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ABSTRACT: The aim of the study was to evaluate the impact of transfer status and distance on in-hospital mortality for acute myocardial infarction (AMI) patients undergoing angioplasty on the same or next day of hospital admission.
Retrospective analysis of English hospital administrative data using logistic regression modelling.
After risk adjustment for the patient baseline characteristics, transferred patients had a higher in-hospital mortality rate than those admitted directly to hospital for angioplasty performed on the same or next day: OR=1.25 (95% confidence interval: 1.02-1.52), P=0.029. There was no statistically significant increased risk of in-hospital mortality with increasing distance between home and angioplasty centre (OR=0.98 (0.84-1.16), P=0.842 for 6-15 km and 1.03 (0.87-1.22), P=0.768 for >15 km when compared with <6 km) or with increasing inter-hospital transfer distance for angioplasty (OR=0.84 (0.55-1.29), P=0.435 for 16-34 km and 0.88 (0.58-1.35), for >34 km when compared with <16 km).
Transfer status is associated with in-hospital mortality rate for AMI patients undergoing angioplasty on the same or next day of hospital admission. No relation between in-hospital mortality and the distance from home to angioplasty centre or inter-hospital transfer distance for angioplasty was found in these patients.
Acute Cardiac Care 03/2012; 14(1):5-12.
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ABSTRACT: Although logistic regression is traditionally used to calculate hospital standardized mortality ratio (HSMR), it ignores the hierarchical structure of the data that can exist within a given database. Hierarchical models allow examination of the effect of data clustering on outcomes.
Traditional logistic regression and random intercepts fixed slopes hierarchical models were fitted to a dataset of patients hospitalized between 2005 and 2007 in Massachusetts. We compared the observed to expected (O/E) in-hospital death ratios between the 2 modeling techniques, a restricted HSMR using only those diagnosis models that converged in both methods and a full hybrid HSMR using a combination of the hierarchical diagnosis models when they converge, plus the remaining diagnoses using standard logistic regression models.
We restricted the analysis to the 36 diagnoses accounting for 80% of in-hospital deaths nationally, based on 1,043,813 admissions (59 hospitals). A failure of the hierarchical models to converge in 15 of 36 diagnosis groups hindered full HSMR comparisons. A restricted HSMR, derived from a dataset based on the 21 diagnosis groups that converged (552,933 admissions) showed very high correlation (Pearson r = 0.99). Both traditional logistic regression and hierarchical model identified 12 statistical outliers in common, 7 with high O/E values and 5 with low O/E values. In addition, the multilevel analysis identified 5 additional unique high outliers and 1 additional unique low outlier, and the conventional model identified 2 additional unique low outliers.
Similar results were obtained from the 2 modeling techniques in terms of O/E ratios. However, because a hierarchical model is associated with convergence problems, traditional logistic regression remains our recommended procedure for computing HSMRs.
Journal of the American College of Surgeons 07/2011; 213(3):392-401. · 4.55 Impact Factor
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ABSTRACT: Serious injuries have been stated as a public health priority in the UK. However, there appears to be a lack of information on population-based rates of serious injury (as defined by a recognised taxonomy of injury severity) at national level from either official statistics or research papers. We aim to address this through a search and review of literature primarily focused within the UK and Europe.
The review summarizes research papers on the subject of population based injury epidemiology published from 1970 to 2008. We examined critically methodological approaches in measuring injury incident rates including data sources, description of the injury pyramid, matching numerator and denominator populations as well as the relationship between injury and socioeconomic status.
National representative rates come from research papers using official statistics sources, often focusing on mortality data alone. Few studies present data from the perspective of an injury pyramid or using a standardized measure of injury severity, i.e. Injury Severity Score (ISS). The population movement that may result in a possible numerator - denominator mismatch has been acknowledged in five research studies and in official statistics. The epidemiological profile shows over the past decades in UK and Europe a decrease in injury death rates. No major trauma population based rates are available within well defined populations across UK over recent time periods. Both fatal and non-fatal injury rates occurred more frequently in males than females with higher rates in males up to 65 years, then in females over 65 years. Road traffic crashes and falls are predominant injury mechanisms. Whereas a straightforward inverse association between injury death rates and socio-economic status has been observed, the evidence of socioeconomic inequalities in non-fatal injuries rates has not been wholly consistent.
New methodological approaches should be developed to deal with the study design inconsistencies and the knowledge gaps identified across this review. Trauma registries contain injury data from hospitals within larger regions and code injury by Abbreviated Injury Scale enabling information on severity; these may be reliable data sources to improve understanding of injury epidemiology.
BMC Public Health 02/2009; 9:226. · 2.00 Impact Factor
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ABSTRACT: Abstract
Background
Serious injuries have been stated as a public health priority in the UK. However, there appears to be a lack of information on population-based rates of serious injury (as defined by a recognised taxonomy of injury severity) at national level from either official statistics or research papers. We aim to address this through a search and review of literature primarily focused within the UK and Europe.
Methods
The review summarizes research papers on the subject of population based injury epidemiology published from 1970 to 2008. We examined critically methodological approaches in measuring injury incident rates including data sources, description of the injury pyramid, matching numerator and denominator populations as well as the relationship between injury and socioeconomic status.
Results
National representative rates come from research papers using official statistics sources, often focusing on mortality data alone. Few studies present data from the perspective of an injury pyramid or using a standardized measure of injury severity, i.e. Injury Severity Score (ISS). The population movement that may result in a possible numerator – denominator mismatch has been acknowledged in five research studies and in official statistics. The epidemiological profile shows over the past decades in UK and Europe a decrease in injury death rates. No major trauma population based rates are available within well defined populations across UK over recent time periods. Both fatal and non-fatal injury rates occurred more frequently in males than females with higher rates in males up to 65 years, then in females over 65 years. Road traffic crashes and falls are predominant injury mechanisms. Whereas a straightforward inverse association between injury death rates and socio-economic status has been observed, the evidence of socioeconomic inequalities in non-fatal injuries rates has not been wholly consistent.
Conclusion
New methodological approaches should be developed to deal with the study design inconsistencies and the knowledge gaps identified across this review. Trauma registries contain injury data from hospitals within larger regions and code injury by Abbreviated Injury Scale enabling information on severity; these may be reliable data sources to improve understanding of injury epidemiology.
BMC Public Health. 01/2009;
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ABSTRACT: Determining population-based rates for major injury poses methodological challenges. We used hospital discharge data over a 10-year period (1996-2005) from a national trauma registry, the Trauma Audit and Research Network (TARN) Manchester, to construct valid numerators and denominators so that we can calculate population-based rates of major injury in the future.
We examined data from all hospitals reporting to TARN for continuity of numerator reporting; rates of completeness for patient postcodes, and clear denominator populations. We defined local market areas (>70% of patients originating from the same postcode district as the hospital). For relevant hospitals we assessed data quality: consistency of reporting, completeness of patient postcodes and for one selected hospital, North Staffordshire Royal Infirmary (NSRI), the capture rate of numerator data reporting. We used an established method based on patient flow to delineate market areas from hospitals discharges. We then assessed the potential competitors, and characterized these denominator areas. Finally we performed a denominator sensitivity analysis using a patient origin matrix based on Hospital Episodes Statistics (HES) to validate our approach.
Sixteen hospitals met the data quality and patient flow criteria for numerator and denominator data, representing 12 hospital catchment areas across England. Data quality issues included fluctuations numbers of reported cases and poor completion of postcodes for some years. We found an overall numerator capture rate of 83.5% for the NSRI. In total we used 40,543 admissions to delineate hospital catchment areas. An average of 3.5 potential hospital competitors and 15.2 postcode districts per area were obtained. The patient origin matrix for NSRI confirmed the accuracy of the denominator/hospital catchment area from the patient flow analysis.
Large national trauma registries, including TARN, hold suitable data for determining population-based injury rates. Patient postcodes from hospital discharge allow identification of denominator populations using a market area approach.
BMC Health Services Research 02/2008; 8:80. · 1.66 Impact Factor
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ABSTRACT: Abstract
Background
Determining population-based rates for major injury poses methodological challenges. We used hospital discharge data over a 10-year period (1996–2005) from a national trauma registry, the Trauma Audit and Research Network (TARN) Manchester, to construct valid numerators and denominators so that we can calculate population-based rates of major injury in the future.
Methods
We examined data from all hospitals reporting to TARN for continuity of numerator reporting; rates of completeness for patient postcodes, and clear denominator populations. We defined local market areas (>70% of patients originating from the same postcode district as the hospital). For relevant hospitals we assessed data quality: consistency of reporting, completeness of patient postcodes and for one selected hospital, North Staffordshire Royal Infirmary (NSRI), the capture rate of numerator data reporting. We used an established method based on patient flow to delineate market areas from hospitals discharges. We then assessed the potential competitors, and characterized these denominator areas. Finally we performed a denominator sensitivity analysis using a patient origin matrix based on Hospital Episodes Statistics (HES) to validate our approach.
Results
Sixteen hospitals met the data quality and patient flow criteria for numerator and denominator data, representing 12 hospital catchment areas across England. Data quality issues included fluctuations numbers of reported cases and poor completion of postcodes for some years. We found an overall numerator capture rate of 83.5% for the NSRI. In total we used 40,543 admissions to delineate hospital catchment areas. An average of 3.5 potential hospital competitors and 15.2 postcode districts per area were obtained. The patient origin matrix for NSRI confirmed the accuracy of the denominator/hospital catchment area from the patient flow analysis.
Conclusion
Large national trauma registries, including TARN, hold suitable data for determining population-based injury rates. Patient postcodes from hospital discharge allow identification of denominator populations using a market area approach.
BMC Health Services Research. 01/2008;
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Roxana Alexandrescu
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ABSTRACT: To describe the characteristics of morbidity in one district of Romania and to identify specific priority areas for preventive measures or further research.
A descriptive retrospective study, cases being identified using routinely collected data on morbidity from a district public health authority. Study population was defined as those resident in Vaslui district, an area characterized by lower socioeconomic development located in the north-eastern region of Romania (Moldova). The main outcome measures were occurrence and prevalence rates of events and patterns of hospital utilization over the study period (1996-2001).
The overall occurrence rate was 46,779.1 per 100,000 person-years with a declining trend over the 6-year study period. The highest values were amongst children. The most common causes were respiratory diseases (36.3%) and digestive diseases (31.4%) followed by infectious/parasitic diseases (5.8%), nervous system/sense organs diseases (5.6%) and skin/subcutaneous diseases (5.0%). The point prevalence rate increased steadily from 8.4% in 1996 to 12.5% in 2001, the picture being dominated by anemia in children and cardiovascular diseases (i.e., hypertension, ischemic cardiopathy) in adults. The leading cause for hospitalization remains respiratory diseases followed by cardiovascular and digestive diseases (39.5% of all diagnoses).
The results suggest a number of prevention priorities amongst Vaslui residents such as respiratory diseases especially in children less than 1 year of age and adults over 65 years, chronic cardiovascular diseases in adults or digestive diseases in the whole population. This work can be used as a starting point for other observational studies on health information systems as well as on broader determinants of health within this community.
Annals of Epidemiology 06/2004; 14(5):346-53. · 3.21 Impact Factor
