Strengthening nutrition routine data using institutionalized health management information systems for decision making: analysis of best practices and lessons learned from implementation in Burkina Faso

Abstract

Strengthening nutrition routine information system is critical to support nutrition programs with relevant data to inform decision-making. This study analyzed the practices and lessons learned from the implementation in Burkina Faso in strengthening nutrition routine data using institutionalized health management information systems for decision making.

Methods This qualitative study was conducted in Burkina Faso in 2022 on the capitalization of best practices after 3 years of implementation through documentary review, semi-structured individual interviews with 64 key implementing informants spread over 2 health districts, 2 regional hospital centers and 2 health regions, and a national triangulation workshop with 40 implementing actors, including 20 from the central level, 15 from the decentralized level, and 5 partners.

Results The results of the study show the best practices and progress identified: (i) the integration of new routine data elements and nutrition indicators into District Health Information Software (DHIS2), which filled the data gap for adequate monitoring of the nutrition program; (ii) the design and use of the nutrition indicator dashboard; (iii) data validation and performance review sessions which have improved the quality and use of routine data in decision-making; and (iv) decentralization of data entry of monthly activity reports of health facilities. Lessons learned included: (i) conducting a small-scale phase to test the indicators is an important step to take before national scale-up of the indicators; (ii) a participatory approach involving all actors at different levels is important; (iii) advocacy is important to integrate prevention indicators into health facilities information systems in a more curative-oriented health system; (iv) the decentralized entry of data is a best practice that improves data quality in terms of timeliness, completeness, and internal consistency.

Conclusion Beyond the inclusion of indicators, special emphasis should be placed on working on data quality. Future experiences in refining routine data related to nutrition-sensitive interventions in the non-health sectors are key next steps that would further contribute to strengthening the national nutrition information system.

Introduction

Malnutrition, in all its forms, is a real public health and development problem in Burkina Faso, as is in other countries in sub-Saharan Africa [1]. The nutritional situation remains serious and has been aggravated in recent years by the humanitarian situation. Notably, the prevalence of stunting fell from 35.1% in 2009 to 21.6% in 2021 [2]. Nevertheless, one out of every ten children under the age of five experiences wasting. Pregnant women present a strikingly high anemia prevalence of 72%, surpassing the World Health Organization (WHO) critical threshold of 40% [2]. However, complementary feeding remains a significant concern in the infant and young child feeding (IYCF) indicators, with 70% of children aged 6 to 23 months not achieving minimum dietary diversity [2].

In a bid to reverse this trend and accelerate the achievement of national objectives and international nutrition targets, since 2018, the government has developed and adopted a Multisectoral strategic Nutrition Plan (MSNP) for 2020–2024 [3]. This national nutrition plan is implemented through nutrition-specific interventions with proven effectiveness in the health sector. These include the Integrated Management of Acute Malnutrition (IMAM) and interventions to prevent malnutrition. These interventions are implemented both at the level of health facilities and at the community level.

However, the NMNP will only yield a substantial impact if it is effectively monitored and evaluated [3]. To assess or monitor progress toward targets and guide the effective implementation of the national nutrition plan, it is essential to ensure the availability of key process, outcome, and impact indicators within the national Nutrition Information System (NIS). A strong national NIS with quality data on the nutrition situation and programming, as well as nutrition investments, is essential for monitoring, decision-making and program adjustment and for policies to respond more effectively to the needs of children and women [4].

Enhanced information can augment the potential of nutritional interventions to promote the health and well-being of individuals, families, and communities. Historically, many countries have limited data on key nutrition issues, including activities to improve nutrition across the health sector. The decentralized nutrition data, collected at both facility and community levels, are currently either not available or are insufficient, especially on a continuous basis or for the most rudimentary service delivery levels. Also, challenges on data quality, analysis and use of routine data are acute in low and middle-income countries (LMIC) [5, 6]. This situation undermines the ability of countries to track progress in nutrition and to strengthen the coverage, quality and continuity of nutrition programs [7,8,9]. There is an urgent need to strengthen routine NIS to support the implementation of nutrition programs [4]. Due to the multisectoral nature of nutrition, the NIS is also multisectoral and is linked to sectors including health, food security, social protection, education, water and sanitation [4]. The national nutrition information system has four key components: (i) routine data, (ii) survey data, (iii) surveillance data and (iv) budgetary and financial data for priority programs by sector [4].

Landscape of nutrition indicators and mapping conducted at baseline in 2018 in Burkina Faso to identify the gaps

In Burkina Faso, a mapping of information system in the health sector that the objective was to carry out a review of the nutrition data available. The mapping focused on the overview of the performance of health management information systems (HMIS) through the conduct of a review of the nutrition indicators collected or not in the DHIS2. The review of indicators also explored program data collected through other sources, such as parallel data collection systems with Excel files and databases. The review identified the needs in terms of availability, quality, analysis, and use of routine nutrition data for monitoring the progress of the national nutrition plan in the health sector. This approach was geared towards optimizing the monitoring of the national nutrition plan and discerning the strengths and weaknesses of the routine HMIS.

The results of this mapping revealed that the HMIS has been digitalized using DHIS2, with routine health data collected monthly. Observed challenges were the non-availability of certain process indicators, insufficient data analysis and a lack of dashboards or scorecard, coupled with deficiencies in data quality and use. The review showed that the indicators of the effects and impacts were collected through population-based surveys. Process indicators were collected from administrative data sources, namely the DHIS2 and Excel databases.

The review of indicators made it possible to identify missing indicators and gaps in routine nutrition data and guide the choice of new nutrition indicators to be introduced into the DHIS2. Initial findings showed that routine HMIS indicators do not allow for adequate monitoring of the new national nutrition plan. Most of the indicators for the integrated management of acute malnutrition (IMAM) were available in the DHIS2, as well as indicators of exclusive breastfeeding, such as early initiation of breastfeeding and low birth weight. However, other indicators were missing. These were the infant and young child feeding indicators (IYCF) and maternal nutrition indicators at the health facility level, which were insufficiently available in the HMIS, such as nutrition counseling during antenatal consultations and infant consultations, iron folic acid or multiple micronutrient supplements (MMS) supplementation in pregnant women, and fortification of infant foods with micronutrient powders. Data from community-level nutrition services, including IYCF components, were not adequately captured using the DHIS2. Routine data were not entered directly by the health facilities; they were transmitted on paper to the district and entries were made by the district data manager.

Other gaps or challenges related to HMIS performance were initially noted in 2018. These included:

• ❖ Problems related to the quality of routine data: consistency of data, completeness, and promptness of monthly reports

• ❖ Parallel collection of vitamin A supplementation campaign data through Excel files

• ❖ Insufficient systematic feedback related to data transmitted to the upper echelon

• ❖ Insufficient integrated analysis and validation of routine nutrition data

• ❖ Absence of nutrition and DHIS2 dashboards not accessible to the public

• ❖ Weak use of routine data

To fill the gaps related to nutrition data, the Ministry of Health of Burkina Faso with the support of its partners, namely UNICEF and the Bill and Melinda Gates Foundation (BMGF), has implemented interventions to strengthen nutrition routine data in the health sector to better guide monitoring in the implementation of the NMNP.

Presentation of the project to strengthen routine nutrition data

Funded by the BMGF, this initiative aimed to strengthen routine nutrition data in Burkina Faso. The intervention focused on four pillars: (1) availability of routine data in the District Health Information Software (DHIS2); (2) quality of data; (3) analysis of data; and (4) use of data to improve program performance. In 2018, a three-year work plan was implemented to address the identified bottlenecks through an initial exploratory qualitative study at the start of the project. The integration of the new nutrition indicators into the DHIS2 began with an initial small-scale phase, which subsequently expanded to a national scale. These indicators were designed to bridge the existing gaps related to the process indicators of nutrition programs. The Fig. 1 below shows the package of actions to strengthen routine nutritional data and the Proximity pillar of the routine health information system (Fig. 1).

Fig. 1

Overview of project interventions for improving routine nutrition data and the pillars of the health information system

The small-scale phase in two health districts

The two health districts of Yako in the northern region and Ziniaré in the central plateau region were selected during the national workshop, in collaboration with all stakeholders, for the implementation of the package of interventions based on the following five key criteria:

• Existence of an integrated package of community measures for IYCF

• Existence of new nutrition-related activities such as maternal nutrition

• High prevalence of stunting

• Presence of several partners in the district

• Existence of equipment for electronic consultation registers (REC) for data entry in health facilities

National scaling-up

Based on the small-scale experience, a list of 11 indicators was selected for broader implementation, aligning with the national review of primary tools within the HMIS across all programs, including nutrition. National scaling started in January 2021, with new routine raw data values consistently reported. Data from newly introduced indicators are now accessible in DHIS2 monthly. The package of other interventions has also gradually moved to the national level.

The objective of this study was to analyze good practices and lessons learned from Burkina Faso in strengthening nutrition routine data using institutionalized health management information systems for decision making, with a focus on progress made, success factors, challenges, and prerequisites for expansion within the health sector.

Methods

This qualitative study was carried out in Burkina Faso in 2022 on the analysis of best practices and lessons learned from the implementation of actions to strengthen the nutrition information system.

Our study entailed the documentation and selection of best practices to scale-up coupled with the extraction of lessons learned in collaboration with the implementing actors.

In March 2022, a qualitative study was carried out by an independent technical team that was not directly involved in the implementation. The methodological approach was carried out in three phases: (i) the selection of best practices to scale-up, (ii) the collection of data through document review and semi-structured individual interviews with the implementing actors in the field and, (iii) a national triangulation workshop.

• ❖ The selection of best practices is based on the following seven criteria: relevance, effectiveness, impact, reproducibility/possibility of scaling up, impact, equity, and feasibility of scale-up [10]. After making a broad list of all the experiences deemed significant to scale-up the seven criteria were used to select the best practices through a classification by order using a rating scale from 1 to 10. The best practices with the highest scores were retained. At the end of the national workshop with the health facility-level participants in attendance, of the 10 good practices identified, 05 most relevant best practices were selected by the technical teams, including:

  • Integration of new routine data elements and nutritional indicators into the DHIS2

  • The design and use of the nutrition indicators dashboard in DHIS2

  • Data validation and performance review sessions

  • Decentralization of the entry of monthly activity reports of health facilities using DHIS2

  • Weekly monitoring of IMAM data in an emergency context

• ❖ Regarding the documentary review, the primary tools (registers) and revised monthly reports of health facilities in force since January 2021 were analyzed to assess the available data, and extractions in DHIS2 made it possible to assess the availability of data, the systematic analysis tools available in the form of a dashboard, and the quality of the data.

• ❖ Twenty investigators, including data managers and nutritionists, were trained for five days on the experience capitalization approach and data collection techniques through individual interviews using the semi-structured interview guide.

The individual interview guide by selected theme were structured around six key points: (i) impacts or changes observed (ii) availability of routine data (success factors, challenges to the implementation), (iii) analysis and quality of routine data (success factors, challenges to the implementation ), (iv) use of routine data to inform decision-making (success factors, challenges to the implementation ), (v) conditions for making the experience sustainable and scaling it up (vi) lessons have been learned from the implementation.

Data were collected until saturation was reached in the individual interviews with a total of 64 key implementing informants spread over 2 health districts, 2 regional hospital centers and 2 health regions. Qualitative data were analyzed, using thematic analysis technique.

The interviews were conducted in French. They were recorded on a dictaphone and transcribed for analysis. The discussions were coded, analysed using QDA Miner software and synthetised by themes and subthemes. Deductive and inductive approaches were used for this interview thematic analysis. This analysis was performed by an assistant researcher and verified by the lead researcher.

The qualitative data collected was synthesized, consolidated, and triangulated with the findings of the documentary review. The results of the study were validated during a 2-days national triangulation workshop with all implementing actors, which brought together 40 participants, including 20 from the central level, 15 from the decentralized level, and 5 partners (UNICEF, WFP, WHO, Alive and Thrive, and Hellen Keller International).

The study on the initial exploratory study and this study on the good practices and lessons learned are linked and combined in the analysis and make it possible to understand the progress made in implementation.

An adaptation of the theoretical framework based on the analysis of best practices, the four key pillars of routine HMIS, and the factors (drivers and challenges) influencing implementation was used to design the study and develop the interview guides. This conceptually adapted framework also includes lessons learned, conditions for sustainability, and scaling up. The framework also guided data analysis (Fig. 2).

Fig. 2

Theoretical framework for analyzing best practices and lessons learned from the implementation of the strengthening of the routine nutrition information system

Results

I. Participants’ characteristics

In 2022, a total of 64 key informants spread over 2 health districts, 2 regional hospital centers and 2 health regions, who were involved in the enhancement of nutrition information, participated in individual interviews. Concerning the different roles of respondents, key informants, the health facility workers are involved in generating data from their frontline service provider roles. The data managers at district ensured the entry of data in DHIS2 and the quality assurance of data. At regional, the actors ensured the quality control to ensure the avaibility of quality data (Table 1).

Table 1 Distribution of respondents for individual interviews by socioprofessional profile and by theme

II. Results of the capitalization study conducted in 2022 on best practices, lessons learned and conditions for sustainability and scaling up

The results of this study showed that the National Nutrition Information System (NNIS) Improvement Project has strengthened the availability of nutrition data in the DHIS2 and the analysis, quality, and use of routine nutrition data to improve the performance of maternal and child nutrition programs. The main themes highlighted by the respondents based on promising practices are presented in Table 2.

Table 2 Results of the analyses of best practices with the progress observed, success factors, challenges, lessons learned and conditions for sustainability and scaling up

II.1 Observed changes, success factors and challenges related to data availability

Introduction of new nutrition indicators in the DHIS2

The results of this study show that the project has enabled the effective integration of nutrition indicators (prevention and care) in the DHIS2 by filling the data gap. In 2018, following a review of indicators, a consensus was reached on 13 new key nutrition indicators that were retained in the small-scale phase. The challenge was to not select “too many” indicators, which could hamper the data quality and workload of service providers. Only tracer indicators that allowed proper monitoring of the implementation of the national nutrition plan were selected for the test phase.

At the end of the small-scale phase, two indicators, namely the number of health workers trained on the IMAM protocol and the number of community-based health workers trained, have not been validated for integration into the national collection tools, as they can be extracted separately from activity reports.

Among the success factors for the introduction of the new nutrition indicators, we noted (i) the leadership and high-level commitment of the Minister of Health, (ii) capacity building of health workers, (iii) a participatory approach throughout the process with the involvement of health districts and key partners, and (iv) search for consensus with exchanges between the implementing actors.

However, the 2019 strike by health workers disrupted the implementation of the small-scale phase.

Additionally, during the process of selecting indicators as part of the overall review of the primary tools of the DHIS2, there was a preference for curative interventions over information education communication.

Data from the vitamin A supplementation (VAS) campaigns in the DHIS2 were also set up and integrated into the DHIS2 instead of Excel files. Some respondents pointed out that this contributed to improving the archiving of VAS data through DHIS2 and the traceability, availability, and quality of VAS data. Dashboard extractions show shortcomings in the quality of the VAS data, with coverage beyond 100% in some districts.

II.2 Observed changes, success factors and challenges related to routine data quality

The results of the study showed an improvement in data quality in term of completeness and promptness rates through the following key actions: (i) decentralization of data entry in DHIS2, (ii) the organization of on-site data quality control as well as validation of data at the district level and (iii) the organization of nutrition performance reviews.

Regarding decentralized data entry, the use of tablets versus paper has greatly improved the quality of data in terms of timeliness, completeness, and internal consistency. This graphic 3 shows the comparative analysis of the completeness and timeliness of monthly reports in an intervention (Yako) and control (Ziniare) health district (Fig. 3).

Fig. 3

Graphic of the comparative analysis of the completeness and timeliness of monthly reports in an intervention (Yako) and control (Ziniare) health district

The existence of the electronic consultation register (REC) with tablets and the training of health workers were factors that facilitated the implementation of decentralized data entry. However, the instability of the internet connection and renewal of data bundles are major challenges for their sustainability.

The data quality control at health facility level with the new process introduced by the project, made it possible to compare the nutrition data from the monthly reports with the data from the registers and to correct the aberrant values. The same is true for the holding of data validation sessions at district level for ensure data quality.

II.3 Observed changes, success factors and challenges related to the systematic analysis of data

Respondents highlighted an improvement in routine data analysis with the development of a dashboard in the form of graphs, tables, and maps; the organization of performance reviews with a systematic analysis of key indicators for monitoring the performance and identification of bottlenecks; and the development of a problem-solving plan. This performance reviews organized at regional level in attendance of data managers, nutritionists, pharmacists and health workers. One of the interviewees declared “With the dashboards, we see a greater appropriation of nutrition issues. The actors are more attentive to the evolution of the indicators”.

These dashboards, present at the national, regional, and district level, have further decentralized to the level of individual healthcare facilities, significantly reinforcing the feedback mechanism (Fig. 4). This Fig. 4 below shows an overview of dashboards of key nutrition indicators in DHIS2.

Fig. 4

Overview of dashboards of key nutrition indicators in DHIS2

Among the success factors that facilitated the systematic analysis are (i) capacity building of actors, (ii) existence of a performance review guide with a directory of key indicators, (iii) availability of qualified staff at the national level for the design of dashboards, and (iv) active participation of key actors. The main constraints encountered were the lack of data validation workshops at the health district level, COVID-19, and the security context.

II.4 Observed changes, success factors and challenges in using data to improve program performance

Factors contributing to the successful use of routine data to inform the national nutrition plan include: (i) the development of a public interface that expands access to nutritional dashboards, (ii) the conducting of nutrition performance reviews across all 13 regions and at the national level, which have contributed to improving the quality, analysis and use of data; (iii) dissemination within district health councils, regional nutrition councils, and during Vitamin A Supplementation review meetings.

Conversely, barriers hindering the use of routine data for decision-making encompass insufficient policy briefs, limited financial resources, and insufficient recommendation follow-ups.

II.5 Lessons learned from the implementation that are relevant to other studies and projects

The experiences from Burkina Faso have yielded valuable insights such as:

• Conducting a small-scale phase to test the indicators is an important step to take prior to scaling up.

• A participatory approach, with the involvement of all actors at different levels is an important point for the introduction of nutrition data in the DHIS2. It enabled a consensus to be reached with stakeholders on the key indicators to be included.

• The absence of standard definitions for routine nutrition indicators does not facilitate an informed choice on the minimum list of indicators to be monitored routinely.

• Specific data disaggregation by gender were overlooked, which may limit the ability of decision-makers to use the data to address gender equality issues.

• Advocacy is important to integrate prevention indicators into health facilities in a more curative-oriented health system. The tracking preventive indicators by the health workers allowed to put emphasis on the prevention. In public health, the prevention came first than the treatment.

• Beyond the inclusion of indicators in DHIS2, special emphasis should be placed on data quality according to the recommendation commonly made by respondents.

• The decentralized entry of data is a best practice that improves data quality in terms of timeliness, completeness, and internal consistency.

• Implementing a weekly IMAM data collection protocol has effectively bolstered nutritional surveillance during crises.

II. 6 Conditions for sustainability, reproducibility and strengthening of the national scale-up

According to the interviewees, the initiative was sustainable as the Ministry of Health carries out the activities, the project is integrated into the HMIS. In addition, parallel data collection with excel files has been gradually abandoned in favor of DHIS2. However, to strengthen the projects’ sustainability, to strengthen the system, the maintaining information systems requires continued investment, a certain number of actions are necessary:

• Continuously enhance the expertise in the Nutrition Information System and integrate training needs into training curricula in health schools.

• Organize supportive supervision to update actors in the field.

• Increase financial resources to bolster logistical capacities in electronic equipment (Smartphone or tablets).

• Ensure the application is regularly updated.

• Strengthen the dissemination of dashboards in existing frameworks.

• Strengthen the regular holding of data validation sessions at the district level.

• Have a stable telephone network and internet connection.

• Regarding the weekly IMAM collection, data is currently collected via telephone and Excel files. Transitioning to tablet-based data collection could enhance the archiving process.

Discussion

Discussion on methodology

This study is grounded in qualitative research, a study of documentation of lessons learned. Although conducted by an independent team that was not directly involved in the implementation, this study has some limitations. Indeed, this study was not exhaustive and all practices for the strengthening of routine nutrition information systems were not included due to the selection criteria used. During interviews, some respondents exhibited recall biases, struggling to recount specific event details. However, the triangulation of sources made it possible to minimize memory bias. Despite these limitations, this paper added the published articles about experiences in strengthening nutrition data for decision making in sub-Saharan Africa and is contributing in a valuable way to the limited evidence about what works to strengthen health sector information systems in ways that enable improved monitoring of nutrition service delivery and progress toward achieving national nutrition plan targets [6, 11].

Discussion on the inclusion of indicators in the DHIS2

The strong leadership of the Minister of Health in favor of nutrition, and the advocacy efforts led by partners, have been important for the introduction of the indicators. One of the challenges was to reduce the number of indicators so as not to increase the workload of health workers, particularly given the volume of monthly reports. The inclusion of community-level indicators in the DHIS2 was faced with the same difficulties, it was necessary to prioritize. The reporting at the community level is done on paper by community health workers whose schooling level is sometimes often low, primary school.

Further, the absence of standard indicators at the global level for DHIS2 did not help guide the country on the selection of indicators in 2018 but nowadays a new global guidance and DHIS2 module is available and address many issues that were raised for the Burkina Faso context [12, 13].

During the overall indicators selection process, the study noted a preference of health professionals for curative interventions over social behavior change communication interventions at community and health facility level [14, 15]. This inclination towards indicators associated with curative interventions over preventive, information and education-based communications interventions might stem from the health system’s inherent emphasis on curative measures over prevention [16, 17]. However, while curative interventions are easy to quantify, validate and verify, activities such as counselling and information education communication are inherently more complex and difficult to validate and verify, thus making data collection and quality more difficult [18].

Data quality discussion

This study shows that certain best practices contributed to improving the quality of routine nutrition data. By decentralizing the data entry into the DHIS2 directly from health facilities using tablets, not only has the workload alleviated for data managers at the district level, but data quality has also seen considerable improvement. However, redefining the role of data managers at the district level is necessary to reorient them towards supervision and data quality control activities.

Despite the progress obtained with the project, the current major challenge that remains is the reporting and completeness of these data in DHIS2. This is especially the case for the IYCF indicator at the community level, supported by partners [16, 19]. Awareness and advocacy is necessary with Non-Governmental Organisations to report on this data in DHIS2 via the circuit of HMIS, respecting deadlines for sending and transmission channels from health facilities [19].

Discussion on data analysis

The creation of dashboards showcasing nutrition tracer indicators has facilitated the automation of systematic data analysis and enhanced feedback mechanisms. However, broadening the reach of these dashboards beyond health workers – to encompass users of nutrition services and local municipalities – would further optimize their use [20, 21]. Additionally, it is also necessary to develop other data quality/validation tools, such as the World Health Organisation Data Quality Tool [6, 22], and the use of target lines in dashboards.

Discussion on using data to guide decision-making

The study showed that best practices have effectively enhanced the dissemination of data to the public, local elected officials, and citizen control committees. This has bolstered budget advocacy in favor of nutrition at the municipal level. However, the use of data depends on several factors such as the effectiveness of dissemination, the quality and reliability of the data, the cost of the proposed solutions and the influence of the actors [3]. Policymakers’ reliance on data for shaping public policies is not solely predicated on empirical evidence; it is, in many instances, a strategic political choice. However, information should be written in a clear and accessible language for decision-makers [17, 19, 23].

Discussion on the context of insecurity

Much like Mali and Niger, the security situation in Burkina Faso has deteriorated considerably since 2018 due to terrorist attacks. The Burkinabè health system has been inevitably impacted by this turmoil, resulting in an escalating number of health facilities either shutting down or operating at minimal capacity. This humanitarian situation has also had consequences on the performance of the HMIS, with a drop in the completeness and timeliness of data, occurring because of cuts to internet connection. Consequently, a weekly reporting mechanism for IMAM data has been instituted [24].

Research component perspective

We intend to continue our investigations following the research carried out within the framework of these studies. Future focal areas include:

• Landscape analysis of nutrition indicators in non-health sectors to assess the data gap. This could include data on food fortification, WASH and food security.

• Documentation of the experiences encountered during the implementation of the monitoring and evaluation plan of national nutrition plan.

• A study on budget data and expenditure allocated to nutrition by both governmental bodies and partners [25]. It would be helpful to reflect on the cost of achieving the NIS best practices implemented during the project and the implications for government to sustain the progress described in this paper.

Implications for the ongoing strengthening of the nutrition information system

The following implications are necessary for the continuous improvement of routine nutrition data:

• Ensure continued advocacy for the mobilization of financial resources for nutrition data [25, 26].

• Accelerate the implementation of the digitization of all primary tools [27].

• Develop a sustainability and scaling-up plan for nutrition data strengthening strategies that are not yet at scale after the small-scale phase.

• Development of policy brief for decision makers [24].

• Maintain an updated inclusion of nutrition indicators in DHIS2 that are yet to be recognized, utilizing the newly launched 2022 global DHIS2 nutrition module as a guideline.

• Strengthen routine nutrition information systems in other nutrition-contributing sectors, implementing DHIS in domains like education and food security.

Conclusion

A qualitative approach was used, underpinned by both conceptualization and contextualization, facilitating a deeper understanding of the research subject. The results of this study highlight the progress made, best practices and lessons learned after three years of implementation. This study can serve as a basis for the design of similar investigations. Lessons learned from Burkina Faso will then be applied in other countries. Strengthening information systems related to nutrition in other sectors outside of health, to bridge existing data gaps, are key next steps that would further contribute to ensuring a more adequate monitoring and evaluation of the national nutrition plan. However, other complementary implementation studies allow for the documentation of additional best practices and promote further reflection to improve decision-making processes.