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Correlation between calorie adequacy of high-risk individuals and selected indicators

Tables 10 and 11 address the third question, i.e. the correlation between individual calorie adequacy and other potential nutrition indicators. The correlations are low, indicating that none of the selected variables are good proxies for the calorie adequacy of individual high-risk household members. Household calorie adequacy, however, is a much better proxy for the calorie adequacy of pregnant and lactating women than for the calorie adequacy of preschoolers. This may be due in part to the greater relative importance of the quantity of food consumed by pregnant and lactating women in relation to overall household food consumption. The implication of this finding, if it is of general validity, is that it is more important to collect data on the individual con gumption of pre-schoolers, while household-level data may suffice to target and assess the impact of policies and programmes for pregnant and lactating women.

Table.10. Simple correlation between calorie adequacy of pre-schoolers and selected proxies (sample size = 375)

 Proxy measure Correlationcoefficient Household calorie consumption/capita 0.42 Household calorie consumption 0.30 Household calorie adequacy 0.27 Household food acquisition (expenditures) 0.12 Household food acquisition/capita (expenditures/capita) 0.11 Household income/capita 0.04 Weight of pre-schooler relative to standard 0.03

Table 11. Simple correlation between calorie adequacy of pregnant and lactating women and selected proxies (sample size = 124)

 Proxy measure Correlationcoefficient Household calorie adequacy 0.52 Household calorie consumption 0.35 Household calorie acquisition/capita (expenditures/capita) 0.17 Household food acquisition (expenditures) 0.12 Household income/capita 0.09 Household food acquisition/capita (calories/capita) 0.09 Household food acquisition (calories) 0.05

Table 12. Simple correlation between weight of pre-schoolers relative to standard and selected proxies (sample size = 2,526)

 Proxy measure Correlationcoefficient Household income/capita 0.22 Household food acquisition/capita (expenditures/capita) 0.17 Household calorie consumption/capita 0.16 Household calorie adequacy 0 15 Household food acquisition (expenditures) 0.12 Household calorie consumption 0.08

Table 12 shows that the correlation between the weight of pre-schoolers relative to standard weights and household-level variables is generally low. It is noteworthy that household incomes per capita appear to be a better proxy for the weight of preschoolers than household calorie variables. This is probably because incomes fluctuate less than calorie consumption and thus are a better long-term indicator of the wellbeing of the household.

CONCLUSIONS

The results presented in this paper lead to the following conclusions:

1. If weight-for-age and weight-for-height of pre-schoolers are deemed to be the most appropriate nutrition indicators, then collection of data on the calorie consumption of individual household members does not appear to be justified for targeting purposes.

2. If the calorie adequacy of pre-schoolers is the indicator chosen to assess their nutritional status and thus is used as the basis for targeting, data should be collected on the calories consumed by these children. In our study, neither household-level variables nor anthropometric measures were found to provide acceptable proxies for the calorie adequacy of individual pre-schoolers.

3. Household calorie adequacy appears to be an acceptable indicator for targeting if the goal of the project is to improve the nutritional status of pregnant and lactating women with large calorie deficits.

Thus, collection of data on the food consumption of individual high-risk household members for the purpose of programme targeting does not appear to be justified if the identification of target households is based on anthropometric measures of preschoolers or calorie adequacy of pregnant and/or lactating women. On the other hand, no acceptable proxy was found for calorie adequacy of pre-schoolers. Efforts to target programmes on households with pre-schoolers showing a large deficit in calorie intakes, therefore, should be based on consumption data for those individuals.

The conclusions we have drawn appear to be valid for our data from the Philippines. Caution should be exercised, however, in interpreting the results and in generalizing the findings beyond the scope of this study. The collection of reliable data on the food consumption of individual household members is extremely difficult, time-consuming, and expensive.(2) In our study, for instance, household and individual consumption were measured (by food weighing) for only one 24-hour period in each survey round. Thus, dayto-day variations may have introduced errors in the estimates, although the consumption of high-risk members relative to total household consumption may not show a large day-today variation. To ensure more reliable data, one would have to expand data collection beyond one 24-hour period to seven or more consecutive days. This would greatly increase costs and reduce the feasible sample size(3) Furthermore, efforts to weigh all foods consumed by individual household members are likely to influence their actual consumption and considerably reduce the reliability of the data. The presence of the researcher during meals would also be likely to interfere in the normal table behaviour during a family meal. Dietary recall, aided by food models, is a less intrusive method. Adequate social preparation must take place, as was done in this survey, in order to ensure accuracy and representativeness of the usual intakes.

In addition to the difficulties of obtaining reasonably accurate measures of calorie consumption, there are also problems with respect to the measurement of calorie requirements. In this analysis, average RDAs for each population subgroup for the Philippines was used (see Appendix to this chapter). However, variation in requirements among individuals within a subgroup may be large and is not reflected in the estimated adequacy levels.

This paper has not addressed the question of whether calorie adequacy or anthropometric measures of high-risk individuals are the most appropriate indicator of nutritional status. Because anthropometric measures reflect the impact of both food and health factors, poor growth performance detected by anthropometric assessment may not be caused by lack of food. Thus, targeting food transfers to households with children with, say, low weight-for-age will only be appropriate if the food and nutrient intake of these children is insufficient as reflected in their calorie or nutrient adequacy. Therefore, food transfer programmes should be targeted on the basis of the degree of calorie deficiency rather than individual anthropometrics.

This study did not identify acceptable proxies for the calorie adequacy of preschoolers. Thus, in spite of the high cost, data must be collected on the food consumption of individual pre-schoolers if programmes are to be targeted on households with pre-schoolers suffering from a high level of calorie or nutrient deficiency.

NOTES

1. This study was jointly supported by grants from the United Nations Development Programme (UNDP) and the National Nutrition Council (NNC) of the Government of the Philippines.

2. It was estimated that the cost of collecting consumption data through food-weighing during one 24-hour period was two-and-a-half times the cost of collecting the data from seven-day recall of food acquisition if the total household consumption was weighed, and four times if foods consumed by each individual were weighed.

3. Food-weighing for each individual household member for each of seven consecutive days was estimated to cost about ten times the cost of obtaining the data from seven-day recall, or, conversely, for a given cost, the relative size of the samples that could be included under each of the two approaches would be I to 10.

REFERENCES

Aligaen, M., and C. Florencio. 1980. Intrahousehold Nutrient Distribution and Adequacy of Food and Nutrition Intake of Filipino Urban Households. Phil. J. Nutr., 1: 11-19.

Food and Nutrition Research Institute. 1977. Recommended Dietary Allowances for Filipinos. Publication 75. FNRI, Manila.

Food and Nutrition Research Institute. 1982. The Second Nationwide Nutrition Survey. NSTA, Manila.

Garcia, M., and P. Pinstrup-Andersen. 1987. The Pilot Food Price Subsidy Scheme in the Philippines: Its Impact on Income, Food Consumption, and Nutritional Status. Research Report 61. International Food Policy Research Institute, Washington, D.C.

Valenzuela, R. 1977. A Study of Nutrient Distribution within the Family and Factors Affecting Nutrient Intake. Mimeo. University of the Philippines, Quezon City.

APPENDIX. RDA for Energy for Filipinos (Kcal/Day)

 Men Girls 20-39 years 2,580 10- 12 years 2,170 40-49 years 2,450 13-15 years 2,200 50-59 years 2,320 16-19years 2,060 60-69 years 2,060 Pregnant 70 and over 1,810 Women 13-15 years 2,630 16-19 years 2,490 20-39 years 1,920 20-39 years 2,350 40-49 years 1,820 40-49 years 2,250 50-59 years 1,730 Lactating (1-6 months) 60-69 years 1,540 70 end over 1,340 13-15years 2,750 Infants (6-11 months) 970 20-39 years 2,470 Children 40-49 years 2,370 1-3 years 1,310 Lactating (6-12 months) 4-6 years 1,640 13-15 years 2,640 7-9 years 1,870 16-19years 2,500 Boys 20-39 years 2,360 40-49 years 2,260 10-12 years 2,270 13-15 years 2,510 16-19years 2,700

Source: Recommended Dietary Allowances for Filipinos, FNRI Publication No. 75 (FNRI, Manila, May 1977). 1977).