any association between type of cooking fuel and anaemia risk. This lack of association with biofuels in adult women could be explained by the body's compensatory response to chronic Hb reduction, whereby the kidneys produce erythropoietin which stimulates production of more red blood cells (Leifert 2008).

Our study has a number of strengths. Demographic and Health Surveys use standardised methodology for data collection and adopt strict quality assurance procedures in data management (Macro International 1996; Rutstein & Rojas 2006) resulting in nationally representative and high-quality data, which increases the reliability of our results. To our knowledge, this is the first study to explore risk factors for moderate-to-severe anaemia among Tanzanian women using nationally representative data. The representativeness of the data increases the generalizability of the results to the country (and to similar settings), provides a better understanding of the determinants of moderate-to-severe anaemia among Tanzanian women and improves the usefulness of the findings, especially in identifying at risk groups and developing targeted interventions. Our study population was large, which enabled us to assess multiple associations between anaemia and various risk factors and to examine possible interactions. Our study is novel in that we were able to examine the effect of individual, household and contextual factors on anaemia risk in Tanzania.

There are some limitations. It was not possible to control for the effect of some factors associated with anaemia, such as HIV and malaria, and micronutrient intake, which may have led to residual confounding. Data on HIV prevalence by zone are consistent with some, but not all, of the geographical differences in anaemia observed in our study (Tanzania Commission for AIDS (TACAIDS), Zanzibar AIDS Commission (ZAC), National Bureau of Statistics (NBS), Office of the Chief Government Statistician (OCGS) & ICF International 2013). Malaria prevalence among children (a proxy for prevalence in women) (Tanzania Commission for AIDS (TACAIDS), Zanzibar AIDS Commission (ZAC), National Bureau of Statistics (NBS), Office of the Chief Government Statistician (OCGS) & ICF International 2013) and the consumption pattern of iron-rich foods among women with children aged <3 years (NBS & ICF Macro 2011) also vary by zone, but do not appear to explain observed differences in anaemia. However, the consumption of vitamin A-rich foods is consistent with variations in anaemia risk in some zones (NBS & ICF Macro 2011). For instance, Zanzibar, which has the highest anaemia risk, has the lowest proportion of women who consumed vitamin A-rich foods. As the TDHS is a cross-sectional study, some observations may be the result of reverse causality. For instance, women who are already anaemic may tend to sleep under a bed net. Finally, as our reference group included women who are mildly anaemic, some of the observed associations might have been underestimated. However, there is evidence that well-known socio-demographic and environmental risk factors for anaemia, including pregnancy, are associated with moderate-to-severe, but not mild, anaemia among African women (Ngnie-Teta et al. 2008). This observation supports the suggestion by some researchers to lower the cut-off point for anaemia among black women to 10 g/dl (Johnson-Spear & Yip 1994), which is in line with our definition of anaemia among pregnant women.


Anaemia among women of reproductive age is a major public health problem in Tanzania. Pregnant women in particular are at greater risk, especially if they are uneducated or in lower wealth groups. Current iron and folic acid supplementation during pregnancy should be addressed to improve the coverage of this important intervention. Antenatal care is an opportunity to prevent anaemia among pregnant women through iron supplementation and malaria prophylaxis. Yet, even though 98% of pregnant women in Tanzania attend antenatal care, only 59% receive iron supplements and 68% receive malaria tablets during these visits (NBS & ICF Macro 2011). The health system needs to be strengthened to reduce these missed opportunities for anaemia prevention and control. The burden of anaemia in eastern Tanzania and Zanzibar could be reduced through improved delivery of existing and new anaemia control interventions. Targeted interventions are needed for women of reproductive age, particularly poor and uneducated women and those with poor access to proper sanitation.