Giuseppe N Giordano1, Henrik Ohlsson2, Kenneth S Kendler3, Marilyn A Winkleby4, Kristina Sundquist5, Jan Sundquist5. 1. Center for Primary Health Care Research, Lund University, Jan Waldenströmsgata 35, CRC, building 28, floor 11, entrance 72, Malmö University Hospital, Malmö, S-205 02, Sweden. Electronic address: giuseppe_nicola.giordano@med.lu.se. 2. Center for Primary Health Care Research, Lund University, Jan Waldenströmsgata 35, CRC, building 28, floor 11, entrance 72, Malmö University Hospital, Malmö, S-205 02, Sweden. 3. Virginia Commonwealth University School of Medicine, PO Box 980126 Richmond, VA 23298, USA. 4. Stanford Prevention Research Center, MSOB, Stanford, CA 94305, USA. 5. Center for Primary Health Care Research, Lund University, Jan Waldenströmsgata 35, CRC, building 28, floor 11, entrance 72, Malmö University Hospital, Malmö, S-205 02, Sweden; Stanford Prevention Research Center, MSOB, Stanford, CA 94305, USA.
Abstract
BACKGROUND: The societal consequences of drug abuse (DA) are severe and well documented, the World Health Organization recommending tracking of population trends for effective policy responses in treatment of DA and delivery of health care services. However, to correctly identify possible sources of DA change, one must first disentangle three different time-related influences on the need for treatment due to DA: age effects, period effects and cohort effects. METHODS: We constructed our main Swedish national DA database (spanning four decades) by linking healthcare data from the Swedish Hospital Discharge Register to individuals, which included hospitalisations in Sweden for 1975-2010. All hospitalized DA cases were identified by ICD codes. Our Swedish national sample consisted of 3078,129 men and 2921,816 women. We employed a cross-classified multilevel logistic regression model to disentangle any net age, period and cohort effects on DA hospitalization rates. RESULTS: We found distinct net age, period and cohort effects, each influencing the predicted probability of hospitalisation for DA in men and women. Peak age for DA in both sexes was 33-35 years; net period effects showed an increase in hospitalisation for DA from 1996 to 2001; and in birth cohorts 1968-1974, we saw a considerable reduction (around 75%) in predicted probability of hospitalisation for DA. CONCLUSIONS: The use of hospital admissions could be regarded as a proxy of the population's health service use for DA. Our results may thus constitute a basis for effective prevention planning, treatment and other appropriate policy responses.
BACKGROUND: The societal consequences of drug abuse (DA) are severe and well documented, the World Health Organization recommending tracking of population trends for effective policy responses in treatment of DA and delivery of health care services. However, to correctly identify possible sources of DA change, one must first disentangle three different time-related influences on the need for treatment due to DA: age effects, period effects and cohort effects. METHODS: We constructed our main Swedish national DA database (spanning four decades) by linking healthcare data from the Swedish Hospital Discharge Register to individuals, which included hospitalisations in Sweden for 1975-2010. All hospitalized DA cases were identified by ICD codes. Our Swedish national sample consisted of 3078,129 men and 2921,816 women. We employed a cross-classified multilevel logistic regression model to disentangle any net age, period and cohort effects on DA hospitalization rates. RESULTS: We found distinct net age, period and cohort effects, each influencing the predicted probability of hospitalisation for DA in men and women. Peak age for DA in both sexes was 33-35 years; net period effects showed an increase in hospitalisation for DA from 1996 to 2001; and in birth cohorts 1968-1974, we saw a considerable reduction (around 75%) in predicted probability of hospitalisation for DA. CONCLUSIONS: The use of hospital admissions could be regarded as a proxy of the population's health service use for DA. Our results may thus constitute a basis for effective prevention planning, treatment and other appropriate policy responses.
Authors: Kenneth S Kendler; Henrik Ohlsson; Hermine H Maes; Kristina Sundquist; Paul Lichtenstein; Jan Sundquist Journal: Drug Alcohol Depend Date: 2015-01-28 Impact factor: 4.492
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Authors: Shadiya L Moss; Julian Santaella-Tenorio; Pia M Mauro; Katherine M Keyes; Silvia S Martins Journal: Addiction Date: 2018-12-18 Impact factor: 6.526
Authors: Kenneth S Kendler; Henrik Ohlsson; Jan Sundquist; Kristina Sundquist Journal: Am J Med Genet B Neuropsychiatr Genet Date: 2019-03-01 Impact factor: 3.568
Authors: Kenneth S Kendler; Henrik Ohlsson; Jan Sundquist; Kristina Sundquist Journal: Am J Med Genet B Neuropsychiatr Genet Date: 2017-12-15 Impact factor: 3.568