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Keywords:

  • anaemia;
  • malaria;
  • case–control study;
  • Tanzania;
  • risk factors

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Study area and population
  6. Study design
  7. Laboratory procedures
  8. Case definitions and statistical methods
  9. Results
  10. Discussion
  11. Acknowledgements
  12. References

In malaria endemic areas anaemia is a usually silent condition that nevertheless places a considerable burden on health services. Cases of severe anaemia often require hospitalization and blood transfusions. The objective of this study was to assess risk factors for admission with anaemia to facilitate the design of anaemia control programmes. We conducted a prospective case–control study of children aged 2–59 months admitted to a district hospital in southern Tanzania. There were 216 cases of severe anaemia [packed cell volume (PCV) < 25%] and 234 age-matched controls (PCV ≥ 25%). Most cases [55.6% (n = 120)] were < 1 year of age. Anaemia was significantly associated with the educational level of parents, type of accommodation, health-seeking behaviour, the child's nutritional status and recent and current medical history. Of these, the single most important factor was Plasmodium falciparum parasitaemia [OR 4.3, 95% confidence interval (CI) 2.9–6.5, P < 0.001]. Multivariate analysis showed that increased recent health expenditure [OR 2.2 (95% CI 1.3–3.9), P = 0.005], malnutrition [OR 2.4 (95%CI 1.3–4.3), P < 0.001], living > 10 km from the hospital [OR 3.0 (95% CI 1.9–4.9), P < 0.001], a history of previous blood transfusion [OR 3.8 (95% CI 1.7–9.1), P < 0.001] and P. falciparum parasitaemia [OR 9.5 (95% CI 4.3–21.3), P < 0.001] were independently related to risk of being admitted with anaemia. These findings are considered in terms of the pathophysiological pathway leading to anaemia. The concentration of anaemia in infants and problems of access to health services and adequate case management underline the need for targeted preventive strategies for anaemia control.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Study area and population
  6. Study design
  7. Laboratory procedures
  8. Case definitions and statistical methods
  9. Results
  10. Discussion
  11. Acknowledgements
  12. References

Anaemia is an important cause of paediatric hospital admission and death in many parts of the world. The prevalence of Hb < 5 g/dl in a number of hospital-based studies ranged from 8% to 29% and was associated with case fatality rates of 9–18% (Emerton 1992; Lackritz et al. 1992; Brewster & Greenwood 1993; Craighead & Knowles 1993; Hedberg et al. 1993; Slutsker et al. 1994; Marsh et al. 1995; Bojang et al. 1997a,b; Schellenberg et al. 1999; Biemba et al. 2000). In some settings anaemia was responsible for about 20% of inpatient paediatric deaths. The non-specific signs and symptoms associated with anaemia lead to under-recognition of the anaemia-related burden of disease and death. This is supported by the observation that more than 20% of anaemic infants [packed cell volume (PCV) < 25%] in one cohort study were only diagnosed at cross-sectional surveys (Menendez et al. 1997).

Anaemia is a multifactorial problem. Causes may be classified according to their position in the pathophysiological process. Socio-economic causes (Tshikuka et al. 1996) and genetic factors such as sickle cell disease and thalassaemia (Fleming & Werblinska 1982; Rana et al. 1993; Thuilliez & Vierin 1997; Koko et al. 1998; Mockenhaupt et al. 2000) may be important underlying factors, although hard to target in anaemia control programmes. Socio-economic factors such as poor housing, crowding and poor education level predispose to illness in parts of Asia and Latin America (Banguero 1984; Buttraporn et al. 1986), although no such association was found in two West African studies of severe anaemia (Koram et al. 1995; Luckner et al. 1998). This may be a reflection of the homogeneity of the West African communities investigated in terms of their socio-economic status. Other socio-cultural factors may be important risk factors for anaemia, such as delayed treatment-seeking and inappropriate medication, both of which are common among the people of sub-Saharan Africa (Mwenesi et al. 1995; Hausmann et al. 1998).

More immediate factors in the pathophysological process may be relatively amenable to control measures. For example, Plasmodium falciparum malaria is a common cause of anaemia in children living in endemic areas (Menendez et al. 1997, 2000; Murphy & Breman 2001) and malaria control with insecticide-treated mosquito nets and chemoprophylaxis reduces the level of anaemia (Alonso et al. 1993; Menendez et al. 1997; Abdulla et al. 2001). Iron deficiency is the most common nutritional cause of anaemia worldwide (Stoltzfus & Dreyfuss 1998). Although iron supplementation is now recommended between the ages of 6 months and 2 years (Stoltzfus & Dreyfuss 1998), this policy is not widely implemented. Even low birthweight children who have limited iron stores (Stockman & Oski 1978; Dallman et al. 1980) are rarely given iron supplements. Marked deficiencies of other minerals, such as zinc, copper and vitamin B12, which are also necessary for haematopoiesis, are also associated with anaemia (Said et al. 1975; Singla et al. 1996). Apart from specific nutritional deficiencies, protein energy malnutrition is a common nutritional disorder in Africa and is also associated with anaemia (Biemba et al. 2000). Some intestinal parasites, in particular hookworm, contribute to the development of anaemia, for example in school-age children (Brooker et al. 1999; Stoltzfus et al. 2000).

In order to optimize effective anaemia control it would be useful to know the relative contribution of the different aetiological factors. In this paper we present a case–control study of the risk factors for admission with severe anaemia in children under 5 years of age in south-eastern Tanzania.

Study area and population

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Study area and population
  6. Study design
  7. Laboratory procedures
  8. Case definitions and statistical methods
  9. Results
  10. Discussion
  11. Acknowledgements
  12. References

The study was conducted in Ifakara in Kilombero District, Morogoro Region, southern Tanzania. The district has a population of 250 000, predominantly subsistence farmers growing rice and maize. Malaria transmission is perennial and the mean annual entomological inoculation rate in a nearby village was more than 300 infectious bites/person/year (Smith et al. 1993), although this may be less in Ifakara town. Malaria control is based on early detection and treatment of cases. Chloroquine resistance is common with a treatment cure rate of only 35.4% on day 7 and 10.3% on day 14 (Hatz et al. 1998).

St Francis Designated District Hospital (SFDDH) is situated in Ifakara town (population 55 000) and has a paediatric department with 70 beds. Among the 6500 annual paediatric admissions the four most common primary clinical diagnoses are malaria (40%), anaemia (20%), pneumonia (20%) and protein energy malnutrition (10%) (unpublished observations).

Study design

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Study area and population
  6. Study design
  7. Laboratory procedures
  8. Case definitions and statistical methods
  9. Results
  10. Discussion
  11. Acknowledgements
  12. References

This was a case–control study frequency matched for age with a target of one control per case. Children aged between 2 and 59 months admitted to the paediatric ward between July and October 2000, whose guardians gave informed consent, were enrolled in the study as cases if their PCV on admission was < 25%, or as controls if their PCV was ≥ 25% and if they had not had a blood transfusion in the last 4 weeks. We used a standard questionnaire to document exposure to risk factors: information on maternal and paternal factors, health-seeking behaviour and socio-economic status (Table 1), length (< 2 years) or height (≥ 2 years), weight, mid upper arm circumference (MUAC) and birthweight of the child (from the growth card).

Table 1.  Potential anaemia risk factors investigated
Socio-economic
Mother's literacy, number of years in school, age at delivery, marital status, parity, number of children alive, birth interval, birth order of this child
Father's literacy and number of years in school
Number of family members, number of meals family gets per day, number of meals child gets per day, food source (bought/grown), type of child care
House owner, type of roof, walls and floor, number of people sleeping in the room with the child
Family source of income, monthly income in shillings, amount of crops harvested, mother's income, father's occupation
Family assets: number of cows, pigs, chickens/ducks, radio, bicycle, TV, fridge
Access to hospital
Distance from hospital > 10 km, duration of illness > 3 days before admission, previous hospital admissions
Health seeking behaviour
Choice of place for treatment: where was the child taken first for treatment
Completion of EPI vaccinations
Weighing contacts: number of weighing contacts
Place this child delivered (home or health facility)
Child factors
Sickle cell status
Low birthweight
Nutritional factors: MUAC, weight/age, weight/height, height/age
Previous blood transfusions
Use of mosquito net
Intestinal parasites: hookworms
P. falciparum parasitaemia & clinical malaria

Laboratory procedures

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Study area and population
  6. Study design
  7. Laboratory procedures
  8. Case definitions and statistical methods
  9. Results
  10. Discussion
  11. Acknowledgements
  12. References

A finger prick blood sample was collected in a microcapillary tube for the measurement of PCV and haemoglobin electrophoresis. Thick blood smears were prepared to look for malaria parasites after staining with Giemsa. Blood slides were read according to standard procedures (Alonso et al. 1994). Briefly, the number of asexual forms per 200 leucocytes was counted independently by at least two slide readers and a parasite concentration calculated by assuming a white cell count of 8000/μl and multiplying by the geometric mean parasite count. Sickle cell status was assessed by electrophoresis on cellulose acetate gel with an alkaline buffer. Wet preparations of stool samples were prepared and read under direct microscopy.

Case definitions and statistical methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Study area and population
  6. Study design
  7. Laboratory procedures
  8. Case definitions and statistical methods
  9. Results
  10. Discussion
  11. Acknowledgements
  12. References

Severe anaemia was defined as a PCV < 25%. This cut-off for anaemia was chosen because it has been associated with increased mortality (Stoltzfus et al. 1997; Schellenberg et al. 1999) and to facilitate comparability with other studies carried out in the area (Menendez et al. 1997; Schellenberg et al. 2001). Clinical malaria was defined as an axillary temperature equal to or higher than 37.5 °C plus asexual P. falciparum parasitaemia of any density. Malnutrition assessed by MUAC was defined as mild if MUAC was 12.5–13.5 cm and severe if MUAC was < 12.5 cm (Shakir & Morley 1974). The NCHS growth standards were used to express measurements as Z-scores for weight for height, weight for age and height for age (WHO 1983). A Z-score of < −1 was considered mild malnutrition, < −2 moderate malnutrition and < −3 severe malnutrition. Data were double entered in a menu driven system written in FoxPro 2.6 (Microsoft Corporation). Statistical analysis was performed using EPI-Info 2000 (v1.0, CDC Atlanta, USA) and Stata version 5.0 (Stata Corp, TX, USA). Multivariate analysis was performed using a logistic regression model adjusting for the possible confounding variables identified on univariate analysis. Two sets of multivariate analysis were performed to ensure that malaria, known to be a major determinant of anaemia in this setting (Menendez et al. 1997) did not obscure the effect of less marked but important upstream determinants.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Study area and population
  6. Study design
  7. Laboratory procedures
  8. Case definitions and statistical methods
  9. Results
  10. Discussion
  11. Acknowledgements
  12. References

Two hundred and sixteen cases and 234 controls were enrolled in the study. Cases and controls alike were from poor families. The mean average total household expenditure for cases and controls was equivalent to US$ 7.65/month. The mean family size was 6.5, with 3.8 adults and 2.7 children. Most families depended on subsistence agriculture, harvesting an average of 10 (SD = 11.8) sacks of rice from 2.3 (SD = 2.4) acres of land. The majority owned the dwellings in which they lived, but these were simple constructions with leaf roofs, mud walls and mud floors. Very few (n = 16; 3.6%) households had electricity and only two had a refrigerator or television/video.

Most cases (55.6%, n = 120) were younger than 1 year (Figure 1). Table 2 shows the risk factors statistically significantly associated with severe anaemia. Anaemic children were more likely than controls to have mothers and fathers who had not completed primary education and fathers who were illiterate. Most children (n = 350, 77.8%) came from families normally growing their own food, but the mean number of meals consumed in the households of controls (2.40, SD = 0.78) was higher than that in the homes of cases [2.22 (SD = 0.80)] (P = 0.02).

image

Figure 1. Age pattern of anaemia.

Download figure to PowerPoint

Table 2.  Univariate analysis of risk factors for severe anaemia
 ControlsCases  
Variablen/N%n/N%Odds Ratio (95% CI)P-value
Socio-economic
Mother's years in school
 < 7 45/234(19.2) 63/216(29.2)1 
 7161/234(68.8)142/216(65.7)0.64 (0.41; 1.00) 0.026
 8–10  7/234(3.0)  3/216(1.4)0.34 (0.08; 1.45) 
 ≥ 11 21/234(9.0)  8/216(3.7)0.29 (0.12; 0.71) 
Father's years in school
 < 7 45/234(19.2) 62/216(28.8)1 
 7161/234(68.8)142/216(66.2)0.65 (0.41; 1.01) 0.031
 8–10  7/234(3.0)  3/216(1.4)0.35 (0.08; 1.43) 
 ≥ 11 21/234(9.0)  8/216(3.7)0.29 (0.12; 0.72) 
Father illiterate 25/234(10.7) 39/215(18.1)1.83 (1.06; 3.16) 0.031
House factors
 Family own a house158/234(64.5)166/216(76.9)1.54 (1.01; 2.4) 0.043
 Paid for housing previous 6 months 33/234(14.1) 13/216(6.01)0.41 (0.21; 0.80) 0.009
 Grass roofing143/234(61.1)169/216(78.2)2.22 (1.46; 3.38)< 0.001
 Earth floor192/234(82.1)196/216(90.7)2.07 (1.17; 3.66) 0.013
 Mud & wood wall116/234(49.6)144/216(66.7)2.00 (1.36; 2.95)< 0.001
Health-seeking & access
 to hospital
Distance to hospital > 10 km118/234(50.4)170/216(78.7)3.66 (2.42; 5.55)< 0.001
Duration of illness > 6 days 66/234(28.2) 69/216(31.9)1.90 (1.16; 3.12) 0.004
Home delivery 63/234(26.9) 90/216(41.7)1.94 (1.30; 2.89) 0.002
Expenditure last 6 months  44/234(18.8) 60/216(27.8)1.78 (1.14; 2.80) 0.012
> 5000 Tsh on health
Child factors
MUAC
 Reference127/233(54.5) 83/216(38.4)1 
 Mild (< 13.5 cm) 50/233(21.5) 69/216(31.9)2.18 (1.36; 3.49) 0.002
 Severe (< 12.5 cm) 56/233(24.0) 64/216(29.6)1.88 (1.16; 3.04) 
Not sleeping under a mosquito net 33/234(14.1) 48/216(22.2)1.80 (1.10; 2.94) 0.020
Previous blood transfusion 11/234(4.7) 37/216(17.1)4.34 (2.17; 9.09)< 0.001
P. falciparum parasitaemia 69/228(29.5)138/213(63.9)4.33 (2.88; 6.49)< 0.001
Clinical malaria 55/227(24.2) 14/211(44.5)2.51 (1.67; 3.79)< 0.001

Most (69%) children lived in houses with grass roofs, but anaemic children were significantly less likely to live under a corrugated iron roof. The minority (13.8%) of children living in a house with a cement floor were also less likely to be anaemic than the majority of children whose houses had earth floors. The 67% of children living in a house with mud and wood walls were more likely to be anaemic than children living in brick-built houses.

There were no significant differences between cases and controls in the numbers of sacks of rice or maize harvested. The proportion of families with a monthly salary below the national minimum wage (TSh 30 000/= – approximately US$ 37.50) was similar for cases (24%) and controls (21%). In the 6 months leading up to the admission, cases spent significantly less on their accommodation (TSh 688 vs. TSh 2322), and significantly more on health issues (TSh 4098 vs. TSh 3401) than controls. Cases also tended to spend less on food and clothes, and to have a lower overall expenditure, than controls, although these differences did not reach statistical significance. Overall, 72% of patients owned a house and all of them lived in the houses they owned. These houses were significantly more likely to be made with non-corrugated roofs (246/324, P < 0.001), mud walls (289/324, P < 0.001) and mud floors (204/324, P < 0.001) than houses which were rented.

Anaemic children were more likely to have been delivered at home than cases. Low birthweight was not associated with an increased risk of anaemia and there was no apparent association between birth order or birth interval and risk of anaemia.

Children who lived more than 10 km away from the hospital were more likely to be anaemic than children living closer to the hospital. The duration of the illness before presenting to hospital was also longer in children admitted with anaemia. Although a history of previous admission was not associated with increased risk of anaemia, a history of previous blood transfusion was (OR 4.34, 95% CI 2.2–9.1).

The different measures of nutritional status produced similar estimates of the prevalence of malnutrition, but only children with a low MUAC were significantly more likely to be anaemic than children not malnourished on this assessment. There was a non-significant tendency for children with low WAZ and HAZ scores to be anaemic (P = 0.07 for each).

Reported net use was high among cases and controls (82%, n = 369) and children not sleeping under nets had nearly double the chance of being cases than children who used nets. Most (64%) cases were P. falciparum positive, a condition associated with a more than four-fold increased risk of anaemia. Increasing parasite density did not increase the risk of anaemia. Only 2.8% of cases had HbSS and 6.9% HbAS, and neither genotype was significantly associated with anaemia. Hookworm ova were only present in 2.9% of children admitted to the paediatric ward (3.7% of anaemia cases and 2.1% of controls, P = 0.36). The prevalence of other intestinal parasites was < 1%.

Multivariate analyses (Table 3) showed that increased health expenditure in the preceding 6 months, malnutrition defined by a reduced MUAC, living >10 km away from the hospital, history of previous blood transfusion and P. falciparum parasitaemia were independent risk factors for admission with severe anaemia. Repeating the analysis having removed P. falciparum parasitaemia did not materially alter the results.

Table 3.  Multivariate analysis of risk factors for severe anaemia
VariableOR95% CIP-value
Health expenditure last 6 months  > 5000 TSh2.231.3; 3. 90.005
Mid upper arm circumference (cm)
 Mild malnutrition 1.6; 5.1< 0.001
 Severe malnutrition2.41.3; 4.3 
Distance from hospital > 10 km3.01.8; 4.9< 0.001
Previous blood transfusion3.81.7; 9.1< 0.001
Asexual P. falciparum parasitaemia9.54.3; 21.3< 0.001

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Study area and population
  6. Study design
  7. Laboratory procedures
  8. Case definitions and statistical methods
  9. Results
  10. Discussion
  11. Acknowledgements
  12. References

The results of this study show that important contributors to the pathogenesis of anaemia included the socio-cultural background, case management of previous anaemia episodes and P. falciparum malaria, which was the most important immediate cause of anaemia in this setting.

The controls were selected from other hospitalized children and hence the risk factors identified are related to the risk of admission with anaemia rather than admission with other conditions. Factors associated more generally with ill health may not have been identified. A striking observation was that cases and controls alike came from poor families, heavily dependent on subsistence agriculture, with very low household expenditure and living in simple dwellings. Despite the homogeneity of the study population in this respect, the study showed that the minority of children living in brick-built houses with cement floors and corrugate iron roofs were less likely to be anaemic than children living in more simple houses. It is not clear whether this is a reflection of improved socio-economic status or whether the better housing per se reduces risk of anaemia, e.g. by being less hospitable for Anopheline mosquitoes. Better nourished children, with higher MUAC and living in households where more meals were available each day, were less likely to be anaemic than their malnourished counterparts. This too may be a reflection of better socio-economic status, itself related to parental education, which was another important determinant of risk of anaemia. That cases came from poorer households is also suggested by the lower total household expenditure of cases than controls. Yet cases spent significantly more on health than controls in the 6 months before questioning, suggesting that these children were more likely to have had prior illnesses than the control children. The former illness may have been malaria, known to predispose to anaemia, or children with anaemia may have been generally less healthy than children with other problems. A potential risk factor, which could account for this observation but which we did not assess, is infection with HIV. Whatever the reason for the increased expenditure, the costs of the new illness episode would exacerbate their poverty, possibly increasing further the risk of subsequent illness episodes, locking them ever more firmly into the illness–poverty cycle.

The observation that children living further away from the hospital were more likely to be anaemic persisted in the multivariate analysis. This may be a reflection of higher malaria transmission beyond the immediate vicinity of SFDDH, impaired access to hospital in those living further away, or a combination of the two factors. The importance of access may explain the finding that the delay in seeking health care, which was more common in cases than controls, did not persist in the multivariate analysis. Delay in seeking health care may therefore be more a function of time taken to travel from the remote home to the hospital, a reflection of ease of physical access to health services, than to health-seeking behaviour. The same considerations apply for the relationship between birth place and anaemia as poor access to hospital would increase the chance of a home delivery. Poor transport facilities and the costs involved in travelling from peripheral rural areas to hospital compound the physical problems of access. In Kilombero, many households are more than 150 km from the district hospital, the only transfusion-capable health facility in the district, and public transport is relatively expensive and, at best, erratic.

The long list of risk factors significantly associated with an increased risk of severe anaemia should not obscure the fact that anaemia cases were concentrated in children younger than 1 year. This is in keeping with earlier work documenting a concentration of anaemia in infants (Slutsker et al. 1994; Schellenberg et al. 1999; Menendez et al. 2000) and the observation that 10% of children aged 6–11 months in the community had a PCV < 15% (D. Schellenberg, unpublished). Anaemia control measures in this setting need to be targeted towards infants. An earlier study estimated that 57% of anaemia in this age group is related to malaria and that 29% is associated with iron deficiency (Menendez et al. 1997). The importance of malaria as a cause of anaemia is reflected by the observation that parasitaemia was associated with risk of anaemia. Increasing density of parasitaemia did not increase the risk of anaemia. Children not sleeping under nets had nearly double the chance of being cases than children sleeping under nets in the univariate analysis. It is impressive that, despite the poverty within the community studied, 82% of children slept under a mosquito net. This is likely to be a reflection of the high mosquito densities of the Kilombero flood plains, with the associated biting nuisance, and the widespread availability of mosquito nets through an active social marketing campaign (Armstrong Schellenberg et al. 1999).

A previous blood transfusion was strongly associated with increased risk of anaemia, suggesting that earlier episodes of anaemia were inadequately treated with a blood transfusion. Studies have shown that the effect of blood transfusion alone is short-lived, lasting only weeks (Lackritz et al. 1992; Holzer et al. 1993). This emphasizes the need for an integrated approach to the management of anaemia, particularly in ensuring that children who receive blood transfusions also receive antimalarials and ferrous sulphate treatment. In our setting, however, routine use of antihelminthics is probably not appropriate as a very low proportion of the young children with anaemia were found to harbour helminths, in keeping with earlier work from this area (Gascon et al. 2000).

In conclusion, this study has shown that factors well upstream in the pathogenic process, such as parental education and type of accommodation, are important determinants of risk of admission with anaemia: such factors are not easy to change and require long-term strategies. Although it was the poorest who were at highest risk of anaemia, they also had the highest health-related expenditures, illustrating their entrapment in the poverty–illness cycle. The plethora of risk factors apparently related to socio-economic status suggest that the prevalence of anaemia, or mean PCV, may be a useful marker of poverty reduction strategies in some settings. Improved case management of anaemia and malnutrition is likely to produce useful health benefits. However, the marked tendency of admitted anaemia cases to be aged < 1 year emphasizes the need to target this most vulnerable age group. Furthermore, as anaemia was more common in those with poorer access to health services, the need to develop preventive strategies is underlined. Taken together, these findings suggest that the approach to anaemia control should be revised so that preventive strategies are targeted at very young children. Several approaches have yielded promising results and are in different stages of implementation and evaluation. These include the use of insecticide treated nets (ITNs), supplementation of infants with iron syrup and intermittent antimalarial treatment delivered alongside routine vaccinations. Further evaluation and deployment of these approaches should now be a priority.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Study area and population
  6. Study design
  7. Laboratory procedures
  8. Case definitions and statistical methods
  9. Results
  10. Discussion
  11. Acknowledgements
  12. References

We thank the parents and guardians of all children involved in the study; Dr P.Kibatala (Medical Director) and the staff of St Francis Designated District Hospital, without whose cooperation this study would not have been possible; the laboratory staff of Ifakara Health Research and Development Centre for performing the lab tests, Irene Ngoja for performing interviews and collecting samples; and the District Medical Officer, Dr F. Lwilla and his District Health Management Team for facilitating the acceptance of the research team in the local community. This study received financial support from the UNDP/World Bank/WHO Special Program for Research Training in Tropical Diseases. Ifakara Health Research and Development Centre receives funds from the Swiss Agency for Development and Cooperation.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Study area and population
  6. Study design
  7. Laboratory procedures
  8. Case definitions and statistical methods
  9. Results
  10. Discussion
  11. Acknowledgements
  12. References
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