3.3: Selecting an appropriate method

The method of choice for assessing food or nutrient intakes depends primarily on the objectives of the study. No method is devoid of random or system­atic errors (Chapter 5), or prevents alterations in the food habits of the respon­dents. However, statis­tical techniques are now available which are designed to mitigate the impact of measurement errors on study results. See Freedman et al. (2011), Kirkpatrick et al. (2018), and Chapter 5. The US National Cancer Institute has devel­oped a Dietary Assessment Primer (2020), a web resource to aid researchers choose the best available dietary assess­ment approach to achieve their research objective. Readers are advised to consult the primer before selecting a self-report dietary instrument.

Box 3.2 provides guidance on the most appropriate methods for assessing food or nutrient intakes in relation to four possible levels of objectives.

Box 3.2: Selection of methodology to measure nutrient intakes to meet four possible levels of objectives.

Level One: Mean nutrient intake of a group

• Preferred approach A single 24h recall, or single weighed or estimated food record, with large number of subjects and adequate representation of all days of the week

Level Two: Proportion of popu­lation “at risk”

• Preferred approach Replicate observations on each indi­vidual or a sub­sample using 24h recalls or weighed or estimated one day food records with an adequate representation of all days of the week

Level Three: Usual intakes of nutrients in indi­viduals for ranking within a group

• Preferred approach Multiple replicates of 24h recalls or food records or a semiquantitative food fre­quency question­naire

Level Four: Usual intakes of foods or nutrients in indi­viduals for counseling or for corre­la­tion or regression analysis

• Preferred approach Even larger number of recalls or records for each indi­vidual. Alter­natively, a semiquanti­tative food fre­quency question­naire or a dietary history can be used.

Note that the number and selection of replicate 24h recalls, or weighed or estimated one day food records required to obtain level two, level three, or level four data, depends on the day-to-day vari­a­tion within one indi­vidual (i.e., within-person vari­a­tion ) of the nutrient of inter­est (Chapter 6). This vari­a­tion depends on the nutrient, the study popu­lation, and the seasonal vari­a­tions of intake. Generally, for nutrients found in high concen­trations in only a few foods, such as vitamins A and D and cholesterol, the number of replicates needed is greater than for those found in a wide range of foods (e.g., protein). Noncon­secutive days should be selected for the replicates when possible, to enhance the statis­tical power of the inform­ation: day-to-day corre­la­tions between intakes often occur when food intake data are collected over consecutive days. The length of time needed between the observation days also depends on the nutrient (IOM, 2000; a 3–10d inter­val after the previous recall is recommended (Tooze, 2020).

Additional factors that should be consid­ered when choosing a method for assessing the food con­sump­tion of indi­viduals are the characteristics of the indi­viduals within the study popu­lation, the respon­dent burden of the method, and the available resources. For instance, certain methods are unsuitable for elderly subjects with poor memories, for busy mothers with young children, or for illiterate indi­viduals. Other methods require highly trained personnel and specialized laboratory and com­put­ing facilities, which may not be available. Generally, the more accurate methods are associated with higher costs, greater respon­dent burden, and lower response rates. Unfor­tu­nately, compromises often have to be made between the collection of precise data on usual nutrient intakes of indi­viduals and a high response rate.

3.3.1 Determining the mean nutrient intake of a group: level one

Level one is the easiest objective to achieve and can be met by measuring the food intake of each subject in the group using a single 24h recall or a one day food record, provided the indi­viduals are representative of the study popu­lation and all the days of the week are proportionately repre­sented in the final sample. Data on mean usual nutrient intakes of a group can be used for inter­national compar­isons across coun­tries of the relationship of nutrient intakes to health and disease. However, this method based on a single intake day per subject should never be used for reporting the distri­bution of intakes (i.e., as percentiles of intake) (Tooze, 2020). To calculate n, the number of subjects required in the group, an estimate of the between-person vari­a­tion for the nutrient of inter­est is needed. This is usually obtained from the literature but may be determined during a pilot study.

As an example, assume the expected mean iron intake obtained from the literature is 10mg/d, with an anticipated stan­dard deviation (s) of 3mg/d. Also assume that we want to be 95% confident that the true mean lies between 9.2 to 10.8mg/d (i.e., the confi­dence inter­val has limits which are 0.8mg/d on either side of the mean). A 95% confi­dence inter­val is calculated approximately as the mean ±2 × e, where e is the stan­dard error of the mean — a measure of the precision of the estimated mean. Hence , the required e = 0.8/2 = 0.4. We can use the following formula to calculate n, the desired group size.

\[n=s_b^2 / e^2\nonumber\]

where \(s_b^2\) is the between-person variance of the nutrient of inter­est, and e is the desired stan­dard error — a measure of precision required for the estimate of the mean intake of the nutrient of inter­est. Hence

\[n=3^2 / 0.4^2=56.25\nonumber\]

and so 56 subjects are required. Alter­natively, if we wanted to be 99% confident that the true mean lies between 9.2 and 10.8mg/d, then more subjects must be studied. A 99% confi­dence inter­val is calculated approximately as the mean ±3 × e. Hence the required e = 0.8/3 = 0.27 Therefore

\[n=3^2 / 0.27^2=123\nonumber\]

and 123 subjects are required.

Clearly, the size of the group (n) necessary to characterize the group mean usual nutrient intake depends on the degree of precision required: more indi­viduals must be studied to achieve higher precision. This calcu­lation of the required group size should be repeated for each of the nutrients of inter­est, and the largest n (i.e., the worst case) should be used if possible.

If the study objective is to demonstrate a significant difference in the mean intakes of two groups, or a significant change in the mean intakes, based on unpaired or paired data, then alternative formulae must be applied; details are given in (Gibson and Ferguson, 2008).

3.3.2 Calculating the popu­lation percentage “at risk": level two

To determine the percentage of the popu­lation “at risk” of inadequate nutrient intakes, an estimate of the distri­bution of usual intakes of the indi­viduals is required. This, in turn, requires that the food con­sump­tion of indi­viduals be measured over more than one day. Hence, repeated 24h recalls, or replicate weighed or estimated one day food records are the methods of choice, again ensuring that all days of the week are proportionately repre­sented in the final sample. Often, it is not feasible to carry out repeated observations on all the indi­viduals, as in the case of a national dietary survey, and the recalls or records are repeated on a sub­sample of the indi­viduals only.

To achieve a level two objective, at least two independent mea­sure­ments of food intake should be obtained on at least a representative random sub­sample of indi­viduals in the survey. The U.S. Food and Nutrition Board (IOM, 2000) recommends that the replicate mea­sure­ments should be independent and made on non-consecutive days 3–10d after the first recall or record. However, if the data can be collected only on consecutive days, then three daily mea­sure­ments should be used. The random sub­sample should consist of at least 50 indi­viduals per demo­graphic group. Where possible, the demo­graphic groups to be sampled should be defined according to the sex and life-stage groups used for the chosen Nutrient Reference Values (Deitchler et al., 2020),

Note that it is more important to have a minimum number of replicate observations in the sub­sample than a minimum proportion of replicate observations. Once the required number of replicate observations have been obtained, methods can be applied to correct for the measurement error associated with both day-to-day variability in intakes for a single person (termed within-person vari­a­tion ) and the vari­a­tion in intakes between indi­viduals (termed between-person vari­a­tion ) (see Chapter 6).

Four statis­tical methods are now available for estimating the distri­bution of usual intake. For more details, the reader is referred to (Tooze, 2020). Of the methods, the first was outlined by the National Research Council and later refined by Iowa State University (ISU) (Nusser et al., 1996) with software devel­oped to use the ISU method. Note, this method does allow for a random sub-sample of repeated 24h recalls or food records, and has the capacity to adjust for season, day of the week, and/or sequence effects, but is not recommended for general use with episod­ically con­sumed foods, food groups, or nutrients (Tooze, 2020). The adjustment process provides esti­mates of the usual nutrient intakes for each specified age and gender-specific subgroup. An example comparing the adjusted distri­butions of usual zinc intakes (using the refined NRC approach of (Nusser et al., 1996) with the observed zinc intakes for New Zealand adult females aged 19–50y is shown in Figure 3.6.

Figure 3.6 Estimates of usual intake distri­bution for zinc for New Zealand adults obtained from 24-hout recall data and adjusted with replicate intake data using the refined NRC method. The y-axis (fre­quency of intake) shows the likelihood of each level of intake in the popu­lation. EAR, Estimated Average Requirement. Modified from (Gibson et al., 2003). Nutrition today, 38(2), 63–70.

The adjustment process used yields a distri­bution with reduced variability (sometimes referred to as “shrinking”) because the within person vari­a­tion has been removed, while preserving the shape of the original observed distri­bution (Gibson et al., 2003).

Later, the National Cancer Institute (NCI) (Tooze, 2020) devel­oped a method which also adjusts for the same covariates (season, day of the week, and/or sequence), but also has the capability of incorporating covariates to identify esti­mates for sub-popu­lations in the survey. In addition, the NCI method, unlike the ISU, can be used with episod­ically con­sumed foods, food groups, or nutrients.

The European Food Con­sump­tion Validation Project have devel­oped the Multiple Source Method(Haubrock et al., 2011) (MSM) that can also be used for episod­ically con­sumed foods, food groups, or nutrients, although caution must be used when employing the program with models containing covariates. The Dutch National Food Con­sump­tion Survey 2007-2010 have devel­oped a program called the Statistical Program to Assess Dietary Exposure (SPADE) , but this program currently requires 24h recalls on at least two-days for all respon­dents in the sample and is not recommended for use with episod­ically con­sumed foods, food groups, or nutrients.

All the methods described briefly above use varying approaches to yield an adjusted distri­bution of “usual" nutrient intakes that can then be used to predict the proportion of the popu­lation at risk of nutrient inadequacy using either the full probability approach, or the Estimated Average Requirement (EAR) cutpoint method; details are given in Chapter 8b. However, the choice of which “usual intake method” method to use depends on whether foods, food groups, or nutrients are episod­ically con­sumed, whether the probability of con­sump­tion and the con­sump­tion-day amount are correlated or not, and whether replicates of the 24h recalls or food records are available on all the popu­lation groups to be surveyed or only on a sub-sample (Souverein et al., 2011).

Table 3.7: “True” and observed prevalence esti­mates and the number of repeated mea­sure­ments needed to reduce the observed prevalence to within 5% of the “true” prevalence. Data for women aged 45–54y from NHANES II(1976–1980). From: Sempos CT, Looker AC, Johnson CL, Woteki CE. (1991). The importance of within-person variability in estimating prevalence. In: Macdonald I (ed.), Monitoring Dietary Intakes, pp. 99–109. Springer-Verlag, Berlin..

Variable	Prevalence (%) "True" Observed		No. of repeated mea­sure­ments needed
Cholesterol > 300mg	15	37	39
Calcium > 800mg	12	21	9

Figure 3.6 shows that, in the example, adjusting the distri­bution significantly using the ISU method reduces the proportion of indi­viduals consid­ered to have intakes below the EAR for zinc. Within-person vari­a­tion can also have a significant effect on esti­mates of the prevalence of abnormally high nutrient intakes. Table 3.7 shows data from NHANES II. The large differences between the observed prevalence and the calculated “true” prevalence represent the effect of removing the within-person vari­a­tion by calcu­lation. In this case, very large numbers of repeated mea­sure­ments on each indi­vidual are required to reduce the observed prevalence of abnormally high intakes of cholesterol and calcium to within 5% of the true prevalence (Sempos et al., 1999). Comparable data are essential for national food policy development and food fortification planning. Food patterns associated with inadequate nutrient intakes can also be identified using this approach, enabling food assistance programs to be designed and improvements in nutrition education made.

More recently, a new method has been devel­oped based on single-day dietary data which can be used to estimate popu­lation distri­butions of usual intake of nearly-daily con­sumed foods and nutrients, provided a suitable external within-person to between-person variance is available (Luo et al., 2019). Nevertheless, researchers are urged to collect replicate data where possible.

3.3.3 Ranking indi­viduals by food or nutrient intake: level three

When the study objective is at level three and involves ranking indi­viduals within a group, often for the purpose of linking dietary intakes with risk of chronic disease, the preferred approach is to obtain multiple observations on each indi­vidual. The number of days required to achieve the level three objective can be calculated from the ratio of the within- to the between-person vari­a­tion in nutrient intakes (often termed the “variance ratio"); for more details see Chapter 6 . Sometimes an estimate of the variance ratio can be obtained from the literature, again preferably from an earlier study on a comparable group. Alter­natively, a pilot study may be necessary to obtain this inform­ation.

Several authors have devel­oped equations for calculating the number of replicate days required to meet level three objectives (Black et al., 1983; Basiotis et al., 1987; Nelson et al., 1989). Black et al. (1983) suggest using the following formula for the number of days (n) of diet records needed:

\[n=\left(r^2 /\left(1-r^2\right)\right) \times\left(s_w^2 / s_b^2\right)\nonumber\]

In this equation, r is the unobservable corre­la­tion between the observed and true mean intakes of indi­viduals over the period of observation, and \(s_w^2\) and \(s_b^2\) are the observed within‑ and between-person variances, respe­ctively. This equation should be used in association with Table 3.8 which shows the proportion of indi­viduals correctly and incorrectly classified in the extreme fractions for dif­fer­ent values of the corrlation coefficient between the observed and true intakes (r). The value of r chosen will depend on the degree of misclas­sification that the inves­tigator is prepared to accept (Table 3.8).

Table 3.8: Proportion of indi­vid­uals correctly and incorrectly classified in the extreme fractions for dif­fer­ent values of the corre­la­tion coefficient (r) . (a), correctly classified in the extreme thirds, fourths, or fifths of the distri­bution of intakes; (b), misclassified into the opposite extreme fraction. From Nelson et al. (1989).

		Correctly and incorrectly classified into extreme fraction
r		Thirds	Fourths	Fifths
0.75	a b	0.69 0.049	0.63 0.013	0.59 0.004
0.80	a b	0.72 0.033	0.68 0.006	0.65 0.002
0.85	a b	0.76 0.018	0.72 0.002	0.69 <0.001
0.90	a b	0.80 0.006	0.77 <0.001	0.75 <0.001
0.95	a b	0.86 <0.001	0.84 <0.001	0.83 <0.001

As an example, assume that the inves­tigator requires that when the indi­viduals are divided into terciles, fewer than 5% (< 0.05) of the indi­viduals are grossly misclassified into the opposite tercile. This will require an r value of 0.75 (Table 3.8). Assuming \(s_w^2 / s_b^2=1.7\), then the number of days (n)

\[\begin{gathered}
n=\left(r^2 /\left(1-r^2\right)\right) \times 1.7 \\
n=0.75^2 /\left(1-0.75^2\right) \times 1.7 \\
n=3 \text { days }
\end{gathered}\nonumber\]

The number of days needed to generate a given r increases as the chosen r increases. If the size of the within-person vari­a­tion \((s_w^2)\) in nutrient intake is small compared with the size of the between-person \((s_b^2)\) vari­a­tion , then fewer replicate days are needed to meet level three objectives.

The U.S. subcommittee on criteria for dietary evaluation (NRC, 1986) recommended using independent days for replicating the mea­sure­ments of one day nutrient intakes to reduce any effect of autocorre­la­tion between intakes on adjacent days.

An alternative approach to achieving level three objectives is to use a semi-quanti­tative food fre­quency question­naire. This approach is often used in epidemio­logical investigations to study associations between intakes and risk of disease and does not require a measurement of absolute nutrient intakes. Although this approach is much simpler, involving only a single inter­view with each subject, it is difficult to quantify the errors involved and to separate the effects of within- and between-person variance.

3.3.3 Determining usual intakes of nutrients of indi­viduals: level four

Reliable esti­mates of usual food or nutrient intakes of indi­viduals that can be used with confi­dence to meet a level four objective, involving corre­la­tion or regression analysis with indi­vidual biochemical measures, are the most difficult to obtain. Large numbers of measurement days for each indi­vidual are required using 24h recalls or estimated or weighed food records.

An estimate of the within-person vari­a­tion for each nutrient of inter­est should be obtained from the literature, preferably from an earlier study on a comparable group or a pilot study, as noted earlier. This estimate may be expressed as the variance, \(s_w^2\); stan­dard deviation, \(s_w\); or as the coefficient of vari­a­tion \((CV_w)\) expressed as a percentage:

\[C V_w=s_w /(\text { mean intake }) \times 100 \%\nonumber\]

This estimate can be used in the following equation to determine the number of days required per indi­vidual to estimate an indi­vidual's nutrient intake to within 20% of their true mean 95% of the time (Beaton et al., 1979).

\[n=\left(Z_\alpha C V_w / D_0\right)^2\nonumber\]

where n = the number of days needed per indi­vidual, Z_α = the normal deviate for the percentage of times the measured value should be within a specified limit (i.e., 1.96 in the example below), CV_w = the within-person coefficient of vari­a­tion (as a percentage), and D₀ = the specified limit (as a percentage of long-term true usual intake) (i.e., 20% in the example given below).

The following example calculates the number of days required to estimate a Malawian woman's zinc intake using 24h recalls to within 20% of the true mean, 95% of the time. In this example, the CV_w (i.e., 34%) for zinc intakes on Malawian women via 24h recalls is taken from the literature (IZiNCG, 2004). Thus if Z_α = 1.96 and CV_w = 34%. then:

\[n=(1.96 \times 34 \% / 20 \%)^2=11 d\nonumber\]

If a pilot study is undertaken in which replicate 24h recalls are conducted, then the actual CV_w for each nutrient of inter­est can be calculated. In this way, the estimate of the number of days required to measure the usual intake of each of the nutrients of inter­est in an indi­vidual, with a required degree of precision, can be defined. In general, consid­erably more days are required to obtain reliable esti­mates of intakes of indi­viduals to meet the level four objective, compared with level three (i.e., relative ranking of subjects into groups) (Palaniappan et al., 2003).

Sometimes, dietary histories or semi-quanti­tative food fre­quency question­naires are used to obtain this level four data on usual nutrient intakes for corre­la­tion with biomarkers (Jacques et al., 1993). Some inves­tigators emphasize, however, that the accuracy of a semi-quanti­tative food fre­quency question­naire is only equivalent to two to three repeat 24h recalls (Sempos et al., 1999).

In some experimentally controlled studies such as balance studies, inform­ation on the actual nutrient intakes of an indi­vidual over a finite time period are required. For such data, weighed food records (Section3. 1.4), completed for the duration of the study period, are the recommended method. Nutrient intakes can then be calculated using food composition data. Alter­natively, duplicate meals can be collected throughout the period for later analysis; details are given in Chapter 4.

3.3.4 Combining dietary instruments

Recognition of the limitation of even two 24h recalls to adequately measure, at the indi­vidual level, usual intake of foods or nutrients that are not con­sumed daily (i.e., episod­ically con­sumed foods) has led to the use of more than one dietary instrument in a given study. In this way, the advantages of each method can be exploited and their weaknesses minimized. Consequently, in large scale epidemio­logical studies, a common approach is the admin­istration of a food fre­quency question­naire to all respon­dents, and 24h recalls or food records in a sub­sample. The food fre­quency question­naire provides inform­ation on foods that are con­sumed less frequently (often, “in the past year") not given by the 24h recalls while the 24h recalls yield richer detail by attempting to correctly quantify portion size for each eating occasion (Subar et al., 2006). Alter­natively, a food fre­quency question­naire could be used as a supplement to one or more quanti­tative 24h recalls admin­istered to the full sample, providing richer detail and less system­atic bias. This approach appears to have the most added value when the research question of inter­est is to estimate diet-health relationships, espe­cially when the estimation of usual dietary intake at the indi­vidual level involves a food, food group, or nutrient that is con­sumed episod­ically (Tooze, 2020) Statistical methods have been devel­oped to combine the dietary data from repeated 24h recalls and food fre­quency question­naires. Conceptually, these statis­tical methods presume that the usual food intake of an indi­vidual equals the probability of consuming a food on a given day (queried as fre­quency of usual intake over a specified time period), multiplied by the average amount of intake of that food on a typical con­sump­tion day. Repeated 24h recalls from the same indi­vidual yield inform­ation on con­sump­tion probability and amount, while the food fre­quency question­naire provides inform­ation on intake fre­quency of rarely con­sumed foods and usual intakes of nutrients when portion size information is available (Conrad and Nothlings, 2017). The reported food frequencies and nutrient intakes (if available) can be used as covariates in both steps of the NCI method (Tooze, 2020) and with MSM (Haubrock et al., 2011) to enhance the estimation of usual intakes from the 24h recall data, espe­cially from foods that are not con­sumed every day. Note, however, that in low income coun­tries where context-specific food fre­quency question­naires are often not available or inappropriate in the study setting, it may be preferable to increase the number of repeat 24h recalls per respon­dent rather than embarking on the development, validation, and collection of dietary data using a food fre­quency question­naire in addition to using the 24h recall method. For more details, the reader is advised to consult (Tooze, 2020). at: Intake.org

3.3.5 Surveys of indi­vidual food consumption at the global level

FAO/WHO Global Individual Food consumption data Tool (FAO/WHO GIFT) is a global data­base of surveys of food consumption at the indi­vidual-level which are collected at the national and sub­national level throughout the world. Only data collected from 1980 by quanti­tative methods such as 24h recalls, food records (weighed or estimated), 12hr recalls, and direct food weighing are considered. Portion sizes of all foods and beverages con­sumed by each survey participant must be assessed, including water if possible. For low‑ or middle-income coun­tries, surveys comprising at least 100 subjects (with no evidence of strong selection bias) are included, whereas for high-income coun­tries, surveys that are nationally repre­sen­tative are an additional criterion. The dietary data generated from FAO/WHO GIFT can be used to inform agricul­tural, nutrition, food safety and environmental policies and programs. A summary of 218 dietary surveys performed in low‑and middle-income coun­tries from 1980 to 2019 and included in the FAO/WHO GIFT inventory is available in de Quadros et al. (2022).

FAO/WHO GIFT apply several criteria to validate the dietary data­bases included. For example, for all foods and drinks con­sumed by each survey participant on each survey day, a complete descrip­tion, the amount con­sumed, and energy and nutrient values for each item must be included in the datasets. Additional compulsory variables are the age and sex of each subject, geo­graph­ical location (country, and region(s) if available), type of area (e.g., rural, urban), and the number of survey days recorded per subject or for a subset of subjects. Inclusion of variables based on anthropometric data such as weight and height and the physiological status of both women (e.g., pregnant, lactation) and infants (i.e., breast­feeding status) are also recommended.

Recipes should be disaggregated whenever possible to provide data on the quantity of each separate ingredient, cooking method used, total amount prepared, number of people served from the recipe, and the quantity con­sumed by the survey participant (or served and leftover). Only with this information can each ingredient be attributed to their appropriate food group for the calculation of the FAO/WHO GIFT indicators and summary statistics.

The food composition values used are provided by the data providers. However, all indi­vidual quanti­tative dietary datasets shared through FAO/WHO GIFT are coded with the FoodEx2 system, a comprehensive and flexible food classification and descrip­tion system. Eligible dietary data­bases are mapped manually by the inves­tigator with FoodEx2 codes for food groups (n=24) and food subgroups based on the descrip­tions provided by FAO/WHO GIFT. FoodEx2 was first devel­oped by the European Food Safety Authority (EFSA) and was later scaled up to the global level in collaboration with FAO and the World Health Organi­zation (WHO). The use of this common food classification and descrip­tion system among dietary surveys from dif­fer­ent coun­tries contributes to the global harmoni­zation of dietary data. Prior to the analysis and format­ting by FAO/WHO GIFT, all eligible datasets are screened for potential errors, missing values and outliers. For further details see FAO/WHO GIFT (Methods)

Provided the eligible dietary datasets have been coded using FoodEx2, the FAO/WHO GIFT platform can compute ready-to-use indicators and summary statistics based on indi­vidual food consumption data in the areas of food consumption, food safety and nutrition. For example, of the indicators, one reflecting dietary diversity at the popu­lation level (a key component of diet quality) entitled the Minimum Dietary Diversity for Women(MDD-W) is computed. This is a food group-based indicator that estimates the proportion of non-pregnant women of repro­ductive age (15‑49y) who con­sumed at least five out of ten defined food groups over the previous 24h.

The summary statistics generated by FAO/WHO GIFT comprise the estimated usual intakes of selected nutrients for pre-defined popu­lation groups by sex and age (except children less than aged 12 months) provided the datasets contain multiple non-consecutive days of 24h recalls / food records for at least a subset of 50 indi­viduals. The statis­tical program chosen to adjust the dietary data for day-to-day vari­a­tion is the Statistical Program to Assess Dietary Exposure (SPADE) by Dekkers et al. (2014). In addition, information on Nutrient Reference Values (NRVs) set by FAO/WHO, the European Food Safety Authority (EFSA) and the Institute of Medicine (IOM) are provided on the FAO/WHO GIFT platform to facilitate comparison of the estimated usual intakes of selected nutrients with NRVs. For more details of the statis­tical program, see (SPADE) and the instruction manual (SPADE - 4100). For NRVs and their applications, see Chapters 8a and 8b, respectively.

The Global Dietary Database (GDD) aims to identify, compile, and standardize indi­vidual-level data on dietary factors related to maternal-child health and chronic diseases. Currently the GDD comprises surveys across 188 coun­tries conducted between 1980 and 2018 and provides empirical evidence on dietary intakes both across and within coun­tries worldwide. Priority is given to nationally or sub-nationally representative dietary surveys based on 24-h recalls, food fre­quency question­naires or short standardized question­naires (e.g., Demo­graphic Health Surveys (DHS). Household-level surveys are included if indi­vidual-level surveys are not available in a country and converted to indi­vidual-level intakes within each household using Adult Male Equivalents (AME), also known as Adult Consumption Equivalents. See: Weisell & Dop (2012). These authors account for the household composition and differing energy intakes by age and sex of household members. See Coates et al. (2017) for discussion of the validity of the application of the AME method for household-level data.

The FoodEx2 categorization system is used to standardize the descrip­tion and classification of foods into food groups. Standardization also includes categorising nutrients and their units; quality assess­ment; aggregation by demo­graphic strata and energy adjustment. For more details on data extraction and standardization, see: (GDD)

Mean intakes of 54 dietary factors by country, year, age, sex, education, urbanicity, and pregnancy / lactation status within nations can now be estimated using the GDD prediction model in 188 coun­tries / territories in Asia, Asia-Pacific high-income coun­tries, Oceania, Former Soviet Union, Latin America and Caribbean, Middle East and North Africa, South Asia, Sub-Saharan Africa, and the Western high-income coun­tries in Australasia, Europe and North America (Miller et al., 2021). The dietary factors selected and defined based on evidence for relationships with maternal-child health or chronic diseases include 14 foods, 7 beverages, 12 macro­nutrients, and 18 micro­nutrients. See (GDD) for more details on data extraction and the standardization used for estimating dietary intakes. Several indicators of global dietary patterns (e.g., Alternative Healthy Eating Index (AHEI); Dietary Approaches to Stop Hypertension (DASH), and the Mediterranean Diet Score (MED)) among children and adults have also been compiled from the GDD sets and compared globally, regionally, and nationally (Miller et al. ,2022).

Institute for Health Metrics and Evaluation (IHME) initiative data­base aims to provide rigorous and comparable measurement of the world’s most important health problems and evaluates the strategies used to prevent them. IHME uses FAO Food Balance Sheet estimates, national product sales, household surveys, and data based on 24h recalls (considered the gold standard). Datasets created by IHME are stored in the IHME data catalogue known as the Global Health Data Exchange and can be freely downloaded from the IHME website.

IHME provides freely available modeled data by country, age, sex and year, based on primary data collected in 204 coun­tries and 87 indicators, including 15 dietary indicators (9 foods and 6 nutrients). The dietary indicators are included in GBD 2017, a worldwide observational epidemiological study that tracks the progress within and between coun­tries of the changing health challenges (Lim et al., 2013).