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Toward the design of a nutrition surveillance system for pregnant women in Zambia
Sm. %I. & Mrcl. Vol. 130. pp. 131 lo 133 Pcrpmon Press Ltd 1979. Punted an Great
Britain
RESEARCH
NOTE
TOWARD THE DESIGN OF A NUTRITION SURVEILLANCE SYSTEM FOR PREGNANT WOMEN IN ZAMBIA KWAMEKWOHE National
Food and Nutrition
Commission, Box 2669, Lusaka. Zambia
Abstract-A l-year nutritional surveillance system is now being tested in parts of Lusaka. the capital of Zambia, with the intent of identifying factors contributing to neonatal health problems. A model has been developed to predict these problems among pregnant women to the end that more efficient nutrition programs can be developed. If successful. the results of this pilot project can be applied to many other parts of Zambia.
BACKGROUND
The National Food and Nutrition Commission (NFNC) of Zambia has established, on the basis of the National Nutrition Surveys conducted in 19691972, that there is a high prevalence (over 60%) of protein energy malnutrition in the country Cl]. Food consumption data from the National Nutrition Surveys also reveal major differences in dietary intakes varying with respect to socioeconomic groups [Z]. For example, the overall quality of the protein intake of pregnant and lactating women, as measured by the serum albumin, was found to be inefficient. Nearly 61% (60.5%) of this vulnerable group had low serum albumin and 34.8% had deficient levels.’ A significant proportion of the pregnant and lactating women (22.2%) were also anemic. The findings further indicate much of the anemia during pregnancy was due to malaria infection. Also, it is suspected that recent economic conditions (inflation, devaluation of the kwacha, effects of warfare in the Southern African region, major shifts in government economic policies) may be aggravating the nutritional status of vulnerable groups (i.e. mothers, infants, school and preschool children) who live in poor communities. PROJECT OBJECTIVES
The principal objectives of this project are: (1) to identify the most significant variables which can be used to define pregnant women who are nutritionally at risk; (2) to identify the possible associated cause of maternal malnutrition; (3) to evaluate existing sources of data and measuring techniques; and (4) to select sensitive predictive indicators for designing a surveillance system for pregnant women in urban and rural areas in Zambia Specifically, this pilot ,project is aimed at verifying a simple three-factor model that could serve as a guide to the design and implementation of an efficient nutrition surveillance system for use by the National Food and Nutrition Commission. The model states 131
that the nutritional status of a pregnant woman is functionally related to her health status and to the family’s ability to purchase adequate nutrients to meet her physiological needs. STUDY AREA
Two high density communities, Mater0 (population 23.000) and Chelston (population 10,000) in Lusaka. the capital of Zambia, are the pilot areas. Both communities have a large working population concentration, and present so&-economic and demographic features that are quite characteristic of the fast-growing towns of Zambia. LITERATURE
REVIEW
The relationship between socio-economic status, maternal nutrition and birth weight are well documented [3,4]. Recent evidence shows that supplementary food during pregnancy can increase birth weight [a. An association between the weight of children at birth and their mothers’ standard of living has been shown to exist between and among developing and developed countries [6]. In countries with a high degree of undernutrition a large proportion of children are born with a weight under 2.4 kg [7,8]. As an index of maternal nutrition, therefore, the percentage of newborn children with a birth weight below 2.0 kg [9] or 2.5 kg [lo] or 3.0 kg [11] have been suggested. The establishment of a “critical limit” for Zambia constitutes an essential part of this pilot project. MODEL AND HYPOTHESES
From the model, presented diagramatically we assert that:
in Fig. 1.
1. the nutritional status (Y,) of a pregnant woman is functionally related to the family’s nutrient adequacy (Q) and her health status (X): 2. the family’s ability to obtain adequate nutrients (Q) depends upon its purchasing power (E): and 3. the nutritional status of a newborn child (Y,)
KWAMI~
Fig.
is functionally mother (Y,).
1. A
nutrition
status
model.
related to the nutritional
status of the
Symbolically, Y, = f(Q. X)
(1)
Q =f(E)
(2) (3)
Y, = f( Y*)
For predictive purposes, it will be necessary to integrate nutrient adequacy (Q) and the family’s purchasing power (E) h_r: (a) calculating the cost of adequate nutrient for a given family per month. and (b) estimating the total monthly income of the family. With these two measures we can derive a summary index (R) to estimate how a family’s access to food compares with its nutritional needs. The index R defined as R=g.
KW~FW
establish the level of income (E) at which a given family will spend. money on food so that it can purchase a nutritionally adequate diet. For practical planning purposes we wish to also predict the probability of a child being born underweight (Y,) given the nutritional status (Y,,,) of the mother. It will be equally useful to predict the level of R at which we can expect a premature or a low birth baby to be born. For planning purposes in developing countries, it is more useful to have some indications of the groups of mothers who stand a high chance of delivering premature and low birth weight babies than routinely counting such babies after they have been born. THE NATURE OF ANALYSIS
We propose to use a logistic response function [ 151 to describe the relationship between the levels of nutrient adequacy (R) and health status (X) to the probability that an individual pregnant woman would be undernourished (Y,). We shall denote, for example. the various levels of nutrient adequacy (R) at which observations are obtained R ,‘... R, The number of observations at level Rj will be denoted N, (i = I.. . . C). Since our dependent variable (Y,) is binary indicating whether or not a pregnant woman is undernourished, i.e. I. or not undernourished, (O), only the total l’s at each R level, rather than the individual Y, values need to be considered. Thus. if we let Mj be the number of l’s at the level R, the proportion of individual I’s at the level R, denoted by pj is:
(4)
should provide us with the simplest means of predicting changes in the degree of nutritional risk faced by a pregnant woman in a given family. Families with the expenditure on consumption just equal to the size of the family’s total monthly income should be considered at risk of undernutrition, since part of the total monthly income will be used for other household items [l2. 133. As an index of the health status of a pregnant woman. we have selected the ieve1 of hemoglobin or packed cell volume [14]. Either of these measures indicate the relative degree of anemia, which is a consequence of inadequate diet, infections and infestations. In deciding on these indicators we have taken into consideration ease of measurement. speed and frequency of data collection, time and cost. We hope the selected indicators can be readily obtained with a minimum of equipment and processing; and. above ah. that they will be continuously avaiiable. The sensitivity of the selected indicators will obviously depend upon the reliability of the measurements as well as the correct determination of the gestation age of pregnancy. We have. of course. carefully weighed the cost of obtaining each indicator against the purpose of the project, including its future transferability to other parts of Zambia. With the above model we hope to be able to determine the statistical relationship which holds between the health status of pregnant women (X) and the frequency with which maternal undernutrition (Y) occurs in a given family. We should also be able to
pi==:. A logistic transformation of the probability p is performed on the sample proportion: _. -, fii = log, -!!pj = 2.303585 log,, -!!I!(6) 1 - pi 1 - pi and pj is used as the dependent variable. The fitted response function for our two independent variables. R.X. will be of the form: p‘ = & + 81 x, + 3, x2
17)
where p’ is the logistic transformation. If we can predict the nutritional status of a pregnant woman (Y,) given R,X, then we should be able to predict the nutritional status of the newborn child ( Y,), since we assert that:
r, = j-cu. If this assumption. is tested and found to be valid. then for the design of the surveillance system we will need to measure only Y, to be able to predict Y,. Furthermore. if this assumption is valid, the determination of the nutritional status of a pregnant woman (Y,) witi involve only her weight and height measurement, the ultimate objective of the surveillance system-a rapid and continuous screening aNd monitoring of the nutritional status of pregnant women with the view of preventing low weight babies-would have been achieved. As stated in the objectives. both the testing of the
133
Design of a nutrition surveillance system for pregnant women model and the designing of the surveillance system are two important components of the project. The main questions to be answered by the pilot project are: (a) can a low-cost nutritionai surveillance system be designed and implemented which will be capable of providing useful input into the Zambian planning
agencies (e.g. National Food and Nutrition Commission) charged with making planning and policy decisions?, and (b) wit1 such a system be used? A l-year test period of the nutritional surveillance system is now underway. This is essential for two principal reasons: first, to adjust the system’s variables (sample size, target group(s), ineasurement techniques, data processing) and, secondly, to integrate the systems’s output into de~sionmaking bodies in Zambia.
In this way it is hoped
the nutrition
surveil-
lance system wili become an integral component the development efforts of Zambia.
of
REFERENCES
FAO, bia:
Nationai Nutrition
Food and
Nurrition
Programme
of Zam-
Stafus Suroey. ESN : DP~M/69/5~
Results.
Conclusions
9.
Archs lat.-amer. Nutr. 22, 255, 1972. The Assessment oj the Nutritiotud of Cammunity. WHO Monograph Series,
Jelliffe D. B.
Status 53.
No.
Geneva, 1966. 10. Bengoa J. M., Jeliiffe D. B. and Perez C. Some Indieaters for a Broad Assessment of the Magnitude of Protein-Calorie Malnutrition in Youna Children in Population Groups. Am. J. Clin. Nut;. 7, 714-720. 1959.
2
Technical Report 2. Rome. 1974. FAO, National Food and Nutrition on Project
5. Lechtig A., Habicht J. P.. Leon E. de and Guzman G. lnfluencia de la nutrition materna sobre el erecimiento fetal en poblaciones rurales de Guatemala. II. Suplementacion Atimentaria. Archs fat-umer. Nutr. 2t, 117, 1972. 6. WHO, ~et~odoi~y of Nutritionai Surveillance. Report of a Joint FAO/UNICEF/WHO Expert Committee. Technical Report Series. Geneva. 1976. 7. Habicht J. P.’ Human Implications of Animal Studies in Prenatal Nutrition and Neurological Development. In Early Malnutrition and Mental Development. Symposia of the Swedish Nutrition Foundation XII, pp. 1I l-l 13. Almqvist & Wiksell, Uppsala. 1974. 8. Lechtig A., Habicht J. P., Guzman G. and Giron E. M. Influencia de las caracteristicas matemas sobre el crecimienta fetal an poblaeiones rurales de Guatemala.
Programme: Report and Recommendations.
ESN : DP/ZAM/69/5 12 Terminal Report. Rome, 19M. WHO. Nutrition in Pregnancy and Luctation: Report ofa WHO Expert Commitree. Wld Hlth Org. Technical Report Series No. 302, Geneva, 1965. Rosa F. W. and furshen M. Fetal Nutrition. Bulletin WHO. 43, 785. 1970.
11. Puffer R. H. and Serrano C. V. Patrerns 01 Mortulity in Chifdhoad. PAHO Scientific Publication No. 262. Washington, 1973. 12. Goreux L. M. Income and Food Consumption. Morterry Bufl. Agricuit. Ecort. Statist. 9, 1960. 13. Perisse J. The Effects of income on the structure of the diet. Nutrition Newsletter 7. I, 1969. 14. WHO, Nutrition Anemias. WHO Technical Report Ser., No. 405, 1968. IS. Nerlove M. and Press J. S. Univarjate and ~ult~~r~ate Lo&ear and La&tic yodels. Santa Monica: Rand Ca. (R-1306 EDA/NIH) December, 1973.
Britain
RESEARCH
NOTE
TOWARD THE DESIGN OF A NUTRITION SURVEILLANCE SYSTEM FOR PREGNANT WOMEN IN ZAMBIA KWAMEKWOHE National
Food and Nutrition
Commission, Box 2669, Lusaka. Zambia
Abstract-A l-year nutritional surveillance system is now being tested in parts of Lusaka. the capital of Zambia, with the intent of identifying factors contributing to neonatal health problems. A model has been developed to predict these problems among pregnant women to the end that more efficient nutrition programs can be developed. If successful. the results of this pilot project can be applied to many other parts of Zambia.
BACKGROUND
The National Food and Nutrition Commission (NFNC) of Zambia has established, on the basis of the National Nutrition Surveys conducted in 19691972, that there is a high prevalence (over 60%) of protein energy malnutrition in the country Cl]. Food consumption data from the National Nutrition Surveys also reveal major differences in dietary intakes varying with respect to socioeconomic groups [Z]. For example, the overall quality of the protein intake of pregnant and lactating women, as measured by the serum albumin, was found to be inefficient. Nearly 61% (60.5%) of this vulnerable group had low serum albumin and 34.8% had deficient levels.’ A significant proportion of the pregnant and lactating women (22.2%) were also anemic. The findings further indicate much of the anemia during pregnancy was due to malaria infection. Also, it is suspected that recent economic conditions (inflation, devaluation of the kwacha, effects of warfare in the Southern African region, major shifts in government economic policies) may be aggravating the nutritional status of vulnerable groups (i.e. mothers, infants, school and preschool children) who live in poor communities. PROJECT OBJECTIVES
The principal objectives of this project are: (1) to identify the most significant variables which can be used to define pregnant women who are nutritionally at risk; (2) to identify the possible associated cause of maternal malnutrition; (3) to evaluate existing sources of data and measuring techniques; and (4) to select sensitive predictive indicators for designing a surveillance system for pregnant women in urban and rural areas in Zambia Specifically, this pilot ,project is aimed at verifying a simple three-factor model that could serve as a guide to the design and implementation of an efficient nutrition surveillance system for use by the National Food and Nutrition Commission. The model states 131
that the nutritional status of a pregnant woman is functionally related to her health status and to the family’s ability to purchase adequate nutrients to meet her physiological needs. STUDY AREA
Two high density communities, Mater0 (population 23.000) and Chelston (population 10,000) in Lusaka. the capital of Zambia, are the pilot areas. Both communities have a large working population concentration, and present so&-economic and demographic features that are quite characteristic of the fast-growing towns of Zambia. LITERATURE
REVIEW
The relationship between socio-economic status, maternal nutrition and birth weight are well documented [3,4]. Recent evidence shows that supplementary food during pregnancy can increase birth weight [a. An association between the weight of children at birth and their mothers’ standard of living has been shown to exist between and among developing and developed countries [6]. In countries with a high degree of undernutrition a large proportion of children are born with a weight under 2.4 kg [7,8]. As an index of maternal nutrition, therefore, the percentage of newborn children with a birth weight below 2.0 kg [9] or 2.5 kg [lo] or 3.0 kg [11] have been suggested. The establishment of a “critical limit” for Zambia constitutes an essential part of this pilot project. MODEL AND HYPOTHESES
From the model, presented diagramatically we assert that:
in Fig. 1.
1. the nutritional status (Y,) of a pregnant woman is functionally related to the family’s nutrient adequacy (Q) and her health status (X): 2. the family’s ability to obtain adequate nutrients (Q) depends upon its purchasing power (E): and 3. the nutritional status of a newborn child (Y,)
KWAMI~
Fig.
is functionally mother (Y,).
1. A
nutrition
status
model.
related to the nutritional
status of the
Symbolically, Y, = f(Q. X)
(1)
Q =f(E)
(2) (3)
Y, = f( Y*)
For predictive purposes, it will be necessary to integrate nutrient adequacy (Q) and the family’s purchasing power (E) h_r: (a) calculating the cost of adequate nutrient for a given family per month. and (b) estimating the total monthly income of the family. With these two measures we can derive a summary index (R) to estimate how a family’s access to food compares with its nutritional needs. The index R defined as R=g.
KW~FW
establish the level of income (E) at which a given family will spend. money on food so that it can purchase a nutritionally adequate diet. For practical planning purposes we wish to also predict the probability of a child being born underweight (Y,) given the nutritional status (Y,,,) of the mother. It will be equally useful to predict the level of R at which we can expect a premature or a low birth baby to be born. For planning purposes in developing countries, it is more useful to have some indications of the groups of mothers who stand a high chance of delivering premature and low birth weight babies than routinely counting such babies after they have been born. THE NATURE OF ANALYSIS
We propose to use a logistic response function [ 151 to describe the relationship between the levels of nutrient adequacy (R) and health status (X) to the probability that an individual pregnant woman would be undernourished (Y,). We shall denote, for example. the various levels of nutrient adequacy (R) at which observations are obtained R ,‘... R, The number of observations at level Rj will be denoted N, (i = I.. . . C). Since our dependent variable (Y,) is binary indicating whether or not a pregnant woman is undernourished, i.e. I. or not undernourished, (O), only the total l’s at each R level, rather than the individual Y, values need to be considered. Thus. if we let Mj be the number of l’s at the level R, the proportion of individual I’s at the level R, denoted by pj is:
(4)
should provide us with the simplest means of predicting changes in the degree of nutritional risk faced by a pregnant woman in a given family. Families with the expenditure on consumption just equal to the size of the family’s total monthly income should be considered at risk of undernutrition, since part of the total monthly income will be used for other household items [l2. 133. As an index of the health status of a pregnant woman. we have selected the ieve1 of hemoglobin or packed cell volume [14]. Either of these measures indicate the relative degree of anemia, which is a consequence of inadequate diet, infections and infestations. In deciding on these indicators we have taken into consideration ease of measurement. speed and frequency of data collection, time and cost. We hope the selected indicators can be readily obtained with a minimum of equipment and processing; and. above ah. that they will be continuously avaiiable. The sensitivity of the selected indicators will obviously depend upon the reliability of the measurements as well as the correct determination of the gestation age of pregnancy. We have. of course. carefully weighed the cost of obtaining each indicator against the purpose of the project, including its future transferability to other parts of Zambia. With the above model we hope to be able to determine the statistical relationship which holds between the health status of pregnant women (X) and the frequency with which maternal undernutrition (Y) occurs in a given family. We should also be able to
pi==:. A logistic transformation of the probability p is performed on the sample proportion: _. -, fii = log, -!!pj = 2.303585 log,, -!!I!(6) 1 - pi 1 - pi and pj is used as the dependent variable. The fitted response function for our two independent variables. R.X. will be of the form: p‘ = & + 81 x, + 3, x2
17)
where p’ is the logistic transformation. If we can predict the nutritional status of a pregnant woman (Y,) given R,X, then we should be able to predict the nutritional status of the newborn child ( Y,), since we assert that:
r, = j-cu. If this assumption. is tested and found to be valid. then for the design of the surveillance system we will need to measure only Y, to be able to predict Y,. Furthermore. if this assumption is valid, the determination of the nutritional status of a pregnant woman (Y,) witi involve only her weight and height measurement, the ultimate objective of the surveillance system-a rapid and continuous screening aNd monitoring of the nutritional status of pregnant women with the view of preventing low weight babies-would have been achieved. As stated in the objectives. both the testing of the
133
Design of a nutrition surveillance system for pregnant women model and the designing of the surveillance system are two important components of the project. The main questions to be answered by the pilot project are: (a) can a low-cost nutritionai surveillance system be designed and implemented which will be capable of providing useful input into the Zambian planning
agencies (e.g. National Food and Nutrition Commission) charged with making planning and policy decisions?, and (b) wit1 such a system be used? A l-year test period of the nutritional surveillance system is now underway. This is essential for two principal reasons: first, to adjust the system’s variables (sample size, target group(s), ineasurement techniques, data processing) and, secondly, to integrate the systems’s output into de~sionmaking bodies in Zambia.
In this way it is hoped
the nutrition
surveil-
lance system wili become an integral component the development efforts of Zambia.
of
REFERENCES
FAO, bia:
Nationai Nutrition
Food and
Nurrition
Programme
of Zam-
Stafus Suroey. ESN : DP~M/69/5~
Results.
Conclusions
9.
Archs lat.-amer. Nutr. 22, 255, 1972. The Assessment oj the Nutritiotud of Cammunity. WHO Monograph Series,
Jelliffe D. B.
Status 53.
No.
Geneva, 1966. 10. Bengoa J. M., Jeliiffe D. B. and Perez C. Some Indieaters for a Broad Assessment of the Magnitude of Protein-Calorie Malnutrition in Youna Children in Population Groups. Am. J. Clin. Nut;. 7, 714-720. 1959.
2
Technical Report 2. Rome. 1974. FAO, National Food and Nutrition on Project
5. Lechtig A., Habicht J. P.. Leon E. de and Guzman G. lnfluencia de la nutrition materna sobre el erecimiento fetal en poblaciones rurales de Guatemala. II. Suplementacion Atimentaria. Archs fat-umer. Nutr. 2t, 117, 1972. 6. WHO, ~et~odoi~y of Nutritionai Surveillance. Report of a Joint FAO/UNICEF/WHO Expert Committee. Technical Report Series. Geneva. 1976. 7. Habicht J. P.’ Human Implications of Animal Studies in Prenatal Nutrition and Neurological Development. In Early Malnutrition and Mental Development. Symposia of the Swedish Nutrition Foundation XII, pp. 1I l-l 13. Almqvist & Wiksell, Uppsala. 1974. 8. Lechtig A., Habicht J. P., Guzman G. and Giron E. M. Influencia de las caracteristicas matemas sobre el crecimienta fetal an poblaeiones rurales de Guatemala.
Programme: Report and Recommendations.
ESN : DP/ZAM/69/5 12 Terminal Report. Rome, 19M. WHO. Nutrition in Pregnancy and Luctation: Report ofa WHO Expert Commitree. Wld Hlth Org. Technical Report Series No. 302, Geneva, 1965. Rosa F. W. and furshen M. Fetal Nutrition. Bulletin WHO. 43, 785. 1970.
11. Puffer R. H. and Serrano C. V. Patrerns 01 Mortulity in Chifdhoad. PAHO Scientific Publication No. 262. Washington, 1973. 12. Goreux L. M. Income and Food Consumption. Morterry Bufl. Agricuit. Ecort. Statist. 9, 1960. 13. Perisse J. The Effects of income on the structure of the diet. Nutrition Newsletter 7. I, 1969. 14. WHO, Nutrition Anemias. WHO Technical Report Ser., No. 405, 1968. IS. Nerlove M. and Press J. S. Univarjate and ~ult~~r~ate Lo&ear and La&tic yodels. Santa Monica: Rand Ca. (R-1306 EDA/NIH) December, 1973.