To provide comparable information about the discriminatory power of FIES, we performed a modified Rasch reliability test 9 , We constructed and assessed scales for SSA subregions, for younger and older adults, and for each country. To obtain comparable prevalence rates, we developed a standard metric based on Rasch modeling results of aggregated SSA data.
We calibrated the item severities of the subregions, of younger and older adults, and for each country against this SSA standard, equating their means and SDs, adjusting them to a common metric. After adjusting for the differences in item dispersion between all scales and the SSA standard metric, we compared the relative positions of item severity parameters to the standard metric using a minimum critical value of 0.
Owing to the higher number of unique items in Congo Brazzaville, Congo Kinshasa, and Somalia, and the small size of non-extreme responses for Mauritius, we excluded data from these countries in the construction of the final SSA standard metric.
Nevertheless, we computed the prevalence rates for these countries using the determined SSA FI thresholds. Data were analyzed through the use of R version 3. Descriptive summaries were determined and compared with the use of chi-square tests. We used logistic regression analysis to examine associations between FI and sociodemographic and economic characteristics, accounting for the complex survey design, and controlled for country and survey year as fixed effects.
The study included 46, Slightly more than half of the sample were women Table 3 shows countries with infit statistics outside the acceptable range of 0. In total, High infits can occur because of small sample sizes as seen in these 2 countries, which had and non-extreme responses, respectively. Small samples provide less precise and unreliable estimates because of their potential to inflate margins of error. Nevertheless, infits in the range 1.
For all other scales, all item infits were near unity. They suggest that at least a few respondents in these countries gave highly improbable responses to these questions based on predictions from their responses to other questions.
No major violations in outfits were seen for the remaining countries, and for both age groups. However, analysis by subregions revealed high outfits of 4. Item severity parameters locate items on a continuum in relation to the level of the underlying latent construct they measure, therefore, items with lower severity parameters would be affirmed by subjects with lesser degrees of FI than for items with higher severity parameters The item severity parameters ranged from —1.
We found similar ranges of item severities in most countries Figure 1 , younger 3. The Island subregion had the highest range 5. On the other hand, the predicted order of item difficulty for items 1—5 was different from their actual order of difficulty, which indicates disordering of the items.
This disordering was seen in the aggregated SSA data Table 4 , for most countries Figure 1 , the subregions, and for both age groups results not shown. Nonetheless, because the disordering of item relative severities is similar across all countries, it indicates that FI is experienced similarly across SSA.
Our results of the item severity order were consistent with the results of response patterns. That is, our results show that as severity of FI measured by the items increased, the proportion of affirmative responses decreased Table 4. For the items measuring less severe FI, although there were inconsistencies in their severity parameters, their proportions of affirmative responses were still generally lower than those of items measuring more severe FI Table 4.
About a third of all respondents There were no other indications of problematic correlations, or indications of multidimensionality. Overall, about one-third Older adults experienced a higher prevalence of SFI than younger adults Also, women, overall, had a higher prevalence of SFI than men There was higher prevalence of SFI among younger women than among younger men The prevalence of SFI at country level ranged from 6.
West Africa had the widest range of SFI, 6. Sociodemographic and economic characteristics of severely food insecure respondents, analysis stratified by age group, within-group comparisons 1. SFI was significantly more prevalent among rural residents, the unemployed, those with lower income, and the less educated Table 5. Older adults with these characteristics were at significantly higher risk of SFI than younger adults. For both age groups, income and educational attainment were most significantly associated with SFI, but the association between rural residence and SFI was no longer significant when controlled for income results not shown.
There were no significant differences in the prevalence of SFI by marital status for younger adults, but a significantly lower proportion of married or partnered older adults experienced SFI compared with those who were single The prevalence of SFI was also higher among individuals in households with more children Table 5 , and the presence of children increased the risk of SFI for older adults more than for younger adults results not shown.
Finally, the prevalence of SFI was also higher among older adults in smaller households compared with younger adults, and among older adults living alone, compared with other respondents FI continues to be highly prevalent in SSA and has been measured by different tools, limiting comparability between countries and monitoring. Existing indicators measure various FI aspects, but given their variability and localization, it can be daunting to decide which tools to use to obtain the desired information 6.
The FIES, developed to provide cross-cultural, multicountry, comparable FI information, provides a unique opportunity to obtain the prevalence of FI across countries through the use of a single metric 9 , Cognitive testing is a qualitative research method used to investigate whether respondents understand questions as intended, focusing on the reasoning behind the responses 18 , 28 , However, even for the items with high outfits, because they had good infits, they are not indicative of any serious violation of the Rasch assumptions Outfits outside the acceptable range present less threat to validity because outfits measure the extent of highly improbable responses on items that are not close to the latent trait of respondents, those considered relatively easy or difficult for them Nonetheless, cognitive testing may better identify reasons for these high outfits, especially because of their occurrence in many countries.
In both cases, improvement of the items should be attempted, or they could be excluded in future studies that use FIES in these countries.
Our results also showed no significant correlations in most countries, and the measure was unidimensional. Because FIES met the assumptions of conditional independence and equal discrimination in most countries, we conclude that FIES has satisfactory psychometric properties, and is a valid tool for use in SSA. If these assumptions are met, other Rasch model assumptions are less likely to be problematic.
However, for Congo Brazzaville, Congo Kinshasa, and Somalia, the large number of items with significantly different severities compared to the SSA standard metric, even after several adjustments, need attention. In these countries, these items may be understood differently and may measure different severity levels of FI compared with the rest of SSA.
Reporting the prevalence of FI in these 3 countries, with the use of the SSA standard metric, should be done with caution. These countries are from Western SSA, hence, they are likely to be more culturally homogeneous, therefore, these 2 items may be understood similarly in all of them. For these countries, 1 of the items could be dropped from the scale or modified to capture a different dimension of FI. The main element of concern noted in all countries was the disordering of items, which is related to construct validity The 5 lower-severity items did not always perform as expected.
This suggests that eating few types of food might be commonplace, probably related to cultural food patterns among other factors 31 , A plausible explanation for this finding might be religiosity, i.
Owing to this item severity disordering, the expected proportion of affirmative responses to FIES items was inconsistent, although there were fewer affirmative responses for items measuring more severe FI. Despite this concern, because the Rasch criteria are very strict, results need to be taken as a whole, and no one criterion is disqualifying A potential reason for disordering of items may be difficulty in consistently discriminating categories due to too many response options or confusing labelling However, FIES does not have too many response options.
To overcome the limitation of the disordering of the threshold items measuring less severe FI, we combined scores for the FS and the MFI categories.
These countries share a recent history of political instability. More in-depth assessment of SFI determinants in these countries is necessary. Our results showed significant associations between FI and sociodemographic and economic characteristics that were previously reported in other studies 35— In our study, those who experienced higher prevalence of SFI tended to have lower incomes, higher dependency levels indicated by having many children, and lower educational attainment; lived in rural areas; were women; and were older adults.
The higher prevalence of SFI among older adults may be due to the associations between income and ageing, and income and rural residence found in this study.
A larger proportion of older adults, than younger adults, were poorer, and more of them were rural residents. These older rural residents were at significantly higher risk of SFI than younger rural residents.
The lower incomes of older adults in our study may be related to limited livelihood strategies in rural SSA, the rural economies being mostly subsistence economies. Older adults have traditionally been respected and cared for in SSA, and they still are. However, owing to economic conditions, there has been rapid rural-urban migration of younger people in SSA, leaving older adults on their own. Because of this, older adults in rural SSA may be more likely to experience insufficient household farm labor and dwindling extended family support 55 , increasing their vulnerability to FI.
Africa has experienced the highest urban growth during the last 2 decades. Our findings show that older adults in smaller households compared with larger ones, and those who lived alone, were at higher risk of SFI. It is possible that in larger households, the risk of SFI among older adults may have been lower owing to the buffering role of social support or to additional income from adults in the same household still in the workforce.
These results indicate the need for paying more attention to the living arrangements of older adults and to the attendant impact on food security. The results suggest that FIES measures the same trait, and that FI is experienced similarly across SSA with few exceptions, therefore, results can be meaningfully compared.
The strengths of our study include the large nationally representative sample from the 37 SSA countries, which makes our results generalizable to this region. The in-person interviews also decreased coverage bias. In addition, the same tool FIES was used to assess the prevalence of FI, making prevalence rates comparable across countries.
FIES contains a limited number of questions, with simple responses, reducing interview time, fatigue, and bias. However, limitations of this study include response bias that may arise from cross-sectional studies. In addition, FIES, like other tools, does not provide information on causes of FI, therefore, the utilization of mixed methods, both qualitative and quantitative, remains important in order to delve into the local causes of FI.
Part of the data is not available for public access, data used for this analysis will be furnished upon request. National Center for Biotechnology Information , U. Curr Dev Nutr. Published online Jul Author information Article notes Copyright and License information Disclaimer.
Nadine R Sahyoun: ude. Address correspondence to NRS e-mail: ude. For commercial re-use, please contact moc. This article has been cited by other articles in PMC. Objectives We aimed to 1 assess the validity of FIES for use in SSA, 2 determine the prevalence of FI by country, age group, and gender, and 3 examine the sociodemographic and economic characteristics of individuals with FI.
Results FIES largely met the Rasch model assumptions of equal discrimination and conditional independence. Keywords: Rasch modeling, food insecurity, sub-Saharan Africa, older adults, younger adults.
Open in a separate window. Sociodemographic and economic variables Sociodemographic and economic variables used in these analyses include gender, residence, marital status, age, household headcount, number of household residents older than 15 y of age, number of children below 15 y of age, educational attainment, employment, and income.
Results Sample characteristics The study included 46, Fit statistics and overall reliability of FIES Table 3 shows countries with infit statistics outside the acceptable range of 0. The ordering of FIES items and correlations Item severity parameters locate items on a continuum in relation to the level of the underlying latent construct they measure, therefore, items with lower severity parameters would be affirmed by subjects with lesser degrees of FI than for items with higher severity parameters TABLE 5 Sociodemographic and economic characteristics of severely food insecure respondents, analysis stratified by age group, within-group comparisons 1.
Chi-square tests were used to evaluate the distributions. Discussion FI continues to be highly prevalent in SSA and has been measured by different tools, limiting comparability between countries and monitoring. Notes The authors reported no funding received for this study.
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