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* Universidad Simon Bolivar, Departamento de Tecnología de Procesos Biológicos y Bioquímicos. Postal Code: 89000, Caracas, Venezuela and
Division of Nutritional Sciences, Cornell University, Ithaca, New York 14850
 |
ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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KEY WORDS: • household food security • food sufficiency • predictors of energy availability • self-perceived HFS scale • community food surveillance
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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),
especially in countries where macroeconomic adjustment programs had
adverse effects on the poor (Baer and Maloney 1997
,
Sharma 1992
).
Household food security
(HFS)3
is defined as secure and permanent access of households to foods,
sufficient in kinds and amounts to enable all individuals to live a
healthy, active and productive life. Evidence suggests that economic
recession and the application of macroeconomic adjustment programs in
many Latin-American countries resulted in a decreased national food
supply and a greater incapacity of the poor to acquire food
(Lorenzana 1998
, Moron 1995
). The
elimination of generalized food subsidies, subsequent increase in food
prices, real income loss and high unemployment rates were among the
main factors that apparently contributed to diminish HFS levels among
the underprivileged (Baer and Maloney 1997
).
Consequently, many countries in the region, Venezuela included,
designed and implemented safety nets including focalized food subsidy
programs to aid the most vulnerable groups suffering from social costs
(Mesa-Lago 1997
). The Venezuelan food subsidy programs,
however, were improvised, and among other deficiencies lacked valid and
precise measures of the nature, magnitude and varying prevalence of
household food insecurity among the poor (Lima 1995
).
Baker and Grosh (1994)
, using different poverty measures
to rank households, reported on "undercoverage" and "leakage"
of social program benefits in Venezuela. Both "undercoverage," the
percentage of those meant to be reached by the program who are not
reached, as well as "leakage," the percentage of program benefits
that are given to those who ought not to receive them, were estimated
to be high: 27 to 67% for the former and 57 to 63% for the latter.
Although "perfect targeting," with only those in need receiving the
benefit, is the "ideal" "in reality no programs are perfectly
targeted or come even very close" (Baker and Grosh 1994
). Nonetheless, short, valid, precise, relatively simple
and noncostly instruments that require a minimum of data to construct,
that can be collected by nonspecialists, and that are easy to analyze
and interpret may contribute significantly to detect and monitor the
target population (Lupien 1994
).
Better estimates of the prevalence of food insecurity not only will
improve targeting of food and nutrition safety nets but also will
facilitate studying the wide-ranging effects of food and nutrition
insecurity on health and well-being. Furthermore, documenting the
relationship between food insecurity and the nutrient intakes of those
affected by it is an important step in assessing public health risks
(Rose and Oliviera 1997
).
Researchers in the United States pioneered the development and
validation of measures of hunger and food insecurity (Alaimo
1998
, Briefel and Woteki 1992
, Radimer et al. 1992
, Wehler et al. 1992
). Approaches have
included construction of a "hunger index" (Wehler et al. 1992
), "food insecurity scales" (Radimer et al. 1992
) and questionnaire items included in national surveys
(Alaimo et al. 1998
, Briefel and Woteki 1992
).
Aguirre (1995)
documented domestic consumption
strategies implemented by poor households in Buenos Aires, Argentina,
during a period of hyperinflation and the subsequent implementation of
macroeconomic adjustment programs (1989–1994). Maxwell (1996)
reported measuring coping strategies as a food
insecurity indicator among semisubsistence farmers in a major African
center. In Latin America, the food basket approach based on core foods
was used to estimate the prevalence of food insecurity among households
in Colombia (Lareo et al. 1990
) and in Cuba (Gay 1997
). To our knowledge no qualitative-quantitative method to
identify and track food insecurity levels of households in Latin
America was developed and validated.
The main concern of this research was to develop an abbreviated
qualitative-quantitative method useful for estimating and eventually
tracking food security levels among the poor based on two measures:
core food sources of household energy availability (the quantitative
measure) and a self-perceived HFS scale (the qualitative measure). The
former estimates energy sufficiency; the latter reflects female
awareness of altered food intake due to constrained resources as well
as her perceptions of hunger experiences of adults or children in the
home (Campbell 1991
, Radimer et al. 1992
,
Scott et al. 1994
).
To assess the validity of using the above measures, the relationships
between key economic, social, and demographic determinants of HFS and
their effect on food security level among the urban poor in a community
in Venezuela were explored. Criterion validity of the proposed measures
can be established if they vary in an expected manner with the
predictor variables in a theoretical model of HFS (Frongillo et al. 1996
).
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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A semistructured questionnaire was pretested among women attending an
out-patient maternal and child health clinic in Caracas. Likewise, the
"Community Childhood Hunger Identification Project" (CCHIP) Hunger
Index (Wehler et al. 1992
) developed and used in the
United States was adapted for the present study. The 2-point, 8-item
scale, indicating perceived food insufficiency or altered food intake
due to resource constraints, was modified into a 4-point, 12-item
scale. With the modified scale, households with zero points were
considered food secure. Twelve or fewer points reflected "mild,"
13–24 points "moderate" and 25 points or more, "severe" food
insecurity. After in-depth iterative pretesting and redesign, the scale
was assumed to have face validity. Subsequently, the scale was applied
to 65 members of poor and very poor households in a community with
characteristics similar to this study's sample population.
Chronbach's reliability analysis of responses (Carmines and Zeller 1979
) produced an alpha coefficient of 0.871, suggesting
that all items belong in the scale.
The study population.
The protocol for this study was reviewed and approved by the Human
Subjects Committee of the Division of Nutritional Science at Cornell
University. Data were gathered on outcome and predictor variables from
a sample of 238 households, in a peri-urban barrio in the Metropolitan
Area of Caracas. All households in the community that complied with
selection criteria, namely, poor and very poor households (based on the
Graffar method for social class, adapted for the Venezuelan population;
Mendez Castellano 1986
), a nonpregnant, nonlactating
householder, household members sharing food resources, and the
female's written consent to participate in the study were identified
through a community census. Of the 433 households identified, 110 did
not comply with selection criteria, 24 females refused to participate,
and another 56 could not be reached during the data collection period
after five home visits. Of the remaining 243 households, five refused a
second interview. The sample of 238 families represents 73.7% of all
eligible households and 55% of all households identified in the
community. Sample size was well above that estimated based on the
prediction of a correlation of 0.25 or more between the response
variable, adequacy of energy availability and key determinants, with
alpha set at <0.05 and beta at 0.1 (for a power of 0.9) (Hulley and Cummings 1988
).
Data collection procedures.
Data were collected by the principal author (P.L.) and four previously
trained nutritionists during March to May of 1995, through personal
interviews of the householder in her home. The list-recall method
(Gibson 1990
) was applied to estimate household food
availability during a 7-d period. Information on household size,
composition, household members' eating-out patterns, food expenditure
decisions, other social and economic criteria, as well as female work
status were likewise gathered. Finally, the self-perceived household
food security scale questionnaire was applied.
Data management and handling.
Food availability data processing and preliminary analysis of energy
and nutrient content were done using the computer program Microsoft
EXCEL. The method of Block et al. (1985)
was adapted for
determining the top food contributors of energy availability. For each
household, energy contributed by each food item reported in the food
availability database was determined and foods ranked in order of their
contribution. The amount of energy accounted for by any item thus
considers not only its energy value but also the frequency of its
consumption, as well as amounts consumed. The number of foods that
contributed at least 85% of energy availability was coded as the
energy predictor score (PREDENAV) for the household. Based on a mean
and SD of 9.99 ± 3.3, the top 12 food contributors of energy
availability were used to construct the abbreviated list of core foods.
Energy and nutrient contributions, as well as percentage contribution
to total energy availability of these foods, were determined.
Statistical analysis.
Principal components and factor analysis were used to assess construct validity (extent to which items designed to measure the same concept related to each other) of the self-perceived HFS scale. All data were analyzed for descriptive, bivariate and regression analyses using the statistical package SYSTAT for Windows.
Since this research is exploratory in nature, regression models to
determine predictors of self-perceived HFS levels were fit with a
backward stepping strategy. For initial exclusion of candidate
predictors, P value was set at 0.2 and for further
exclusions 0.1. Significance of interactions between predictor
variables was then assessed. Forward stepping was used to regress total
energy availability on the amount of energy provided by each of the top
12 food contributors of energy availability. Cumulative
R2 represents the percentage variation in the
outcome variable explained by the short list of food predictors
(Byers et al. 1985
, Lee et al. 1992
).
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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Principal components and factor analysis of the self-perceived HFS
scale identified two factors: altered food intake and hunger
experiences of adults and children using as a criterion an Eigenvalue
of 1.0 or greater (Table 1
).The rotated loadings indicated seven factor loads "cohering" in
factor 1 with a range of factor loadings between 0.56 to 0.88. In
factor 2, the five cohering factor loadings ranged from 0.67 to 0.91.
Factor 1 explained 37.2% and factor 2, 32.1% of total variance. These
results make it evident that the self-perceived HFS scale exhibits
content validity. The items "hang together" as a unit, and they
reveal an underlying dimension of the concept being measured
(Frongillo et al. 1996
). Results reported high
reliability of factors (0.91 and 0.88) and a very high reliability of
the 12-item scale (0.92) (Carmines and Zeller 1979
).
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The most common intrahousehold indicator of food insecurity was lack of
food money. The items that follow appear to reflect within-the-home
adjustment strategies to cope with constrained resources: buying fewer
essential foods for children, reducing the usual number of home meals,
household members eating less food, and adults reducing the number of
usual meals or eating less at the main meal. Noteworthy, experiences of
adult hunger are more commonly reported than children reducing usual
number of meals. Furthermore, the data suggest that an adult may go to
bed hungry before a child eats less at the main meal. Finally,
complaint of hunger by children or children going to bed hungry are the
least-reported indicators of perceived food insecurity and thus may
reflect the household's incapacity to cope with constrained access to
food. The percentage distribution of responses to these items seems to
indicate that the rationing of adults' food intake comes before that
of children. Results of the present study are very similar to those
reported by Scott et al. (1994)
with the use of the
CCHIP scale among hungry households in the United States. Likewise,
Maxwell (1996)
reported limiting portion size, skipping
meals and skipping eating for whole days as indicators of food
insecurity among a sample of underprivileged farmers in Kampala,
Uganda.
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, a greater percentage of very
poor compared to poor households reported adjustment strategies to
constrained food resources, as well as perceived experiences of hunger
among adults and children. Remarkably, despite a lower proportion of
households with children, a greater percentage of very poor compared to
poor households reported that children ate less at the main meal,
complained of hunger or went to bed hungry for lack of food.
Self-perceived HFS levels for all households and by poverty level are
presented in Table 3.
For the entire sample, less than a fourth (22%) of women perceived
their homes to be food secure. Overall, 59, 14 and 5% of females
perceived their households to be mildly, moderately and severely
insecure, respectively, based on the criteria employed in this study.
As described, this study's HFS scale was adapted from the CCHIP scale
(Wehler et al. 1992
). The latter was used to assess
hunger in a stratified sample of 2,211 poor families in the United
States (Scott et al. 1994
). The CCHIP study detected
26% of poor U.S. households to be food-sufficient, 54% at risk of
food insufficiency and 19% as food-insufficient. Worthy of note, the
researchers reported that between 29 and 79% of the entire sample
participated in one of several food assistance programs in the country.
In contrast, only 13% of the households studied in this community
reported receiving government-subsidized foods.
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. More than
twice the proportion of poor households were categorized as secure
compared to very poor households. Conversely, more than twice of very
poor households were categorized as severely insecure in contrast to
their poor counterparts (P = <0.001).
To test for accuracy and overall validity of the HFS measure proposed
in this study, multivariate regression analysis was applied relating
these measures to the social, economic and demographic factors that the
literature suggest are associated with the phenomena being measured.
The key determinants of self-perceived HFS identified are presented in
Table 4
and show that all predictors influence self-perceived HFS in the
expected direction. Predictors of energy availability (PREDENAV) score
or the number of foods that contribute 85% or more to household energy
availability emerged as a significant determinant (beta =
-0.298). Tle lower the PREDENAV score, the quantitative measure of
food insufficiency used in this study, the higher the self-perceived
household food insecurity level. This provides evidence of a positive
relationship between household energy sufficiency and self-perceived
food security level, contributing support to the validity of this
qualitative-quantitative method of estimating HFS levels among the
poor.
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, showing a very
significant association between perceived food insecurity level and
poverty level. Finally, number of children also emerged as a
determinant, and the direction was positive: the higher the number of
children in the household the greater the level of female-perceived
food insecurity. These four predictors explain 34.3% of the variation
in self-perceived HFS score. Clearly, these data provide evidence for
the criterion validity of the measure of the qualitative dimension of
HFS used in this study: the measure varies in an expected manner with
the predictor variables in a theoretical model of food insecurity
(Frongillo et al. 1996
). Predictors of energy availability.
Several problems make traditional food and nutrient assessment methods
impractical for food and nutrition surveillance. The household food
record method, which involves the weighing or recording in household
measures of all food consumed at each meal (Burk and Pao 1976
) is impractical if data are needed on a frequent and
continuous basis. The 24-h recall involves one or more visits to the
families to obtain complete consumption data for all family members for
a single day. The food inventory method that estimates household food
stocks before and after a given consumption period is highly invasive.
Clearly, simpler, less-expensive but valid and reliable methods are
needed to monitor household food availability or intake (Lareo et al. 1990
, Lupien 1994
).
Several studies reported that a limited number of foods explains a
substantial proportion of the total energy intake of population groups.
Lee et al. (1992)
identified 20 food items as major food
predictors for total energy intake based on a dietary survey of 539
households in Taiwan. Block et al. (1985)
previously
reported quantitative information on the role of individual foods in
total population intake of energy and specific nutrients in the United
States, using data from the Second National Health and Nutrition
Examination Survey (NHANES II). Thirty food products accounted for 75%
of total energy intake. Research in Cali, Colombia, revealed that the
15 foods most commonly purchased by the majority of the population in
14 communities provided sufficient energy to cover at least 70% of
recommended allowances for these nutrients (Lareo et al. 1990
).
As explained in the section on methods, forward stepping was used to
regress total energy availability on the amount of energy provided by
each of the top 12 food contributors of energy availability. This
strategy was previously used by Byers et al. (1985)
who
examined the question of how many foods might minimally be required to
estimate specific nutrient intakes for epidemiologic purposes. The
authors reported that 21 foods "explained" 90% of the variance
(cumulative R2) for total energy intake of 1,682
individuals in the Upstate New York Dietary Study.
We found that the top 12 foods identified in our study population
contributed 80.5% to total energy availability and explained 90.7% of
the variance in total energy availability (Table 5
).To explain the other 9.3% requires measuring all the other foods
available to the household. This, then, is the primary advantage of
this abbreviated measure of food sufficiency: it is short; precise;
easy to use, analyze and interpret compared to traditional methods,
making it a useful instrument for measuring food sufficiency and thus
for monitoring HFS among the poor.
|
Study implications for policy and programming.
Currently there is a paucity of data concerning links between
urbanization and HFS. In low-income and middle-income countries, most
nutrition-related research as well as food- and nutrition-related
program design, implementation and evaluation are based on rural
populations' needs. It is politically imperative for governments to
address the urban plight more efficiently. "Failure to implement more
effective urban nutrition programs undermines political stability, and
therefore the capacity of governments to perform their broad
development functions" (Monteiro 1987
).
The specialized agencies of the United Nations launched the concept of
food and nutrition surveillance (FNS) over 20 years ago (WHO
1976
). To date FNS has been adopted in many of the developing
countries virtually as the responsibility of the health sector alone.
Food and nutrition surveillance can be an instrument of food security
from the national to the individual levels if information generated
through surveillance is used to make wise decisions. However,
information needs will vary from one level to the next. A decentralized
system may have the advantage, in addition to shortening the time
between data generation and decision making and action, of reducing the
burden of interinstitutional coordination.
It has been argued that an FNS system designed, operated and
continously evaluated by the community can be a powerful instrument for
local, self-reliant development and food security actions
(Immink 1988
, Lorenzana 1998
). Community
FNS systems have data requirements that focus on simplicity of methods
capable of generating timely information relative to needs of
decisionmakers. Besides, minimizing real costs should be an important
concern. Therefore an aim of community-based FNS systems is to identify
methods that require a minimum of data to construct, that can be
collected by nonspecialists, and that are easy to analyze and
interpret.
Given the current changes in many countries toward greater
decentralization and community-based activities, assessment information
must also be accessible to, and understood by, a much wider audience
than in the past. Additional work is needed to develop better and more
cost-effective dietary assessment methods and to integrate the results
of such methods into overall information systems that lead to better
decisionmaking for policies and activities to improve nutrition
(Lupien 1994
).
Results of this study appear promising. The short method proposed above seems appropriate for community-based monitoring of HFS among the poor. However, HFS poses basic challenges for monitoring: there is no single measure or indicator of HFS that can be universally applied in the way that anthropometry was used to assess malnutrition. Besides, the determinants of HFS may differ from community to community. For the identification of groups, resource-related correlates as well as other intrahousehold issues are often very useful indicators. Rapid assessment procedures that rely on multidisciplinary strategies to quickly gather information related to certain practices or problems within a given community can be useful for identifying baseline data. The short method proposed in this study may be used to identify particularly vulnerable groups of people, to track their food security levels, to select certain households as recipients of direct food assistance, and possibly to evaluate food subsidy projects and programs intended to have some beneficial impact on HFS. In short, it may have potential use for targeting, monitoring, program design and evaluation. Undoubtedly, the external validity of the method needs to be established.
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FOOTNOTES |
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1 The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 USC section 1734 solely to indicate this fact.
3 Abbreviations used: CCHIP, Community Childhood
Hunger Identification Project; FNS, food and nutrition surveillance;
HFS, household food security; PREDENAY, energy prediction score.
Manuscript received August 12, 1998. Initial review completed December 1, 1998. Revision accepted December 16, 1998.
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