Indicator 2.1.2 Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES)

Goal 2. End hunger, achieve food security and improved nutrition and promote sustainable agriculture

Target 2.1. By 2030, end hunger and ensure access by all people, in particular the poor and people in vulnerable situations, including infants, to safe, nutritious and sufficient food all year round

Indicator 2.1.2. Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES)

Proportion of households experiencing food insecurity, 2015/16 KIHBS, 2020 KCHS, 2020 Covid Wave 2, KDHS 2022 (Data is available but indicator not computed)

10.05.2024

2.1.1, 2.2.1, 2.2.2, 2.2.3, 12.3.1

Kenya National Bureau of Statistics (KNBS)

Senior Manager

Food Monitoring, Nutrition and Environment Statistics Division

Compilation of Food Security and Nutrition Statistics

+254-735-004-401, +254-202-911-000, +254-202-911-001

P.O. Box 30266 – 00100, Nairobi. Kenya.

dps@knbs.or.ke

Definition:

The indicator measures the percentage of individuals in the population who have experienced food insecurity at moderate or severe levels during the reference period. The severity of food insecurity, defined as a latent trait, is measured on the Food Insecurity Experience Scale global reference scale, a Last updated: 2023-05-15 measurement standard established by FAO through the application of the Food Insecurity Experience Scale in more than 140 countries worldwide, starting in 2014.

Concepts:

Extensive research over more than 25 years has demonstrated that the inability to access food results in a series of experiences and conditions that are fairly common across cultures and socio-economic contexts and that range from being concerned about the ability to obtain enough food, to the need to compromise on the quality or the diversity of food consumed, to being forced to reduce the intake of food by cutting portion sizes or skipping meals, up to the extreme condition of feeling hungry and not having means to access any food for a whole day. Typical conditions like these form the basis of an experience-based food insecurity measurement scale. When analysed through sound statistical methods rooted in Item Response Theory, data collected through such scales provide the basis to compute theoretically consistent, cross country comparable measures of the prevalence of food insecurity. The severity of the food insecurity condition as measured by this indicator thus directly reflects the extent of households’ or individuals’ inability to regularly access the food they need.

Per cent (%)

This indicator is reported at National, Rural and Urban levels.

In 2014, there was development of Coping Strategy Index (CSI) which was measuring food insecurity levels. The CSI is a composite calculation of the frequency and severity of coping strategies that households adopt when facing lack of food or money to purchase food. A higher CSI score indicates more serious food security situation. The minimum possible CSI score is 7 and maximum possible score is 56. (KDHS 2014). It was developed by World Food Programme (WFP).

2015/16 Kenya Integrated Household Budget Survey (KIHBS)

Kenya Continuous Household Survey Program (KCHSP), 2019 – 2021

Kenya Demographic Health Survey (KDHS), 2014 and 2022

Covid Wave 2, 2020

Surveys

KIHBS – Every 5 years, KCHSP – Adhoc, KDHS – Every 5 years

KIHBS 2024/25, KDHS 2027

KNBS

KNBS

The 2006 Statistics Act mandates the Bureau to be the principal agency of the Government for collecting, analysing and disseminating statistical data in Kenya and to be the custodian of official statistical information (Section 4.(1)).

Food insecurity at moderate levels of severity is typically associated with the inability to regularly eat healthy, balanced diets. As such, high prevalence of food insecurity at moderate levels can be considered a predictor of various forms of diet-related health conditions in the population, associated with micronutrient deficiency and unbalanced diets. Severe levels of food insecurity, on the other hand, imply a high probability of reduced food intake and therefore can lead to more severe forms of undernutrition, including hunger. Short questionnaires like the FIES are very easy to administer at limited cost, which is one of the main advantages of their use. The ability to precisely determine the food insecurity status of specific individuals or households, however, is limited by the small number of questions, a reason why assignment of individual respondents to food insecurity classes is best done in probability terms, thus ensuring that estimates of prevalence rates in a population are sufficiently reliable even when based on relatively small sample sizes. As with any statistical assessment, reliability and precision crucially depend on the quality of the survey design and implementation. One major advantage of the analytic treatment of the data through the Rasch model-based methods is that it permits testing the quality of the data collected and evaluating the likely margin of uncertainty around estimated prevalence rates, which should always be reported.

An average of less than three minutes of survey time is estimated to collect FIES data in a well-conducted face-to-face survey, which should make it possible to include the FIES-SM in a nationally representative survey in every country in the world, at a very reasonable cost. The FAO provides versions of FIES-SM adapted and translated in each of the more than 200 languages and dialects. Compared to other proposed non-official indicators of household food insecurity, the FIES based approach has the advantage that food insecurity prevalence rates are directly comparable across population groups and counties. Even if they use similar labels (such as “mild”, “moderate” and “severe” food insecurity) other approaches have yet to demonstrate the formal comparability of the thresholds used for classification, due to lack of the definition of a proper statistical model that links the values of Last updated: 2023-05-15 the “indexes” or “scores” used for classification, to the severity of food insecurity. For this reason, care should be taken when comparing the results obtained with the FIES with those obtained with these other indicators, even if, unfortunately, similar labels are used to describe them.

Data at the individual or household level is collected by applying an experience-based food security scale questionnaire within a survey. The food security survey module collects answers to questions asking respondents to report the occurrence of several typical experiences and conditions associated with food insecurity. The data is analysed using the Rasch model (also known as one-parameter logistic model, 1-PL), which postulates that the probability of observing an affirmative answer by respondent i to question j, is a logistic function of the distance, on an underlying scale of severity, between the position of the respondent, , and that of the item, .

Parameters and can be estimated using maximum likelihood procedures. Parameters , in particular, are interpreted as a measure of the severity of the food security condition for each respondent and are used to classify them into classes of food insecurity.

The FIES considers the three classes of (a) food security or mild food insecurity; b) moderate or severe food insecurity, and (c) severe food insecurity, and estimates the probability of being moderately or severely food insecure () and the probability of being severely food insecure () for each respondent, with . The probability of being food secure or mildly food insecure can be obtained as .

Given a representative sample, the prevalence of food insecurity at moderate or severe levels (FImod+sev), and at severe levels (FIsev) in the population are computed as the weighted sum of the probability of belonging to the moderate or severe food insecurity class, and to the severe food insecurity class, respectively, of all individual or household respondents in a sample:

and

where are post-stratification weights that indicate the proportion of individual or households in the national population represented by each element in the sample.

It is important to note that if are individual sampling weights, then the prevalence of food insecurity refers to the total population of individuals, while if they are household weights, the prevalence refers to the population of households. For the calculation of the indicator 2.1.2, objective is to produce a prevalence of individuals. This implies that:

if a survey is at household level, and provides household sampling weights, they should be transformed to individual sampling weights by multiplying the weights by the household size. This individual weighting system can then be used to calculate the individual prevalence rates in formulas (1) and (2)

If the survey includes only adults, then the adult weights applied to the probabilities in formulas (1) and (2) provide the adult prevalence rates (). In this case, to calculate the prevalence in the total population, then the proportion of children who live in households where at least one adult is food insecure must also be calculated. This can be done by dividing the adult weights by the number of adults in the household and multiplying those approximate household weights by the number of children in the household. Once the approximate child weights are obtained, the prevalence of food insecurity of children who live in households where at least one adult is food insecure () can be calculated by applying these weights to the probabilities of food insecurity in formulas (1) and (2). The prevalence of food insecurity in the total population is finally calculated as:

and

Where and are the adult and children populations in the country.

When applied to the country total population, the prevalence of food insecurity in the total population provides the number of individuals who live in food insecure households (or in households where at least one adult is food insecure) in a country, at different levels of severity ( and ). In the database, the number of food insecure people are expressed in thousands.

All stakeholder were invited to participate in validation of the data before publication

Experience-based food security scales data are collected through population surveys (either household or individual surveys) using questionnaires/modules that are adapted to the country language and condition.

The Kenya National Bureau of Statistics is ISO certified based on 9001:2015 Standard requirements. The processes of compilation, production, publication and dissemination of data, including quality control, are carried out following the methodological framework and standards established by the KNBS, in compliance with the Internationally acceptable standards.

The KNBS adheres to Kenya Statistical Quality Assurance Framework (KesQAF) that underlines principles to be assured in managing the statistical production processes and output.

Data consistency and quality checks are conducted through Technical Working Groups (TWGs) before publication and dissemination.

The processes of compilation, production, publication and dissemination of data, including quality control are subjected to a set criteria and standards to ensure conformity.

Data availability:

Data available in KDHS 2014, 2015/16 KIHBS, 2019-2021 KCHS Surveys and KDHS 2022.

Disaggregation:

The data is available at National and area of residence (Rural and Urban) levels. Because of the large sample, data can be disaggregated at County levels.

This indicator is computed using the recommended international standards such as the Rash model and as such it does not deviate from international standards.

KDHS 2014, KIHBS 2015/2016, KCHS 2019-2021, KDHS 2022

https://www.knbs.or.ke/download/the-statistics-act2006-2/

https://www.knbs.or.ke/download/basic-report/

https://www.knbs.or.ke/download/the-kenya-poverty-report-2019/

https://www.knbs.or.ke/download/the-kenya-poverty-report-2020/

https://www.knbs.or.ke/download/the-kenya-poverty-report-2021/

https://www.knbs.or.ke/download/kenya-dhs-2022-main-report-volume-1/

https://www.knbs.or.ke/download/kenya-dhs-2022-main-report-volume-2/

https://www.knbs.or.ke/download/2014-kenya-demographic-and-health-survey/