Abstract
Food insecurity (FI) is a major global health challenge despite the actions taken to combat it. Social and economic factors can affect food security. This study aimed to systematically review research evidence to investigate socioeconomic determinants of FI in the healthy Iranian population. Three databases and search engines, including PubMed, Scopus, and Web of Science as well as Iranian scientific search engines, including Scientific Information Database (SID) and Magiran were systematically searched in English and Persian to identify relevant articles from 1990 until October 2022. A total of 6624 studies were identified. After screening, 115 cross-sectional and 6 case–control studies were included. We separated the results based on rural, urban, and suburban areas. Socioeconomic factors which were correlated to FI in both urban and rural areas were family head education level, parent’s occupation, family head occupation, occupation diversity, number of working persons in a family, marital status of the family head, family size, number of children, household’s economic status, house area, house ownership, and living facilities. Many socioeconomic determinants affect IF in the urban, rural, and suburban areas; so, policymakers should have comprehensive attention on these factors, specifically to improve food security status and support society’s health.
Background
Food insecurity (FI) occurs when there is inadequate access to adequate, safe, and nutritious food based on cultural preferences for normal and active life (Napoli et al., 2011). According to the World Food Program (WFP), nearly 12% of the global population faced FI at severe levels in 2020, representing 928 million people (World Food Programme, 2021). The COVID-19 (coronavirus disease of 2019) pandemic has had an impressive impact on various dimensions of food security worldwide (Béné et al., 2021). A meta-analysis performed by Behzadifar et al. (2016) showed that 49% of families in Iran suffered from FI. Iran has been faced many sanctions, especially after 2018, so the prevalence of FI has increased (Hejazi and Emamgholipour, 2020). FI has several consequences, such as depression (Pourmotabbed et al., 2020), obesity (Moradi et al., 2019), cardiovascular disease, and mortality (Sun et al., 2020).
It has several components, including physical availability of food, economic and physical access to it, food utilization, and stability of the food sources. The lack of any component leads to FI (Gross et al., 2000). It is affected by socioeconomic factors that might be different across different regions of the world. Earlier studies have shown that family social factors, including household head’s education level, occupation and marital status, family economic status, and family size, may affect food security (Ramesh et al., 2010; Safarpour et al., 2013; Tabrizi et al., 2018). The likelihood of experiencing FI is higher among households with low income relying on social assistance, those headed by a divorced person, and/or the head of the household who got a low education level (Bulawayo et al., 2019; Kharisma and Abe, 2020; Ramesh et al., 2010). In a systematic review performed in Brazil, income has been shown to be at the center of factors linking social factors to FI (Lignani et al., 2020).
An understanding of the determinants of FI is a crucial part of making policies to prevent the problem. FI could be measured at different levels including global, regional, country, city, household, or individual (Shapouri and Stacy, 2003). Although several documents concerning the FI-associated factors have been published in Iran, each study has reported region-specific factors in this area. However, policymaking in the country needs comprehensive information. There is no systematic review investigating the socioeconomic determinants of FI in Iran. Iran, similar to other countries, has its own social, economic, and cultural features. A previous review performed by Alimoradi et al. (2015) in Iran did not systematically assess and report factors contributing to FI (Alimoradi et al., 2015).
Therefore, the aim of this systematic review was to identify socioeconomic determinants of FI in Iran. This evidence could guide future policies to improve the food security situation.
Method
Search strategy
We selected articles published from 1990 until October 2022 by searching through PubMed, Scopus, Web of Science, and Google Scholar. We further searched in SID (Scientific Information Database) and Iranian Magazines Database (Magiran) using Persian and English keywords, including food security and FI. Due to the sanctions, we have not had access to Scopus since 2022, so we could not search in Scopus after this time. The following search string was used in our literature search (“Food Supply”[Mesh] OR “food security” OR “food insecurity” OR “food adequacy” OR “food insufficiency” OR hunger OR starvation OR “nutrition* security” OR “nutrition* insecurity”) AND (Iran) OR Persia*). The search strategy in other databases is presented in Supplementary Table 1. No other limitation including language was applied. The gray literature including documents that have not been formally published in a peer-reviewed indexed format was not included. Duplicate citations were also removed. The relevant full-text articles were obtained. If necessary, we contacted the corresponding author. Reference lists of both the identified articles and review articles were searched for other relevant publications.
Selection
All types of studies (except for ecological, qualitative, and studies that were conducted in the drought period) assessing the socioeconomic determinants of FI, including education level and occupation of both parents and the participants, number of working persons in the family, marital status of the head of household, family size, number of children, having child/children <18 in the household, household income level, non-agricultural income, savings, living facilities, occupational diversity, household economic status, house area, house, and car ownership, number of rooms, residence (urban/rural), agriculture land area, and other FI relative factors were included in this systematic review. Studies that only reported FI prevalence or did not have statistical analysis were excluded for the study. We also excluded studies conducted on immigrants. Only studies that used valid and reliable questionnaires were included in the study; therefore, publications that assessed FI by calorie intake categorization or comparison of standard food basket or food diversity score were not included. We did not include studies without statistical analysis.
Data abstraction
Titles and abstracts from the electronic searches were screened by two investigators (S.N., M.B.) for inclusion based on exposures and outcome measures of the study. If it was not clear whether the study met the inclusion criteria, the full text of the article was reviewed. Disagreements were referred to the principal investigator.
Data synthesis and analysis
Required data were extracted using a data collection form. The main outcome of interest in this study was FI. The following data were extracted: the first author’s last name, date of publication, study design, participants’ age range, sample size, city or place of investigation, method of assessment of FI, FI prevalence, results of correlation, and/or association of socioeconomic factors with FI and statistical analysis.
Quality assessment of studies
The quality of the eligible studies and risk of bias were evaluated according to the Newcastle-Ottawa Scale (NOS) (Wells et al., n.d.). Based on this scale, each study was scored for the selection of study group/groups (based on representativeness of the sample, sample size, non-respondents, and ascertainment of the exposure), comparability of the subjects (adjustment for confounders), assessment of the outcome (using validated tool or method for assessing FI), and statistical analysis. In this method, each study has a score between 0 and 10. We assumed studies with a score of <4 as unsatisfactory.
Results
Study characteristics in total
A total of 6624 studies were identified using search strategy. Many papers were excluded from the examination of abstracts as they did not meet the inclusion criteria. The results of the screening process and reasons for exclusion are given in Figure 1.
Flow chart of the selection study.
The quality score in the final sample ranged from 3 to 10 with an average of 6.8. Of the included articles, 12 studies had unsatisfactory quality, 51 had good quality, 38 were satisfactory, and 20 were very good. Data are presented in Table 1 (cross-sectional studies) and Table 2 (case–control studies).
The Newcastle–Ottawa Scale for cross-sectional studies.
Representativeness of the sample:
Truly representative of the average in the target population (*all subjects or random sampling).
Somewhat representative of the average in the target group (*non-random sampling).
Selected group of users/convenience sample.
No description of the derivation of the included subjects.
Sample size:
Justified and satisfactory (*including sample size calculation).
Not justified.
No information provided.
Non-respondents:
Proportion of target sample recruited attains prespecified target or basic summary of non-respondent characteristics in sampling frame recorded.*
Unsatisfactory recruitment rate, no summary data on non-respondents.
No information provided.
Ascertainment of the exposure (risk factor):
If the study applied validated measurement tool to ascertain the risk factors, we assigned two stars. In addition, we still assigned one star to the study applied non-validated measurement tool, which was available or described.
Comparability of subjects in different outcome groups on the basis of design or analysis. Confounding factors controlled.
Data/results adjusted for relevant predictors/risk factors/confounders.**
Data/results not adjusted for all relevant confounders/risk factors/information not provided.
Assessment of outcome:
Independent blind assessment using objective validated methods.**
Unblinded assessment using objective validated methods.**
Used non-standard or non-validated methods with gold standard.*
No description/non-standard methods used.
Statistical test:
Statistical test used to analyze the data clearly described, appropriate and measures of association presented including confidence intervals and probability level (p value).*
Statistical test not appropriate, not described, or incomplete.
The Newcastle–Ottawa Scale for case–control studies.
A study can be awarded a maximum of one star for each numbered item within the Selection and Exposure categories. A maximum of two stars can be given for Comparability.
Is the case definition adequate?
yes, with independent validation *
yes, eg record linkage or based on self-reports
no description
Representativeness of the cases
consecutive or obviously representative series of cases *
potential for selection biases or not stated
Selection of Controls
community controls *
hospital controls
no description
Definition of Controls
no history of disease (endpoint) *
no description of source
Comparability of cases and controls on the basis of the design or analysis
study controls for the most important factor *
study controls for any additional factor *
Ascertainment of exposure
secure record (eg surgical records) *
structured interview where blind to case/control status *
interview not blinded to case/control statusd) written self-report or medical record onlye) no description
Same method of ascertainment for cases and controls
yes *
no
Non-Response rate
same rate for both groups *
non-respondents described
rate different and no designation
Ten studies recognized as double publications (overlapping with another publication of the same group) were removed. Two studies were excluded from the study because the authors compared the prevalence of severe FI with mild or moderate FI. After the screening procedure, 121 studies were included in the review. In all, 1 out of 115 cross-sectional studies was a longitudinal study and the baseline characteristics were considered in the review. In addition, 6 out of the published articles were case–controls. It should be noted that we only included case–control studies which had healthy controls and statistical analysis was done in the control group separately. In all, 5 out of the 115 studies were conducted specifically during COVID-19 (Ezzeddin et al., 2022; Ghanbari Movahed et al., 2022; Pakravan-Charvadeh et al., 2021b; Tezerji and Nazari Robati, 2021; Yazdanpanah et al., 2021). The reference lists of some of the excluded articles are presented in Supplementary 2.
Of the published cross-sectional articles, 65 studies were reported in English, and 56 were in Persian; 36 studies were conducted in the rural areas, 66 were performed in the urban areas, 13 studies consisted of both rural and urban areas, 3 in the suburbs, and 2 consisted of both urban and suburb areas. One study has been done in an industrial company.
There were eight studies on pregnant women (Barzegar et al., 2019; Fathi Beyranvand et al., 2019; Hojaji et al., 2015; Kazemi et al., 2018; Rajizadeh et al., 2019; Rasty et al., 2016; Sharifi et al., 2018; Yadegari et al., 2017), six in the elderly (Alipour et al., 2021; Cheraghi and Kazemi, 2018; Eghrari et al., 2020; Fallah Tafti et al., 2015; Hosseinpour et al., 2019; Rafat et al., 2020), and the remaining are conducted in growers; children and adolescents (Alipour et al., 2016; Esfandiari et al., 2018; Nejati Salehkhani et al., 2018); and based on the household level. It should be noted that articles that assessed FI based on Radimer–Cornell have reported FI in three categories of household, individual, and child level. The articles were published between 2006 and 2022.
The overall prevalence of FI ranged from 10.4% in a study conducted in Tabriz (Koohi, 2014) to 99.6% in another study conducted in Nahavand (GalamBahri et al., 2017). In studies conducted in the rural areas, the highest and lowest prevalence rates were 99.6 in Nahavand (GalamBahri et al., 2017) and 40.8 in Neyshabour villages (Gholami et al., 2013), respectively. In urban households, the highest and lowest prevalence rates were 98.6% in the Ghazi Tabatabie et al.’s (2011) study performed in district 20 in Tehran and 10.4% in a study conducted in Tabriz (Koohi, 2014), respectively. Among pregnant women, the highest prevalence rate was related to Rajizadeh’s study, which reported 54.4% (12) and the lowest rate was 30.9% in the study of Yadegari et al. (2017).
Measurement of FI
In 58 articles, FI was measured using the USD18-item, 39 studies used HFIAS, and 8 employed the Radimer–Cornell questionnaire; 12 studies used a 6-item USDA questionnaire, and 4 applied the 8-item adapted version.
Assessment of socioeconomic variables
All of the included studies evaluated socioeconomic factors through a self-report questionnaire.
Statistical analysis and adjustment for confounders
All studies used statistical analysis including chi-square test, independent t-test, correlation coefficient, one-way analysis of variance (ANOVA), univariate and multiple logistic regression, and logit model. The number and type of potential confounders used for statistical adjustment varied between studies, of which most included age, social class, and sex when applied. Only one study reported a p-value without representation of statistical analysis (Afshar et al., 2018). Seven studies (Mokari-Yamchi et al., 2020; Sharifi et al., 2018; Tabrizi et al., 2018; Tutunchi et al., 2020) adjusted their results for different confounding factors including age, gender, education level, family size, and 44 used multiple regression analysis, which is a way of adjustment. Some of the studies employed more than one statistical analysis test.
The socioeconomic determinants of FI are different in the urban, rural, and suburb settings, so we decided to present the results in separate sections. In this way, the presentation and interpretation of the results would be better and help policymakers have more accurate and precise information about the variables.
Characteristics of studies performed in rural areas
In this category, there were 36 studies. The results are presented in Table 3. The majority of the articles had an acceptable quality score and only 1 of them had unsatisfactory quality; 11 studies had good quality, 7 were satisfactory, 7 were very good. All of the studies had a cross-sectional design. It should be mentioned that there were two articles conducted by Savari et al. (2014a, 2014b) and Sharafkhani et al. (2010, 2011), who sliced the results and reported them in separate articles.
Characteristics of studies performed in rural regions.
1: Family size; 2: Financial aid receive; 3: Family head education level; 4: Number of working person in family; 5: Household income; 6: Crop income; 7: Agricultural lands; 8: Ownership of agricultural machinery; 9: Agricultural techniques; 10: Information resources; 11: Social participation; 12: Household’s economic status; 13: Social capital; 14: Family head job; 15: Marital status of family head; 16: Having < 18-year-old child; 17: Number of educated family members; 18: Work experience; 19: Maternal education level; 20: House Ownership; 21: Insurance; 22: Number of rooms; 23: Employment ratio; 24: Garden area; 25: Arable land area; 26: Supporting institution; 27: Water arable land ownership; 28: Number of dependents; 29: Non-agricultural incomes; 30: Distance from the shopping center; 31: Saving; 32: Number of trips to the city; 33: Number of livestock animals; 34: Banking facilities; 35: Mother’s job; 36: Job diversity; 37: Food storage; 38: Food processing facilities; 39: Economic sustainability; 40: social sustainability; 41: Social communication with town; 42: Subsidy; 43: Number of children; 44: House area; 45: Household constant monthly income; 46: Credit access; 47: Being under the coverage of a supporting center; 48: Participation in home loans; 49: Household welfare index; 50: Car ownership; 51: Presence of children; 52: Facilities; 53: Number of working children; 54: Economic determinants; 55: Social determinants (including social support, social trust, community relations, . . .); 56: Distance from city; 57: Working in secondary labor market; 58: Household head seasonal job; 59: Family head constant job; 60: Arable and rain fed land ownership; 61: Family type (extended or nuclear); 62: Woman education; 63: Paternal education level; 64: Father’s job; 65: Number of birds; 66: Number of educated children; 67: Number of person with non-agricultural incomes in family; 68: Women participation; 69: Collaboration with social organizations; 70: Subsistence risks; 71: Woman economic ability; 72: Mixed production style; 73: Having <5-year-old child; 74: Having elderly in household; 75: Number of markets; 76: Households received additional subsidies (in addition to the national subsidy); 77: Transport expenditure; 78: Clothes expenditure; 79: Financial capital.
FI: Food insecurity; ANOVA: analysis of variance; CI: confidence interval; OR: odds ratio; USDA: United States Department of Agriculture; HFIAS: Household Food Insecurity.Access Scale; FS: Food Secure; SEM: Structural Equation Modeling.
Of the published articles, 10 studies were reported in English and 26 were written in Persian.
One of the studies was conducted on elderly women (Cheraghi and Kazemi, 2018); one on women as head of the family (Saadi and Moaddab, 2013), and two studies on female-headed households (Naderi Mahdei and Jalilian, 2016; Soufi and Mirakzadeh, 2021).
Measurement of FI
FI was measured using the USDA 18-item questionnaire in 16 studies, 2 studies with Radimer–Cornell (Khosravipour et al., 2017), and 14 studies with HFIAS. Four studies used a 6-item USDA questionnaire (Gholami et al., 2013; Khodabakhshzadeh et al., 2018; Sharafkhani et al., 2010, 2011).
Characteristics of studies performed in urban areas
In this category, there were 66 studies. The results are presented in Table 4. It should be noted that there were two studies performed by Payab et al. (2012, 2014), who sliced the results and reported them in separate articles.
Characteristics of studies performed in urban areas.
1: Participant education, 2: Participant job, 3: Family size, 4; Income, 5: Number of Children, 6: Household income, 7: House ownership, 8: Family head education level, 9: Family head job, 10: Mother’s education level, 11: Father’s education level, 12: House area, 13: Mother’s job, 14: Father’s job, 15: Health insurance, 16: Elderly’s education level, 17: Elderly marriage status, 18: Economic fund, 19: Type of admission, 20: Room number, 21: Household’s economic status, 22: Marital status of the family head, 23: Women’s education level, 24: Husband education, 25: Social capital, 26: Facilities, 27: Elderly’s job, 28: Having < 18 y child, 29: Number of working persons in the family, 30: Participant marital status, 31: Social participation, 32: Women’s job, 33: Husband job, 34: Household head birth location (rural), 35: House mortgage, 36: Car ownership, 37: Number of computers, 38: Number of cars, 39: Mother’s marital status, 40: Saving power 41: Number of educated family members 42: Number of subsidy recipient members 43: Number of male children 44: Number of female children 45: Food storage.
FI: food insecurity; ANOVA: analysis of variance; CI: confidence interval; OR: odds ratio; USDA: United States Department of Agriculture; HFIAS: Household Food Insecurity Access Scale; FS: Food Secure; SEM: Structural Equation Modeling.
The majority of the articles had an acceptable quality score: 10 articles had unsatisfactory quality; 29 studies had good quality, 21 were satisfactory, and 6 were very good. There were 6 case–control studies (Hasan-Ghomi et al., 2015; Mirshekar et al., 2017; Mirzadehahari et al., 2015; Moradi et al., 2016; Rasty et al., 2016) and 60 cross-sectional designs.
Five studies were conducted on children (Alipour et al., 2016; Esfandiari et al., 2018; Jafari et al., 2017; Nejati Salehkhani et al., 2018; Parsavala et al., 2013); seven on pregnant women (Barzegar et al., 2019; Fathi Beyranvand et al., 2019; Hojaji et al., 2015; Rajizadeh et al., 2019; Rasty et al., 2016; Yadegari et al., 2017), five on the elderly (Alipour et al., 2021; Anbari-Nogyni et al., 2022; Eghrari et al., 2020; Fallah Tafti et al., 2015; Hosseinpour et al., 2019), and one case–control study on arthritis rheumatoid patients and healthy controls; however, the statistical analysis was done independent of health status, so we included this article (Moradi et al., 2016). In this category, some studies reported their results based on household status, individual level, and the details are presented in Table 4.
Measurement of FI
FI was measured by using the USDA 18-item questionnaire in 31 articles, 20 studies by HFIAS. Five studies used a 6-item USDA questionnaire and three studies employed the 8-item questionnaire. Radimer–Cornell questionnaire has been used in six studies and one study used a valid and reliable 18-item author-made questionnaire (Moosavian et al., 2019).
Characteristics of studies performed in urban or rural areas or suburbs
In this category, there were 19 studies. The results are presented in Table 5. Some of the studies were conducted on both the rural and urban populations. The rest was performed in the rural and urban areas or suburbs.
Characteristics of studies performed in urban and rural areas or suburbs.
1: Family head education level, 2: Household income, 3: Family Size, 4: Mother’s education level, 5: Father’s education level, 6: Marital status of a family head, 7: Household constant monthly income, 8: causes of migration to slum (economical or socio-cultural), 9: Mother’s job, 10: Father’s job, 11: Number of working persons in a family, 12: House area, 13: Residence, 14: Having < 18 y child, 15: Number of children, 16: Number of elderly, 17: House Ownership, 18: Car ownership, 19: Family head job, 20: Room number, 21: household expenditure, 22: Household’s economic status, 23: Husband’s education, 24: Family head constant job, 25: Family head seasonal job, 26: Saving, 27: Subsidy, 28: House rent, 29: House mortgage, 30: Membership in organizations, 31: Participant’s education, 32: Participant’s job, 33: Insurance, 34: Husband’s job.
FI: Food insecurity; CI: confidence interval; ANOVA: analysis of variance; USDA: United States Department of Agriculture; HFIAS: Household Food Insecurity Access Scale; CPS-FSSM: Current Population Survey-Food Security Survey Module.
The majority of the articles had an acceptable quality score. Of these, 8 studies had good quality, 8 were satisfactory, two studies had very good quality and only one had unsatisfactory quality. All of the studies had the cross-sectional design. One of the studies was conducted on workers of an industrial company in Tabriz (Tutunchi et al., 2020). Dastgiri et al. (2011), Amiresmaeili et al. (2021) and Ghasemzadeh et al. (2022) conducted their studies in the suburbs. In all, 2 studies were done in the urban and suburban settings (Abbasi et al., 2016; Pasdar et al., 2019), while 13 studies chose their sample from the urban and rural regions (Akbarpoor et al., 2016; Arzhang et al., 2019; Daneshzad et al., 2015; Fallah Madvari et al., 2015; Ghodsi et al., 2016; Kazemi et al., 2018; Pakravan-Charvadeh et al., 2020; Rafat et al., 2020; Rahimi Moghadam et al., 2015; Ramezani Ahmadi et al., 2017; Salarkia et al., 2016; Shahraki et al., 2016; Tabrizi et al., 2018). Kazemi et al.’s (2018) study was done among pregnant women.
Of the published articles, 14 studies were reported in English and 5 were in Persian. The articles were published between 2011 and 2022. The results of studies in this category were presented based on household status or individual level.
Measurement of FI
FI was measured using the USDA 18-item questionnaire in 10 articles, and 5 using HFIAS. Three studies used a 6-item USDA questionnaire and one study employed the 8-item adapted version (Rafat et al., 2020).
Correlations of FI in studies performed in urban areas
Socioeconomic factors correlated with FI in urban studies were as follows: Parents’ education level, Household head’s education level, Parental occupation, Number of educated family members, Household head’s occupation, Occupational diversity, Number of persons working in the household, Marital status of the head of household, Family size, Number of children, Number of male children, Number of female children, Having children/child <18 in the household, Household income level, Household’s economic status, House area, House ownership, House mortgage, Number of rooms, Social participation, Social capital, Living facilities, Car ownership, Household head birth location, Health insurance, Type of admission in university, Saving power, Food storage and Number of subsidy recipient members. In the following, we provide information about some of the most important of them.
Correlations of FI with socioeconomic factors
In all, 17 studies assessed maternal occupation; 9 out of 17 studies showed that there was no significant association between FI and maternal occupation (Alipour et al., 2016; Ekhlaspour et al., 2019; Esfandiari et al., 2018; Mirshekar et al., 2017; Mohammadzadeh et al., 2010; Payab et al., 2012, 2014; Rajizadeh et al., 2019; Salarkia et al., 2014). Eight studies reported an inverse association between FI and maternal occupation (Alimoradi et al., 2022; Hakim et al., 2012; Hojaji et al., 2015; Moosavian et al., 2019; Mortazavi et al., 2017; Safarpour et al., 2013; Siahipour et al., 2019; Yeganeh et al., 2019). Ahmadi et al. (2020) and Sharifi et al. (2018) reported an inverse association between maternal occupation and FI. Inverse association indicated that housekeepers were more food insecure compared with the employees.
Paternal occupation was evaluated in 12 studies; 2 out of 12 studies showed no significant association between FI and paternal occupation (Alipour et al., 2016; Rajizadeh et al., 2019). Nine studies reported an inverse association between FI and paternal occupation (Alimoradi et al., 2022; Farzaneh et al., 2017; Hakim et al., 2012; Mohammadzadeh et al., 2010; Moosavian et al., 2019; Safarpour et al., 2013; Siahipour et al., 2019; Yeganeh et al., 2019; Zerafati Shoae et al., 2007). Fallah Tafti et al. (2015) reported an inverse association between elderly occupation and their FI status.
Household head’s occupation was assessed in 20 studies; 2 out of 20 studies showed no significant association between FI and household head’s occupation (Hojaji et al., 2020; Mortazavi et al., 2017). Fourteen studies reported an inverse association between FI and household head’s occupation (Afshar et al., 2018; Dastgiri et al., 2006; Ebadi-Vanestanagh et al., 2019; Ekhlaspour et al., 2019; Ghazi Tabatabie et al., 2011; Koohi, 2021; Mirshekar et al., 2017; Mohammadi et al., 2012, 2016; Parvin et al., 2020; Payab et al., 2012, 2014; Salarkia et al., 2014; Yadegari et al., 2017). Mirzadehahari et al. (2015) and Rasty et al. (2016) reported no significant association between FI and household head’s occupation in the control group. Hassan Ghomi’s study reported no significant association between a person’s occupation and FI (Hasan-Ghomi et al., 2015).
Seven studies assessed the number of persons working in the household; 2 out of 7 studies showed no significant association between FI and the number of persons working in the household (Ahmadi et al., 2020; Pakravan-Charvadeh et al., 2021a). Three studies reported an inverse association between FI and the number of persons working in the household (Hashemzadeh et al., 2022; Moosavian et al., 2019; Parsavala et al., 2013). Rasty et al. (2016) reported no significant association between FI and the number of persons working in the household in the control group.
Household income level was assessed in 28 studies; 1 out of 28 studies showed no significant association between FI and household income level (Alipour et al., 2016); 24 studies reported that there was an inverse association between FI and household income level (Abbasalizad Farhangi et al., 2015; Abedi et al., 2013; Afshar et al., 2018; Alimoradi et al., 2022; Anbari-Nogyni et al., 2022; Azizi et al., 2013; Barzegar et al., 2019; Dastgiri et al., 2006; Ebadi-Vanestanagh et al., 2019; Hojaji et al., 2015, 2020; Jafari et al., 2017; Mirzadehahari et al., 2015; Mohammadi et al., 2012, 2016; Moosavian et al., 2019; Noorollah Noorivandi, 2018; Pakravan-Charvadeh et al., 2021a; Shabanali Fami et al., 2021; Sharifi et al., 2018; Siahipour et al., 2019; Yadegari et al., 2017; Yeganeh et al., 2019; Zerafati Shoae et al., 2007). Fallah Tafti, Alipour, and Eghrari reported that income level of the elderly was inversely correlated with FI (Alipour et al., 2021; Eghrari et al., 2020; Fallah Tafti et al., 2015).
It should be mentioned that 19 studies evaluated the effect of household economic status on FI and reported an inverse association between FI and household economic status (Asadi-Lari et al., 2019; Azizi et al., 2013; Farzaneh et al., 2017; Hosseinpour et al., 2019; Mirshekar et al., 2017; Mohammadzadeh et al., 2010; Moradi et al., 2016; Mortazavi et al., 2017; Narmaki et al., 2017; Parsavala et al., 2013; Parvin et al., 2020; Payab et al., 2014; Rajizadeh et al., 2019; Ramesh et al., 2010; Rasty et al., 2016; Safarpour et al., 2013; Shakibay Novin et al., 2022; Yadegari et al., 2017; Bayat et al., 2021). In their study, Hezarjaribi et al. assessed household economic fund and reported an inverse association between FI and it (Hezarjaribi and F, 2013) and in the Pakravan-Charvadeh et al. (2021a) study, the number of subsidy recipient members in the family and saving power were inversely associated with FI. Food storage was only assessed in one study and did not show any significant association (Shabanali Fami et al., 2021).
House ownership was assessed in 20 studies; 5 out of 20 studies showed no significant association between FI and house ownership (Amin et al., 2022; Farzaneh et al., 2017; Mohammadi et al., 2012; Moosavian et al., 2019; Pakravan-Charvadeh et al., 2021a), and all of the remaining studies reported an inverse association between FI and house ownership (Abedi et al., 2013; Alipour et al., 2021; Ekhlaspour et al., 2019; Esfandiari et al., 2018; Hojaji et al., 2020; Koohi, 2021; Mirshekar et al., 2017; Moradi et al., 2016; Parvin et al., 2020; Payab et al., 2012; Rasty et al., 2016; Safarpour et al., 2013; Shakibay Novin et al., 2022; Siahipour et al., 2019; Yeganeh et al., 2019). It should be noted that 4 studies investigated the number of rooms and reported an inverse (Fallah Tafti et al., 2015; Mohammadi et al., 2012; Moosavian et al., 2019) and not significant association between FI and room number (Anbari-Nogyni et al., 2022).
House mortgage was assessed in the Pakravan study and had positive and not significant association with FI in urban and rural areas, respectively (Pakravan-Charvadeh et al., 2020).
House area was also assessed in 12 studies and 3 of which showed no significant association between FI and house area (Alipour et al., 2016, 2021; Anbari-Nogyni et al., 2022). Surprisingly, Yadegari et al. (2017) reported that FI was positively associated with house area, and all the remaining studies showed an inverse association between FI and house area (Fallah Tafti et al., 2015; Farzaneh et al., 2017; Mohammadi et al., 2012; Mohammadzadeh et al., 2010; Salarkia et al., 2014; Sharifi et al., 2018; Tezerji and Nazari Robati, 2021).
Seven studies assessed living facilities and showed an inverse association between FI and living facilities (Ekhlaspour et al., 2019; Fallah Tafti et al., 2015; Farzaneh et al., 2017; Mohammadi et al., 2012; Mohammadzadeh et al., 2010; Shabanali Fami et al., 2021; Tezerji and Nazari Robati, 2021). Moosavian et al. (2019) reported that the number of computers was inversely correlated with FI. It should be noted that in two studies, car ownership and the number of cars were inversely correlated with FI (Mohammadi et al., 2012; Moosavian et al., 2019).
In all, 30 studies assessed the maternal education level; 6 out of 30 studies showed no significant association between FI and maternal education level (Alipour et al., 2016; Azizi et al., 2013; Mirshekar et al., 2017; Mohammadzadeh et al., 2010; Rajizadeh et al., 2019; Turpin and Lorentzen, 1996). Twenty studies reported an inverse association between FI and maternal education level (Alimoradi et al., 2022; Dorosty et al., 2008; Ekhlaspour et al., 2019; Esfandiari et al., 2018; Farzaneh et al., 2017; Hakim et al., 2012; Hojaji et al., 2015; Jafari et al., 2017; Khorramrouz et al., 2020; Moosavian et al., 2019; Mortazavi et al., 2017; Parvin et al., 2020; Payab et al., 2012, 2014; Rasty et al., 2016; Safarpour et al., 2013; Salarkia et al., 2014; Siahipour et al., 2019; Yeganeh et al., 2019; Zerafati Shoae et al., 2007). Four studies reported that there was an inverse association between FI and women education level (Barzegar et al., 2019; Hojaji et al., 2020; Narmaki et al., 2017; Sharifi et al., 2018).
In all, 17 studies evaluated the paternal education level; 3 out of 17 studies showed no significant association between FI and the paternal education level (Alipour et al., 2016; Mohammadzadeh et al., 2010; Rajizadeh et al., 2019); 12 studies reported inverse association with FI (Alimoradi et al., 2022; Ekhlaspour et al., 2019; Esfandiari et al., 2018; Farzaneh et al., 2017; Hakim et al., 2012; Jafari et al., 2017; Moosavian et al., 2019; Safarpour et al., 2013; Siahipour et al., 2019; Yeganeh et al., 2019; Zerafati Shoae et al., 2007; Hamedi-Shahraki et al., 2021). One study assessed the education level of the elderly and reported that there was an inverse association between FI and it (Fallah Tafti et al., 2015). Hasan-Ghomi et al. showed no significant association between FI and the educational level of the control group (Hasan-Ghomi et al., 2015). In Abbasalizad Farhangi et al.’s (2015) study, the participants’ educational level did not significantly affect FI status. Only Pakravan-Charvadeh et al. (2021a) assessed the number of educated family members and reported inverse association. In Sharifi et al.’s study (Sharifi et al., 2018), husbands’ educational level did not affect women’s FI status; however, Barzegar et al. and Hojaji et al. (2020) showed an inverse association between FI and the husbands’ education level (Barzegar et al., 2019). Noorivand reported an inverse association between crop growers’ educational level and their FI status (Noorollah Noorivandi, 2018).
In all, 17 studies evaluated the household head’s education level. Among them, only 1 study by Mortazavi et al. (2017) showed no significant association between FI and the household head’s education level. Sixteen studies reported an inverse association between FI and the household head’s education level (Afshar et al., 2018; Anbari-Nogyni et al., 2022; Dastgiri et al., 2006; Ebadi-Vanestanagh et al., 2019; Ghazi Tabatabie et al., 2011; Hashemzadeh et al., 2022; Mirshekar et al., 2017; Mohammadi et al., 2012, 2016; Pakravan-Charvadeh et al., 2021a; Parvin et al., 2020; Payab et al., 2012, 2014; Salarkia et al., 2014). Mirzadehahari et al. (2015) and Rasty et al. (2016) reported that FI was inversely related to the household head’s education level in the control group in their case–control studies.
One study assessed the education level of the elderly, showing an inverse association between FI and it (Fallah Tafti et al., 2015). In Hassan Ghomi et al.’s study, the education level of the control group did not have any significant association with FI (Hasan-Ghomi et al., 2015). In Sharifi et al.’s study, the husband’s education level did not have any effect on the women’s FI status (Sharifi et al., 2018).
In all, 11 studies evaluated the household head’s marital status; 8 out of 11 studies showed no significant association between FI and the household head’s marital status (Eghrari et al., 2020; Hashemzadeh et al., 2022; Mirzadehahari et al., 2015; Mohammadi et al., 2012; Moradi et al., 2016; Parsavala et al., 2013; Payab et al., 2014; Shakibay Novin et al., 2022). One study reported that there was an inverse association between FI and the household head’s marital status (Mohammadi et al., 2016), meaning that married women were found to be more food insecure than those who were single. Alipour et al. (2021) reported that elderly’s marital status did not affect food security of them. In Alimoradi et al.’s (2022) study, households with two parents including mother and father had better food security situation compared with households living with mother only.
Azizi et al. (2013) reported no significant association between women’s marital status and FI; however, in another study, widows were more FI (Payab et al., 2012). The mother’s marital status did not affect FI in the Safarpour et al.’s study (Safarpour et al., 2013). Fallah Tafti et al. (2015) reported that the elderly who were single were more food insecure than those who were married and also in Hassan Ghomi’s study, there was no significant association between marital status and FI in the control group (Hasan-Ghomi et al., 2015).
Family size
It is supposed that the risk of FI would increase with higher family size because with decreasing the budget, there are more persons to eat. In all, 37 studies evaluated family size; 14 out of 37 studies showed no significant association between FI and family size (Abbasalizad Farhangi et al., 2015; Ahmadi et al., 2020; Alimoradi et al., 2022; Anbari-Nogyni et al., 2022; Barzegar et al., 2019; Ghazi Tabatabie et al., 2011; Hashemzadeh et al., 2022; Hojaji et al., 2020; Mirzadehahari et al., 2015; Mohammadi et al., 2012; Parsavala et al., 2013; Parvin et al., 2020; Ramesh et al., 2010; Salarkia et al., 2014); 23 studies reported that FI was positively associated with family size (Abedi et al., 2013; Asadi-Lari et al., 2019; Azizi et al., 2013; Dastgiri et al., 2006; Ekhlaspour et al., 2019; Esfandiari et al., 2018; Fallah Tafti et al., 2015; Farzaneh et al., 2017; Hakim et al., 2012; Jafari et al., 2017; Koohi, 2021; Mohammadzadeh et al., 2010; Mortazavi et al., 2017; Nejati Salehkhani et al., 2018; Pakravan-Charvadeh et al., 2021a; Payab et al., 2012, 2014; Rasty et al., 2016; Shakibay Novin et al., 2022; Sharifi et al., 2018; Siahipour et al., 2019; Tezerji and Nazari Robati, 2021; Zerafati Shoae et al., 2007).
Number of children
Previous studies showed that an increasing number of children are a risk factor for FI, because children do not have an active role in income. In the present systematic review among urban studies, this variable was assessed in 14 studies: 4 out of 14 studies showed no significant association between FI and number of children (Anbari-Nogyni et al., 2022; Azizi et al., 2013; Fathi Beyranvand et al., 2019; Ramesh et al., 2010); 10 studies reported that FI was positively associated with the number of children (Abedi et al., 2013; Ahmadi et al., 2020; Alimoradi et al., 2022; Ebadi-Vanestanagh et al., 2019; Eghrari et al., 2020; Fallah Tafti et al., 2015; Hojaji et al., 2015; Mirzadehahari et al., 2015; Nejati Salehkhani et al., 2018; Payab et al., 2014). Only Pakravan-Charvadeh et al. (2021a) assessed the relation of male or female number of children and no significant association was seen. It should be noted that five studies evaluated the effect of having children under 18 in the household on FI (Hakim et al., 2012), Mortazavi et al. (2017), Ramesh et al. (2010), and Safarpour reported a positive association and only Yadegari et al. (2017) showed an inverse association, meaning that households with children <18 years of age were more food secure.
Two studies assessed social participation and had (Noorollah Noorivandi, 2018) showed no significant association between FI and social participation (Hezarjaribi and F, 2013). One study reported the protective effect of social capital on food security (Koohi, 2014). Social capital is a measure of trust, reciprocity, and social networks and according to the literature it is positively associated with household food security, independent of household-level socioeconomic factors. Social capital is associated with decreased risk of hunger.
Health insurance was inversely associated with FI in the Amin et al. (2022) study; also type of admission to college was not significantly associated with FI.
Household head birth location was positively associated with FI, meaning that those who were born in rural areas were more food insecure compared with those born in urban areas (Koohi, 2021).
Correlations of FI in studies performed in rural areas
Correlations of FI with the socioeconomic factors in rural studies were as follows: Family Size, Financial aid receive, Household’s economic status, Family head education level, Number of working person in family, Household income, Crop income, Agricultural Lands, Ownership of agricultural machinery, Agricultural techniques, Information resources, Social participation, Social capital, Family head job, Marital status of family head, Having <18-year-old child, Number of educated family members, Work experience, Non-agricultural incomes, Distance from the shopping center, Saving, Number of livestock animals, Mother’s job, Job diversity, Food storage, Processing facilities, Social communication with town, Number of trips to the city, Distance from city, Maternal education level, Number of children, House area, House Ownership, Car ownership, Presence of children in the family, Facilities, Insurance, Supporting institution, Being under the coverage of a supporting center, Number of working children, Number of dependents, Paternal education level, Father’s job, Household head seasonal or constant job, Number of rooms, Household constant monthly income, Number of birds, Number of educated children, Number of person with non-agricultural incomes in family, Woman education, Women Participation, Woman economic ability, Collaboration with social organizations, Subsistence risks, Mixed production style, Having <5-year-old child, Having elderly in household, Number of markets, Financial capital, Economic sustainability, Social sustainability, Working in secondary labor market, Garden area, Arable land area, Water arable land ownership, Arable and rain fed land ownership, Household expenditure (transport and clothes expenditure), Participation in home loans, Credit access, Households received additional subsidies (in addition to the national subsidy), Economic determinants, Family type (extended or nuclear), Household welfare index, Subsidy and Social determinants. In the following, we provide information about some of the most important of them.
Correlations of FI with socioeconomic factors
Four studies evaluated maternal occupation; three out of four studies showed no significant association between FI and maternal occupation (Cheraghi and Kazemi, 2018; Saadi and Moaddab, 2013; Savari et al., 2014a). Taheri et al. (2016) reported that families with working mothers were more food insecure.
In all, 9 studies evaluated the household head’s occupation: 3 out of 9 studies showed no significant association between FI and the household head’s occupation (Aazami et al., 2018; Ghanbari Movahed et al., 2022; Najafianzadeh et al., 2015). Bagheri et al. (2020), Khodabakhshzadeh et al. (2018), Mahmoudi et al. (2020), Maleki Fard et al. (2021) and Yazdanpanah et al. (2021) showed that the head of the household working as an employee was less food insecure; however, GalamBahri reported that there was an inverse relationship between FI and the household head’s occupation (GalamBahri et al., 2017). Maleki Fard et al. (2021) also reported that work experience and family head constant and seasonal job compared with workless, were associated with better food security. Working in the secondary labor market had not significantly correlated with food security status in Khosravi et al.’s (2021) study. It should be noted that no significant association (Taheri et al., 2016) and also an inverse association was found between FI and paternal occupation (Rostami et al., 2015).
Three studies evaluated occupational diversity. One out of three studies showed no significant association between FI and occupational diversity (Cheraghi and Kazemi, 2018) and also the remaining two studies reported an inverse association (Cheraghi et al., 2018; GalamBahri et al., 2017). The head of the household with occupational diversity had the opportunity to earn more money and did not rely on only one source of funding.
The number of persons working in the household has been assessed in 6 studies; 3 out of 4 studies showed no significant association between FI and the number of persons working in the household (Maleki Fard et al., 2021; Rostami et al., 2015; Savari et al., 2014a); 2 studies reported an inverse association between FI and the number of persons working in the household (Asgharian Dastenaii et al., 2013; Ghadiri Masoum et al., 2017; Ghanbari Movahed et al., 2022). Khosravipour et al. (2017) found no significant association between the number of children working in the household and FI.
In all, 15 studies assessed household income level: 2 out of 15 studies showed no significant association between FI and household income level (Najafianzadeh et al., 2015; Shakiba et al., 2021). The remaining 13 studies reported an inverse association between FI and household income level (Asgharian Dastenaii et al., 2013; Bayanati et al., 2022; GalamBahri et al., 2017; Ghadiri Masoum et al., 2017; Gholami et al., 2013; Khodabakhshzadeh et al., 2018; Khosravipour et al., 2017; Maleki Fard et al., 2021; Mokari-Yamchi et al., 2020; Naderi Mahdei and Jalilian, 2016; Rostami et al., 2015; Savari et al., 2014a; Sheikhi et al., 2022). It should be mentioned that four studies evaluated the effect of household constant monthly income on FI and it was not significant in Rostami et al.’s (2015) study; however, others found an inverse association (Ghanbari Movahed et al., 2022; Maleki Fard et al., 2021; Sharafkhani et al., 2011). Also, six studies evaluated the effect of household economic status on FI and reported an inverse association (Aazami et al., 2018; Najafianzadeh et al., 2015; Saadi et al., 2015; Taheri et al., 2016; Yazdanpanah et al., 2021) and not significant association (Shakiba et al., 2021). Only one study conducted by Cheraghi (Cheraghi and Kazemi, 2018) assessed and showed a protective effect of the children’s financial aid on FI. Jozi et al. (2021) reported that economic determinants have an important and significant role in the food security status of family.
The non-agricultural incomes have been assessed in three studies with an inverse association between FI and itself (Cheraghi et al., 2016, 2018; Ghadiri Masoum et al., 2017). Saadi et al. (2015) also reported that there was an inverse association between FI and the number of people with non-agricultural income.
One study assessed crop income and showed an inverse association between FI and it (Asgharian Dastenaii et al., 2013).
The amount or having savings has been assessed in five studies. Three out of five studies reported an inverse association between FI and savings amount (Cheraghi and Kazemi, 2018; Ghanbari Movahed et al., 2022; Maleki Fard et al., 2021) and also the remaining two studies showed no significant association (Ghadiri Masoum et al., 2017; Shabanali Fami et al., 2020).
Ghadiri Masoum et al. (2017) evaluated the positive effect of the subsidy on food security; however, in the Sheikhi et al. (2022) study, receiving additional subsidies in addition to the national subsidy did not have a meaningful association.
One study assessed the family production style and showed an inverse association between FI and it, meaning that households with mixed production styles are more food secure than those with agricultural/industrial production or business production only (Savari et al., 2014a).
House ownership was assessed in eight studies. Three out of eight studies showed no significant association between FI and house ownership (Ghadiri Masoum et al., 2017; Naderi Mahdei and Jalilian, 2016; Sheikhi et al., 2022), and the remaining five studies reported an inverse association (Bayanati et al., 2022; Gholami et al., 2013; Khodabakhshzadeh et al., 2018; Saadi and Moaddab, 2013; Sharafkhani et al., 2011). Five studies assessed the house area. Three out of five studies showed no significant association between FI and it (Ghadiri Masoum et al., 2017; Naderi Mahdei and Jalilian, 2016; Rostami et al., 2015) and also the remaining two studies found an inverse relationship (Gholami et al., 2013; Sharafkhani et al., 2011). It should be noted that three studies investigated the number of rooms and non-significant (Rostami et al., 2015) and inverse associations were seen between FI and this variable (Bayanati et al., 2022; Sharafkhani et al., 2011).
Receiving financial assistance from supportive organizations was assessed in five studies. In their study, Asgharian Dastenaii et al. (2013) reported that families under the coverage of supportive systems still suffer from FI; however, Khodabakhshzadeh et al. (2018) found the inverse and the remaining reported non-significant association between FI and this variable (Ghanbari Movahed et al., 2022; Maleki Fard et al., 2021; Rostami et al., 2015). Jamini in 2017 reported that there was no significant relationship between the type of supporting system and FI (Jamini et al., 2017). Participation in home loans can act as a supportive factor; however, two studies evaluated participation in home loans and did not find any significant association (Ghanbari Movahed et al., 2022; Maleki Fard et al., 2021).
Two studies evaluated the work experience of the family head. One study showed no significant association (Bagheri et al., 2020), however, another reported an inverse association between FI and this variable (Maleki Fard et al., 2021).
Cheraghi and Kazemi (2018)reported that the area of the garden and the amount of rainfed areas were inversely associated with FI, however, Maleki Fard did not report a significant association between water arable land and FI; also the Ghanbari Movahed et al. (2022) study did not show a significant association between arable and rain fed land ownership and FI.
Four studies assessed the number of livestock animals (small and large). Three out of four studies showed an inverse association between FI and this variable (Cheraghi and Kazemi, 2018; Ghadiri Masoum et al., 2017; Saadi et al., 2015), while Rostami et al. (2015) did not find any significant association. Maleki Fard et al. (2021) and Ghanbari Movahed et al. (2021) did not find a significant association between livestock ownership and FI. One study evaluated the number of birds and showed no significant association between FI and the variable (Rostami et al., 2015).
Four studies assessed the distance from the shopping center and two of them showed that this variable was positive with FI because it reduced access to food (Cheraghi and Kazemi, 2018; Sharafkhani et al., 2011); however, the remaining did not report a significant association (Ghanbari Movahed et al., 2022; Maleki Fard et al., 2021). It should be mentioned that four studies assessed the distance from the village to city and three of them reported a positive association between FI and this variable (Gholami et al., 2013; Khodabakhshzadeh et al., 2018; Sheikhi et al., 2022); however, Savari et al. (2014a) did not find a significant association. The positive effect of the number of visits to the city on food security status had been assessed and reported in Cheraghi and Kazemi’s (2018) study, showing an inverse association between FI and this variable; Ghadiri Masoum et al.’s (2017) study also found no significant association. The economic relationship with the city was only assessed in one study showing an inverse association between FI and this variable (Ghadiri Masoum et al., 2017).
The number of markets in the village was assessed only in one study showing an inverse association between FI and this variable (Sharafkhani et al., 2011).
One study examined the subsistence risks that were positively associated with FI (Savari and Ghanian, 2019).
Four studies evaluated the living facilities and found an inverse association between FI and this variable (Cheraghi et al., 2018; Jamini et al., 2017; Khodabakhshzadeh et al., 2018; Rostami et al., 2015). It should be noted that in three studies, car ownership or the number of cars had an inverse (Gholami et al., 2013; Sharafkhani et al., 2011) or no significant association with FI (Maleki Fard et al., 2021).
Two studies evaluated insurance and showed inverse (Bayanati et al., 2022) and no significant association between FI and this variable (Jamini et al., 2017). Cheraghi showed that taking advantage of banking facilities can also protect against FI (Cheraghi and Kazemi, 2018). Access to credit was only assessed in the Ghanbari Movahed study and showed a protective effect against FI (Ghanbari Movahed et al., 2022).
One study evaluated ownership of agricultural machinery and showed an inverse association between FI and this variable (Asgharian Dastenaii et al., 2013).
Six studies evaluated agricultural land area (which is the total area of land cultivated to food and cash crop by households
The educational level of mothers was assessed in 10 studies; 4 out of 10 studies found no significant association between FI and this variable (Ghadiri Masoum et al., 2017; Naderi Mahdei and Jalilian, 2016; Najafianzadeh et al., 2015; Rostami et al., 2015). Six studies reported an inverse association between FI and maternal education level (Bayanati et al., 2022; Saadi et al., 2015; Saadi and Moaddab, 2013; Savari et al., 2014a; Sharafkhani et al., 2011; Taheri et al., 2016).
Four studies evaluated paternal education level. Two out of four studies showed no significant association between FI and maternal education level (Najafianzadeh et al., 2015; Rostami et al., 2015). Two studies reported an inverse association between FI and this variable (Saadi et al., 2015; Taheri et al., 2016).
In all, 11 studies assessed the household head’s education level; 3 out of the 11 studies showed no significant association between FI and this variable (Aazami et al., 2018; Ghadiri Masoum et al., 2017; Sheikhi et al., 2022). Seven studies reported an inverse association between FI and the household head’s education level (Asgharian Dastenaii et al., 2013; Bagheri et al., 2020; Gholami et al., 2013; Khodabakhshzadeh et al., 2018; Mahmoudi et al., 2020; Maleki Fard et al., 2021; Mokari-Yamchi et al., 2020). Surprisingly, Khosravipour et al. (2017) found that the prevalence of FI in families with a higher educational level was higher.
Family head marital status was assessed in five studies. Two out of five studies did not find any significant association (Mokari-Yamchi et al., 2020; Sheikhi et al., 2022); however, Gholami (Gholami et al., 2013) and Sharafkhani et al. (2011) showed that families with single parents were more food insecure. Bagheri et al. (2020) found that people who were married suffered more from FI compared with those who were single. Family type was not significantly different between food secure and insecure families in Maleki Fard Study. There are two types of family: extended and nuclear. Extended families include father, mother, married children and their children, grandfather and grandmother. A nuclear family involves the father, mother, and single children (Maleki Fard et al., 2021).
In all, 18 studies assessed family size; 9 out of 18 studies showed no significant association between FI and family size (Ghadiri Masoum et al., 2017; Gholami et al., 2013; Maleki Fard et al., 2021; Mokari-Yamchi et al., 2020; Rostami et al., 2015; Savari et al., 2014a; Shakiba et al., 2021; Sharafkhani et al., 2011; Taheri et al., 2016). Nine studies reported a positive association between FI and this variable (Asgharian Dastenaii et al., 2013; Bagheri et al., 2020; Cheraghi and Kazemi, 2018; Ghanbari Movahed et al., 2022; Jamini et al., 2017; Khodabakhshzadeh et al., 2018; Mahmoudi et al., 2020; Sheikhi et al., 2022; Soufi and Mirakzadeh, 2021). Surprisingly, Sharafkhani et al. (2010) showed an inverse association, indicating that the higher the family size, the better the food security status of the family. Five studies assessed the role of dependency burden (which is the ratio of dependent young and old to the population of working age) and reported that it was positively associated with FI (Bayanati et al., 2022; Naderi Mahdei and Jalilian, 2016; Saadi et al., 2015; Saadi and Moaddab, 2013; Soufi and Mirakzadeh, 2021). The number of family members with a higher education level did not have any significant association in Bagheri et al.’s (2020) study. Having an elderly person in the household did not show any significant association with FI, which was examined in one study (Sharafkhani et al., 2010).
Four studies evaluated the number of children and showed no significant association between FI and this variable (Ghadiri Masoum et al., 2017; Khodabakhshzadeh et al., 2018; Najafianzadeh et al., 2015; Saadi et al., 2015). It should be noted that six studies evaluated the effect of having children under 18 years in the household on FI status and only Saadi reported a positive association (Saadi and Moaddab, 2013), and the remaining studies did not find any significant association (Bagheri et al., 2020; Bayanati et al., 2022; Ghanbari Movahed et al., 2022; Maleki Fard et al., 2021; Najafianzadeh et al., 2015). Gholami et al. (2013) found that the presence of children did not have any association with FI status, and Sharafkhani et al. (2010) showed that having children under 5 years old is accompanied by FI of the household (Sharafkhani et al., 2010). One study evaluated the number of children with a higher education level and found an inverse association between FI and this variable (Saadi et al., 2015).
Agricultural techniques were assessed in two studies and one study showed an inverse relationship between FI and this variable (Asgharian Dastenaii et al., 2013) and another study showed no significant association (Jamini et al., 2017).
One study evaluated social participation and did not show any significant association between FI and this variable (Asgharian Dastenaii et al., 2013).
Two studies evaluated social capital. One study reported an inverse relationship between FI and social capital (Aazami et al., 2018) and another study showed no significant association (Jamini et al., 2017). Yazdanpanah et al. (2021) found an inverse association between social assets and FI. Jozi et al. (2021) could not find a significant association between social determinants and FI. They considered factors such as social relations, social supports, social trust, and other related matters under this general heading.
Women’s participation was assessed in two studies and an inverse association between FI and this variable (Saadi et al., 2015; Shabanali Fami et al., 2020). Two studies evaluated women’s economic and social ability and found an inverse association between FI and this variable (Savari et al., 2014b; Shabanali Fami et al., 2020).
One study assessed collaboration with social organizations and showed no significant association between FI and this variable (Saadi et al., 2015). Social communication with the city had a protective effect against FI in the Ghadiri Masoum et al. study.
Financial capital mainly refers to the total quantity of cash accessible to the public and may also include access to credit and loans; in the Yazdanpanah et al. (2021) study, it was inversely associated with FI.
Economic and social sustainability in rural areas are important factors that can prevent FI. Eskandari Shahraki reported that economic sustainability was inversely associated with FI; however, social sustainability was not significantly correlated. Sustainable tourism leads to a sustainable economy and creates social welfare, creating job opportunities alongside agricultural and livestock activities in rural areas (Eskandari Shahraki et al., 2022). The household welfare index was assessed in two studies and showed an inverse (Sheikhi et al., 2022) and not significant (Ghanbari Movahed et al., 2022) association. To calculate the household’s welfare index, the household’s amenities and belongings were recorded and weighed based on price, necessity, and importance between 0 and 10 in these articles.
Life expenditures like transport and clothes expenditure had a negative effect on the food security situation of the family (Soufi and Mirakzadeh, 2021).
Correlations of FI in studies performed in urban or rural areas or suburbs
There were studies that have not been done only in urban or rural settings or conducted in suburbs. Correlations of socioeconomic variables with FI in studies with a mix of rural and urban or suburb settings were as follows: Family head education level, Household income, Family Size, Mother’s education level, Participant’s education, Father’s education level, Mother’s job, Family head job, Father’s job, Participant’s job, Husband’s job, Family head constant job, Household’s economic status, Number of working persons in a family, House area, Residence, Having <18-year-old child, Marital status of a family head, Number of children, Number of elderly, Household constant monthly income, House Ownership, House mortgage, Car ownership, Husband’s education, Room number, Insurance, Household expenditure, Cause of migration to slum (economical or sociocultural), Family head seasonal job, Subsidy, Saving, House rent and Membership in organizations. In the following, we provide information about some of the most important among them.
Correlations of FI with socioeconomic factors
Mothers’ occupation was assessed in eight studies; six out of eight studies showed no significant association between FI and this variable (Daneshzad et al., 2015; Fallah Madvari et al., 2015; Kazemi et al., 2018; Ramezani Ahmadi et al., 2017; Salarkia et al., 2016; Shahraki et al., 2016). Two studies reported that housekeepers were more food insecure (Arzhang et al., 2019; Rahimi Moghadam et al., 2015).
Seven studies evaluated the household head occupation and reported an inverse association between FI and itself (Fallah Madvari et al., 2015; Ghasemzadeh et al., 2022; Kazemi et al., 2018; Pasdar et al., 2019; Ramezani Ahmadi et al., 2017; Tabrizi et al., 2018; Tutunchi et al., 2020). Rafat et al. (2020) showed that occupation of an elderly person had an inverse association with FI. It should be noted that paternal occupation has been reported in five studies and showed no significant association between FI and this variable (Rahimi Moghadam et al., 2015) and also reported an inverse association (Arzhang et al., 2019; Daneshzad et al., 2015; Ghodsi et al., 2016; Shahraki et al., 2016). The categorization of occupation was different among studies, however, the common result indicated that having work protects against FI compared with workless.
One study evaluated the number of persons working in the household and showed an inverse association between FI and this variable (Daneshzad et al., 2015). Pakravan et al. reported that household head constant job was inversely associated with FI in both rural and urban areas, and seasonal job surprisingly had a significant association only in the urban areas (Pakravan-Charvadeh et al., 2020).
Eight studies assessed the household income level; one out of eight studies showed no significant association between FI and this variable (Fallah Madvari et al., 2015; Pakravan-Charvadeh et al., 2020). The remaining studies reported an inverse association between FI and household income level (Abbasi et al., 2016; Akbarpoor et al., 2016; Daneshzad et al., 2015; Pakravan-Charvadeh et al., 2020; Pasdar et al., 2019; Rahimi Moghadam et al., 2015; Shahraki et al., 2016).
One study evaluated insurance and showed an inverse association between FI and this variable (Rafat et al., 2020). Also, six studies investigated the effect of household economic status on FI. Two out of six studies showed no significant between FI and household economic status (Kazemi et al., 2018; Ramezani Ahmadi et al., 2017). The remaining studies reported an inverse association (Arzhang et al., 2019; Ghodsi et al., 2016; Rafat et al., 2020; Tutunchi et al., 2020).
Four studies evaluated house ownership. The results of Dastgiri et al., Hashemzadeh et al. and Pakravan-Charvadeh showed an inverse association between FI and house ownership (Dastgiri et al., 2011; Ghasemzadeh et al., 2022; Pakravan-Charvadeh et al., 2020) and one study showed no significant association (Kazemi et al., 2018). It should be noted that in Pakravan-Charvadeh et al.’s study, this result was only significant in the rural areas.
Five studies investigated the house area; three out of the five studies found an inverse association between FI and this variable (Dastgiri et al., 2011; Ghasemzadeh et al., 2022; Rafat et al., 2020), while the remaining study showed no significant association (Daneshzad et al., 2015). The house area had no significant association with FI in the urban areas, however, it was surprisingly positively associated with FI in the rural areas in Pakravan-Charvadeh et al. (2020) study. The house rent was only assessed in the Pakravan study and had no significant relation with FI in both the urban and rural areas (Pakravan-Charvadeh et al., 2020). It should be noted that four studies investigated room numbers and showed an inverse association between FI and this variable (Ghasemzadeh et al., 2022; Pakravan-Charvadeh et al., 2020; Pasdar et al., 2019; Rafat et al., 2020). This finding was common between the rural and urban areas in the Pakravan-Charvadeh et al. study.
It should be noted that in two studies, car ownership was inversely related to FI (Dastgiri et al., 2011). Pakravan-Charvadeh et al. (2020) reportedly found that it was inversely associated with FI in urban areas; however, no significant association was seen in the rural areas.
In all, 11 studies evaluated the maternal education level; 6 out of 11 studies showed no significant association between FI and this variable (Daneshzad et al., 2015; Fallah Madvari et al., 2015; Ghodsi et al., 2016; Kazemi et al., 2018; Pasdar et al., 2019; Ramezani Ahmadi et al., 2017). The remaining five studies reported an inverse association between FI and the maternal education level (Akbarpoor et al., 2016; Arzhang et al., 2019; Rahimi Moghadam et al., 2015; Salarkia et al., 2016; Shahraki et al., 2016).
Eight studies evaluated the paternal education level; three out of the eight studies showed no significant association between FI and this variable (Daneshzad et al., 2015; Ghodsi et al., 2016; Pasdar et al., 2019). The remaining five studies reported an inverse association between FI and the paternal education level (Akbarpoor et al., 2016; Arzhang et al., 2019; Fallah Madvari et al., 2015; Rahimi Moghadam et al., 2015; Shahraki et al., 2016). In two studies conducted on pregnant women, a nonsignificant association (Kazemi et al., 2018) and an inverse association were observed between husbands’ education level and FI (Ramezani Ahmadi et al., 2017).
The educational level of the household head was assessed in five studies and four out of five showed an inverse association between FI and this variable (Abbasi et al., 2016; Dastgiri et al., 2011; Tabrizi et al., 2018; Tutunchi et al., 2020). In the urban areas in the Pakravan-Charvadeh et al. (2020) study, household head education was positively associated with FI; however, in the rural areas they did not find a significant association. One study conducted on the elderly reported an inverse association between the education level of an elderly person and FI (Rafat et al., 2020).
Three studies investigated the household head’s marital status, but Fallah Madvari et al. (2015) did not find any significant association between FI and this variable. Dastgiri showed that families with a single parent were more food insecure. However, Tabrizi et al. (2018) showed that families with married parents were more food insecure.
In all, 13 studies evaluated family size; 2 out of 11 studies showed no significant association between FI and this variable (Daneshzad et al., 2015; Ramezani Ahmadi et al., 2017). The remaining studies reported a positive association between FI and family size (Akbarpoor et al., 2016; Fallah Madvari et al., 2015; Ghasemzadeh et al., 2022; Ghodsi et al., 2016; Pasdar et al., 2019; Rahimi Moghadam et al., 2015; Shahraki et al., 2016; Tabrizi et al., 2018; Tutunchi et al., 2020); however, Salarkia et al. (2016) and Amiresmaeili (Amiresmaeili et al., 2021) reported an inverse association, indicating that families with more family numbers were more food secure (Salarkia et al., 2016).
Seven studies examined the number of children. Kazemi, Tabrizi, and Ghasemzadeh did not find any significant association between FI and the number of children (Ghasemzadeh et al., 2022; Kazemi et al., 2018; Tabrizi et al., 2018). Three out of six studies reported a positive association between FI and this variable (Dastgiri et al., 2011; Rafat et al., 2020; Shahraki et al., 2016); however, Salarkia et al. (2016) found an inverse association. It should be noted that only one study in this category evaluated the effect of having children under 18 years old on FI and did not report any significant association (Daneshzad et al., 2015).
One study investigated the number of elderly persons in the household and showed a positive association between FI and this variable (Dastgiri et al., 2011).
In all, 10 studies evaluated the residence; 7 out of the 10 showed no significant association between FI and the residence (Daneshzad et al., 2015; Fallah Madvari et al., 2015; Ghodsi et al., 2016; Kazemi et al., 2018; Pasdar et al., 2019; Salarkia et al., 2016; Tabrizi et al., 2018) the remaining 3 studies showed that rural families suffered more from FI (Rafat et al., 2020; Rahimi Moghadam et al., 2015; Shahraki et al., 2016); however, Ramezani Ahmadi et al. (2017) reported that FI was more seen in urban families.
Discussion
This systematic review aimed to summarize evidence from Iranian studies conducted on the associations of the socioeconomic factors with FI in the urban, rural areas, and suburbs. Providing accurate data on the determinants of FI can help policymakers prioritize and initiate appropriate interventions to improve food security. Following the recent economic downturns and sanctions in Iran, there has been a rise in FI (Hejazi and Emamgholipour, 2020) and COVID-19 affected it by different mechanisms (Rad et al., 2021). This was the first systematic review identifying socioeconomic determinants of FI in Iran comprehensively.
According to our findings, FI was more prevalent in rural areas, and the highest and lowest prevalence rates were 99.6% in Nahavand (GalamBahri et al., 2017) and 40.8% in Neyshabour villages (Gholami et al., 2013), respectively. In the urban population the situation was different; so the highest and lowest prevalence rates were 98.6% in district 20 in Tehran (Ghazi Tabatabie et al., 2011) and 10.4% in a study conducted in Tabriz (Koohi, 2014). It should be noted that the reported prevalence of the same provinces sometimes differs due to different samples, periods, and tools that were used for measuring FI.
The questionnaire-based prevalence of FI in Iran was 49.2% in a systematic review performed by Daneshi-Maskooni et al. (2017). As suggested by Gundersen et al. (2017) and Grimaccia and Naccarato (2019), the prevalence and determinants of FI differ according to the location. However, we cannot have a precise judgment about the prevalence of FI because this was not the main goal of the present study. In this review, most studies (54%) were conducted in urban and 30% in rural areas, and the rest in mixed areas. Rural studies usually investigated more socioeconomic variables than urban ones. A large number of urban studies focused on the role of parents’ education level and occupation, income, economic status of a family, family size, number of children, and house ownership. A higher level of education and having occupation can provide more income and nutritional information, so food security can be achieved. Education level is assumed to have a positive effect on household food security. Educated household heads in rural households can update and adopt appropriate technology that can help them to have more income than non-educated households (Muche et al., 2014); however, in the studies that showed a negative effect of household head education on food security, it is probable that family heads with higher education have less time and attention for household food and expenditure for education, which results in limited resources for food in the family. The results are consistent with the previous study by Harris (Harris-Fry et al., 2015). Occupation and education level of mothers in rural studies were less investigated, and the reported relations were less significant. In rural areas, women have different and diverse roles, including food processing activities, food products marketing, farming activities, and labor for farm work. Women with formal education are more likely to increase productivity in their chosen careers and improve the food security of their households (Olumakaiye and Ajayi, 2006). In addition, it is important to pay attention to women’s social participation, skills, and financial abilities. Regarding women’s occupation, no proper judgment can be made because a large number of included studies in all three categories did not show any significant association. Women’s occupation can add a financial resource to the family; however, housekeepers compared with working mothers have more time and energy to handle family and children’s nutritional status (Taheri et al., 2016). Increasing family participation in community organizations, social capital, and women’s social participation can improve household food security status (Maharjan and Joshi, 2011) and rural studies focused more on such variables, although the number of such studies is low. It is therefore recommended that future studies need to pay more attention to such variables and their roles at all rural and urban areas, and suburbs.
Only five rural studies assessed the dependency burden and showed that the higher dependency burden, the greater FI. This finding is in agreement with Pakistani and Ethiopian studies (Asghar and Muhammad, 2013; Sisha, 2020).
Rural studies investigated the household head’s occupational diversity and it was shown to be protective against FI because it can provide more secure funding for the family. Policymakers should provide facilities and training courses for family members to educate and improve their skills (Djibouti and Headquarters, 2012).
Family size in urban studies was dominantly seen as a risk factor for FI which is in agreement with foreign studies (Maharjan and Joshi, 2011; Titus and Adetokunbo, 2007); however, in rural studies, each family member can have an active role in earning money and management of food security. No rural study has shown a significant association between the number of children in the household and the family FI, since children have an active role in the family and are not dependent members. However, in towns, children and the elderly have financial dependence on other members. It is better to investigate the effect of children’s sex on household food security in further studies because, due to cultural differences, girls and boys have different roles and responsibilities in the family.
The household head’s marital status in all three areas had inconsistent results, which means that in some of the studies, the married parents’ head families were more food secure; however, others showed an inverse relationship or nonsignificant association. Usually, married family heads have more children and the dependency burden is higher in these families; however, single-family heads have little time and resources to pay attention to the nutrition status of the family. Also, in a study performed in Ethiopia, there was no significant association (Muche et al., 2014); however, in some studies, single, divorced, or widowed household heads were more food insecure (Magaña-Lemus et al., 2016). Therefore, no proper conclusion about the role of the family head’s marital status can be made.
Household income level and economic status in all three categories had the strongest inverse association with FI, which is in agreement with previous studies (Lignani et al., 2020). Rural studies assessed receiving subsidy and showed an inverse association between FI and this variable. Subsidy can provide a constant income resource, so can protect against FI; however, the prolonged effect should be evaluated because it is not identical in the urban and rural provinces (Hosseini et al., 2017).
House area is commonly a representative of the economic status of the family and the better economic status is accompanied with larger house area; however, in the Pakravan-Charvadeh study, it was positively associated with FI in the rural areas because the larger house area needs more fuel and living expenditure, so it can have a negative effect on food security.
House and car ownership seems like a household economic status marker and reflect food security in most of the studies. A positive association was observed between agricultural land area and improvement in household income level and food security, which was in agreement with previous studies by Jayne TS (Jayne et al., 2005) and Muluken et al. (2008); however, the number of studies assessing this variable is not enough and should be assessed in future studies.
It has been shown that residence is one of the FI determinants, although these results are not conclusive and it seems that future studies are needed to be conclusive. Foreign studies demonstrated that residence has a role in food security situations (Coleman-Jensen, 2012). Researchers should focus on studies in suburbs because the nature of suburbs, their health status, and nutritional and behavioral features are not well understood in Iran and the number of studies in suburbs in the present study was low and was not sufficient to reflect the FI situation. Also, marginalization is growing in some of the cities in Iran (Zebardast, 2006).
The number of studies assessing association of FI determinants including occupational diversity, women’s social and economic ability, social participation, social stability, economic stability, living expenditure, amount of rainfed areas and garden lands, family head work experience, number of educated persons in the family, and savings was rare. Also, the effects of family size, the household head’s marital status, maternal occupation, number of children, and residence need more assessment.
A systematic review performed by Zaçe et al. (2020) demonstrated the prevalence and correlations of FI among children in high-income European countries. Low-income level, households headed by single parents, a higher number of children, household’s parental structure, parents’ occupation and education level, household head’s age, depressive symptoms in parents, and ethnicity were correlated with FI.
Lignani et al. in 2020 conducted a systematic review to identify which social indicators are associated with FI in Brazilian households and how these relationships are explained. They reported three possible justifications for the association between social indicators and FI; a direct relationship, a relationship mediated by income, or the relationship mediated by another social indicator and income (Lignani et al., 2020). In a review performed by Alimoradi et al. (2015) to identify assessment tools, prevalence, predicting factors, and outcomes of FI among Iranian households, a significant association of food security with parents, age, education and occupation, income, house ownership, facilities, ethnicity was reported; however, no significant association was seen for sex, age, number of children, and the elderly in the household. The effect of age was not exactly reported in that study (Alimoradi et al., 2015). As explained earlier, the study had some limitations.
Our study had some strength. We included only studies with valid and reliable questionnaires. We separated different settings (rural, urban, and suburban) to better investigate and explain the determinants and we conducted the search in both English and Persian languages. However, there were some limitations. The gray literature was not included. Different FI tools with different duration and number of questions, different tools for assessing socioeconomic factors, and different explanations and categorizations in studies prevent accurate and precise interpretation. There were some studies with no adjustment for confounders. Also, some of the reported results were secondary findings with fewer accurate and precise statistics. Ultimately, we could not have judgment about causality or correlation for some of the reported results.
Conclusion
Many socioeconomic determinants affect FI in Iran. So, policymakers should pay comprehensive attention to these factors to improve the food security status and support society’s health. The majority of studies conducted in Iran employed a cross-sectional method, so the causality justification is not very well understood. It is therefore recommended that further studies be conducted in Iran in order to evaluate determinants of FI in the different geographical locations, especially in suburbs. Since most of the studies were conducted by nutritionists or agriculture experts separately and each of them focused on some determinants, it is suggested that studies be carried out with joint cooperation in order to synergize and determine the factors more accurately and completely. To have better justification, a meta-analysis of studies and regression models seems to be appropriate to discover the determinants’ priority.
Supplemental Material
sj-docx-1-jas-10.1177_00219096231161893 – Supplemental material for Socioeconomic Determinants of Food Insecurity in Iran: A Systematic Review
Supplemental material, sj-docx-1-jas-10.1177_00219096231161893 for Socioeconomic Determinants of Food Insecurity in Iran: A Systematic Review by Saba Narmcheshm, Ahmad Esmaillzadeh, Mina Babashahi, Elham Sharegh Farid and Ahmad Reza Dorosty in Journal of Asian and African Studies
Supplemental Material
sj-docx-2-jas-10.1177_00219096231161893 – Supplemental material for Socioeconomic Determinants of Food Insecurity in Iran: A Systematic Review
Supplemental material, sj-docx-2-jas-10.1177_00219096231161893 for Socioeconomic Determinants of Food Insecurity in Iran: A Systematic Review by Saba Narmcheshm, Ahmad Esmaillzadeh, Mina Babashahi, Elham Sharegh Farid and Ahmad Reza Dorosty in Journal of Asian and African Studies
Footnotes
Acknowledgements
The authors are grateful to Bahareh Barkhi Darian and Dr Azadeh Aminianfar for their guidance.
Author contributions
The authors’ contributions are as follows: S.N., and A.R.D., and A.E. designed the study. S.N. and M.B. conducted the research. S.N. wrote the draft. All authors critically revised the draft and approved the final manuscript.
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Data would be available for the journal in the case of urgency.
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All of the authors read the manuscript and are aware of submission.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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The author(s) received no financial support for the research, authorship, and/or publication of this article.
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