Keywords |
| food insecurity, quality of life, Bachok |
Introduction |
| Food insecurity is defined as the limited or uncertain availability of nutritionally adequate and safe
food.1 Food insecurity is associated with a wide range of health outcomes for adults.2 Households
suffering from food insecurity are more likely to have adults who have lower nutrient intakes,3,4 greater
probabilities of mental health problems,5 long-term physical health problems,6 higher levels of
depression,7 higher levels of chronic diseases,8 and lower scores on physical and mental health
evaluations.9 Food-insecure elderly are more likely to have limitations in activities of daily living.10
Campbell (1991) elaborated on a concept of food insecurity; the risk factors and consequences. She
indicated two sets of potential consequences of food insecurity11 namely, typical physical and
psychological symptoms of suboptimal nutritional status.12 Food insecurity rates in rural areas exceed
those in suburbs and metropolitan areas but not those in central city areas.13 Quality of life, which is
central for the development of social policy, is one of the most important issues facing the world today.
Various literatures asserted that food insecurity not only affects the health of children negatively but
also exacerbates acute diseases and speeds the onset of degenerative disease among the elderly.14
Hamelin and colleagues discussed this association with the following report: “according to
respondents’ description, the experience of household food insecurity is characterized by two
categories of manifestations: (a) the core characteristics of the phenomenon which are reflected by not
having enough food in the present, by worrying about having enough in the future and by expressing a
feeling of alienation; and (b) a related set of actions and reactions by the household to these core
manifestations”.15 The consequences of food insecurity at the household level are categorized into
three areas: physical impairments, psychological suffering, and socio-familial perturbations.15,16
Physical impairments related to insufficient food include illness, fatigue, or both illness and fatigue. In
Malaysia, several public and private food assistance programs are offered at the national, state, and
local community levels to alleviate food insecurity and hunger. However, in spite of these efforts, high
rates of food insecurity still exist in low-income households, especially in rural areas. One of the latest
food insecurity studies in rural Malaysia reported a higher prevalence of household food insecurity
(77.5%) 17 compared with those of other studies.18,19 The present study aims to investigate the
association between food insecurity and quality of life in low-income households in rural peninsular
Malaysia. |
Material & Methods |
Study location |
| This study was conducted in eight sub districts (i.e., Tawang, Perupok, Telong, Gunung, Mahligai,
Tanjong Pauh, Melawi, and Bekelam) of Bachok; a coastal district situated 25 km east of Kota Bharu,
a capital city of Kelantan state, Malaysia. Kelantan had the lowest mean monthly income (RM 1, 829) among all the states of Malaysia in 2004. Kelantan is categorized as a less developed state because of
its high poverty incidence (10.6%) and low GDP growth rate in peninsular Malaysia.20 |
Subjects |
| A cross-sectional survey of households receiving monthly allowances was conducted. A total of 223
mothers aged 18 years to 55 years old who are neither lactating nor pregnant and have at least one
child aged 2 years to 12 years old were purposively selected. Mothers were recruited because they are
responsible for food production, acquisition, preparation, and security.21,22 Mother-child pairs are
involved in most household food security studies because the attitudes and practices of mothers may
influence the eating habits of their children, and the health of mothers can be adversely affected by
food scarcity and maternal hardship.5,9 In the Bachok district, 12 villages with Malay ethnic groups
comprise the majority of the population. Based on population density, eight of the largest villages were
selected for the cross-sectional study. No probability sampling was conducted and all respondents were
purposively selected from the records of the Welfare Department until the calculated sample size (n =
223) was obtained because of the strict inclusion criteria of households (Welfare assistance recipients
in Bachok District, mother aged 18 to 55, mothers who were neither pregnant nor lactating during the
study period, households having children aged 2 to 12 and living with the mother in the same
household and signed the consent form). If there is more than one mother living in the same targeted
household, only one mother who is responsible for food purchasing and preparation was interviewed to
avoid the overlapping of household information such as household size, income, numbers of children
and other dietary information. Prior to the data collection, list of names of the recipient of financial
assistance from the Department of Social Welfare, Bachok District, Kelantan, Malaysia were
identified. All the recipients received financial assistance as monthly allowances form. The total
number of the recipients in Bachok District was 3,635 which has been a sampling frame for this study.
The sample size was calculated using the single proportion formula. The calculated sample size of our
study was 202.7 to which we added 10% as a non-response rate. Therefore, the final sample size was
as follows: 202.7 + 10% non response = 202.7 + 20.2 = 222.7 = 223. |
Data Collection |
| All study procedures were reviewed and approved by the Ethical Committee of the Universiti Sains
Malaysia prior to data collection. Two trained research assistants conducted visits to the houses of
respondents with the guidance of the village head. The lists of mothers who receive monthly
allowances were obtained from the welfare office of the Bachok district. The interviewers collected all
pertinent research information through in-depth and face-to-face individual interviews with the
respondents. This study employed a pre-tested questionnaire consisting of socio-demographic
questions, the 10-item Radimer/Cornell hunger scale,23,24 and the Medical Outcome Study Short Form-
36 (SF-36). |
Measurement |
| Medical Outcome Study Short Form (SF-36): The functional health and well-being of individuals or
groups were evaluated using the SF-36, a multipurpose survey consisting of 36 items representing an eight-scale profile. The SF-36 does not rely on traditional parameters, namely, specific age, disease, or
treatment.25,26 The 36 items comprising the SF-36 are divided in the following eight domains: physical
function (ten items), role limitations caused by physical health problems (four items), bodily pain (two
items), general health perception (five items), vitality (four items), social function (two items), role
limitations caused by emotional problems (three items), and mental health (five items). The score on
each scale ranges from 0 to 100 with a low score indicating poor health or severe disability.27 These
eight domains can characterize both favorable and unfavorable self-evaluations of general health
status.27 Close-ended questions in the SF-36 were specifically designed to encourage respondents to
select responses from a set of possible answers assembled by Ware and Sherbourne 28 and to comply
with the methodological guideline for close-ended questions.29 The SF-36 has been used in the
Medical Outcomes Study.30 Furthermore, the SF-36 is helpful in research, clinical practice, general
population surveys, health policies, and health practices evaluation.31 The SF-36 questionnaire was
translated to Malay for the present study. This instrument is valid, reliable, and can be used in
Malaysia.32 The raw score of each SF-36 dimension was derived by calculating the item scores and
converting the calculated score to a value ranging from zero (i.e., the worst possible health state
measured by the questionnaire) to 100 (i.e., the best possible health state). Thereafter, the raw score
was re-calculated across the dimension. The raw score transformation formula 31 is presented as
follows: |
 |
| Household food insecurity: The 10-item Radimer/Cornell scale was designed to detect food
insecurity at the household, adult, and child levels. The conceptual framework indicates that food
insecurity is a “managed process” with sequential responses that arise as food supplies become more
limited. First, anxiety and concern about food supply are experienced at the household level, which is
classified at mild severity. Consequently, the household makes budget adjustments that may affect diet
quality. At the adult level, which is of moderate severity, adults limit the quantity and quality of food
they consume. Finally, children experience the direct effects of limited food supplies such as hunger at
the child level, which is the most severe stage. |
| Data Management and Analysis |
| All survey data were entered into electronic format using a key. The model was verified to minimize
data transcription errors and enhance data integrity. All analyses were completed using PASW SPSS
(version 18.0) for Windows. Food security categories were computed to yield four groups: food-secure
households, food-insecure households, households with food-insecure adults, and households with
child hunger. The SF-36 scales were computed using the guidelines by Ware et al. 27 Independent ttest,
chi-square test, and Fisher’s exact test with STATA software were used to compare the socioeconomic
and demographic characteristics of all households. Analysis of variance was used to examine
the differences between the categories of food security and the scale scores of the SF-36. When the p
values of the omnibus F test were less than 0.05, Bonferroni corrections were applied to compare each
of the food security groups. Single linear regression (SLnR) was used to examine the relationship
between each of the eight domains of the quality of life (outcome variables) and the predicted variables, particularly food insecurity status. Depending on SLnR results, significant factors, that is,
independent variables with a p value < 0.25 and variables that might be related to quality of life (i.e.,
food insecurity status, age of mothers, occupation, type of household, BMI of mothers, and income per
capita), were included in the stepwise multiple linear regression (MLR) analysis. |
Result |
| Table 1 describes the participants in this study. Among the 223 respondents, 187 (83.9%) reported
certain levels of food insecurity with 66 (29.6%), 43 (19.3%), and 78 (35%) categorized under foodinsecure
households, households with food-insecure adults, and households with child hunger,
respectively. The Radimer/Cornell scale showed an acceptable internal consistency (Cronbach’s alpha
= 0.88). |
| The majority of the mothers (60.5%) are at a moderate age (i.e., 31 years to 45 years old). Foodinsecure
households are more likely to have larger household sizes and greater numbers of children
compared with other households. In addition, mothers from food-insecure households are less likely to
have graduated from high school; some participants (10.4%) did not even receive any formal
education. Almost 60% of the households are headed by mothers who are either widowed or divorced.
No significant difference was found between the proportion of household types or the marital status of
mothers and food-secure and food-insecure households. The monthly income of food-secure
households is significantly higher than that of their counterparts. Among the 223 households, 44.3%
are living below the poverty line.33 |
| The respondents in each of the three categories of food-insecure households reported poorer functional
health status compared with those in food-secure households (Table 2). The mean differences between
the scores of food-secure households and households with child hunger were significant for all health
domains. No significant mean difference was found between food-secure households, food-insecure
households, and households with food-insecure adults regarding role limitations caused by physical
health problems and bodily pain. No differences in health status were observed among food-insecure
groups. |
| Prior to the stepwise MLnR analysis, SLnR was conducted to investigate the associations between
each of the eight scales of SF-36 (dependent variables), household food insecurity status, and other
predicted variables (independent variables). Simple linear regression (SLnR) was conducted to identify
significant independent variables for multivariable analysis. Simple Linear Regression was performed
on each independent variable. The assumption of normality for the score and the linearity for each
domain were found to be approximately normally distributed; residuals appeared linear and randomly
scattered. |
| The direction of the association between household food insecurity and the quality of life via stepwise
MLnR in obtaining the final model are shown in Table 3. The following are the MLnR results for the
eight models. For physical function, the final model of the MLnR analysis implied a significant
association between household food insecurity status and physical function (p < 0.001) as well as
between physical function and the BMI of the mothers (p < 0.05). An increase of one percent in food
insecurity decreased the physical function score by 9.6 (b = −9.64). Moreover, a one-point increase in
the BMI decreased the physical function of the mothers by 0.38. For role limitations caused by physical health problems, a significant association was found between the dependent variable (i.e., role
limitations caused by physical health problems) and the two predictor variables, that is, household food
insecurity status (p = 0.003) and employment status of the mother (p = 0.025). An increase of one
percent in food insecurity decreased the score for role limitations caused by physical health problems
by 18.5 (b = −18.50, 95% CI, −30.18, −6.33). For bodily pain, a significant association was found
between bodily pain scale and food security status (p = 0.001); no significant association was recorded
for the other predictor variables. An increase of one percent in food insecurity decreased the bodily
pain score by 11.8 (b = −11.782). For general health, a significant association was found between the
dependent variable (i.e., general health) and the independent variable (i.e., household food insecurity
status; p < 0.001), age of mothers (p = 0.044), and type of household (p = 0.021). An increase of one
percent in food insecurity decreased the general health score by 15.0 (b = −15.05). An increase in the
age of the mothers decreased general health by 0.33. For vitality, a significant association between the
criterion variable (i.e., vitality) and the predictor variable (i.e., household food insecurity status) was
noted (p < 0.001); however, no significant association was recorded for the other predictor variables.
An increase of one percent in household food insecurity decreased the vitality score by 13.0 (b = −13.03).
For social function, a significant association was established between the criterion variable (i.e., social
function) and the predictor variable (i.e., household food insecurity status) at p < 0.001. An increase of
one percent in food insecurity decreased the social function score by 20.76 (b = −20.76). Income per
capita was also associated with the social function score (p = 0.019). For role limitations caused by
emotional health problems, a significant association was found between the criterion variable (i.e., role
limitation caused by emotional factors) and the predictor variable (i.e., food security status) at p <
0.001; no significant association was found for the other included predictor variables. An increase of
one percent in food insecurity decreased the role limitation score by 36.2 (b = −36.23). For mental
health, a significant association between the criterion variable (i.e., mental health) and the predictor
variable (i.e., food security status) was noted at p < 0.001; however, no significant association was
recorded for the other predictor variables. Furthermore, 14% of the variations in mental health are
explained by food security status (R square = 0.14). An increase of one percent in food insecurity
decreased the mental health score by 15.6 (b = −15.68). |
| The results of the MLR analysis show a significant association between each of the eight SF-36 scales
and the predictor variable (i.e., household food insecurity status). |
Discussion |
| The region where this study was conducted is characterized by low socio-economic status. Kelantan
had the lowest mean monthly income (RM 1, 829) among all the states of Malaysia in 2004. Kelantan
is classified as a less developed state because of its high poverty incidence (10.6%) and low GDP
growth rate in peninsular Malaysia.20 This study reports a higher prevalence of household food
insecurity in low-income households in rural Malaysia than those cited in previous studies.17–19 This
result can be explained by the higher percentage of households (44.3%) living under the poverty line
and larger household size in our study sample. The average household size in this study 6.71 (2.29) is
higher than the reported household size for rural areas in Malaysia (4.6).34 |
| The findings of the present study indicate that food insecurity status is associated with the quality of
life and may lead to adverse effects on the well-being of individuals in low-income communities. The
current findings are consistent with those of Campbell’s model, which asserts that food insecurity may lead to suboptimal quality of life and health (i.e., physical, social, and mental well-being).
Furthermore, the findings in this study is in agreement with those by Tarasuk,6 who found that women
in food-insecure households are more likely to report their health as fair, poor, or very poor with
longstanding health conditions or activity limitations. Parents in food-insecure households are highly
vulnerable to feelings of anxiety and helplessness, loss of control, family dysfunction, and
psychological impairment. When accompanied by concerns on how to procure food, this condition
may engage parents in undesirable activities such as borrowing money, selling assets, or even
stealing.4 The constant psychological stress linked with food insecurity may increase the risk for
depression, particularly for single mothers, who are more likely to report poorer mental health than
married or partnered mothers. Single, unemployed mothers are twice as likely to report higher levels of
distress compared with other groups. Single mothers in general, regardless of employment status, are
more likely to report higher personal and chronic stress.35 This interpretation is consistent with our
results, in which single mothers constitute 60% of the respondents. |
| This study shows that social function and social interaction are associated with food insecurity. The
results of the present study are in agreement with those reported in previous studies, 36 which found
that parents of food-insecure households compromise their diet to protect their children and seek
socially stigmatized means of food acquisition. The health and positive sense of self and outlook of
parents in food-insecure households may suffer, leading to negative physical and/or mental health
outcomes. These parents may experience distress because of the manner in which they feed their
children, such as using food banks, borrowing money or food, or sending their children to charities
where free meals are offered. A parent’s shame or embarrassment concerning inadequacy in providing
food to his/her children can translate to social elimination as well as a sense of isolation and ignorance
from the community. |
| The findings of the current study were confirmed by several previous studies that measured food
insecurity and general health status.37–40 Pheley et al.37 found that food-insecure adults have poorer
functional status compared with food-secure respondents based on all SF-36 scales in a clinical/nonclinical
setting in Appalachia. |
| Scores of the quality of life domains in our study are higher than those in other studies. This result
might be attributed to the characteristic of rural communities, which tend to have higher social
cohesion within and outside the family compared with other communities. These findings are in
agreement with other studies that reported the association of living in rural areas with better overall
mental health.41 Rural areas exhibit relatively higher average health and less prevalence of premature
mortality.42, 43 Rural dwellers are also less likely to rate their health as fair or poor.44, 45 |
| In view of the sampling and recruitment strategies utilized in this project, the use of a convenience
sample may result in selection biases that could affect the generalizibility of the findings. |
Conclusion |
| Child hunger is associated with poor health status on each scale in the SF-36 instrument, whereas
household food insecurity and adult food insecurity are associated with six scales. No differences in
health status were observed between the various levels of food insecurity (household food insecurity,
adult food insecurity, and child hunger). The MLR analyses demonstrate a consistent, independent relationship of each SF-36 scale with food insecurity. Hence, food insecurity, to a certain extent, is one
of the strongest predictors of each health status construct. Although an association between food
security and health status is suggested in this convenience sample, future investigations are needed to
examine the relationship between more objective measures of health status, household food
inventories, and other direct measures of food availability in more diverse and larger populations. |
Acknowledgement |
| We would like to express our sincerest gratitude and appreciation to Ms. Fiona Lim Wei Ting and Mr.
Azizi Bin Mohamed Zain for their assistance, the Social Welfare Department of Malaysia for giving us
permission to conduct this study, Dr. Kamarul Imran for his help and guidance in data analysis, and to
all participants and staff who made this study possible. This work is supported by the Universiti Sains
Malaysia (No. 1001/PPSK/812022) and Research University Postgraduate Research Scheme from
Institute of Postgraduate Studies Universiti Sains Malaysia. |
Author’s contributions |
| Ihab A.N. wrote the manuscript and performed data collection, data entry, and data analysis. Rohana
A.J. is the principal investigator for this study. She also contributed to the design of the study, liaised
with the authorities involved, supervised data collection in the field and wrote the initial draft of the
manuscript. Wan Manan W.M responsible for the application of grant, budget, and ethical approval.
Wan Suriati W.N. was fully involved in data collection as a trained research assistant and managing
technical problems in the field. Zalilah M.S. and Mohamed Rusli A. participated in the design of the
study. All authors participated in the review of the manuscripts and approved the final version. |
| Competing interests: The authors declare no competing interests. |
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