Improved Multisectoral Nutrition Governance in Kenya is Influenced by Ministry Affiliation and Level of Nutrition Prioritization in Line Ministries

Abstract

Background

Kenya has shown progress in social, economic, and health indicators over the past 2 decades. Yet, malnutrition remains a critical public health issue. Effective Multisectoral Nutrition Governance (MNG) is an enabling determinant of nutrition ensures that nutrition policies are well-coordinated, adequately funded, and effectively implemented, leading to better nutrition outcomes.

Objective

Our study assessed the status, evolution, and factors associated with MNG in Kenya using a cross-sectional design at 2 distinct time points (2012 and 2023). The 2 distinct time points provided valuable comparative perspectives allowing for the assessment of progress and trends in MNG allowing further analysis.

Methods

The study targeted 96 government and nongovernmental staff from eligible ministries at the national level. It used a structured closed-ended questionnaire adapted from a validated Nutrition Governance Index.

Results

Our findings reveal an improvement in MNG status, from 58.4% in 2012 to 71.1% in 2023 (P < .01). The 6 MNG domains demonstrated varying performance levels, with mean scores ranging from 3.1 to 4.0 (out of 5). Despite the performance variation, the domains contributed equally to the MNG status. A positive relationship was found between the MNG score and respondent affiliation with the Ministry of Health and the level of prioritization of nutrition in a ministry (P < .05).

Conclusions

Further efforts are needed to strengthen specific MNG domains, particularly nutrition financing, transparency, accountability, and capacity, to ensure progress in tackling malnutrition in Kenya. These findings can inform efforts to enhance MNG strategies to improve nutrition outcomes sustainably in Kenya.

Plain language title

Study to Assess the Status of Effective Governance for Delivering Multisectoral Nutrition Programs in Kenya and Identify Factors that Influence Its Effectiveness

Plain language summary

Why was the study done?

Malnutrition remains a significant public health challenge in Kenya despite improvements in some nutrition indicators such as stunting. More effort across various sectors is required to sustain progress in some areas and address gaps in areas in which the country is off-track. Ensuring nutrition programs are effectively planned, managed, and implemented across different sectors requires strong/good governance. However, little is known about how well governance structures support multisectoral nutrition programs in Kenya and what factors contribute to their effectiveness. This study aimed to assess the status of governance for nutrition programs and identify key factors that influence its success over the past decade.

What did the researchers do?

The research team surveyed 96 government and nongovernment officials from 8 different ministries using a standardized questionnaire to understand how governance for nutrition programs was functioning. The questionnaire covered nutrition governance constituents/domains such as leadership, coordination, financing, and capacity to deliver nutrition programs. They compared nutrition governance effectiveness over the past 10 years (2023 and 2012). They also analyzed factors that may influence nutrition governance such as stakeholder roles, the specific government ministries they work for/support, and number of years they have worked in their sectors.

What did the researchers find?

The findings showed that nutrition governance improved from 58.4% in 2012 to 71.1% in 2023. Governance domains such as enhanced political commitment and leadership for nutrition, adoption of nutrition policy frameworks, and improved coordination observed the greatest improvements. Nutrition financing, accountability, and capacity to deliver nutrition are some of the areas that require improvement. Furthermore, the findings showed that respondents from ministries that placed a higher priority on nutrition and those linked to the Ministry of Health had higher governance scores.

What do the findings mean?

This study highlights areas where governance for nutrition programs has improved and where further work is needed. Strengthening nutrition financing, improving accountability, and strengthening capacity are essential to sustaining progress and ensuring that multisectoral nutrition programs are effectively delivered in Kenya. These findings can guide policymakers in refining governance strategies to ensure long-term improvements in nutrition outcomes across the country.

Keywords

Multisectoral Nutrition Governance multisectoral nutrition approaches nutrition policy nutrition programming Kenya

Introduction

In the past 2 decades, Kenya has improved the indicators of social, economic, and health and well-being.¹ Despite progress, malnutrition remains a public health concern demonstrated by the high prevalence of stunting, wasting, micronutrient deficiencies, and rising prevalence of overweight and obesity and diet-related noncommunicable diseases in some regions of the country.^2-4 Malnutrition has long-term implications on health, cognitive development, and economic productivity. Children who suffer from malnutrition are at a higher risk of mortality and morbidity, and those who survive often face lifelong consequences such as impaired cognitive and physical development.⁵ Additionally, malnutrition imposes an economic burden on Kenya, contributing to lost productivity and increased healthcare costs.^6-8 Kenya's progress toward achieving the World Health Assembly (WHA) targets has been mixed. While some indicators have shown improvement, the country is not on track to meet targets related to stunting, wasting, and underweight.^9,10 Socioeconomic inequalities, climate change, and inadequate access to health and social services among other issues further complicate efforts to address malnutrition.^11-14 The current nutritional challenges require innovative and effective strategies to address underlying, immediate, and enabling determinants of malnutrition sustainably.

Multisectoral Nutrition Governance (MNG) refers to the systems, policies, and processes that ensure an enabling environment for effective planning, implementation, and monitoring of nutrition actions across sectors. It encompasses various elements or domains including political commitment and leadership, policy coherence, multisectoral coordination and collaboration, financing, capacity to implement nutrition programs, and accountability mechanisms that drive the success of multisectoral nutrition programs. Achieving MNG requires effective political commitment, policy coherence, multisectoral coordination and collaboration, sustainable financing, implementation capacity, and accountability mechanisms. All these factors must be enabling within and across sectors which underscores the complexity of achieving effective MNG.^15-17 There are no universally standardized indicators for assessing the effectiveness of MNG. However, previous studies have evaluated it through proxy measures such as the strength of cross-sectoral coordination mechanisms, institutional anchoring within high-level government bodies, policy coherence across sectors, incorporation of nutrition in country development plans, and the functionality of multisectoral platforms.^18,19 MNG is crucial for improving public health outcomes. Effective MNG frameworks ensure that nutrition policies are well-coordinated, adequately funded, and effectively implemented, leading to better nutrition and health outcomes. Countries like Brazil, Ethiopia, Guatemala, and Nepal, for instance, have demonstrated that integrated nutrition governance is beneficial in achieving reductions in malnutrition.^8,17,20-22 Despite these successes, gaps persist in current nutrition governance frameworks. Challenges in implementing effective nutrition governance frameworks include limited political commitment and leadership, inadequate technical capacity, limited coordination, and weak monitoring, evaluation, and results measurement systems among others.^17,23,24

Qualitative assessments focusing on effective monitoring and measurement of MNG have provided valuable insights, however, there are inadequate quantitative evaluations that can offer more precise and actionable data.^16,17 The quantification of MNG status and identification of factors associated provides new insights into the effectiveness of governance interventions. This approach provides a comprehensive understanding of both progress and constraints which is essential for informing targeted improvements. This study assessed the status, evolution, and factors associated with MNG in Kenya. It employed a cross-sectional research design at 2 time points using a validated tool. Our study asked the following questions: (i) What is the current MNG status and how has it evolved over the past decade? (ii) What is the relative contribution of individual domains to the overall MNG status? (iii) Which contextual and respondent factors influence the current MNG status?

Methods

Study Design

This study was nested within a larger study designed to assess MNG and its drivers within Kenya's national nutrition landscape using both quantitative and qualitative approaches. It assessed the status, evolution, and factors associated with MNG in Kenya using a cross-sectional design for 2 time points (2012 and 2023). The 2 time points facilitated the assessment of temporal shifts in MNG over time and provided valuable insights into the evolution of MNG frameworks, enabling the identification of trends, progress, and challenges. The year 2012 marked a critical juncture in Kenya's nutrition landscape. The 2 key frameworks, the National Food and Nutrition Security Policy (NFSNP) and the first Kenya National Nutrition Action Plan (KNNAP), were adopted in 2012. In addition, Kenya strengthened its commitment to global nutrition initiatives by endorsing the WHA nutrition targets and joining the Scaling Up Nutrition (SUN) Movement around the same period.²⁵ This ensured that the participants could easily recall MNG-related events in 2012.

Ethical Approval and Consent

Ethical approval for this study was obtained from the Texas Tech University (TTU) Institutional Review Board (IRB) (approval number IRB2023-493) and Amref Ethics and Scientific Review Committee (ESRC) (approval number ESRC P1443/2023). A research permit was obtained from the National Commission for Science, Technology and Innovation (NACOSTI) (license number NACOSTI/P/23/27685). In addition, a letter of support to conduct the study was provided by the Office of the Director General, Ministry of Health Kenya (number Ref: MOM/ADM/1/1/82/361). Written informed consent was sought from all respondents to collect the data as well as to publish it.

Study Area and Target Population

The Overall Context in Kenya

The Republic of Kenya is a unitary state divided into 47 counties. The country is governed by the National Government and 47 County Governments. The 2 levels of government work in close consultation as espoused in Article 6 subsection 2 of the Constitution of Kenya, 2010.²⁶ At the national level, 21 ministries are responsible for policy-making and overseeing the implementation of various policies related to specific areas of governance. Each ministry has departments, divisions, units, and autonomous agencies which are responsible for implementing policies, programs, and projects developed by the ministries. The government ministries, departments, and agencies are supported by development partners/donors, United Nations agencies, Civil Society Organizations, private sector, and other nongovernmental partners who mainly provide technical assistance, capacity building, and funding.¹ Although service delivery responsibilities in sectors such as health and agriculture had been devolved to county governments under Kenya's 2010 Constitution, the national government continued to play a critical role in providing strategic guidance, technical assistance, and capacity building to ensure coherent and coordinated nutrition governance across the 2 levels of government.²⁶ Planning and implementation of nutrition actions both at the national and county levels in Kenya have been addressed primarily by the Ministries of Health, Agriculture, Education, and Social Protection. However, secondary ministries such as Water and Sanitation; National Treasury and Economic Planning; Public Service, Gender and Affirmative Action; Trade, Investments, and Industry; and Devolution and Planning increasingly play an important role in efforts to improve nutrition in Kenya. Ministries such as Defense, Energy, Foreign Affairs, Tourism and Wildlife, Information and Communication, Environment and Forestry, Mining and Blue Economy, East African Community, Youth Affairs and Sports, and Interior and National Administration play a role, but it is peripheral as compared to 9 aforementioned ministries.^25,27,28

Study Area and Target Population

This study was conducted at the national level in Kenya. The target population consisted of government staff working as program managers/directors and program officers in various line ministries implementing nutrition. It also included nongovernmental organizations/institutions’ program managers/directors, and program officers supporting the nutrition sector at the national level. Both program managers/directors and program officers were selected because MNG involves decision-making, policy development, implementation of strategies, and monitoring and accountability.^15,17,22

Sampling Design and Inclusion and Exclusion Criteria

A representative multistage process was used to select the study respondents. The sampling process entailed mapping of government staff and nongovernmental working and supporting ministries respectively as program managers/directors and program officers. Inclusion criteria entailed government staff working as program managers/directors and program officers in departments, divisions, or units related to nutrition in ministries mapped for this study and their willingness to participate in the study. Program managers/directors and program officers from nongovernmental institutions directly supporting the nutrition sector at the national level to plan and implement nutrition actions were included. Exclusion criteria covered government staff from line ministries that play no direct role in nutrition planning and programming, eg, administration, human resources, and legal services units. Program managers/directors from nongovernmental institutions that do not directly support the nutrition sector at the national level and those who do not support nutrition programs were excluded from the study.

Mapping of eligible respondents in the line ministries and nongovernmental staff was conducted to identify the target population/sampling frame for the study (N = 124). Respondents were mapped with the support of the Division of Family Wellness, Nutrition, and Dietetics which is in charge of coordinating nutrition programs in Kenya. The target population was then divided into 3 quotas: (i) government staff working as program managers and officers in the 4 primary line ministries (n = 52), (ii) government staff working as program managers and officers in the secondary line ministries (n = 11), and (iii) nongovernmental program managers and officers from institutions which directly support the nutrition sector at the national level (n = 61). Finally, 90% of the respondents were selected from each of the 3 quotas to form the sample size (n = 112).

Data Collection Tool/MNG Questionnaire

A structured closed-ended adapted questionnaire building on the validated Nutrition Governance Index questionnaire by Namirembe et al¹⁷ and leveraging on the understanding of MNG by the authors^15,22,29 was used to collect quantitative data (Supplemental file S1). The questions were broad to cover the 6 MNG domains and relevant for both program managers/directors and program officers in government and nongovernmental institutions.

The questionnaire had 27 interrelated 5-point Likert-type questions/items (ranging from 1 strongly disagree to 5 strongly agree) covering 6 MNG domains. Each question had a “Don't know” option which helped to mitigate recall bias by preventing respondents from guessing when they have no information or cannot accurately remember past events. The questionnaire covered 2 time points (i) the present/current situation (2023), and (ii) the situation previously—2012 when the NFNSP and the KNNAP were first adopted. In addition, the respondents were asked to rate (on a scale of 1–10) the current status of MNG and the level of prioritization of nutrition in their ministries. Furthermore, participants were asked contextual/background characteristics questions such as their roles, the specific government ministries they work for/support, number of years they have worked in their sectors among others. Collecting these characteristics allowed for the analysis of responses by sector, role, and tenure among others, highlighting potential differences in perceptions and priorities across sectors (Supplemental file S1).

The researcher conducted a pretest to identify and address potential issues with the research instrument before administering the tools. First, peers from the Texas Tech University, Department of Nutritional Sciences were invited to review the tool and provide suggestions for improvement voluntarily. Following this, 3 experts, who are part of the researcher's dissertation committee, were also asked to evaluate the questionnaire/survey items and offer specific recommendations on which to include, exclude, or correct. After incorporating the peer and expert feedback, the improved survey was returned to the 2 experts for a final review to confirm that all issues had been addressed. As recommended by Bryman and Bell,³⁰ this comprehensive pretesting approach helped ensure the survey was clear, and adequately covered the research questions, with an attractive layout for the respondents. The reliability analysis on a small sample pilot (N = 16) assessed the internal consistency of the questionnaire items using Cronbach's alpha (α), a measure that indicates how well a set of items/questions measures a single unidimensional latent construct (domains). The pretest participants who were not part of the study were selected.³¹ They included government staff and nongovernmental staff who are conversant with Kenya's nutrition context but who do not play roles in nutrition sector policies and programs. Cronbach's α ranges from 0 to 1 but a desired level ranges from .65 to .80. A Cronbach's α of .94 indicated that the tested items were highly correlated.³² To determine test–retest reliability the same questionnaire was administered to the same pilot participants over a duration of 1 to 3 weeks to determine the stability coefficient (g) of the questionnaire. A higher g is desirable since it indicates a better stability of the questionnaire. A stability coefficient result of 0.8 indicated that the tool was reliable.³³

The MNG questionnaire was programmed and administered through the Qualtrics Software, Survey platform, Version XM/os2, July 2023. Administering the survey through the Qualtrics Survey platform had several advantages. It allowed minor flow refinements early in the rollout phase to enhance respondent experience, the use of logic features to reduce response error and improve completion rates, flexible distribution methods to increase reach, and adherence to adequate data security standards.³⁴ It is important to highlight that no substantive changes to the questionnaire content were made after piloting. All modifications that were limited to skip logic and sequencing and were implemented by the researchers were finalized before full-scale data collection. However, it introduced multiple survey versions, response formats, inconsistent naming, and additional metadata leading to inconsistent data structure. This meant that significant effort was put into cleaning the data, particularly to ensure the process was reproducible.

Measures

Measuring MNG Status

The MNG status metric was derived through a multistage hierarchical process. First, the mean of each domain item (27 questions in respective domains) was calculated for the 2023 and 2012 segments. For example, the political commitment and leadership domain had 6 ordinal Likert-type items/questions with scores ranging from 1 to 5. Second, the item means were summed to compute domain mean scores (interval in nature ranging from 1 to 5) (Supplemental material 1). Averaging, rather than using raw item summation to compute domain means, was chosen to handle missing data and normalize the data.³⁵ Finally, the 6 domain means were aggregated to obtain the MNG score/index for the 2023 and 2012 segments. Computing the MNG index based on 27 questions and aggregating them into an index provides a more comprehensive and reliable measure of the MNG construct compared to a single question. This is because it reduces the impact of random errors associated with individual questions.^35,36

All the domains contributed equally to the MNG index, ie, no weighting of domains was applied. This approach was considered because the MNG domains are interrelated and interdependent. For instance, adequate results monitoring and measurement cannot be realized without adequate capacity and capability. Likewise, transparency, accountability, and coordination cannot be achieved without a proper system to measure and monitor results. Similarly, the realization of each MNG domain requires adequate financing.^15,23

Computing New Categorical Variables

Original variables were aggregated and transformed to create new variables. The new variables permitted further understanding and analysis of MNG status and factors associated. Some of the new variables created include:

MNG 2023 and MNG 2012 status/index in percentage format—in this case the raw MNG scores (ranging from 6 to 30) were converted to percentages for easier interpretation and comparison.

Categorizing the responses for the question that required respondents to rate MNG and prioritization of nutrition on a scale of 1 to 10—the categorization was effected as follows: very low (1–3), low (3–5), medium (5–7), high (7–9), and very high (9–10).³⁷

Categorizing all the respondents to “primary” or “secondary” line ministries depending on the ministries they work for or support.

Segmenting respondents to “government” and “nongovernment” depending on whether they are government or nongovernment organization (NGO) staff.

In addition, the qualitative question on how nutrition is perceived and prioritized in various ministries was thematically coded and categorized into very low, low, medium, high, and very high similar to the MNG rating question.

Statistical Analyses

Assumption Checks

Conducting assumption checks before performing inferential statistics (t-test and multiple linear regression analyses) was essential to ensure the robustness of the analysis. This was beneficial considering the relatively small sample size for the study. Assumption checks confirmed whether the data met the specific criteria required by the parametric statistical tests, such as normality, homogeneity of variance, linearity, and independence. The Shapiro–Wilk test was used to assess the normality of each interval variable, providing a P-value to determine if the data significantly deviates from a normal distribution. Levene's test was used to assess the equality of variances for each interval variable across the groups defined by factor variables. Lastly, for regression models including all relevant factor variables, the Durbin–Watson test was used to check for autocorrelation in the residuals, ensuring the independence of errors.³⁸

Main Statistical Analyses

The R language within the R-studio environment, Version 4.3.3 was used to analyze quantitative data.³⁹ Frequencies, percentages, and means were used to depict respondents’ characteristics. In addition, descriptive statistics means and standard deviation were used to describe the current MNG status in 2023, and the MNG status in 2012. In terms of inferential statistics, a paired-samples t-test was conducted to compare the means of MNG status in 2023 and 2012. Multiple linear regression was used to assess whether the 6 nutrition governance domains contributed differently to achieving the current overall MNG. Similarly, multiple linear regression was conducted to assess the contextual and respondent factors (independent variables) that significantly influence the current MNG status (dependent variable). This statistical analysis allowed the study to model the relationship between multiple independent and dependent variables. According to Field (2018), multiple regression analysis can be applied when the independent variables are of different types, including categorical (dummy-coded), ordinal, or continuous (interval or ratio), as long as the dependent variable is continuous. The P-value cut-off for significance testing was set a priori at P < .05. The assumptions of normality, linearity, and homoscedasticity were checked and met before the t-test and multiple linear regression analyses were conducted.^33,38

Results

Response Rate, Survey Completion, and Questionnaire Reliability

Response Rate

The response rate for completing the survey was 77.4% (96 out of 124) (Table 1). A response rate over 70% is considered robust in survey research targeting highly specialized target groups.⁴⁰ The high response rate was due to a clearly defined target population, the strong interest of the respondents in the subject matter of our research, and frequent follow-up to complete the survey. As part of the follow-up, respondents who encountered challenges were encouraged to complete the survey by sharing fresh survey links.

Table 1.

Respondents Characteristics (N = 96).

Characteristic	Category^a	n/N	Percent
Survey response and completion	Yes	96/124	77.4
Survey response and completion	No	28/124	22.6
Respondents role	Program managers and directors	60/96	62.5
Respondents role	Program officers	36/96	37.5
Category of respondent's ministry	Primary	89/96	92.7
Category of respondent's ministry	Secondary	7/96	7.3
Government or nongovernment respondents	Government	50/96	52.1
Government or nongovernment respondents	Nongovernment	46/96	47.9
Ministry respondents work for or support	Health	52/96	54.2
	Agriculture	18/96	18.8
	Education (includes government academia)	11/96	11.5
	Labor and social protection	8/96	8.3
	Water and sanitation	3/96	3.1
	Treasury and economic planning	2/96	2.1
	Mining, blue economy, and maritime affairs	1/96	1.0
	Public service and gender	1/96	1.0
Ministry government respondents represent^b	Health	14/50	28.0
	Education (includes government academia)	11/50	22.0
	Agriculture	10/50	20.0
	Labor and social protection	8/50	16.0
	Water and sanitation	3/50	6.0
	Treasury and economic planning	2/50	4.0
	Mining, blue economy, and maritime affairs	1/50	2.0
	Public service and gender	1/50	2.0
Ministry NGO respondents support	Health	38/46	82.6
Ministry NGO respondents support	Agriculture	8/46	17.4
NGO respondents’ organization type^c	Civil society organizations	24/46	52.2
	Development partners/donors	11/46	23.9
	United Nations agencies	6/46	13.0
	Academia (private research and academic)	4/46	8.7
	Private sector	1/44	2.2
Number of years respondents have worked or supported	>10 years	69/96	71.9
	5–9 years	17/96	17.7
	<5 years	10/96	10.4

Abbreviation: NGO, nongovernment organization.

The chi-square tests on the respondent characteristic variables groups showed significant differences (P < .05) within groups indicating that the observed differences occurred by chance.

Primary line ministries—Health, Agriculture and Livestock, Education, Labor and Social Protection. Secondary line ministries—Water and Sanitation, Treasury and Economic Planning, Public service and Gender, and Mining, Blue economy, and maritime Affairs.

This is according to the Scaling Up Nutrition Movement categorization.

Survey Completion, Missing Data, and Symmetry of Data

Each of the 96 recorded responses had total missing cases of <20% missing data. The total proportion of missing response cases in the dataset was 1.1%. The proportion of missing cases was slightly higher in the 2012 segment (1.5%) compared to the 2023 segment (0.5%). This suggests that some respondents may have skipped certain questions because they were not in their roles in 2012 or they could not recall information from the distant past. The overall proportion of missing responses was below the common threshold of 5.0%, minimizing its impact on the findings. The domain scores were fairly symmetrically distributed, with skewness ranging from −0.46 to 0.51, except for the results measurement and management domain, which had a higher skew (0.80), likely due to having only 2 items/questions. Kurtosis values ranged from 2.36 to 3.09, indicating no substantial deviations from normality, except for the results measurement and management domain, which had a higher kurtosis (3.78), suggesting a more peaked distribution due to limited variance.³⁸

Questionnaire Reliability

The reliability analysis assessed the internal consistency of the questionnaire items using Cronbach's α, a measure that indicates how well a set of items/questions measures a single unidimensional latent construct (domains). The reliability analysis of the questionnaire items revealed high internal consistency, with an overall Cronbach's α of .94. This indicates that the items are highly correlated and effectively measure the same underlying construct. The signal-to-noise ratio was 15, indicating a high level of reliability. The average interitem correlation was 0.55, which falls within the optimal range, suggesting a balance between correlation and redundancy. The 95% confidence interval (CI) for Cronbach's α ranged from .92 to .96 further confirming the high reliability of the scale. Moreover, the analysis shows that removing any single item maintains a Cronbach's α of .93, showing that no single item was critical for the scale's reliability. Individual items also exhibited strong item-total correlations, with mean scores ranging from 2.6 to 4.0.³²

Respondents’ Characteristics

Table 1 presents the respondents’ characteristics. Most respondents were program managers and directors (n = 60, 62.5%). Nearly all the respondents worked for or supported primary line ministries (n = 89, 92.7%) with Health and Agriculture ministries dominating (n = 70, 73.0%). Slightly more respondents (n = 50, 52.1%) worked for the government as compared to those who worked for NGOs (n = 46, 47.9%). The majority of respondents (n = 69, 71.9%) had worked in or supported their ministries for over 10 years, indicating that they could reliably recall MNG-related dynamics from the past decade. The chi-square tests on the respondent characteristic groups’ variables showed significant differences (P < .05) within groups showing that the observed differences occurred by chance.

What is the Current MNG Status and How Has it Evolved in the Last 10 Years?

The current MNG status (2023) score/index constructed by the 6 domains was 71.1% (SD = 13.61). The domains showed varying levels of performance in their percentage scores, that is, (i) political commitment and leadership (79.8%), (ii) policy coherence and coordination (76.0%), (iii) transparency and accountability (74.0%), (iv) results measurement and monitoring (68.8%), (v) capacity and capability (68.8%), and (vi) financing (3.1%). In addition to the aggregated MNG score, respondents were asked to rate the current MNG status from 1 to 10, the median score was (60.0%, interquartile range, 50.0-70.0). Table 2 compares the findings of the total MNG scores and the 6 domains in 2023 and 2012. The 2023 group consistently scored higher with less variability. For example, the overall MNG status in 2023 had a mean of 71.1 and SD of 13.6, while the MNG status in 2012 had a mean of 58.4 and SD of 17.1. Similar trends were observed in all the domains.

Table 2.

MNG Domains and MNG Status Descriptive Statistics.

Variable	Mean	Min.	Max.	SD	P ^b	CI lower	CI upper	t	df	F	d
MNG status in 2023 (%)	71.07	42.3	98.9	13.61	<.01	8.31	17.1	5.7	190	0.63	0.82
MNG status in 2012 (%)	58.36	10.0	100.0	17.08
Political commitment and leadership in 2023	3.99	2	5	0.71	<.01	0.32	0.79	4.62	186	0.59	0.67
Political commitment and leadership in 2012	3.43	1	5	0.92
Policy coherence and coordination in 2023	3.80	1.4	5	0.81	<.01	0.47	0.97	5.64	188	0.75	0.82
Policy coherence and coordination in 2023	3.08	1	5	0.93
Transparency and accountability in 2023	3.44	1.3	5	0.86	<.01	0.25	0.75	3.9	182	0.99	0.57
Transparency and accountability in 2012	2.94	1	5	0.87
Financing in 2023	3.05	1.5	5	0.83	<.01	0.21	0.73	3.6	183	0.74	0.53
Financing in 2012	2.57	1	5	0.96
Capacity and capability in 2023	3.44	1	5	0.9	0.03	0.16	0.7	3.09	188	0.8	0.45
Capacity and capability in 2012	3.01	1	5	1.01
Results measurement and monitoring in 2023	3.69	1	5	0.81	<.01	0.23	0.72	3.88	187	0.85	0.56
Results measurement and monitoring in 2012	3.21	1	5	0.88

Abbreviations: CI, confidence interval; MNG, Multisectoral Nutrition Governance.

Grouping (group 2023 and group 2012) permitted assessment between groups.

t-Test used to assess differences in means between 2023 and 2012 groups.

Figure 1 illustrates the comparative mean MNG scores for 2012 and 2023 across key respondent categories. The black lines represent the change in performance over time, with red markers indicating 2012 scores and blue markers representing 2023 scores. MNG scores improved across all groups in 2023 compared to 2012. The largest increases were observed among nongovernment staff (+14.6), program directors or managers (+14.1), and respondents with more than 10 years of experience (+12.5). Respondents from other ministries recorded a larger increase (+15.2) compared to those from the health ministry (+10.6).

Figure 1.

Comparative MNG Scores for 2012 and 2023 Across Key Respondent Categories. Abbreviation: MNG, Multisectoral Nutrition Governance.

Is There a Significant Difference in the Means of MNG 2023 and MNG 2012 Status?

To assess assumptions for the paired-samples t-test, we examined the distribution of the difference scores using the Shapiro–Wilk test, skewness, kurtosis, histogram, and Q–Q plot. The Shapiro–Wilk test was significant (W = 0.97, P = .03), suggesting a deviation from normality. However, skewness (0.58) and kurtosis (3.84) remained within acceptable ranges, indicating only mild departures. The histogram displayed a bimodal shape, and the Q–Q plot showed minor tail deviations, suggesting some departure from normality. Given the mild nonnormality observed, a Wilcoxon signed-rank test was conducted as a nonparametric alternative, which confirmed the significance of the effect, V = 3969, P < .01, rank-biserial correlation (r_b) = .73. Since the P-value was below the significance level chosen a priori (α < .05), we reject the null hypothesis and conclude that the MNG status in 2023 is higher than in 2012. The scatter plot in Figure 2 provides a visual representation of the difference in MNG status and MNG domains between 2023 and 2012.

Figure 2.

Scatter plot of the mean difference in MNG status between 2023 and 2012. Abbreviation: MNG, Multisectoral Nutrition Governance.

What is the Relative Contribution of Individual Domains to the Current MNG Status?

Key regression assumptions were tested before assessing the relative contribution of the 6 MNG domains to the overall MNG status in 2023. Linearity was visually confirmed through scatter plots which showed no major deviations. The Durbin–Watson test (DW = 2.10; P = .68) indicated no significant autocorrelation. Multicollinearity was not a concern, as all variance inflation factor (VIF) values were below 5 (VIF_max = 2.79). However, the Shapiro–Wilk test (W = 0.92; P < .01) indicated significant deviation from normality which was further supported by the histogram and Q–Q plot of residuals. Given this, bootstrapping with 1000 resamples was applied to generate robust CIs.

To test whether all 6 domains contribute equally to MNG status, bootstrapped relative importance analysis (Lindeman, Merenda, and Gold [LMG] metric Method) was conducted. This approach decomposes the total explained variance (R²) among the 6 governance domains, quantifying their unique contributions. The bootstrapped LMG method was appropriate compared to traditional regression-based inference considering the expected strong linear association between the independent variables and the dependent variables. Table 3 presents the relative importance (LMG values) and bootstrapped 95% CI, allowing statistical comparisons. The results indicate that all 6 governance domains contributed similarly to the explained variance in MNG status. Despite the differences, the overlapping bootstrapped CI suggests no statistically significant differences in domain contributions, supporting the assumption of equal weighting. Given these findings, we fail to reject the null hypothesis and conclude that the various MNG domains contribute equally to MNG status.

Table 3.

Bootstrapped Relative Importance of MNG Domains.

MNG domain	Relative importance (LMG)	CI lower	CI upper
Political commitment and leadership	0.16	0.14	0.18
Policy coherence and coordination	0.17	0.14	0.19
Transparency and accountability	0.19	0.17	0.21
Financing	0.15	0.13	0.18
Capacity and capability	0.17	0.15	0.19
Results measurement and monitoring	0.19	0.13	0.18

Abbreviations: CI, confidence interval; LMG, Lindeman, Merenda, and Gold; MNG, Multisectoral Nutrition Governance.

Which Factors Influence MNG Status?

Is There a Relationship Between Current MNG Status and Respondent Factors?

Regression assumptions were tested before assessing the contextual and respondent factors influencing MNG status. Linearity was visually confirmed using scatterplots, while the independence of observations was supported by the Durbin–Watson test (DW = 1.94; P = .39) which indicated no significant autocorrelation. All VIF values remained below 5 suggesting minimal redundancy among predictors which indicated that multicollinearity was not a concern. The Shapiro–Wilk test (W = 0.99; P = .32) and residual plots confirmed normality, supporting the validity of the linear model. The residuals versus fitted plot further indicated no heteroscedasticity concerns, supporting the assumption of homoscedasticity. Given that all assumptions were met, multiple linear regression was appropriate to assess the relationship.

A multiple linear regression analysis was conducted to identify significant contextual and respondent factors influencing MNG status. The model explained 63.7% of the variance (R² = .64, F(10, 82) = 14.39, P < .01), indicating that the included predictors accounted for a substantial proportion of the observed variation. The results show that respondents who worked or supported the Health Ministry (β = 1.60, P = .04) scored MNG higher compared to their Agriculture Ministry counterpart. Respondents who perceived that nutrition was not prioritized in their ministries reported significantly lower MNG scores (β = −7.82, P < .01) compared to those who felt that nutrition was highly prioritized (Table 4). The null hypothesis that there are no contextual and respondent factors that significantly influence the current MNG status was rejected. Although the respondents’ role, working for government or NGO, and years worked were not statistically significant, their inclusion in the model provided a more comprehensive understanding of the governance landscape.

Table 4.

Factors Influencing the Current MNG Status.

Predictors	β	SE	t	CI lower	CI upper	P
Intercept	27.97	1.27	21.99	21.52	34.43	<.01
Role: manager or director (Ref: Program officer)	−1.00	0.58	−1.72	−2.15	0.15	.09
Government vs NGO (Ref: Government)	−1.05	0.73	−1.43	−2.5	0.4	.16
Ministry: Education (Ref: Agriculture)	1.15	1.11	1.03	−1.04	3.34	.31
Ministry: Health (Ref: Agriculture)	1.6	0.77	2.07	0.09	3.11	.04
Ministry: Other (Ref: Agriculture)	1.96	1.04	1.88	−0.10	4.02	.06
Years worked: <10 years (Ref: >10 years)	−0.40	0.66	−0.62	−1.7	0.89	.54
Perceptions of nutrition prioritization: low (Ref: high)	−11.25	1.30	−8.63	−13.82	−8.68	<.01

Abbreviations: CI, confidence interval; Govt, government; MNG, Multisectoral Nutrition Governance; NGO, nongovernment organization.

Note: Reference categories are indicated in parentheses.

Model fit: R² = .64, F(10, 82) = 14.39, P < .01.

Discussion

Summary of the Findings

Our study aimed to assess the status, evolution, and factors associated with MNG in Kenya. Our findings show a significant improvement in MNG status, from 58.4% in 2012 to 71.1% in 2023 (P < .01). The 6 MNG domains showed varying levels of performance in their mean scores in 2023, ie, (i) political commitment and leadership (79.8%), (ii) policy coherence and coordination (76.0%), (iii) transparency and accountability (74.0%), (iv) results measurement and monitoring (68.8%) (v) capacity and capability (68.8%), and (vi) financing. Despite their variation, the domains contributed equally to the MNG status. A positive relationship was found between the MNG score and respondent's ministry affiliation as well as the level of prioritization of nutrition in a ministry (P < .05).

Status of MNG

The improvement in MNG status from 58.4% in 2012 to 71.1% in 2023 demonstrates a measurable advancement in MNG Kenya. The 12.7% increase in the past 10 years indicates increased commitment and action in strengthening MNG frameworks supporting nutrition in Kenya. This progress is driven by enhanced policy frameworks such as the NFSNP and KNNAP, which have integrated nutrition into sectoral strategies and mobilized resources.^25,27 Improved political commitment has enabled nutrition to be embedded in national development plans like Kenya Vision 2030 and medium term plans, reinforcing its prioritization. The establishment of coordinating mechanisms such as the Kenya Food Security Steering Group, the National Nutrition Interagency Coordination Committee, the National Nutrition Technical Forums, and the SUN movement has improved cross-sectoral collaboration, ensuring a more cohesive approach to addressing malnutrition. Additionally, stakeholder engagement spanning government agencies, civil society, and international organizations has fostered shared responsibility, strengthening accountability and resource allocation.^41,42 Nonetheless, progress remains uneven, particularly in accountability, financing, and implementation capacity. These persistent challenges stem from factors such as fragmented mandates across sectors, inconsistent county-level leadership, overdependence on donor funding, and limited enforcement of coordination mechanisms.

The improvements in MNG in Kenya align with global trends. Countries such as Bangladesh, Ethiopia, Nepal, and Uganda have demonstrated similar progress through enhanced political commitment, multisectoral coordination, and effective monitoring systems among others.^15,43,44 However, like Kenya, progress remains uneven, particularly in nutrition financing, transparency and accountability, and the capacity to implement multisectoral actions. The World Bank reports that progress in reducing child undernutrition has stagnated partly due to governance inefficiencies.²² Similarly, the Global Hunger Index (2023) highlights that since 2015, nutrition governance progress has slowed, aggravated by the COVID-19 pandemic, economic downturns, and political instability.⁴⁵ The improvement in MNG in Kenya and other developing countries reflects a growing recognition of the need for effective governance mechanisms that set a precedence for integrated strategies tackling malnutrition.^46,47 Kenya's experience offers valuable lessons for institutionalizing multisectoral governance. It illustrates both the opportunities and ongoing challenges to achieving sustainable nutrition outcomes.

Contributors to MNG Status

Recent studies have highlighted several factors contributing to effective nutrition governance. These include political commitment,²⁴ coherent policies that integrate nutrition in other sectors such as education, and social protection,⁴³ coordination⁴⁸ robust mechanisms to track progress and measure the impact of nutrition policies and programs,⁴⁹ transparency and accountability, capacity building,⁵⁰ and adequate and sustained financing.⁵¹ The contributors to MNG identified in this study align with previous research, highlighting the significance of political commitment, policy coherence, transparency, monitoring, capacity building, and financing in improving nutrition outcomes. While inferential analysis in our study did not reveal significant relative contributions among the domains, the descriptive data offers valuable insights. Political commitment and leadership scored the highest (4.0), indicating robust governmental resolve in supporting MNG. Policy coherence and coordination (3.8) also performed well, underscoring the importance of aligned policies. Lower scores in transparency and accountability (3.4), results measurement and monitoring (3.7), capacity and capability (3.4), and financing (3.1) suggest areas needing further enhancement. The lack of significant relative contributions can be attributed to the high interdependence among the 6 domains. This interdependence implies that improvements in 1 domain are likely to influence and bolster others, emphasizing the need for a comprehensive and integrated governance approach. Such a holistic strategy is essential for effective MNG, as isolated improvements in individual domains may not yield significant overall progress. This interconnectedness, while beneficial, can also pose challenges in identifying specific areas for targeted intervention, complicating policy and resource allocation decisions.^45,52,53 Despite the challenge of interconnectedness, our study introduces new insights into the interdependence of these domains, suggesting that their combined effect rather than individual contributions may drive MNG improvements. The findings highlight the importance of an integrated approach to governance which requires balanced improvements across all domains to ensure sustainable nutrition outcomes. However, it is essential to acknowledge that high interdependence can complicate pinpointing specific areas for targeted intervention, thus complicating resource allocation and programming efforts.^51,53

Factors Associated With MNG

MNG is influenced by a range of individual contextual and respondent factors including roles of stakeholders, ministry type, organizational affiliation, and the prioritization of nutrition within ministries. Our study established a positive relationship between the MNG score and respondent affiliation with the Ministry of Health as well as the level of prioritization of nutrition in a ministry (P < .05). Various studies have identified diverse factors influencing MNG either as a whole or within specific domains. The role of stakeholders within an organization or ministry is crucial. Program managers/directors and program officers often have varying degrees of influence on nutrition policy implementation. For instance, program managers and directors usually have strategic roles, making high-level decisions that shape nutrition governance frameworks.^15,24 Similarly, the type of ministry staff work for, such as Health, Education, or Agriculture, can significantly impact the level of prioritization of nutrition within that specific ministry.⁵⁴ Ministries that prioritize nutrition such as Health tend to allocate more resources, foster better intersectoral collaboration, and implement comprehensive nutrition programs. Conversely, low prioritization can lead to fragmented efforts and limited impact.⁵⁵ Additionally, whether someone works for the government or a nongovernmental organization can influence their approach to nutrition governance. Government employees often have a more direct role in policy implementation and regulation.⁵⁶ Staff from health ministries are typically more directly involved in nutrition-specific interventions, whereas those from education and agriculture ministries may engage in nutrition-sensitive programs. This diversity can influence the comprehensiveness and effectiveness of nutrition governance.^23,57 The divergent findings observed in various studies may be attributed to unique contexts and methodological approaches. Recognizing these differences can help tailor more effective strategies for improving MNG based on specific contextual needs. In our study, it is crucial to acknowledge that respondents from the Health Ministry, which coordinates nutrition programs and prioritizes nutrition highly, may have provided favorable scores compared to their counterparts in other ministries suggesting a potential bias. Acknowledging these variations and limitations is essential for designing context-specific strategies and for guiding future research. For example, our study was limited by a relatively small sample size, particularly within individual sectors, which constrained our ability to robustly model relationships between respondent characteristics and governance perceptions. Future research should involve larger and more diverse samples across national and subnational levels to examine these dynamics more systematically.

Strengths and Limitations

Our study has several notable strengths. The rigorous assessment of MNG status using a validated quantitative tool ensures high reliability and accuracy of the findings. This study provides a quantified analysis of nutrition governance which is disaggregated into constituent domains. This approach allows for a nuanced understanding of how political, institutional, and structural factors that influence MNG, which some studies may have overlook. By quantifying governance dimensions and tracking changes over time, this research offers a precise evaluation of how governance structures have evolved and their impact on nutrition outcomes. This approach aligns with recent calls for more rigorous and detailed assessments of governance to better inform policy and intervention strategies.^17,23,58 The applicability of the findings at both national and county levels allows for a nuanced understanding of MNG across different governance structures. Additionally, this study contributes to growing evidence on robust assessment of factors associated with MNG, addressing a critical gap in the literature.

Although the study primarily focused on the national level government, the findings may apply to individual counties for various reasons. First, the majority of functions related to nutrition programming, eg, those in Health and Agriculture are devolved to counties. Second, the 2 levels of government have shared objectives as highlighted in the link between the KNNAP and County Nutrition Action Plans (CNAPs), and third, the central role the national government plays in providing capacity building, developing standards and regulations and policy and strategic guidance on nutrition to counties.^25,26 Despite these strengths, the study has certain limitations. The cross-sectional design used to assess MNG at 2 distinct time points presents challenges, particularly in capturing continuous shifts in MNG dynamics. Specifically, the higher proportion of missing cases in the 2012 segment (1.5%) compared to the 2023 segment (0.5%) indicates difficulties in accurately recalling events from the distant past. Moreover, the study relied on staff perceptions, some of whom may not have held their positions a decade earlier.

Conclusion

This study provides a comprehensive assessment of MNG in Kenya, highlighting significant advancements from 2012 to 2023. Using a validated tool to rigorously evaluate MNG status, the study offers reliable and relevant insights at both the national and county levels. The findings underscore the critical role of political commitment and leadership and policy coherence and coordination in enhancing MNG and nutrition programming. Despite some limitations, such as the reliance on 2 time points, the study establishes a foundation to explore MNG dynamics further. The study's implications for MNG in Kenya are insightful. Strengthening governance frameworks by prioritizing nutrition across various sectors may lead to significant improvements in health outcomes. Policymakers are encouraged to develop integrated, cross-sectoral strategies that ensure comprehensive and coordinated efforts. For future research, it is crucial to delve deeper into the causal pathways linking MNG to nutritional outcomes, utilizing more extensive longitudinal data and exploring the interaction of governance socioeconomic and other contextual variables. These steps are essential for crafting effective, evidence-based nutrition policies that can be implemented to sustainably improve nutrition outcomes in Kenya.

Supplemental Material

sj-docx-1-fnb-10.1177_03795721251357628 - Supplemental material for Improved Multisectoral Nutrition Governance in Kenya is Influenced by Ministry Affiliation and Level of Nutrition Prioritization in Line Ministries

Supplemental material, sj-docx-1-fnb-10.1177_03795721251357628 for Improved Multisectoral Nutrition Governance in Kenya is Influenced by Ministry Affiliation and Level of Nutrition Prioritization in Line Ministries by Jacob Korir, Wanjiku N. Gichohi-Wainaina, Surya Niraula, Nikhil Dhurandhar and Wilna Oldewage-Theron in Food and Nutrition Bulletin

Supplemental Material

sj-docx-2-fnb-10.1177_03795721251357628 - Supplemental material for Improved Multisectoral Nutrition Governance in Kenya is Influenced by Ministry Affiliation and Level of Nutrition Prioritization in Line Ministries

Supplemental material, sj-docx-2-fnb-10.1177_03795721251357628 for Improved Multisectoral Nutrition Governance in Kenya is Influenced by Ministry Affiliation and Level of Nutrition Prioritization in Line Ministries by Jacob Korir, Wanjiku N. Gichohi-Wainaina, Surya Niraula, Nikhil Dhurandhar and Wilna Oldewage-Theron in Food and Nutrition Bulletin

Footnotes

Acknowledgments

The authors thank Clementina Ngina Musyoki for her assistance in following up with the study participants.

Authors’ Contributions

Jacob Korir did conceptualization, development of methodology, data collection, data curation, formal analysis, visualization, writing original draft, review and editing, and validation.

Wanjiku N. Gichohi-Wainaina did conceptualization, development of methodology, data collection, formal analysis, writing original draft, review and editing, and validation.

Surya Niraula did formal analysis, visualization, and validation.

Nikhil Dhurandhar did conceptualization, review and editing, and validation.

Wilna Oldewage-Theron did conceptualization, development of methodology, data collection, formal analysis, writing original draft, review and editing, supervision, and validation.

Consent to Participate

Written informed consent was obtained from all respondents to collect the data as well as to publish the findings.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Considerations

This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the Texas Tech University (TTU) Institutional Review Board (IRB) (approval number IRB2023-493), and Amref Ethics and Scientific Review Committee (ESRC) (approval number ESRC P1443/2023). In addition, a research permit was obtained from the National Commission for Science, Technology and Innovation (NACOSTI) (license number NACOSTI/P/23/27685). In addition, a letter of support to conduct the study was provided by the Office of the Director General, Ministry of Health Kenya (number Ref: MOM/ADM/1/1/82/361).

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jacob Korir

Supplemental Material

Supplemental material for this article is available online.

Correction (August 2025):

Article updated online to add the reference, ‘Ouedraogo, O. (2019)’ in the article and the subsequent references have been renumbered.

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