Search

Additional file 2. Pen’s Parade: SCL-10 = Symptoms checklist 10; ten items scale for measuring mental health status/psychological distress. The figure shows distribution in SCL-10 mean values at baseline (n = 707) by fixed black line. The dotted lines represent the mean reported SCL-10 score of the Norwegian reference population (1.36) and standard reference of 1.85 indicating one or more mental disorders above this cut-off, respectively. Source: Strand BH, Dalgard OS, Tambs K, Rognerud M: Measuring the mental health status of the Norwegian population: a comparison of the instruments SCL-25, SCL-10, SCL-5 and MHI-5 (SF-36). Nordic journal of psychiatry 2003.

Additional File 1. Supplementary tables and figures

Additional file 3. STROBE Statement. Checklist of items that should be included in reports of cohort studies

Additional file 1. STROBE Statement. Checklist of items that should be included in reports of cohort studies.

Additional file 1. STROBE Statement. Checklist of items included in reports of cohort studies.

Additional file 5. Mean (SD) item scores for single items on the FSS-9 at baseline and follow-up. Legend: FSS-9: Nine-item Fatigue Severity Scale; I: Item; OAT: Opioid agonist therapy; SD: Standard Deviation. Follow-up: The FSS-9 score on the last health assessment during the study period among patients with two or more annual health assessments. Mean (SD) item scores for single items on the FSS-9 for patients using methadone or buprenorphine as OAT opioid.

Additional file 3. Aspartate aminotransferase to platelet ratio index (APRI). Description: APRI: Aspartate aminotransferase to platelet ratio index. The figure displays the APRI score equation. The AST upper limit of normal range was defined as 45 IU/L (male) and 35 IU/L (female).

Additional file 4. Potential correlations between sociodemographic and clinical factors and fatigue. The figure shows that potential sociodemographic and clinical comorbidities may affect fatigue among patients with substance use disorders.

Additional file 6. Linear mixed model of fatigue (FSS-9) adjusted for sociodemographic and clinical factors among patients receiving methadone as an OAT opioid at baseline (N = 209). Legends: APRI: Aspartate transaminase to platelet ratio index; CI: Confidence interval; CRP: C-reactive protein; FSS-9: Nine-item Fatigue Severity Scale; eGFR: estimated glomerular filtration rate; HIV: Human Immunodeficiency virus; kPa: Kilopascal; OAT: Opioid Agonist Therapy. 1) Age per 10 years was centred according to mean age (43 years) in the study sample at baseline. 2) Includes amphetamine or cocaine use. The OAT opioid ratio: a ratio between the received dose of OAT opioids per day divided by the expected mean dose of OAT opioids (buprenorphine 18 mg, buprenorphine-naloxone 18/4.5 mg or methadone 90 mg). The educational level: highest level of education was coded 0–4 with 4 as the highest educational level. Unstable housing situation: living on the street, homeless shelter, or with family and friends at any time in the past 30 days prior to the health assessment. Debt difficulties: struggling with repaying current illegal and legal debt. Injecting substance use: Having injected a substance in the past 12 months prior to the health assessment. Frequent use of substances: at least weekly during the past 12 months prior to the health assessment. Viral load of HCV: From − 0.5 to 0.5, where the range ≥ − 0.5 to 0 identifies the high viral load (HCV PCR ≥ 800,000 IE/ml). Zero (0) defined patients without HCV infection. When using the Bonferroni corrected p-values (αaltered = 0.05 / 43 = 0.0012), the predictors did not affect the FSS-9 sum score significantly. The table displays a linear mixed model analysis (Restricted Maximum Likelihood regression) evaluating sociodemographic and clinical factors’ (predictors) changes in the FSS-9 sum score at baseline and their influence on changes in the FSS-9 sum score (time trend) per year from baseline among patients receiving methadone as an OAT opioid. The predictors’ effect estimates and time trends estimate adjusted mean differences in the FSS-9 sum score.

Additional file 4. Equations to calculate diazepam equivalents. Legends: Equations used to calculate lowest, mean, and highest equipotency dose of included benzodiazepines and z-hypnotics.

Additional file 2. The US-English and the Norwegian versions of FSS-9. Description: Legends: FSS-9; Nine-item Fatigue Severity Scale. All items in the FSS-9 are ranged as a Likert scale from 1 to 7, where 1 indicates “strongly disagree” and 7 “strongly agree”.

Additional file 3. Opioid agonist therapy and hepatitis C treatment in Norway and Sweden

Additional file 1. The number of months from baseline to the second or third health assessment. No.: Number of patients; SD: Standard deviation; ref.: Reference. The table displays the number of patients with one, two, and three health assessments, including a nine-item Fatigue Severity Scale score. The table displays the time between baseline and the second and third health assessments.

Additional file 7. Linear mixed model of fatigue (FSS-9) adjusted for sociodemographic and clinical factors among patients receiving buprenorphine as an OAT opioid at baseline (N = 321). Legends: APRI: Aspartate transaminase to platelet ratio index; CI: Confidence interval; CRP: C-reactive protein; FSS-9: Nine-item Fatigue Severity Scale; eGFR: estimated glomerular filtration rate; HIV: Human Immunodeficiency virus; kPa: Kilopascal; OAT: Opioid Agonist Therapy. 1) Age per 10 years was centred according to mean age (43 years) in the study sample at baseline. 2) Includes amphetamine or cocaine use. The OAT opioid ratio: a ratio between the received dose of OAT opioids per day divided by the expected mean dose of OAT opioids (buprenorphine 18 mg, buprenorphine-naloxone 18/4.5 mg or methadone 90 mg). The educational level: highest level of education was coded 0–4 with 4 as the highest educational level. Unstable housing situation: living on the street, homeless shelter, or with family and friends at any time during the past 30 days prior to the health assessment. Debt difficulties: struggling with repaying current illegal and legal debt. Injecting substance use: Having injected a substance during the past 12 months prior to the health assessment. Frequent use of substances: at least weekly during the past 12 months prior to the health assessment. Viral load of HCV: From − 0.5 to 0.5, where the range ≥ − 0.5 to 0 identifies the high viral load (HCV PCR ≥ 800,000 IE/ml). Zero (0) defined patients without HCV infection. When using the Bonferroni corrected p-values (αaltered = 0.05 / 43 = 0.0012), the predictors did not affect the FSS-9 sum score significantly. The table displays a linear mixed model analysis (Restricted Maximum Likelihood regression) evaluating sociodemographic and clinical factors’ (predictors) changes in the FSS-9 sum score at baseline and their influence on changes in the FSS-9 sum score (time trend) per year from baseline among patients receiving buprenorphine as an OAT opioid. The predictors’ effect estimates and time trends estimate adjusted mean differences in the FSS-9 sum score.

Additional file 1. Methodology description: estimating chronic hepatitis C (HCV) prevalence among people on opioid agonist therapy (OAT) in Norway and Sweden

Background: The use of antiseizure medications (ASMs) in the pediatric population is poorly studied. The purpose of this study was to investigate changes in the use of ASMs in children and adolescents compared to adults, and to elucidate safety considerations of certain drugs. Method: In this population-based pharmacoepidemiological study we used the Norwegian Prescription Database (NorPD), 2009–2018. The use of ASMs is presented as 1-year prevalence per 1000: number of ASM users in a year * 1000 / number of inhabitants that year. Variables included predetermined 5-year age groups, gender, ASMs, diagnosis-specific reimbursement codes, user, and population numbers. Selected ASMs used for specific indications or subgroups included ethosuximide, sulthiame, rufinamide, stiripentol, and clobazam. Gender differences in the use of valproate was examined due to safety considerations in girls/women. Results: The total number of ASM users (all indications) for the age groups 0–19 and 20–59 years was 5807 and 47,481 respectively in 2009, and 5906 and 61,447 respectively in 2018. The 1-year prevalence for children/ adolescents (0–19 years) using ASMs in epilepsy remained stable from 2008 to 2018, 4.3–4.2/1000 inhabitants, as compared to 8.2–7.6/1000 in adults (20–59 years). Valproate, lamotrigine, and levetiracetam were the three most used ASMs in epilepsy in children/adolescents, similar to adults. The selected ASMs were mainly used in children/ adolescents, accounting for 0.74/1000 in 2018 versus 0.17/1000 in adults. A significant increase was seen for sulthiame (8-fold), ethosuximide (4-fold), clobazam (3-fold), and stiripentol (2-fold). The use of ASMs in non-epilepsy indications was limited and stable (17% in 2018); mainly lamotrigine in psychiatry in adolescents (15–19 years). This finding was in contrast to extensive non-epilepsy use in adults (71% in 2018). Conclusion: Changes in the use of ASMs in children/adolescents differ as compared to adults, most notably extensive and increasing use of selected ASMs and limited non-epilepsy. This is an important part of pharmacovigilance and patient safety evaluations.

Table S1 QOL10 item responses among 531 patients entering substance use disorder treatment, N(%) (DOCX 14 kb)

Table S2 QOL10 item-total correlations (DOCX 14 kb)

ORs and 95 % CIs for the association between nighttime road traffic noise (Lnight) and sleep medication use by 5 dB increase in noise level. (DOCX 24 kb)

Simplified directed acyclic graph for the association between road traffic noise and sleep medication use. Some variables have been grouped for legibility. a Includes the variables age, sex, and having children â ¤ 5 years b Includes the variables education and household income c Includes the variables smoking status, alcohol use, caffeine use, physical activity, and night shift work. (DOCX 64 kb)

Introduction and Aims: Crime rates are high in substance‐using populations; therefore, investigation of factors associated with crime in these populations is highly relevant. We describe crime prevalence and associations between crime, pattern of substance use and psychosocial factors, such as self‐control and social network. Design and Methods: This is a cross‐sectional study including substance users (n = 549; mean age 34 years; 27% women) entering treatment at 21 treatment centres across Norway (December 2012 to April 2015). Data on demographics, substance use, psychosocial variables and crime in the 6 months prior to treatment were obtained through interviews. Adjusted odds ratios (aOR) with 95% confidence intervals (CI) were estimated through logistic regression. Results: Sixty‐four percent of participants had committed crime in the 6 months prior to treatment. Of these, 93% committed income‐generating crime. Several factors were associated with increased likelihood of having committed crime: use of stimulants (aOR = 1.82, 95% CI 1.04–3.17), use of a higher number of different substances (aOR = 1.16, 95% CI 1.04–1.31) and spending most of their time with family or friends using addictive substances (aOR = 2.38, 95% CI 1.10–5.16 and aOR = 2.22, 95% CI 1.32–3.73). Protective factors associated with decreased likelihood of committing crime were being older (aOR = 0.95, 95% CI 0.92–0.97) and having higher self‐control (aOR = 0.94, 95% CI 0.91–0.97). Discussion and Conclusions: Stimulant use, higher number of different substances used, lower self‐control, primarily a substance‐using social network and being younger were associated with crime in this substance‐using population. Treatment clinics should consider these risk factors for crime, and suitable interventions should be implemented and evaluated. This research has been published in Drug and Alcohol Review. © 2016 Wiley

Shows the questions concerning medication use from the Norwegian Mother and Child cohort study questionnaire Q4 (6 months of age) and Q5 (18 months of age). (DOCX 971 kb)

Shows the questions concerning medication use from the Norwegian Mother and Child cohort study questionnaire Q4 (6 months of age) and Q5 (18 months of age). (DOCX 971 kb)

Bakgrunn: I 2008 ble det gjort endringer i blåreseptordningen slik at pasienter med kroniske sterke smerter kunne få forskrevet smertestillende legemidler av spesialist gjennom blåreseptordningen med refusjonskode -71 (kroniske, sterke smerter). I 2016 ble blåreseptforskriften endret slik at også fastlegene kunne initiere søknad om individuell refusjon og forskrive opioider på blå resept til pasienter med kroniske sterke smerter. Fastleger kan nå søke om individuell refusjon for opioider for behandling med opptil 100 mg orale morfinekvivalenter (OMEQ) per døgn. Leger ved tverrfaglige smerte-klinikker kan søke om å forskrive daglige mengder over dette, med et øvre tak på 300 mg OMEQ. Hensikt: Studere om retningslinjer hos pasienter med kroniske, sterke smerter som bruker opioider på blå resept med refusjonskode -71 (kroniske, sterke smerter) blir fulgt. Metode: Reseptregisteret inneholder informasjon om alle legemidler som er forskrevet og utlevert fra apotek og mengde legemiddel utlevert registreres i definerte døgndoser (DDD). Personer i Reseptregisteret som i løpet av året fikk minst én opioidutlevering (ATC-gruppe N02A) på blå resept med refusjonskode -71 er definert som studiepopulasjon. Antall mg orale morfinekvivalenter (OMEQ) er beregnet ut fra antall definerte døgndoser (DDD) på ordinasjonsnivå for alle utleveringer (på varenummernivå) brukerne fikk i 2018. Morfin ekvianalgetisk ratio, som angitt i tabellen til Helfo er brukt i beregningene. Videre ble total mengde OMEQ utlevert per år aggregert på individnivå. Deretter ble antall pasienter som fikk doser på over 300 mg OMEQ per døgn i 2018 beregnet. Resultater: I denne studien var det totalt 17 383 pasienter ≥18 år som hentet ut minst ett opioid på blå resept med refusjonskode -71 (kroniske, sterke smerter) i 2018, som utgjør rundt 0,41 % av Norges befolkning. Av disse var 11 661 (67 %) kvinner og gjennomsnittsalderen for de 17 383 pasientene var på 62 år. Det var 331 pasienter som hadde en døgndose som var over 300 mg orale morfinekvivalenter per døgn på blå resept. 75 % av de 17 383 pasientene hadde også hentet ut minst ett opioid på hvit resept. Ved å legge til det som også var hentet ut av opioider på hvit resept, var det 631 pasienter som hadde en dose på over 300 mg orale morfinekvivalenter per døgn. 22,5 % av de 17 383 pasientene hentet ut sitt første opioid på blå resept med refusjonskode -71 i 2018. Konklusjon: Sett i forhold til hvor mange av Norges befolkning som oppgir å ha langvarige smerter, er det et lite antall personer som er del av blåreseptordningen ved bruk av opioider for kroniske, sterke smerter (-71). Det var en liten andel av disse pasientene som brukte en døgndose som var på mer enn det som dekkes i blåreseptordningen, men noen av pasientene brukte svært høye og urovekkende doser. Dette er pasienter som kan ha utviklet avhengighet og som kan ha behov for behandling av spesialister i rusmedisin. Background: In 2008, changes were made to the reimbursement regimen with reimbursement code -71 (strong, chronic pain), enabling patients with strong, chronic pain to get analgesic drugs prescribed by a specialist. In 2016, the reimbursement regulation was changed to also enable General Practitioners (GPs) to apply for individual reimbursement of opioids to patients with strong chronic pain. GPs can now apply for individual reimbursement for treatment up to 100 mg oral morphine equivalents (OMEQ) daily. Physicians at interdisciplinary pain clinics can apply for prescribing daily dosages above this, with a ceiling at 300 mg OMEQ. Purpose: To study whether the policies are followed in the use of reimbursed opioids with reimbursement code -71 (strong chronic pain) for patients with strong chronic pain. Method: The Norwegian Prescription Database (NorPD) contains information regarding all drugs prescribed and filled by pharmacies. The number of drugs filled is registered in defined daily dose (DDD). Individual registered in the Prescription Database who received reimbursed opioids with reimbursement code -71 at least once during the year (ATC group N02A), are defined as the population of this study. The amount of mg OMEQ is calculated based on the number of DDD at ordination level for all user prescriptions filled in 2018 (at article number level). Morphine equianalgesic dose ratios, specified in Helfo’s table, are used in the calculations. Next, the total amount of OMEQ filled each year was aggregated at individual level. Lastly, the number of patients receiving dosages above 300 mg OMEQ daily in 2018 was calculated. Results: A total of 17 383 patients ≥18 years old filled at least one reimbursed opioid with reimbursement code -71 in 2018. This makes up approximately 0.41 % of Norway’s population. Their mean age was 62 years. Of these, 11 661 (67 %) were women. 331 patients had a DDD above 300 mg OMEQ. 75 % of the patients also filled at least one non-reimbursed opioid prescription during the year. Adding both reimbursed and non-reimbursed opioids, it was found that 631 patients had a DDD above 300 mg OMEQ. Of the 17 383 patients, 22.5 % filled their first reimbursed opioid with reimbursement code -71 in 2018. Conclusion: Considering the proportion of Norway’s population that are stating that they experience prolonged pain, only a small number is part of the opioid reimbursement regimen for strong chronic pain. A small proportion of these patients had a DDD above what can be received through the reimbursement regimen, and some patients had disturbingly high dosages. These patients may have developed an addiction and could need treatment from addiction specialists.