Limits...
A proposed approach in defining population-based rates of major injury from a trauma registry dataset: delineation of hospital catchment areas (I).

Alexandrescu R, O'Brien SJ, Lyons RA, Lecky FE, Trauma Audit and Research Netwo - BMC Health Serv Res (2008)

Bottom Line: We found an overall numerator capture rate of 83.5% for the NSRI.The patient origin matrix for NSRI confirmed the accuracy of the denominator/hospital catchment area from the patient flow analysis.Patient postcodes from hospital discharge allow identification of denominator populations using a market area approach.

View Article: PubMed Central - HTML - PubMed

Affiliation: Trauma Audit and Research Network, Clinical Science Building, Hope Hospital, University of Manchester, Eccles Old Road, Salford, M6 8HD, UK. Roxana.Alexandrescu@postgrad.manchester.ac.uk

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.

Show MeSH

Related in: MedlinePlus

Map of Stoke on Trent area with numbers indicating the districts within Stoke on Trent area, dots representing NSRI and the hospitals within a 20 miles radius (labelled according to the postcode district location), and the hospital catchment area for NSRI delineated in black.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2365946&req=5

Figure 1: Map of Stoke on Trent area with numbers indicating the districts within Stoke on Trent area, dots representing NSRI and the hospitals within a 20 miles radius (labelled according to the postcode district location), and the hospital catchment area for NSRI delineated in black.

Mentions: We present a detailed example to show how we applied the market area technique to determine the catchment area for NSRI, in Stoke on Trent (Table 4). In line with our a priori criteria a high percentage of postcodes were available (i.e. 95.6%, 4898/5125) and over 70% of patients discharged from the hospital NSRI originate from the same area where the hospital is located – Stoke on Trent (i.e. 75.8%, 3886/5125). Table 4 also shows patient's postcodes of residence by postcode districts in descending order of frequency, after removing from the database the patients who lived out of the area as well as all admissions during 1996 (56.2% completeness rate for postcodes) and 2005 (delay in data reporting). In bold are the postcode districts that account for the threshold of 80% of hospital's admissions: ST1 to ST7, ST10 and ST13. We then visualised these districts on map and delineated a contiguous catchment area (Figure 1).


A proposed approach in defining population-based rates of major injury from a trauma registry dataset: delineation of hospital catchment areas (I).

Alexandrescu R, O'Brien SJ, Lyons RA, Lecky FE, Trauma Audit and Research Netwo - BMC Health Serv Res (2008)

Map of Stoke on Trent area with numbers indicating the districts within Stoke on Trent area, dots representing NSRI and the hospitals within a 20 miles radius (labelled according to the postcode district location), and the hospital catchment area for NSRI delineated in black.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2365946&req=5

Figure 1: Map of Stoke on Trent area with numbers indicating the districts within Stoke on Trent area, dots representing NSRI and the hospitals within a 20 miles radius (labelled according to the postcode district location), and the hospital catchment area for NSRI delineated in black.
Mentions: We present a detailed example to show how we applied the market area technique to determine the catchment area for NSRI, in Stoke on Trent (Table 4). In line with our a priori criteria a high percentage of postcodes were available (i.e. 95.6%, 4898/5125) and over 70% of patients discharged from the hospital NSRI originate from the same area where the hospital is located – Stoke on Trent (i.e. 75.8%, 3886/5125). Table 4 also shows patient's postcodes of residence by postcode districts in descending order of frequency, after removing from the database the patients who lived out of the area as well as all admissions during 1996 (56.2% completeness rate for postcodes) and 2005 (delay in data reporting). In bold are the postcode districts that account for the threshold of 80% of hospital's admissions: ST1 to ST7, ST10 and ST13. We then visualised these districts on map and delineated a contiguous catchment area (Figure 1).

Bottom Line: We found an overall numerator capture rate of 83.5% for the NSRI.The patient origin matrix for NSRI confirmed the accuracy of the denominator/hospital catchment area from the patient flow analysis.Patient postcodes from hospital discharge allow identification of denominator populations using a market area approach.

View Article: PubMed Central - HTML - PubMed

Affiliation: Trauma Audit and Research Network, Clinical Science Building, Hope Hospital, University of Manchester, Eccles Old Road, Salford, M6 8HD, UK. Roxana.Alexandrescu@postgrad.manchester.ac.uk

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.

Show MeSH
Related in: MedlinePlus