The cold chain for health products is an essential process for ensuring the efficacy and safety of vaccines, thermosensitive medicines, and other biological products [2]. Its interruption can lead to a loss of therapeutic potential, increased health risks, and substantial economic losses. Effective cold chain management is therefore a fundamental pillar of quality healthcare delivery, ensuring that appropriate temperature conditions are maintained throughout the storage and distribution of health products [3].

However, maintaining an optimal cold chain remains a major challenge, particularly in resource-limited countries, where infrastructure and human capacity are often insufficient [4]. In developed countries, cold chain management is generally well controlled thanks to modern infrastructure, high-performance equipment, and rigorous procedures, thereby ensuring the quality and availability of health products (WHO, 2020) [1]. In contrast, in many developing countries, this management is hindered by multiple constraints, including economic difficulties, inadequate infrastructure, and a shortage of qualified human resources [5].

In these contexts, health facilities and pharmacies frequently face problems such as inadequate refrigeration equipment, recurrent power outages, and a lack of staff training. These shortcomings compromise the quality of health products and expose populations to increased health risks [6].

Furthermore, cold chain failures are not merely a logistical challenge but represent a genuine threat to public health, population well-being, and socioeconomic development [7]. In Benin, the southern zone concentrates approximately 70% of health infrastructure, placing it at the heart of issues related to health system performance. The resulting wastage of health products generates substantial economic losses estimated at several billion CFA francs each year, representing resources that could otherwise be reallocated to other development priorities.

Consequently, improving cold chain management appears to be a strategic opportunity to strengthen Benin’s health system. It would not only reduce losses but also improve the quality of care and strengthen public trust. The issue thus extends beyond the health sector alone, as it also contributes to safeguarding population health and the country’s sustainable development [8][9].

The primary assessment “Assessment of cold chain performance for health products in health facilities in southern Benin in 2025” was based on standardized criteria, organized by cold chain components (Inputs, Implementation, and Results), to which binary scores (0 or 1) were assigned. The scores obtained, after review of each of these components, were used to determine both component-level performance and overall performance.

Scoring principle: Each criterion was scored from 0 - 1.

One (1) if the criterion met the standards and zero (0) if it did not;Sub-component score = Total of all criterion scores;Component score = Total of all sub-component scores;Main component score = Total of all independent component scores.

Overall cold chain performance was based on an expected total score of 4200 points, of which 2558 points were obtained, corresponding to 60.90%. The Inputs component reached 61.98%, the Implementation component achieved 68.23%, and the results component recorded 47.36%. Given these results, overall cold chain performance in health facilities in southern Benin was judged poor, as performance is considered good if the score obtained is greater than or equal to 75%, and poor if the score obtained is below 75%.

The sampling method and technique, as well as the sample size calculation for the primary assessment, are the same as those used in the study of associated factors described in the manuscript.

In each selected health facility, a single healthcare provider was retained for the survey. This was the staff member directly responsible for cold chain management of health products or, failing that, the agent performing this role at the time of the survey. Thus, a single respondent was included per health facility.

Study setting:We conducted an analytical cross-sectional study in 120 health facilities drawn from 17 health zones across 6 departments in southern Benin.

Southern Benin comprises seven departments, among the most populous and dynamic in the country: Atlantique, Littoral, Ouémé, Plateau, Mono, Couffo, and Zou. Characterized by a high level of urbanization, it is the country’s most populous and most developed region. It includes several major cities, notably Cotonou, Porto-Novo, Ouidah, Abomey, Allada, and Lokossa, and concentrates significant economic, political, and cultural infrastructure. As a result, southern Benin represents the country’s economic and administrative core, with a high concentration of population and infrastructure.

The health system in this area is organized to meet the needs of a dense and diverse population. It is structured into several health zones, each responsible for coordinating health services. In February 2025, more than 150 managers were trained and deployed across the country’s 34 health zones to strengthen patient safety and improve quality of care [10]. In addition, the region hosts referral health facilities, notably the Hubert Koutoukou Maga National Teaching Hospital Center (CNHU-HKM), the country’s leading hospital institution, offering a wide range of medical services and serving as a training center for health professionals.

Study type and period:This was an analytical cross-sectional study conducted in 2025.

Study population:The study population comprised two categories: a primary population and a secondary population.

Primary population

This consisted of functional public health facilities in the six (6) departments of southern Benin included in the study.

Secondary population

This consisted of stakeholders involved in cold chain management within public health facilities in southern Benin.

Study criteria

Inclusion criteria:Health facilities and stakeholders involved in the study had to meet the following criteria:

For health facilities(primary population)

Be a public health facility located in southern Benin;Have a cold chain system for the storage of vaccines and other thermosensitive products;Have usable data on cold chain management (temperature logs, supervision reports, etc.) covering a period of at least six (6) months prior to the survey.

For stakeholders involved in the cold chain(secondary population)

Be involved in the management, supervision, or maintenance of the cold chain (pharmacists, logisticians, nurses, maintenance technicians);Have at least three (3) years of experience at the selected health facility;Have provided informed consent to participate in the study.

Non-inclusion criteria

The following were not included in the study:

For health facilities(primary population)

Health facilities without usable data on cold chain management.

For stakeholders involved in the cold chain(secondary population)

Staff absent during the data collection period;Staff who declined to participate in the study.

General characteristics of health facilities

The study covered 120 health facilities distributed across six departments in southern Benin (see Table A1). The most represented departments were Zou (24.17%), followed by Atlantique (20.83%) and Ouémé (19.17%). The health facilities came from 17 health zones, with the DAA health zone being the most represented (10%), followed by CBGH, PAK, and ZoBoZa (9.17% each).

Characteristics of respondents(seeTable A2)

Regarding respondents’ position within the cold chain, the majority were cold chain managers (91.15%). About 70% of these staff had more than three (3) years of experience in cold chain management.

Women accounted for 55.83% of respondents, versus 44.17% men. The mean age of respondents was 43.8 ± 6.3 years, ranging from 27 to 56 years. The most represented age group was 42 years and older (72.5%).

Factors associated with cold chain performance

After adjustment for the variables included in the model, the multivariate analysis identified the following factors independently associated with cold chain performance:

Staff training on cold chain management (aOR [95% CI] = 4.06 [1.01 - 16.35], p = 0.0485);Refrigerator age ≤ 5 years (aOR [95% CI] = 4.10 [1.33 - 12.63], p = 0.0168).Availability of a preventive maintenance schedule (aOR [95% CI] = 7.72 [1.78 - 33.47], p = 0.0084);Availability of a dedicated budget line (aOR [95% CI] = 5.98 [1.97 - 18.12], p = 0.0014).

Achievement of study objectives

The main objective of this study was to identify the factors associated with cold chain performance for health products in health facilities in southern Benin in 2025.

The multivariate analysis identified four factors independently associated with performance:

Staff training on cold chain management;Refrigerator age less than or equal to five (5) years;Availability of a preventive maintenance schedule;The existence of a dedicated budget line or annual plan.

The identification of these factors confirms that the study’s objective was achieved and highlights concrete intervention levers, both technical, organizational, and financial.

Quality and validity of the results

The robustness of the results rests on several methodological elements.

First, data collection was carried out using a structured, standardized questionnaire, ensuring the homogeneity of the information gathered. Second, the statistical analysis was conducted using Epi Info software, combining descriptive, bivariate, and multivariate analyses.

The use of multivariate logistic regression is a major strength of the study. It allowed the associations observed in the bivariate analysis to be adjusted and the factors independently associated with performance to be isolated, while controlling for confounding effects.

However, certain limitations must be acknowledged. This was a cross-sectional study, which does not allow a strict causal relationship to be established between the factors studied and the observed performance. In addition, some variables relied on self-reports from health facility managers, which exposes the study to a potential risk of reporting bias or social desirability bias.

Despite these limitations, the internal consistency of the results, their organizational plausibility, and their concordance with the international literature give the study satisfactory internal validity.

Factorsindependentlyassociated and comparison with the literature

Among the variables significant in the bivariate analysis (see Table A3), only four retained a statistically significant association after adjustment in the multivariate model (see Table A4). These independent factors reflect the essential dimensions of a high-performing system, namely human competencies, equipment quality, structured maintenance, and dedicated financing.

Staff training on the cold chain

Staff training emerged as a major independent factor (aOR = 4.06; p = 0.0485). Health facilities with trained staff had an approximately four-fold higher probability of showing good performance.

Training strengthens technical skills related to temperature monitoring, interpretation of temperature logs, management of temperature excursions, and basic equipment maintenance. It also improves organizational capacity and responsiveness to incidents. These results are consistent with those of Yakum et al. (2015) in Cameroon, who showed that insufficient staff training was associated with poor compliance with vaccine storage practices [12]. Similarly, WHO [1][13] emphasizes that continuing education is one of the fundamental pillars of a high-performing cold chain. Recent studies conducted in Sub-Saharan Africa confirm that strengthening human capacity significantly improves the quality of thermal monitoring and reduces vaccine wastage (Zipursky et al., 2014) [14].

Refrigerator age ≤ 5 years

Refrigerator age of five (5) years or less was significantly associated with better performance (aOR = 4.10; p = 0.0168). Newer equipment is generally more reliable, compliant with WHO PQS (Performance, Quality and Safety) standards, and less prone to technical breakdowns.

Equipment obsolescence is recognized as a major risk factor for cold chain disruption, particularly in resource-limited settings. Ashok et al. (2017) [15] showed that equipment modernization is a key intervention for improving the reliability of storage systems. Similarly, initiatives supported by GAVI through the Cold Chain Equipment Optimization Platform (CCEOP) have demonstrated that targeted equipment renewal significantly improves logistical performance [16].

Preventive maintenance schedule available

The availability of a preventive maintenance schedule emerged as the factor most strongly associated with performance (aOR = 7.72; p = 0.0084). Health facilities with such a system in place were nearly eight times more likely to show good performance.

Preventive maintenance makes it possible to anticipate failures, reduce service interruptions, and extend equipment lifespan. According to WHO recommendations, a structured maintenance program is essential to ensure continuity of the cold chain [13]. Lydon et al. (2014) also emphasize that optimizing maintenance generates substantial economic benefits by reducing vaccine losses [17].

This result highlights the importance of proactive rather than reactive equipment management.

Dedicated budget line/annual plan available

The availability of a dedicated budget line was also independently associated with performance (aOR = 5.98; p = 0.0014). This factor reflects the structural and institutional dimension of the system.

Financial planning ensures the availability of resources needed for maintenance, replacement of defective parts, equipment acquisition, and staff training. The absence of dedicated funding compromises the sustainability of technical interventions [18].

These results are consistent with analyses by GAVI and WHO, which identify sustainable financing as an essential condition for the performance of vaccine logistics systems [1][19].

Thus, the four independent determinants identified—staff training, refrigerator age, preventive maintenance, and dedicated financing—constitute the pillars of a high-performing cold chain management system. They reflect the interaction between human resources, technical infrastructure, organization, and financial governance.

The evaluation of cold chain management in health facilities in southern Benin provided a precise overview of logistical performance and identified the independent factors influencing this performance.

The multivariate analysis identified four independent determinants:

Staff training on cold chain management;Refrigerator age of five (5) years or less;Availability of a preventive maintenance schedule;The existence of a dedicated budget line or annual plan.

These results show that cold chain performance relies on a balance between human competencies, equipment quality, structured maintenance organization, and sustainable financing.

Accordingly, priority interventions should aim to:

Continuously strengthen staff capacity;Progressively renew aging equipment;Institutionalize preventive maintenance;Secure dedicated financial resources.

The coordinated implementation of these actions will help sustainably improve the performance of health facilities and strengthen the safety of vaccination and public health programs.

This study was conducted in accordance with applicable ethical principles. Authorization for data collection was granted by Benin’s Ministry of Health: No. 3988/MS/DC/SGM/DFRS/SA dated August 4, 2025.

Participants gave their informed consent prior to inclusion. No specific funding likely to influence the results was received for this research.

What is known about this topic

The performance of the cold chain for health products in health facilities.

What this study adds

This study provided the general characteristics of health facilities;The factors associated with cold chain performance for health products in health facilities;Our results could serve as a reference for other studies.

Mr. Rigobert Missikpodé: Conceptualization and study design; development of the methodology; supervision of data collection; analysis and interpretation of results; drafting of the manuscript (initial version); overall coordination of the research project.

Pr. Patrick Makoutodé: Co-conceptualization of the study; field data collection; contribution to statistical analysis; critical revision of the manuscript.

Pr. Charles Jérôme Sossa: Scientific supervision; validation of results; critical revision of intellectual content; approval of the final version submitted for publication.

The authors thank the authorities of Benin’s Ministry of Health and the cold chain managers of health facilities in southern Benin.

Table A1.General characteristics of health facilities.

Table A2.General characteristics of respondents.

Table A3.Bivariate analysis of factors associated with cold chain performance in health facilities.

PR: prevalence ratio; CI: confidence interval. Bold p-values indicate statistical significance (p < 0.05).

Table A4.Multivariate analysis of factors associated with cold chain performance in health facilities (logistic regression).

aOR: adjusted odds ratio; CI: confidence interval.