Background
Anaemia is a global public health problem affecting over 1.62 billion people [1]. It affects all age groups of people but pregnant women and children are more vulnerable. Iron deficiency is the leading cause of anaemia among pregnant women globally [2]. Other causes of anaemia in pregnancy are heavy blood loss as may occur during menstruation and parasitic infections, conditions such as malaria and HIV which lower blood haemoglobin (Hb) concentrations, and micronutrient deficiencies [1]. Low intake and poor absorption of iron especially during growth and pregnancy when iron requirements are higher remain risk factors for anaemia [3]. The World Health Organisation defines anaemia in pregnant women as Hb concentration less than 11.0 g/dl [4]. In pregnant women, anaemia increases risk for maternal and child mortality and has negative consequences on the cognitive and physical development of children [5], and on work productivity [6, 7]. Severe anaemia is associated with fatigue, weakness, breathlessness, dizziness, drowsiness and perceived paleness of the skin [8]. In the developing world, anaemia is a priority nutritional problem because of the economic, social, and other negative consequences associated with it [9]. Africa carries a high burden of anaemia with a prevalence of 65.8 % among pregnant women [1]. In Uganda, the prevalence of anaemia among women of child bearing age has been reported to be 24 % overall, and 13.1 and 18.8 % in Northern and Western Uganda, respectively [10].
Poverty is one of the risk factors for iron deficiency in pregnant women [11, 12], and given the fact that Northern region is the poorest region in Uganda [13] with high rates of malnutrition, the problem of anaemia cannot be underestimated. Despite the known consequences of anaemia in pregnancy, there is scanty information on the prevalence of anaemia in pregnant women in Northern Uganda since the end of the Lord’s Resistance Army (LRA) rebellion in 2006. The two decades of civil war in Northern Uganda led to the destruction of social services and left the majority of the population in poverty. Western Uganda on the other hand remained peaceful during the same period. It was therefore necessary to compare the prevalence of anaemia in the two regional hospitals. This would give an insight into the long term effect of the war on women of child bearing age. The objective of this study was to determine the prevalence of anaemia and associated risk factors among pregnant women attending antenatal care at Gulu and Hoima Regional Hospitals so that evidence-based interventions can be put in place.
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Methods
Study design and setting
We conducted a cross sectional study at Gulu and Hoima Regional Hospitals from July to October 2012. Gulu and Hoima Regional Hospitals are located in Northern and Western Uganda, respectively. Gulu district headquarters are located approximately 340 km North of Uganda’s capital city, Kampala. The population of Gulu District is 443,733 [14]. The coordinates of the district are: 02 45 N, 32 00E. Hoima district headquarters are located approximately 230 km Northwest of Kampala. The population of Hoima district is 573,903 [14]. The coordinates of the district are: 01 24 N, 31 18E.
Study population
Our study participants were pregnant women attending antenatal care in the two regional hospitals. All pregnant women who consented to the study and who reported that they did not have sickle cell disease were eligible to participate in the study. This is because persons who have sickle cell disease usually have lower Hb levels than the normal persons.
Sample size and sampling procedures
The sample size for this study was calculated using Kish Leslie formula. We considered 95 % confidence interval, 5 % margin of error, and 45 and 64 % prevalence of anaemia for Gulu (Northern Uganda) and Hoima (Western Uganda), respectively as was reported by Ugandan Demographic and Health surveys [15]. We factored a 10 % non response rate in the sample size calculation. The two hospitals were purposively sampled since they are regional referral hospitals in the study areas with high patient load, from different backgrounds and settings. We used simple random sampling to select the study participants. Each day, before the provision of health education, the names of every pregnant woman attending the antenatal clinic was taken. Fifty percent of the women were then randomly selected until the required sample size was attained. The probability sampling was employed to avoid selection bias.
Socio-demographic data
Data on socio-demographic characteristics were collected using structured questionnaires (Additional file 1). The questionnaire was pre-tested in an area with similar settings to those of the study hospitals. The questionnaire was then refined to further improve its validity and reliability. The questionnaires were both in English and the local languages. Trained research assistants who were fluent in both English and the local language (Luo or Lunyoro) conducted face-to-face interviews with the pregnant women. The interviews were conducted in privacy to maintain confidentiality, within the hospital premises.
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Collection and analysis of blood samples
The vein puncture site was cleaned using a swab containing 70 % alcohol and using aseptic methods, an appropriate vein was identified and a hypodermic needle introduced into the vein. About 3–4 ml of venous blood was drawn into a syringe and then transferred into a sterile vacutainer containing EDTA and transported to the laboratory for analysis. Trained laboratory technicians did the analyses both in Gulu and Hoima Regional Hospitals. Laboratory analysis was done using an automated analyser, (Celltac, Automated Haematology Analyzer, MEK-6400. NIHON KOHDEN). The manufacturer supplied controls were run every morning to ensure that the analyser was operating within 2.0 standard deviations. The closed mode of blood sampling was used; the analyser automatically sampled blood, processed, analysed and printed out the haemoglobin concentration levels. Pregnant women with haemoglobin concentration of less than 11.0 g/dl were categorised as anaemic. Anaemia was considered severe when haemoglobin concentration was less than 7.0 g/dL, moderate when haemoglobin was between 7.0 and 9.9 g/dL, and mild from 10 to 10.9 g/dL [4].
Statistical analysis
Both the laboratory and questionnaire data were checked and cleaned for completeness and consistency. Participants with missing data on haemoglobin level were excluded from the analyses. Statistical analysis was performed using Stata version 12. Quantitative variables were categorised into groups basing on either biologically recognised groupings like trimester in pregnancy or societal recognised groupings like education levels. Descriptive statistics was employed for the analysis of demographic data. We used odds ratios as a measure of association, with a 95 % confidence interval. Variables with p-values <0.2 at bivariable analysis and those with biological plausibility with respect to anaemia were put into backward stepwise multivariable logistic regressions to determine the independent predictors for anaemia in pregnancy. Statistical significance was set at P < 0.05.
Ethical considerations
The study was approved by Gulu University Research Ethics Committee. Written informed consent was obtained from each study participant before data collection. Privacy and confidentiality was maintained throughout the study process.
Results
A total of 743 pregnant women took part in this study for a response rate of 91.1 %. Some participants withdrew from the study at the point of blood collection. A large majority of the study participants were below the age of 25 years (Table 1). A total of 164 (22.1 %) women were anaemic. The prevalence of anaemia in Gulu was 32.9 % (117/356) and that in Hoima was 12.1 % (47/387). The prevalence of mild, moderate and severe anaemia in Gulu were 23, 9, and 0.8 % respectively; and in Hoima were 9, 2.5, and 0.5 %, respectively (Table 2). In the bivariable analysis, anaemia was significantly associated with the level of education attained, occupation, being in or out of school, and being a resident of Gulu district (Table 3). Being a housewife (AOR = 1.7, 95 % CI: 1.05–2.68) and a resident in Gulu districts (AOR = 3.6, 95 % CI: 2.41–5.58) were independent risk factors for anaemia (Table 4).
Table 1
Socio-demographic characteristics of the study participants (N = 743)
Hoima (N = 387) | Gulu (N = 356) | |||
|---|---|---|---|---|
Characteristic | Number | Percent | Number | Percent |
Age | ||||
≤19 | 102 | 26.4 | 97 | 27.3 |
20–24 | 116 | 30.0 | 127 | 35.7 |
25–29 | 91 | 23.5 | 87 | 24.4 |
30–34 | 54 | 14.0 | 32 | 9.0 |
35–39 | 22 | 5.7 | 9 | 2.5 |
>39 | 2 | 0.5 | 4 | 1.2 |
Education level | ||||
No education | 26 | 6.7 | 23 | 6.5 |
Primary | 186 | 48.1 | 204 | 57.3 |
Secondary | 149 | 38.5 | 104 | 29.2 |
Tertiary | 26 | 6.7 | 25 | 7.0 |
Still in school | ||||
Yes | 26 | 6.7 | 13 | 3.7 |
No | 361 | 93.3 | 343 | 96.3 |
Marital status | ||||
Married | 321 | 83.0 | 332 | 93.3 |
Single | 60 | 15.5 | 19 | 5.3 |
Widowed | 1 | 0.3 | 3 | 0.8 |
Separated/divorced | 5 | 1.3 | 2 | 0.6 |
Occupation | ||||
Farming | 130 | 33.6 | 92 | 25.8 |
Trader | 92 | 23.8 | 62 | 17.4 |
Formal employment | 51 | 13.2 | 32 | 9.0 |
Handicraft | 0 | 0.0 | 10 | 2.8 |
Housewife | 114 | 29.4 | 160 | 44.9 |
Household size | ||||
1–5 | 286 | 73.9 | 249 | 69.9 |
6–10 | 94 | 24.3 | 96 | 27.0 |
>10 | 7 | 1.8 | 11 | 3.1 |
Wealth index | ||||
Lowest | 278 | 71.8 | 184 | 51.7 |
Second lowest | 85 | 22.0 | 149 | 41.9 |
Medium | 15 | 3.9 | 17 | 4.8 |
High | 9 | 2.3 | 6 | 1.7 |
Residence | ||||
Rural | 164 | 42.4 | 137 | 38.5 |
Urban | 223 | 57.6 | 219 | 61.5 |
Trimester | ||||
First | 42 | 10.8 | 13 | 3.7 |
Second | 200 | 51.7 | 105 | 29.5 |
Third | 145 | 37.5 | 238 | 66.8 |
Gravidity | ||||
1–4 | 309 | 79.8 | 301 | 84.6 |
>4 | 78 | 20.2 | 55 | 15.4 |
Delivery gap | ||||
Never delivered | 130 | 33.6 | 120 | 33.7 |
1–11 months | 10 | 2.6 | 12 | 3.4 |
12–24 | 75 | 19.4 | 41 | 11.5 |
25–36 | 60 | 15.5 | 79 | 22.2 |
>36 | 112 | 28.9 | 104 | 29.2 |
Table 2
Anaemia prevalence in Gulu and Hoima districts
District | Number | Percent |
|---|---|---|
Gulu | ||
Total anaemia | 117 | 32.9 |
Mild anaemia | 82 | 23.0 |
Moderate anaemia | 32 | 9.0 |
Severe anaemia | 3 | 0.8 |
Hoima | ||
Total anaemia | 47 | 12.1 |
Mild anaemia | 35 | 9.0 |
Moderate anaemia | 10 | 2.5 |
Severe anaemia | 2 | 0.5 |
Table 3
Chi- square tests of anaemia and associated risk factors among pregnant women in Gulu and Hoima Regional Hospitals
Variable | Anaemic | Non anaemic | P-value | ||
|---|---|---|---|---|---|
Number | Percentage | Number | Percentage | ||
Age | |||||
≤19 | 47 | 23.6 | 152 | 76.4 | |
20–24 | 48 | 19.8 | 195 | 80.2 | 0.62 |
25–29 | 42 | 23.6 | 136 | 79.4 | |
30–34 | 21 | 24.4 | 65 | 75.6 | |
35–39 | 4 | 12.9 | 27 | 87.1 | 0.58b
|
>39 | 2 | 33.3 | 4 | 66.7 | |
Education | |||||
No education | 11 | 45.8 | 13 | 54.2 | |
Primary | 87 | 22.8 | 295 | 77.2 | 0.01a
|
Secondary | 59 | 20.9 | 224 | 79.1 | |
Higher | 7 | 13.0 | 47 | 87.0 | |
Still in school | |||||
Yes | 3 | 7.7 | 36 | 92.3 | 0.03b
|
No | 161 | 22.9 | 543 | 77.1 | 0.03a
|
Occupation | |||||
Farmer | 38 | 17.1 | 184 | 81.9 | |
Trader | 31 | 20.1 | 123 | 79.9 | 0.03a
|
Formal employment | 15 | 18.1 | 68 | 81.9 | |
Handicraft | 3 | 30.0 | 7 | 70.0 | 0.03b
|
Housewife | 77 | 28.1 | 197 | 71.9 | |
Residence | |||||
Rural | 67 | 22.3 | 234 | 77.7 | 0.9 |
Urban | 97 | 22.0 | 345 | 78.0 | |
Marital status | |||||
Single | 14 | 17.7 | 65 | 82.3 | 0.2 |
Married | 145 | 22.2 | 508 | 77.8 | |
Widowed | 2 | 50.0 | 2 | 50.0 | 0.1b
|
Separated/divorced | 3 | 42.9 | 4 | 57.1 | |
District | |||||
Hoima | 47 | 12.1 | 340 | 87.9 | <0.01a
|
Gulu | 117 | 32.9 | 239 | 67.1 | |
Trimester | |||||
First | 8 | 14.6 | 47 | 85.4 | 0.2 |
Second | 63 | 20.7 | 242 | 79.3 | |
Third | 93 | 24.3 | 290 | 75.7 | |
Gravidity | |||||
1–4 | 133 | 21.8 | 477 | 78.2 | 0.7 |
>5 | 31 | 23.3 | 102 | 76.7 | |
Delivery gap (months) | |||||
Never delivered | 61 | 24.4 | 189 | 75.6 | 0.8 |
1–11 | 5 | 22.7 | 17 | 77.3 | |
12–24 | 23 | 19.8 | 93 | 80.2 | |
25–36 | 31 | 22.3 | 108 | 77.7 | |
>36 | 44 | 20.4 | 172 | 79.6 | |
Household size | |||||
1–5 | 111 | 20.8 | 424 | 79.2 | 0.3 |
6–10 | 48 | 25.3 | 142 | 74.7 | |
>10 | 5 | 27.8 | 13 | 72.2 | |
Wealth index | |||||
Lowest | 100 | 21.7 | 362 | 78.3 | 0.7 |
Second lowest | 56 | 23.9 | 178 | 76.1 | |
Middle | 5 | 15.6 | 27 | 84.4 | |
High | 3 | 20.0 | 12 | 80.0 | |
Table 4
Multivariable logistic regression analysis for risk factors for anaemia in Gulu and Hoima Regional Hospitals
Variable | Number | Anaemic (%) | Crude OR (95 % CI) | AOR (95 % CI) |
|---|---|---|---|---|
Age | ||||
<20 | 199 | 47 (23.6) | 1.0 | 1.0 |
20–24 | 243 | 48 (19.8) | 0.8 (0.51–1.60) | 1.8 (0.50–1.32) |
25–29 | 178 | 42 (23.6) | 1.0 (0.62–1.61) | 1.1 (0.63–1.80) |
30–34 | 86 | 21 (24.4) | 1.0 (0.58–1.89) | 1.3 (0.68–2.56) |
35–39 | 31 | 4 (12.9) | 0.5 (0.16–1.45) | 0.6 (0.19–1.85) |
≥40 | 6 | 2 (33.3) | 1.6 (0.29–9.16) | 1.4 (0.22–8.99) |
Education | ||||
No education | 24 | 16 (32.7) | 1.0 | 1.0 |
Primary | 382 | 86 (22.1) | 0.3 (0.15–0.81) | 0.5 (0.27–1.10) |
Secondary | 283 | 55 (21.7) | 0.3 (0.13–0.74) | 0.6 (0.30–1.31) |
Higher | 547 | 7 (13.0) | 0.2 (0.52–0.60) | 0.4 (0.11–1.20) |
Occupation | ||||
Farmer | 222 | 38 (17.1) | 1.0 | 1.0 |
Trader | 154 | 31 (20.1) | 1.2 (0.72–2.07) | 1.3 (0.73–2.21) |
Formal employment | 83 | 15 (18.1) | 1.1 (0.55–2.07) | 1.4 (0.63–2.96) |
Handicraft | 10 | 3 (30.0) | 2.1 (0.51–8.44) | 1.1 (0.25–4.45) |
Housewife | 274 | 77 (28.1) | 1.9 (1.22–2.94) | 1.7 (1.05–2.68) |
Residence | ||||
Rural | 301 | 67 (22.3) | 1.0 | |
Urban | 442 | 97 (22.0) | 1.0 (0.69–1.40) | |
Marital status | ||||
Single | 79 | 14 (17.7) | 1.0 | 1.0 |
Married | 653 | 145 (22.2) | 1.3 (0.72–2.43) | 0.8 (0.43–1.64) |
Widowed | 4 | 2 (50) | 4.6 (0.58–37.46) | 2.0 (0.21–18.89) |
Separated/divorced | 7 | 3 (42.9) | 3.0 (0.68–17.92) | 3.0 (0.55–16.85) |
District | ||||
Hoima | 387 | 47 (12.1) | 1.0 | 1.0 |
Gulu | 356 | 117 (32.9) | 3.5 (2.40–5.23) | 3.6 (2.41–5.58) |
Trimester | ||||
First | 55 | 8 (14.6) | 1.0 | 1.0 |
Second | 305 | 63 (20.7) | 1.5 (0.69–3.41) | 1.5 (0.63–3.39) |
Third | 383 | 93 (24.3) | 1.9 (0.86–4.14) | 1.2 (0.52–2.74) |
Gravidity | ||||
1–4 | 610 | 133 (21.80) | 1.0 | |
≥5 | 133 | 31 (23.3) | 1.1 (0.70–1.70) | |
Delivery gap | ||||
None | 250 | 61 (24.2) | 1.0 | |
1–11 | 22 | 5 (22.7) | 0.9 (0.32–2.58) | |
12–24 | 116 | 23 (19.8) | 0.8 (0.45–1.32) | |
25–36 | 139 | 31 (22.3) | 0.9 (0.54–1.46) | |
>36 | 216 | 44 (20.4) | 0.8 (0.51–1.23) | |
Household size | ||||
1–5 | 535 | 111 20.8 | 1.0 | |
6–10 | 190 | 48 25.3 | 1.3 (0.25–1.79) | |
>10 | 18 | 5 27.8 | 1.5 (0.51–4.21) | |
Wealth index | ||||
Lowest | 462 | 100 (21.7) | 1.0 | |
Second lowest | 234 | 56 (23.9) | 1.1 (0.78–1.66) | |
Middle | 32 | 5 (15.6) | 0.7 (0.25–1.79) | |
High | 15 | 3 (20.0) | 0.9 (0.25–3.27) | |
Still in school | ||||
Yes | 39 | 3 (7.7) | 1.0 | 1.0 |
No | 704 | 161 (22.9) | 3.7 (1.11–12.10) | 2.6 (0.73–9.26) |
Discussion
The overall prevalence of anaemia was 22.1 % which agrees with findings in Ethiopia and Nigeria, [16, 17] but lower than those reported elsewhere [18‐21], and the WHO estimate of 40–60 % in developing countries [1]. The variations in the prevalence of anaemia may be due to the fact that the interventions employed to address anaemia in pregnancy vary in different settings.
The prevalence of anaemia was significantly higher in Gulu than in Hoima (P < 0.001). Gulu district was ravaged by a twenty-year war between the LRA and the Government of Uganda that left most of the social services in the district in ruins and a large majority of its population in poverty [22]. Our findings also show that being a housewife is an independent risk factor for anaemia. Because most housewives depend solely on their husbands’ earnings for their financial needs, the majority of them tend to be of low socio-economic status which has been reported as a known determinant of anaemia [23, 24]. This is also manifested by the finding that anaemia was more prevalent among women who had low monthly family income. Anaemia was also more prevalent among women who live in big households (>5 people) compared to those who live in small households. These might be low income families that were displaced from their family land during the LRA insurgency but have since decided to remain in the urban settings for a better life. Education level attained was also found to be associated with anaemia. In Uganda, low level of education is associated with unemployment, which consequently leads to poverty, a known risk factor for anaemia in pregnancy [21, 24].
Anaemia prevalence was highest (24.3 %) during the third trimester as compared to the first trimester (14.6 %) and second trimester (20.7 %). Haemodilution in pregnancy increases to peak during the second trimester which may explain the high prevalence of anaemia during this period. However, the increased incidence of anaemia during the third trimester may also indicate poor antenatal care and nutrition. These findings agree with that of Karaoglu and the WHO report [23, 25], but differ to those from Porto Novo, Cape Verde and Abeokuta, Nigeria [24, 26]. Although a study conducted in Trinidad and Tobago reported increased presence of anaemia with increasing gravidity [27], this study found no evidence of increased incidence of anaemia in a grand multigravid woman as compared to primigravid, secundigravid or multigravid woman. This finding is in agreement with other studies [16]. Perhaps the health education provided to pregnant women during antenatal visits leads to better health seeking behaviour and dietary habits, especially during pregnancy.
These findings will go a long way in addressing the problem of anaemia, which can affect psychological and physical behaviour. This is especially important because even very mild forms of anaemia have been reported to influence the sense of well being, lessen resistance to fatigue, lower productivity [28], aggravate other disorders, and affect work capacity [26]. In pregnant women, anaemia can result in increased risk of maternal and perinatal mortality, low birth weight, [29] and reduced resistance to blood loss with the result that death may occur from the blood loss associated with delivery. The strength of this study is the use of power formula to calculate the sample size and the random sampling of the study participants which enhances its generalisability.
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Limitations of this study
We did not consider other factors like parasitic infections which can lead to anaemia. We were therefore not able to determine their contribution to anaemia in our study population. Being a cross sectional study, we could not identify the cause and effect relationship.
Conclusion
The prevalence of anaemia in pregnant women in Gulu is higher than in Hoima. Amongst pregnancy women, being a housewife is an independent risk factor for anaemia. Greater efforts are required to encourage early antenatal attendance from women in these at risk groups. This would allow iron and folic acid supplementation during pregnancy, which would potentially reduce the prevalence of anaemia.
Acknowledgements
We are grateful to Dr Benjamin Hopwood, Medical Officer, Paediatric Department, Hoima Regional Referral Hospital, who edited the manuscript to address typographical and grammatical errors. This work was supported by Training Health Researchers into Vocational Excellence in East Africa (THRiVE); grant number 087540 funded by the Welcome Trust. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the supporting offices.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
GO participated in designing the study, data collection, and statistical analyses and served as the lead author of the manuscript. RW conceived and participated in designing the study, data collection and data entry. PO participated in designing the study, data collection and proof read the manuscript. All authors read and approved the final manuscript.