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Additional file 5. Primary end point analyses of mean and sum scores of FSS-9 at baseline and EOT12 Legends: The table displays the mean and sum scores of FSS-9 at baseline and EOT12 among participants who were included in the intention-to-treat and per-protocol analyses, respectively. The FSS-9 sum score ranges from 9 points, no fatigue, to 63 points, worst fatigue. Each item was ranged on a Likert scale from 1 point, no fatigue, to 7 points, worst fatigue. EOT12: 12 weeks after the end of treatment; SD: Standard deviation.

Additional file 2. The US-English and the Norwegian versions of the FSS-9. Legends: FSS-9; Nine-items 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 indicates “strongly agree”.

Additional file 1. Norwegian national HCV treatment guidelines during the study period.

Additional file 7. A linear prediction of changes in FSS-9 sum scores from baseline to EOT12. Legends: The figure displays the linear predictionincluding 95 % confidence intervals of changes in FSS-9 sum scorefrom baseline to EOT12 for integrated and standard HCV treatment groups. EOT12: 12 weeks after the end of HCV treatment; FSS-9: Nine-item fatigue severity scale; HCV: Hepatitis C virus.

Additional file 8. Pen’s parades of FSS-9 sum scores at baseline and EOT12. Legends: The figures display participants who received integrated and standard HCV treatment and were included in the per-protocol analysis. The graphs demonstrate the FSS-9 sum scores at baseline/prior to the HCV treatment and EOT12. The red line represents the FSS-9 sum scores at baseline when the participants are in sorted order by FSS-9 sum scores to highestscore). The blue spikes demonstrate the FSS-9 sum score at EOT12. The length of the spikes mark the changes in the FSS-9 sum score from baseline to EOT12. Participants without spikes did not complete FSS-9 assessment at EOT12. The dotted line demonstrates the cut-off value for severe fatigue. EOT12: 12 weeks after the end of treatment; FSS-9: Nine-item Fatigue Severity Scale.

Additional file 3. The relationship between adults ADHD self-report scale version 1.1, part A (ASRS-A), and clinically diagnosed ADHD (presented by numbers in each category). Legends: ADHD: Attention-deficit/hyperactivity disorder; ASRS-A: The adults ADHD self-report scale version 1.1, part A. ASRS-A + and ASRS-A – were defined as exceeding and not reaching the symptom cutoff for at least four of six questions in the ASRS, part A, respectively. ADHD + and ADHD – were defined as being registered or not registered with a diagnosis of ADHD according to medical records. 1) A total of 10 patients were prescribed central stimulants. 2) A total of one patient was prescribed central stimulants. ASRS-A + and ADHD +: Spearman’s rho = 0.3130. Sensitivity = 29/36 = 0.806; Specificity = 117/189 = 0.619.

Additional file 12. Linear mixed model of the association between sociodemographic factors, injecting drug use, and drug use and FSS-9 sum score. Legends: The table displays a linear mixed model analysis regression of the impact of sociodemographic factors, injecting drug use, and drug use on FSS-9 sum scores at baseline and from baseline to EOT12in the study sample. The FSS-9 sum score ranges from 9 points, no fatigue, to 63 points, worst fatigue. “Educational attainment” was defined as the highest level of education completed. “Injecting substance use” was defined as having injected any substance at least once during the 12 months leading up to the first health assessment. Drug use was categorized according to the use during the past year. Frequent drug use was defined as consuming at least one of the drugs in the five drug classes more than weekly during the year leading up to the first health assessment. Participants who did not use drugs or used them less than weekly during the year were categorized as having “no frequent use of drugs”. Missing values were identified in 1.4% of FSS-9 scores, 1.1 % of educational attainment, 2.5 % of injecting drug use and 3.3 % of drug use at baseline and 29.9% of FSS-9 score at EOT12, and all were handled as “missing at random” and replaced with estimated values using expectation- maximization algorithm. Except for the “achieving SVR” predictor, we kept all the predictor variables constant at the baseline level in predicting changes in the FSS-9 sum scores from baseline to EOT12. To explore whether predictors predicted changes in the FSS-9 score from baseline to EOT12, the interaction between these factors and timeand EOT12) were added. EOT12: 12 weeks after the end of HCV treatment; FSS-9: Nine-item fatigue severity scale; HCV: Hepatitis C virus; SVR: Sustained virological response.

Additional file 2. Longitudinal ordinal multilevel mixed-effect logistic regression analysis of the association of ASRS–memory and –attention (combined outcome variable) with sociodemographic characteristics and substance use at baseline and over time (per year). Legends: ADHD: Attention-deficit/hyperactivity disorder; ASRS-A: The adults ADHD self-report scale version 1.1, part A. 1) The combined outcome variable was generated as a sum score of the responses to ASRS, part A, question 3 (ASRS–memory) and ASRS, part B, question 9 (ASRS–attention), ranged from 0 to 8. Patients who completed one health assessment visit and only responded to one of the two ASRS questions were excluded (n = 35). A total of 666 patients responded to ASRS–memory and attention questions. Of those, 241 had completed two or more occasions, rendering 257 repeated responses to the questions. The correlation between ASRS–memory and –attention questions was 0.39. ASRS–memory: How often do you have problems remembering appointments or obligations? ASRS–attention: How often do you have difficulty concentrating on what people say to you, even when they are speaking to you directly? The responses were answered on a Likert scale ranging from never (0) to very often (4) for each question, which generated a sum score from 0 to 8.

Additional file 4. Distribution of FSS-9 sum scores for integrated HCV treatmentand standard HCV treatmentgroups at baseline. Legends: The two graphsdisplay the FSS-9 sum scores for integrated HCV treatmentand standard HCV treatmentat baseline. The red lines demonstrate the distribution of the FSS-9 sum scores with skewness –0.8and –0.5and kurtoses 2.7 and 2.3. The FSS-9 sum scores ranges from 9 points, no fatigue, to 63 points, worst fatigue.

Additional file 1. US-English and Norwegian versions of the Adult ADHD self-report scale version 1.1, part A, and part B, question 9. Legends: ADHD: Attention-deficit/hyperactivity disorder.

Additional file 6. Linear mixed model of ΔFSS-9 sum score from baseline to EOT12 for integrated HCV treatment (per-protocol) (number of participants = 212, number of observations: 424). Legends: The table displays a linear mixed model analysis (Restricted Maximum Likelihood) regression of the impact of integrated HCV treatment on changes in FSS-9 sum scores (ΔFSS-9 sum scores) from baseline to EOT12 (per-protocol analysis), adjusted for acheiving SVR at EOT12. The FSS-9 sum score ranges from 9 points, no fatigue, to 63 points, worst fatigue. EOT12: 12 weeks after the end of HCV treatment; FSS-9: Nine-item fatigue severity scale.

Additional file 9. Linear mixed model of ΔFSS-9 sum scores from baseline to EOT12 for integrated HCV treatment (intention-totreat, sensitivity analysis without computed data), adjusted for SVR at EOT12 (number of participants = 189, number of observations: 378). Legends: The table displays a linear mixed model analysis (Restricted Maximum Likelihood) regression of the impact of integrated HCV treatment and SVR on changes in FSS-9 sum scores (ΔFSS-9 sum score) from baseline to EOT12 (intention-to-treat analysis without computed data by the expectation–maximization algorithm). The FSS-9 sum score ranges from 9 points, no fatigue, to 63 points, worst fatigue. EOT12: 12 weeks after the end of HCV treatment; FSS-9: Nine-item fatigue severity scale; SVR: Sustained virological response.

Additional file 3. Aspartate aminotransferase to platelet ratio index. Legends: The figure displays the equation to calculate APRI score. AST upper limit of normal range was defined as 45 IU/Land 35 IU/L.

Additional file 10. Linear mixed model of ΔFSS-9 sum scores from baseline to EOT12 for integrated HCV treatment, adjusted for SVR . Legends: The table displays a linear mixed model analysis regression of the impact of integrated HCV treatment and SVR on changes in FSS-9 sum scoresfrom baseline to EOT12. The FSS-9 sum score ranges from 9 points, no fatigue, to 63 points, worst fatigue. EOT12: 12 weeks after the end of HCV treatment; FSS-9: Nine-item fatigue severity scale; SVR: Sustained virological response.

Supplemental material, sj-docx-1-sat-10.1177_11782218221111840 for The Role of Physical Activity in Opioid Substitution Therapy: A Systematic Review of Interventional and Observational Studies by Silvia Eiken Alpers, Einar Furulund, Ståle Pallesen, Asgeir Mamen, Sindre M Dyrstad and Lars Thore Fadnes in Substance Abuse: Research and Treatment

Supplemental material, sj-docx-4-sat-10.1177_11782218221111840 for The Role of Physical Activity in Opioid Substitution Therapy: A Systematic Review of Interventional and Observational Studies by Silvia Eiken Alpers, Einar Furulund, Ståle Pallesen, Asgeir Mamen, Sindre M Dyrstad and Lars Thore Fadnes in Substance Abuse: Research and Treatment

Supplemental material, sj-docx-1-sat-10.1177_11782218221111840 for The Role of Physical Activity in Opioid Substitution Therapy: A Systematic Review of Interventional and Observational Studies by Silvia Eiken Alpers, Einar Furulund, Ståle Pallesen, Asgeir Mamen, Sindre M Dyrstad and Lars Thore Fadnes in Substance Abuse: Research and Treatment

Supplemental material, sj-docx-2-sat-10.1177_11782218221111840 for The Role of Physical Activity in Opioid Substitution Therapy: A Systematic Review of Interventional and Observational Studies by Silvia Eiken Alpers, Einar Furulund, Ståle Pallesen, Asgeir Mamen, Sindre M Dyrstad and Lars Thore Fadnes in Substance Abuse: Research and Treatment

Supplemental material, sj-docx-3-sat-10.1177_11782218221111840 for The Role of Physical Activity in Opioid Substitution Therapy: A Systematic Review of Interventional and Observational Studies by Silvia Eiken Alpers, Einar Furulund, Ståle Pallesen, Asgeir Mamen, Sindre M Dyrstad and Lars Thore Fadnes in Substance Abuse: Research and Treatment

Supplemental material, sj-docx-4-sat-10.1177_11782218221111840 for The Role of Physical Activity in Opioid Substitution Therapy: A Systematic Review of Interventional and Observational Studies by Silvia Eiken Alpers, Einar Furulund, Ståle Pallesen, Asgeir Mamen, Sindre M Dyrstad and Lars Thore Fadnes in Substance Abuse: Research and Treatment

Additional file 1.

Supplemental material, sj-docx-3-sat-10.1177_11782218221111840 for The Role of Physical Activity in Opioid Substitution Therapy: A Systematic Review of Interventional and Observational Studies by Silvia Eiken Alpers, Einar Furulund, Ståle Pallesen, Asgeir Mamen, Sindre M Dyrstad and Lars Thore Fadnes in Substance Abuse: Research and Treatment

Einar Furulund,1– 3 Mitra Bemanian,2,3 Nina Berggren,3 Tesfaye Madebo,2,4 Sara Hydle Rivedal,3 Torgeir Gilje Lid,1,5 Lars Thore Fadnes2,3 1Centre for Alcohol and Drug Research, Stavanger University Hospital, Stavanger, Norway; 2Bergen Addiction Research, Department of Addiction Medicine, Haukeland University Hospital, Bergen, Norway; 3Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway; 4Department of Respiratory Medicine, Stavanger University Hospital, Stavanger, Norway; 5Department of Public Health, University of Stavanger, Stavanger, NorwayCorrespondence: Einar FurulundCentre for Alcohol and Drug Research, Stavanger University Hospital, Stavanger, NorwayTel +4751518000Email Einar.Furulund@sus.noBackground: The role of nutrition and dietary patterns has been widely investigated in cancer, cardiovascular disease, and diabetes, but there are limited data on nutritional impact on COPD. This systematic review (PROSPERO-reg. no: CRD42020172712) aimed to investigate the effect of nutritional interventions on pulmonary and physical function, inflammation, and health-related quality of life among individuals with COPD.Methods: Systematic searches were conducted in Medline, Embase, and Cochrane, resulting in 3861 references. Studies focusing exclusively on inpatient-stays, underweight or obese patients were excluded. Double screening, extraction and bias assessment were conducted. Bias was assessed according to the Cochrane risk of bias tool for randomized controlled trials. Thirteen randomized controlled trials with 916 participants were included.Results: These trials investigated effects of protein supplementation, beetroot juice, increased fruit and vegetable intake, black seed oil, Tualang honey, Chlorella vulgaris-extract, whey-peptide containing nutritional drink, and increased macronutrient intake. The durations of the interventions were from weeks to a few months, and only one with duration > 1 year (investigating increased fruit/vegetable intake). The intervention increasing fruit/vegetables found improvement in pulmonary function tests. Some interventions observed effects on systemic inflammation, health-related quality of life and physical function, although with some mixed results. Five were classified as poor, five as fair, and three as good in terms of risk of bias and quality.Conclusion: Increasing intake of fruits and vegetables over prolonged periods might have positive effects on lung function in individuals with COPD. Some nutritional interventions also observed effects on systemic inflammation, health-related quality of life, and physical function, although with some mixed results. Many of the trials were underpowered, had high dropout rates, or had a high risk of bias. Further research should investigate effect of prolonged dietary interventions.Keywords: nutrition, lung function, inflammation, physical function, health-related quality of life

Additional file 3. The Cronbach’s α if-item-deleted and Item-Total correlation for the FSS-3 and FSS-9, and these scales including the VAFS. FSS-9: Nine-item Fatigue Severity Scale; FSS-3: Three-item Fatigue Severity Scale; VAFS: Visual Analogue Fatigue Scale; No.: Number of patients. *Due to high internal consistency and almost equal Cronbach’s α of the remaining items, we deleted item 8 using clinical judgement. Item 8 could be difficult to answer if patients were affected by substances or were going through substance withdrawals. **We deleted item 9 using clinical judgement due to high internal consistency and almost equal Cronbach’s α in the remaining items and the fact that unemployment was frequently reported among patients with SUDs. Tables a) and c) show the Cronbach’s α if-item-deleted for the FSS-3 and FSS-9, and these scales including the VAFS using data from the first health assessment (baseline) (Table a) and the second health assessment (Table c). Tables b) and d) show the Item-Total correlation for the FSS-3 and FSS-9, and these scales including the VAFS using data from the first health assessment (baseline) (Table b) and the second health assessment (Table d). The values in bold represent the FSS-3’s Cronbach’s α if-item-deleted and Item-Total correlation.

Additional file 1. The number of months from baseline to the second or third measurements. No.: Number of patients; SD: Standard deviation; ref.: Reference. Tables display the number of patients with one, two, and three health assessments, including the Fatigue Severity Scale and the Visual Analog Fatigue Scale measurements. The table displays the time interval between the second and third health assessments and baseline.

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.

Supplemental material, sj-docx-1-sjp-10.1177_14034948211027817 for How the Norwegian population was affected by non-pharmaceutical interventions during the first six weeks of the COVID-19 lockdown by Silje Mæland, Ragnhild Bjørknes, Stine Lehmann, Gro Mjeldheim Sandal, William Hazell, Åsgeir Kjetland Rabben, Øystein Vedaa, Jens Christoffer Skogen and Lars Thore Fadnes in Scandinavian Journal of Public Health

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

BAKGRUNN Forekomsten av rastløse bein er rundt 5–10 % blant voksne i den generelle befolkningen. Få studier er gjort om forekomsten blant pasienter som oppsøker fastlege. Det er også få studier om sammenheng med andre vanlige plager som irritabel tarm, kronisk utmattelse eller kronisk muskel- og ryggsmerte. MATERIALE OG METODE Studien ble gjennomført som en spørreskjemaundersøkelse ved legekontorer på Sør- og Vestlandet høsten 2017 og våren 2018, der pasienter som ventet på time hos fastlegen fortløpende ble invitert til å delta. 2 634 personer deltok (62,2 % kvinner, gjennomsnittsalder 49,6 år). Svarprosenten var 86,8. Rastløse bein ble definert basert på internasjonale kriterier. Assosiasjoner mellom rastløse bein og irritabel tarm, kronisk utmattelse og kronisk muskel- og ryggsmerte ble analysert med khikvadrattester og logistisk regresjon. RESULTATER OG FORTOLKNING Andelen pasienter med rastløse bein var 14,3 %. Blant pasientene med rastløse bein rapporterte 44,8 % at symptomene var moderate til veldig plagsomme, og 85,8 % at de ikke brukte medikamenter for dette. Andelen pasienter med rastløse bein var signifikant høyere blant pasienter med irritabel tarm (21,8 % versus 13,6 %, p = 0,009), kronisk utmattelse (18,2 % versus 13,1 %, p = 0,003) og kronisk muskel- og ryggsmerte (23,2 % versus 12,2 %, p < 0,0005). Fastleger bør være oppmerksomme på at mange pasienter har rastløse bein og at tilstanden er assosiert med andre vanlige plager. HOVEDFUNN Forekomsten av rastløse bein var 14,3 % blant pasienter som oppsøker fastlegen. Rundt halvparten av dem oppga mye og hyppige plager, men et fåtall benyttet medikamentell behandling. Rastløse bein var hyppigere blant pasienter med andre plager som irritabel tarm (justert oddsratio (OR) 1,73), kronisk utmattelse (OR 1,48) og kronisk muskel- og ryggsmerte (OR 2,06), sammenliknet med pasienter uten disse tilstandene. publishedVersion

Supplemental material, sj-docx-1-sjp-10.1177_14034948211027817 for How the Norwegian population was affected by non-pharmaceutical interventions during the first six weeks of the COVID-19 lockdown by Silje Mæland, Ragnhild Bjørknes, Stine Lehmann, Gro Mjeldheim Sandal, William Hazell, Åsgeir Kjetland Rabben, Øystein Vedaa, Jens Christoffer Skogen and Lars Thore Fadnes in Scandinavian Journal of Public Health

Supplemental material, sj-pdf-1-sjp-10.1177_1403494821993714 for Perceived consequences and worries among youth in Norway during the COVID-19 pandemic lockdown by Stine Lehmann, Jens Christoffer Skogen, Ellen Haug, Silje Mæland, Lars Thore Fadnes, Gro Mjeldheim Sandal, Mari Hysing and Ragnhild Bjørknes in Scandinavian Journal of Public Health

Additional file 2 Adjusted linear mixed model for the Substance Use Severity Index (SUSI) for patients with two or more SUSI measurements (N = 170). CI: Confidence Interval; OAT: Opioid Agonist Therapy; cSUSI: Mean Change in Substance Use Severity Index; SUD: Substance Use Disorder. 1) The SUSI is a continuous variable ranging from 0 to 1, where 0 indicates no substance use and 1 indicates daily substance use for all substances (cannabis, amphetamines, cocaine, opioids, benzodiazepines, and alcohol). 2) The value shows the difference in adjusted SUSI between “going to inpatient detoxification” and “not going to inpatient detoxification” at baseline (before detoxification). 3) Interpretation: the cSUSI per year from baseline of a constant predictor and its comparator (e.g., the cSUSI of “ongoing injecting substance use” (constant predictor) and “no ongoing inject substance use” (constant comparator) per year from baseline). 4) Interpretation: the cSUSI of a time-varying predictor and its time-varying comparator per year from baseline (e.g., the cSUSI per year of “going to inpatient detoxification” and “not going to inpatient detoxification” from baseline). The table displays a linear mixed model analysis (Restricted Maximum Likelihood regression) evaluating the impact of inpatient intoxication, injecting substance use, age, and gender on the SUSI at baseline and from baseline (over time) among patients undergoing outpatient SUD treatment with two or more substance use measurements.

Additional file 1. Table S1. Mothers’ OIDP by HIV status, socio-demographic and self-reported clinical variables. Unadjusted and adjusted OLR and negative binomial regression. Table S2. Evaluation of effects of interaction of covariates* with HIV exposure regressed with oral health impacts.

Supplemental material, sj-pdf-1-sjp-10.1177_1403494821993714 for Perceived consequences and worries among youth in Norway during the COVID-19 pandemic lockdown by Stine Lehmann, Jens Christoffer Skogen, Ellen Haug, Silje Mæland, Lars Thore Fadnes, Gro Mjeldheim Sandal, Mari Hysing and Ragnhild Bjørknes in Scandinavian Journal of Public Health

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: Community based breastfeeding promotion programmes have been shown to be effective in increasing breastfeeding prevalence. However, there is limited data on the cost-effectiveness of these programmes in sub-Saharan Africa. This paper evaluates the cost-effectiveness of a breastfeeding promotion intervention targetingmothers and their 0 to 6 month old children. Methods: Data were obtained from a community randomized trial conducted in Uganda between 2006– 2008, and supplemented with evidence from several studies in sub-Saharan Africa. In the trial, peer counselling was offered to women in intervention clusters. In the control and intervention clusters, women could access standard health facility breastfeeding promotion services (HFP). Thus, twomethods of breastfeeding promotion were compared: community based peer counselling (in addition to HFP) and standard HFP alone. A Markov model was used to calculate incremental cost-effectiveness ratios between the two strategies. The model estimated changes in breastfeeding prevalence and disability adjusted life years. Costs were estimated from a provider perspective. Uncertainty around the results was characterized using one-way sensitivity analyses and a probabilistic sensitivity analysis. Findings: Peer counselling more than doubled the breastfeeding prevalence as reported by mothers, but there was no observable impact on diarrhoea prevalence. Estimated incremental cost-effectiveness ratios were USD 68 per month of exclusive or predominant breastfeeding and USD 11,353 per disability adjusted life year (DALY) averted. The findings were robust to parameter variations in the sensitivity analyses Conclusions: Our strategy to promote community based peer counselling is unlikely to be cost-effective in reducing diarrhoea prevalence and mortality in Uganda, because its cost per DALY averted far exceeds the commonly assumed willingness-to-pay threshold of three times Uganda’s GDP per capita (USD 1653). However, since the intervention significantly increases prevalence of exclusive or predominant breastfeeding, it could be adopted in Uganda if benefits other than reducing the occurrence of diarrhoea are believed to be important. publishedVersion

Background: Immediate and exclusive initiation of breastfeeding after delivery has been associated with better neonatal survival and child health and are recommended by the WHO. We report its impact on early infant feeding practices from the PROMISE-EBF trial. Methods: PROMISE-EBF was a cluster randomised behaviour change intervention trial of exclusive breastfeeding (EBF) promotion by peer counsellors in Burkina Faso, Uganda and South Africa implemented during 2006-2008 among 2579 mother-infant pairs. Counselling started in the last pregnancy trimester and mothers were offered at least five postnatal visits. Early infant feeding practices: use of prelacteal feeds (any foods or drinks other than breast milk given within the first 3 days), expressing and discarding colostrum, and timing of initiation of breastfeeding are presented by trial arm in each country. Prevalence ratios (PR) with 95% confidence intervals (95%CI) are given. Results: The proportion of women who gave prelacteal feeds in the intervention and control arms were, respectively: 11% and 36%, PR 0.3 (95% CI 0.2, 0.6) in Burkina Faso, 13% and 44%, PR 0.3 (95% CI 0.2, 0.5) in Uganda and 30% and 33%, PR 0.9 (95% CI 0.6, 1.3) in South Africa. While the majority gave colostrum, the proportion of those who expressed and discarded it in the intervention and control arms were: 8% and 12%, PR 0.7 (95% CI 0.3, 1.6) in Burkina Faso, 3% and 10%, PR 0.3 (95% CI 0.1, 0.6) in Uganda and 17% and 16%, PR 1.1 (95% CI 0.6, 2.1) in South Africa. Only a minority in Burkina Faso (