Objectives: To examine the relative importance of patients' socio-demographic characteristics, health service factors, health conditions, day and season of admission as predictors of emergency admissions among Blackpool residents.Study design: Population-based cross-sectional study using routine patient admission data. Records of admissions among Blackpool patients were analysed to examine the effects of socio-demographic factors, health service factors, health conditions, day and season of admissions on emergency admissions. Principal results:The emergency admission risk has declined over the study period. Compared with admissions in people aged 35 to 44, admissions in those aged 85 and above were 3.2 times more likely to be emergencies. Admissions from 4th IMD quintile areas were 43% more likely to be emergencies compared with those from 2nd IMD quintile areas. Admissions among patients registered with Blackpool GPs were less likely to be emergencies compared with those registered with GPs outside Blackpool. Admissions to geriatric medical specialties were most likely to be emergencies. Emergency admission risk was highest for admissions attributed to accidents. Emergency admission risk was also highest for Saturday admissions and lowest for Wednesday admissions. No significant association was observed between emergency admissions and patients' sex. Conclusions:The risk of emergency admissions among Blackpool patients has declined over the period of this study. Socio-demographic and health service factors, health conditions, day and season of admission were independently associated with emergency admissions. [12]. A provider spell is the time that a patient stays with one hospital care provider from admission to discharge, transfer or death [13]. Records of admission spells for Blackpool patients over the period were extracted for analyses.Emergency admissions are admissions that are unpredictable and at short notice because of clinical needs [12]. Admissions classified as emergencies in this study were those coded as 21, 22, 23, 24 and 28 [12]. Primary diagnoses at admission were classified according to the World Health Organization's ICD-10 (International Classification of Disease, 10 th revision).[14] Specialty classifications used broadly followed those contained in the dataset [12] (see Appendix 1 for details). We used the ONS classification of the seasons used in the excess winter deaths calculations [15]. GP practices were coded A to V to eliminate the risk of identifying patients because of the relatively small practice list sizes.Postcodes of patients' places of residence were linked to their respective 2007 Indices of Multiple Deprivation (IMD) [16] which are composite scores derived from seven deprivation domains. The scores were further grouped into their corresponding national score quintiles -the higher the quintile the more deprived an area is. The resultant dataset was analysed to describe emergency admission patterns and identify factors associated with emergency admission. Statistica...

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Before I had even finished reading the first chapter of Diane Macunovich's new book, three things were crystal clear:People matter: a society's demographics need to be considered explicitly when trying to understand or to forecast its economic behavior.Einstein's conclusions about relativity apply to economies: changes in the relative size and age composition of a population can lead to major changes in its social and economic behavior.Economic demographers rule! From now on, users of long-term forecasting models will need to include information on changes in age structure and cohort size if they wish to forecast events more than a few years ahead.