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Malnutrition and basic needs surveys in Thailand
SOC. Sci. Med. Vol. 23, No. 5, pp. 485491. 1986 Printed in Great Britatn. All rights reserved
MALNUTRITION
Copyright g
AND BASIC NEEDS
SURVEYS
0277s9536;86 53.00 + 0.00 1986 PergamonJournals Ltd
IN THAILAND
TOP*‘GCHAI SAVASDISARA,WALTERE. J. TIPS,* DIRK FORDEYNand RACHADAPORN CHAI~~JT Division of Human Settlements Development, Asian Institute of Technology, P.O. Box 2754, Bangkok 10501. Thailand Abstract-Data on various socio-economic characteristics and the nutritional status of the population of the Province of Nakhon Rachasima had been collected in a basic human needs survey for the purpose of integrated rural development planning. The collected data represent the conditions of an entire province. An assessment of the correlations between various agricultural, socio-economic and sanitation
parameters is made. Malnutrition of children under 5 years of age is correlated with some of the assessed parameters. But, the usefulness of general basic needs surveys for designing nutrition intervention projects is limited due to a lack of specific information on the points of leverage to bring about change in the nutritional status. Key
words--malnutrition,
basic needs, nutrition intervention, socio-economic survey, nutritional surveil-
INTRODUCTION Many major development programs have been launched in the past decade to improve the nutritional status of the rural population of Thailand. A massive commitment of funds has been made to attain that goal under the five National Economic and Social Deyelopment Plans, and more specifically in the last of these plans. Governmental decisionmakers are establishing policies and programmes, and they are allocating resources in numerous projects run by a number of government agencies and NGOs. However, specific criteria to assess and monitor the impact of nutrition and food production projects remain largely undefined (see [l, 21 though). Also, the assessment and monitoring of the projects seems to be difficult because of the sheer magnitude of the development effort [3]. It would be desirable to identify a uniform and comprehensive system to monitor a number of different types of development projects that are implemented in the same area [4]. Such a monitoring system makes even more sense in the case of rural Thailand, because by the enforcement of a recent decentralization, the provincial administrations have become responsible for running the operations and projects of all central line ministries. Thus, the Province has become responsible for a great many different aspects of rural development. The rationale for setting up a survey system that comprises one questionnaire dealing with all sectors of government involvement in the development effort is of course related to manpower, financial and logistics constraints. The initial efforts of the Korat Development Pilot Project to establish a basic needs survey as the database system for rural development planning have been followed up by the National Economic and Social Development Board [S]. In the period 1986-1987, all provinces will be involved in what is now called the ‘Quality of Life Mass Campaign.’ It *Address all correspondence to Walter E. J. Tips.
is concerned with creating self-awareness among the villagers by having them identify their own basic needs [6,7]. Planning in a more participatory way should be the result as well as stimulating selfawareness, self-help and self-reliance in the face of reduced government expenditure on rural development services. It is likely that the future Sixth National Economic and Social Development Plan (1987-1992) will advocate the use of these so-called basic needs surveys. Different disciplines, represented by different points of view and responsibilities among the Royal Thai Government (RTG) Ministries, such as Agriculture and Cooperatives, Public Health, Education and Interior have tended to approach the issue of nutritional status in terms of their specific strategies. However, attempts are now being made to further develop the measurement of nutrition and other rural development aspects into a more comprehensive and meaningful way in order that decision makers could use such considerations in their policies. Attempts to identify the determinants of nutritional status on the basis of socio-economic assessments and to develop ways of forecasting nutritional effects of rural and agriculture projects have been reported [S]. Also, it is being attempted to gain insights in how the effect of nutrition improvement and food production projects in relation to other rural development projects can be measured. An assessment of correlations between nutritional status and rural development through, e.g. land reform and increased cash cropping in Thailand as well as, specifically, in African countries has become the subject of increasing and widespread concern, although some controversy over real impacts remains [g-15]. There is a legitimate concern that cash crops earning hard currency both at the farm level and in international markets replace traditional crops and divert farm labour away from those inputs in the diet that are produced on the farm level without entering the formal market system. Thus, rural or agricultural development would produce nutritional deficiencies in rural areas where subsistence and traditional ways
485 s S.M. 2,r+Il
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of food production that satisfy the dietary requirements existed previously. For such pre-project assessments a system of indicators and identification of linkages between various rural development projects would be needed [ 161. The objectives of this article are the following: 1. To report on the findings of a large scale investigation into the food intake of the population of Nakhon Rachasima in north-east Thailand. In particular, the focus is on the variation in food taken during different meals. 2. TO assess the correlations that may exist between the nutritional status of the population and various agricultural, sanitation and socio-economic characteristics of the villages. This would help to identify the points of leverage for nutrition intervention projects. IMETHODS
In this study, the main information to assess food availability and nutritional status comes from a socio-economic survey at the household level. Data on malnutrition levels based on weight-for-age (according to a modified Gomez-classification) are routinely collected by the Ministry of Public Health officials in all sub-districts. The malnutrition cases are classified in three levels. In the Thai modified version of the Gomez classification for protein-energy malnutrition*, mild malnutrition or level 1 corresponds to 75-85% of the reference weight, moderate malnutrition or level 2 corresponds to 6@-75% of the reference weight, and severe malnutrition or level 3 corresponds to less than 60% of the reference weight (see [17] for English-language reference). The source of comprehensive data on rural village life that was available as an example of general basic needs surveys was the so-called Korat Development Pilot Project (Korat is a vernacular name for Nakhon Rachasima) [Il. The project consists of a socioeconomic survey focusing on the basic needs concept and efforts to plan more systematically for the achievement of the goals of the national development plan through identification of rural development projects. It emphasizes the use of the provincial administration as a key element to set up a planning mechanism for the implementation of rural development in Thailand. The following nine areas of interest were integrated in the survey: (1) nutrition requirements; (2) housing and surrounding environment; (3) employment and working environment; (4) community facilities and services; (5) security of lives and property; (6) upkeeping of traditional values; (7) income, household consumption and wealth; (8) family planning and access to contraceptives; (9) local participation and environmental protection. These parameters are the indicators for each of the development phenomena to be monitored and initially specified by the National Economic and Social Development Board (NESDB) of Thailand. They had been established through a series of workshops in 1983-84 for officers of the four Royal Thai Government ministries involved in rural development, i.e. the
by Dr Pensri Khajanahthiti, Mahidol University, Department of Paediatrics, Ramathibodi Hospital.
*Developed
Ministry of Agriculture and Cooperatives, the Ministry of Education, the Ministry of Public Health, and the Ministry of Interior. The questionnaire eventually contained items to be surveyed on two levels: the household and the village. The target respondents are some 2500 households living in 44 selected villages in the Province of Nakhon Rachasima. The survey took place during June and July 1984. The nutrition situation in the sample villages was relatively severe. in particular, mild cases are rampant. On average. level 1 mainutrition was prevalent among 33.88% (SD 32.74) of the children under 5 years of age. Levels 2 and 3 accounted for 4.97% (SD 7.64) and 1.64% (SD 8.06) on average, respectively, among the children under 5 years of age. Out of the total number of villages in the sample only eight had no level 1 cases while only four villages had level 3 cases. There are two levels of analyses included in the study: a household level and a village level assessment. The data for analyses on the household level in this paper, however, have been taken by random selection of approx. 25 households in each village. This results in a total number of 1038 cases for statistical analysis (some villages did not have 25 complete questionnaires). Data from the household level questionnaire include the following: (1) educational attainment of the head of the household; (2) educational attainment of the wife of the household; (3) land ownership (land tenure); (4) quality of drinking water; (5) type of toilet; (6) proximity to the market outlet for agricultural produce; (7) rainy season major/and minor crops; (8) dry season major/and minor crops; (9) food sufficiency for babies in the household; (10) food sufficiency for adult members in the household; (11) types of food usually taken in each meal; (12) kinds of kitchen crops grown; (13) kinds and number of livestock raised; (14) kind of food preservation services received from either, the governmental and/or the private sector; (15) number of people in the household; (16) width of the dwelling unit; (17) length of the dwelling unit. Five other variables were selected from the village level questionnaire: (1) number of households in the village; (2) number of children under 5 years of age; (3) number of level 1 malnourished children under 5 years of age; (4) number of level 2 malnourished children under 5 years of age; (5) number of level 3 malnourished children under 5 years of age. RESULTS AND
DlSCUSSlOS
Results on the household level The data obtained from the surveys reveal how often people usually eat in a day, what they usually eat, what kind of kitchen crops they grow, what kind of livestock they raise, and whether there is any correlation between these characteristics. Results from the study (Table 1) show that 88.2% of the sample take an early morning meal (5.00 a.m.-8.00 a.m.); 12.9% take a late morning meal (9.00 a.m.-11.00 a.m.); at midday (11.30 a.m.-2.00 p.m.) 99.2% of the respondents eat; supper is taken by 46.0% (5.00 p.m.-7.00 p.m.); and late evening meals are taken by 53.6% (9.00 p.m.-11.00 p.m.).
Malnutrition and basic needs surveys in Thailand
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Table 1. Daily meals and food consumption by household (n = 1038)
1. 2. 3. 4. 5. 6. 7. a. 9. 10. NA
Rice Fish Meat Vegetables Fish sauce Fruits Milk Eggs Ikans Others
Early morning (%)
morning (%)
Midday W)
Supper W)
Late evening W)
87.9 71,s 58.9 86.5 86.0 33.5 5.5 62.2 41.9 4.2
12.3 9.7 7.6 12.1 Ii.8 3.8 0.1 7.5 4.5 0.7
98.7 71.3 56.6 9i.i 96.2 37.4 4.4 54.9 40.8 3.9
45.4 33.1 29.5 44.1 43.4 17.1 3.0 28.7 20.0 1.8
52.9 44.1 35.6 50.6 51.5 la.1 2.6 33.0 23.2 3.0
It.8
87.1
0.8
54.0
46.4
Late
NA: no answer/not applicable.
There seems to be some shift in the respondents that eat supper or late evening meals. However, it is understood that the two Thai time markers ‘ton yen’ and ‘ton kham’ that were used in the questionnaire are respectively defining late afternoon (from around 5 p.m. to 6 p.m.) and the early evening (the time around sunset between 6.30 p.m. and 7 p.m.). Since the survey was carried out during the rice planting season when many families may have worked until sunset, the noticed shift for the evening periods could be an artifact of the survey time and the terms in which the question was cast. However, it should be pointed out that if the number of meals taken daily were classified as a morning meal by combining an early and late morning meals together, a midday meal and an evening meal by combining supper and late evening meals together, analysis will yield a similar result. That is, almost all people in the sample are taking an early meal, whereas 99.2 and 99.6% are taking midday and evening meals respectively. By using the dietary history method with an undetermined time framework no quantitative data could be expected; only categories of food usually eaten are indicated together with their rank order in importance in the diet. The more detailed food recall method for a definite time period would be needed to assess where shortfalls in calorie-protein consumption appear [ 191. However, the needed precision for this would be beyond the constraints of general basic needs surveys. In regard to what they eat, results from the analysis indicate that rice, vegetables and fish sauce constitute the main ingredients of each meal. Eggs and beef seem equal in popularity, but second in rank to fish by far, while beans as another important protein source are much less consumed. Fruits come next in rank of importance, whilst milk and other food items have hardly any meaning in the diet. Chi-square tests for the intake of one type of food during different meals do not indicate a significant difference among the usual foods selected during different meals. Also findings from the study indicate that there is a correlation between food consumption and food production. There is a significant relationship between those who eat fruits and those who grow fruits. This is represented by a chi-square of 125.09 (1 df at 0.001 level of significance; Table 2). Through calculation of an odds ratio it can be found that the odds
of eating fruits are nearly ten times higher among those that grow fruits than among those who do not grow fruits. Table 2 indicates that 49.2% of the sample households grow fruit trees as kitchen crops whereas 50.8% do not grow them. Also 50.8% of the sample usually eat fruits in at least one of the daily meals while 49.2% of the sample do not eat fruits. Among the group of people who grow fruit trees as kitchen crops (49.2% of the sample), 68.5% report they usually eat fruits. This figure accounts for 33.7% of the sample. The other half of the group (31.5%), accounting for 15.5% of the sample, are those who grow fruits as kitchen crops but do not usually eat them with their daily meals. Also from Table 2 it can be seen that there are a total number of 350 people, accounting for 33.7% of the sample, who do not grow fruit trees and do not eat fruits with their meals. Not eating fruits for the people belonging to this group may be the reason for not growing fruit trees or vice versa. It is interesting, however, to find that there is another group of people in the sample who do not usually eat fruits with their meals but grow some fruit trees as kitchen crops. This group of people accounts for 15.5% of the sample. Food production for this group seems to be a source of cash income. Furthermore, the analysis on the household level indicates that there is a weak relationship between those who usually eat eggs and those who raise poultry. This relationship is represented by a chisquare of 3.31 (1 df at 0.06 level of significance; Table 3). However, the odds that those raising poultry will also eat eggs more frequently than those who do not raise poultry are very small (in fact smaller than 1). Table 2. Correlations between eating and growing fruits Freq. row pet, co1 pet, tot pet, Those who eat fruits
Yes
No
Column Total
Those who grow fruit tras Yes
No
350 66.4 68.5 33.7
::.6 33.6 17.1
161 31.5 31.5 15.5 511 49.2
350 68.5 66.4 33.7 527
50.8
Row total 527 50.8
511 49.2 1038
100.0
TONGCW
488
Table 3. Correlationsbetweenraising poultry and eating Freq. Thosewho
Those who eat
row pd. col pet,
raise poultry
tot pet.
Yes
eggs
Row
No
total
Yes
682 83.3 77.9 65.7
137 16.7 S4.6 13.2
819 78.9
No
194 88.6 22.1 18.7 876 84.4
25 11.4 15.4 2.4 162 15.6
219 21.1
eggs
Column
Total
SAVASDISARA et al.
1038 100.0
Table 3 also shows that there are 15.6% of the sample who do not raise poultry whereas 84.4% do. Among those who do not raise poultry, 84.6%, accounting for 13.2% of the sample, usually eat eggs in at least one of their daily meals while 15.4%, accounting for 2.4% of the sample, do not eat eggs. The 77.9% who raise poultry, accounting for 65.7% of the sample, usually eat eggs in at least one of the daily meals, while 22.1%, accounting for 18.7% of the sample do not eat eggs. In addition to the assessment of correlations between those who raise poultry and those who usually eat eggs, analysis on the household level also tries to test whether there is any difference if the correlation between these two variables is assessed in a different way. That is, will an assessment produce a different result if poultry is subdivided into specific categories; duck, geese and chicken. Results from the analysis are presented in Table 4. Table 4 indicates that there is some difference when the correlations are assessed between those who usually eat eggs and those who raise poultry, classified as specific categories, i.e. those who only raise duck, geese or chicken. Correlations as expressed by a gamma coefficient* between those who raise chicken or geese and those who eat eggs are stronger than the correlation obtained for those who raise duck and those who eat eggs. In contrast to the above mentioned findings, there is only a statistically insignificant relationship between those who usually eat vegetables and those who grow vegetables. Analyses on the village level
Analyses exploring whether there is a relationship between the proportion of malnourished children, some socio-economic characteristics and the average rate of food production and food consumption of the people in the village are reported. The socioeconomic characteristics include variables such as average level of educational attainment of the head and wife of the household, land ownership, average proximity to the market, quality of drinking water,
*Gamma is expressed as P -Q/P + Q, where P is the number of concordant pairs and Q is the number of discordant pairs [20].
type of toilet facility, average size of dwelling unit, average intra-household density and some agricultural-related activities. In the analysis, the proportion of level 1 malnourished children under 5 years of age in the village is classified as low, fair, high and very high. This was done by transforming the raw scores @roportions of level 1 malnourished children) to normalized or zscores. These scores were subsequently grouped according to whether they are low, fair, high or very high on the following scale: -0.5 and less was rated low, -0.5 to 0.0 was fair, 0.0 to 0.5 was high and very high was 0.5 and above. Also, the level of educational attainment of the head of the household was defined as either low or fair. Low represents those who have no schooling but are either illiterate or literate, and high represents those obtaining some schooling, usually less than grade 4. It should be pointed out, however, that in the sample the highest average educational attainment of the head of the household in each village falls only between grade 4 and 7. Results from the analysis on the village level indicate a significant relationship between the average level of educational attainment of the head of the household and the proportion of level 1 malnourished children in the village. This relationship is represented by a gamma coefficient of 0.61 (Table 5). But, the result does not indicate a strong relationship between the malnutrition of children under 5 years and the average level of educational attainment of the wife in the household. Also, results from the analyses show that there is a significant negative relationship between the average intra-household density in the village, i.e. the crowding of household members in the house, and the proportion of level 1 malnourished children. Intra-household density in this study is the number of persons per square meter. The average intrahousehold density for all houses in the village in the sample ranges from 0.2 to 0.7 persons per square meter. These numbers were grouped into four categories; 0.2 to 0.3 was rated low density, 0.4 was a fair density, 0.5 was rated as a high density and from 0.6 to 0.7 was a very high density. The relationship is indicated by a gamma coefficient of -0.31 (Table 6). The negative correlation which is not easily explained, suggests that there might be some interaction of other factors in the relationship between these two variables. Also, the analysis on the village level attempts to find out whether there is a relationship between different average levels of sanitation of the village, classified in regard to the average use of various types of septic toilets, and the proportion of malnourished children under 5 years. Types and quality of toilet facilities are measured as poor, fair and good. Poor represents the households with no toilet, fair represents households with pit-toilets, and good represents those with either septic tank or flush-toilets. Results from the analysis indicate that the proportion of level 1 malnourished children is significantly correlated with types of toilet facility respondents in the village have ,in their house (the factor is measured by averaging all households). The higher the index for standard toilets of people in the village is on average, the lower the rate of malnourishment is. The
and basic needs surveys in Thailand
Malnutrition
489
Table 4. Gamma coefficients of correlation between raising duck, geese, chicken and eating eggs (N = 1038) Raising duck
Raisinn geese
Raisinn chicken
0.09
0.39
0.20
Eating eggs
Table 5. Correlation between the average level of educational attainment of the head of the household and the proportion of malnutrition in 44 villages in Nakhon Rachasima Roportion of malnutrition
Low
Fair
High
Very high
3 IS
0 13
0 8
0 5
Low Fair
Education of the household head
Gamma coefficient of correlation = 0.612. Table 6. Correlation between the proportion of malnutrition and intra-household density in 44 villages in Nakhon Rachasima Proportion
of malnutrition
Low
Fair
Hinh
Very hiah
0 3 7 8
:
0 3 3 2
2
Low Fair High Very high
Average level of intra-household density
5 4
I 0 2
Gamma coefficient of correlation = -0.306. Table 7. Correlation between the proportion of malnutrition of the children and the type of toilet facilities in 44 villages Prooortion of malnutrition
Low
Fair
High
Very hieh
2 8 8
0 8 5
2 3 3
2 2 1
Poor Fair Good
Quality of toilet Facility
Gamma coefficient of correlation = -0.252. Table 8. Pearson’s product moment correlations between malnutrition and agricultural activities for 44 villages in Nakhon Rachasima VI V6
-0.02 (P = 0.45)
v2 -0.06 (P = 0.35)
v3 -0.24 (P = 0.06)
v4 -0.22 (P = 0.08)
VS -0.22 (P = 0.08)
Vl = The proportion of people not growing vegetables. V2 = The proportion of people not growing fruits. V3 = The proportion of people not raising poultry. V4 = The proportion of people not eating vegetables. VS = The proportion of people not eating fruits. V6 = Total proportions of malnourished children in the village. This is a weighted average whereby level 1 is weighted as I and level 2 and 3 are weighted 2 and 3 respectively.
correlation is represented by a gamma coefficient of -0.25 (Table 7). In addition, an attempt was made to investigate the possible relationship between the types of land ownership and the proportion of malnourished children under 5 years. It was hypothesized that the types of land ownership-whether a person owns or rents land-affect the nutritional status of the household members [21]. People who are able to own land for cultivation themselves are usually better off socioeconomically than those who rent land and therefore the average malnutrition status of their household members is usually assumed to be better than those of the renters’ families. Results from the analysis at the village level are in disagreement with this hypothesis. The correlation obtained is a negative gamma coefficient of - l.O.* Therefore, villages with a high
*It should be noted that gamma can be 1.0 or - 1.0 under a variety of circumstances other than when all cases fall on a diagonal.
average level of renting land are less likely to be associated with the malnutrition problem. Apart from the above mentioned findings, correlations are also assessed between the proportion of malnourished children and some other variables such as quality of drinking water, classified according to the sources where drinking water is collected, proximity to the central market or location of marketing for cash crops. Findings obtained, however, do not indicate any strong relationships among these variables. Regarding correlations between the proportion of malnourished children under 5 years and patterns of. food production and food consumption of the people in the village (Table 8), analysis at the village level indicates that there are insignificant relationships only among the total proportion of malnourished children and the proportion of people who grow kitchen crops (- 0.02 at 0.45 level of significance) and fruits (- 0.06 at 0.35 level of significance). There is a weak relationship between the proportion of malnourished children under 5 years and the proportion
TONGCHAI SAVASDISARAet al.
190
Table 9. Pearson’s product moment correlations between malnutrition and intake of fruits and vegetables for 44 villages in Nakhon Rachasima v4 VS V6 VI = The V2 = The V3 = The V4 = The V5 = The V6 = The
Vl -0.17 (P = 0.15) -0.17 (P =0.15) -0.08 (P = 0.30)
v2 -0.18 (P =0.13) -0.13 (P = 0.20) -0.08 (P = 0.30)
v3 0.18 (P = 0.13) 0.15 (P = 0.17) 0.09 (P = 0.28)
proportion of people not eating vegetables. proportion of people not eating fruits. prOpOniOn of people neither eating vegetables nor fruits. proportion of malnourished children level I. proportion of malnourished children level 2. proportion of malnourished children level 3.
of households raising poultry (-0.24 at 0.06 level of significance). However, the strength of these relationships can be improved when the assessment is changed to relating the rates of malnourished children and the rates of those who actually eat vegetables (-0.22 at 0.08 level of significance) and fruits (-0.22 at 0.08 level of significance; Table 9). Apart from the above mentioned findings, there is also a linear relationship among the proportion of malnourished children under 5 years on levels 1, 2 and 3 and the proportion of people in the village not eating either vegetables or fruits. The strength of this relationship tends to be improved when the proportion of malnourished children on levels 1,2 and 3 are correlated with the proportion of people eating neither vegetables nor fruits. CONCLUSIONS
To summarize, this study has assessed the nutritional status of the population and its correlation with activities deemed important in rural development by using socio-economic data, which had already been collected in the province of Nakhon Rachasima, north-east Thailand. The data from the survey were collected both on the household level and on the village level by using a basic minimum needs concept that includes nutritional aspects of the rural population. The data used have proven to be relatively useful in assessing correlations among several factors involved in the socio-economic life and agricultural production as related to nutritional status of the people in the province. Thus, some points of leverage to improve the nutritional status based on rural or agricultural development projects can be identified [22]. But, nevertheless, there are some obvious limitations with this kind of surveys, mostly stemming from the lack of more specific information concerning food intake and preservation. They are most obvious in aspects related to adoption behaviour for any supplementary foods that would be prepared and in the precise identification of daily diets. Also, parts of the general basic needs survey would have to be repeated during the major seasons in the agricultural calendar to indicate any seasonal effects since malnutrition is seasonal as well [23,24]. More specific questionnaires need to be designed for a more detailed survey to monitor changes in the nutritional status for the in-depth identification of nutrition intervention projects [19]. For planning
general surveys may prove useful since they indicate the relationship between nutrition projects and other rural development planning efforts. For monitoring and evaluation of nutrition intervention projects general basic needs surveys repeated periodically may only yield results over extended time periods. This
would make such an effort pointless. Detailed monitoring of individuals based on physiological properties, e.g. weight-for-age and weight-for-height data collection, would be indispensable to detect shorterlived improvements due to nutrition intervention projects. A correlation between improved socio-economic status and improved nutritional status of children has been established for north-eastern Thai villages [25]. But, the mechanisms that mediate this improved nutritional status had not been identified yet. From this research it can be concluded that there are linkages-as they appear from correlations-between several determinants of nutritional status. The agricultural production base seems to have an impact on food intake and, therefore, improvements in some components of the village economy will eventually improve the nutritional intake. Food consumption is correlated with food production although sometimes in a complex way. For some foods there is no direct correlation, but, obviously such items are sold for cash which, perhaps depending on the educational level of household members, is used for food or luxury items instead. In contradiction to previous research, we found that land ownership is not related to level 1 malnutrition. The influence of environmental sanitation aspects, such as crowding in houses and improved toilet facilities, cannot be easily identified. The complex mechanisms and linkages that are involved in nutritional impacts of agricultural and rural development projects warrant the setting up of monitoring systems and careful project identification for nutrition interventions. Such SYSterns of monitoring should look into indicators that are closely related to potential points of leverage to influence the daily diet. The points of leverage themselves should be firmly established on the basis of the village economy and the seasonal pattern of agricultural production. Also, cultural and social factors that are not often studied in enough detail in general surveys for rural development are mediating the intervention via adoption behaviour. Indicators from general basic needs surveys are obviously too crude to allow monitoring of changes in production and their influence on the food intake. Finally, monitoring of food intake by a food recall method only can establish the causality of improved nutritional status, diversified or more intense production and actual changes in the diet. Acknowledgement-We
are grateful for research funding by the Commission of the European Communities, Dir.-Gen. of Science, Research and Development under contract TSD. M. 056. TH(H) of the Science and Technology for Development Programme. REFERENCES
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22. FAO. Integrating Nurrition into Agriculrural and Rural Developmenr Projects: A Manual. Nutrition in Agricuiture No. 1. FAO, Rome, 1982. 23. Chen L. C. et al. Seasonal dimensions of energy protein malnutrition in rural Bangladesh: the role of agriculture, dietary practices and infection. Ecol. Fd Nulr. 8, 175-187, 1979. 24. Longhurst R. and Payne P. Seasonal Aspects of Nutrition: Review of Evidence and Policy Impjicaliok.
Disc. Pauer No. 145. Institute of Develooment Studies. Universiiy of Sussex, Brighton, 1979. A 25. Egoramaiphol S. et al. The influence of improved socio-economic development on health and nutritional status. Nutr. Rep. Int. 31, 117-722, 1985.
MALNUTRITION
Copyright g
AND BASIC NEEDS
SURVEYS
0277s9536;86 53.00 + 0.00 1986 PergamonJournals Ltd
IN THAILAND
TOP*‘GCHAI SAVASDISARA,WALTERE. J. TIPS,* DIRK FORDEYNand RACHADAPORN CHAI~~JT Division of Human Settlements Development, Asian Institute of Technology, P.O. Box 2754, Bangkok 10501. Thailand Abstract-Data on various socio-economic characteristics and the nutritional status of the population of the Province of Nakhon Rachasima had been collected in a basic human needs survey for the purpose of integrated rural development planning. The collected data represent the conditions of an entire province. An assessment of the correlations between various agricultural, socio-economic and sanitation
parameters is made. Malnutrition of children under 5 years of age is correlated with some of the assessed parameters. But, the usefulness of general basic needs surveys for designing nutrition intervention projects is limited due to a lack of specific information on the points of leverage to bring about change in the nutritional status. Key
words--malnutrition,
basic needs, nutrition intervention, socio-economic survey, nutritional surveil-
INTRODUCTION Many major development programs have been launched in the past decade to improve the nutritional status of the rural population of Thailand. A massive commitment of funds has been made to attain that goal under the five National Economic and Social Deyelopment Plans, and more specifically in the last of these plans. Governmental decisionmakers are establishing policies and programmes, and they are allocating resources in numerous projects run by a number of government agencies and NGOs. However, specific criteria to assess and monitor the impact of nutrition and food production projects remain largely undefined (see [l, 21 though). Also, the assessment and monitoring of the projects seems to be difficult because of the sheer magnitude of the development effort [3]. It would be desirable to identify a uniform and comprehensive system to monitor a number of different types of development projects that are implemented in the same area [4]. Such a monitoring system makes even more sense in the case of rural Thailand, because by the enforcement of a recent decentralization, the provincial administrations have become responsible for running the operations and projects of all central line ministries. Thus, the Province has become responsible for a great many different aspects of rural development. The rationale for setting up a survey system that comprises one questionnaire dealing with all sectors of government involvement in the development effort is of course related to manpower, financial and logistics constraints. The initial efforts of the Korat Development Pilot Project to establish a basic needs survey as the database system for rural development planning have been followed up by the National Economic and Social Development Board [S]. In the period 1986-1987, all provinces will be involved in what is now called the ‘Quality of Life Mass Campaign.’ It *Address all correspondence to Walter E. J. Tips.
is concerned with creating self-awareness among the villagers by having them identify their own basic needs [6,7]. Planning in a more participatory way should be the result as well as stimulating selfawareness, self-help and self-reliance in the face of reduced government expenditure on rural development services. It is likely that the future Sixth National Economic and Social Development Plan (1987-1992) will advocate the use of these so-called basic needs surveys. Different disciplines, represented by different points of view and responsibilities among the Royal Thai Government (RTG) Ministries, such as Agriculture and Cooperatives, Public Health, Education and Interior have tended to approach the issue of nutritional status in terms of their specific strategies. However, attempts are now being made to further develop the measurement of nutrition and other rural development aspects into a more comprehensive and meaningful way in order that decision makers could use such considerations in their policies. Attempts to identify the determinants of nutritional status on the basis of socio-economic assessments and to develop ways of forecasting nutritional effects of rural and agriculture projects have been reported [S]. Also, it is being attempted to gain insights in how the effect of nutrition improvement and food production projects in relation to other rural development projects can be measured. An assessment of correlations between nutritional status and rural development through, e.g. land reform and increased cash cropping in Thailand as well as, specifically, in African countries has become the subject of increasing and widespread concern, although some controversy over real impacts remains [g-15]. There is a legitimate concern that cash crops earning hard currency both at the farm level and in international markets replace traditional crops and divert farm labour away from those inputs in the diet that are produced on the farm level without entering the formal market system. Thus, rural or agricultural development would produce nutritional deficiencies in rural areas where subsistence and traditional ways
485 s S.M. 2,r+Il
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of food production that satisfy the dietary requirements existed previously. For such pre-project assessments a system of indicators and identification of linkages between various rural development projects would be needed [ 161. The objectives of this article are the following: 1. To report on the findings of a large scale investigation into the food intake of the population of Nakhon Rachasima in north-east Thailand. In particular, the focus is on the variation in food taken during different meals. 2. TO assess the correlations that may exist between the nutritional status of the population and various agricultural, sanitation and socio-economic characteristics of the villages. This would help to identify the points of leverage for nutrition intervention projects. IMETHODS
In this study, the main information to assess food availability and nutritional status comes from a socio-economic survey at the household level. Data on malnutrition levels based on weight-for-age (according to a modified Gomez-classification) are routinely collected by the Ministry of Public Health officials in all sub-districts. The malnutrition cases are classified in three levels. In the Thai modified version of the Gomez classification for protein-energy malnutrition*, mild malnutrition or level 1 corresponds to 75-85% of the reference weight, moderate malnutrition or level 2 corresponds to 6@-75% of the reference weight, and severe malnutrition or level 3 corresponds to less than 60% of the reference weight (see [17] for English-language reference). The source of comprehensive data on rural village life that was available as an example of general basic needs surveys was the so-called Korat Development Pilot Project (Korat is a vernacular name for Nakhon Rachasima) [Il. The project consists of a socioeconomic survey focusing on the basic needs concept and efforts to plan more systematically for the achievement of the goals of the national development plan through identification of rural development projects. It emphasizes the use of the provincial administration as a key element to set up a planning mechanism for the implementation of rural development in Thailand. The following nine areas of interest were integrated in the survey: (1) nutrition requirements; (2) housing and surrounding environment; (3) employment and working environment; (4) community facilities and services; (5) security of lives and property; (6) upkeeping of traditional values; (7) income, household consumption and wealth; (8) family planning and access to contraceptives; (9) local participation and environmental protection. These parameters are the indicators for each of the development phenomena to be monitored and initially specified by the National Economic and Social Development Board (NESDB) of Thailand. They had been established through a series of workshops in 1983-84 for officers of the four Royal Thai Government ministries involved in rural development, i.e. the
by Dr Pensri Khajanahthiti, Mahidol University, Department of Paediatrics, Ramathibodi Hospital.
*Developed
Ministry of Agriculture and Cooperatives, the Ministry of Education, the Ministry of Public Health, and the Ministry of Interior. The questionnaire eventually contained items to be surveyed on two levels: the household and the village. The target respondents are some 2500 households living in 44 selected villages in the Province of Nakhon Rachasima. The survey took place during June and July 1984. The nutrition situation in the sample villages was relatively severe. in particular, mild cases are rampant. On average. level 1 mainutrition was prevalent among 33.88% (SD 32.74) of the children under 5 years of age. Levels 2 and 3 accounted for 4.97% (SD 7.64) and 1.64% (SD 8.06) on average, respectively, among the children under 5 years of age. Out of the total number of villages in the sample only eight had no level 1 cases while only four villages had level 3 cases. There are two levels of analyses included in the study: a household level and a village level assessment. The data for analyses on the household level in this paper, however, have been taken by random selection of approx. 25 households in each village. This results in a total number of 1038 cases for statistical analysis (some villages did not have 25 complete questionnaires). Data from the household level questionnaire include the following: (1) educational attainment of the head of the household; (2) educational attainment of the wife of the household; (3) land ownership (land tenure); (4) quality of drinking water; (5) type of toilet; (6) proximity to the market outlet for agricultural produce; (7) rainy season major/and minor crops; (8) dry season major/and minor crops; (9) food sufficiency for babies in the household; (10) food sufficiency for adult members in the household; (11) types of food usually taken in each meal; (12) kinds of kitchen crops grown; (13) kinds and number of livestock raised; (14) kind of food preservation services received from either, the governmental and/or the private sector; (15) number of people in the household; (16) width of the dwelling unit; (17) length of the dwelling unit. Five other variables were selected from the village level questionnaire: (1) number of households in the village; (2) number of children under 5 years of age; (3) number of level 1 malnourished children under 5 years of age; (4) number of level 2 malnourished children under 5 years of age; (5) number of level 3 malnourished children under 5 years of age. RESULTS AND
DlSCUSSlOS
Results on the household level The data obtained from the surveys reveal how often people usually eat in a day, what they usually eat, what kind of kitchen crops they grow, what kind of livestock they raise, and whether there is any correlation between these characteristics. Results from the study (Table 1) show that 88.2% of the sample take an early morning meal (5.00 a.m.-8.00 a.m.); 12.9% take a late morning meal (9.00 a.m.-11.00 a.m.); at midday (11.30 a.m.-2.00 p.m.) 99.2% of the respondents eat; supper is taken by 46.0% (5.00 p.m.-7.00 p.m.); and late evening meals are taken by 53.6% (9.00 p.m.-11.00 p.m.).
Malnutrition and basic needs surveys in Thailand
487
Table 1. Daily meals and food consumption by household (n = 1038)
1. 2. 3. 4. 5. 6. 7. a. 9. 10. NA
Rice Fish Meat Vegetables Fish sauce Fruits Milk Eggs Ikans Others
Early morning (%)
morning (%)
Midday W)
Supper W)
Late evening W)
87.9 71,s 58.9 86.5 86.0 33.5 5.5 62.2 41.9 4.2
12.3 9.7 7.6 12.1 Ii.8 3.8 0.1 7.5 4.5 0.7
98.7 71.3 56.6 9i.i 96.2 37.4 4.4 54.9 40.8 3.9
45.4 33.1 29.5 44.1 43.4 17.1 3.0 28.7 20.0 1.8
52.9 44.1 35.6 50.6 51.5 la.1 2.6 33.0 23.2 3.0
It.8
87.1
0.8
54.0
46.4
Late
NA: no answer/not applicable.
There seems to be some shift in the respondents that eat supper or late evening meals. However, it is understood that the two Thai time markers ‘ton yen’ and ‘ton kham’ that were used in the questionnaire are respectively defining late afternoon (from around 5 p.m. to 6 p.m.) and the early evening (the time around sunset between 6.30 p.m. and 7 p.m.). Since the survey was carried out during the rice planting season when many families may have worked until sunset, the noticed shift for the evening periods could be an artifact of the survey time and the terms in which the question was cast. However, it should be pointed out that if the number of meals taken daily were classified as a morning meal by combining an early and late morning meals together, a midday meal and an evening meal by combining supper and late evening meals together, analysis will yield a similar result. That is, almost all people in the sample are taking an early meal, whereas 99.2 and 99.6% are taking midday and evening meals respectively. By using the dietary history method with an undetermined time framework no quantitative data could be expected; only categories of food usually eaten are indicated together with their rank order in importance in the diet. The more detailed food recall method for a definite time period would be needed to assess where shortfalls in calorie-protein consumption appear [ 191. However, the needed precision for this would be beyond the constraints of general basic needs surveys. In regard to what they eat, results from the analysis indicate that rice, vegetables and fish sauce constitute the main ingredients of each meal. Eggs and beef seem equal in popularity, but second in rank to fish by far, while beans as another important protein source are much less consumed. Fruits come next in rank of importance, whilst milk and other food items have hardly any meaning in the diet. Chi-square tests for the intake of one type of food during different meals do not indicate a significant difference among the usual foods selected during different meals. Also findings from the study indicate that there is a correlation between food consumption and food production. There is a significant relationship between those who eat fruits and those who grow fruits. This is represented by a chi-square of 125.09 (1 df at 0.001 level of significance; Table 2). Through calculation of an odds ratio it can be found that the odds
of eating fruits are nearly ten times higher among those that grow fruits than among those who do not grow fruits. Table 2 indicates that 49.2% of the sample households grow fruit trees as kitchen crops whereas 50.8% do not grow them. Also 50.8% of the sample usually eat fruits in at least one of the daily meals while 49.2% of the sample do not eat fruits. Among the group of people who grow fruit trees as kitchen crops (49.2% of the sample), 68.5% report they usually eat fruits. This figure accounts for 33.7% of the sample. The other half of the group (31.5%), accounting for 15.5% of the sample, are those who grow fruits as kitchen crops but do not usually eat them with their daily meals. Also from Table 2 it can be seen that there are a total number of 350 people, accounting for 33.7% of the sample, who do not grow fruit trees and do not eat fruits with their meals. Not eating fruits for the people belonging to this group may be the reason for not growing fruit trees or vice versa. It is interesting, however, to find that there is another group of people in the sample who do not usually eat fruits with their meals but grow some fruit trees as kitchen crops. This group of people accounts for 15.5% of the sample. Food production for this group seems to be a source of cash income. Furthermore, the analysis on the household level indicates that there is a weak relationship between those who usually eat eggs and those who raise poultry. This relationship is represented by a chisquare of 3.31 (1 df at 0.06 level of significance; Table 3). However, the odds that those raising poultry will also eat eggs more frequently than those who do not raise poultry are very small (in fact smaller than 1). Table 2. Correlations between eating and growing fruits Freq. row pet, co1 pet, tot pet, Those who eat fruits
Yes
No
Column Total
Those who grow fruit tras Yes
No
350 66.4 68.5 33.7
::.6 33.6 17.1
161 31.5 31.5 15.5 511 49.2
350 68.5 66.4 33.7 527
50.8
Row total 527 50.8
511 49.2 1038
100.0
TONGCW
488
Table 3. Correlationsbetweenraising poultry and eating Freq. Thosewho
Those who eat
row pd. col pet,
raise poultry
tot pet.
Yes
eggs
Row
No
total
Yes
682 83.3 77.9 65.7
137 16.7 S4.6 13.2
819 78.9
No
194 88.6 22.1 18.7 876 84.4
25 11.4 15.4 2.4 162 15.6
219 21.1
eggs
Column
Total
SAVASDISARA et al.
1038 100.0
Table 3 also shows that there are 15.6% of the sample who do not raise poultry whereas 84.4% do. Among those who do not raise poultry, 84.6%, accounting for 13.2% of the sample, usually eat eggs in at least one of their daily meals while 15.4%, accounting for 2.4% of the sample, do not eat eggs. The 77.9% who raise poultry, accounting for 65.7% of the sample, usually eat eggs in at least one of the daily meals, while 22.1%, accounting for 18.7% of the sample do not eat eggs. In addition to the assessment of correlations between those who raise poultry and those who usually eat eggs, analysis on the household level also tries to test whether there is any difference if the correlation between these two variables is assessed in a different way. That is, will an assessment produce a different result if poultry is subdivided into specific categories; duck, geese and chicken. Results from the analysis are presented in Table 4. Table 4 indicates that there is some difference when the correlations are assessed between those who usually eat eggs and those who raise poultry, classified as specific categories, i.e. those who only raise duck, geese or chicken. Correlations as expressed by a gamma coefficient* between those who raise chicken or geese and those who eat eggs are stronger than the correlation obtained for those who raise duck and those who eat eggs. In contrast to the above mentioned findings, there is only a statistically insignificant relationship between those who usually eat vegetables and those who grow vegetables. Analyses on the village level
Analyses exploring whether there is a relationship between the proportion of malnourished children, some socio-economic characteristics and the average rate of food production and food consumption of the people in the village are reported. The socioeconomic characteristics include variables such as average level of educational attainment of the head and wife of the household, land ownership, average proximity to the market, quality of drinking water,
*Gamma is expressed as P -Q/P + Q, where P is the number of concordant pairs and Q is the number of discordant pairs [20].
type of toilet facility, average size of dwelling unit, average intra-household density and some agricultural-related activities. In the analysis, the proportion of level 1 malnourished children under 5 years of age in the village is classified as low, fair, high and very high. This was done by transforming the raw scores @roportions of level 1 malnourished children) to normalized or zscores. These scores were subsequently grouped according to whether they are low, fair, high or very high on the following scale: -0.5 and less was rated low, -0.5 to 0.0 was fair, 0.0 to 0.5 was high and very high was 0.5 and above. Also, the level of educational attainment of the head of the household was defined as either low or fair. Low represents those who have no schooling but are either illiterate or literate, and high represents those obtaining some schooling, usually less than grade 4. It should be pointed out, however, that in the sample the highest average educational attainment of the head of the household in each village falls only between grade 4 and 7. Results from the analysis on the village level indicate a significant relationship between the average level of educational attainment of the head of the household and the proportion of level 1 malnourished children in the village. This relationship is represented by a gamma coefficient of 0.61 (Table 5). But, the result does not indicate a strong relationship between the malnutrition of children under 5 years and the average level of educational attainment of the wife in the household. Also, results from the analyses show that there is a significant negative relationship between the average intra-household density in the village, i.e. the crowding of household members in the house, and the proportion of level 1 malnourished children. Intra-household density in this study is the number of persons per square meter. The average intrahousehold density for all houses in the village in the sample ranges from 0.2 to 0.7 persons per square meter. These numbers were grouped into four categories; 0.2 to 0.3 was rated low density, 0.4 was a fair density, 0.5 was rated as a high density and from 0.6 to 0.7 was a very high density. The relationship is indicated by a gamma coefficient of -0.31 (Table 6). The negative correlation which is not easily explained, suggests that there might be some interaction of other factors in the relationship between these two variables. Also, the analysis on the village level attempts to find out whether there is a relationship between different average levels of sanitation of the village, classified in regard to the average use of various types of septic toilets, and the proportion of malnourished children under 5 years. Types and quality of toilet facilities are measured as poor, fair and good. Poor represents the households with no toilet, fair represents households with pit-toilets, and good represents those with either septic tank or flush-toilets. Results from the analysis indicate that the proportion of level 1 malnourished children is significantly correlated with types of toilet facility respondents in the village have ,in their house (the factor is measured by averaging all households). The higher the index for standard toilets of people in the village is on average, the lower the rate of malnourishment is. The
and basic needs surveys in Thailand
Malnutrition
489
Table 4. Gamma coefficients of correlation between raising duck, geese, chicken and eating eggs (N = 1038) Raising duck
Raisinn geese
Raisinn chicken
0.09
0.39
0.20
Eating eggs
Table 5. Correlation between the average level of educational attainment of the head of the household and the proportion of malnutrition in 44 villages in Nakhon Rachasima Roportion of malnutrition
Low
Fair
High
Very high
3 IS
0 13
0 8
0 5
Low Fair
Education of the household head
Gamma coefficient of correlation = 0.612. Table 6. Correlation between the proportion of malnutrition and intra-household density in 44 villages in Nakhon Rachasima Proportion
of malnutrition
Low
Fair
Hinh
Very hiah
0 3 7 8
:
0 3 3 2
2
Low Fair High Very high
Average level of intra-household density
5 4
I 0 2
Gamma coefficient of correlation = -0.306. Table 7. Correlation between the proportion of malnutrition of the children and the type of toilet facilities in 44 villages Prooortion of malnutrition
Low
Fair
High
Very hieh
2 8 8
0 8 5
2 3 3
2 2 1
Poor Fair Good
Quality of toilet Facility
Gamma coefficient of correlation = -0.252. Table 8. Pearson’s product moment correlations between malnutrition and agricultural activities for 44 villages in Nakhon Rachasima VI V6
-0.02 (P = 0.45)
v2 -0.06 (P = 0.35)
v3 -0.24 (P = 0.06)
v4 -0.22 (P = 0.08)
VS -0.22 (P = 0.08)
Vl = The proportion of people not growing vegetables. V2 = The proportion of people not growing fruits. V3 = The proportion of people not raising poultry. V4 = The proportion of people not eating vegetables. VS = The proportion of people not eating fruits. V6 = Total proportions of malnourished children in the village. This is a weighted average whereby level 1 is weighted as I and level 2 and 3 are weighted 2 and 3 respectively.
correlation is represented by a gamma coefficient of -0.25 (Table 7). In addition, an attempt was made to investigate the possible relationship between the types of land ownership and the proportion of malnourished children under 5 years. It was hypothesized that the types of land ownership-whether a person owns or rents land-affect the nutritional status of the household members [21]. People who are able to own land for cultivation themselves are usually better off socioeconomically than those who rent land and therefore the average malnutrition status of their household members is usually assumed to be better than those of the renters’ families. Results from the analysis at the village level are in disagreement with this hypothesis. The correlation obtained is a negative gamma coefficient of - l.O.* Therefore, villages with a high
*It should be noted that gamma can be 1.0 or - 1.0 under a variety of circumstances other than when all cases fall on a diagonal.
average level of renting land are less likely to be associated with the malnutrition problem. Apart from the above mentioned findings, correlations are also assessed between the proportion of malnourished children and some other variables such as quality of drinking water, classified according to the sources where drinking water is collected, proximity to the central market or location of marketing for cash crops. Findings obtained, however, do not indicate any strong relationships among these variables. Regarding correlations between the proportion of malnourished children under 5 years and patterns of. food production and food consumption of the people in the village (Table 8), analysis at the village level indicates that there are insignificant relationships only among the total proportion of malnourished children and the proportion of people who grow kitchen crops (- 0.02 at 0.45 level of significance) and fruits (- 0.06 at 0.35 level of significance). There is a weak relationship between the proportion of malnourished children under 5 years and the proportion
TONGCHAI SAVASDISARAet al.
190
Table 9. Pearson’s product moment correlations between malnutrition and intake of fruits and vegetables for 44 villages in Nakhon Rachasima v4 VS V6 VI = The V2 = The V3 = The V4 = The V5 = The V6 = The
Vl -0.17 (P = 0.15) -0.17 (P =0.15) -0.08 (P = 0.30)
v2 -0.18 (P =0.13) -0.13 (P = 0.20) -0.08 (P = 0.30)
v3 0.18 (P = 0.13) 0.15 (P = 0.17) 0.09 (P = 0.28)
proportion of people not eating vegetables. proportion of people not eating fruits. prOpOniOn of people neither eating vegetables nor fruits. proportion of malnourished children level I. proportion of malnourished children level 2. proportion of malnourished children level 3.
of households raising poultry (-0.24 at 0.06 level of significance). However, the strength of these relationships can be improved when the assessment is changed to relating the rates of malnourished children and the rates of those who actually eat vegetables (-0.22 at 0.08 level of significance) and fruits (-0.22 at 0.08 level of significance; Table 9). Apart from the above mentioned findings, there is also a linear relationship among the proportion of malnourished children under 5 years on levels 1, 2 and 3 and the proportion of people in the village not eating either vegetables or fruits. The strength of this relationship tends to be improved when the proportion of malnourished children on levels 1,2 and 3 are correlated with the proportion of people eating neither vegetables nor fruits. CONCLUSIONS
To summarize, this study has assessed the nutritional status of the population and its correlation with activities deemed important in rural development by using socio-economic data, which had already been collected in the province of Nakhon Rachasima, north-east Thailand. The data from the survey were collected both on the household level and on the village level by using a basic minimum needs concept that includes nutritional aspects of the rural population. The data used have proven to be relatively useful in assessing correlations among several factors involved in the socio-economic life and agricultural production as related to nutritional status of the people in the province. Thus, some points of leverage to improve the nutritional status based on rural or agricultural development projects can be identified [22]. But, nevertheless, there are some obvious limitations with this kind of surveys, mostly stemming from the lack of more specific information concerning food intake and preservation. They are most obvious in aspects related to adoption behaviour for any supplementary foods that would be prepared and in the precise identification of daily diets. Also, parts of the general basic needs survey would have to be repeated during the major seasons in the agricultural calendar to indicate any seasonal effects since malnutrition is seasonal as well [23,24]. More specific questionnaires need to be designed for a more detailed survey to monitor changes in the nutritional status for the in-depth identification of nutrition intervention projects [19]. For planning
general surveys may prove useful since they indicate the relationship between nutrition projects and other rural development planning efforts. For monitoring and evaluation of nutrition intervention projects general basic needs surveys repeated periodically may only yield results over extended time periods. This
would make such an effort pointless. Detailed monitoring of individuals based on physiological properties, e.g. weight-for-age and weight-for-height data collection, would be indispensable to detect shorterlived improvements due to nutrition intervention projects. A correlation between improved socio-economic status and improved nutritional status of children has been established for north-eastern Thai villages [25]. But, the mechanisms that mediate this improved nutritional status had not been identified yet. From this research it can be concluded that there are linkages-as they appear from correlations-between several determinants of nutritional status. The agricultural production base seems to have an impact on food intake and, therefore, improvements in some components of the village economy will eventually improve the nutritional intake. Food consumption is correlated with food production although sometimes in a complex way. For some foods there is no direct correlation, but, obviously such items are sold for cash which, perhaps depending on the educational level of household members, is used for food or luxury items instead. In contradiction to previous research, we found that land ownership is not related to level 1 malnutrition. The influence of environmental sanitation aspects, such as crowding in houses and improved toilet facilities, cannot be easily identified. The complex mechanisms and linkages that are involved in nutritional impacts of agricultural and rural development projects warrant the setting up of monitoring systems and careful project identification for nutrition interventions. Such SYSterns of monitoring should look into indicators that are closely related to potential points of leverage to influence the daily diet. The points of leverage themselves should be firmly established on the basis of the village economy and the seasonal pattern of agricultural production. Also, cultural and social factors that are not often studied in enough detail in general surveys for rural development are mediating the intervention via adoption behaviour. Indicators from general basic needs surveys are obviously too crude to allow monitoring of changes in production and their influence on the food intake. Finally, monitoring of food intake by a food recall method only can establish the causality of improved nutritional status, diversified or more intense production and actual changes in the diet. Acknowledgement-We
are grateful for research funding by the Commission of the European Communities, Dir.-Gen. of Science, Research and Development under contract TSD. M. 056. TH(H) of the Science and Technology for Development Programme. REFERENCES
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