Costs and health-related quality of life among patients with multimorbidity in Malawi and Tanzania .
1
Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
2
Community Medicine, KCMC University, Moshi, Tanzania, United Republic of
3
Division of Infectious Diseases & International Health, Department of Medicine, Duke University, North Calorina, United States
Popul. Med. 2026;8(Supplement Supplement 1):A3621
ABSTRACT
BACKGROUND:
Multimorbidity, the presence of >2 chronic conditions, presents complex resourcing challenges for health systems in Sub-Saharan Africa, which are vertically orientated toward infectious diseases. This study evaluates healthcare costs, and health-related quality of life (HRQoL) among patients with multimorbidity in Malawi and Tanzania.
METHODS:
The study was nested within a prospective cohort study, recruiting participants from medical admissions at district and tertiary hospitals in Malawi and Tanzania. Clinical records and point-of-care tests were used to diagnose HIV, hypertension, diabetes, heart failure, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, chronic liver disease and chronic kidney disease. Healthcare costs and EQ-5D-5L data (used to calculate utility scores (HRQoL)), were collected over 1 year, and then analysed and compared for participants with and without multimorbidity.
RESULTS:
Total healthcare costs were significantly higher for participants with multimorbidity (Malawi: $56.79 vs $30.93, p = 0.028; Tanzania: $310.28 vs $169.34, p = 0.002). Participants with multimorbidity incurred significantly higher medical costs in Tanzania ($207.33 vs $63.75, p = 0.002) than in Malawi ($11.73 vs $3.52, p = 0.153). Participants with multimorbidity incurred significantly higher non-medical costs in Malawi ($22.54 vs $14.96, p = 0.030) compared to Tanzania ($9.36 vs $12.36, p = 0.110). Catastrophic costs were more prevalent in multimorbidity (without multimorbidity 8/139 (5.8%), with multimorbidity 15/194 (7.70%), p = 0.485). Participants with multimorbidity had lower health utility scores at baseline in Tanzania (mean 0.276 vs 0.407, p = 0.05) but not Malawi (0.417 vs 0.490, p = 0.12). At the end-line, health utility remained lower for those with multimorbidity (Malawi: 0.832 vs 0.917, p = 0.006; Tanzania: 0.838 vs 0.914, p = 0.094).
CONCLUSIONS:
The economic and HRQoL impact of multimorbidity is substantial in Malawi and Tanzania. Context-specific strategies are needed to manage multimorbidity and optimise health outcomes, resource use, and financing priorities.
Multimorbidity, the presence of >2 chronic conditions, presents complex resourcing challenges for health systems in Sub-Saharan Africa, which are vertically orientated toward infectious diseases. This study evaluates healthcare costs, and health-related quality of life (HRQoL) among patients with multimorbidity in Malawi and Tanzania.
METHODS:
The study was nested within a prospective cohort study, recruiting participants from medical admissions at district and tertiary hospitals in Malawi and Tanzania. Clinical records and point-of-care tests were used to diagnose HIV, hypertension, diabetes, heart failure, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, chronic liver disease and chronic kidney disease. Healthcare costs and EQ-5D-5L data (used to calculate utility scores (HRQoL)), were collected over 1 year, and then analysed and compared for participants with and without multimorbidity.
RESULTS:
Total healthcare costs were significantly higher for participants with multimorbidity (Malawi: $56.79 vs $30.93, p = 0.028; Tanzania: $310.28 vs $169.34, p = 0.002). Participants with multimorbidity incurred significantly higher medical costs in Tanzania ($207.33 vs $63.75, p = 0.002) than in Malawi ($11.73 vs $3.52, p = 0.153). Participants with multimorbidity incurred significantly higher non-medical costs in Malawi ($22.54 vs $14.96, p = 0.030) compared to Tanzania ($9.36 vs $12.36, p = 0.110). Catastrophic costs were more prevalent in multimorbidity (without multimorbidity 8/139 (5.8%), with multimorbidity 15/194 (7.70%), p = 0.485). Participants with multimorbidity had lower health utility scores at baseline in Tanzania (mean 0.276 vs 0.407, p = 0.05) but not Malawi (0.417 vs 0.490, p = 0.12). At the end-line, health utility remained lower for those with multimorbidity (Malawi: 0.832 vs 0.917, p = 0.006; Tanzania: 0.838 vs 0.914, p = 0.094).
CONCLUSIONS:
The economic and HRQoL impact of multimorbidity is substantial in Malawi and Tanzania. Context-specific strategies are needed to manage multimorbidity and optimise health outcomes, resource use, and financing priorities.
| eISSN: | 2654-1459 |
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