INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

Effect of Training and Capacity Building on the use of Routine Health

Data in Public Health Programs: Evidence from a Quasi-Experimental

Study in Kenya

Joshua, M. Gitonga¹; Prof. John Paul Oyore²; Prof. George Ochieng Otieno³

¹²³Department of Family Medicine, Community Health and Epidemiology Kenyatta University

DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100043

Received: 07 November 2025; Accepted: 14 November 2025; Published: 28 November 2025

ABSTRACT

The effective use of Routine Health Data (RHD) remains a critical yet underutilized component of health

system governance in many low- and middle-income settings. This quasi-experimental study examined the

impact of a structured training intervention on the capacity of County Health Management Teams (CHMTs) in

Kenya to apply in public health decision-making. Twelve counties were selected across six regional blocs, with

six receiving the intervention and six serving as controls. Data collected at baseline and endline using

structured questionnaires were analysed using descriptive statistics, chi-square tests, and a Difference-in-

Differences (DiD) model. Results showed significant improvements in analytical, interpretive, and application

competencies among trained CHMT members, with a 0.45-unit increase in perceived data-use capacity relative

to controls. The findings underscore that systematic capacity-building enhances data-driven decision-making

and should be institutionalized within county health leadership frameworks.

Keywords-Routine health data; capacity building; data utilization; health management; decision-making;

evidence-informed governance

INTRODUCTION

Background to the Study

Effective decision-making in health systems relies fundamentally on the capacity of managers to interpret and

apply RHD in planning, resource allocation, and performance monitoring. Although most countries in sub-

Saharan Africa have invested substantially in strengthening Health Information Systems (HIS), including the

widespread adoption of digital platforms such as the District Health Information Software (DHIS2), the

translation of available data into actionable decisions remains inconsistent (Nutley & Reynolds, 2013; Aqil et

al., 2014). Weak analytical skills, insufficient confidence in data interpretation, and the absence of institutional

incentives for evidence use have perpetuated a persistent gap between data generation and data application

(Mboera et al., 2020; Nsubuga et al., 2018). Consequently, health managers often engage in routine data

reporting without systematically applying information to guide decisions, undermining the potential of HIS to

strengthen accountability, efficiency, and service delivery equity within public health systems.

In Kenya, the devolution of health services in 2013 shifted substantial decision-making authority to the county

level, amplifying the importance of managerial competence in data analysis and use. CHMTs are central to this

process, as they are responsible for transforming health information into actionable plans, budgets, and

policies. However, while data reporting rates through the Kenya Health Information System (KHIS) have

improved markedly, evidence indicates that county managers still face barriers to effective data use (Oluoch et

al., 2020; Wako et al., 2018). These challenges stem from limited technical capacity, insufficient training in

data interpretation, and weak institutional mechanisms for continuous learning (O’Meara et al., 2022). As a

result, decisions in planning, supervision, and resource allocation are often based on intuition or precedent

rather than empirical evidence, compromising health sector responsiveness and efficiency.

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

Although national policies emphasize the need for evidence-informed management, empirical studies on

interventions that build CHMT members’ capacity to use RHD remain limited. Few evaluations have examined

how structured training influences knowledge, confidence, and analytical ability to apply data in managerial

decisions. This gap underscores the need for empirical evidence on the effectiveness of capacity-building

interventions in institutionalizing data-driven decision-making within Kenya’s devolved health governance

framework. By addressing this gap, the present study contributes to understanding how targeted training can

transform data-use practices from compliance-oriented reporting into a core component of strategic and

accountable health management.

Problem Statement

The strategic potential of substantial investments in HIS across sub-Saharan Africa, including the widespread

adoption of digital platforms such as DHIS2, remains significantly underrealized (Mutale et al., 2013;

Ndabarora et al., 2014). Effective decision-making relies on managerial capacity to interpret and apply RHD

for planning, resource allocation, and performance monitoring (Nutley & Reynolds, 2013). However, a

persistent and critical issue is that the generation of data has consistently outpaced the capacity to interpret and

use it, weakening the translation of evidence into management actions (AbouZahr & Boerma, 2015; Nutley &

Reynolds, 2013).

This challenge is keenly reflected in Kenya's devolved structure, where CHMTs are central to transforming

health information into actionable plans and policies (Oluoch et al., 2020). Despite marked improvements in

data reporting rates through the Kenya Health Information System (KHIS), county managers face barriers to

effective data use (Oluoch et al., 2020; Wako et al., 2018). The constraints on data utilization are directly

linked to managerial shortcomings like poor analytical skills and low confidence in reading data, further

aggravated by the lack of institutional encouragement and systems necessary for ongoing professional

development. As such, the reliance on intuition or administrative precedent frequently supersedes the use of

empirical evidence in critical decisions concerning planning, supervision, and resource allocation, thereby

compromising the responsiveness and efficiency of the health sector (Wako et al., 2018).

Although national policies emphasize the necessity of evidence-informed management, empirical studies

evaluating the effectiveness of interventions designed to build CHMT members’ capacity to use RHD remain

limited (Aqil et al., 2009). Few evaluations have examined how structured training influences the specific

competencies of knowledge, confidence, and analytical ability required to apply data in core managerial

functions (Nsubuga et al., 2018). This gap necessitates research to demonstrate the causal effect of targeted

capacity-building on data-driven decision-making. By providing empirical evidence, this study contributes to

understanding how training can transform data-use practices from compliance-oriented reporting into a core

component of strategic and accountable health management within Kenya’s devolved governance framework.

Objective of the Study

The overall objective of this study was to assess the effect of structured training and capacity-building

interventions on the ability of CHMTs to utilize RHD in making public health decisions within Kenya’s

devolved health system. Specifically, the study examined how training influenced CHMT members’ analytical

competence, interpretive capacity, and confidence in applying RHD to managerial and strategic functions.

The study was guided by the following specific objectives:

1. To examine CHMT members’ ability to use RHD for public health decision-making between baseline and

endline across intervention and control counties.

2. To assess the ability of CHMT members to apply RHD for decision-making across selected demographic

and institutional characteristics.

3. To determine the effect of structured training and capacity-building interventions on the integration of

RHD into public health decision-making among CHMTs.

The study also sought to assess the following hypotheses:

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

1. H₀₁: There is no statistically significant difference in the use of RHD for public health decision-making

between intervention and control counties.

2. H₀₂: There are no statistically significant variations in the ability of CHMT members to apply RHD for

decision-making across demographic and institutional characteristics.

3. H₀₃: Structured training and capacity-building interventions have no statistically significant effect on the

integration of RHD into public health decision-making among CHMTs.

Significance of the Study

This study holds considerable significance for strengthening evidence-based governance within Kenya’s

devolved health system. By assessing how structured training interventions enhance the ability of CHMTs to

interpret and apply RHD, the study provides concrete evidence of how capacity development can bridge the

persistent gap between data generation and its practical use in managerial decision-making. The findings

demonstrate that equipping health managers with analytical and interpretive competencies not only improves

data-use practices but also promotes institutional accountability, transparency, and efficiency in public health

management. Beyond its empirical contribution, the study offers strategic guidance for policymakers and

development partners on how to embed data-use capacity-building into health sector reforms and leadership

programs. It underscores the need for continuous professional development and system-level support to sustain

data-driven decision-making practices. Further, this research contributes to the broader discourse on

institutionalizing a culture of evidence-informed management within devolved governance structures.

MATERIALS AND METHODS

Study Design and Setting

The study adopted a quasi-experimental pretest–post-test design comprising intervention and control groups.

This design was selected to enable assessment of how a structured capacity-building intervention influenced

the ability of CHMTs to interpret and apply RHD in decision-making processes. The approach allowed

comparison of changes over time between counties that received the training intervention and those that did

not, thereby isolating the effect of the intervention from other contextual or temporal factors.

A total of twelve counties were purposively selected from Kenya’s six regional economic blocs to ensure

diversity in geography, governance structures, and health system capacity. Six counties formed the intervention

group and six served as the control group. The intervention counties received a structured training program

adapted from the MEASURE Evaluation data-use curriculum, customized to Kenya’s devolved health

governance context. The program emphasized analytical skill development, practical exercises in data

interpretation, and application of evidence in real-world decision-making scenarios. Baseline data were

collected prior to the training, and endline data were obtained after the intervention was rolled out.

County selection ensured regional representation across the six economic blocs. From the Lake Region

Economic Bloc, Kisii served as the intervention county and Kisumu as the control; in the North Rift Economic

Bloc, Uasin Gishu represented the intervention arm and Turkana the control. Within the Mt. Kenya and

Aberdare Bloc, Kiambu was included as the intervention county and Meru as the control. Garissa and Marsabit

were drawn from the Frontier Counties Development Council, Kilifi and Kwale from the Jumuiya ya Kaunti za

Pwani, and Machakos and Makueni from the Southeastern Kenya Economic Bloc. This cross-regional

composition captured counties with differing levels of infrastructure, managerial experience, and information

system maturity, providing a comprehensive basis for examining how contextual diversity affects training

outcomes.

Each CHMT was treated as a distinct analytical unit because it represents the institutional focal point for

evidence interpretation and operational decision-making at the county level. Ethical approval for the study was

obtained from a recognized Institutional Review Board, and authorization was granted by the Ministry of

Health. Participation was voluntary, informed consent was obtained from all respondents, and confidentiality

was maintained throughout data collection, analysis, and reporting.

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

Study Population and Sampling

The study targeted members of the CHMTs who hold administrative and technical positions central to health

sector management within Kenya’s devolved governance structure. The study population therefore comprised

County Directors of Health, County Health Administrative Officers, Nursing Officers, Clinical Officers, Health

Records and Information Officers, Public Health Officers, and program leads representing various functional

departments. This multidisciplinary composition reflects the institutional core of county health governance,

combining strategic and operational expertise necessary for evidence-based decision-making.

A combination of purposive and random sampling techniques was used to ensure both representativeness and

contextual diversity. All consenting CHMT members from these counties were included, yielding a total

sample of 200 participants. The sample size was determined using Cochran’s (1977) formula and adjusted to

accommodate potential attrition. This approach ensured balanced representation across counties while

providing sufficient power to detect differences in the effects of training on data-use capacity.

Data Collection Procedures

Data were collected at two distinct points in time, baseline and endline. The baseline phase was conducted

prior to the implementation of the capacity-building intervention to establish the initial conditions across both

the intervention and control counties. The endline phase followed the completion of the training program and

was designed to capture post-intervention outcomes in data-use practices. A structured self-administered

questionnaire served as the principal tool for data collection.

Data collection was undertaken by trained research assistants under the direct supervision of the principal

investigator to ensure methodological rigor and consistency. Enumerators received comprehensive instruction

on research ethics, informed consent, confidentiality, and standardized administration procedures. Both the

baseline and endline exercises adhered to identical procedures to maintain comparability across phases.

Data Analysis Techniques

Data were analysed using both descriptive and inferential statistical techniques in line with the study

objectives. Descriptive statistics were computed to summarize the characteristics of the respondents and

provide a clear overview of data-use patterns at both baseline and endline.

Inferential analysis was then conducted to examine whether the observed differences in data-use practices

between intervention and control counties, as well as across time, were statistically significant. The chi-square

test was applied to determine associations between key variables and to evaluate variations across

demographic and institutional characteristics such as gender, education level, tenure, and age. To estimate the

causal impact of the capacity-building intervention, a Difference-in-Differences (DiD) regression model was

employed. This approach compared the magnitude of change between baseline and endline in both groups,

using the control counties as a counterfactual to isolate the net effect of the training.

RESULTS

The study sought to assess the effect of targeted training and capacity-building interventions on the ability of

CHMTs to utilize RHD in decision-making. Using baseline and endline observations from intervention and

control counties, the results trace both descriptive patterns and inferential relationships to determine whether

structured capacity strengthening led to measurable improvements in data-driven management. In doing so, the

findings illuminate the extent to which professional development initiatives can transform managerial practice

from routine data reporting toward sustained, evidence-based governance within Kenya’s devolved health

system.

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

Socio-Demographic Characteristics of Respondents

Table 1 presents the socio-demographic and professional characteristics of CHMTs members who participated

in the study during both the baseline and endline assessments.

Table 1: Socio-Demographic Characteristics of Respondents

Variable

Category

Baseline

Endline

Interventio

n

Intervention Control

Control

46.6%

52.9% (101)

(89)

Total Respondents

Age

47.1% (90)

53.4% (102)

20 - 29 Years

30 - 39 Years

1.6% (3)

4.7% (9)

11% (21)

1.6% (3)

4.7% (9)

11% (21)

5.8% (11)

5.2% (10)

21.5%

(41)

21.5%

(41)

40 - 49 Years

25.1% (48)

14.7% (28)

24.6% (47)

22.5% (43)

25.1% (48)

14.7% (28)

25.1% (48)

21.5% (41)

50 Years and

Above

15.7%

(30)

16.2%

(31)

29.8%

(57)

29.8%

(57)

Gender

Male

23.6%

(45)

Female

23% (44)

Education Level

Diploma Certificate 4.7% (9)

9.4% (18) 4.7% (9)

9.4% (18)

15.2%

(29)

Master’s Degree

13.6% (26)

0.5% (1)

13.6% (26)

(29)

PhD

2.6% (5)

0.5% (1)

2.6% (5)

25.7%

(49)

26.2%

(50)

28.3% (54)

27.7% (53)

CHMT Membership

Duration

Less than 1 year

5.8% (11)

5.8% (11) 5.8% (11)

6.8% (13)

24.1%

26.2%

(50)

2-5 years

6-9 years

26.7% (51)

8.4% (16)

25.7% (49)

(46)

11% (21)

12% (23)

8.9% (17)

6.3% (12)

9.4% (18)

11% (21)

10 years and above 6.3% (12)

The composition of respondents revealed a balanced gender representation, with males constituting

approximately 55% and females 45% of participants across both time points. Most respondents were aged

between 40 and 49 years (around 47%), indicating that the study engaged primarily mid-career health

managers with substantial professional experience. Educational attainment was notably high, with over half of

the respondents holding undergraduate qualifications and nearly one-third possessing master’s degrees,

reflecting a technically proficient managerial workforce. Only a small proportion held diploma or doctoral-

level credentials, collectively underscoring an academically well-prepared cohort.

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

Descriptive Statistics on Data Use for Common Public Health Decisions

The descriptive analysis examined how the structured training intervention influenced CHMT members’ self-

assessed capacity to analyse, interpret, and apply RHD, comparing mean agreement scores between baseline

and endline across intervention and control counties.

Table 2: Mean Change in Perceived Impact of Training on Routine Health Data Utilization

Variable

Baseline

Endline

Inter Cont Over Inter Cont Over

venti rol

on

all

venti rol

on

all

Training has enhanced my analytical skills and ability to

effectively utilize RHD for decision-making.

3.67 3.66 3.66 4.15 3.59 3.85

(0.98 (1.0 (0.9 (0.68 (1.0 (0.9

)

0)

9)

)

1)

Through training, I have gained skills in setting targets,

calculating program coverage, and assessing service utilization

using RHD.

3.66 3.68 3.67 4.03 3.65 3.83

(1.01 (0.9 (0.9 (0.68 (0.9 (0.8

)

5)

7)

)

6)

The training has improved my capacity to interpret, present, and

communicate insights from RHD to stakeholders.

3.61 3.71 3.66 4.17 3.69 3.91

(0.98 (0.8 (0.9 (0.73 (0.8 (0.8

)

6)

I am better equipped to identify and address data quality issues in 3.53 3.61 3.58 3.97 3.63 3.79

RHD as a result of the training. (1.03 (0.8 (0.9 (0.70 (0.9 (0.8

9) 6) 0) 3)

I have successfully applied the skills and knowledge gained from 3.64 3.76 3.71 3.91 3.75 3.82

2)

)

6)

3)

)

the training to improve my use of RHD in my daily work within

the CHMT.

(0.99 (0.9 (0.9 (0.78 (0.9 (0.8

2) 5) 2) 6)

)

Training has enhanced my analytical skills and ability to

effectively utilize RHD for decision-making.

3.60 3.70 3.65 3.92 3.66 3.78

(0.90 (0.8 (0.8 (0.61 (0.8 (0.7

)

7)

8)

)

8)

Impact of Training Overall

3.62 3.69 3.66 4.03 3.66 3.83

(0.90 (0.7 (0.8 (0.51 (0.7 (0.6

)

9)

4)

)

6)

8)

At baseline, the overall pattern across both study arms reflected moderate to high self-assessed competency in

key data-use domains, suggesting that CHMT members already possessed a foundational level of analytical

and managerial experience. However, the endline data revealed a marked divergence between intervention and

control counties. Respondents from intervention counties consistently recorded higher mean scores across all

assessed variables, reflecting substantial improvements in core data competencies.

In contrast, the control counties showed either negligible changes or marginal declines in comparable

indicators. This suggests that the observed improvements were directly associated with the structured training

rather than broader system-wide effects or external factors, demonstrating that the capacity-building initiative

enhanced CHMT members’ practical engagement with RHD.

The graphical analysis presents a comparative overview of how the training intervention influenced the

perceived capacity of CHMTs to utilize RHD in decision-making. Figure 1 displays the distribution of

respondents across low, moderate, and high levels of perceived training impact, while Figure 2 illustrates the

corresponding changes in average index scores.

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

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Figure 1: Change in Levels of Perceived Impact of Training on Routine Health Data Utilization

Figure 2: Change in Overall Mean Scores for Perceived Impact of Training on Routine Health Data

Utilization

As shown in Figure 1, perceptions among respondents in the control counties remained relatively stable across

the study period. The distribution of responses across the moderate and low categories showed minimal

variation, indicating that in the absence of formal training, perceptions of capacity did not evolve significantly.

In contrast, the intervention counties exhibited a clear upward movement (Figure 2). This shift suggests that

the structured training program helped transform participants’ self-assessed ability to interpret and apply data

in their operational contexts, promoting greater uniformity and confidence in data-use capability.

Inferential Statistics

Inferential analysis was undertaken to determine whether the training intervention led to statistically

meaningful changes in the perceived ability of CHMTs to utilize RHD for decision-making. Chi-square tests

were first applied to evaluate differences across time, between study arms, and across demographic and

institutional characteristics. This approach addressed the first two study objectives by examining temporal and

cross-sectional patterns in perceived training impact.

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Table 3: Chi-square Test for Perceived Impact of Training on Routine Health Data Utilization

Comparison

N

χ2

df

2

4

7

8

p-value

0.814

<0.001

0.571

0.008

0.172

0.591

0.290

203

179

193

189

382

381

382

0.41

13.7

1.12

9.33

6.38

5.57

9.66

Control: Baseline vs Endline

Intervention: Baseline vs Endline

Baseline: Control vs Intervention

Endline: Control vs Intervention

Control vs Intervention by gender

Control vs Intervention by Education Level

Control vs Intervention by Duration CHMT

Membership

382

8.51

8

0.385

Control vs Intervention by Age

The chi-square results demonstrated that the observed changes were concentrated in the intervention counties.

The control group showed no significant difference between baseline and endline, validating the stability of

perceptions in the absence of training. Conversely, the intervention group reported a statistically significant

shift (p < 0.001), confirming a measurable program effect. Furthermore, while the two study arms were

comparable at baseline, a clear and significant divergence had emerged by endline (p = 0.008), reflecting the

successful elevation of perceived data utilization competence among trained CHMT members. Analysis across

demographic and institutional variables indicated that the intervention's benefit was inclusive. None of the

subgroup comparisons, including gender, education level, tenure, or age, yielded statistically significant

results.

To further isolate the causal influence of the training intervention, a Difference-in-Differences (DiD)

regression model was employed as shown below.

Table 4: DiD Regression Results for Impact of Training RHD Utilization

Term

Estimate Std. Error

t-value

p-value 95% CI (Lower,

Upper)

3.6871

-0.0649

-0.0283

0.4510

0.0766

0.1116

0.1080

0.1578

48.15

-0.58

-0.26

2.86

<0.001

0.5610

0.7935

0.0045

[3.5366, 3.8377]

[-0.2842, 0.1544]

[-0.2407, 0.1841]

[0.1407, 0.7613]

Intercept (Control Baseline)

Intervention (Baseline

Difference)

Post (Time Effect in Control)

Intervention × Post (Treatment

Effect)

The regression results revealed a significant treatment effect (β = 0.4510, p = 0.0045), demonstrating that the

training intervention increased CHMT members’ mean perception of data utilization capacity by nearly half a

point on the five-point scale compared to the control group. This effect remained statistically robust after

controlling for baseline differences and time-related changes. The magnitude of the treatment effect indicates

that participation in the training substantially enhanced participants perceived analytical skills, data

interpretation capabilities, and confidence in applying data for operational and strategic decision-making.

The chi-square and DiD findings provide strong empirical support for the effectiveness of structured training in

strengthening data-use capacity among county health managers. These results underscore the importance of

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continuous capacity development in institutionalizing a culture of data-driven governance, where evidence

informs decision-making as a routine managerial practice.

DISCUSSION

Interpretation of Findings

The results of this study demonstrate that targeted training interventions significantly improved the ability of

CHMTs to utilize RHD in decision-making. Participants in the intervention counties showed a consistent shift

toward structured and purposeful data use across key managerial domains, including planning, budgeting,

monitoring, and policy formulation. This pattern represents a transition from compliance-oriented data

handling to an institutional culture where evidence guides administrative judgment. The observed changes

were not limited to technical proficiency but extended to the normalization of analytical thinking within county

health leadership. Importantly, the effects were consistent across demographic and professional subgroups,

indicating that the intervention fostered an inclusive transformation rather than benefiting a specific cadre.

These findings illustrate that deliberate and well-designed training can reposition RHD from a passive

reporting tool to a resource for accountability, performance improvement, and strategic management within

devolved health systems.

Comparison with Existing Literature

The findings of this study are consistent with global and regional evidence demonstrating that institutional

capacity enhancement is central to promoting the use of health data for decision-making. Nutley and Reynolds

(2013) emphasize that investments in HIS yield tangible results only when they are accompanied by

interventions that empower decision-makers to interpret and apply data effectively. Similarly, Aqil et al. (2014)

found that structured mentorship and data-use training were critical in closing the gap between information

availability and actual utilization in public health programs. Comparable evidence from Uganda and Tanzania

also confirms that decision-support training leads to measurable improvements in evidence-based planning and

supervision (Nsubuga et al., 2018; Mboera et al., 2020). The uniform improvements across gender, education,

and tenure observed in this study align with the argument advanced by Hotchkiss et al. (2012), who assert that

sustained data use is primarily shaped by institutional systems and leadership practices rather than individual

attributes. Collectively, these parallels reinforce that data utilization improves most where technical training is

coupled with organizational reinforcement and accountability mechanisms.

Implications for County Health Governance

The outcomes of this study have significant implications for the strengthening of county health governance in

Kenya. The demonstrated gains highlight the importance of embedding structured training and mentorship

within routine management systems to institutionalize data-informed decision-making. Integrating continuous

learning on data interpretation, performance analysis, and feedback mechanisms into county health leadership

development programs would ensure that these improvements are sustained beyond the life of specific

projects. Counties should also establish routine data review and learning forums where managers collectively

examine trends, reflect on progress, and adjust plans based on emerging evidence. Such platforms can promote

transparency and encourage the practical application of data in resource allocation, program monitoring, and

service delivery optimization.

CONCLUSION AND RECOMMENDATIONS

Summary Of Conclusions

This study concludes that structured training and capacity-building initiatives play a pivotal role in

strengthening the ability of CHMTs to interpret and apply RHD in managerial decision making. The findings

demonstrated that participants who underwent targeted training showed measurable gains in analytical

proficiency, confidence in data interpretation, and capacity to communicate evidence for action. These

improvements were reflected in higher mean scores and statistically significant differences between

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intervention and control counties, confirming that deliberate, skills-focused interventions can shift institutional

behaviour toward more consistent data use. By equipping health managers with the technical and conceptual

tools needed to transform data into actionable insight, the intervention contributed to building a foundation for

evidence-informed governance within Kenya’s devolved health system. The study therefore establishes that

capacity development is not an ancillary activity but a central enabler of effective and accountable public

health management.

Policy and Practice Recommendations

The results of this study highlight the need to institutionalize training for data-driven decision making as a

routine component of health sector management. The Ministry of Health, in partnership with county

governments, should embed structured training on data analytics, interpretation, and application within the

professional development frameworks of CHMT members and other health managers. Counties should

develop mentorship mechanisms and peer-learning platforms where newly acquired competencies can be

continuously reinforced through practice and collaborative reflection. Furthermore, regular performance

review sessions that integrate data analysis into planning and supervision processes should be mandated to

ensure that data use becomes an ingrained managerial habit. Investments in supportive infrastructure, such as

functional digital systems, reliable data repositories, and analytical tools, will further enhance the practical

utility of training outcomes. By aligning managerial capacity with system readiness, counties can sustain a

culture where training translates directly into improved performance and informed health sector governance.

Suggestions for Further Research

Future research should investigate the mechanisms through which training influences the long-term

institutionalization of data use in county health systems. Longitudinal studies are needed to determine whether

the observed improvements in analytical and interpretive skills lead to measurable gains in service delivery,

efficiency, and equity. Additional qualitative studies could explore how mentorship, leadership engagement,

and organizational culture mediate the relationship between training and sustained practice. Comparative

analyses across different cadres of health managers and sectors could also reveal contextual factors that either

enable or constrain the translation of skills into action.

ACKNOWLEDGMENT

The author sincerely acknowledges the unwavering guidance and mentorship of Prof. John Paul Oyore from

the Department of Family Medicine, Community Health and Epidemiology, and Prof. George Otieno from the

Department of Health Management and Informatics, both of Kenyatta University. Their intellectual support,

constructive feedback, and commitment throughout the research process were instrumental in shaping the

study’s direction and rigor. The author also extends appreciation to the County Health Management Teams who

participated in the study for their time, insights, and invaluable contribution to advancing evidence-based

decision-making within Kenya’s devolved health system.

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