Factors associated with prescribing costs: analysis of a nationwide administrative database

Objective All health care systems in the world struggle with rising costs for drugs. We sought to explore factors impacting on prescribing costs in a nationwide database of ambulatory care in Germany. Factors identified by this research can be used for adjustment in future profiling efforts. Methods...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Hirsch, O. [verfasserIn] Schulz, M. Erhart, M. Donner-Banzhoff, N.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Drug prescriptions Drug costs Ambulatory care Regression analyses

Anmerkung:	© The Author(s) 2018

Übergeordnetes Werk:	Enthalten in: Cost effectiveness and resource allocation - London : BioMed Central, 2003, 16(2018), 1 vom: 08. Feb.
Übergeordnetes Werk:	volume:16 ; year:2018 ; number:1 ; day:08 ; month:02

Links:	Volltext

DOI / URN:	10.1186/s12962-018-0091-1

Katalog-ID:	SPR028894502

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520			\|a Objective All health care systems in the world struggle with rising costs for drugs. We sought to explore factors impacting on prescribing costs in a nationwide database of ambulatory care in Germany. Factors identified by this research can be used for adjustment in future profiling efforts. Methods We analysed nationwide prescription data of physicians having contractual relationships with statutory health insurance funds in 2014. Predictor and outcome variables were aggregated at the practice level. We performed analyses separately for primary care and specialties of cardiology, gastroenterology, neurology and psychiatry, pulmology as well as oncology and haematology. Bivariate robust regressions and Spearman rank correlations were computed in order to find meaningful predictors for our outcome variable prescription costs per patient. Results Median age of patients and proportion of DDD issued were substantial predictors for prescription costs per patient in Primary Care, Cardiology, and Pulmology with explained variances between 41 and 61%. In Neurology and Psychiatry only proportion of patients with polypharmacy ≥ 2 quarters was a significant predictor for prescription costs per patient, explaining 20% of the variance. For gastroenterologists, oncologists and haematologists no stable models could be established. Conclusions Any analysis of prescribing behaviour must take the degree into account to which an individual physician or practice is responsible for prescribing patients’ medication. Proportion of prescriptions/DDDs is an essential confounder for future studies of drug prescribing.
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allfields	10.1186/s12962-018-0091-1 doi (DE-627)SPR028894502 (SPR)s12962-018-0091-1-e DE-627 ger DE-627 rakwb eng Hirsch, O. verfasserin (orcid)0000-0003-4496-2554 aut Factors associated with prescribing costs: analysis of a nationwide administrative database 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Objective All health care systems in the world struggle with rising costs for drugs. We sought to explore factors impacting on prescribing costs in a nationwide database of ambulatory care in Germany. Factors identified by this research can be used for adjustment in future profiling efforts. Methods We analysed nationwide prescription data of physicians having contractual relationships with statutory health insurance funds in 2014. Predictor and outcome variables were aggregated at the practice level. We performed analyses separately for primary care and specialties of cardiology, gastroenterology, neurology and psychiatry, pulmology as well as oncology and haematology. Bivariate robust regressions and Spearman rank correlations were computed in order to find meaningful predictors for our outcome variable prescription costs per patient. Results Median age of patients and proportion of DDD issued were substantial predictors for prescription costs per patient in Primary Care, Cardiology, and Pulmology with explained variances between 41 and 61%. In Neurology and Psychiatry only proportion of patients with polypharmacy ≥ 2 quarters was a significant predictor for prescription costs per patient, explaining 20% of the variance. For gastroenterologists, oncologists and haematologists no stable models could be established. Conclusions Any analysis of prescribing behaviour must take the degree into account to which an individual physician or practice is responsible for prescribing patients’ medication. Proportion of prescriptions/DDDs is an essential confounder for future studies of drug prescribing. Drug prescriptions (dpeaa)DE-He213 Drug costs (dpeaa)DE-He213 Ambulatory care (dpeaa)DE-He213 Regression analyses (dpeaa)DE-He213 Schulz, M. aut Erhart, M. aut Donner-Banzhoff, N. aut Enthalten in Cost effectiveness and resource allocation London : BioMed Central, 2003 16(2018), 1 vom: 08. Feb. (DE-627)369555570 (DE-600)2119372-1 1478-7547 nnns volume:16 year:2018 number:1 day:08 month:02 https://dx.doi.org/10.1186/s12962-018-0091-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2018 1 08 02
spelling	10.1186/s12962-018-0091-1 doi (DE-627)SPR028894502 (SPR)s12962-018-0091-1-e DE-627 ger DE-627 rakwb eng Hirsch, O. verfasserin (orcid)0000-0003-4496-2554 aut Factors associated with prescribing costs: analysis of a nationwide administrative database 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Objective All health care systems in the world struggle with rising costs for drugs. We sought to explore factors impacting on prescribing costs in a nationwide database of ambulatory care in Germany. Factors identified by this research can be used for adjustment in future profiling efforts. Methods We analysed nationwide prescription data of physicians having contractual relationships with statutory health insurance funds in 2014. Predictor and outcome variables were aggregated at the practice level. We performed analyses separately for primary care and specialties of cardiology, gastroenterology, neurology and psychiatry, pulmology as well as oncology and haematology. Bivariate robust regressions and Spearman rank correlations were computed in order to find meaningful predictors for our outcome variable prescription costs per patient. Results Median age of patients and proportion of DDD issued were substantial predictors for prescription costs per patient in Primary Care, Cardiology, and Pulmology with explained variances between 41 and 61%. In Neurology and Psychiatry only proportion of patients with polypharmacy ≥ 2 quarters was a significant predictor for prescription costs per patient, explaining 20% of the variance. For gastroenterologists, oncologists and haematologists no stable models could be established. Conclusions Any analysis of prescribing behaviour must take the degree into account to which an individual physician or practice is responsible for prescribing patients’ medication. Proportion of prescriptions/DDDs is an essential confounder for future studies of drug prescribing. Drug prescriptions (dpeaa)DE-He213 Drug costs (dpeaa)DE-He213 Ambulatory care (dpeaa)DE-He213 Regression analyses (dpeaa)DE-He213 Schulz, M. aut Erhart, M. aut Donner-Banzhoff, N. aut Enthalten in Cost effectiveness and resource allocation London : BioMed Central, 2003 16(2018), 1 vom: 08. Feb. (DE-627)369555570 (DE-600)2119372-1 1478-7547 nnns volume:16 year:2018 number:1 day:08 month:02 https://dx.doi.org/10.1186/s12962-018-0091-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2018 1 08 02
allfields_unstemmed	10.1186/s12962-018-0091-1 doi (DE-627)SPR028894502 (SPR)s12962-018-0091-1-e DE-627 ger DE-627 rakwb eng Hirsch, O. verfasserin (orcid)0000-0003-4496-2554 aut Factors associated with prescribing costs: analysis of a nationwide administrative database 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Objective All health care systems in the world struggle with rising costs for drugs. We sought to explore factors impacting on prescribing costs in a nationwide database of ambulatory care in Germany. Factors identified by this research can be used for adjustment in future profiling efforts. Methods We analysed nationwide prescription data of physicians having contractual relationships with statutory health insurance funds in 2014. Predictor and outcome variables were aggregated at the practice level. We performed analyses separately for primary care and specialties of cardiology, gastroenterology, neurology and psychiatry, pulmology as well as oncology and haematology. Bivariate robust regressions and Spearman rank correlations were computed in order to find meaningful predictors for our outcome variable prescription costs per patient. Results Median age of patients and proportion of DDD issued were substantial predictors for prescription costs per patient in Primary Care, Cardiology, and Pulmology with explained variances between 41 and 61%. In Neurology and Psychiatry only proportion of patients with polypharmacy ≥ 2 quarters was a significant predictor for prescription costs per patient, explaining 20% of the variance. For gastroenterologists, oncologists and haematologists no stable models could be established. Conclusions Any analysis of prescribing behaviour must take the degree into account to which an individual physician or practice is responsible for prescribing patients’ medication. Proportion of prescriptions/DDDs is an essential confounder for future studies of drug prescribing. Drug prescriptions (dpeaa)DE-He213 Drug costs (dpeaa)DE-He213 Ambulatory care (dpeaa)DE-He213 Regression analyses (dpeaa)DE-He213 Schulz, M. aut Erhart, M. aut Donner-Banzhoff, N. aut Enthalten in Cost effectiveness and resource allocation London : BioMed Central, 2003 16(2018), 1 vom: 08. Feb. (DE-627)369555570 (DE-600)2119372-1 1478-7547 nnns volume:16 year:2018 number:1 day:08 month:02 https://dx.doi.org/10.1186/s12962-018-0091-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2018 1 08 02
allfieldsGer	10.1186/s12962-018-0091-1 doi (DE-627)SPR028894502 (SPR)s12962-018-0091-1-e DE-627 ger DE-627 rakwb eng Hirsch, O. verfasserin (orcid)0000-0003-4496-2554 aut Factors associated with prescribing costs: analysis of a nationwide administrative database 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Objective All health care systems in the world struggle with rising costs for drugs. We sought to explore factors impacting on prescribing costs in a nationwide database of ambulatory care in Germany. Factors identified by this research can be used for adjustment in future profiling efforts. Methods We analysed nationwide prescription data of physicians having contractual relationships with statutory health insurance funds in 2014. Predictor and outcome variables were aggregated at the practice level. We performed analyses separately for primary care and specialties of cardiology, gastroenterology, neurology and psychiatry, pulmology as well as oncology and haematology. Bivariate robust regressions and Spearman rank correlations were computed in order to find meaningful predictors for our outcome variable prescription costs per patient. Results Median age of patients and proportion of DDD issued were substantial predictors for prescription costs per patient in Primary Care, Cardiology, and Pulmology with explained variances between 41 and 61%. In Neurology and Psychiatry only proportion of patients with polypharmacy ≥ 2 quarters was a significant predictor for prescription costs per patient, explaining 20% of the variance. For gastroenterologists, oncologists and haematologists no stable models could be established. Conclusions Any analysis of prescribing behaviour must take the degree into account to which an individual physician or practice is responsible for prescribing patients’ medication. Proportion of prescriptions/DDDs is an essential confounder for future studies of drug prescribing. Drug prescriptions (dpeaa)DE-He213 Drug costs (dpeaa)DE-He213 Ambulatory care (dpeaa)DE-He213 Regression analyses (dpeaa)DE-He213 Schulz, M. aut Erhart, M. aut Donner-Banzhoff, N. aut Enthalten in Cost effectiveness and resource allocation London : BioMed Central, 2003 16(2018), 1 vom: 08. Feb. (DE-627)369555570 (DE-600)2119372-1 1478-7547 nnns volume:16 year:2018 number:1 day:08 month:02 https://dx.doi.org/10.1186/s12962-018-0091-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2018 1 08 02
allfieldsSound	10.1186/s12962-018-0091-1 doi (DE-627)SPR028894502 (SPR)s12962-018-0091-1-e DE-627 ger DE-627 rakwb eng Hirsch, O. verfasserin (orcid)0000-0003-4496-2554 aut Factors associated with prescribing costs: analysis of a nationwide administrative database 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Objective All health care systems in the world struggle with rising costs for drugs. We sought to explore factors impacting on prescribing costs in a nationwide database of ambulatory care in Germany. Factors identified by this research can be used for adjustment in future profiling efforts. Methods We analysed nationwide prescription data of physicians having contractual relationships with statutory health insurance funds in 2014. Predictor and outcome variables were aggregated at the practice level. We performed analyses separately for primary care and specialties of cardiology, gastroenterology, neurology and psychiatry, pulmology as well as oncology and haematology. Bivariate robust regressions and Spearman rank correlations were computed in order to find meaningful predictors for our outcome variable prescription costs per patient. Results Median age of patients and proportion of DDD issued were substantial predictors for prescription costs per patient in Primary Care, Cardiology, and Pulmology with explained variances between 41 and 61%. In Neurology and Psychiatry only proportion of patients with polypharmacy ≥ 2 quarters was a significant predictor for prescription costs per patient, explaining 20% of the variance. For gastroenterologists, oncologists and haematologists no stable models could be established. Conclusions Any analysis of prescribing behaviour must take the degree into account to which an individual physician or practice is responsible for prescribing patients’ medication. Proportion of prescriptions/DDDs is an essential confounder for future studies of drug prescribing. Drug prescriptions (dpeaa)DE-He213 Drug costs (dpeaa)DE-He213 Ambulatory care (dpeaa)DE-He213 Regression analyses (dpeaa)DE-He213 Schulz, M. aut Erhart, M. aut Donner-Banzhoff, N. aut Enthalten in Cost effectiveness and resource allocation London : BioMed Central, 2003 16(2018), 1 vom: 08. Feb. (DE-627)369555570 (DE-600)2119372-1 1478-7547 nnns volume:16 year:2018 number:1 day:08 month:02 https://dx.doi.org/10.1186/s12962-018-0091-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2018 1 08 02
language	English
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We sought to explore factors impacting on prescribing costs in a nationwide database of ambulatory care in Germany. Factors identified by this research can be used for adjustment in future profiling efforts. Methods We analysed nationwide prescription data of physicians having contractual relationships with statutory health insurance funds in 2014. Predictor and outcome variables were aggregated at the practice level. We performed analyses separately for primary care and specialties of cardiology, gastroenterology, neurology and psychiatry, pulmology as well as oncology and haematology. Bivariate robust regressions and Spearman rank correlations were computed in order to find meaningful predictors for our outcome variable prescription costs per patient. Results Median age of patients and proportion of DDD issued were substantial predictors for prescription costs per patient in Primary Care, Cardiology, and Pulmology with explained variances between 41 and 61%. In Neurology and Psychiatry only proportion of patients with polypharmacy ≥ 2 quarters was a significant predictor for prescription costs per patient, explaining 20% of the variance. For gastroenterologists, oncologists and haematologists no stable models could be established. Conclusions Any analysis of prescribing behaviour must take the degree into account to which an individual physician or practice is responsible for prescribing patients’ medication. 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topic_title	Factors associated with prescribing costs: analysis of a nationwide administrative database Drug prescriptions (dpeaa)DE-He213 Drug costs (dpeaa)DE-He213 Ambulatory care (dpeaa)DE-He213 Regression analyses (dpeaa)DE-He213
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title	Factors associated with prescribing costs: analysis of a nationwide administrative database
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title_full	Factors associated with prescribing costs: analysis of a nationwide administrative database
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title_sort	factors associated with prescribing costs: analysis of a nationwide administrative database
title_auth	Factors associated with prescribing costs: analysis of a nationwide administrative database
abstract	Objective All health care systems in the world struggle with rising costs for drugs. We sought to explore factors impacting on prescribing costs in a nationwide database of ambulatory care in Germany. Factors identified by this research can be used for adjustment in future profiling efforts. Methods We analysed nationwide prescription data of physicians having contractual relationships with statutory health insurance funds in 2014. Predictor and outcome variables were aggregated at the practice level. We performed analyses separately for primary care and specialties of cardiology, gastroenterology, neurology and psychiatry, pulmology as well as oncology and haematology. Bivariate robust regressions and Spearman rank correlations were computed in order to find meaningful predictors for our outcome variable prescription costs per patient. Results Median age of patients and proportion of DDD issued were substantial predictors for prescription costs per patient in Primary Care, Cardiology, and Pulmology with explained variances between 41 and 61%. In Neurology and Psychiatry only proportion of patients with polypharmacy ≥ 2 quarters was a significant predictor for prescription costs per patient, explaining 20% of the variance. For gastroenterologists, oncologists and haematologists no stable models could be established. Conclusions Any analysis of prescribing behaviour must take the degree into account to which an individual physician or practice is responsible for prescribing patients’ medication. Proportion of prescriptions/DDDs is an essential confounder for future studies of drug prescribing. © The Author(s) 2018
abstractGer	Objective All health care systems in the world struggle with rising costs for drugs. We sought to explore factors impacting on prescribing costs in a nationwide database of ambulatory care in Germany. Factors identified by this research can be used for adjustment in future profiling efforts. Methods We analysed nationwide prescription data of physicians having contractual relationships with statutory health insurance funds in 2014. Predictor and outcome variables were aggregated at the practice level. We performed analyses separately for primary care and specialties of cardiology, gastroenterology, neurology and psychiatry, pulmology as well as oncology and haematology. Bivariate robust regressions and Spearman rank correlations were computed in order to find meaningful predictors for our outcome variable prescription costs per patient. Results Median age of patients and proportion of DDD issued were substantial predictors for prescription costs per patient in Primary Care, Cardiology, and Pulmology with explained variances between 41 and 61%. In Neurology and Psychiatry only proportion of patients with polypharmacy ≥ 2 quarters was a significant predictor for prescription costs per patient, explaining 20% of the variance. For gastroenterologists, oncologists and haematologists no stable models could be established. Conclusions Any analysis of prescribing behaviour must take the degree into account to which an individual physician or practice is responsible for prescribing patients’ medication. Proportion of prescriptions/DDDs is an essential confounder for future studies of drug prescribing. © The Author(s) 2018
abstract_unstemmed	Objective All health care systems in the world struggle with rising costs for drugs. We sought to explore factors impacting on prescribing costs in a nationwide database of ambulatory care in Germany. Factors identified by this research can be used for adjustment in future profiling efforts. Methods We analysed nationwide prescription data of physicians having contractual relationships with statutory health insurance funds in 2014. Predictor and outcome variables were aggregated at the practice level. We performed analyses separately for primary care and specialties of cardiology, gastroenterology, neurology and psychiatry, pulmology as well as oncology and haematology. Bivariate robust regressions and Spearman rank correlations were computed in order to find meaningful predictors for our outcome variable prescription costs per patient. Results Median age of patients and proportion of DDD issued were substantial predictors for prescription costs per patient in Primary Care, Cardiology, and Pulmology with explained variances between 41 and 61%. In Neurology and Psychiatry only proportion of patients with polypharmacy ≥ 2 quarters was a significant predictor for prescription costs per patient, explaining 20% of the variance. For gastroenterologists, oncologists and haematologists no stable models could be established. Conclusions Any analysis of prescribing behaviour must take the degree into account to which an individual physician or practice is responsible for prescribing patients’ medication. Proportion of prescriptions/DDDs is an essential confounder for future studies of drug prescribing. © The Author(s) 2018
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title_short	Factors associated with prescribing costs: analysis of a nationwide administrative database
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In Neurology and Psychiatry only proportion of patients with polypharmacy ≥ 2 quarters was a significant predictor for prescription costs per patient, explaining 20% of the variance. For gastroenterologists, oncologists and haematologists no stable models could be established. Conclusions Any analysis of prescribing behaviour must take the degree into account to which an individual physician or practice is responsible for prescribing patients’ medication. 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Factors associated with prescribing costs: analysis of a nationwide administrative database

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