PubMed · 15551698
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Marc Farr. 2004-10-28. Data briefing. Mapping needs.. https://pubmed.ncbi.nlm.nih.gov/15551698/
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A number of North American and European studies have observed a higher proportion of winter births in schizophrenia patients. Thus, seasonal fluctuation of unknown environmental factors may affect brain development in ways that alter susceptibility to schizophrenia. Specification of these factors may help elucidate the etiopathological mechanism of the disease, about which little is certain. A small number of studies have investigated this issue in Asian populations, and the findings are not as consistent as those of Western populations. No remarkable excess of winter births has been observed in Japanese or Korean studies, while some studies have reported a significant decrease of summer births. We further investigated the issue in Japanese patients with schizophrenia (n=3927). No significant excess of winter births was observed, but a decrease in the summer births was found in male subjects. This is largely consistent with previous Japanese studies; however, when the subjects were confined to those born in a colder and higher latitude area of Japan (n=1338), a consistent trend for both a winter increase, and a summer decrease, was found. However, the results did not consistently reach statistical significance, possibly due to the lack of statistical power. Environmental factors that correlate with latitude might play a role in the development of the seasonality of births in schizophrenia. Further studies in a larger sample size are required to test these possibilities.
BACKGROUND: The description of patient travel patterns and variations in health care utilization may guide a sound health care planning process. In order to accurately describe these differences across regions with homogeneous populations, small area analysis (SAA) has proved as a valuable tool to create appropriate area models. This paper presents the methodology to create and characterize population-based hospital service areas (HSAs) for Switzerland. METHODS: We employed federal hospital discharge data to perform a patient origin study using small area analysis. Each of 605 residential regions was assigned to one of 215 hospital provider regions where the most frequent number of discharges took place. HSAs were characterized geographically, demographically, and through health utilization indices and rates that describe hospital use. We introduced novel planning variables extracted from the patient origin study and investigated relationships among health utilization indices and rates to understand patient travel patterns for hospital use. Results were visualized as maps in a geographic information system (GIS). RESULTS: We obtained 100 HSAs using a patient origin matrix containing over four million discharges. HSAs had diverse demographic and geographic characteristics. Urban HSAs had above average population sizes, while mountainous HSAs were scarcely populated but larger in size. We found higher localization of care in urban HSAs and in mountainous HSAs. Half of the Swiss population lives in service areas where 65% of hospital care is provided by local hospitals. CONCLUSION: Health utilization indices and rates demonstrated patient travel patterns that merit more detailed analyses in light of political, infrastructural and developmental determinants. HSAs and health utilization indices provide valuable information for health care planning. They will be used to study variation phenomena in Swiss health care.
BACKGROUND: It is difficult to measure and compare workload in UK general practice. A GP/health economist team recently proposed a means of calculating the unit cost of a GP consulting. It is therefore now possible to extrapolate to the costs of other clinical tasks in a practice and then to compare the workloads of caring for different patients and compare between practices. OBJECTIVES: The study aims were: (i) to estimate the relative costs of daily clinical activities within a practice (implying workload); and (ii) to compare the costs of caring for different types of patients categorized by gender, by age, and by socio-economic status as marked by the Council Tax Valuation Band (CTVB) of home address. METHODS: The study design was a cross-sectional cost comparison of all clinical activity aggregated, by patient, over one year in an English semi-rural general practice. The subjects were 3339 practice patients, randomly selected. The main outcome measures were costs per clinical domain and overall costs per patient per year; both then compared by gender, age group and by CTVB. RESULTS: CTVB is as significant a predictor of patient care cost (workload) as is patient gender and age (both already known). CONCLUSIONS: It is now possible to estimate the cost of care of different patients in such a way that NHS planning and especially resource allocation to practices could be improved.