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European Journal of Clinical Nutrition (2003) 57, 1212–1221 & 2003 Nature Publishing Group All rights reserved 0954-3007/03 $25.00 www.nature.com/ejcn ORIGINAL COMMUNICATION Predicting dietary intakes with simple food recall information: a case study from rural Mozambique D Rose1* and D Tschirley2 1 Department of Community Health Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA; and 2Department of Agricultural Economics, Michigan State University, East Lansing, MI, USA Objective: Improving dietary status is an important development objective, but monitoring of progress in this area can be too costly for many low-income countries. This paper demonstrates a simple, inexpensive technique for monitoring household diets in Mozambique. Design: Secondary analysis of data from an intensive field survey on household food consumption and agricultural practices, known as the Nampula/Cabo Delgado Study (NCD). Subjects: In total, 388 households in 16 villages from a stratified random sample of rural areas in Nampula and Cabo Delgado provinces in northern Mozambique. Methods: The NCD employed a quantitative 24-h food recall on two nonconsecutive days in each of the three different seasons. A dietary intake prediction model was developed with linear regression techniques based on NCD nutrient intake data and easyto-collect variables, such as food group consumption and household size The model was used to predict the prevalence of low intakes among subsamples from the field study using only easy-to-collect variables. Results: Using empirical data for the harvest season from the original NCD study, 40% of the observations on households had low-energy intakes, whereas rates of low intake for protein, vitamin A, and iron, were 14, 94, and 39, respectively. The model developed here predicted that 42% would have low-energy intakes and that 12, 93, and 35% would have low-protein, vitamin A, and iron intakes, respectively. Similarly, close predictions were found using an aggregate index of overall diet quality. Conclusions: This work demonstrates the potential for using low-cost methods for monitoring dietary intake in Mozambique. Sponsorship: Michigan State University and the Mozambican Ministry of Agriculture and Fisheries. European Journal of Clinical Nutrition (2003) 57, 1212–1221. doi:10.1038/sj.ejcn.1601671 Keywords: Africa; Mozambique; household study; dietary assessment methods; diet quality index; nutrient intake Introduction A well-nourished population is not only important to a country’s long-term development, but it is also a desirable outcome objective in itself. Unfortunately, monitoring of progress in meeting this objective can be expensive, since large-scale quantitative surveys are time-consuming and resource-intensive. Obtaining information on dietary intake *Correspondence: Dr D Rose, Department of Community Health Sciences, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 2301, New Orleans, LA 70112, USA. E-mail: [email protected] Guarantor: D Rose. Contributors: DR designed this study and performed the data analysis. DT is co-director of MSU’s Mozambique Food Security Project, under which both the original field work and this secondary data analysis were conducted. DR was the lead author and DT was the second author on the write-up of this paper. Received 9 May 2002; revised 14 September 2002; accepted 16 September 2002 is especially difficult in Mozambique, as it is in other SubSaharan African countries, because of its large size, the lack of adequate roads and transportation, the diversity of languages spoken, and the relative inexperience in fielding complex dietary questionnaires. Yet the information from dietary surveys is essential for monitoring and program design, as well as for geographical targeting to population groups in need of emergency interventions. Since a full and accurate assessment of dietary intake is a costly and time-consuming activity, a number of attempts have been made to develop relatively simple and inexpensive methods to do this. One such measure uses food variety to predict dietary adequacy. In Mali, researchers weighed the food intakes of household members and compared the nutrients consumed in this food to simple measures of dietary diversity (Hatly et al, 1998). These researchers found that the number of different food groups consumed in a 3-day period was useful for distinguishing those with Predicting dietary intakes in Mozambique D Rose and D Tschirley 1213 inadequate diets from those with adequate ones. Unlike the approach in Mali, analysts in Zambia developed a scoring system that weighted foods differently depending on the food group to which they belong. For example, consumption of foods from the nutrient-rich meats group received four points, whereas those from the cereals group received two points. After adding up the points from all the foods consumed in a 24-h period, household diets were evaluated based on pre-established cut-points (FHANIS/CSO, 1998). In rural Mozambique, the types of foods, their availability and nutritional content, as well as the consumption patterns and nutritional problems in the population are not the same as those in Mali or Zambia. Neither are the constraints and opportunities with regards to data collection efforts. A simplified field tool would be an enormous advantage to government agencies, donors, and national and international nongovernmental organisations involved in food security interventions, since all of these organisations lack the resources to regularly conduct intensive dietary surveys. Even though there is difficulty in fielding intensive data collection efforts, there are many local analysts in the country who are quite practised at using statistical packages on microcomputers. This human resource in data processing implies that while field instruments should be kept simple for data collection purposes, more sophisticated techniques can be used to process the data, once obtained. Both the Mali and Zambian tools described above begin with a simple listing of all foods consumed yesterday. Processing of both tools is quite simple as wellFeither a simple count of the number of different foods or a simple summing of points. Here we develop a tool that is simple to field, but is followed by more complex processing techniques. This approach takes full advantage of all the information collected in a simple instrument as well as the human resources in Mozambique capable of processing this information. Figure 1 outlines the overall strategy for this approach. Obtaining a detailed quantitative data set on household dietary intake is a necessary first step. For our work, we rely on a previous study conducted in northern Mozambique, known as the Nampula/Cabo Delgado (NCD) study. Since the NCD study collected quantitative information on food consumption, it allows us to get estimates of household nutrient intake in the study area. In the second phase, statistical relations are explored between easy-to-collect variables in the NCD database, variables similar to those that could be collected in future efforts by various organisations, with these quantitative measures of household nutrient intake. In this analysis, a dietary intake prediction model is developed that allows prediction of a household’s dietary intake level given some relatively simple information, such as the types of foods eaten by the household in a 24-h period or the number of members in the household. The development of this modelFthat is, phase II in Figure 1Fand its performance are the subjects of this paper. In phases III and IV, the model can then be used with Figure 1 Overview of strategy to get low-cost estimates for monitoring dietary intakes. information from a new survey to get predictions of household dietary intake in that area. The techniques developed here are focused at the household level. Although malnutrition is a condition experienced by individuals, the household is the entry point for most interventions that seek to improve this condition. The science of determining nutrient requirements at the household level is less developed than it is for individuals (FNB, 2000). However, it is more practical to monitor conditions at the household level, since most agricultural and health projects, as well as the surveys that monitor them, are targeted at households. As is the case with the 24-h recall instrument on which our approach is based, the targeting or monitoring that can be done with this technique is designed for populations of households, rather than for individual ones. Methods Study area The Nampula/Cabo Delgado (NCD) study was originally designed to identify the impacts of various smallholder cotton-growing schemes on household incomes and food security in Mozambique (MAF and MSU, 1996, Strasberg, 1997). The study was conducted in Montepuez District in the province of Cabo Delgado and in Monapo and Meconta Districts of Nampula Province. These districts were purposively selected both because they encompassed the variety of different cotton-growing schemes present and because they are typical of the interior of northern Mozambique, where maize- and cassava-based cropping systems predominate and where cashew, as well as cotton, are often grown. Villages, and households within them, were then selected using a stratified random cluster design. In total, 388 households in European Journal of Clinical Nutrition Predicting dietary intakes in Mozambique D Rose and D Tschirley 1214 16 villages were selected for the original study. An extensive description of sampling procedures has been published previously (MAF and MSU, 1996). Dietary assessment Household food consumption was measured using a 24-h recall technique. Trained enumerators asked the person who most often did the food preparation and cooking to identify the type and quantity of each food ingredient prepared and consumed during the previous 24 h. To facilitate recall of the quantities used, the household respondent indicated the amounts of each ingredient used with reference to her own household utensils (eg, a cup, a can, a spoon). A quantitative determination of the volume of the amounts indicated by the household respondent was then made. To do this, the utensil was filled to the level indicated by the respondent with a food item of known density, such as dried maize, and the weight of that amount of maize was then determined. Dividing the weight of this maize by its density gave us the volume of the particular food used by the respondent. Volumetric determination was necessary, as opposed to direct weighing, because households often did not have extra amounts of the foods consumed in the previous day. Interviews and weighings were always conducted in the household, which facilitated this recall and volumetric measurement. Information was solicited on foods eaten other than the main meal items. Although it is possible that some snacks between meals or foods eaten away from home were not observed by the household respondent, snacking and away-from-home food consumption were relatively limited in the poor rural communities of Mozambique in 1995. The weight of each food was then calculated by multiplying the volume of the food consumed by its density. Through its work in Mozambique since the early 1990s, the Michigan State University Food Security Project accumulated a database on average densities of the most common food items. Information on the densities of additional foods not found in this database was obtained from research in a neighbouring country (IFPRI, 1997). For most fruits and vegetables, volumetric information was not used; weight information was obtained directly by multiplying the number consumed (eg, three tomatoes) by mean weights of these items. The nutrients consumed from each food were obtained by multiplying the weight of the food consumed times the nutrients per 100 g of that food. The latter information on the nutrient content of foods was obtained from standard food composition tables used in Mozambique and elsewhere in Africa (Leung et al, 1968; West, 1988; MISAU, 1991). Two recall interviews, separated by about 1 week, were conducted during each of three rounds: in May 1995 (‘harvest season’), in September 1995 (‘postharvest season’), and in January 1996 (‘hungry season’). Nutrients for all foods European Journal of Clinical Nutrition listed in the 24-h period were summed for each interview day and then averaged over the two interview days to get a mean daily household intake for each round. This yielded 1140 observations on 388 households (94% of households had dietary data in all three periods). Household intakes were compared with international standards to develop nutrient intake ratios (NIRs) for each household. These ratios are similar to a nutrient adequacy ratio (NAR) for an individual (Guthrie & Scheer, 1981), except that the numerator is a sum of all household members’ intakes and the denominator is a sum of the recommended allowances for each person in attendance at household meals on the day preceding the recall. We find the NIR to be useful for reporting nutrient intakes, since it allows one to compare households with different age– gender compositions. We purposefully avoid the previously used ‘nutrient adequacy ratio’ terminology, in part because ours is a household measure, and in part to avoid misinterpretation regarding the nutrient adequacy of household diets. Energy allowances were based on reference weight data for Mozambique (James & Schofield, 1994) and include the energy needed to maintain weight as well as energy necessary for occupational and ‘socially desirable’ activities (FAO/WHO/UNU, 1985). Occupational activities were assumed to be characteristic of a rural population in a developing country, that is, requiring moderate to heavy energy expenditures. NIRs were also calculated for protein, vitamin A, and iron, with recommended intakes obtained from appropriate sources (FAO/WHO/ UNU, 1985; FAO and WHO, 1988). These four nutrients were highlighted because of their importance for public health and nutrition in Mozambique. See Table 7 (of the Appendix) for a detailed information on the recommendations used. Mozambique Diet Quality Index (MDQI) While information on intakes of specific nutrients is useful for designing applied interventions to address specific nutrition problems, policymakers often need simple summary measures of nutrition, so they can assess overall progress in this area. We created the MDQI to summarise dietary information related to the most important nutritional problems in Mozambique. Other than iodine deficiency, which cannot be assessed with our dietary instruments, the key nutritional problems are protein and energy malnutrition, vitamin A deficiency, and iron deficiency. Other nutritional deficiencies, such as niacin deficiency and vitamin C deficiency, have also been documented in Mozambique, though not as frequently (GISMAV, 1998). Thus, MDQI also gives weight to other important nutrients by including a composite index, a mean intake ratio composed of seven nutrients (MIR7)Fthiamin, riboflavin, niacin, vitamin B6, folic acid, vitamin C, and calcium. Zinc deficiency is common in developing countries and is likely to be a problem in Mozambique, although it has not been Predicting dietary intakes in Mozambique D Rose and D Tschirley 1215 documented there. We have not included it in our index, since our food composition databases do not have information on this nutrient. The score on this MDQI ranges from 0 to 10 and is a sum of each of the five component scores related to energy, protein, vitamin A, iron, and the composite index MIR7. To compute each component score, the NIR is first computed, then truncated at 1.0 if the household consumed more than the recommended amount, and then multiplied by 2. Truncation reflects the fact that excesses in consumption of one nutrient do not make up for deficiencies in other nutrients. Multiplying each of the ratios by 2 is simply a means of converting the MDQI to a more convenient range of 0–10, rather than 0–5. Based on the scores on this index, household diets were divided into three categories: acceptable, low quality, and very low quality. Households that scored 7.5 or greater on this index were considered to have acceptable diets. Households that scored 6.0 or greater, but less than 7.5 points on this index were considered to have low-quality diets. Those that scored less than 6.0 points were considered to have very low-quality diets. These cutoff points were based on a combination of scientific judgement and practical policy concerns. The Food and Nutrition Board (FNB, 1986) of the US National Research Council outlined conditions for when the mean nutrient requirement can be used as a cutoff point indicating inadequate intakes for nutrients other than energy. Using a typical assumption about the requirement distribution of a nutrient (ie, the coefficient of variation is 0.15), it can be shown that the mean nutrient requirement is 76.9% of the recommendation for a safe level of intake. We use 75% as a rough approximation to this figure, largely to facilitate comparisons with other literature on this topic. This corresponds to 7.5 on the 10-point MDQI scale as a cutoff point for an acceptable diet. Based on the same rationale, the intake of specific nutrients is described, elsewhere in this paper, as low when the NIR is less than 75%. Practical concerns about interventions that could be targeted to areas of highest priority motivated our decision to split low intakes on the MDQI into two categories, those that were low (6.0–7.5) and those that were very low (o6.0). It can never be known whether a given diet is truly low or acceptable for a given household. However, the terminology is useful for categorising groups of households based on relative dietary quality. Dietary intake prediction model To begin developing a prediction model, variables were considered that would be easy to collect in the field, and which were also included in the NCD survey. These variables included, for example, food items that were consumed in the previous day or the age and gender composition of the household. Note that food consumption variables are identified here as easy-to-collect, since reference is made to a simple listing of which foods were eaten during the day. In contrast, quantitative information on how much was consumed would be quite complex to collect. Since there are over 70 different food items in the original NCD food consumption database, a number of different food grouping systemsFincluding ones that contained 7, 11, 13, and 15 different food groupsFwere explored to reduce this into a manageable number. The goal was to find reasonably aggregated food groups, which would be broad enough to encompass local foods from different parts of the country. However, food groups that were sufficiently disaggregated were needed so that nutrient content would be relatively homogenous across foods within the same group, a necessity for getting good predictions of nutrient intakes. A system of 11 food groups was developed which balanced these concerns. The complete list of food groups and individual food items in each group is listed in Table 1. Linear regression was used to develop a prediction model that would map food group consumption to nutrient intakes. The household intake of a nutrient (expressed as a percent of its recommendation, ie its NIR) was the dependent variable and the consumption of foods and other easy-to-collect variables were the independent variables. There were four main nutrients of interest: energy, protein, vitamin A, and iron. There were also seven nutrients that made up the summary measure of dietary variety, referred to as MIR7 in the previous section. Thus, a total of 11 regression models, one for each nutrient, were estimated. All models were estimated with ordinary least-squares regression using all independent variables listed in Table 3 and the ‘regression’ command in SPSS. Other than for vitamin A and calcium, which were estimated linearly, all models were estimated with dependent variables in logarithmic form. Several different expressions of the food consumption variables were tested. One approach used a single variableF the number of different food groups consumed in the previous day. Another approach used a set of dichotomous indicators of whether or not the household consumed a food from each food group on the previous day. The final approach used a set of variables expressing the number of times per day the household consumed a food from each of the 11 food groups. This latter approach performed best among the different food variable alternatives. We also experimented with a number of socioeconomic variables, such as those related to household size (measured in adult equivalents), land tenure, agricultural production and agricultural sales, as well as seasonal indicators. Household size was a significant predictor in every nutrient intake model, but none of the other socioeconomic variables improved the prediction significantly enough to warrant inclusion in the final models. Statistical weights accompanying the NCD data set were used for all descriptive statistics reported here (Tables 1, 4–6). Regression analyses were performed unweighted. European Journal of Clinical Nutrition Predicting dietary intakes in Mozambique D Rose and D Tschirley 1216 Table 1 Food items in each of the 11 food groups Food group Food items Grains Dried maize, maize flour, other maize products, sorghum, sorghum flour, fresh sorghum, bread, rice, pasta, cookies Cassava flour, dried cassava Dried beans, dried peas Dried peanuts, coconut, pumpkin seeds, sesame seeds, sunflower seeds, cashew nuts Dried fish, fresh fish, beef, chicken, rat, bird, pigeon, snail, crustaceans, grasshopper, frog, milk, eggs Pumpkin, dark leafy greens, red pepper leaves, cassava leaves, bean leaves, pumpkin leaves, sweet potato leaves, cashew leaves, red peppers, mango Papaya, lime, fresh cassava, fresh sweet potato (pale), tomato, fresh beans, fresh peas, fava beans Mushrooms, onions, bananas, fresh maize, fresh yams, okra, apples, fresh peanuts Sugar, sugar cane, honey Oil Beverages (including maize beer, cashew juice, cashew wine, tea, coffee), salt, candy Tubers Beans Nuts and seeds Animal products Vitamin A-rich fruits and vegetables Vitamin C-rich fruits and vegetables Other fruits and vegetables Sugars Oils Other foods Results Table 2 displays the mean nutrient intakes as a percent of recommendations from the NCD study. Data were collected during three different seasons. For the harvest season, mean intake for energy was 93% of the recommendation, while for vitamin A it was only 28%. The second data column presents mean intakes during the postharvest season, when intakes for most nutrients were at their highest. Intakes were at their lowest during the hungry season, except for vitamin A, since this was the time when households substituted leaves and other vegetables for staple grains. Additional details on food and nutrient intakes in the NCD study area have been published previously (Rose et al, 1999). The preferred set of models derived from this work is displayed in Table 3. Each column describes a model that predicts the intake of a particular nutrient. The coefficient estimates, derived from linear regression models, reflect three main relations. First, they are affected by the nutrient content of particular foods. For example, the largest coefficient in the vitamin A model, 0.446, is on the vitamin A-rich fruits and vegetable group. Consumption of beans, nuts and seeds, which are good sources of protein, positively affects the intake of protein. This can be seen by the sizable positive coefficients on these foods. Second, the coefficients in Table 3 may also reflect the amount of food consumed at a given eating occasion. For example, animal products are a rich source of protein, but relatively small quantities are consumed at any one time in Nampula and Cabo Delgado. Thus, the coefficient on animal products in the protein equation is smaller than the coefficients on some other food groups, such as nuts and seeds or grains. Third, the coefficients in Table 3 also reflect substitutions between various food groups. For example, there is a negative coefficient on the oils food group in the protein model. Oils have no protein content and when substituted for other foods that do have significant protein European Journal of Clinical Nutrition Table 2 Mean nutrient intakes in the Nampula/Cabo Delgado sample by season Mean intake as a percent of recommendation (standard errors in parentheses) Nutrient Energy Protein Vitamin A Iron Harvest seasona (n=379) 92.7 150.2 28.1 103.4 (2.4) (4.3) (1.3) (3.5) Postharvest season (n=386) 102.0 151.6 19.5 155.2 (2.1) (3.8) (0.8) (4.7) Hungry season (n=375) 66.4 76.2 55.0 85.4 (1.8) (2.6) (3.0) (2.9) a Harvest season data were collected in May 1995, postharvest season data were collected in August 1995 and hungry season data were collected in January 1996. content, they could lower overall protein intake of households. This might occur if respondents substitute oils for the amount of peanuts they use in the preparation of vegetable dishes. The coefficients in Table 3 form the basis of the dietary intake prediction model. To apply this model in practice, one would assemble a data set with information on the number of times in a day foods from each of the 11 food groups were consumed by each household. (This information could come from a qualitative-type 24-h diet recall, which simply yielded a listing of each food item consumed at each meal or snack time in the previous day.) Then, to calculate the prediction of vitamin A intake at the household level, one would multiply the coefficient on each food group from the vitamin A column in Table 3 by the number of times that food group was consumed by the household. One would also need to multiply the household size coefficient by the number of adult equivalents in the household. The sum of these products plus the intercept would yield a prediction for the vitamin A NIR for that household for that day. In this Predicting dietary intakes in Mozambique D Rose and D Tschirley 1217 case the formula for a given household would be: Pred VitA NIR ¼ 0:006 ngrains þ 0:090 nbeans 0:014 ntubers 0:033 nnuts þ 0:084 nanimals þ 0:446 nvitAfv þ 0:105 nvitCfv þ 0:050 notherfv 0:082 nsugars þ 0:018 noils þ 0:096 notherfds 0:054 hhsize þ 0116; where Pred_VitA_NIR is the predicted vitamin A NIR for that household, nfoodgroup refers to the number of times an item from a particular food group was consumed in the previous day (eg, ngrains refers to the number of times grain foods were consumed in the previous day), and hhsize is the household size in adult equivalent units. This description is intended to show only the mechanics of how the dietary intake prediction model gets applied to household-level data. However, this tool is not designed for targeting of resources to individual households. It should be used only when considering groups or populations of households. In practice, the above calculations would be automated with a computer program. This program would make a similar calculation for each nutrient for each household in the data set under consideration. In the present study, these calculations were made in order to compare the results from the prediction models with the actual results of nutrient intake from the detailed quantitative method used in the NCD survey. The results of this comparison are presented for protein intake in the harvest season in Table 4. There were 379 household observations; 52 (14%) had low intakes of protein and 327 had acceptable intakes as determined by the quantitative recall measurement technique implemented in that study (see the far right column of Table 4). The dietary intake prediction model predicted that from this sample, 46 (12%) would have low intakes and 333 would have acceptable intakes (see the bottom row of this table). Of the 52 households with low intakes on the quantitative measurement technique, 31 of them were also low using the prediction model, so this model had a sensitivity rate of 60%. Of the 327 households with acceptable intakes using the measurement technique, 312 were also acceptable with the prediction model, for a specificity rate of 95%. For vitamin A (not shown), the sensitivity of the prediction model was much better, at 96%, but the specificity was worse, at 49%. In all of these cases, we define a low intake to be less than 75% of the recommendation. As discussed in the Methods section, it can never be known whether a given diet is truly low or acceptable for a specific household, but we find this a useful threshold for comparing groups of households over time. Information on the frequency of low intakes as actually measured is compared with results obtained for the prediction models for the four main nutrients in Table 5. The first two columns display statistics for the harvest season. For the most part, the predicted percent of the sample with low intakes is fairly close to the results Table 3 Dietary intake prediction model for selected nutrients Dependent variablea Independent variableb Grains Beans Tubers Nuts/seeds Animal products Vitamin A-rich fruits and vegetables Vitamin C-rich fruits and vegetables Other fruits and vegetables Sugars Oils Other foods Household size Intercept Adjusted R2 N F Energy 0.316** 0.298** 0.394** 0.240** 0.122** 0.050* 0.062** 0.100** 0.016 0.089 0.098 0.147** 0.739** 0.554 1140 118.68 Protein Vitamin A Coefficient estimates 0.289** 0.006 0.612** 0.090** 0.007 0.014 0.324** 0.033* 0.209** 0.084** 0.035 0.446** 0.071** 0.105** 0.100* 0.050* 0.071 0.082* 0.144* 0.018 0.146 0.096 0.145** 0.054** 0.457** 0.116** Model statistics 0.646 0.565 1140 1140 174.16 124.14 Iron 0.201** 0.746** 0.492** 0.164** 0.119** 0.012 0.088** 0.129* 0.102 0.141 0.153 0.162** 0.545** 0.477 1140 87.46 a Dependent variables are nutrient intake ratios, the sum of intakes of individuals in the household divided by the sum of their recommended allowances. Food group variables refer to the number of times a food was consumed from each group per day. Household size is expressed in adult equivalents. * Coefficient estimate significant at Po0.05. ** Coefficient estimate significant at Po0.001. b European Journal of Clinical Nutrition Predicting dietary intakes in Mozambique D Rose and D Tschirley 1218 derived from measurements of dietary intake. For example, using the dietary intake prediction model, 42% of households are predicted to have low intakes of energy, whereas 40% had low intakes based on the full quantitative dietary measurements. Measured and predicted low-intake rates for the postharvest and hungry seasons are also displayed in Table 5. Predictions were also made on the MDQI scores for each household. To do this, we first used the dietary intake prediction model to make a prediction of each household’s NIR for each of the 11 nutrients that form the MDQI, that is, energy, protein, vitamin A, iron, and the seven nutrients that make up the composite measure, known as MIR7. We then calculated the MDQI for each household as described in the Methods section, but used predicted NIRs rather than observed values. As can be seen in Table 6, in the harvest season this methodology predicted that 49% of households consumed low or very low-quality diets, which is quite close to the measured results of 52%. Predictions of the aggregate percent of the population with either low or very low-quality diets at different times of the yearFpostharvest or hungry seasonsFwere also close to measured results. In the hungry season, the model predicted that 81% of the sample would have low or very low intakes, whereas 77 were actually measured to have intakes falling in this category. Discussion This paper demonstrates an inexpensive method for monitoring household diets in Mozambique. It uses a previously conducted, intensive and quantitative study of dietary intake to develop a prediction model. This prediction model allows one to use simple, easy-to-collect information on food group consumption to make assessments of overall dietary quality in a population. Comparisons of predictions using this technique with results obtained from the quantitative measurements of dietary intake in Nampula and Cabo Delgado provinces indicate a model with a relatively robust set of coefficients. As shown in Tables 5 and 6, it does reasonably well at predicting nutrient intakes at vastly different times of the year, that is, at both the low (hungry) and high (postharvest) points in terms of consumption. There are three plausible explanations for the reasonable predictions yielded by the model. First, we extract a maximum amount of information from a simple list of foods. Rather than just a basic count of the number of different food items consumed, as is done in some proxies for diet quality, we categorise the foods into 11 homogenous groups and count up how many times foods from each group are consumed per day. This information allows us to develop a set of coefficients that map food group consumption to nutrient intake. Second, our application of this technique is on a relatively monotonous rural diet with limited variety both in food selection and in recipes. The entire Mozambique food composition table has only 119 food items (MISAU, 1991), a small number Table 4 Comparing the predictions of low intakes of protein in the harvest season with those obtained from a quantitative measurement method Predictions Measured results Acceptable (X75% RDA) Low (o75% RDA) Totals Acceptable (X75% RDA) Count Row (%) Col (%) 312 95 94 15 5 33 327 100 86 Low (o75% RDA) Count Row (%) Col (%) 21 40 6 31 60 67 52 100 14a 333 88 100 46 12a 100 379 100 100 Totals Count Row (%) Col (%) a Bold-faced percentages, 14 and 12, represent the percent with low intakes as determined by the quantative measurement method and by using the dietary intake prediction model, respectively. Table 5 Measured frequency of low intakes compared with predicted frequency using the dietary intake prediction model Harvesta (n=379) Nutrient Postharvest (n=386) Measured (% low)b Predicted (% low) Measured (% low) 40 14 94 39 42 12 93 35 25 10 99 14 Energy Protein Vitamin A Iron a Predicted (% low) 28 15 100 16 Hungry (n=375) Measured (% low) Predicted (% low) 63 58 71 55 68 70 81 59 Harvest season data were collected in May 1995, postharvest season data were collected in August 1995 and Hungry season data were collected in January 1996. A low intake refers to intakes less than 75% of the recommendation. b European Journal of Clinical Nutrition Predicting dietary intakes in Mozambique D Rose and D Tschirley 1219 Table 6 Measured and predicted results on the Mozambique Diet Quality Index (MDQI)a Harvestb (n=379) Percent of households with: Acceptable diets Low or very low Low-quality diets Very low-quality diets Post-harvest (n=386) Hungry (n=375) Measured (%) Predicted (%) Measured(%) Predicted (%) Measured (%) Predicted (%) 48 52 29 23 51 49 29 20 55 45 32 12 52 48 36 12 23 77 26 51 19 81 34 47 a The MDQI is a composite measure based on the nutrient intakes of energy, protein, vitamin A, iron, and seven other nutrients. On a scale of 0–10, acceptable, low and very low quality diets are those with 7.5 or more points, 6.0–7.5 points, or less than 6.0 points, respectively. b Harvest season data were collected in May 1995, postharvest season data were collected in August 1995 and hungry season data were collected in January 1996. compared to the 1500 food items found in the South African database (MRC, 1999) or the 6200 items in that of the US (USDA, 1999). Previous research specific to the NCD area has shown that, on average, only four different food items are consumed per day (Rose et al, 1999). Third, it should be emphasised that model development and predictions were made on overlapping data sets. As discussed below, the entire data set was used for model development, whereas predictions were made with data from one season at a time. Consumption does vary from one season to the next, so these results are useful to study. However, a true test of the success of the model would require comparison of predicted and measured values using an entirely different data source in northern Mozambique. Unfortunately, no such data set exists. In order to have the most representative prediction model, we estimated our regressions pooling observations from three different times during the yearF the harvest, postharvest, and hungry seasons. This allows one to use the coefficients from this model to develop estimates of nutrient intake for any time during the year in which food consumption data can be collected. The advantage of such a system is that dietary quality can be monitored whenever it is feasible for the monitoring agency, provided that subsequent monitoring surveys are conducted at the same time of the year to ensure comparability. Although feasibility is an important concern, seasonal issues are complex and their importance should not be underestimated. The most convenient time to do agricultural surveys is typically in the postharvest season, which is usually the most food secure time of the year. However, in the Nampula/Cabo Delgado sample, even though this season was the best time of the year, close to half of the households had low or very low-quality diets. Thus, if this tool were to be used in the postharvest season as a means for targeting resources or for monitoring improvements, there would be no problem in finding priority areas of need or in detecting changes over time. Still, we have seen that household correlations from one season to the next are not always strong. That is, those that do worst in the postharvest season are not all the same households as those with the lowest intakes in the hungry season. Further research is needed on the variability of household intakes across seasons and its implications for designing realistic monitoring systems. Another concern with this approach is that the simple dietary surveys needed to implement it are likely to be based on just one day of data. As has been shown previously, there is significant intraindividual variation in intakes from one day to the next (FNB, 1986). Thus, a distribution of intakes based on 1 day of data will be more dispersed than a distribution based on averages of intakes on 2 or more days from the same individuals, or in this case households. We found this to be the case in the NCD survey when we looked at the frequency of low intakes based on 1 day of data as compared with 2 days of data, the latter being what we report on in this paper. In practice, this means that prevalence estimates of low intakes based on just 1 day of data will be higher than our results reported here for NCD. This should not be a problem, as long as monitoring agencies that begin collecting 1-day consumption data continue to collect 1-day data in the future to ensure comparability. The results presented here suggest that simple food recall instruments, when used in tandem with prediction models developed from quantitative dietary surveys, can provide reasonable assessments of household dietary intake in Mozambique. It should be highlighted that this technique is designed for monitoring purposes at the population level, with the focus on detecting global changes over time. This technique is likely to have applicability in other developing countries both because of the simple nature of the food recall data and because of the widespread existence of microcomputers needed to develop and implement the dietary intake prediction model. This prediction model will need to be calibrated wherever it is to be used, which implies that conducting or obtaining data European Journal of Clinical Nutrition Predicting dietary intakes in Mozambique D Rose and D Tschirley 1220 from a detailed quantitative dietary survey is a necessary first step in applying this technique in other countries. Acknowledgements We would like to acknowledge the hard work of Paul Strasberg, who coordinated the original field data collection effort as part of his doctoral dissertation, as well as all the Mozambican analysts and field workers, including Jose Jaime Jeje, Ana Paula Santos, Higino Marrule, and Rui Benfica who assisted us with this work. References Food and Agriculture Organisation and World Health Organisation (FAO and WHO) (1988): Requirements of Vitamin A, Iron, Folate, and Vitamin B12. FAO Food and Nutrition Series 23 Rome Food and Agriculture Organisation. 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Strasberg P (1997): Smallholder Cash Cropping, Food Cropping, and Food Security in Northern Mozambique. Doctoral dissertation, Department of Agricultural Economics, Michigan State University, East Lansing. US Department of Agriculture, Agricultural Research Service (USDA) (1999): USDA Nutrient Database for Standard Reference, Release 13. Nutrient Data Laboratory Home Page, http: //www.nal.usda.gov/ fnic/foodcomp. West CE, Pepping F & Temalilwa CR (1988) The Composition of Foods Commonly Eaten in East Africa. Wageningen: Wageningen Agricultural University. Appendix Nutrient reference standards (see Table 7). Predicting dietary intakes in Mozambique D Rose and D Tschirley 1221 Table 7 Males Age o1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20–29 30–49 50–59 X60 Pregnant Lactating Females Energya Proteinb Vitamin Ac Ironc Energya Proteinb 785 1307 1456 1604 1729 1812 1910 1992 2056 2066 2088 2152 2180 2297 2397 2449 2528 2618 2987 2987 2987 2928 2928 2018 14.0 23.6 26.6 29.2 32.8 32.5 35.8 30.0 33.4 35.9 38.2 42.9 43.8 49.7 50.4 54.1 55.8 59.1 56.6 56.6 56.6 56.6 56.6 56.6 400 400 400 400 400 400 400 500 500 500 500 500 500 600 600 600 600 600 600 600 600 600 600 600 8 8 8 8 9 9 9 16 16 16 16 16 16 24 24 24 15 15 15 15 15 15 15 15 741 1107 1255 1397 1546 1698 1785 1771 1835 1810 1901 1914 1974 2029 2087 2143 2143 2150 2183 2183 2183 2186 2186 1834 þ285 þ500 13.3 19.1 23.4 26.5 30.2 31.8 35.5 29.1 33.2 36.5 41.6 44.6 44.0 48.5 50.2 55.5 51.7 52.1 49.7 49.7 49.7 49.7 49.7 49.7 þ7 þ 18 Vitaminc Ironc 400 400 400 400 400 400 400 500 500 500 500 500 500 600 600 600 500 500 500 500 500 500 500 500 þ100 þ 250 8 8 8 8 9 9 9 16 16 16 16 16 16 27 27 27 27 27 27 27 29 29 13 13 29 29 a These recommendations are based on reference weight data for Mozambique (James & Schofield 1994) and include energy needed to maintain weight as well as energy necessary for occupational and ’socially desirable’ activities (FAO/WHO/UNU, 1985). Occupational activities are assumed to be characteristic of a rural population in a developing country, that is, requiring moderate to heavy energy expenditures. b These levels are safe intakes (average requirement þ 2 s.d.) based on recommendations in FAO/WHO/UNU (1985) as applied to a Nigerian cassava-diet, that is corrected for a reduced digestibility of 85%, and for reduced protein quality of 72% for ages 1–6 y, and 95% for ages 6–12 y (see Table 40 in FAO/WHO/UNU 1985). Additional protein requirements for pregnancy and lactation are from the same source and assume a digestibility of 85% (see Table 50 in FAO/WHO/UNU, 1985). Since protein recommendations are listed in grams of intake per kilograms of body weight, assumptions about weight were needed to calculate values in the above table. We used reference weight data for Mozambique (James & Schofield, 1994). c Recommended levels of intake for vitamin A are in micrograms of retinol equivalents and for iron are in milligrams. These are safe levels, that is, average requirements plus a safety factor, to meet the needs of most healthy people (FAO and WHO, 1988). Iron standards are based on the requirement to prevent anemia from a low bioavailability diet (5%). For pregnancy and lactation, the requirement for menstruating women is assumed. For women over age 50, the iron standard is reduced to 13 mg/day. European Journal of Clinical Nutrition