Save the Children (UK), Democratic Republic of Congo

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1 Final Report Cross-sectional anthropometric surveys of children aged 6-59 months living in Mapela, Tshimungu, Kimbanseke and Lobiko Aires de Sante in Masina and Kimbanseke communes, Eastern Kinshasa Democratic Republic of Congo April 2001 Save the Children (UK), Democratic Republic of Congo 1

2 EXECUTIVE SUMMARY Objectives To estimate the nutritional status of children aged 6 to 59 months To determine the demographic composition of households To estimate morbidity of children under five years To estimate retrospective mortality of children under five years To evaluate the coverage of feeding programmes in the population surveyed Methodology On April, Save the Children (SC) conducted a cross-sectional anthropometric surveys of children aged 6-59 months living in 4 Aires de Sante (Mapela,Tshimungu, Kimbanseke, Lobiko) in Masina and Kimbanseke communes in the East of Kinshasa. 938 children were selected for the measurement of height, weight and oedema using two stage 30 by 30 cluster sampling. Information was also collected on current morbidity and attendance of feeding centres. Additional information from the 817 households visited on household composition and under fives mortality was also collected. Data was cross-checked before analysis and 6 children s records were subsequently disqualified, leaving a total of 931 children for analysis. Results The survey found an overall prevalence of 11.0% global acute malnutrition and 2.1 % severe malnutrition. In the absence of aggrevating factors, a prevalence above 10 % global malnutrition represents a risky situation for which targeted supplementary and therapeutic feeding appropriate responses (WHO, 2000) 1. According to a recent ACF/CEPLANUT 2 nutrition survey, prevalence of global acute malnutrition in Kimbanseke commune (where 3 of the aires de sante in the SC survey were located) more than trebled in the period between September 1999 and February 2001 from 3.8% to 12.2%. It is apparent from these statistics that the nutritional status in Kimbanseke commune and most probably Masina and other vulnerable communes in Kinshasa has dramatically increased since the end of 1999 to unacceptably high levels and should be a cause for concern and appropriate action. Table 1: Summary of results of SC Nutrition Survey, Kinshasa, April 2001, by aires de sante Aires de Sante Global maln Global maln 95% CI Chronic Maln Chronic maln 95% CI Feeding Centre Coverage Morbidity (last 24 hrs) Tshimungu (n=435) 11.3 % (7.1% - 5.5%) 33.9 % 27.5% - 0.3% 8.2% 19.4 % 0.82 Mapela (n=224) 8.5 % (3.3% - 3.7%) 31.1 % 22.5% - 9.7% 15.8% 17.0 % 0.73 Kimbanseke (n=119) 5.0 % (-0.5% -0.5%) 33.3 % 20.9% - 5.7% 16.7% 12.6 % 1.1 Lobiko (n=153) 18.3% (9.7% %) 41.7 % 30.6% - 2.8% 3.6% 17.7 % 0.32 Total (n=931) 11.0% (8.3% %) 34.4% 30.0% % 8.8% 17.6% 0.77 U5s mortality per/10,000/ day The table above shows considerable variation in prevalence of malnutrition and other health indicators between the four aires de sante surveyed. 1 The Management of Nutrition in Major Emergencies, WHO, Kinshasa Enquetes Nutritionnelles Communes de Kimbanseke, Selembao, Bumbu et Kisenso. ACF/CEPLANUT. April

3 Prevalence of global acute malnutrition varied considerably between the four aires de sante, from an acceptable 5.0% in Kimbanseke, to an alarmingly high level of 18.3 % in Lobiko. Overall chronic malnutrition affected approximately one third (34.4%) of the sample and was particularly high in Lobiko (41.7 %). All aires de sante had an under fives mortality rate below 2.0 (calculated as an average for the previous 12 months) which implies that the health situation is not serious and so is not a cause of undue concern 3. Overall, 17.6 % of children were reported to have been ill in the 24 hours previous to the survey. In terms of symptoms of illness, fever was the most common (9.2%), followed by cough (2.6%) and diarrhoea (2.5%). The order and prominence of these three symptoms reflects the three major causes of morbidity and mortality in Kinshasa, namely malaria, respiratory diseases and diarrhoeal diseases. There was a positive statistical association between morbidity and malnutrition, with the proportion of malnourished who were also ill (30.7%) being almost double that of children who were not malnourished (16.0%). The feeding centre coverage was calculated as 8.8%, which means that only 8.8% of malnourished children in the survey were attending a feeding centre. The coverage varied considerably between aires de sante. Lobiko, which had the highest amount of malnutrition had an extremely and unacceptably low coverage of 3.6% whilst Kimbanseke which had the lowest prevalence of malnutrition had the highest coverage of 16.7 %. Lobiko The high prevalence of 18.3 % global acute malnutrition found in Lobiko is particularly worrying and represents a serious situation according to WHO guidelines. Approximately one fifth of households (17.8 %) and parcelles (20.2 %) in Lobiko were caring for at least one malnourished child, whilst 2.2 % of parcelles had two or three malnourished children. Lobiko also had the highest prevalence of chronic malnutrition (41.7%) suggesting a long term nutritional problem, as well as the lowest feeding centre coverage (3.6%). The latter means that only 3.6 % of malnourished children in Lobiko were attending a feeding centre for treatment. The under fives mortality rate of 0.32 was low and not critical. The particularly high level of malnutrition in Lobiko may be explained by its inaccessibility/limited road access, sandy soil (making it difficult to grow crops), and poor infra-structure including provision of potable water, latrines, health care and schools. Lobiko is essentially rural in nature with poor road access severely limiting the opportunity for engaging in trade with major markets. The absence of potable water is a huge constraint, with many households having to obtain water from the river. These problems appear particularly pronounced in the remote southern hilly areas furthest away from the road where there is no access to portable water and no latrines. Recommendations for SC health programme It is proposed that any nutrition input made by SC in Kinshasa should be targeted at Lobiko in order to tackle its alarmingly high rates of acute and chronic malnutrition and unacceptably low rates of feeding centre coverage. Recommended actions for SC health team 1. Ensure that the results of the recent SC nutrition survey are passed onto the relevant government/health authorities, feeding centres, religious groups and NGOs, and that the implications of this and other recent assessments are explained to them, highlighting the need for a co-ordinated and effective response in this and possibly other vulnerable areas of Kinshasa. 3 The management of Nutrition in Major Emergencies. WHO

4 2. Build good working relationships with appropriate authorities (particularly CEPLANUT), health/feeding centres, BDOM, ACF etc and initiate discussion with them to identify effective and sustainable strategies to reduce malnutrition/increase feeding centre coverage in Lobiko and other vulnerable areas of Kinshasa 3. Encourage the relevant authorities to initiate and support programmes that would help improve provision of water, sanitation, and health care in Lobiko. 4. Strengthen nutrition screening and referrals in Lobiko, both at the health centres and by Maman Bongisas. This should involve general training in the identification of malnutrition and should include the following: Maman Bongisas how to recognise oedema and how to use MUAC as a quick, cheap and relatively effective method for screening malnourished children for referral to health centres. Health centre staff how to identify oedema and how to use weight for height charts for identifying malnourished children for referral to feeding centres. 5. Identify realistic, effective and sustainable ways in which to improve the treatment provided at feeding centres (particularly Stage III-TFC), in partnership with those running the centres. This should involve training of feeding centre staff in how to treat malnutrition effectively. 6. Provide training for health & feeding centre staff, and Mamans Bongisas in the prevention of malnutrition, with an emphasis on the importance of breast feeding and good weaning practises. This should encourage improved screening, referrals and the counselling of mothers in good feeding practises. 7. Alert the general community to the problem of malnutrition, its causes, health implications and how to prevent it, e.g. input school curriculum, women s groups, etc 8. Encourage the development of protocols for effective nutrition surveillance through the regular collection, analysis and interpretation of the data on malnutrition/health to allow the nutrition situation to be closely monitored. 9. Strengthen first line treatment and preventative strategies for malaria, diarrhoea and respiratory infections. This would include the provision of free drugs to those attending feeding centres Mary Atkinson Nutrition Consultant MAY

5 1.0 INTRODUCTION 1.1 Background Despite the signing of the Lusaka Peace Accord in 1999, the almost three year old multi-nation war in the Democratic Republic of Congo (DRC) continues. In addition to the large population displacement caused by the fighting, the economy is in crisis and is unable to sustain an ever-growing urban population. The health care system continues to deteriorate at an alarming rate. A sharp increase in epidemic diseases including cholera, measles, polio and meningitis reflects poor access to potable water (42%) and limited access to primary health care services (26%). It has been estimated that 2.2 million persons in the DRC suffer grave food insecurity, whilst 4 million reside in inaccessible areas vulnerable to food insec urity 4. As with the rest of the country, the capital Kinshasa suffers from constant petrol shortages, with much of the transport infra-structure in a state of collapse. Urban residents struggle under the effects of rampant inflation (estimated at 890% in 2000), together with unrealistic and volatile exchange rates. The inaccessibility of rebel controlled areas in the east of the country which used to provide the capital with a significant proportion of its food has helped lead to restricted food supplies and extremely high food prices. The tremendous fluctuations in the price of food are mirrored in the prices of non-food items and have meant that salaries and wage rates are usually reviewed and adjusted on a monthly basis. Salaries of civil servants in government offices are the only salaries reported not to be adjusted on a regular basis. According to the last government census in , Kinshasa has a total population of 5,500,000 which represents 11% of the total population of DRC and makes Kinshasa the largest city in Central Africa. The population of Kinshasa has more than doubled in the last 12 years. Kinshasa is divided into 24 administrative units (Communes), grouped into 4 districts, which are further divided into Quartiers. The city is also divided into 22 health zones (Zones de Sante/Enquêtées) which are further divided into Aires de Santes. 1.2 Nutritional situation in Kinshasa Previous nutrition surveys Kinshasa is diverse in character. Some parts of the capital are established population dense inner city areas, whereas others on the outskirts of the city are semi-rural and have only been recently urbanised. The different areas of the city vary in prosperity, density, accessibility, and health environment. Consequently, food security and nutritional status can vary significantly both between and within Communes. Up until 1996, most of the nutritional information collected in Kinshasa related to the city as a whole and was not disaggregated down to commune level. In an attempt to rectify this, CEPLANUT 6 has carried out four nutritional surveys (1996, 1998, 1999, and 2000) that aim to identify differences in malnutrition rates between different zones in the city. Areas of the city occupied by the military and rich residential areas are not included in these categories. Since 1998, nutritional surveys have been carried out in three distinct zones or strates in the city. The consistently most vulnerable zone in terms of malnutrition is Cities excentriques at semi-rural which includes Kimbanseke, Kisenso, Masina, Mont-Ngafula, Maluku, and N sele communes. The most recent CEPLANT survey in June 2000 found 6.1 % global acute malnutrition and 31.5 % chronic malnutrition in this most vulnerable zone. Overall, 5.2 % global acute malnutrition and 26.8% global malnutrition was found in Kinshasa. This represents a significant decrease in prevalence of malnutrition since the previous survey in June 1999 which found 10.5 % global acute malnutrition and 31.1 % chromic malnutrition. Both surveys took place in June, a time of year when the staple foods maize and cassava, tend to be readily available and so are not inflated in price. 4 UNICEF, La Division Urbanie des Affaires Intérieures de la ville de Kinshasa, CEPLANUT is the national centre for nutrition and is part of the Ministry of Health 5

6 Action Contre la Faim and CEPLANUT conducted a nutrition survey in Kimbanseke, Bumbu, Kisenso and Selembao communes in February of this year 7. Global acute malnutrition ranged from 8.5 % to 12.2 % in the four communes surveyed. They found that the prevalence of malnutrition had more than trebled in Kimbanseke and almost doubled in Selembao since the time of their previous survey in these two communes in September Nutrition programmes in Kinshasa The treatment of malnutrition is Kinshasa is almost totally run by religious organisations, of which BDOM (Bureau Diocésain des Oeuvres Médicales) is the largest and is the co-ordinating body of all the NGOs and faith groups involved with feeding centres in the entire city. There are approximately 48 feeding centres (supplementary Stage II, and therapeutic Stage III) in total. All centres follow the same protocols and PAM supplies all the food: usually maize, CSB, beans, sugar and oil, although not all of these commodities are always available. Community volunteers known as Maman Bongisa are responsible for locating and referring malnourished children to the health centres, the care of children in the feeding centres and the follow up of children who have been discharged Background to survey locations in Kinshasa The purpose of this nutritional survey is to estimate the prevalence of malnutrition and the coverage of existing feeding programmes in what are thought to be the worst affected areas of the Eastern region of Kinshasa. Four Aires de Sante within Masina and Kimbanseke communes were chosen as the survey locations after having been identified as being particularly vulnerable to malnutrition. Both Masina and Kimabanseke communes are peripheral and semi-rural in character and have been identified as being particularly vulnerable from previous CEPLANT nutrition surveys. A unpublished study conducted by Save the Children (SC) in 1999, found that a large proportion of street children, including those that were working, originated from families in Masina and its neighbouring communes N djili and Kimbanseke, which also suggests that these communes are particularly vulnerable. In addition, a Household Food Economy Assessment conducted by Save the Children in June provided further evidence of the vulnerability of households in Masina commune. Kimbanseke is considered to be the poorest commune in Kinshasa. Its population has increased in size five times since 1979 and is now the most densely populated commune in the capital with an estimated population of 526, The population of Masina commune is estimated to be 281,620 6 and can be divided into broad economic zones the more prosperous southern area immediately above Boulevard Lumumba and Quartier No 1 and the poorer less developed northern area along the railway and marshland that borders the river Congo. The four Aires de Santes chosen for the survey (Tshimungu, Mapela, Kimbanseke, Lobiko) were identified from an assessment of the access to food, caring capacities, and health services and environments in five vulnerable Zones de Sante (Masina, Kimbanseke, Biyela, Kingasani, and Kikimi) within Mapela and Kimbanseke communes. The latter review included input from the Medicines des Zones and also took into consideration the results of a Lot Quality Assurance Sampling (LQAS) nutrition assessment conducted by SC in January in Lobiko, Mapela and Kimbanseke Aires de Santes 10. The results of the latter nutritional assessment suggested that the nutrition situation was not good (89.58% confident that acute malnutrition >5 %) and was possibly bad (80.95 % confident that acute malnutrition > 15%) in the three Aires de Sante assessed. 7 Kinshasa Enquetes Nutritionnelles Communes de Kimbanseke, Selebao, Bumbu et Kisenso. CEPLANUT. April Rapid Household Food Economy Assessment of Masina Commune, Kinshasa. SC. June ACF (USA), 9 La Division Urbaines des Affaires Intérieures de la ville de Kinshasa, Lot Quality Assurance Sampling Assessment of Masina commune, Kinshasa. SC. Jan Unpublished 6

7 2.0 OBJECTIVES Main objective To estimate the prevalence of malnutrition in children aged 6 to 59 months Secondary objectives To evaluate the coverage of feeding programmes in the population surveyed To determine the demographic composition of households To estimate morbidity of children under five years To estimate retrospective mortality of children under five years 3.0 METHODOLOGY 3.1 Sampling methodology Sample population Table 3.1: Sample population Communes Commune Zones de Sante Aires de Sante Aires de Sante Population population * population ** <5 yrs (20%) ** Masina 273,309 Masina Mapela 25,480 5,096 Kimbanseke 526,386 Biyela Tshimungu 59,121 1,182 Kikimi Lobiko 18,898 3,780 Kimbanseke Kimbanseke 18,219 3,644 Total 121,718 24,344 * La Division Urbanes des Affaires Intérieures de la ville de Kinshasa, 1998 ** Population figures collected by health authorities for national vaccination campaign, 2000 The cluster sampling method was used to select children for measurement. calculated to provide at least 4% precision using this method of sampling. A sample size of 900 was Selection of sample population In the first stage of cluster sampling 30 streets (clusters) were selected from a complete list of streets and their respective estimated population size in the four Aires de Sante surveyed (see Appendix I). In the second stage, a parcelle was randomly chosen from each of selected streets as the starting location for each team. Subsequent parcelles were chosen by locating the nearest parcelle to the right. 3.2 Data collection All households/families living at each parcelle (compound), were included in the survey. All children aged 6-59 months with heights between 65cm and 110cm living in each household visited were measured. The following information was collected from each household and child and recorded on the survey questionnaire (see Appendix II) From every household: Household composition (number 0-5 months, 6-59 months, 60 months and above) Under fives mortality in last 12 months (number of deaths, date of death) 7

8 For every child aged 6-59 months: Age (calculated using date on child s vaccination card, or local events calendar (see Appendix III). Where age was not available, selection was based on height excluding all children below 65cm and above 110cm) Sex Weight (using a Salter Scale, precision 0.1 Kg, weighed without clothes) Height (using a measuring board, precision of 0.1cm. Children less than 2 years or 85 cm measured lying down. Children greater than 2 years or equal to 85 cm measured standing up) Bilateral oedema (assessed by applying thumb pressure to both shins for three seconds) Morbidity in last 24 hours (most severe complaint recorded) Feeding Centre Coverage (current attendance of Stage II SFP, or Stage III - TFC feeding programme) Where an eligible child was absent from a household, an absence form was completed and arrangements made to return. Where an eligible child was currently admitted in the TFC (Stage III), or hospital, the team located the child at the feeding centre/hospital to obtain their measurements. 3.3 Indicators Acute malnutrition Acute malnutrition was determined using weight for height (W/H) index values. Both z-scores and percentage of the median were used to express severe and global malnutrition in the sample population. Only z-scores have true statistical meaning and so only these will be used to look at malnutrition according to the different sub-categories. Z-score % of median Oedema Global malnutrition < - 2 z < 80 % absent/present Severe malnutrition <- 3 z < 70 % absent/present Moderate malnutrition > -3 z to < - 2z > 70 % to < 80% present Chronic malnutrition Chronic malnutrition was determined using weight for height (W/H) index values and was expressed as z- scores. Z score Global malnutrition < - 2z Severe malnutrition < - 3 z Moderate malnutrition > - 3 z to < - 2 z Feeding Centre Coverage The Feeding Centre Coverage is defined as the percentage of malnourished children currently frequenting a feeding centre (Stage II, Stage II), divided by the number of malnourished children. Coverage = Number of malnourished children attending feeding centre sample x 100 Total number of malnourished children 8

9 3.3.4 Retrospective Crude Under Fives Mortality Rate (CMR) Retrospective Crude Under Five s Mortality Rate (CMR) was calculated as number of deaths per 10,000 per day, both as an average over the last 12 months, and as an average since the beginning of March 2001, as follows (WHO, ): U5s CMR = Average number of U5s deaths in the population per day x Total number of U5s in the population 3.4 Team composition and training The survey team was made up of four SCF health programme staff, together with health workers from the health zones and national NGOs. Each of the five survey teams was composed of a team supervisor and two additional team members. All team supervisors had experience in methodologies used in nutritional surveys. Prior to the survey, all team members attended a two day Save the Children (SC) training workshop to explain the basics of the survey methodology and anthropometric data collection. The workshop included rigorous training and assessment in measuring weight, height, identification of oedema and how to complete the questionnaires. Teams were also sent out into the field to practice the selection and measurement of children. Teams were managed in the field by one ex-patriot supervisor. The survey took place between April Data Analysis Data processing and analysis were carried out using EPI-INFO 5.0. The calculation and analysis of the anthropometric indicators were carried out using EPINUT. 11 The Management of Nutrition in Major Emergencies. Geneva, World Health Organisation,

10 4.0 RESULTS I HOUSEHOLD INFORMATION All households (families) within each selected parcelle (compound) were included in the survey. A total of 578 parcelles and 817 households were interviewed. 4.1 Demographic information Composition of parcelle (compound) The average number of people living in a parcelle was The mean number of households (families)living in parcelle ranged from 1 to 6, with an average of Composition of households (families) Table 4.1: Age group composition of households/families, April 2001 Total (n=809) Tshimungu (n=386) Mapela (n=183) Kimbanseke (n=99) Lobiko (n=141) Age group N Mean % N Mean % N Mean % N Mean % N Mean % 0-5 months months months Household Table 4.1 shows that the total number of people living in a household ranged from 1 to 29, with a mean of On average, 17.5 % of the population surveyed was under five years of age. 4.2 Retrospective Crude Under Five s Mortality A total of 0.35 % (28) families reported at least one under five s death in the last year. 3.9 % (15) of families in Tshimungu, 3.8 % (7) in Mapela, 5.2 % (5) in Kimbanseke and 1.4 % (2) in Lobiko reported at least one under fives death in the last 12 months. Retrospective under fives mortality was calculated as 0.58 per 10,000 per day using mortality figures from the beginning of March 01 and 0.77 per 10,000 per day when calculated as a daily average over the last 12 months. Table 4.2: Retrospective under fives mortality (deaths per 10,000 per day, calculated as an average for the last 12 months) according to Aires de Sante, April 2001 Aires de Sante Retrospective Under Fives Mortality per 10,000/ day (average last 12 months) Tshimungu 0.82 Mapela 0.73 Kimbanseke 1.1 Lobiko 0.32 Total 0.77 The highest under fives mortality rate was found in Kimbanseke (1.1 per 10,000 day calculated as an average over the last 12 months). 10

11 Figure 4.1: Date of under fives deaths over last 12 months, April 2001 Number of deaths Apr-00 May-00 Jun-00 Jul-00 Aug-00 Sep-00 Oct-00 Nov-00 Dec-00 Jan-01 Feb-01 Mar-01 Date Figure 4.1 shows that the under fives death rate peaked between December and February. This period corresponds to one of two rainy seasons in DRC when prevalence of malaria might be raised. It does not however, appear to correspond to a time of year that food availability is reduced. II INFORMATION ON CHILDREN MEASURED 4.3 Nutrition Anthropometry A total of 931 children qualified for data entry and analysis. weight and/or height measurements, or missing values. Six children were flagged due to extreme Acute malnutrition was determined using weight for height (W/H) index values (see Methodology section). Both z-scores and percentage of the median were used to express severe and global malnutrition in the sample population. Only z-scores have true statistical meaning and so only these will be used to look at malnutrition according to the different sub-categories Distribution by age and sex Table 4.3 Distribution of children aged 6-59 months by age group and sex, April 2001 Age Boys Girls Total Sex ratio (months) N % N % N % % % % % % % % % % % % % % % % 1.7 Total % % % 1.0 The sex ratio distribution shows an approximately equal representation of boys and girls throughout the different age groups in the sample, implying that no selection bias has occurred. 11

12 4.3.2 Prevalence of Acute Malnutrition Table 4.4: Prevalence of severe and global malnutrition, z-score, % median, April 2001 % Severe malnutrition % Global malnutrition z-score (95% CI) 2.0 % (1.0 % %) 11.0 % (8.3% %) % of median (95% CI) 1.6 % (0.7 % %) 7.1 % (5.0 % %) Table 4.4 shows the distribution of global malnutrition and severe malnutrition by z-score and % median in the sample population. Global acute malnutrition expressed in z-scores was 11.0 % (95% Confidence Interval 8.3% %) including 2.0 % severe malnutrition (CI: 1.0 % 3.9 %). Expressed as a percentage of the median, the overall global malnutrition rate was 7.1 % (CI: 5.0 % %), including 1.6 % (CI: 0.7 % %) severe malnutrition Prevalence of Oedema Table 4.5: Distribution of Marasmus (wasting), Kwashiorkor, and combined Marasmus/Kwashiorkor, Z- scores, April 2001 Wasting (< -2 Z scores) No Wasting (> - 2 Z scores) Oedema Marasmus/Kwashiorkor 5 (0.5 %) Kwashiorkor 6 (0.6 %) No oedema Marasmus 91 (9.8 %) Normal 829 (89.0 %) Table 4.5 shows the distribution of pure marasmus (wasting), pure kwashiorkor and combined marasmus/kwashiorkor. Eleven cases of oedema were found in the sample Acute malnutrition by age Table 4.6: Distribution of malnutrition by age, Z-scores, April 2001 Severe malnutrition Moderate malnutrition No malnutrition Oedema AGE N < - 3 Z scores >-3 to <-2 Z scores >-2 Z scores (months) n % N % N % n % % % % % % % % % % % % % % % % % % % % % Total % % % % Table 4.7: Acute malnutrition by age group, Z-scores, April 2001 Age groups Severe acute Global acute N % N % 6-29 months (n=416) % % (CI) (0.8 % %) (9.8 % %) 6-59 months (n=498) % % (CI) (0.9 % %) (8.2% %) Tables 4.6 and 4.7 demonstrate a higher level of severe acute and global acute malnutrition in the younger 6-29 month age groups. Such a difference is common, since this age group represents the weaning period of young children. 12

13 Statistical tests revealed that the 6-29 month age group had a higher risk of malnutrition (14.2 % global) compared to the month age group (10.8 % global), Yates Corrected Chi Square = 8.25, p= The relative risk (RR) is 1.77 (95% CI: ). This means that children in the 6-29 age group had a risk multiplied by 1.77 to be malnourished, compared to children of months. Note that the prevalence of global malnutrition for the sample population was slightly reduced (10.8 % in the above analysis according to age group, compared to the previously stated figure of 11.0 %). This is because two children with moderate malnutrition and one child with oedema were not able to provide information on their age and so could not be included in the analysis according to age group Acute malnutrition by gender Table 4.8: Acute malnutrition by gender, Z-scores, April 2001 Gender Severe Acute Moderate acute Oedema Global n % N % n % n % Males (n=471) % % % % Females (n=457) % % % % Table 4.8 shows that prevalence of global malnutrition was approximately equal in both boys and girls in the sample Acute malnutrition by Aires de Sante Table 4.9: Acute malnutrition by Aires de Sante, Z-Scores, April 2001 Aires de Sante Severe Acute Moderate acute Oedema Global 95% CI n % N % n % n % (global) Tshimungu (n=435) % % % % (7.1% %) Mapela (n=224) % % % % (3.3% %) Kimbanseke (n=119) % % % % (-0.5% %) Lobiko (n=153) % % % % (9.7% %) Total (n=931) % % % % (8.3% %) Prevalence of malnutrition varied considerably between the four Aires de Sante surveyed. Global malnutrition remained below 10% in Kimbanseke (5.0%) and Mapela (8.5%), but was above 10% in Tshimungu (11.3%) and Lobiko (18.3%). The prevalence of acute malnutrition in Lobiko was alarmingly high Acute malnutrition by parcelle and household Table 4.10: Acute malnutrition by household (family), Z-Scores, April 2001 Number of malnourished children per households/families Aires de Sante n % n % n % n Tshimungu (n=388) % % % % Mapela (n=183) % % % % Kimbanseke (n=101) % 6 5.9% % % Lobiko (n=140) % % % % Total (n=812) % % % 0 0.0% Table 4.10 shows that on average, 10.8% of households visited during the survey had at least one malnourished child living with them. 1.7% of households had two malnourished children. Nearly one fifth (17.8%) of households questioned in Lobiko had at least one malnourished child. 13

14 Table 4.11: Acute malnutrition by parcelle, Z-Scores, April 2001 Number of malnourished children per households/families Aires de Sante n % n % n % n Tshimungu (n=267) % % % % Mapela (n=114) % % % % Kimbanseke (n=78) % % % % Lobiko (n=114) % % % % Total (n=573) % % % % Table 4.11 shows that on average, 12.8% of parcelles visited in the survey had at least one malnourished child living there. One fifth (20.2 %) of parcelles visited in Lobiko had at least one malnourished child Prevalence of Chronic Malnutrition Table 4.12: Distribution of chronic malnutrition by age, Z -scores, April 2001 Severe malnutrition Moderate malnutrition Global No malnutrition N < - 3 Z scores >-3 to <-2 Z scores <-2 z scores >-2 Z scores n % n N n % 95% CI n % Tshimungu % % % 27.5% % % Mapela % % % 22.5% % % Kimbanseke % % % 20.9% % % Lobiko % % % 30.6% % % Total % % % 30.0% -38.8% % Table 4.12 shows the prevalence of chronic malnutrition in the different Aires de Sante where the survey took place. Overall, approximately one third (34.4 %) of children measured were chronically malnourished (<-2.0 Z score), including 14.8% severely chronically malnourished children. Lobiko had the highest prevalence of chronic malnutrition (41.7 %) of the four Aires de Sante, whilst Mapela had the lowest (31.1 %). 4.4 Morbidity in last 24 hours Table 4.13: Under fives morbidity in last 24 hours by Aires de Sante, Z-scores, April 2001 Total Tshimungu Mapela Kimbanseke Lobiko Morbidity n % n % n % n % n % No illness % % % % % Fever % % % % % Cough % % % % % Diarrhoea % % % % % Other % % % % % Vomiting % % % % % Bloody diarrhoea % % % % % Measles % % % % % Total % % % % % The table above shows that 17.6 % of the under fives sampled were reported to have been ill in the last 24 hours. The most frequent symptom of illness reported was fever (9.2%) followed by cough (2.6%) and diarrhoea (2.6%). The prevalence and order of the different symptoms was similar for each of the Aires de Sante. 14

15 Table 4.14: Morbidity in last 24 hours by acute malnutrition, Z-scores, April 2001 Total No malnutrition Global malnutrition n % n % n % No morbidity % % % Morbidity % % % Total % % % Table 4.14, shows that the proportion of ill children amongst the malnourished was almost double that found in children who were not malnourished. This higher risk of illness among the malnourished was found to be statistically significant, Yates Corrected Chi Square = 12.41, p= The relative risk (RR) of 1.91 (95% CI: ) means malnourished children had a risk multiplied by 1.91 to be ill, compared to children who were not malnourished. 4.5 Attendance of Stage II (SFP) and Stage III (TFC) All children measured in the survey were asked whether they were currently attending a feeding programme, either Stage II (supplementary Feeding) or Stage III (therapeutic Feeding). Table 4.15: Feeding Centre Coverage by Aires de Sante, April 2001 Aires de Sante No attending feeding No malnourished children % attendance programme Tshimungu % Mapela % Kimbanseke % Lobiko % Total % Table 4.15 shows that the Feeding Centre Coverage for the total surveyed population was 8.8 %. This means that 8.8 % of the children who were malnourished in the survey were attending one of the feeding centres. The Feeding Centre Coverage was particularly low for Lobiko (3.6 %) but was significantly higher in the other Aires de Santes and in particular Kimbanseke, where the coverage was 16.7 %. 15

16 5. 0 DISCUSSION 5.1 Prevalence of Acute Malnutrition The prevalence of global acute malnutrition found in the survey population was 11.0 %, with approximately 1 in 11 households supporting at least one malnourished child. According to WHO criteria, a prevalence of global malnutrition above 10 % (with no aggrevating factors ) represents a risky situation for which targeted supplementary and therapeutic feeding programmes are appropriate responses 12. It is important to take into account however, the fact that this survey took place in April, at the end of the rainy season, when prices of the staple foods (maize and cassava) are at their highest and so at a time when the population are particularly vulnerable to malnutrition. Action Contre La Faim (ACF) and CEPLANUT conducted a nutrition survey in Kimbanseke commune (where three of the four Aires de Sante in this survey are located), in February 2001 and found similar levels of malnutrition (12.2 % global and 2.4 % severe) to this survey 13. The prevalence of malnutrition found by ACF/CEPLANUT in Kimbanseke in February however, was more than three times higher than that found by them in a previous survey in September 1999 (3.8% global and 1.0 % severe). ACF/CEPLANUT also conducted a survey in Selembao commune in February where prevalence of global malnutrition was seen to have almost doubled since their previous survey there in September 1999 (from 6.7 % in 1999 to 12.0 % in 2001). Both of the previous ACF surveys were conducted in September 1999 when availability of food is usually good and so prevalence of malnutrition is not expected to be particularly high, whilst the most recent surveys took place in February 2001 when malnutrition is expected to be raised. This expected seasonal trend however, can not fully explain the huge rise in prevalence over the last 17 months. It is apparent from these statistics that the nutritional status of the population in Kimbanseke, Selembao and most probably other vulnerable communes in Kinshasa, has dramatically decreased since 1999 and should be a cause for serious concern and appropriate action Prevalence of Acute malnutrition by Aires de Sante The results of this survey indicate that the prevalence of malnutrition varies considerably between the four Aires de Sante surveyed. Global acute malnutrition was below 10% in Kimbanseke (5.0%) and Mapela (8.5%) but was above 10% in Tshimungu (11.3%) and Lobiko (18.3%). Some degree of caution is required for the interpretation of prevalence figures calculated for these sub-categories of the survey population however, since they represent much smaller population groups and so will be less accurate than the prevalence figures obtained for the total population. The 95% confidence intervals calculated for each of the four Aires de Santes indicate the wider range of values within which the true rate of malnutrition is expected to lie. The following discussion on the nutritional situation in each of the Aires de Santes, is based on information obtained from various sources, including a recent SC Food Economy Assessment in Masina commune 14, and a study of the health situation in Tshimungu and Lobiko Aires de Sante conducted by L Ecole de Santé Public and SC Lobiko The high global prevalence of malnutrition in Lobiko (18.3 %) is particularly worrying and according to WHO criteria represents a serious situation requiring the provision of general rations, blanket 12 The Management of Nutrition in Major emergencies, WHO Kinshasa Enquetes Nutritionnelles Communes de Kimbanseke, Selembao, Bumbu et Kisenso. ACF/CEPLANUT. April Rapid Household Food Economy Assessment of Masina Commune, Kinshasa. SCF. June Etude de la situation sanitaire des enfants ages de 0 a 18 ans dans les zones de sante de biyela et de kikimi a Kinshasa. L Ecole de Santé Public, SCF. August

17 supplementary feeding for all members of vulnerable groups, and therapeutic feeding for the severely malnourished 16. Even when taking into account the lower confidence interval of 9.7%, the level of malnutrition in Lobiko is unacceptably high. In addition, the upper confidence interval of 26.9 % is alarmingly high. Approximately one fifth of households (17.8 %) and parcelles (20.2 %) in Lobiko was caring for at least one malnourished child, whilst 2.2 % of parcelles had two or three malnourished children living there. The particularly high level of malnutrition in Lobiko may be explained by its inaccessibility/limited road access, sandy soil (making it difficult to grow crops), and poor infra-structure including provision of potable water, latrines, health care and schools. Lobiko is essential rural in nature with poor road access severely limiting the opportunity for engaging in trade with major markets. The absence of potable water is a huge constraint, with many households having to obtain water from the river. These problems appear particularly pronounced in the remote southern hilly areas furthest away from the road where there is no access to portable water and no latrines. The results of this survey and a previous health assessment by L Ecole de Santé and SCF 17, clearly indicate the urgent need for programmes to improve the infra-structure of Lobiko, including the provision of water, sanitation and health care Tshimungu The high prevalence of global malnutrition in Tshimungu (11.3 %) is also cause for concern. Tshimungu is very densely populated, so limiting the availability of plots of land for household food production. It also has sanitation problems, particularly in the wet season when latrines tend to flood, so increasing the risk of cholera. Being closer to Boulevard Lumumba, Lobiko does however have much better accessibility and consequently better opportunities for trading compared to Lobiko Mapela The 8.5 % prevalence of global malnutrition is Mapela is also unacceptable. Being situated in the north of Masina commune, Mapela has limited access to the markets in the south of the Zones de Sante. A SC Food Economy assessment in Masina found that the poorer, and therefore more vulnerable families, were mainly landless agricultural households who either rent the land they work on, or work on the land of richer households for a daily wage. There appears to be sanitation problems including a limited number of latrines which tend to flood in the rainy season. Discussions with mothers of malnourished children in Masina, together with findings on the average food intake of poor households, suggest that malnutrition may be less a result of lack of food at household level and more related to poor child feeding practises, early weaning, illness and inappropriate intra-household distribution Kimbanseke Kimbanseke has a more acceptable level of global malnutrition of 5.0 %. This Aires de Sante benefits from better accessibility and consequently a more active economy than the other three Aires de Sante. Availability of water and land for garden cultivation is also relatively good, as is the provision of health care. 5.2 Prevalence of Chronic Malnutrition Height for age indices were calculated in order to determine prevalence of chronic malnutrition in the sample population. Overall, chronic malnutrition (<-2.0 z scores) affects approximately one third (34.4%) of the sample population. This is a similar level to that found by ACF/CEPLANUT in Kimbanseke commune both in their recent nutrition survey in February of this year (35.9%) and in their previous survey September 1999 (35.8 %). This result can also be compared to the prevalence of 31.1% chronic malnutrition found in the zone exentriques et semi-rural (which included Masina and Kimbanseke communes) in the most recent CEPLANUT nutrition survey in June The Management of Nutrition in Major emergencies, WHO Etude de la situation sanitaire des enfants ages de 0 a 18 ans dans les zones de sante de biyela et de kikimi a Kinshasa. L Ecole de Santé Public, SCF. August Rapid Household Food Economy Assessment of Masina Commune, Kinshasa. SCF. June

18 It is interesting to note that chronic malnutrition was particularly high in Lobiko (41.7 %) which suggests a more pronounced long term nutritional problem in this Aires de Sante. 5.3 Retrospective Under Five s Crude Mortality Rate(CMR) The Retrospective Under Five s Crude Mortality Rates (CMR) calculated for the sample population as a whole and the different Aires de Sante were all below 2.0 which implies that the health situation is not serious 19 and so is not a cause of undue concern. In addition, they compare favourably with the results of the recent ACF/CEPLANUT survey which found a CMR of 1.1 per 10,0000 per day in Kimbanseke commune. The CMR calculated as an average from March up to the time of the survey in April (0.58 per 10,0000 per day) was lower than that calculated as an average for the previous 12 months (0.77 per 10,000 per day). This suggests that mortality is low during March and April compared to the rest of year. Aside from Lobiko, the CMR did not vary considerably between the different Aires de Santes. Lobiko had a surprisingly low CMR of 0.3, particularly considering its high prevalence of malnutrition and poor health care environment. Only 2 (1.4%) of families in Lobiko reported having an under fives death in the past 12 months. It is important to note however, that collection of data on retrospective mortality without any independent verification is frequently unreliable and so the results obtained should be interpreted with some degree of caution. 5.4 Under fives morbidity in the last 24 hours The mother/carer of children measured in the survey were asked if the child had been ill in the past 24 hours. If the child was reported to have been ill, the major symptom of illness was determined and recorded. Overall, 17.6 % of children were reported to have been ill in the previous 24 hours. Aside from Kimbanseke which had the lowest prevalence of morbidity of 12.6%, this proportion did not vary considerably between the Aires de Santes surveyed. It is interesting to note that Kimbanseke also had the lowest prevalence of malnutrition. Fever was the most common symptom (9.2 %), followed by cough (2.6 %), and then diarrhoea (2.5 %). Although fever is a symptom of a number of diseases, it commonly indicates malaria. The order and prominence of these three symptoms reflects those of the three major causes of morbidity and mortality in Kinshasa, namely malaria, respiratory diseases and diarrhoeal diseases 20. It was found that there was a positive and statistically significant association between morbidity and malnutrition in the children surveyed, with the proportion of malnourished children who were also ill (30.7%), being almost double that of children who were not malnourished (16.0%). This association is not surprising given the strong association between malnutrition and disease. Interestingly, a SC investigation into the causes of malnutrition, which involved interviewing 100 mothers with malnourished children admitted into Stage III (therapeutic) feeding centres in Kinshasa in January 2001, found that over half of children admitted had fallen sick before being admitted into the centre. Malaria and fever were the most common conditions of these malnourished children. 5.5 Feeding Centre Coverage Overall, the Feeding Centre Coverage was calculated to be 8.8 %. This means that only 8.8% (9) children who were malnourished in the survey were attending a feeding centre for treatment. Seven out of these nine children were attending Stage II (supplementary) feeding centres. 19 The Management of Nutrition in Major emergencies, WHO Statistics compiled by Bureaux Centraux des Zones de Sante de Kinshasa,

19 The Feeding Centre Coverage calculated for the different Aires de Santes varied considerably. Lobiko, which had the highest amount of malnutrition and morbidity had an extremely low coverage of 3.6%, whilst Mapela and Kimbanseke had a Feeding Centre Coverage of 15.8 % and 16.7 % respectively. This low coverage of malnourished children indicates that there is tremendous opportunity for improved nutrition screening and referral both by the Health Centres and the Maman Bongisas, particularly in Lobiko. 6.0 RECOMMENDATIONS 1. Improve feeding centre coverage by strengthening nutrition screening and referrals both at the health centres and by the Mamans Bongisas in the community. 2. Provide training for health staff and Mamans Bongisas on both the prevention and treatment of malnutrition, with an emphasis on the importance of breastfeeding and appropriate weaning practises. 3. Provide community based programmes to raise awareness of malnutrition and nutrition related issues within the general population. 4. Strengthen the nutrition surveillance system for the regular collection and analysis of data on malnutrition in the Zones de Sante to allow the nutritional situation to be closely monitored. 5. Continue to monitor the nutritional and food security situation in the most vulnerable Eastern zones of Kinshasa, through regular anthropometric surveys. 6. Strengthen first line treatment and preventative strategies for diarrhoea, malaria and respiratory infections. 7. Encourage programmes to improve the economy and infra-structure of Lobiko, particularly with regard to the provision of improved sanitation and access to clean water. 19

20 8.0 APPENDIX I Sampling information II Survey Questio nnaire III Local Calendar of Events 20