PubMed · 12279529
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T P Dekker, E D Van De Gronden. 1981. [Census and census management].. https://pubmed.ncbi.nlm.nih.gov/12279529/
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In some countries, patients requiring elective surgery can access comparative waiting time information for various surgical units. What someone can deduce from this information will depend upon how the statistics are derived, and how waiting lists behave. However, empirical analyses of waiting list behaviour are scarce. This study analysed three years of waiting list data collected at one hospital in Sydney, Australia. The results highlight various issues that raise questions about using particular waiting time statistics to make inferences about patient waiting times. In particular, the results highlight the considerable variation in behaviour that can exist between surgeons in the same specialty, and that can occur over time.
There is a growing interest in the use of small area analyses in investigating the relationship between socioeconomic status and health, and in informing resource allocation decision-making. However, few such studies have been undertaken in low- and middle-income countries (LMICs). This paper reports on such a study undertaken in South Africa. It both looked at the feasibility of developing a broad-based area deprivation index in a data scarce context and considered the implications of such an index for geographic resource allocations. Despite certain data problems, it was possible to construct and compare three different indices: a general index of deprivation (GID), compiled from census data using principal component analysis; a policy-perspective index of deprivation (PID), based on groups identified as priorities within policy documents; and a single indicator of deprivation (SID), selected for relevance and feasibility of use. The findings demonstrate clearly that in South Africa deprivation is multi-faceted, is concentrated in specific areas within the country and is correlated with ill-health. However, the formula currently used by the National Treasury to allocate resources between geographic areas, biases these allocations towards less deprived areas within the country. The inclusion of the GID within this formula would dramatically alter allocations towards those areas suffering from human development deficits. The area in which analysis was undertaken was not, however, sufficiently small to identify pockets of deprivation within the less deprived metropolitan areas. These findings suggest that it is feasible to conduct small area analyses in LMICs but that specific attention needs to be given to the size of the geographic unit used in analysis. In addition, they highlight the importance of considering deprivation in resource allocation mechanisms if vertical equity goals are to be promoted through resource allocation, particularly within decentralized health systems.
In the housing unit method, population is calculated as the number of households times the average number of persons per household (PPH), plus the population residing in group quarters facilities. Estimates of households and the group quarters population can be derived directly from concurrent data series, but estimates of PPH have traditionally been based on previous values or estimates for larger areas. In our study, we developed several regression models in which PPH estimates were based on symptomatic indicators of PPH change. We tested these estimates using county-level data in four states and found them to be more precise and less biased than estimates based on more commonly used methods.