Food-poverty status and food insecurity in rural West Lombok based on mothers’ food expenditure equivalency

Tina Rosalina, Lindawati Wibowo, Arnfried A. Kielmann, and Avita Aliza Usfar Abstract Background. When the Central Bureau for Statistics (CBS) developed a national food-poverty line for Indonesia, some aspects, such as food availability, food beliefs, and food habits, were not considered. In addition, the reference population was determined on the basis of their nonfood expenditures. Objective. To develop and use a method applicable in any given sociocultural setting, as well as to determine food-poverty status in rural West Lombok, Indonesia, using mothers’ food expenditure equivalency (FEE). Method. Mothers’ actual food intake determined by a modified 24-hour recall served to establish FEE. The results were verified with household food-security measures based on the US Household Food Security/Hunger Measurement (US HFSSM), and the mothers’ nutritional status was assessed by the body-mass index (BMI). Results. Most mothers (72%) were food-poor and 79% were also food-insecure. Food poverty has a positive correlation with household food insecurity. The severely food poor also had the highest risk of household food insecurity. The nutritional status of mothers showed no correlation with food-poverty status and therefore was not found to be an appropriate indicator of food poverty in this cultural setting. Conclusions. Because most food consumed by mothers was purchased, financial security plays a key role in determining family food sufficiency, in terms of both quantity and variety. Mothers’ BMI status differed between the food-poor and non–food-poor groups, but the difference was not statistically significant, suggesting

The authors are affiliated with the Research Division, Southeast Asian Ministry of Education Organization, Tropical Medicine Regional Centre for Community Nutrition, University of Indonesia, Jakarta, Indonesia. Please direct queries to the corresponding author: Lindawati Wibowo, Research Division, SEAMEO—TROPMED RCCN UI, Jl. Salemba Raya No. 6, PO Box 3852, Jakarta, Indonesia 10430; e-mail: [email protected]; [email protected].

that in our setting the food-poverty line cannot be used to identify physiological need but is rather more of a social and economic indicator. We suggest the use of US HFSSM questionnaires as a simple alternative means to assess both food-poverty and food-security status, mainly because the method is simple to apply and corroborates our findings using area-specific FEEs. Finally, our study results suggest a number of follow-up investigations.

Key words: Food poverty, food security, nutritional status

Introduction Poverty is pronounced deprivation in well-being, where well-being can be measured by an individual’s possession of income, health, nutrition, education, assets, housing, and certain rights in a society, such as freedom of speech [1]. A person, a family, or a nation is not deemed poor only because of low income [2]. Poverty may lead to a multiplicity of deprivations, which translate into lack of financial resources or income, lack of health facilities, lack of knowledge, and the tendency to live in communities that have weak institutions and have social norms that are not conducive to good nutrition and health. In Indonesia in 2003, 37 million people (17.4% of the population) lived under the poverty line. The percentage of poor people in rural areas (20%) is higher than in urban areas (14%) [3]. The present study focuses on rural food poverty. The study area was in rural West Nusa Tenggara Province, where both the poverty index (26.3%) [4] and the prevalence of underweight children (22%) [5] are high. Various approaches have been taken to estimate the poverty line. In general, it is established through one of three measures: direct calorie intake, food energy intake, and the cost-of-basic-needs method [6]. Indonesia’s Central Bureau for Statistics (CBS) used the costof-basic-needs method to establish its national poverty

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line. The index is defined as the expenditure required to procure a minimum standard of food, the so-called “food-poverty line,” as well as essential nonfood commodities, per capita per month [7]. In this study, we focused exclusively on measuring food poverty, given that food shortage would seem to be an excellent proxy indicator reflecting chronic poverty [8]. The food-poverty line developed by the CBS was determined by calculating the Indonesian rupiah value of adequate food intake, arbitrarily set at 2,100 kcal per person per day (the recommendation of the National Workshop on Food and Nutrition in 1978). The method of translating the nutritional requirement into its rupiah equivalent uses a food-commodity basket selected by the CBS. This food basket consists of 52 food items and was defined for each province on the basis of the consumption pattern of a population group living just above the poverty line according to the National Household Socioeconomic Survey (SUSENAS). The reference population is a group of people living just above the poverty line on the basis of National Survey data, and hence their pattern of consumption can be used as a standard of minimum consumption. The intakes of those selected 52 food items are then transformed into their rupiah equivalents by multiplying both the volume and the frequency of consumed food items by their average market prices or prices paid by this reference population. Additionally, the caloric composition of the food consumed from among the selected 52 food items is determined. This amounted to 1380.6 and 1517.9 kcal per adult for urban and rural areas, respectively. The weighted average of the price per calorie is then computed by dividing the total Rupiah value of food intake by the respective calorie content. To arrive at the 2,100 kcal-equivalent food sufficiency, the resulting weighted average price per calorie is multiplied by 2,100 [7]. Some limitations in the above (cost-of-basic-needs) method for estimating the food-poverty line need to be pointed out. First, the 52 selected food items took into account the differences between provinces but did not distinguish between urban and rural areas [9], except with respect to the volume and price of each selected food item. However, the food-poverty line still reflects the specific characteristics of each area (urban vs. rural) and is “location specific,” but other essential and determinant aspects, such as food availability, utilization, habits, and belief, were not considered in the calculation. Since there are many different ethnic and religious groups in Indonesia and each of them has its own food habits and beliefs, some of the food items in the list might not be available to or consumed by a specific population group. Another limitation is that in 1998, Indonesia changed its government structure from centralization to decentralization. All policies are now formulated at the

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district level. Therefore, data on the food-poverty line at the district level should be based on the usual food intake of the population in each district in order to assist the local government to develop a food-poverty alleviation program based on local needs and resources. Finally, relying on the household as the economic unit for determining food poverty, or for measuring the effects of price fluctuations and income changes on food poverty, overlooks intra-household distribution issues, especially for women [10]. With the above shortcomings in mind, we developed a novel method for establishing food-poverty lines. In this study, food poverty was simply defined as the financial inability of the mother, as a representative of the household, to have adequate food intake. As with the cost-of-basic-needs method, we calculated the weighted average price per calorie but used a different reference population and methodology to obtain the usual calorie intake. Unlike the cost-of-basic-needs method, which used a food basket to obtain the usual calorie intake, ours is based on a 24-hour dietary recall. The reference population in the cost-of-basicneeds method was the reference poor group from the SUSENAS data, whereas in our method we randomly selected mothers of normal BMI. The results of this study provide data on the foodpoverty line at the district level. We selected young mothers as study subjects for three main reasons: they were the most affected household members when prices and incomes changed [11]; we assumed that their food intake would reflect their households’ food intake, because in most societies mothers are also responsible for feeding the family; and food intakes of other members of the household (fathers and children) are neither representative of the household as a whole nor easily obtained, and hence their use might lead to biased rather than precise results. Gender “valuation” and, conversely, discrimination is mirrored in allocation patterns of lifesaving resources, such as food and health services. Men tend to eat larger quantities and frequently exaggerate their food consumption in surveys, whereas women underreport theirs [12]. But aside from differentially perceived intakes, a study by Gittelsohn in Nepal showed that adult women were less likely to meet their nutrient requirements for energy than men of the same age. Women eat last after the largest and choicest portions have gone to men and boys [13]. A case study of eight provinces in China by Luo et al. found that in most cases, males had a higher proportion of nutrient intake than females, particularly in the young adult group, where men presented a higher discrepancy score than women for energy, protein, calcium, iron, vitamin C, and retinol [14]. In situations of food scarcity, Indonesian women tend to reduce their own food intake before reducing that of their children and

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husbands [15]. In rural settings, where intensive labor is common, meals are usually provided by employers for their employees, who commonly are men. Consequently, men’s food intake does not reflect household food intake. Schoolchildren frequently have snacks at school, and preschoolers often are given snacks by neighbors. Accurate assessment of the food intake of infants is particularly difficult, especially when infants are receiving both breastmilk and complementary food [16]. In lieu of the food basket, we utilized a 24-hour food-recall method in assessing the usual calorie intake of the reference population to compute the weighted average of the price per calorie. Twenty-four-hour food recall is one of the internationally accepted methods for assessing the usual calorie intake of individuals and has been used in some national nutrition surveys. With a large number of subjects and adequate representation of all days of the week, the usual intakes of a population group can be obtained by using the same method [16]. In large-scale studies, the 24-hour recall gives a reasonably accurate picture of the average nutrient intake of the population [16, 17]. Aside from information on usual calorie intake, our modified 24-hour dietary recall form also allowed us to collect data on usual food expenditures. The following steps were carried out to obtain the weighted average of the price per calorie up to and including the 2,100 kcal-equivalent food sufficiency level. Two methods were employed to determine mothers’ usual food expenditures. The first was based on the food that had actually been bought and consumed by the mothers, called real food expenditure (RFE). In a second calculation, we included all food that was actually eaten, including food produced and received as gifts, to obtain the food expenditure equivalency (FEE). This was done because in rural areas people might obtain food from relatives or neighbors as well as from their own cultivated land. In this manner, food availability, concepts, and beliefs prevailing in the study area and their effects on food utilization are accounted for. Food insecurity and poor nutritional status are almost inevitably the result of poverty. Under normal circumstances, where individuals and households have sufficient resources, they are also able to access and make available sufficient food for their needs. Nutritional status is considered a powerful indicator of nutrition security and well-being of an individual and reflects the nutrition and poverty situation of a household [18]. In Indonesia, the body-mass index (BMI) can reflect maternal malnutrition caused in part by the tendency of women to reduce their own food intake before reducing that of their children and husbands [15]. In this study, we also examined whether the resultant food-poverty status of mothers demonstrated their nutritional and household food-security

status in order to compare our results with those of Melgar-Quinonez et al. [19], who showed that the food-security status measured by the US Household Food Security/Hunger Measurement (US HFSSM) may serve as a proxy for measuring poverty. Their findings confirmed the expected negative correlation of food insecurity with food expenditure, as well as demonstrating that an increase in the level of food insecurity is associated with decreased consumption of animal-source foods.

Methods Subjects

Women (n = 240) 20 to 45 years of age, married with at least one child, who were free from illness according to external appearance and on interrogation and were free from conditions or ailments that might influence their appetites were enrolled in the study. Pregnant and handicapped mothers were excluded because their BMI measurements and energy requirements might have introduced a bias. Study site

The study was conducted in two selected subdistricts in West Lombok, Lingsar and Narmada, where the prevalence of underweight children was 37% and 18%, respectively [5]. Because these two subdistricts were the most fertile areas of West Lombok District and supplied water for all districts in Lombok Island, one may assume that the resultant food-poverty status is representative of all West Lombok District. If anything, the results in the remainder of the district might be worse. Study design

The study was cross-sectional in design and was conducted in 10 villages from August 2004 through June 2005. The period of the study included the fruit harvest season from December 2004 through June 2005. Determination of food-poverty line

The food-poverty line was calculated according to the CBS formula as the mean food expenditure (FEE/ RFE) divided by the mean food intake of the reference population multiplied by the respective energy intakes as derived from the national recommended dietary allowances (RDAs). The reference group in this study was a group of mothers with normal BMI. Mean food intake of the population

Mean food intakes of the population were assessed

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by using a modified 24-hour recall method and considered existing food availability and food habits and preferences. We suspended data collection during the entire month following the Prophet Muhammad’s birthday because, unlike the rest of Indonesia, West Lombok traditionally celebrates the birthday for all of that month, during which the amount and quality of food intake do not represent the norm. To determine other constraints that might have influenced the food intake of the population, accessibility of food in the market was also assessed by indepth interviews when it was found that focus group discussions did not yield the required information. A total of 12 in-depth interviews were held. Respondents were recruited from two different areas, one close to markets and the other distant from them. Interviews were conducted by four local enumerators fluent in the local language, i.e., Sasaknese, and Indonesian. Both languages as well as local subdialects were used, since the mothers generally preferred talking to people from their own culture about their food habits and beliefs, even though some of them understood and spoke Indonesian. The 24-hour recall interviews were held 6 days a week on Monday through Saturday and were carried out by three trained enumerators. Prior to actual data collection, preliminary tests were conducted to reduce intra- and interobserver errors for each enumerator. The respondent was asked about all foods and beverages consumed during the previous 24-hour period preceding the interview. Detailed descriptions of all food and beverage intakes, including cooking methods and brand names (for commercial foods), were recorded, together with the time of consumption [16, 20]. Glasses and bowls in common use, standard measuring spoons, and three-dimensional food models helped mothers estimate the size of individual food portions [20]. Some of the food items were weighed to determine the precise amount of food. The weighing was done at the base camp by a trained enumerator on the day of data collection. This procedure was followed to avoid using interview time to weigh food, to avoid estimating the weight of food items, which would have introduced a bias because some of the food items did not have standard portions, and to reduce measurement errors that might have arisen as result of unsuitable environmental conditions in the field. In a preliminary survey, the food intakes of 50 mothers (20% of the sample) were determined by 24hour recall on 3 consecutive days. This served to validate subsequent results obtained from 24-hour recall interviews carried out on the remainder of the study  Milad al-Nabi, the Prophet Muhammad’s birthday, is based on the Islamic calendar, and its dates shift from year to year in the Gregorian calendar. In 2005, the period of celebration was mid April to mid May.

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population, as well as to get an estimate of the day-today variability of food intake for the same individual. The respondents for the repeat survey were randomly selected from among all women included in the study. The results for energy intake obtained by the two methods were then compared. Food intakes determined by either the 1-day or the 3-day recall were equal, justifying the use of 24-hour recall on all respondents. The types of food consumed by the subsample of 50 mothers were also recorded in their 1-day and 3-day repeat 24-hour recalls. The frequency of consumption of each food type by the woman was classified as always, often (mentioned in two or three recalls out of four), and rarely or never. Mean food expenditure

The food expenditure of mothers (RFE/FEE) was the total expenditure for all food items consumed on a given day. Food expenditure was determined by dividing the amount of food consumed (by weight) by the total amount (by weight) of food items used to prepare the dish, including cost for fuel, cooking oil, and condiments, multiplied by the total price paid for all items. For example, if the total expenditure, including expenditure for all food items used in preparation, fuel, and condiments, for a bowl of soup amounted to Rp 1000**, and the bowl yielded 10 tablespoons, then the expenditure for the two tablespoons consumed by the mother came to Rp 200. This procedure was followed for each food item consumed. The resultant rupiah value was set as the food-poverty line for that population. Three methods were used to determine the price of the food consumed by mothers: asking the mothers directly during the 24-hour recall interview, observing food prices in several markets close to the mother’s house in order to cover memory lapses (to determine variations in food prices, a survey was carried out in two subdistrict markets), and informing ourselves daily about prevailing food prices from the local newspaper (Lombok Post). To establish food prices, we mainly used the first two methods. The third method was employed only when the respondent did not know the price and it could not be obtained by direct market survey on the day of the interview. Food prices remained stable during the entire period of data collection. We also asked the mothers how many of their children had died, if any, within the 5 years preceding the enquiry. However, in order not to jeopardize acceptance of the main questions, we did not probe or insist if the mother was reluctant to talk about child deaths. Respective energy intake

Energy allowances are established differently from those for other specific nutrients because they are set ** One USD is approximately 9200 Indonesian Rupiah.

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at a level to meet average energy requirements for a group of comparable individuals. Energy needs vary from person to person; however, an additional allowance to cover this variance would be inappropriate, since it could lead to obesity in the average person, i.e., one with average energy requirements. Over the long term, a surplus of energy intake from any source is stored as fat, which may be detrimental to health [18, 21]. Energy requirements for Indonesia are based on several studies. The resultant energy expenditures were determined by measuring oxygen intake and were compared with Food and Agriculture Organization/ World Health Organization/United Nations University (FAO/WHO/UNU) recommendations. The results showed no significant differences, suggesting that the (FAO/WHO/UNU) recommendations are suitable to be used in Indonesia after adjustment for body size, age, and activity [22]. In this study, the energy intake results were classified according to the national RDA standard of energy requirements for the above age group (20 to 45 years), which amounted to 2,200 kcal/day [23, 24]. However, if an individual already achieved 70% of the energy requirement, i.e., 1,540 kcal, he or she was not considered deficient in energy intake [24, 25]. Mothers who consumed ≥ 70% of the national RDA for energy were classified as non–food-poor, and those who consumed < 70% of the national RDA were classified as food-poor. The food-poor group in turn was subdivided into the moderately food-poor, with ≥ 50% to < 70% of the national RDA, and the severely food-poor, with < 50% of the national RDA (i.e., 1,100 kcal). Household food-security assessment

Household food-security status was assessed by two trained enumerators using US HFSSM questionnaires [26] translated into Indonesian. The questionnaires had previously been used in five studies conducted by SEAMEO/RCCN/UI from February 2004 to August 2005 in West and North Sumatera, West and East Nusa Tenggara, Jakarta, and East Java Provinces that included 3,704 households (45% urban and 55% rural) with children below 5 years of age. The enumerators were trained before actual data collection to ensure that they had a uniform understanding of each question. Since there had been no prior comprehensive qualitative assessment of the acceptability and applicability to the local setting of the US HFSSM questionnaire, we used our enumerators’ familiarity with local customs and norms to adapt the questionnaire to the specific cultural setting. In the course of this process, both the wording and the cul South East Asian Ministers of Education Organization/Research Center for Community Nutrition/University of Indonesia.

tural acceptability of each question were assessed, and if necessary the question was revised without omitting or changing its original meaning. Subsequently, each question in the US HFSSM questionnaire was also field tested on three consecutive days for its applicability to the local setting. The results of pretesting were discussed on the same days that field testing was carried out. Some questions were modified, without changing their main objectives, to make them both more relevant and more easily understood by the respondents. For example, in the question We worried whether our food would run out before we got money to buy more. Was that often true, sometimes true, or never true for your household in the last 12 months?, we replaced the word “food” by “rice.” In the local setting, rice is the food most commonly stored, since it is culturally perceived as an essential food. As such, it is perceived as enabling survival, especially in times of food scarcity, even in the absence of other food items. Most of the respondents did not know the meaning of “balanced” diet and tended to perceive it as referring to culturally favored foods, i.e., primarily meat, fish, and other foods of animal origin. Using some suitable illustration during the interview, we usually informed respondents that a balanced diet requires more than just meat and fish, and gave the example of a combination of rice plus dishes from animal and plant sources. In the question In the last 12 months, excluding December, did you or other adults in your household ever cut the size of your meal or skip meals because there wasn’t enough money for food?, we replaced the month of December with the dates of local festivals, which were usually religious festivals of importance to the Moslem or Hindu faiths, the main religions on the island. To help the respondents clearly understand the meaning of each question, the interviews were mainly conducted in the local language of Sasaknese. The respondents were asked whether they had experienced deprivation of basic food items in their households during the previous 12 months. The questionnaires consisted of 16 items. A score of 0–2 indicated food security, 3 to 7 food insecurity without hunger, 8 to 12 food insecurity with moderate hunger, and 13 to 18 food insecurity with severe hunger. Ultimately, we grouped the last three categories under one heading called food insecurity. Data on food security were collected 1 month after data on food intake. A total of only 217 mothers were available and willing to be asked about their household food-security status. The remaining 23 had moved to other villages, were divorced, or declined to continue participation in the study. Nutritional status assessment

The weights of mothers (wearing minimal clothing)

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were determined to the nearest 0.1 kg with an electronic SECA 770 platform scale by one researcher assisted by two enumerators. The mothers’ heights were measured to the nearest millimeter by a microtoise scale. A wooden board was used to standardize the measurement if no straight wall was found at the respondent’s house [16]. Mothers with BMI of 18.5 kg/m2 or greater were classified as having normal weight and those with BMI less than 18.5 kg/m2 as underweight. Data analysis

The results of the in-depth interviews were analyzed according to the method of Creswell [27]. Nutrient intakes were determined from 1-day, 24-hour-recall data by NutriSurvey for Windows (SEAMEO— TROPMED, University of Indonesia, Jakarta, 2004) using the Indonesian food-composition database. This method calculates the daily intakes of energy, protein, and other nutrients by mothers. Statistical analyses were performed with SPSS for Windows (version 11.5) and StatXact4 for Windows. Normality of the data was verified by the KolmogorovSmirnov test. The baseline characteristics of foodpoor and food-sufficient mothers were compared by Student’s t-test for normally distributed data and by the Pearson chi-square, Mann-Whitney, and KruskalWallis tests for non-normally distributed data. The correlation of food-poverty status as response variable with other variables, such as mother’s education (1 = illiterate or ≤ 6 years of school; 0 = > 6 years of school), father’s income status (1 = irregular; 0 = regular), dependency ratio (replacing the variable “number

of household members”), employment rate, household food-security score (higher food-security scores indicate lower food-security status), and mother’s nutritional status, as influencing variables was determined by the Pearson chi-square test and Spearman’s rho test, followed by multinomial logistic regression. Ethical considerations

This study followed the ethical guidelines of the Council for International Organization of Medical Science [28] and received ethical approval from the Faculty of Medicine, University of Indonesia (No. 125/PT02.FK/ ETIK/2004), prior to implementation of the fieldwork. Informed consent was obtained from all mothers; the study was explained to the participants orally, and those who agreed to participate were asked to sign or affix a fingerprint to the approval form.

Results Two hundred forty apparently healthy, nonpregnant mothers entered the study, of whom 217 were both available and willing to talk about their household’s condition with respect to food security. The nutritional status of all 240 mothers was determined. Baseline characteristics

Table 1 gives an overview of the population’s baseline characteristics. The educational level of most mothers (63%) was low, and a few mothers had never

TABLE 1. Socioeconomic characteristics of the mothers according to their food-poverty status Nonpoor

Moderately poor

Severely poor

Total

n = 68

n = 62

n = 107

N = 237

5 (7.4)* 33 (48.5)* 17 (25)* 13 (19.1)* 4 (3, 7) 66.66 (33.33, 150) 50 (25, 100)

6 (9.7) 43 (69.4) 6 (9.7) 7 (11.3) 4 (3, 7) 100 (50, 150) 50 (25, 100)

10 (9.3) 74 (69.2) 18 (16.8) 5 (4.7) 4 (3, 8) 100 (33.33, 200) 50 (25, 100)

21 (8.9) 150 (63.3) 41 (17.3) 25 (10.5) 4 (3, 7) 100 (33.33, 150) 50 (25, 100)

Mother’s occupational statusa Housewife (%) Working mother (%)

57 (83.8) 11 (16.2)

54 (87.1) 8 (12.9)

90 (84.1) 17 (15.9)

201 (84.8) 36 (15.2)

Father’s income statusa Regular income (%) Irregular income (%) Preschool child mortality ratioa

34 (50)* 34 (50)* 3 (0.9)

30 (48.4) 32 (51.6) 1 (0.3)

35 (33.3) 70 (66.7) 8 (1.5)

99 (42.1) 136 (57.9) 12 (1.0)

Variable Mother’s educational levela Illiterate (%) Elementary school (%) Junior high school (%) Senior high school and above (%) Household sizeb Dependency ratio (%)b Employment rate (%)b

* Significantly different from severely poor group (p < .05) a. Pearson chi-square test: number (% or mortality ratio within food-poverty status group). b. Kruskal-Wallis test: median (5th percentile, 95th percentile).

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attended school. The median household size was 4, and the dependency ratio was 100, i.e., on average the proportions of unproductive and productive persons in the household were equal and that the nonproductive depended on the productive. On the average, for each employed household member there were two unemployed members. Most mothers were housewives (85%), and more than half of the husbands (58%) had irregular incomes. Relatively few mothers (5%) had experienced one or more child deaths within the past 5 years. Since child mortality was not one of the questions under investigation, no major effort was made to probe, nor was the denominator population of preschool children determined. It is likely that 5% is an underestimate, since it would suggest a very low infant mortality rate of less than 5/1,000 live births/per year. Instead, and assuming underreporting of child deaths to be the same in all groups, we used child mortality as a rough proxy indicator for the effects, if any, of food poverty and food security on child health. There were no statistically significant differences among food-poverty groups in household size, dependency ratio, employment rate, mother’s occupational status, or number of preschool child deaths. Significantly more fathers in the non–food-poor group than

in the food-poor group had a regular income. Similarly, the educational status of mothers in the non–food-poor group was significantly higher than in the severely food-poor group (table 1). Food-poverty line

The food-poverty line was calculated by using food expenditure equivalency of the mothers, which did not differ from real food expenditure (table 2) since most of the food consumed was bought. Since the majority (82%) of respondents had a normal BMI, we no longer required the reference population and we included all mothers. The median value of the mother’s food intake and her food expenditure equivalency was used, since these were not normally distributed. The median energy intake of the total population was 1,122 kcal per person per day, 51% of adequate intake according to national RDA standards. The median food expenditure equivalency of mothers was Rp 2,150/day. The resultant food-poverty lines were ≥ Rp 2,950/day (Rp 88,500/month) for the non–foodpoor group, Rp 2,100 to Rp 2,950/day (Rp 63,000 to Rp 88,500/month) for the moderately food-poor, and < Rp 2,100/day (Rp 63,000/month) for the severely

TABLE 2. Mother’s nutritional status, food intake and expenditure on food, and household food-security status according to their food-poverty status Variable Mother’s nutritional status Mothers’ height (cm)a Mothers’ weight (kg)b Mother’s BMI (kg/m2)b Underweight (%)c Overweight or obese (%)c Mother’s energy intake (kcal/day)b* Mother’s protein intake (g/day)b* Mother’s food expenditure equivalency (Rp/day) b* Mother’s real food expenditure (Rp/day) b* Household food- security statusc Secure (%) Insecure (%)

Nonpoor

Moderately poor

Severely poor

Total

n = 68

n = 64

n = 108

N = 240

149 ± 4.81 45.95 (37.59; 65.71) 20.72 (17.46; 27.92) 10 (14.7) 13 (19.1) 1,426.76 (832.85; 2,509.15) 60 (30.36; 114.42)

149.06 ± 6.68 45.9 (36.12; 63.05) 21.04 (16.45; 26.37) 13 (20.3) 7 (10.9) 1,120.32 (750.66; 1,867.84) 42.64 (23.46; 89.33)

150.31 ± 5.55 47.3 (37.8; 64.71) 20.78 (16.72; 28.20) 19 (17.8) 15 (14.0) 1,011.75 (500.29; 1,784.98) 34 (16.21; 78.71)

149 ±5.69 46.9 (37.51; 63.99) 20.79 (17.07; 27.74) 42 (17.6) 35 (14.6) 1,121.56 (599.46; 1,940.10) 42.27 (19.00; 94.29)

3,650 (3,000; 5,510) 2,425 (2,100; 2,842.50) 1,650 (1,100; 2,000)

2,150 (1,200; 4,995)

3,350 (1,480; 5,310)

2,400 (1857.5; 2842.5)

1,600 (600; 2,000)

2,000 (902.5; 4,350)

n = 51

n = 48

n = 88

N = 187

23 (37.1)** 39 (62.9)**

12 (21.4) 44 (78.6)

11 (11.1) 88 (88.9)

46 (21.2) 171 (78.8)

BMI, body mass index * Significant difference between food-poverty status groups (p < .05). ** Significantly different from moderately and severely poor groups (p < .05). a. Analysis of variance (ANOVA) (mean ± SD). b. Kruskal-Wallis test: median (5th percentile, 95th percentile). c. Pearson chi-square test: number (% within food-poverty group).

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food-poor. On the basis of these cutoff points, most of the mothers would be considered food-poor. Only 28% of mothers were not food-poor, 27% were moderately food-poor, and 45% were severely food-poor. Food poverty in relation to food variety

Table 3 shows food variety, type, and frequency of consumption over a 4-day recall. On ordinary days, vegetables and beans constituted the main meals. Non– food-poor respondents consumed a greater variety of food items than the severely food-poor. The non–foodpoor ate fruits, beans, and snacks more frequently and in larger amounts than the other two groups. The lowest consumption of fruits and snacks was among mothers in the severely food-poor group. Beans formed the smallest part of the diet among the moderately food-poor, although the difference was not statistically significant. The consumption of meat (including eggs) in the non–food-poor group was higher than

that among the moderately and severely food-poor groups. Most mothers (83%) in the non–food-poor group ate meat and eggs often. However, only 10% of total protein was animal protein, suggesting that the actual amount of animal protein in the diet was very small. The median daily per capita protein intake was 42 g (85% of the national RDA). The non–food-poor group had a significantly higher protein intake than the moderately and severely food-poor groups. Food items such as rice, cooking oil, vegetables, meat, eggs, chicken, beans, fruits, and condiments were always available and could readily be found in the nearest market or obtained from itinerant sellers. Mothers residing near markets could purchase all required items there. Those living far away purchased food from local sellers. A few whose homes were far from the market visited the nearest facility at times to buy large amounts of food to last them for several days. For most mothers, cost was the determining factor that influenced both the nature and the amount of food consumed.

TABLE 3. Variation, type, and frequency of food consumed by the mothers based on 24-hour recall interviews over a 4-day period according to their food-poverty status Nonpoor

Moderately poor

Severely poor

Total

n = 12

n = 10

n = 28

N = 50

3.8 (2.75, 4.75)*

3 (2.25, 5)

2.75 (1.5, 4.75)

2.87 (1.5, 5)

Meat, chicken, egg, Always (%) Often (%) Never or rarely (%)

0 (0) 10 (83.3) 2 (16.7)

1 (10) 5 (50) 4 (40)

3 (10.7) 7 (25) 18 (64.3)

4 (8) 22 (44) 24 (48)

Fruitsb Always (%) Often (%) Never or rarely (%)

2 (16.7) 5 (41.7) 5 (41.7)

0 (0) 4 (40) 6 (60)

1 (3.6) 4 (14.3) 23 (82.1)

3 (6) 13 (26) 34 (68)

10 (100)

28 (100)

50 (100)

Variable Food varietya fishb

Vegetablesb Always (%)

12 (100)

Beansb Always (%) Often (%) Never or rarely (%)

1 (8.3) 6 (50) 5 (41.7)

2 (20) 2 (20) 6 (60)

1 (3.6) 14 (50) 13 (46.4)

4 (8) 22 (44) 24 (48)

Tofu or tempeb Always (%) Often (%) Never or rarely (%)

5 (41.7) 3 (25) 4 (33.3)

0 8 (80) 2 (20)

1 (36) 15 (53.6) 12 (42.9)

6 (12) 26 (52) 18 (36)

Snackb Always (%) Often (%) Never or rarely (%)

1 (8.3) 9 (75) 2 (16.7)

2 (20) 3 (30) 5 (50)

1 (3.6) 12 (42.9) 15 (53.6)

4 (8) 24 (48) 22 (44)

Plant protein as %of RDAa

89.8 (63.14, 98.04)

89.01 (74.24, 100)

95.15 (63.26, 100)

91.73 (63.14, 100)

Animal protein as % of RDA a

10.19 (1.96, 36.86)

10.98 (0, 25.76)

4.48 (0, 36.74)

8.26 (0, 36.86)

* Significantly different from severely poor group (p < .05). a. Kruskal-Wallis test: median (minimum, maximum). b. Pearson chi-square test: number (%).

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Household food security and its relation to food poverty

Nutritional status and its relation to food-poverty status

Although originally we had established three groups for the classification of food security—secure, insecure without hunger, and insecure with hunger (moderate and severe)—we ended up keeping only two groups: household food-secure and household food-insecure, since none of our respondents fell into the severely deprived group. Most mothers (79%) resided in food-insecure households. The percentage of foodsecure households was significantly higher among the non–food-poor (37%) than among the moderately food-poor (21%) and severely food-poor groups (11%) (table 2). In the food-poor groups (the moderately and severely food-poor groups combined), those from food-secure households had higher levels of education and a larger proportion of their spouses enjoyed a regular income (table 4). Even though the difference in child mortality ratios (defined as the number of preschool child deaths per 100 mothers per year) between food-secure and foodinsecure households is not statistically significant, it is noteworthy that all nine deaths occurred in foodinsecure households.

Few mothers (17.6%) were underweight (BMI < 18.5 kg/m2), and most (67.8%) had a normal BMI (18.5 to 24.9). The smallest percentage (14.6%) were overweight (BMI ≥ 25.0). There were no significant differences in mothers’ BMI among the food-poverty groups. The median BMIs of the non–food-poor, moderately foodpoor, and severely food-poor groups were normal and equal (21%). However, the moderately food-poor group had the highest percentage of underweight mothers. There were no significant correlations between mothers’ nutritional status and food-poverty condition (table 2). Food-poverty level determined by its predictors

Table 5 presents results from multinomial logistic regression for the association between food-poverty status as the response variable and its explanatory variables, such as food-security score, dependency ratio, employment rate, mother’s education, and father’s income. The variable dependency ratio was highly correlated with the variable number of household members; this might lead to collinearity problems. However, we

TABLE 4. Socioeconomic characteristics of moderately and severely poor mothers according to their household food-security status Variable Mother’s educational levela Illiterate or ≤ 6 yr school (%) > 6 yr school (%) sizeb

Household Dependency ratio (%)b Employment rate (%)b Mother’s occupational statusa Housewife (%) Working mother (%) Father’s income Regular Irregular

statusa

Preschool child mortality ratioa,c

Food-secure

Food-insecure

Total

n = 22

n = 131

N = 153

12 (54.5)* 10 (45.5)*

108 (82.4)   23 (17.6)

120 (78.4)   33 (21.6)

n = 22

n = 130

N = 152

4 (3, 10.85) 100 (35.83, 192.5) 50 (25, 100)

4 (3, 7) 100 (33.33, 200) 50 (25, 100)

4 (3, 7) 100 (33.33, 200) 50 (25, 100)

n = 22

n = 131

N = 153

4 (18.2) 18 (81.8)

20 (15.3) 111 (84.7)

24 (15.7) 129 (84.3)

n = 22

n = 129

N = 151

12 (57.1)*   9 (42.9)*

38 (33.6) 75 (66.4)

50 (37.3) 84 (62.7)

n = 22

n = 131

N = 153

0 (0)

9 (1.4)

9 (1.2)

* Significantly different from food-insecure group (p < .05). a. Pearson chi-square test: number (%) b. Mann-Whitney test: median (5th percentile, 95th percentile) c. Arbitrarily defined as the number of preschool child deaths per 100 mothers per year.

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TABLE 5. Multinomial regression model of food-poverty status and its predictors Food-poverty statusa

Variable

Severely poor

Moderately poor

B

SE

Significance

Intercept Dependency ratio (%) Food-security scoreb Employment rate (%) Mother’s education = 0 Mother’s education = 1c Father’s income = 0 Father’s income = 1c

–0.941 0.005 0.292 0.007 –0.816 0 –0.523 0

0.857 0.004 0.088 0.01 0.418

0.272 0.285 0.001 0.505 0.051

0.393

0.184

Intercept Food-security scoreb Employment rate (%) Mother’s education = 0 Mother’s education = 1c Father’s income = 0 Father’s income = 1c Food-security scoreb

–1.487 0.009 0.166 0.006 –0.753 0 –0.055 0

0.929 0.005 0.096 0.011 0.46

0.109 0.052 0.082 0.575 0.101

0.43

0.898

a. The reference category is nonpoor. b. The higher the score, the higher the degree of food insecurity. c. This parameter is set to zero because it is redundant. Mother’s education: 0 = illiterate or ≤ 6 years of school; 1 = > 6 years of school. Father’s income: 0 = regular; 1 = irregular.

assumed that the variable dependency ratio was more accurate as an independent variable for poverty than the variable number of household members. The results showed different predictors for different levels of poverty. Employment rate and father’s income did not come out as significant predictors of food-poverty status in the model. Households at risk for food insecurity, i.e., those with higher food-security scores, had a higher probability of being food-poor. The probability of being food-poor due to food insecurity, after other predictors in the model had been controlled for, was even higher in the severely food-poor than in the moderately food-poor. Mothers with low educational levels were more likely to be severely food-poor than non–foodpoor. Households with high dependency ratios had a higher probability of being moderately food-poor than non–food-poor.

Discussion The Central Bureau for Statistics (CBS) used the 52 selected food items to calculate the mean intake of the population. The selection of the reference foodcommodity basket used by the CBS is determined on the basis of the number of calories consumed and the frequency of households consuming the commodity [7]. A food item that was consumed mainly in one area might not appear in the CBS list because the majority of the Indonesian population did not consume it. Conversely, many food items that were not consumed by one population group might have been included in the calculation because they featured in the CBS food-

commodity basket. In this study, several food items on the CBS list were neither consumed nor available in the study area. The CBS food-poverty line is based only on the amount of food used by the household, yet food used is not equivalent to food consumed. The caloric content derived from the food used would tend to overestimate caloric intakes derived from the food actually consumed by the household members [29]. Unlike the CBS standard, in this study the food-poverty line was derived independently of preestablished norms and did not include nonfood expenditures. To estimate the usual intake of a population, this study used a quantitative daily consumption method with 1-day, 24-hour recall, validated with 3-day recall. The modified 24-hour recall was a tedious exercise for the assessment of food-poverty status. However, it provided important and detailed information, such as usual food intake of the population, food expenditure, food availability, and variations in the pattern of food consumption. This kind of information will help local governments to develop relevant and appropriate interventions to alleviate food poverty based on local food sources. According to the validation results, energy intakes as determined by 1-day, 24-hour recall in this study were no different from those obtained with a longer recall period. This held true for both quality and quantity of food items. We may also assume these results to correctly reflect usual food intakes of the population, given the large number of randomly selected mothers included in the study [16]. In this way, food expenditure was determined only for food available in the area and actually consumed by respondents, more accurately reflecting food poverty

Food-poverty status and food insecurity

for that population. Additionally, and since the method excluded nonfood expenditures, both the sensitivity and the specificity of the formula as an indicator of food poverty were enhanced. Our food-poverty line reflects the existing conditions in the area or region at the particular time it was established. This is because by taking account of food habits, food beliefs, and usual dietary patterns, it was based on actual and habitual food intake. The transformation of food intake to expenditure (rupiah) was based on locally prevailing food prices. Our food-poverty line is, hence, location specific. The CBS used the price of the raw food and included the cost of kerosene in nonfood expenditures, whereas in our study, since the results are based on actual food consumption, food expenditure calculations used the price of cooked food. Therefore, the cost of kerosene was included in the food expenditure items. The resultant rupiah value in this study also would tend to be higher than that of the CBS because women may have consumed more types of low-calorie foods. Even though in this study determination of the food-poverty line was based entirely on mothers’ food intake, one may confidently assume that the food consumption of all household members was no worse. In our in-depth interviews, and contrary to expectations that mothers would have the lowest priority in access to food, we found no differences among household members in the amount or quality of food consumed. However, if the reputed self-sacrificial behavior of mothers generally holds true in most households, then if the mother’s food intake is adequate we may confidently assume the same to be true for the remaining household members. The converse assumption, that is, when the mother is food-poor other members of the household are also food-poor, may be less convincing. However, we argue that if the mother has inadequate food intake, with or without other household members having adequate food intakes, the household is still food-poor. The fact that one individual—the mother—has to sacrifice her share to make up for the missing food of the others still means that overall there is not enough food, and hence if the food were evenly distributed, everyone in the household would be fooddeprived to some extent. Thus, measuring only the mother’s food intake provides a good proxy indicator for household food poverty. The food-poverty line in this study was 30% higher than that of the CBS. The food expenditure determined by CBS for the rural population in West Lombok district was Rp 68,317 per capita per month, equivalent to 71.78% of the food-poverty line of West Nusa Tenggara Province [4]. Unfortunately, the CBS does not provide data on the percentage of population that is food-poor either at the national or the district level. These differences in food-poverty lines may be a result of different methods of calculating the caloric intake of the population.

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On the basis of the CBS food-poverty line, only 12.5% of our population is food-poor; according to our methodology, 72% are food-poor. This means that 59.5% of our food-poor population, who consumed less than 70% of the RDA or less than 1,540 kcal of energy per day, based on their usual intake, was classified as non–food-poor on the basis of the CBS food-poverty line of 2,100 kcal/day. Consequently, 59.5% of the population classified as food-poor would be excluded from many food intervention programs because they would be considered non–food-poor. With regard to comparability, i.e., the concern about being able to use derived poverty lines across space and time, Asra and Santos-Francisco [6] argue that most of the time there is a trade-off between specificity and comparability in the estimation of poverty lines that includes “minimum” food and nonfood expenditures. This indeed holds true if one uses a “mere” poverty line instead of a food-poverty line. It should be quite difficult, if not impossible, to derive a poverty line that lends itself to comparisons over space and time, if only because the standard of living, i.e., expectations of what is minimally required for basic existence, will differ between different regions of the world and even of the same country. These differences derive from a host of factors that range from social and cultural expectations and real differences in the cost of living to the nature of the social services available to the population. Within the same subdistrict, per capita food expenditure may be higher in richer than in poorer areas, and if one makes an error in sampling, the resultant food-poverty lines will not be comparable between the richer and the poorer areas. However, if one obtains the minimum cost for food expenditure that is representative of the whole subdistrict, then the same food-poverty line may indeed be applied. By determining the food-poverty line from a reference population that was selected randomly from the entire study population, as we have done, the differences between the food-poverty lines for richer and poorer population groups are similarly comparable. Food is the most basic need. A person or household that has to sacrifice food intake can be assumed to be very poor, as is a household that falls below the food-poverty line. To establish a gold standard for a food-poverty line of a given ecosystem would require a captive population and externally controlled provision of food. The latter would need to satisfy normal physiological requirements, as determined by the prevailing activity and morbidity levels of the chosen population, as well as their ingrained food habits. Food items would need to be derived from primarily locally available sources at the prevailing average price. Food items that are locally unavailable yet indispensable for normal health, such as iodized salt in an iodine-deficient area, would be purchased from outside sources. Although a standard food-poverty line might, in theory, be established in this way, it would not be of much use,

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given that few if any households would be able to stick to such a regimen or would have the knowledge required to optimize their food procurement process to such a narrow margin of error. The poverty line is different, because it includes expenditures on nonfood items that may or may not be “sacrificed” and transformed into food expenditures. A household determined to be “poor” according to the poverty line is not necessarily poor in terms of food. The food-poverty line, therefore, is considerably more sensitive for identifying poor households, especially very poor ones, than the poverty line. Using the food-poverty line to classify households as food-poor and non–food-poor provides better comparability across regions and time. Although food-poverty lines derived in different places or at different times or in urban versus rural regions, might differ because they are based on different standards of living, classifications based on food-poverty lines rather than poverty lines would still be comparable. Poverty as such implicitly becomes an economic concept, with income traditionally being considered the main determinant of a person’s well-being [8]. Income elasticity of calories was found to be linear, implying that an increase in income results in an almost proportionate increase in caloric intake [8]. In our study, most mothers could be considered “poor” in terms of food intake, even in the non–food-poor group. Since food poverty was judged by monetary value, one was not necessarily food-sufficient if one had spent a large amount of money to purchase food and vice versa. If in a certain area the price of food is high, then the amount of food expenditure will also be high without necessarily providing a high energy intake. Conversely, when the price of food is low or when a person has a low-quality diet, such as one high in calories but low in other nutrients, then the low food expenditure will not necessarily provide a low energy intake. Thus, using consumption of energy instead of other nutrients, such as animal protein, to measure food poverty might lead to under- or overestimation of food poverty. Although we selected the mothers randomly from the population, the discrepancy between food poverty and energy adequacy could not be totally eliminated. Caloric adequacy does not guarantee the absence of food poverty, nor does an inadequate calorie intake necessarily equal food poverty. The mean caloric intake of the mothers was lower than that expected on the basis of national RDAs (2,200 kcal); only 21% of mothers consumed at least 70% of the RDA, 33.3% consumed from 50% to less than 70%, and 46% consumed less than 50%. Surprisingly, such low energy intakes were not reflected in lower BMI values, a result suggesting either that national RDAs are inappropriate for the population studied, since they do not take into account the shorter stature of Indonesian women (mean = 149.62 cm, SE = 0.37 cm, as compared with mean = 162.1 cm, SE = 0.14 cm for US adult women) or that energy expenditures in

T. Rosalina et al.

this population are lower, or both. The fact that in our population the BMI values of mothers in the food-poor and food-adequate groups were numerically different, but not different at a statistically significant level, prevents the food-poverty line from being used to identify physiological need and makes it more of a social and economic indicator. Differences in the quality of food intake, such as the relatively low inclusion of fruits in the diet of the food-poor, suggests an educational etiology, considering that fruits in this area are abundant, cheap, or even free, and available throughout the year. This might explain why the mothers’ education affected food expenditure, especially in the severely food-poor group. The very low food expenditure in that group was due not only to financial insecurity but also to the poor knowledge of the mothers, whereas in the moderately food-poor group, the low food expenditure tended to be determined only by financial insecurity, as reflected by its significant effect on the variable dependency ratio. Food-security status was significantly associated with household food supplies and some measures of dietary intake [30]. Access to markets significantly affects food security [31]. Contrary to our expectations, in-depth interviews showed food items to be readily available in the study area and within easy reach of the population, but our study population could barely afford them. Households lack the resources to fulfill their basic needs [1]. Most of the women are housewives and depend almost entirely on the income from their husbands to satisfy their nutritional needs. As we learned in the course of the interviews, most of the study population did not own land. Even though they lived in the most fertile areas of Lombok Island, they had to depend on their financial resources to provide food for the household. This finding is supported by the fact that when the variable father’s income was entered in the regression model, its effect on food poverty was diminished by food-security status. Thus it is not surprising that the percentage of food-poor households is highest among food-insecure households in which the head of the household has an irregular income [32]. This finding compares well with the results of an investigation by Sarlio-Lahteenkorva and Lahelma [33], in which low household income, recent unemployment, and economic problems were the predictors of food insecurity. Tarasuk [34] also showed that food insecurity appears inextricably linked to the level of financial security. In this study, food poverty was positively correlated with household food insecurity. Severely food-poor mothers had a higher probability of belonging to food-insecure households than non–food-poor and moderately foodpoor mothers. The more food-insecure a household, the lower the food-poverty status. Mothers in the non–food-poor group, where income was regular and a constant proportion of the income of the head of household may have been used to purchase

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food, enjoyed a greater variety of food. The monthly income of Rp 88,500 permitted the non–food-poor to consume at least three different types of food in addition to vegetables and rice, which were eaten daily, thus fulfilling almost 100% of their protein requirements from plant sources. Because most food consumed by mothers was purchased, financial security plays a key role in determining family food sufficiency, in terms of both quantity and variety. The recent increase in the price of fuel by 61% caused a nonproportional rise in the cost of food and demonstrated the necessity for periodic revision of that indicator. Considering that the rise in income usually lags behind and is minimal to begin with, one may expect a nonproportional increase also in the number of food-insecure households.

Conclusions and recommendations Most mothers were considered poor in term of food. The median energy intake of the population was 1,121.6 kcal. The resultant food-poverty lines were ≥ Rp 2,950 for the non–food-poor group (equivalent to ≥ 1,540 kcal), Rp 2,100 to less than Rp 2,950 for the moderately food-poor group (equivalent to ≥ 1,100 kcal to < 1,540 kcal), and below Rp 2,100 for those in the severely food-poor group (equivalent to < 1,540 kcal). The food-poverty line determined in this study is higher than that of the CBS. Used in this setting, the food-poverty lines as well as the US HFSSM would seem to be more related to poverty than to food need. Once physical growth is completed, adaptive mechanisms, such as physiological stunting, might have “kicked in” so that by adulthood the actual nutritional requirements would be less than those in a population not subjected to early food deprivation. In assessing the adequacy of actual food intake of the population, at a minimum their anthropometric features (e.g., height and weight) as well as their energy expenditure patterns would need to be considered. Both determine and influence the food intakes required to maintain metabolic balance, i.e., to maintain a normal BMI as well as allow normal physiological performance. Although we did find normal, i.e., mean BMI values for the three food-poverty groups, we did not assess the physiologic performance of the subjects. Consequently, we cannot assess whether the intakes we recorded were also adequate with respect to physiological demands. Food-poverty status based on mothers’ food intake has a strong positive correlation with food-security status. The severely food-poor mother is at higher risk for household food insecurity. On the assumption that our study population had

adapted to chronic nutritional deprivation by reducing physical growth and physical performance, we recommend that the severely food-poor be given preferential treatment by nutrition intervention programs, such as nutrition education and, possibly, “rice-for-the-poor.” Launched in 1998, “rice-for-the-poor” is a program delivered by the central government to strengthen food security among low-income populations. We also recommend that each district elaborate its food-poverty line in order to develop relevant and appropriate interventions to alleviate district-specific food poverty by making use of local resources. To calculate the cost of alleviating food poverty, the study result has to be completed with other variables such as the head count and poverty gap index. (The “head count” is the percentage of poor out of the total population; the poverty “gap” is that between income and the poverty line, expressed as a ratio.) The US HFSSM questionnaires were suggested as a simple method to assess both food-security and food-poverty status. Finally, three types of study are recommended to answer some of the more important questions that arose out of this investigation. The first type consists of metabolic studies to redetermine actual nutritional requirements in line with body stature and physiological needs, particularly for Indonesians. The second would be socioanthropological investigations of the two “extreme” groups of mothers with respect to their ability to cope with available resources. In what ways do the “achievers,” who managed to have an adequate caloric intake despite being severely foodpoor, differ from the “nonachievers,” who despite being non–food-poor in relation to the food-poverty line, had daily intakes of less than 1,100 kcal. The third type of study would be a detailed survey of preschool child deaths in the preceding 5 years among representative samples of households from the two extreme groups, i.e., the severely food-poor and the non–food-poor, for the purpose of clearly identifying pathological effects of household food poverty on the most vulnerable groups. All three types of study would lend themselves well to postgraduate student and faculty research. For this purpose, families fitting the required poverty states in addition to those included in this study would need to be sought out.

Acknowledgments We thank the subjects for their interest and willingness to participate in this study and Puguh B. Irawan, MA, PhD, Purnawan Djunadi, MD, MPH, PhD, and other anonymous resource persons who provided many valuable suggestions and inputs to the study. The study was supported by the Neys van Hoogstraten Foundation, the Netherlands.

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