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Can survey data facilitate local priority setting? Experience from the Igunga and Nzega districts in Tanzania
Malale Tungu1, Gasto Frumence2, Mughwira Mwangu2
1Department of Development Studies, School of Public Health and Social Sciences, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania. malaletungu@gmail.com.
Purpose:
This study aimed to investigate whether a local survey applying EQ-5D and SAGE could provide data valuable in setting priorities.
Methodology:
A cross-sectional household survey was used to collect information from a total of 1,899 elderly individuals aged 60 years and over living in the Nzega and Igunga districts using the WHO-SAGE and EQ-5D questionnaires. QALY weights were generated using the average of an EQ-5D index. A multivariable regression model was performed to analyse the effect of socioeconomic factors and self-rated health status on the EQ-5D index, using a linear regression model.
Results:
The confidence interval estimates indicate higher HRQoL among men, married, urban dwellers, and elderly rated with good health than in women, unmarried, rural dwellers, and elderly rated with bad/moderate health, and it decreases with age. Income and education level have a positive relationship with HRQoL. The regression analysis; Model 1 (not adjusted with SAGE variables): age in all groups (p = 0.01, 0.00 and 0.02) and marital status (p = 0.01) have an influence on HRQoL. Model 2 (adjusted with SAGE variables): self-rated health (p < 0.00), the age for the 80-89 group (p = 0.01), marital status (not married), and high income have an influence on HRQoL. Sex, education, and residence were not statistically significant (in either model) to affect the HRQoL of the elderly.
Conclusion:
Local surveys, applying a combination of EQ-5D and SAGE, generate relevant and valuable information for policy makers when setting priorities at the district level. Therefore, this paper provides an empirical analysis for decision makers to consider the importance of combining EQ-5D, SAGE, and socioeconomic factors when setting priorities to improve HRQoL among the elderly.
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