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OCR for page 83
7
Estimating Eligibility Based on
Meeting Nutritional Risk Criteria
To be fully eligible for WIC benefits, applicants who meet categorical
and income eligibility requirements also must be deemed nutritionally at
risk by meeting at least one nutritional risk criterion. Five types of nutri-
tional risk criteria are considered in determining whether a person is nutri-
tionally at risk: anthropometric, biochemical, clinical/health/medical, di-
etary, and other. Examples of each type of risk appear in Table 7-1, as does
the number of criteria considered for each type. Each nutritional risk crite-
rion includes an indicator of nutritional risk and a cutoff point. For ex-
ample, for young children, a blood lead value equal to or greater than 10
micrograms per deciliter is an approved criterion for nutritional risk, so
that a child with a blood lead level above the 10 microgram level would
qualify as nutritionally at risk.
To determine whether an applicant meets at least one of the nutrition
risk criteria, a competent professional authority at the local WIC office
administers a nutritional risk screen to the applicant. For example, an
applicant's height, weight, and hemoglobin values are measured and com-
pared with the cutoff values for the respective nutritional risk criteria.
Checks for health conditions that confer eligibility are also made. In most
cases, the staff member asks the applicant or caregiver for information about
the applicant's food intake. Generally, this involves either a 24-hour diet
recall (asking what foods and beverages were consumed the previous day,
and in what amounts) or a food frequency questionnaire (obtaining infor-
mation on the frequency with which the applicant consumed specified
foods and the portion sizes usually consumed).
83
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84 ESTI~TING ELIGIBILI>~D PAR TICIPATION FOR THE ~CPROG~
TABLE 7-1 Types of Nutritional Risk Criteria Used in WIC, Numbers
of Criteria, and Examples
Type of Risk Criteria
Number
of Criteria
by Typea
Anthropometric
Biochemical
Cl ini cal/h ealth /medical
Dietary
Other
18
43
19
14
Underweight, overweight
Low hematocrit
Diagnosed diabetes mellitus
Food intake that does not meet food guide
pyramid specifications, improper dilution
of formula
Regression, migrancy, homelessness
aNumbers are based on WIC Policy Memorar~d?vm 98-9 (U.S. Department of Agricul-
ture, 1998~. Some criteria have subcriteria, such as specific kinds of gastrointestinal
disorders. Some but not all criteria apply to every categorical group (women, infants,
and children). For example, many of the criteria applicable to infants do not apply to
any other category.
To account for the nutritional risk requirement in estimating WIC
eligibility, the current USDA method adjusts the estimated number of in-
come-eligible persons in each categorical group downward using the ad-
justment factors listed in Table 7-2. The results are estimates of fully eli-
gible individuals in each category. The adjustment factors for all categories
TABLE 7-2 Adjustment Factors Currently Used to Estimate the Number
of Income-Eligible People Who Also Meet Nutritional Risk Eligibility
Criteria
Category
Adjustment Factor
Infants
Children
Pregnant women
NonbreastSeeding postpartum women
BreastSeeding postpartum women
0.950
0.752
0.913
0.933
0.889
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ESTIMATING ELIGIBILITY BASED ON NUTRITION RISK CRITERIA 85
except infants were based on estimates of nutritional risk for income-eligible
individuals obtained from the first WIC Eligibility Study (WES I), which
used data collected in the early 1980s (U.S. Department of Agriculture,
19871. The procedure for determining these adjustment factors was to de-
velop a list of the nutritional risk criteria most commonly used by the states
(modal nutritional risk criteria) and to use nationally representative data
sets to estimate the proportion of income-eligible women, infants, and
young children who met one or more of these criteria. Modal nutritional
risk criteria were used because, until 1998, regulations allowed each state to
establish its own nutritional risk criteria. Prior to 1998, the states used
different numbers and kinds of indicators of nutritional risk and different
cutoff points. To produce the adjustment factors, the study combined data
from two surveys the 1980 National Natality Survey and the 1978-1980
National Health and Nutrition Examination Survey.
In 1991, USDA increased the adjustment factor for infants from the
WES I value of 0.752 to 0.950. The higher value was adopted to account
for the high percentage of infants who met a "predisposing" nutritional risk
criterion (and thus were WIC eligible) based on "other" risk specifically,
their mother's participation or eligibility for participation in WIC (see the
discussion of criterion 701 in the section "Method for Infants to Age 1
Year". WES II proposed higher adjustment factors for the nutritional risk
of women and children, but USDA has not adopted them (U.S. Depart-
ment of Agriculture, 1999a).
This chapter critiques the current method used to make national esti-
mates of the proportions of income-eligible persons who meet at least one
nutritional risk criterion (and thus are fully eligible) and discusses alterna-
tive methods for estimating those who meet a criterion. In discussing alter-
natives, the difficulties of assessing nutritional risk in the field and of esti-
mating the prevalence of nutritional risk with survey data are considered.
To give a conservative estimate of the level of nutritional risk in the in-
come-eligible population, lower bound estimates of the prevalence of nu-
tritional risk are presented. We find that for all groups for which quality
data are available, even the lower bound estimates of the prevalence of
nutritional risk are very close to 100 percent. For one group, children ages
1 to 2, data limitations prevent us from presenting lower bound estimates.
The chapter also contains a discussion of the costs and benefits of using a
dietary risk screen to determine eligibility. Finally, it provides recommen-
dations regarding methods to estimate the percentages of categorically eli-
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86 ESTI~TING ELIGIBILI>~D PAR TICIPATION FOR THE ~CPROG~
gible and income-eligible individuals who meet at least one nutritional
. . . .
risk crlterlon.
CRITIQUE OF CURRENT METHOD
The panel's Phase I report concludes that "the estimates of nutritional
risk currently used may not accurately reflect the actual number at nutri-
tional risk" (National Research Council, 2001:61. That report identifies a
number of concerns with the current USDA nutritional risk adjustment
factors and with the adjustment factors estimated in WES II (U.S. Depart-
ment of Agriculture, 1999b). These concerns include the use of old data,
the method used to account for variation in nutritional risk criteria across
states, the use of data on only one day of diet recall, and the method used to
combine separate estimates of risk from different data sources.
The adjustment factors for the categorical groups other than infants
need to be reconsidered for three major reasons: (1) they are based on sur-
vey data that are more than 20 years old; (2) states have adopted a relatively
standardized set of anthropometric, biochemical, clinical/health/medical,
predisposing, and certain dietary risk criteria from an approved list (U.S.
Department of Agriculture, 19981; and (3) a recent Institute of Medicine
(IOM) report recommends presuming that all income-eligible women and
children ages 2 years and older are at dietary risk (Institute of Medicine,
20021.1 As shown in Table 7-1, the term dlietary risk refers to a type of risk
that encompasses many specific criteria. All the dietary criteria relate to
some aspect of dietary intake. The recommendation of the IOM is made in
a report that does not address infants or children under age 2 years, but the
presumption of dietary risk for women and children at least 2 years of age
also would be a presumption of nutritional risk. USDA has not yet taken
an official position on the IOM recommendation concerning presumption
of dietary risk.
This recommendation is based on the IOM report's two major findings: (1) studies
suggest that nearly all children ages 2 years and older and all women in the childbearing years
are at dietary risk because they fail to meet the dietary guidelines as translated by recommen-
dations of the food guide pyramid and (2) no known assessment methods can identify or
hold promise of accurately identifying the small percentages of women and children who do
meet the proposed criterion "failure to meet dietary guideline" with the limited amount of
on-site information about food intake that is available to WIC field staffs.
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ESTIMATING ELIGIBILITY BASED ON NUTRITION RISK CRITERIA 87
POSSIBLE METHODS TO ESTIMATE NUTRITIONAL RISK
The standard method of estimating the prevalence of a risk is to
operationalize the definition of risk in quantitative terms (by specifying an
indicator and a cutoff value) and use survey data to determine the percent-
age of individuals who fall above or below the specified cutoff value. An
example of a nutritional risk prevalence is the percentage of children ages 1
to 5 years who have been diagnosed with diabetes mellitus. Since nutri-
tional risk may take many forms, however, there are many approved nutri-
tional risk criteria for each categorical group served by WIC. This means
that the method used to estimate the prevalence of nutritional risk within a
categorical group must consider the risk of failing to meet at least one of
the many criteria applicable to that group.
The panel considered new approaches to estimate the risk of meeting
at least one nutritional risk criterion in the income-eligible population.
Different data sources were considered. As we discuss in this chapter, the
lack of relevant national data about dietary risk of children ages 1 to 2 years
limits our ability to estimate the percentage of these children who meet
income eligibility requirements but not nutritional risk criteria. For the
other groups, the panel made what we consider to be conservative, lower
bound estimates of the prevalence of nutritional risk. The following section
discusses how these estimates were made and presents our lower bound
estimates.
National Data Sets for Estimating Risk Prevalence
A big obstacle to estimating the proportion of the income-eligible
population that meets at least one criterion for nutritional risk is the lack of
a single data source that contains information regarding all the risk criteria
for the relevant population groups. Two nationally representative surveys
that measure many nutritional risks the Continuing Survey of Food In-
take by Individuals (CSFII) and the National Health and Nutrition Exami-
nation Survey (NHANES) provide data related to the nutritional risk
criteria. Neither survey, however, covers all of the nearly 100 approved nu-
tritional risk criteria. For example, neither CSFII nor NHANES provides
data to estimate the percentage of income-eligible people with food aller-
gies, infectious disorders, pica, or severe nausea and vomiting, which are
risk criteria for one or more categorical groups. Table 7-3 lists the indicators
for approved nutritional risks, and identifies which survey, if any, provides
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88 ESTIMATING ELIGIBILI~YANDPARTICIPATIONFOR THE WICPROGRAM
TABLE 7-3 Available Data Related to Estimating Nutritional Risk, by
Survey
Categorical
Groups to
Nutritional Risk Indicatora Which Related Data Available, by Survey
(Code, Description) for Criterion Is
Criterion Applicable NHANES CSFII
101-103,Lowweightforheight Each Measured Self-reported
111-114, High weight for Each Measured Self-reported
height
121, Short stature
134, Failure to thrive
135, Inadequate growth
141, Low birthweight
142, Prematurity
151, Small for gestational age
152, Low head circumference
153, Large for gestational age
201, Low hematocrit/
Infants,
children
Infants
Infants,
children
Infants
Infants
Infants
Infants
Infants
Each
low hemoglobin
211, Elevated blood lead Each
311, History of preterm delivery Pregnant
women
312, History of low birthweight Pregnant
women
321, History of spontaneous Pregnant
abortion, fetal or neonatal women
loss
331, Pregnancy at a young age Pregnant
women
332, Closely spaced pregnancies Pregnant
women
333, High parity and young age Pregnant
women
334, Lack of adequate prenatal Pregnant
care
women
335, Multifetal gestation Pregnant
women
336, Fetal growth restriction Pregnant
women
Measured
Self-reported
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ESTIMATING ELIGIBILITY BASED ON NUTRITION RISK CRITERIA 89
TABLE 7-3 Continue]
Categorical
Groups to
Nutritional Risk Indicatora Which Related Data Available, by Survey
(Code, Description) for Criterion Is
Criterion Applicable NHANES CSFII
337, History of birth of an
infant who is large for women
gestational age
338, Pregnant woman currently Pregnant
breastSeeding
339, History of birth with Pregnant
Pregnant
women
nutrition-related congenital women
or birth defect
341, Nutrient deficiency diseases Each
342, Gastrointestinal disorders Each
343, Diabetes mellitus Pregnant Yes
women
344, Thyroid disorders Each
345, Hypertension, chronic or Each Yes
pregnancy induced
346, Renal disease Each
347, Cancer Each Yes
348, Central nervous system Each
disorders
349, Genetic and congenital Each
disorders
350, Pyloric stenosis
351, Inborn errors of
metabolism
352, Infectious diseases
353, Food allergies
354, Celiac disease
355, Lactose intolerance
356, Hypoglycemia
357, Drug-nutrient interactions
358, Eating disorders
Infants
Each
Each
Each
Each
Each
Each
Each
Pregnant
women
YYes
:es
359, Recent major surgery, Each
trauma, burns
360, Other medical conditions Each Yes Yes
361, Depression Each
corltirlued
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90 ESTIMATING ELIGIBILI~YANDPARTICIPATIONFOR THE WICPROGRAM
TABLE 7-3 Continue]
Nutritional Risk Indicatora
(Code, Description) for
, - . .
Criterion
Categorical
Groups to
Which
Criterion Is
Applicable NHANES CSFII
Related Data Available, by Survey
Each
362, Developmental, sensory, or
motor disabilities interfering
with the ability to eat
371, Maternal smoking Pregnant
women
372, Alcohol and illegal drug Pregnant
use
381, Dental problems
401, Failure to meet
USDA/DHHS Dietary
Guidelines for Americans
402, Vegan diets
403, Highly restrictive diets
Each
Each
Each
Each
women
411, Inappropriate infant Infants
feeding practices
412, Early introduction of solid
foods
413, Feeding cow milk during
the first 12 months
414, No dependable source of
iron for infants at 6 months
of age or later
415, Improper dilution of
formula
416, Feeding other foods low in
. , .
essential nutrients
417, Lack of sanitation in
Infants
Infants
Infants
Infants
Infants
Infants
preparation, handling, and
storage of formula or
expressed breastmilk
418, Infrequent breastSeeding as Infants
sole source of nutrients
Yes
Yes. Asks about
amount of foods
eaten.
Yes. Asks when an
infant is fed
breastmilk,
formula, milk,
and solid foods.
Yes. See above.
Yes. See above.
Yes. Asks about
2 days' food
consumption.
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ESTIMATING ELIGIBILITY BASED ON NUTRITION RISK CRITERIA 91
TABLE 7-3 Continue]
Categorical
Groups to
Nutritional Risk Indicatora Which Related Data Available, by Survey
(Code, Description) for Criterion Is
Criterion Applicable NHANES CSFII
419, Inappropriate use of
nursing bottles
420, Excessive caffeine intake
Infants
Pregnant
women
421, Pica Each
422, Inadequate diet Each
423, Inappropriate or excessive Each Yes. Asks about all Yes. Asks
intake of dietary prescription and about
supplements nonprescription intake.
. . . . .
1ncluc .lng vltamlns, mlnera .s, vitamins,
and herbal remedies minerals, dietary
424, Inadequate vitamin/
mineral supplementation
425, Inappropriate feeding
practices for children
426, Inadequate folic acid
supplements.
Each Yes. See above. Yes
Children
Pregnant
intake to prevent neural tube women
defects
501, Possibility of regression
502, Transfer of certification
503, Presumptive eligibility for
Each
Each
Pregnant
pregnant women women
601, BreastSeeding mother of Pregnant
infant at nutritional risk
602, BreastSeeding
complications or potential
. .
comp. .lcatlons
603, BreastSeeding
complications or potential
.
comp. .lcatlons
701, Infant up to 6 months old Infants
of WIC mother or of a
woman who would have been
eligible during pregnancy
702, BreastSeeding infant of
woman at nutritional risk
women
Pregnant
women
Infants
Infants
Yes. See above. Yes
corltirlued
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92 ESTIMATING ELIGIBILII~YANDPARTICIPATIONFOR THE WICPROGRAM
TABLE 7-3 Continued
Nutritional Risk Indicatora
(Code, Description) for
, - . .
Criterion
Categorical
Groups to
Which
Criterion Is
Applicable NHANES CSFII
Related Data Available, by Survey
703, Infant born of woman with Infants
mental retardation or alcohol
or drug abuse during most
recent pregnancy
801, Homelessness
802, Migrancy
901, Recipient of abuse
902, Woman or infant/child of
. . · . . - · .
primary giver wltn 1lmltea
ability to make feeding
decisions and/or prepare food
903, Foster care
Each
Each
Each
Each
Each
aThe code numbers and brief descriptions are from U.S. Department of Agriculture
(1998~. This memorandum provides detailed information about each criterion for nu-
tritional risk. CSFII = Continuing Survey of Food Intake by Individuals; NHANES =
National Health and Nutrition Examination Survey
NOTE: Neither survey provides data related to more than 50 of the approved nutri-
.
tlona . rls. ~ criteria.
data related to the indicator. The panel used both data sets when consider-
ing lower bound estimates of the proportion of individuals meeting at least
one criterion. The next section describes these two data sources. We note
that in the future the CSFII will be discontinued and incorporated into
NHANES.
Continuing Survey of Food Intake Icy Individuals
The CSFII surveys fielded in 1994-1996 and 1998 provide the most
recent dietary intake data available from a nationwide food consumption
survey. CSFII data have a large sample size for children categorically eli-
gible for WIC (those less than 5 years of age) and include an oversample of
low-income persons. However, the survey includes only a small number of
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ESTIMATING ELIGIBILITY BASED ON NUTRITION RISK CRITERIA 93
pregnant and breastleeding women and does not identify other postpartum
women. The 1998 supplementary CSFII survey was conducted to increase
the sample size for children from birth through age 9 years and was de-
signed so that the combined 1994-1998 sample of children constitutes a
nationally representative probability sample. The combined 1994-1998
CSFII includes over 2,500 children ages 2 to 5 years who live in households
with incomes at or below 185 percent of federal poverty guidelines.
The 1994-1998 CSFII is a reliable nationwide data source for estimat-
ing the proportion of income-eligible individuals for WIC who meet the
dietary risk criterion failure to meet dietary guidlelines as specified in the
report Dietary Risk Assessment in the WIC Program (Institute of Medicine,
2002~. The 1994-1998 CSFII collects two nonconsecutive 24-hour recalls
of dietary intake for each individual in the sample (this replicate diet recall
is missing for a negligibly small proportion of individuals in the sample).
Replicate diet recalls offer an advantage when the quantity of interest is the
usual dietary intake of a food or food group. Because food intake is variable
from day to day, a single day's food intake provides a very unreliable esti-
mate of the usual or habitual intake of an individual. The CSFII sample
includes respondents from every state except Alaska and Hawaii. The sur-
vey collects data during all months and seasons of the year in urban, subur-
ban, and rural areas. CSFII collects data on respondents' participation in
food assistance programs (including WIC), on income, and on other
sociodemographic variables.
Nonetheless, the CSFII is limited for estimating the proportion of in-
dividuals who meet at least one of the many nutritional risk criteria. The
survey does not contain information on most of the nondietary measures of
nutritional risk, such as biochemical and clinical/health/medical status.
Furthermore, the anthropometric data it includes are self-reported rather
than standardized measurements, and the survey lacks information on the
consumption of dietary supplements.
National Health and/Nutrition Examination Survey
NHANES provides nationally representative data relevant to many of
the nutritional risk criteria in four of the five risk categories. Using highly
standardized methods, NHANES obtains anthropometric and biochemi-
cal measurements and a broad range of data related to health and medical
problems. In addition, it collects one-day dietary intake data through the
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102 ESTIMATING ELIGIBILII~YANDPARTICIPATIONFOR THE WICPROGRAM
Lower BoundI Estimates for Infants
If a postpartum woman participates in WIC or would have been eli-
gible for WIC during her pregnancy, her infant is automatically considered
to be at risk and fully eligible (criterion number 701 for "other" nutritional
risk U.S. Department of Agriculture, 19981. This is a widely used crite-
rion: according to 1998 WIC Participant and Program Characteristics (U.S.
Department of Agriculture, 2000b), 74 percent of all infants who partici-
pate in WIC had mothers who were eligible or participating in WIC dur-
ing pregnancy. However, this percentage is likely to be an underestimate for
two major reasons:
· Only about 64 percent of states record all the nutritional risk crite-
ria under which a person is found eligible (U.S. Department of
Agriculture, 2000b). Risk under this criterion might not be re-
ported, for example, for infants at high risk because of a medical
cone ition.
· If, as discussed above, more than 97 percent of income-eligible preg-
nant women are at dietary risk, at least 97 percent of infants would
have mothers who were at dietary risk during pregnancy.
.
Since infants ordinarily are certified for a one-year period, the above
information implies that an adjustment factor of 0.97 is a reasonable lower
bound for obtaining estimates of income-eligible infants who also meet a
nutritional risk criterion. This is slightly higher than the value of 0.95,
which is currently used by USDA.
Lower Boundl Estimates for Childiren Ages 1 to 2 Years
In 1998, 65 percent of children ages 1-2 years have an identified di-
etary risk, a majority of them because of inadequate or inappropriate nutri-
ent intake (U.S. Department of Agriculture, 2000b). The next most com-
mon category of nutritional risk is anthropometric: 38 percent of children
of this age meet at least one of the relevant anthropometric criteria (e.g.,
low or high weight for height or inappropriate growth or weight gain pat-
tern). As stated previously, these percentages are likely to be underestimates
of the WIC participants meeting these criteria, since not all state WIC
agencies report all applicable nutritional risks.
Criterion 425, "inappropriate feeding practices for children," actually
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ESTIMATING ELIGIBILITY BASED ON NUTRITION RISK CRITERIA 103
includes nine subcriteria, any one of which could be used to establish di-
etary risk of children in this age group. For many of these subcriteria, sur-
vey data are not available to estimate the prevalence of young children who
meet one or more of them. The identification of some of the risks would
rely on information that is not collected by either CSFII nor NHANES.
One such subcriterion is "Routine consumption or feeding of foods low in
essential nutrients and high in calories that replace age-appropriate nutri-
ent dense foods needed for growth and development between 12 and 24
months of age."
We do not have data to estimate the lower bound of the prevalence of
nutritional risk among children ages 1 to 2 years. However, considering the
very large variation in day-to-day intake by children of these ages, the many
subcriteria that could be used to confer dietary risk, the relatively high
percentage of children with an anthropometric risk, and the array of other
nutritional risk criteria, it is reasonable to expect that a very high percent-
age of these children would have at least one nutritional risk. Furthermore,
for previously certified children without other nutritional risks, criterion
501, "possibility of regression," may be used in certain circumstances. Such
children are considered at nutritional risk when the competent professional
authority at the WIC site determines a possibility of regression of nutri-
tional status if the applicant does not continue to receive WIC benefits.
This criterion reflects the preventive nature of WIC.
Assessing Nutritional Risk in the Field
Compared with estimating the percentage of individuals in a popula-
tion who meet at least one nutritional risk criterion, screening for nutri-
tional risk, especially for dietary risk, is an even more daunting task in the
WIC service site. Since WIC field staff are required to screen for nutri-
tional risk to determine full eligibility for WIC, and since dietary risk is the
most common risk reported for women and children, effective screening
for nutritional risk requires an accurate screening method for dietary risk.
This section briefly describes the inherent limitations of the methods avail-
able to WIC staff for screening for dietary risk.
To assess dietary risk, WIC field staff generally obtain a single 24-hour
diet recall or administer a food frequency questionnaire. Regardless of the
skill of the staff member, both of these instruments have serious shortcom-
ings if the goal is to determine whether or not the individual's usual intake
of the food groups meets the criterion for dietary risk. Significant measure-
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104 ESTIMATING ELIGIBILITYAND PAR TICIPATION FOR THE ~CPROGR~
ment error is associated with these instruments, albeit of a different nature
for each.
In the case of food frequency questionnaires, the measurement error is
due to the failure of the instrument to accurately capture the usual or long-
run average intake of foods (e.g., Kipnis et al., 19991. Moreover, studies
have shown that food frequency questionnaires do a poor job of measuring
"true" energy intake (Kipnis et al., 19991.
A single 24-hour recall, designed to capture daily food intake, provides
limited information about the individual's usual intake of food. Two recent
IOM reports (Institute of Medicine, 2000, 2002) have documented that it
is very difficult to assess the usual dietary intake of an individual accurately
when only one or a few days of dietary intake data are available. In fact,
information on daily dietary intake is subject to so much error that one
could conclude that a person does not meet the criterion for dietary risk
(that is, her habitual intake of a food group is at least equal to the cutoff
point) only if the person's mean intake of that food group were consider-
ably higher than the cutoff point (Institute of Medicine, 20001.
The following example illustrates the problem with the 24-hour recall.
If the applicant is a child age 2 to 5 years, then he or she would need to have
a usual intake of two or more servings of fruit and six or more servings of
grains (and also satisfy other dietary criteria) to be considered ineligible for
WIC. However, the WIC staff member has only one day's intake, not usual
intake. Given that fruit and grain consumption varies from day to day, how
high would a single-day intake of fruits and grains (or other food group)
need to be to conclude, with some degree of certainty, that the child's usual
intake makes him or her ineligible? To answer this question, it is necessary
to know the day-to-day variance in the child's daily intake of fruits and
grains. Using the 1994-1998 CSFII data for children ages 2 to 5 years, the
panel estimated the day-to-day standard deviation of number of servings of
fruits and grains to be 0.98 and 1.64 servings, respectively. Then the panel
computed the mean intake based on one day of data that would result in
rejection of the hypothesis that the child's usual intake does not meet the
criterion. They did this under the assumptions that daily intake of fruits
and grains for the child is normally distributed and that the child's day-to-
day variance in intake is similar to the population estimate. For a confi-
dence level of 97.5 percent, the calculation is the following:
one day mean 2 1.96 x SD of daily intake + threshold,
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ESTIMATING ELIGIBILITY BASED ON NUTRITION RISK CRITERIA 105
which in the case of fruits, results in
one day mean2 1.96x0.98 +2.
Thus, the one-day intake of fruits reported by the child would have to
be at least 3.9 servings before the WIC staff member could confidently
conclude that the child's usual fruit consumption meets the threshold of
two servings per day. In the case of grains, the daily reported intake would
need to be slightly higher than nine servings for the WIC staff to be confi-
dent that the child is meeting the grain servings criterion. Clearly, a single
24-hour recall provides little information about the child's usual intake of
the food. Therefore, a WIC field member would need to observe a very
high intake on one day before she could be sure that, on the average, the
child consumes enough of the food.
Regardless of the instrument used by the WIC field staff, assessing
dietary intakes for an individual is very challenging, even under the best of
circumstances. With the inherent limitations of practical methods to assess
dietary intake of individuals, it is arguably impossible for WIC field staff to
distinguish the persons who do not meet the dietary risk criterion from
those who do.
COST-BENEFIT ANALYSIS OF ASSESSING THE DIETARY RISK
OF WIC APPLICANTS FOR DETERMINING ELIGIBILITY
Considering the limitations of methods to screen for dietary risk, the
panel examined the costs and benefits of screening for dietary risk. It is
possible that because of inaccuracies in the screening process for dietary
risk, individuals who truly meet a dietary risk criterion for nutritional risk
and who would benefit from the WIC program might be excluded from
participating, while others who do not meet the criterion might be allowed
. , in,
to enroll.
Two potential remedies could reduce the costs of these errors in dietary
risk eligibility determination.4 One remedy would be to improve the accu-
racy of the screening process. The other would be to presume that all cat-
egorically and income-eligible individuals are at dietary risk an approach
4Benefits and costs here are defined broadly, including all the benefits of the program to
society and all the costs to society associated with the program.
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106 ESTIMATING ELIGIBILITYAND PAR TICIPATION FOR THE ~CPROGR~
that was recommended for women and children over age 2 years in the
recent IOM report (Institute of Medicine, 20021.
The principal way to improve the accuracy of the eligibility screen to
assess the dietary component of nutritional risk would be to collect several
additional days of information on an applicant's food intake using the best
methods available. However, collecting this additional information would
increase the burden on the applicant and increase the administrative costs
of the program in the time and effort needed to collect and review the
information. Increasing the burden on the WIC applicant might be a bar-
rier to participation and thus result in an increased number of unserved
people who are nutritionally at risk. Assuming that WIC benefits reduce
nutritional risk in the eligible population, if fewer eligible individuals apply
because of an additional burden, then fewer eligible people would receive
the nutritional benefits of WIC and more people would be at nutritional
risk.
The panel finds the presumption of nutritional risk a more appealing
approach to consider. This approach is consistent with the IOM recom-
mendation to presume that all categorically and income-eligible women
and children ages 2 to 5 years are at dietary risk (and thus at nutritional
risk).5 If this remedy were applied, then it would no longer be necessary to
account for nutritional risk in the estimates of the number of WIC-eligible
individuals for budgetary purposes.
Presuming that all are at nutritional risk could have at least one nega-
tive and at least one positive effect. In particular, it could increase the pro-
portion of participants who are not at nutritional risk and who thus would
benefit less from the program.6 However, presuming that all are at nutri-
tional risk in determining eligibility would eliminate the possibility of in-
correctly denying eligibility to any applicants who are at risk and would
benefit from the program.
We illustrate these two possible effects of ignoring the nutritional risk
screen with two examples one in which the nutritional risk screen is used
5The Institute of Medicine (2002) report emphasized that the assessment of nutritional
risk remains valuable for tailoring the contents of the food package and the nutritional edu-
cation and referral services that should be given to an individual. Moreover the assessment of
anthropometric, biochemical, and medical/clinical risks is necessary for application of the
priority system, should funding be insufficient to serve all who apply.
6Program data are unavailable to determine the percentage of applicants who are found
ineligible based on lack of nutritional risk alone.
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ESTIMATING ELIGIBILITY BASED ON NUTRITION RISK CRITERIA 107
and one in which the nutritional risk screen is ignored. For both examples,
assume that 1,000 individuals are both categorically eligible and income
eligible for the program, and that 95 percent of those categorically eligible
and income eligible are truly at nutritional risk, as defined by meeting a
dietary criterion for nutritional risk. Further assume that, because of limi-
tations inherent in the screening procedure, the chance of excluding an
individual who is truly at risk is 10 percent (sensitivity equals 90 percent),
and the chance of incorrectly certifying an individual as nutritionally at risk
is also 10 percent (specificity equals 90 percent). Considering the poor
accuracy of the screening tests, it is highly unlikely that both the sensitivity
and specificity would be this high. Thus, the calculations probably repre-
CC 1 ''
sent a nest case scenario.
In the first example, when the nutritional risk screen is employed, 5 of
the 50 truly ineligible persons would be screened as eligible and 95 of the
950 truly eligible persons would be screened as ineligible. A total of 860
individuals would be screened as fully eligible. These results are summa-
rized in Table 7-5, part A.
In the second example, when the nutritional screen is not employed to
determine eligibility, all of the 1,000 individuals would be certified as fully
eligible. Of these, 50 would not be truly eligible (part B). However, as can
be seen by comparing part A with part B. the 95 at-risk individuals who
would not have been certified on the basis of the inaccurate nutritional
screen would now be eligible for benefits.
Is it economically rational to presume that all are at nutritional risk
and thus fully eligible? This depends on whether the net social benefits of
providing WIC benefits to an additional 95 individuals who are at risk are
greater than the net social costs of providing WIC benefits to the 45 indi-
viduals who are not at risk and would not pass the nutritional risk screen.
The panel formalized this cost-benefit calculation. Table 7-6 presents a
set of the critical values that the net social benefits of the WIC program
would have to be in order to warrant ignoring the costs associated with the
presumption that all income-eligible individuals are at nutritional risk and
thus fully eligible for WIC (see Appendix B for the formalization of this
analysis). These different critical values are calculated assuming different
true levels of the prevalence of nutritional risk in the income-eligible popu-
lation, different levels of the relative predictive power of the screening pro-
cedure (the ratio of the probability that someone who is truly not at risk is
screened as at risk to the probability that someone who is truly at risk is
screened as at risk), and different values of the net social benefits of WIC to
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108 ESTIMATING ELIGIBILI~YANDPARTICIPATIONFOR THE WICPROGRAM
TABLE 7-5 Effects of Using or Not Using a Screen for Nutritional Risk
on the Number Found Eligible or Ineligible, by Their True Nutritional
Risk Status
A
Numbers eligible and ineligible when the nutritional screen is used.
Fully Eligible Based on Nutritional Risk Screen
Truly at Nutritional Risk Yes No Total
Yes 855 95 950
No 5 45 50
Total 860 140 1,000
B
Numbers eligible and ineligible when the nutritional screen is not used.
Fully Eligible (No Nutritional Risk Screen)
Truly at Nutritional Risk Yes No Total
Yes 950 0 950
No 50 0 50
Total 1,000 0 1,000
NOTE: Both panels assume that 95 percent of income-eligible populations are truly at
nutritional risk and that the nutrition risk screen has a 90 percent sensitivity and 90
.^ .
percent spec1~1c1ty.
those fully eligible. Examining the table, if the true proportion of income-
eligible persons at nutritional risk is 0.90 and if the probability of accu-
rately screening someone who is truly not at risk equals the probability of
inaccurately screening someone who is truly at risk, then the net benefits of
WIC should be at least 1.56 to justify presumption of nutritional risk
that is, for each dollar of program expenditures, program benefits must be
equal to $1.56. As the screening procedure becomes more accurate (the
relative probability of correctly identifying those not at risk increases-
moving down columns), the net benefits of WIC must be larger to justify
presumption of nutritional risk. As the true prevalence of nutritional risk in
the population increases (moving from left to right across rows), the net
benefits of WIC needed to justify presumption of nutritional risk decrease.
Several studies have made estimates of the net benefits of the WIC
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ESTIMATING ELIGIBILITY BASED ON NUTRITION RISK CRITERIA 109
TABLE 7-6 Critical Values, by Prevalence of Nutritional Risk and
Hypothetical Values of the Accuracy of the Screening Procedure, of Net
Social Benefits of WIC Needed to Ignore the Nutritional Screening
Procedure
Prevalence of Nutritional Risk in the
Income-Eligible Population
Accuracy of Screen 0.90 0.95 0.99
1.0
2.0
5.0
10.0
1.28
1.31
1.39
1.53
1.26
1.28
1.32
1.38
1.25
1.26
1.26
1.28
NOTE: Accuracy level of the screen for nutritional risk is measured as the probability
the screen will accurately assess someone not truly at risk divided by the probability the
screen will inaccurately assess someone who truly is at risk as not at risk. See Appendix B
for details on how the net social benefit levels needed to ignore the screen are calculated.
program. The most robust findings on the net benefits of the program in
the WIC evaluation literature have examined the effect of WIC on preg-
nancy outcomes. For example, a General Accounting Office (GAO) study
(U.S. General Accounting Office, 1992) found that for every $1 spent on
pregnant women, WIC saved $3.50 on medical and disability costs because
there were fewer low-birthweight births. In a study that attempted to ac-
count for selection bias in the GAO estimates, Devaney et al. (1992) found
savings of $2.29 for every dollar of WIC expenditures. If the GAO esti-
mates or the Devaney et al. estimates are correct, then it is clear that the net
benefits of WIC for pregnant women are large enough to justify the pre-
sumption of nutritional risk for eligibility purposes. Only if the true ben-
efits of WIC are much lower than these estimates is it inadvisable to pre-
sume all are at nutritional risk. For example, if the screening procedure can
accurately identify those not at nutritional risk (predictive power ratio of at
least 5), and if the true prevalence of nutritional risk is 90 percent, then a
net benefit of 1.39 would not be great enough to justify ignoring the screen
and presuming that all are at nutritional risk.
Whether the presumption of nutritional risk should be made for cat-
egorical groups other than pregnant women depends on four factors: as-
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110 ESTIMATING ELIGIBILITY AND PAR TICIPATION FOR THE WIC PROGRAM
gumptions about the net benefits of WIC participation to these groups, the
percentage of income-eligible persons who are truly at nutritional risk, the
accuracy of the screening method, and the assumptions about the excess
burden of raising tax money to fund the program (see Appendix B). Our
lower bound estimates of the prevalence of nutritional risk for women,
infants, and children ages 2 to 5 years are well over 90 percent. Further-
more, as the preceding section discussed, the dietary risk screen used to
determine WIC eligibility has a high level of inaccuracy. Given these two
factors even using lower bound estimates of the net benefits of WIC—
presuming that all are at nutrition risk, is justified. However, if new infor-
mation about the prevalence of nutritional risk or of WIC's benefits be-
comes available, or if a more accurate screen is found, this presumption
should be reexamined. The calculations outlined here and in Appendix B
give the framework for such an analysis.
SUMMARY
In this chapter, the panel critiqued current methods used to adjust the
number of categorically and income-eligible persons to account for those
who do not meet at least one criterion for nutritional risk. The chapter also
presented lower bound estimates of the prevalence of nutritional risk and
discussed the inherent limitations of accurate assessment of the dietary risk
of an individual. Finally, the chapter examined cost-benefit ratios needed in
order to presume that all income-eligible persons meet nutritional risk cri-
teria and are therefore fully eligible for WIC.
The cost-benefit analysis found that, based on estimates of the net
benefits of WIC, ignoring the nutritional risk screen to determine eligibil-
ity is justified. A nutritional risk screen would be justifiable, however, if a
revised, highly accurate screen that correctly identifies individuals who are
not at nutritional risk were available, and if the actual prevalence of nutri-
tional risk was considerably lower than the current estimate. Lower bound
estimates of dietary risk among income-eligible infants, women, and chil-
dren ages 2 to 5 years all are at least 97 percent, and those children ages 1 to
2 are likely to be that high as well.
CONCLUSION: Given very high estimates of the prevalence of nu-
tritional risk among income-eligible populations, gross inaccuracies in
screening procedures for dietary risk, and cost-benefit calculations of
administering the screen, the panel concludes that a nutritional risk
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ESTIMATING ELIGIBILITY BASED ON NUTRITION RISK CRITERIA 111
screen is not useful for determining eligibility. If USDA drops this
aspect of eligibility determination, no adjustment for the prevalence of
nutritional risk is needed to estimate eligibility.
The IOM report recommends that all women and children ages 2 to 5
years who meet all other eligibility requirements should be presumed to
meet the requirement of nutritional risk through the failure to meet dietary
guidelines criterion (Institute of Medicine, 20021. The dietary guidelines
used in the criterion do not apply to infants and children between the ages
of 1 and 2, so the IOM recommendation does not specifically apply to
children of these ages. However, if the recommendation is adopted, all in-
fants will necessarily also be considered at nutritional risk because an infant
whose mother was considered to be nutritionally at risk during pregnancy
is also considered to meet nutritional risk requirements. Thus, an implica-
tion of the IOM recommendation is that all infants will also be presumed
to be at nutritional risk. If the IOM recommendation is not adopted by
USDA, then the lower bound estimates ofthe prevalence of nutritional risk
given earlier in this chapter should be used to estimate the number fully
eligible for WIC. These lower bound estimates are: 0.97 for pregnant
women, 1.00 for postpartum women, 0.97 for infants, and 0.98 for chil-
dren ages 2 to 5. There are no data to make a lower bound estimate of the
prevalence of nutritional risk among children ages 1 to 2. However, given
that the diets of children at this age are probably not that different from the
diets of children ages 2 to 5, and the many other criteria that could be used
to confer nutritional risk of children at this age, the prevalence of nutri-
tional risk among children ages 1 to 2 is also likely to be very high.
If all income-eligible people are considered to be nutritionally at risk
and no downward adjustment for nutritional risk is made to the estimates
of those who are income eligible, the number of those estimated to be
eligible for WIC will increase. Eligibility estimates for children will increase
the most because the current adjustment factor for nutritional risk for chil-
dren is 0.752 lower than that for any other group. In 1999, 6.4 million
children were estimated to be income eligible for WIC and 4.8 million
were estimated to be both income eligible and nutritionally at risk.
USDA should periodically assess the findings leading to the conclu-
sion that the nutritional risk screen is not useful to determine eligibility.
Better data to measure the prevalence of nutritional risk may become avail-
able, or if the program is highly successful at reducing nutritional problems
or nutritional behaviors of the population otherwise improve, the preva-
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112 ESTIMATING ELIGIBILITYANDPARTICIPATIONFOR THE WICPROGRAM
fence of nutritional risk in the population may decrease. If it decreases
significantly, and if the screen for nutritional risk becomes more accurate,
screening would become more important in targeting WIC's benefits to
intended groups. The eligibility estimates would then need to be adjusted
accordingly (i.e., by the percentage of the income-eligible population at
nutritional risk).
Representative terms from entire chapter:
dietary risk
