Can survey data facilitate local priority setting? Experience from the Igunga and Nzega districts in Tanzania
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Can survey data facilitate local priority setting? Experience from the Igunga and Nzega districts in Tanzania Malale Tungu1 · Gasto Frumence1 · Mughwira Mwangu1 · Anna‑Karin Hurtig2 · Lars Lindholm2 Accepted: 8 June 2020 © Springer Nature Switzerland AG 2020
Abstract 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 1 disease 69 0.68
95% ConfiVAS dence intervals
95% Confidence intervals
0.83–0.85 0.69–0.71 0.29–0.52
72.0 56.90 35.09
70.72–73.28 55.93–57.87 28.62–41.56
0.74–0.77 0.72–0.74
63.79 59.35
62.31–65.28 58.32–60.38
0.74–0.75 0.67–0.72
61.11 58.81
60.21–62.02 56.37–61.25
0.73–0.75 0.59–0.71
60.93 57.64
60.07–61.79 53.05–62.23
0.73–0.75 0.25–1.07
60.80 66.33
59.95–61.65 16.01–116.65
0.73–0.75 0.68–0.73
60.76 61.24
59.88–61.65 58.24–64.24
0.73–0.75 0.64–0.75
60.96 54.21
60.10–61.81 48.00–60.42
0.73–0.75 0.62–0.69
60.88 59.42
60.01–61.75 55.47–63.37
0.75–0.77 0.70–0.72
60.82 60.79
59.68–61.96 59.51–62.07
0.73–0.75 0.72–0.79
61.26 53.78
60.39–62.14 50.54–57.01
0.73–0.75 0.59–0.75
60.84 59.57
59.97–61.70 55.11–64.03
0.73–0.75 0.62–0.87
60.78 64.00
59.93–61.63 51.68–76.32
0.78–0.81 0.70–0.72 0.68–0.72
62.10 60.45 59.11
60.68–63.51 59.22–61.69 57.04–61.17
0.74–0.76 0.68–0.72 0.63–0.72
61.89 58.28 55.41
60.90–62.88 56.56–59.99 50.15–60.66
ZW Zimbabwe tariffs and VAS Visual Analogue Scale
13
Quality of Life Research
Table 6 Factors associated with health status in terms of EQ-5D (n = 1899) EQ-5D index
Model 1 Coefficient of variables
Model 2 (adjusted with SAGE variables) p value
Sex Female (ref.) 0 Male 0.01 0.36 Age group (60–69 = reference) 60–69 0 70–79 − 0.04 0.01 80–89 − 0.11 0.00 90+ − 0.13 0.02 Marital status (married = reference) Married 0 Not married − 0.04 0.01 Income (low = reference) Low 0 Middle 0.02 0.22 High 0.04 0.06 Education level (low = reference) Low 0 Middle 0.00 0.97 High 0.06 0.05 Residence (rural = reference) Rural 0 Urban 0.02 0.
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