The Value of Mortality Data for Learning Dynamics and Decision-Making in Mozambique’s Health System
1
Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland
2
University of Basel, Basel, Switzerland
3
Manhiça Health Research Centre, Vila da Manhica, Mozambique
Popul. Med. 2026;8(Supplement Supplement 1):
ABSTRACT
INTRODUCTION:
Health systems should be able to learn from the data and evidence that is routinely generated at different levels. As such, mortality data on the fact and the cause of death should be the center of informed public health policy. However, health systems do not learn from mortality data in ways that shape national and local health strategies. Using the Learning Health System (LHS) lens, we examined how mortality data are accessed, interpreted, and used in Mozambique.
METHODS:
We conducted a qualitative case study from October to November 2024, involving 20 key informants from the Ministry of Health (at both national and subnational levels), NGOs, data producers, and users. Data were collected through semi-structured interviews, transcribed, coded, and thematically analysed, guided by LHS dimensions: learning levels, loops, and means.
RESULTS:
Participants recognised the value of mortality data for learning and decision-making. Use was concentrated at the MoH central level, where information supported double-loop learning by questioning goals, reprioritising strategies, and reallocating resources. At subnational levels, mortality data primarily supported single-loop learning via routine reports that triggered immediate adjustments in clinical practice and outbreak response. Delays in data access, low data literacy among decision-makers, fragmented information systems, and centralised decision-making appear to be the greatest challenges to the effective use of mortality data. Heavy dependence on central directives limited local and its ability to act, preventing new ways of learning and working.
CONCLUSIONS:
Mortality data already support operational and strategic learning in Mozambique, particularly through single- and double-loop processes. Systemic transformation in the way the system learns remains limited and aggravated by structural barriers in the health system. Priorities include improving timely access to mortality information across levels, strengthening analytic and interpretive capacity, integrating fragmented systems, and widening delegated decision space. This will ultimately enhance evidence-based planning and health outcomes.
Health systems should be able to learn from the data and evidence that is routinely generated at different levels. As such, mortality data on the fact and the cause of death should be the center of informed public health policy. However, health systems do not learn from mortality data in ways that shape national and local health strategies. Using the Learning Health System (LHS) lens, we examined how mortality data are accessed, interpreted, and used in Mozambique.
METHODS:
We conducted a qualitative case study from October to November 2024, involving 20 key informants from the Ministry of Health (at both national and subnational levels), NGOs, data producers, and users. Data were collected through semi-structured interviews, transcribed, coded, and thematically analysed, guided by LHS dimensions: learning levels, loops, and means.
RESULTS:
Participants recognised the value of mortality data for learning and decision-making. Use was concentrated at the MoH central level, where information supported double-loop learning by questioning goals, reprioritising strategies, and reallocating resources. At subnational levels, mortality data primarily supported single-loop learning via routine reports that triggered immediate adjustments in clinical practice and outbreak response. Delays in data access, low data literacy among decision-makers, fragmented information systems, and centralised decision-making appear to be the greatest challenges to the effective use of mortality data. Heavy dependence on central directives limited local and its ability to act, preventing new ways of learning and working.
CONCLUSIONS:
Mortality data already support operational and strategic learning in Mozambique, particularly through single- and double-loop processes. Systemic transformation in the way the system learns remains limited and aggravated by structural barriers in the health system. Priorities include improving timely access to mortality information across levels, strengthening analytic and interpretive capacity, integrating fragmented systems, and widening delegated decision space. This will ultimately enhance evidence-based planning and health outcomes.
| eISSN: | 2654-1459 |
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