Abstract
BackgroundThe two-week illness prevalence rate is an important and comparable indicator of health service needs. High-spatial-resolution, age-specific risk mapping of this indicator can provide valuable information for health resource allocation. The age-prevalence relationships may be different among areas of the study region, but previous geostatistical models usually ignored the spatial-age interaction.MethodsWe took Guangdong province, the province with the largest population and economy in China, as a study case. We collected two-week illness data and other potential influencing predictors from the fifth National Health Services Survey in 2013 and other open-access databases. Bayesian geostatistical binary regression models were developed with spatial-age structured random effect, based on which, high-resolution, age-specific two-week illness prevalence rates, as well as number of people reporting two-week illness, were estimated. The equality of health resource distribution was further evaluated based on the two-week illness mapping results and the health supply data.ResultsThe map across all age groups revealed that the highest risk was concentrated in the central (i.e., Pearl River Delta) and northern regions of the province. These areas had a two-week illness prevalence > 25.0%, compared with 10.0–20.0% in other areas. Age-specific maps revealed significant differences in prevalence between age groups, and the age-prevalence relationships also differed across locations. In most areas, the prevalence rates decrease from age 0 to age 20, and then increase gradually. Overall, the estimated age- and population-adjusted prevalence was 16.5% [95% Bayesian credible interval (BCI): 14.5–18.6%], and the estimated total number of people reporting illness within the two-week period was 17.5 million (95% BCI: 15.5–19.8 million) in Guangdong Province. The Lorenz curve and the Gini coefficient (resulted in 0.3526) showed a moderate level of inequality in health resource distribution.ConclusionsWe developed a Bayesian geostatistical modeling framework with spatial-age structured effect to produce age-specific, high-resolution maps of the two-week illness prevalence rate and the numbers of people reporting two-week illness in Guangdong province. The methodology developed in this study can be generalized to other global regions with available relevant survey data. The mapping results will support plans for health resource allocation.
Highlights
The two-week illness prevalence rate is an important and comparable indicator of health service needs
To further show the application potential of the outcomes, we evaluated the equality of health resource distribution based on the two-week illness mapping results and the health supply data
The raw two-week illness prevalence was 18.5%
Summary
The two-week illness prevalence rate is an important and comparable indicator of health service needs. There may be “supplier-introduced demand” [8] In this regard, the prevalence of illness within a two-week period, known as the two-week illness prevalence rate, directly reflects the risk of illness, breaks through the above limitations, and becomes an important and comparable indicator for health service needs [9,10,11]. The prevalence of illness within a two-week period, known as the two-week illness prevalence rate, directly reflects the risk of illness, breaks through the above limitations, and becomes an important and comparable indicator for health service needs [9,10,11] This indicator is usually estimated by the self-reported illness status in health surveys, and the time frame “two-week” is considered reasonable for accurate recall of information about the illness and treatment [12, 13]. Two-week illness has been applied in various health surveys worldwide [10, 14,15,16,17,18]
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