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Bayesian inference for generalized linear mixed models (GLMM) is appealing, but its widespread use has been hampered by the lack of a fast implementation tool and the difficulty in specifying prior distributions. In this paper, we conduct an extensive simulation study to evaluate the performance of INLA for estimation of the hierarchical Poisson regression models with overdispersion in comparison with JAGS and Stan while assuming a variety of prior specifications for variance components. Further, we analysed the influence of different factors such as small number of observations per cluster, different values of the cluster variance and estimation from a misspecified model. A simulation study has shown that the approximation strategy employed by INLA is accurate in general and that all software leads to similar results for most of the cases considered. Estimation of the variance components, however, is difficult when their true value is small for all estimation methods and prior specifications. The estimates obtained for all software tend to be biased downward or upward depending on the assumed priors.

BACKGROUND Sleep disturbances and poor health-related quality of life (HRQoL) are common in people with rheumatoid arthritis (RA). Sleep disturbances, such as less total sleep time, more waking periods after sleep onset, and higher levels of nonrestorative sleep, may be a driver of HRQoL. However, understanding whether these sleep disturbances reduce HRQoL has, to date, been challenging because of the need to collect complex time-varying data at high resolution. Such data collection is now made possible by the widespread availability and use of mobile health (mHealth) technologies. OBJECTIVE This mHealth study aimed to test whether sleep disturbance (both absolute values and variability) causes poor HRQoL. METHODS The quality of life, sleep, and RA study was a prospective mHealth study of adults with RA. Participants completed a baseline questionnaire, wore a triaxial accelerometer for 30 days to objectively assess sleep, and provided daily reports via a smartphone app that assessed sleep (Consensus Sleep Diary), pain, fatigue, mood, and other symptoms. Participants completed the World Health Organization Quality of Life-Brief (WHOQoL-BREF) questionnaire every 10 days. Multilevel modeling tested the relationship between sleep variables and the WHOQoL-BREF domains (physical, psychological, environmental, and social). RESULTS Of the 268 recruited participants, 254 were included in the analysis. Across all WHOQoL-BREF domains, participants’ scores were lower than the population average. Consensus Sleep Diary sleep parameters predicted the WHOQoL-BREF domain scores. For example, for each hour increase in the total time asleep physical domain scores increased by 1.11 points (β=1.11, 95% CI 0.07-2.15) and social domain scores increased by 1.65 points. These associations were not explained by sociodemographic and lifestyle factors, disease activity, medication use, anxiety levels, sleep quality, or clinical sleep disorders. However, these changes were attenuated and no longer significant when pain, fatigue, and mood were included in the model. Increased variability in total time asleep was associated with poorer physical and psychological domain scores, independent of all covariates. There was no association between actigraphy-measured sleep and WHOQoL-BREF. CONCLUSIONS Optimizing total sleep time, increasing sleep efficiency, decreasing sleep onset latency, and reducing variability in total sleep time could improve HRQoL in people with RA.

Background Elective total knee replacement (TKR) is a safe and cost-effective surgical procedure for treating severe knee osteoarthritis (OA). Although complications following surgery are rare, prediction tools could help identify those patients who are at particularly high risk who could then be targeted with preventative interventions. We aimed to develop a simple model to help inform treatment choices. Methods We trained and externally validated adverse event prediction models for patients with TKR using electronic health records (EHR) and claims data from the US (OPTUM, CCAE, MDCR, and MDCD) and general practice data in the UK (IQVIA Medical Research Database ([IMRD], incorporating data from The Health Improvement Network [THIN], a Cegedim database). The target population consisted of patients undergoing a primary TKR, aged ≥40 years and registered in any of the contributing data sources for ≥1 year before surgery. LASSO logistic regression models were developed for four adverse outcomes: post-operative (90-day) mortality, venous thromboembolism (VTE), readmission, and long-term (5-year) revision surgery. A second model was developed with a reduced feature set to increase interpretability and usability. Findings A total of 508,082 patients were included, with sample size per data source ranging from 1,853 to 158,549 patients. Overall, 90-day mortality, VTE, and readmission prevalence occurred in a range of 0.20%-0.32%, 1.7%-3.0% and 2.2%-4.8%, respectively. Five-year revision surgery was observed in 1.5%-3.1% of patients. The full model predicting 90-day mortality yielded AUROC of 0.78 when trained in OPTUM and yielded an AUROC of 0.70 when externally validated on THIN. We then developed a 12 variable model which achieved internal AUROC of 0.77 and external AUROC of 0.71 in THIN. The discriminative performances of the models predicting 90-day VTE, readmission, and 5-year revision were consistently poor across the datasets (AUROC Interpretation We developed and externally validated a simple prediction model based on sex, age, and 10 comorbidities that can identify patients at high risk of short-term mortality following TKR. Our model had a greater discriminative ability than the Charlson Comorbidity Index in predicting 90-day mortality. The 12-feature mortality model is easily implemented and the performance suggests it could be used to inform evidence based shared decision-making prior to surgery and for appropriate precautions to be taken for those at high risk. The other outcomes examined had low performance. Funding This activity under the European Health Data & Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. The sponsor of the study did not have any involvement in the writing of the manuscript or the decision to submit it for publication. The research was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). DPA is funded by a National Institute for Health Research Clinician Scientist award (CS-2013-13-012). TDS is funded by the Department of Health of the Generalitat de Catalunya under the Strategic Plan for Research and Innovation in Health (PERIS; SLT002/16/00308). The views expressed in this publication are those of the authors and not those of the NHS, the National Institute for Health Research or the Department of Health. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. Key Points Question Is it possible to predict adverse events following total knee replacement? Findings Mortality was the only adverse event studied that we were able to predict with adequate performance. We produced a 12 variable prediction model for 90-day post-operative mortality that achieved an AUROC of 0.77 on internal test validation (Optum) and 0.71 when externally validated in THIN. The model also showed adequate calibration. Meaning Patients can now be presented with an accurate risk assessment for short term mortality such that they are well-informed before the decision for surgery is taken. Importance Total Knee Replacement is generally a safe, effective procedure that is performed on thousands of patients each year. However, a small number of those patients will experience adverse events. Due to the surgery’s elective nature, a well calibrated, high performing risk model could pre-emptively inform the patient and clinician decision making process and help to guide preventative treatment.

Background To better understand the profile of individuals with severe coronavirus disease 2019 (COVID-19), we characterised individuals hospitalised with COVID-19 and compared them to individuals previously hospitalised with influenza. Methods We report the characteristics (demographics, prior conditions and medication use) of patients hospitalised with COVID-19 between December 2019 and April 2020 in the US (Columbia University Irving Medical Center [CUIMC], STAnford Medicine Research data Repository [STARR-OMOP], and the Department of Veterans Affairs [VA OMOP]) and Health Insurance Review & Assessment [HIRA] of South Korea. Patients hospitalised with COVID-19 were compared with patients previously hospitalised with influenza in 2014–19. Results 6,806 (US: 1,634, South Korea: 5,172) individuals hospitalised with COVID-19 were included. Patients in the US were majority male (VA OMOP: 94%, STARR-OMOP: 57%, CUIMC: 52%), but were majority female in HIRA (56%). Age profiles varied across data sources. Prevalence of asthma ranged from 7% to 14%, diabetes from 18% to 43%, and hypertensive disorder from 22% to 70% across data sources, while between 9% and 39% were taking drugs acting on the renin-angiotensin system in the 30 days prior to their hospitalisation. Compared to 52,422 individuals hospitalised with influenza, patients admitted with COVID-19 were more likely male, younger, and, in the US, had fewer comorbidities and lower medication use. Conclusions Rates of comorbidities and medication use are high among individuals hospitalised with COVID-19. However, COVID-19 patients are more likely to be male and appear to be younger and, in the US, generally healthier than those typically admitted with influenza.

Objectives Patients with RA are frequently treated with glucocorticoids (GCs), but evidence is conflicting about whether GCs are associated with hypertension. The aim of this study was to determine whether GCs are associated with incident hypertension in patients with RA. Methods A retrospective cohort of patients with incident RA and without hypertension was identified from UK primary care electronic medical records (Clinical Practice Research Datalink). GC prescriptions were used to determine time-varying GC use, dose and cumulative dose, with a 3 month attribution window. Hypertension was identified through either: blood pressure measurements >140/90 mmHg, or antihypertensive prescriptions and a Read code for hypertension. Unadjusted and adjusted Cox proportional hazards regression models were fitted to determine whether there was an association between GC use and incident hypertension. Results There were 17 760 patients in the cohort. A total of 7421 (42%) were prescribed GCs during follow-up. The incident rate of hypertension was 64.1 per 1000 person years (95% CI: 62.5, 65.7). The Cox proportional hazards model indicated that recent GC use was associated with a 17% increased hazard of hypertension (hazard ratio 1.17; 95% CI: 1.10, 1.24). When categorized by dose, only doses above 7.5 mg were significantly associated with hypertension. Cumulative dose did not indicate a clear pattern. Conclusion Recent GC use was associated with incident hypertension in patients with RA, in particular doses ≥7.5 mg were associated with hypertension. Clinicians need to consider cardiovascular risk when prescribing GCs, and ensure blood pressure is regularly monitored and treated where necessary.

Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19., Detailed knowledge of the characteristics of COVID-19 patients helps with public health planning. Here, the authors use routinely-collected data from seven databases in three countries to describe the characteristics of >30,000 patients admitted with COVID-19 and compare them with those admitted for influenza in previous years.

Background Physician prescribing behaviour has been described as a key driver of rising opioid prescriptions and long-term opioid use. However, the effect of prescribers requires interpretation within context. No studies have investigated the extent to which regions, practices, prescribers, vary in opioid prescribing by considering this hierarchy together, whilst accounting for case-mix. Objectives: (i)quantify and identify risk factors for the transition from new-users to long-term opioid users (ii) quantify variation of long-term use attributed to region, practice, prescriber, accounting for patient mix and chance variation. Methods We conducted a retrospective observational study between 2006-2017 using Clinical Practice Research Datalink. New users of opioids, ≥18 years, without cancer were identified. Long-term opioid use was defined as ≥ 3 opioid prescriptions within a 90-day period from index date, or ≥ 1 opioid prescription lasting at least 90-days in the first year. A multi-level random-effects logistic regression model was used to examine the association of patient characteristics with the odds of becoming a long-term opioid-user. To examine variation in opioid use among prescribers, GP-practices and region after adjusting for case-mix, we used a nested random-effect structure. A ‘high-risk’ region, prescriber or practice was defined as those where the entire adjusted 95% CI lay above population average. Results 1,968,742 new opioid users were included; 14.6% transitioned to long-term use. In the fully-adjusted model, factors associated with higher-odds of long-term use included high morphine-milligram equivalents (MME)/day at first prescription, older-age, deprivation, fibromyalgia, rheumatological conditions and prior surgery (Table 1). After adjustment for case-mix, the North-West, Yorkshire and South-West were found to be high-risk regions for long-term use. 103 practices (25.6%) and 540 prescribers (3.5%) were associated with a significantly higher risk of long-term use. The odds of becoming a long-term user for patients belonging to these prescribers reached up to > 3.5 times than the population average. Conclusion Prescribing factors, age, deprivation and conditions including fibromyalgia and rheumatological conditions were associated with higher odds of long-term opioid use. In the first UK study evaluating long-term opioid prescribing with patient-level characteristics adjustment, variation in regions, especially practices and prescribers were observed. Our findings support greater calls for action to reduce practice/prescriber variation by promoting safe practice in opioid-prescribing. Disclosures M. Jani: None. B. Birlie-Yimer: None. T. Sheppard: None. M. Lunt: None. W.G. Dixon: None.

Background Oral glucocorticoids (GC) are frequently prescribed to patients with rheumatoid arthritis (RA), however GC use is associated with several potential side effects. Hypertension is cited as a possible side effect, but few studies have specifically investigated GC-associated hypertension in patients with RA with conflicting results. The aim of this study was to determine whether GCs were associated with an increased risk of incident hypertension in a cohort of patients with RA. Methods A retrospective cohort of patients with incident RA and no hypertension at RA diagnosis were identified from UK primary care electronic health records (Clinical Practice Research Datalink). GC prescriptions were used to determine time-varying GC use and dose, categorised as: no use, >0-4.9 mg/day, 5-7.4 mg/day, 7.5-14.9 mg/day, ≥15mg/day. A 3-month risk attribution model was used where patients continued to remain at risk for 3 months after the end of prescriptions. Hypertension was identified if a patient had either: 1) 2 consecutive systolic blood pressure (BP) measurements >140mmHg within a year, 2) 2 consecutive diastolic BP measurements >90mmHg within a year or 3) antihypertensive prescriptions on at least two occasions and a Read code for hypertension. Unadjusted and adjusted Cox proportional hazards (PH) regression models were fitted to determine if there was an association between GC use and hypertension. Results There were 17,760 patients with incident RA and no hypertension. The cohort had a mean age of 56.3 ± 12.7 years and were predominantly female (68%). 7,421 (42%) were prescribed GCs during follow-up. There were 6,243 cases of incident hypertension. The Cox PH model indicated that recent GC use was associated with a 17% increased hazard of hypertension (hazard ratio: 1.17 (95% CI 1.10 to 1.24)). When categorised by dose, only doses above 7.5mg were significantly associated with hypertension (Table 1). Conclusion In this large cohort of patients with RA and without hypertension, recent GC use was associated with incident hypertension. Doses ≥7.5mg were associated with hypertension while the association with lower doses was inconclusive. Clinicians need to consider cardiovascular risk when prescribing GCs and ensure BP is regularly monitored. Disclosures R.E. Costello None. M. Jani None. B.B. Yimer None. W.G. Dixon None.

Background Smartphone location data can be used for observational health studies (to determine participant exposure or behavior) or to deliver a location-based health intervention. However, missing location data are more common when using smartphones compared to when using research-grade location trackers. Missing location data can affect study validity and intervention safety. Objective The objective of this study was to investigate the distribution of missing location data and its predictors to inform design, analysis, and interpretation of future smartphone (observational and interventional) studies. Methods We analyzed hourly smartphone location data collected from 9665 research participants on 488,400 participant days in a national smartphone study investigating the association between weather conditions and chronic pain in the United Kingdom. We used a generalized mixed-effects linear model with logistic regression to identify whether a successfully recorded geolocation was associated with the time of day, participants’ time in study, operating system, time since previous survey completion, participant age, sex, and weather sensitivity. Results For most participants, the app collected a median of 2 out of a maximum of 24 locations (1760/9665, 18.2% of participants), no location data (1664/9665, 17.2%), or complete location data (1575/9665, 16.3%). The median locations per day differed by the operating system: participants with an Android phone most often had complete data (a median of 24/24 locations) whereas iPhone users most often had a median of 2 out of 24 locations. The odds of a successfully recorded location for Android phones were 22.91 times higher than those for iPhones (95% CI 19.53-26.87). The odds of a successfully recorded location were lower during weekends (odds ratio [OR] 0.94, 95% CI 0.94-0.95) and nights (OR 0.37, 95% CI 0.37-0.38), if time in study was longer (OR 0.99 per additional day in study, 95% CI 0.99-1.00), and if a participant had not used the app recently (OR 0.96 per additional day since last survey entry, 95% CI 0.96-0.96). Participant age and sex did not predict missing location data. Conclusions The predictors of missing location data reported in our study could inform app settings and user instructions for future smartphone (observational and interventional) studies. These predictors have implications for analysis methods to deal with missing location data, such as imputation of missing values or case-only analysis. Health studies using smartphones for data collection should assess context-specific consequences of high missing data, especially among iPhone users, during the night and for disengaged participants.

Feeding ecology of the African civet, Civettictis civetta (Schreber, 1778), was studied in selected coffee forest habitat for two seasons between December 2012 and October 2013. The study was conducted in Limu Seka district, south-west Ethiopia. Faecal analysis was employed to assess the diet spectrum, seasonal abundance and relative importance of food items. Over 55 different food items were identified from the analysis of 387 scat samples collected during the dry and wet seasons. In the coffee forest habitat, African civets showed omnivorous feeding habit with plant-to-animal diet biomass ratio of 1 : 1.36. A slight diet specialization was observed during the wet season (BA = 0.46) favouring animal prey. However, during the dry season, they showed generalist feeding habit (BA = 0.87) with more plant biomass in their diet. With over 64% occurrence and 14.4% biomass, coffee berries significantly contributed to the civets dry season plant diets. The excreted coffee beans, after civets ingested ripe coffee berries, are the tastiest product used by farmers for consumption and market. Seasonal collection of civet coffee from coffee forest floor economically supports the farmers while increasing the importance of civets in the habitat and hence contributing for its conservation.

Background There is uncertainty around whether to use unicompartmental knee replacement (UKR) or total knee replacement (TKR) for individuals with osteoarthritis confined to a single compartment of the knee. We aimed to emulate the design of the Total or Partial Knee Arthroplasty Trial (TOPKAT) using routinely collected data to assess whether the efficacy results reported in the trial translate into effectiveness in routine practice, and to assess comparative safety. Methods We did a population-based network study using data from four US and one UK health-care database, part of the Observational Health Data Sciences and Informatics network. The inclusion criteria were the same as those for TOPKAT; briefly, we identified patients aged at least 40 years with osteoarthritis who had undergone UKR or TKR and who had available data for at least one year prior to surgery. Patients were excluded if they had evidence of previous knee arthroplasty, knee fracture, knee surgery (except diagnostic), rheumatoid arthritis, infammatory arthropathies, or septic arthritis. Opioid use from 91–365 days after surgery, as a proxy for persistent pain, was assessed for all participants in all databases. Postoperative complications (ie, venous thromboembolism, infection, readmission, and mortality) were assessed over the 60 days after surgery and implant survival (as measured by revision procedures) was assessed over the 5 years after surgery. Outcomes were assessed in all databases, except for readmission, which was assessed in three of the databases, and mortality, which was assessed in two of the databases. Propensity score matched Cox proportional hazards models were fitted for each outcome. Calibrated hazard ratios (cHRs) were generated for each database to account for observed differences in control outcomes, and cHRs were then combined using meta-analysis. Findings 33 867 individuals who received UKR and 557 831 individuals who received TKR between Jan 1, 2005, and April 30, 2018, were eligible for matching. 32 379 with UKR and 250 377 with TKR were propensity score matched and informed the analyses. UKR was associated with a reduced risk of postoperative opioid use (cHR from meta-analysis 0·81, 95% CI 0·73–0·90) and a reduced risk of venous thromboembolism (0·62, 0·36–0·95), whereas no difference was seen for infection (0·85, 0·51–1·37) and readmission (0·79, 0·47–1·25). Evidence was insufficient to conclude whether there was a reduction in risk of mortality. UKR was also associated with an increased risk of revision (1·64, 1·40–1·94). Interpretation UKR was associated with a reduced risk of postoperative opioid use compared with TKR, which might indicate a reduced risk of persistent pain after surgery. UKR was associated with a lower risk of venous thromboembolism but an increased risk of revision compared with TKR. These findings can help to inform shared decision making for individuals eligible for knee replacement surgery.

Background: The opioid epidemic in the U.S. has led to similar concerns about prescribed opioids in countries within Europe. In new users, the rate of escalation to more potent opioids is likely to contribute to long-term prescriptions, which in turn may be associated with opioid dependency, addiction and overdose. The scale of such escalation however is unclear in the U.K for non-cancer pain. Objectives: We sought to: (i) describe trends of prescribed opioids for non-cancer pain in the UK primary care setting over a 10-year period (ii) assess the sequential transition of opioid strength from index date over a 2-year period. Methods: We conducted a retrospective observational study over a 10-year period from 1/1/2006 to 31/12/2015 using the Clinical Practice Research Datalink (CPRD). CPRD collects de-identified patient data from a network of GP practices across the UK. New users of opioids, 18 years or over, without cancer in the 2 years prior to index date were included. The number of prescriptions for each drug were calculated by each calendar year accounting for the number of eligible patients registered in CPRD for that year. Sunburst plots were created to evaluate the sequential transition of opioids over time. A 4-state hidden Markov model was used to estimate the transition probability for individuals escalating to more potent opioids over a 2 year period. States were defined as (i) no drug (ii) weak opioid (codeine, dihydrocodeine) (iii) moderate opioid (tramadol) (iii) strong opioid (all others in CPRD). Methadone prescriptions were excluded for the purposes of this analysis. Results: 1,026,955 opioid users were included: mean age (SD) was 55(18) years; 58% being female. New users of opioids were most commonly prescribed codeine (n=723,102; 70.8%), followed by dihydrocodeine (n=179,831; 17.6%), tramadol (n=93,338, 9.1%) with n=24,808 (2.4%) strong opioid prescriptions. The rate of prescribing strong opioids/10,000 population increased 12 fold from 2006-2013, followed by a gradual decline till 2015 (Figure 1). This trend was most marked with certain opioids: morphine, oxycodone, buprenorphine and fentanyl (28.5 prescriptions per 10,000 population to a peak of 353.0 prescriptions per 10,000 population in 2013 and 303.1 prescriptions per 10,000 population in 2015). Using sunburst plots, of the new users prescribed weak opioids as their first prescription at index date, 5.5% transitioned to moderate opioids, 4.1% to strong opioids over 2 years (Figure 2). Transition probability of moving from weak to strong opioid at a given time point over 2 years was 0.001, whilst staying on a strong opioid (if first prescription at index date) was 0.97. Conclusion: Strong opioid prescribing increased till 2013-14 gradually decreasing following UK initiatives to improve monitoring and use of controlled drugs. Although less potent codeine prescriptions made up the majority of first prescriptions, the transition probability of staying on a strong opioid at 2 years remained high if prescribed first as a new user. Disclosure of Interests: None declared

Background:Prescribing behaviour of physicians has been described as a key driver of rising opioid prescriptions and long-term opioid use. However, the effect of prescribers requires interpretation within context. No studies have investigated the extent to which regions, practices and prescribers, vary in opioid prescribing accounting for case-mix by considering this hierarchy together.Objectives:(i) Quantify and identify risk factors for the transition from new-user to long-term opioid user (ii) Quantify variation of long-term use attributed to region, practice and prescriber, accounting for patient mix and chance variation.Methods:We conducted a retrospective observational UK study between 2006-2017 using Clinical Practice Research Datalink. Opioids new users, ≥18 years, without cancer were identified. Long-term opioid use was defined as ≥3 opioid prescriptions issued within a 90-day period from index date, or ≥1 opioid prescription lasting at least 90 days in the first year. A multi-level random-effects logistic regression model was used to examine the association of patient characteristics with the odds of becoming a long-term opioid user. To examine variation in opioid use amongst prescribers, GP practices and region after adjusting for patient case-mix, we used a nested random-effect structure. A ‘high-risk’ region, prescriber or practice was defined as those where the entire adjusted 95% CI lay above the population average.Results:1,968,742 new opioid users were included; 14.6% patients transitioned to long-term use. In the fully adjusted model, factors associated with higher odds of long-term opioid use included high morphine milligram equivalents (MME)/day at first prescription, older age, deprivation, fibromyalgia, rheumatological conditions, major surgery (Table). After adjustment for case-mix, the North-West, Yorkshire and South-West were found to be high-risk regions for long-term use. 103 practices (25.6%) and 540 prescribers (3.5%) were associated with a significantly higher risk of long-term use. The odds of becoming a long-term user for a patient belonging to these prescribers reached up to >3.5 times than the population average.Conclusion:Prescribing factors, age, deprivation and conditions including fibromyalgia and rheumatological conditions were associated with higher odds of long-term opioid use. In the first UK study evaluating long-term opioid prescribing with adjustment for patient-level characteristics, variation in regions and especially practices and prescribers were observed. Our findings support greater calls for action to reduce practice and prescriber variation by promoting safe practice in opioid prescribing.Table.Factors associated with long-term opioid use using a multi-level model accounting for clustering of individuals within prescriber, practice and regionIndividual factorsAdjusted Odds Ratio (95% CI) *Prescribing factorsIndex daily MME >2007.59 (6.29, 9.16)Index daily MME 100-2001.12 (1.03, 1.21)Index daily MME 50-1001.58 (1.49, 1.68)Index daily MME RefGabapentinoid use2.51 (2.43, 2.60)Psychotropic use1.28 (1.17, 1.40)Age>754.35 (4.26, 4.45)65-753.57 (3.50, 3.65)55-653.03 (2.96, 3.09)35-551.91 (1.88, 1.95)Age RefDeprivation (Townsend score)Quintile 5 (Most deprived)1.54 (1.51, 1.57)Quintile 41.34 (1.31, 1.36)Quintile 31.20 (1.18, 1.22)Quintile 21.09 (1.07, 1.11)Quintile 1 (Least deprived)RefPre-existing conditions/ prior proceduresFibromyalgia1.81 (1.49, 2.20)Substance use disorder1.76 (1.70, 1.83)Suicide and self-harm1.56 (1.51, 1.61)Rheumatological conditions Ψ1.54 (1.49, 1.59)Alcohol abuse1.50 (1.45, 1.55)Depression1.28 (1.26, 1.30)Major Surgery1.09 (1.06, 1.13)Abbreviations: MME, Morphine Milligram Equivalent; *pDisclosure of Interests:Meghna Jani Speakers bureau: Grifols, Belay Birlie Yimer: None declared, Therese Sheppard: None declared, Mark Lunt: None declared, William Dixon Consultant of: Bayer and Google

Background:Oral glucocorticoids (GC) are frequently prescribed to patients with rheumatoid arthritis (RA), however GC use is associated with a number of potential side effects. Hypertension is cited as a possible side effect, but few studies have specifically investigated GC-associated hypertension in patients with RA with conflicting results.Objectives:The aim of this study was to determine whether GCs were associated with an increased risk of incident hypertension in a cohort of patients with RA.Methods:A retrospective cohort of patients with incident RA and no hypertension at RA diagnosis were identified from UK primary care electronic health records (Clinical Practice Research Datalink). GC prescriptions were used to determine time-varying GC use and dose, categorised as: no use, >0–4.9 mg/day, 5–7.4 mg/day, 7.5–14.9 mg/day, ≥15mg/day. A 3-month risk attribution model was used where patients continued to remain at risk for 3 months after the end of prescriptions. Hypertension was identified if a patient had either: 1) 2 consecutive systolic blood pressure (BP) measurements >140mmHg within a year, 2) 2 consecutive diastolic BP measurements >90mmHg within a year or 3) antihypertensive prescriptions on at least two occasions and a Read code for hypertension. Unadjusted and adjusted Cox proportional hazards (PH) regression models were fitted to determine if there was an association between GC use and hypertension. Models were adjusted for baseline age, gender, baseline body mass index, baseline ever smoking, time-varying synthetic disease-modifying anti-rheumatic drug use, time-varying non-steroidal anti-inflammatory drug use and baseline Charlson comorbidity index.Results:There were 17,760 patients with incident RA and no hypertension. The cohort had a mean age of 56.3 ± 12.7 years and were predominantly female (68%). 7,421 (42%) were prescribed GCs during follow-up. There were 6,243 cases of incident hypertension over 97547 person years (pyrs) of follow-up, giving an incident rate of 64.1 per 1000 pyrs. Of those 1321 cases were in those exposed to GCs and 4922 were in those unexposed, giving incident rates of 87.6 per 1000 pyrs and 59.7 per 1000 pyrs, respectively. The adjusted Cox PH model indicated that recent GC use was associated with a 17% increased hazard of hypertension (hazard ratio: 1.17 (95% CI 1.10 to 1.24)). When categorised by dose, the adjusted model indicated only doses above 7.5mg were significantly associated with hypertension (Table 1).Table 1.Unadjusted and adjusted Cox proportional hazards regression model resultsUnadjustedHR (95% CI)Age and gender adjustedHR (95% CI)Fully adjusted* HR (95% CI)Recent GC use1.44(1.35 to 1.53)1.23(1.16 to 1.31)1.17(1.10 to 1.24)Recent GC doseNo GC useReferenceReferenceReference>0 – 4.9mg1.35(1.21 to 1.53)1.13(1.01 to 1.28)1.10(0.98 to 1.24)5mg – 7.4mg1.40(1.22 to 1.60)1.11(0.97 to 1.27)1.07(0.93 to 1.23)7.5mg – 14.9mg1.44(1.33 to 1.57)1.26(1.16 to 1.38)1.18(1.08 to 1.29)15mg and over1.60(1.40 to 1.84)1.45(1.27 to 1.66)1.36(1.18 to 1.56)* Adjusted for: Baseline age, gender, baseline body mass index, baseline ever smoking, synthetic disease-modifying anti-rheumatic drug use (time-varying), non-steroidal anti-inflammatory drug use (time-varying) and baseline Charlson comorbidity index.Conclusion:In this large cohort of patients with RA and without hypertension, recent GC use was associated with incident hypertension. In particular doses ≥7.5mg were associated with hypertension while the association with lower doses was inconclusive. Clinicians need to consider cardiovascular risk when prescribing GCs and ensure BP is regularly monitored.Disclosure of Interests:Ruth E Costello: None declared, Belay Birlie Yimer: None declared, Meghna Jani Speakers bureau: Grifols, William Dixon Consultant of: Bayer and Google

Introduction: Efforts have been made to reduce HIV/AIDS-related mortality by delivering antiretroviral therapy (ART) treatment. However, HIV patients in resource-poor settings are still dying, even if they are on ART treatment. This study aimed to explore the factors associated with HIV/AIDS-related mortality in Southwestern Ethiopia. Method: A non-concurrent retrospective cohort study which collected data from the clinical records of adult HIV/AIDS patients, who initiated ART treatment and were followed between January 2006 and December 2010, was conducted, to explore the factors associated with HIV/AIDS-related mortality at Jimma University Specialized Hospital (JUSH). Survival times (i.e., the time from the onset of ART treatment to the death or censoring) and different characteristics of patients were retrospectively examined. A best-fit model was chosen for the survival data, after the comparison between native semi-parametric Cox regression and parametric survival models (i.e., exponential, Weibull, and log-logistic). Result: A total of 456 HIV patients were included in the study, mostly females (312, 68.4%), with a median age of 30 years (inter-quartile range (IQR): 23-37 years). Estimated follow-up until December 2010 accounted for 1245 person-years at risk (PYAR) and resulted in 66 (14.5%) deaths and 390 censored individuals, representing a median survival time of 34.0 months ( IQR: 22.8-42.0 months). The overall mortality rate was 5.3/100 PYAR: 6.5/100 PYAR for males and 4.8/100 PYAR for females. The Weibull survival model was the best model for fitting the data (lowest AIC). The main factors associated with mortality were: baseline age (>35 years old, AHR = 3.8, 95% CI: 1.6-9.1), baseline weight (AHR = 0.93, 95% CI: 0.90-0.97), baseline WHO stage IV (AHR = 6.2, 95% CI: 2.2-14.2), and low adherence to ART treatment (AHR = 4.2, 95% CI: 2.5-7.1). Conclusion: An effective reduction in HIV/AIDS mortality could be achieved through timely ART treatment onset and maintaining high levels of treatment adherence. ispartof: International Journal of Environmental Research and Public Health vol:14 issue:3 ispartof: location:Switzerland status: published

Supplementary Appendix: Evaluation of the Durability of First-line Highly Active Antiretroviral Therapy in Southwest Ethiopia Using Multistate Survival Model. (PDF 218 KB)

Despite the marked progress in coverage of prevention of mother to child HIV transmission (PMTCT) programs, mother to child HIV transmission (MTCT) rate is not well documented in Southwest Ethiopia. A retrospective follow up study was carried at Jimma University Specialized Hospital PMTCT clinic to quantify MTCT rate and its predictors among HIV-exposed infants. Data were extracted from medical records of HIV-infected women and exposed infants between September 2010 and December 2012. Univariate and multivariate logistic regression analyses were carried out to identify potential factors predicting MTCT. A total of 146 infants born to HIV-infected mothers were included in the analysis. Out of 146 infants, 25 (17%, 95% CI: 11%-23.2%) were HIV positive. In the adjusted multivariate logistic regression analysis, mothers being on late AIDS stage (AOR= 5.8; 95% CI: 1.6-16.5), absence of maternal PMTCT interventions (AOR= 4.9; 95% CI: 1.4-16.5), home delivery (AOR= 8.1; 95% CI: 2.1-31.9) and mixed infant feeding (AOR= 5.6; 95% CI: 1.4-41.2) were independently associated with MTCT. We documented a high rate of MTCT among exposed infants in Southwest Ethiopia. All pregnant HIV positive mothers should be enrolled in PMTCT programs at earlier stage and exclusive breast feeding should be encouraged so as to decrease MTCT.

Age at marriage is of particular interest because it marks the transition to adulthood in many societies; the point at which certain options in education, employment, and participation in society are foreclosed. This study aimed to investigate demographic and socioeconomic factors affecting age at first marriage in Ethiopian women. The data source used for the analysis was the 2011 Ethiopian Demographic and Health Survey, which is country representative survey. The study considered 10,417 women aged 15-49 years from nine regions and one city administration. Accelerated failure time model was used for identifying factors associated with age at first marriage. The median time for age at first marriage was 17 years (95% CI: 16.90, 17.10). Based on Akaike’s information criterion (AIC) the Log-logistic accelerated failure time model was found to be the best model in describing the age at first marriage among other candidate models. The result based on this model showed that region, women’s educational level, wealth index and religion significantly affect timing of first marriage. Women who had secondary and higher education prolonged time-to-first marriage by the factor of ɸ =1.42 and ɸ =1.46, respectively. Women from Oromia, Somali, SNNP and Dire Dawa have prolonged time to age at first marriage by ɸ=1.02, ɸ=1.05, ɸ=1.08, and ɸ=1.09 respectively. However, women from Amhara region (ɸ =0.89), Benishangul-Gumuz region (ɸ =0.95) and Gambela region (ɸ =0.95) had a significantly higher risk of early first marriage compared to their counterparts in the Tigray region. The acceleration factors for middle wealth index and rich are 0.99 and 0.98 respectively using poor household reference. This implied that poor household women have longer time-to-age at first marriage. Improving girls and young women access to education is important for rising the women’s age at first marriage, which is vital for empowering them and enhancing their participation in any sector.