Health Resource Utilisation and Disparities: an Ecological Study of Admission Patterns Across Ethnicity in England Between 2017 and 2020
Background and Aim The COVID-19 pandemic highlighted adverse outcomes in Asian, Black, and ethnic minority groups. More research is required to explore underlying ethnic health inequalities. In this study, we aim to examine pre-COVID ethnic inequalities more generally through healthcare utilisation...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Toal, C. M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of racial and ethnic health disparities - Cham : Springer Internat. Publ., 2014, 10(2022), 6 vom: 05. Dez., Seite 2872-2881 |
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| Übergeordnetes Werk: |
volume:10 ; year:2022 ; number:6 ; day:05 ; month:12 ; pages:2872-2881 |
| Links: |
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| DOI / URN: |
10.1007/s40615-022-01464-7 |
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| Katalog-ID: |
SPR053743350 |
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| 520 | |a Background and Aim The COVID-19 pandemic highlighted adverse outcomes in Asian, Black, and ethnic minority groups. More research is required to explore underlying ethnic health inequalities. In this study, we aim to examine pre-COVID ethnic inequalities more generally through healthcare utilisation to contextualise underlying inequalities that were present before the pandemic. Design This was an ecological study exploring all admissions to NHS hospitals in England from 2017 to 2020. Methods The primary outcomes were admission rates within ethnic groups. Secondary outcomes included age-specific and age-standardised admission rates. Sub-analysis of admission rates across an index of multiple deprivation (IMD) deciles was also performed to contextualise the impact of socioeconomic differences amongst ethnic categories. Results were presented as a relative ratio (RR) with 95% confidence intervals. Results Age-standardised admission rates were higher in Asian (RR 1.40 [1.38–1.41] in 2019) and Black (RR 1.37 [1.37–1.38]) and lower in Mixed groups (RR 0.91 [0.90–0.91]) relative to White. There was significant missingness or misassignment of ethnicity in NHS admissions: with 11.7% of admissions having an unknown/not-stated ethnicity assignment and ‘other’ ethnicity being significantly over-represented. Admission rates did not mirror the degree of deprivation across all ethnic categories. Conclusions This study shows Black and Asian ethnic groups have higher admission rates compared to White across all age groups and when standardised for age. There is evidence of incomplete and misidentification of ethnicity assignment in NHS admission records, which may introduce bias to work on these datasets. Differences in admission rates across individual ethnic categories cannot solely be explained by socioeconomic status. Further work is needed to identify ethnicity-specific factors of these inequalities to allow targeted interventions at the local level. | ||
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| 700 | 1 | |a Puthucheary, Z. |4 aut | |
| 700 | 1 | |a Prowle, J. R. |4 aut | |
| 700 | 1 | |a Wan, Y. I. |4 aut | |
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10.1007/s40615-022-01464-7 doi (DE-627)SPR053743350 (SPR)s40615-022-01464-7-e DE-627 ger DE-627 rakwb eng Toal, C. M. verfasserin (orcid)0000-0001-9969-242X aut Health Resource Utilisation and Disparities: an Ecological Study of Admission Patterns Across Ethnicity in England Between 2017 and 2020 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background and Aim The COVID-19 pandemic highlighted adverse outcomes in Asian, Black, and ethnic minority groups. More research is required to explore underlying ethnic health inequalities. In this study, we aim to examine pre-COVID ethnic inequalities more generally through healthcare utilisation to contextualise underlying inequalities that were present before the pandemic. Design This was an ecological study exploring all admissions to NHS hospitals in England from 2017 to 2020. Methods The primary outcomes were admission rates within ethnic groups. Secondary outcomes included age-specific and age-standardised admission rates. Sub-analysis of admission rates across an index of multiple deprivation (IMD) deciles was also performed to contextualise the impact of socioeconomic differences amongst ethnic categories. Results were presented as a relative ratio (RR) with 95% confidence intervals. Results Age-standardised admission rates were higher in Asian (RR 1.40 [1.38–1.41] in 2019) and Black (RR 1.37 [1.37–1.38]) and lower in Mixed groups (RR 0.91 [0.90–0.91]) relative to White. There was significant missingness or misassignment of ethnicity in NHS admissions: with 11.7% of admissions having an unknown/not-stated ethnicity assignment and ‘other’ ethnicity being significantly over-represented. Admission rates did not mirror the degree of deprivation across all ethnic categories. Conclusions This study shows Black and Asian ethnic groups have higher admission rates compared to White across all age groups and when standardised for age. There is evidence of incomplete and misidentification of ethnicity assignment in NHS admission records, which may introduce bias to work on these datasets. Differences in admission rates across individual ethnic categories cannot solely be explained by socioeconomic status. Further work is needed to identify ethnicity-specific factors of these inequalities to allow targeted interventions at the local level. Ethnicity (dpeaa)DE-He213 Socioeconomic deprivation (dpeaa)DE-He213 Secondary care (dpeaa)DE-He213 Health inequality (dpeaa)DE-He213 Fowler, A. J. aut Pearse, R. M. aut Puthucheary, Z. aut Prowle, J. R. aut Wan, Y. I. aut Enthalten in Journal of racial and ethnic health disparities Cham : Springer Internat. Publ., 2014 10(2022), 6 vom: 05. Dez., Seite 2872-2881 (DE-627)780379705 (DE-600)2760524-3 2196-8837 nnns volume:10 year:2022 number:6 day:05 month:12 pages:2872-2881 https://dx.doi.org/10.1007/s40615-022-01464-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 10 2022 6 05 12 2872-2881 |
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10.1007/s40615-022-01464-7 doi (DE-627)SPR053743350 (SPR)s40615-022-01464-7-e DE-627 ger DE-627 rakwb eng Toal, C. M. verfasserin (orcid)0000-0001-9969-242X aut Health Resource Utilisation and Disparities: an Ecological Study of Admission Patterns Across Ethnicity in England Between 2017 and 2020 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background and Aim The COVID-19 pandemic highlighted adverse outcomes in Asian, Black, and ethnic minority groups. More research is required to explore underlying ethnic health inequalities. In this study, we aim to examine pre-COVID ethnic inequalities more generally through healthcare utilisation to contextualise underlying inequalities that were present before the pandemic. Design This was an ecological study exploring all admissions to NHS hospitals in England from 2017 to 2020. Methods The primary outcomes were admission rates within ethnic groups. Secondary outcomes included age-specific and age-standardised admission rates. Sub-analysis of admission rates across an index of multiple deprivation (IMD) deciles was also performed to contextualise the impact of socioeconomic differences amongst ethnic categories. Results were presented as a relative ratio (RR) with 95% confidence intervals. Results Age-standardised admission rates were higher in Asian (RR 1.40 [1.38–1.41] in 2019) and Black (RR 1.37 [1.37–1.38]) and lower in Mixed groups (RR 0.91 [0.90–0.91]) relative to White. There was significant missingness or misassignment of ethnicity in NHS admissions: with 11.7% of admissions having an unknown/not-stated ethnicity assignment and ‘other’ ethnicity being significantly over-represented. Admission rates did not mirror the degree of deprivation across all ethnic categories. Conclusions This study shows Black and Asian ethnic groups have higher admission rates compared to White across all age groups and when standardised for age. There is evidence of incomplete and misidentification of ethnicity assignment in NHS admission records, which may introduce bias to work on these datasets. Differences in admission rates across individual ethnic categories cannot solely be explained by socioeconomic status. Further work is needed to identify ethnicity-specific factors of these inequalities to allow targeted interventions at the local level. Ethnicity (dpeaa)DE-He213 Socioeconomic deprivation (dpeaa)DE-He213 Secondary care (dpeaa)DE-He213 Health inequality (dpeaa)DE-He213 Fowler, A. J. aut Pearse, R. M. aut Puthucheary, Z. aut Prowle, J. R. aut Wan, Y. I. aut Enthalten in Journal of racial and ethnic health disparities Cham : Springer Internat. Publ., 2014 10(2022), 6 vom: 05. Dez., Seite 2872-2881 (DE-627)780379705 (DE-600)2760524-3 2196-8837 nnns volume:10 year:2022 number:6 day:05 month:12 pages:2872-2881 https://dx.doi.org/10.1007/s40615-022-01464-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 10 2022 6 05 12 2872-2881 |
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10.1007/s40615-022-01464-7 doi (DE-627)SPR053743350 (SPR)s40615-022-01464-7-e DE-627 ger DE-627 rakwb eng Toal, C. M. verfasserin (orcid)0000-0001-9969-242X aut Health Resource Utilisation and Disparities: an Ecological Study of Admission Patterns Across Ethnicity in England Between 2017 and 2020 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background and Aim The COVID-19 pandemic highlighted adverse outcomes in Asian, Black, and ethnic minority groups. More research is required to explore underlying ethnic health inequalities. In this study, we aim to examine pre-COVID ethnic inequalities more generally through healthcare utilisation to contextualise underlying inequalities that were present before the pandemic. Design This was an ecological study exploring all admissions to NHS hospitals in England from 2017 to 2020. Methods The primary outcomes were admission rates within ethnic groups. Secondary outcomes included age-specific and age-standardised admission rates. Sub-analysis of admission rates across an index of multiple deprivation (IMD) deciles was also performed to contextualise the impact of socioeconomic differences amongst ethnic categories. Results were presented as a relative ratio (RR) with 95% confidence intervals. Results Age-standardised admission rates were higher in Asian (RR 1.40 [1.38–1.41] in 2019) and Black (RR 1.37 [1.37–1.38]) and lower in Mixed groups (RR 0.91 [0.90–0.91]) relative to White. There was significant missingness or misassignment of ethnicity in NHS admissions: with 11.7% of admissions having an unknown/not-stated ethnicity assignment and ‘other’ ethnicity being significantly over-represented. Admission rates did not mirror the degree of deprivation across all ethnic categories. Conclusions This study shows Black and Asian ethnic groups have higher admission rates compared to White across all age groups and when standardised for age. There is evidence of incomplete and misidentification of ethnicity assignment in NHS admission records, which may introduce bias to work on these datasets. Differences in admission rates across individual ethnic categories cannot solely be explained by socioeconomic status. Further work is needed to identify ethnicity-specific factors of these inequalities to allow targeted interventions at the local level. Ethnicity (dpeaa)DE-He213 Socioeconomic deprivation (dpeaa)DE-He213 Secondary care (dpeaa)DE-He213 Health inequality (dpeaa)DE-He213 Fowler, A. J. aut Pearse, R. M. aut Puthucheary, Z. aut Prowle, J. R. aut Wan, Y. I. aut Enthalten in Journal of racial and ethnic health disparities Cham : Springer Internat. Publ., 2014 10(2022), 6 vom: 05. Dez., Seite 2872-2881 (DE-627)780379705 (DE-600)2760524-3 2196-8837 nnns volume:10 year:2022 number:6 day:05 month:12 pages:2872-2881 https://dx.doi.org/10.1007/s40615-022-01464-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 10 2022 6 05 12 2872-2881 |
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10.1007/s40615-022-01464-7 doi (DE-627)SPR053743350 (SPR)s40615-022-01464-7-e DE-627 ger DE-627 rakwb eng Toal, C. M. verfasserin (orcid)0000-0001-9969-242X aut Health Resource Utilisation and Disparities: an Ecological Study of Admission Patterns Across Ethnicity in England Between 2017 and 2020 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background and Aim The COVID-19 pandemic highlighted adverse outcomes in Asian, Black, and ethnic minority groups. More research is required to explore underlying ethnic health inequalities. In this study, we aim to examine pre-COVID ethnic inequalities more generally through healthcare utilisation to contextualise underlying inequalities that were present before the pandemic. Design This was an ecological study exploring all admissions to NHS hospitals in England from 2017 to 2020. Methods The primary outcomes were admission rates within ethnic groups. Secondary outcomes included age-specific and age-standardised admission rates. Sub-analysis of admission rates across an index of multiple deprivation (IMD) deciles was also performed to contextualise the impact of socioeconomic differences amongst ethnic categories. Results were presented as a relative ratio (RR) with 95% confidence intervals. Results Age-standardised admission rates were higher in Asian (RR 1.40 [1.38–1.41] in 2019) and Black (RR 1.37 [1.37–1.38]) and lower in Mixed groups (RR 0.91 [0.90–0.91]) relative to White. There was significant missingness or misassignment of ethnicity in NHS admissions: with 11.7% of admissions having an unknown/not-stated ethnicity assignment and ‘other’ ethnicity being significantly over-represented. Admission rates did not mirror the degree of deprivation across all ethnic categories. Conclusions This study shows Black and Asian ethnic groups have higher admission rates compared to White across all age groups and when standardised for age. There is evidence of incomplete and misidentification of ethnicity assignment in NHS admission records, which may introduce bias to work on these datasets. Differences in admission rates across individual ethnic categories cannot solely be explained by socioeconomic status. Further work is needed to identify ethnicity-specific factors of these inequalities to allow targeted interventions at the local level. Ethnicity (dpeaa)DE-He213 Socioeconomic deprivation (dpeaa)DE-He213 Secondary care (dpeaa)DE-He213 Health inequality (dpeaa)DE-He213 Fowler, A. J. aut Pearse, R. M. aut Puthucheary, Z. aut Prowle, J. R. aut Wan, Y. I. aut Enthalten in Journal of racial and ethnic health disparities Cham : Springer Internat. Publ., 2014 10(2022), 6 vom: 05. Dez., Seite 2872-2881 (DE-627)780379705 (DE-600)2760524-3 2196-8837 nnns volume:10 year:2022 number:6 day:05 month:12 pages:2872-2881 https://dx.doi.org/10.1007/s40615-022-01464-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 10 2022 6 05 12 2872-2881 |
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10.1007/s40615-022-01464-7 doi (DE-627)SPR053743350 (SPR)s40615-022-01464-7-e DE-627 ger DE-627 rakwb eng Toal, C. M. verfasserin (orcid)0000-0001-9969-242X aut Health Resource Utilisation and Disparities: an Ecological Study of Admission Patterns Across Ethnicity in England Between 2017 and 2020 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background and Aim The COVID-19 pandemic highlighted adverse outcomes in Asian, Black, and ethnic minority groups. More research is required to explore underlying ethnic health inequalities. In this study, we aim to examine pre-COVID ethnic inequalities more generally through healthcare utilisation to contextualise underlying inequalities that were present before the pandemic. Design This was an ecological study exploring all admissions to NHS hospitals in England from 2017 to 2020. Methods The primary outcomes were admission rates within ethnic groups. Secondary outcomes included age-specific and age-standardised admission rates. Sub-analysis of admission rates across an index of multiple deprivation (IMD) deciles was also performed to contextualise the impact of socioeconomic differences amongst ethnic categories. Results were presented as a relative ratio (RR) with 95% confidence intervals. Results Age-standardised admission rates were higher in Asian (RR 1.40 [1.38–1.41] in 2019) and Black (RR 1.37 [1.37–1.38]) and lower in Mixed groups (RR 0.91 [0.90–0.91]) relative to White. There was significant missingness or misassignment of ethnicity in NHS admissions: with 11.7% of admissions having an unknown/not-stated ethnicity assignment and ‘other’ ethnicity being significantly over-represented. Admission rates did not mirror the degree of deprivation across all ethnic categories. Conclusions This study shows Black and Asian ethnic groups have higher admission rates compared to White across all age groups and when standardised for age. There is evidence of incomplete and misidentification of ethnicity assignment in NHS admission records, which may introduce bias to work on these datasets. Differences in admission rates across individual ethnic categories cannot solely be explained by socioeconomic status. Further work is needed to identify ethnicity-specific factors of these inequalities to allow targeted interventions at the local level. Ethnicity (dpeaa)DE-He213 Socioeconomic deprivation (dpeaa)DE-He213 Secondary care (dpeaa)DE-He213 Health inequality (dpeaa)DE-He213 Fowler, A. J. aut Pearse, R. M. aut Puthucheary, Z. aut Prowle, J. R. aut Wan, Y. I. aut Enthalten in Journal of racial and ethnic health disparities Cham : Springer Internat. Publ., 2014 10(2022), 6 vom: 05. Dez., Seite 2872-2881 (DE-627)780379705 (DE-600)2760524-3 2196-8837 nnns volume:10 year:2022 number:6 day:05 month:12 pages:2872-2881 https://dx.doi.org/10.1007/s40615-022-01464-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 10 2022 6 05 12 2872-2881 |
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M.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-9969-242X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Health Resource Utilisation and Disparities: an Ecological Study of Admission Patterns Across Ethnicity in England Between 2017 and 2020</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background and Aim The COVID-19 pandemic highlighted adverse outcomes in Asian, Black, and ethnic minority groups. More research is required to explore underlying ethnic health inequalities. In this study, we aim to examine pre-COVID ethnic inequalities more generally through healthcare utilisation to contextualise underlying inequalities that were present before the pandemic. Design This was an ecological study exploring all admissions to NHS hospitals in England from 2017 to 2020. Methods The primary outcomes were admission rates within ethnic groups. Secondary outcomes included age-specific and age-standardised admission rates. Sub-analysis of admission rates across an index of multiple deprivation (IMD) deciles was also performed to contextualise the impact of socioeconomic differences amongst ethnic categories. Results were presented as a relative ratio (RR) with 95% confidence intervals. Results Age-standardised admission rates were higher in Asian (RR 1.40 [1.38–1.41] in 2019) and Black (RR 1.37 [1.37–1.38]) and lower in Mixed groups (RR 0.91 [0.90–0.91]) relative to White. There was significant missingness or misassignment of ethnicity in NHS admissions: with 11.7% of admissions having an unknown/not-stated ethnicity assignment and ‘other’ ethnicity being significantly over-represented. Admission rates did not mirror the degree of deprivation across all ethnic categories. Conclusions This study shows Black and Asian ethnic groups have higher admission rates compared to White across all age groups and when standardised for age. There is evidence of incomplete and misidentification of ethnicity assignment in NHS admission records, which may introduce bias to work on these datasets. Differences in admission rates across individual ethnic categories cannot solely be explained by socioeconomic status. 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Toal, C. M. |
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Toal, C. M. misc Ethnicity misc Socioeconomic deprivation misc Secondary care misc Health inequality Health Resource Utilisation and Disparities: an Ecological Study of Admission Patterns Across Ethnicity in England Between 2017 and 2020 |
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Health Resource Utilisation and Disparities: an Ecological Study of Admission Patterns Across Ethnicity in England Between 2017 and 2020 Ethnicity (dpeaa)DE-He213 Socioeconomic deprivation (dpeaa)DE-He213 Secondary care (dpeaa)DE-He213 Health inequality (dpeaa)DE-He213 |
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health resource utilisation and disparities: an ecological study of admission patterns across ethnicity in england between 2017 and 2020 |
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Health Resource Utilisation and Disparities: an Ecological Study of Admission Patterns Across Ethnicity in England Between 2017 and 2020 |
| abstract |
Background and Aim The COVID-19 pandemic highlighted adverse outcomes in Asian, Black, and ethnic minority groups. More research is required to explore underlying ethnic health inequalities. In this study, we aim to examine pre-COVID ethnic inequalities more generally through healthcare utilisation to contextualise underlying inequalities that were present before the pandemic. Design This was an ecological study exploring all admissions to NHS hospitals in England from 2017 to 2020. Methods The primary outcomes were admission rates within ethnic groups. Secondary outcomes included age-specific and age-standardised admission rates. Sub-analysis of admission rates across an index of multiple deprivation (IMD) deciles was also performed to contextualise the impact of socioeconomic differences amongst ethnic categories. Results were presented as a relative ratio (RR) with 95% confidence intervals. Results Age-standardised admission rates were higher in Asian (RR 1.40 [1.38–1.41] in 2019) and Black (RR 1.37 [1.37–1.38]) and lower in Mixed groups (RR 0.91 [0.90–0.91]) relative to White. There was significant missingness or misassignment of ethnicity in NHS admissions: with 11.7% of admissions having an unknown/not-stated ethnicity assignment and ‘other’ ethnicity being significantly over-represented. Admission rates did not mirror the degree of deprivation across all ethnic categories. Conclusions This study shows Black and Asian ethnic groups have higher admission rates compared to White across all age groups and when standardised for age. There is evidence of incomplete and misidentification of ethnicity assignment in NHS admission records, which may introduce bias to work on these datasets. Differences in admission rates across individual ethnic categories cannot solely be explained by socioeconomic status. Further work is needed to identify ethnicity-specific factors of these inequalities to allow targeted interventions at the local level. © The Author(s) 2022 |
| abstractGer |
Background and Aim The COVID-19 pandemic highlighted adverse outcomes in Asian, Black, and ethnic minority groups. More research is required to explore underlying ethnic health inequalities. In this study, we aim to examine pre-COVID ethnic inequalities more generally through healthcare utilisation to contextualise underlying inequalities that were present before the pandemic. Design This was an ecological study exploring all admissions to NHS hospitals in England from 2017 to 2020. Methods The primary outcomes were admission rates within ethnic groups. Secondary outcomes included age-specific and age-standardised admission rates. Sub-analysis of admission rates across an index of multiple deprivation (IMD) deciles was also performed to contextualise the impact of socioeconomic differences amongst ethnic categories. Results were presented as a relative ratio (RR) with 95% confidence intervals. Results Age-standardised admission rates were higher in Asian (RR 1.40 [1.38–1.41] in 2019) and Black (RR 1.37 [1.37–1.38]) and lower in Mixed groups (RR 0.91 [0.90–0.91]) relative to White. There was significant missingness or misassignment of ethnicity in NHS admissions: with 11.7% of admissions having an unknown/not-stated ethnicity assignment and ‘other’ ethnicity being significantly over-represented. Admission rates did not mirror the degree of deprivation across all ethnic categories. Conclusions This study shows Black and Asian ethnic groups have higher admission rates compared to White across all age groups and when standardised for age. There is evidence of incomplete and misidentification of ethnicity assignment in NHS admission records, which may introduce bias to work on these datasets. Differences in admission rates across individual ethnic categories cannot solely be explained by socioeconomic status. Further work is needed to identify ethnicity-specific factors of these inequalities to allow targeted interventions at the local level. © The Author(s) 2022 |
| abstract_unstemmed |
Background and Aim The COVID-19 pandemic highlighted adverse outcomes in Asian, Black, and ethnic minority groups. More research is required to explore underlying ethnic health inequalities. In this study, we aim to examine pre-COVID ethnic inequalities more generally through healthcare utilisation to contextualise underlying inequalities that were present before the pandemic. Design This was an ecological study exploring all admissions to NHS hospitals in England from 2017 to 2020. Methods The primary outcomes were admission rates within ethnic groups. Secondary outcomes included age-specific and age-standardised admission rates. Sub-analysis of admission rates across an index of multiple deprivation (IMD) deciles was also performed to contextualise the impact of socioeconomic differences amongst ethnic categories. Results were presented as a relative ratio (RR) with 95% confidence intervals. Results Age-standardised admission rates were higher in Asian (RR 1.40 [1.38–1.41] in 2019) and Black (RR 1.37 [1.37–1.38]) and lower in Mixed groups (RR 0.91 [0.90–0.91]) relative to White. There was significant missingness or misassignment of ethnicity in NHS admissions: with 11.7% of admissions having an unknown/not-stated ethnicity assignment and ‘other’ ethnicity being significantly over-represented. Admission rates did not mirror the degree of deprivation across all ethnic categories. Conclusions This study shows Black and Asian ethnic groups have higher admission rates compared to White across all age groups and when standardised for age. There is evidence of incomplete and misidentification of ethnicity assignment in NHS admission records, which may introduce bias to work on these datasets. Differences in admission rates across individual ethnic categories cannot solely be explained by socioeconomic status. Further work is needed to identify ethnicity-specific factors of these inequalities to allow targeted interventions at the local level. © The Author(s) 2022 |
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| title_short |
Health Resource Utilisation and Disparities: an Ecological Study of Admission Patterns Across Ethnicity in England Between 2017 and 2020 |
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https://dx.doi.org/10.1007/s40615-022-01464-7 |
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Fowler, A. J. Pearse, R. M. Puthucheary, Z. Prowle, J. R. Wan, Y. I. |
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Fowler, A. J. Pearse, R. M. Puthucheary, Z. Prowle, J. R. Wan, Y. I. |
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| score |
7.399643 |