Applying Trial-Derived Treatment Effects to Real-World Populations: Generalizing Cost-Effectiveness Estimates When Modeling Complex Hazards.

OBJECTIVES: Generalizability of trial-based cost-effectiveness estimates to real-world target populations is important for decision making. In the context of independent aggregate time-to-event baseline and relative effects data, complex hazards can make modeling of data for use in economic evaluation challenging. Our article provides an overview of methods that can be used to apply trial-derived relative treatment effects to external real-world baselines when faced with complex hazards and follows with a motivating example. METHODS: Approaches for applying trial-derived relative effects to real-world baselines are presented in the context of complex hazards. Appropriate methods are applied in a cost-effectiveness analysis using data from a previously published study assessing the real-world cost-effectiveness of a treatment for carcinoma of the head and neck as a motivating example. RESULTS: Lack of common hazards between the trial and target real-world population, a complex baseline hazard function, and nonproportional relative effects made the use of flexible models necessary to adequately estimate survival. Assuming common distributions between trial and real-world reference survival substantially affected survival and cost-effectiveness estimates. Modeling time-dependent vs proportional relative effects affected estimates to a lesser extent, dependent on assumptions used in cost-effectiveness modeling. CONCLUSIONS: Appropriately capturing reference treatment survival when attempting to generalize trial-derived relative treatment effects to real-world target populations can have important impacts on cost-effectiveness estimates. A balance between model complexity and adequacy for decision making should be considered where multiple data sources with complex hazards are being evaluated.

IncidencePrevalence: An R package to calculate population-level incidence rates and prevalence using the OMOP common data model.

PURPOSE: Real-world data (RWD) offers a valuable resource for generating population-level disease epidemiology metrics. We aimed to develop a well-tested and user-friendly R package to compute incidence rates and prevalence in data mapped to the observational medical outcomes partnership (OMOP) common data model (CDM). MATERIALS AND METHODS: We created IncidencePrevalence, an R package to support the analysis of population-level incidence rates and point- and period-prevalence in OMOP-formatted data. On top of unit testing, we assessed the face validity of the package. To do so, we calculated incidence rates of COVID-19 using RWD from Spain (SIDIAP) and the United Kingdom (CPRD Aurum), and replicated two previously published studies using data from the Netherlands (IPCI) and the United Kingdom (CPRD Gold). We compared the obtained results to those previously published, and measured execution times by running a benchmark analysis across databases. RESULTS: IncidencePrevalence achieved high agreement to previously published data in CPRD Gold and IPCI, and showed good performance across databases. For COVID-19, incidence calculated by the package was similar to public data after the first-wave of the pandemic. CONCLUSION: For data mapped to the OMOP CDM, the IncidencePrevalence R package can support descriptive epidemiological research. It enables reliable estimation of incidence and prevalence from large real-world data sets. It represents a simple, but extendable, analytical framework to generate estimates in a reproducible and timely manner.

Calculating daily dose in the Observational Medical Outcomes Partnership Common Data Model.

PURPOSE: We aimed to develop a standardized method to calculate daily dose (i.e., the amount of drug a patient was exposed to per day) of any drug on a global scale using only drug information of typical observational data in the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) and a single reference table from Observational Health Data Sciences And Informatics (OHDSI). MATERIALS AND METHODS: The OMOP DRUG_STRENGTH reference table contains information on the strength or concentration of drugs, whereas the OMOP DRUG_EXPOSURE table contains information on patients' drug prescriptions or dispensations/claims. Based on DRUG_EXPOSURE data from the primary care databases Clinical Practice Research Datalink GOLD (United Kingdom) and Integrated Primary Care Information (IPCI, The Netherlands) and healthcare claims from PharMetrics® Plus for Academics (USA), we developed four formulas to calculate daily dose given different DRUG_STRENGTH reference table information. We tested the dose formulas by comparing the calculated median daily dose to the World Health Organization (WHO) Defined Daily Dose (DDD) for six different ingredients in those three databases and additional four international databases representing a variety of healthcare settings: MAITT (Estonia, healthcare claims and discharge summaries), IQVIA Disease Analyzer Germany (outpatient data), IQVIA Longitudinal Patient Database Belgium (outpatient data), and IMASIS Parc Salut (Spain, hospital data). Finally, in each database, we assessed the proportion of drug records for which daily dose calculations were possible using the suggested formulas. RESULTS: Applying the dose formulas, we obtained median daily doses that generally matched the WHO DDD definitions. Our dose formulas were applicable to >85% of drug records in all but one of the assessed databases. CONCLUSION: We have established and implemented a standardized daily dose calculation in OMOP CDM providing reliable and reproducible results.

Time Series Methods to Assess the Impact of Regulatory Action: A Study of UK Primary Care and Hospital Data on the Use of Fluoroquinolones.

PURPOSE: To illustrate the interest in using interrupted time series (ITS) methods, this study evaluated the impact of the UK MHRA's March 2019 Risk Minimisation Measures (RMM) on fluoroquinolone usage. METHODS: Monthly and quarterly fluoroquinolone use incidence rates from 2012 to 2022 were analysed across hospital care (Barts Health NHS Trust), primary care (Clinical Practice Research Datalink (CPRD) Aurum and CPRD GOLD), and linked records from both settings (East Scotland). Rates were stratified by age (19-59 and ≥ 60 years old). Seasonality-adjusted segmented regression and ARIMA models were employed to model quarterly and monthly rates, respectively. RESULTS: Post-RMM, with segmented regression, both age groups in Barts Health experienced nearly complete reductions (> 99%); CPRD Aurum saw 20.19% (19-59) and 19.29% ( ≥ $$ \ge $$ 60) reductions; no significant changes in CPRD GOLD; East Scotland had 45.43% (19-59) and 41.47% ( ≥ $$ \ge $$ 60) decreases. Slope analysis indicated increases for East Scotland (19-59) and both CPRD Aurum groups, but a decrease for CPRD GOLD's ≥ $$ \ge $$ 60; ARIMA detected significant step changes in CPRD GOLD not identified by segmented regression and noted a significant slope increase in Barts Health's 19-59 group. Both models showed no post-modelling autocorrelations across databases, yet Barts Health's residuals were non-normally distributed with non-constant variance. CONCLUSIONS: Both segmented regression and ARIMA confirmed the reduction of fluoroquinolones use after RMM across four different UK primary care and hospital databases. Model diagnostics showed good performance in eliminating residual autocorrelation for both methods. However, diagnostics for hospital databases with low incident use revealed the presence of heteroscedasticity and non-normal white noise using both methods.

Trends of use and characterisation of anti-dementia drugs users: a large multinational-network population-based study.

BACKGROUND: An updated time-trend analysis of anti-dementia drugs (ADDs) is lacking. The aim of this study is to assess the incident rate (IR) of ADD in individuals with dementia using real-world data. SETTING: Primary care data (country/database) from the UK/CPRD-GOLD (2007-20), Spain/SIDIAP (2010-20) and the Netherlands/IPCI (2008-20), standardised to a common data model. METHODS: Cohort study. Participants: dementia patients ≥40 years old with ≥1 year of previous data. Follow-up: until the end of the study period, transfer out of the catchment area, death or incident prescription of rivastigmine, galantamine, donepezil or memantine. Other variables: age/sex, type of dementia, comorbidities. Statistics: overall and yearly age/sex IR, with 95% confidence interval, per 100,000 person-years (IR per 105 PY (95%CI)). RESULTS: We identified a total of (incident anti-dementia users/dementia patients) 41,024/110,642 in UK/CPRD-GOLD, 51,667/134,927 in Spain/SIDIAP and 2,088/17,559 in the Netherlands/IPCI.In the UK, IR (per 105 PY (95%CI)) of ADD decreased from 2007 (30,829 (28,891-32,862)) to 2010 (17,793 (17,083-18,524)), then increased up to 2019 (31,601 (30,483 to 32,749)) and decrease in 2020 (24,067 (23,021-25,148)). In Spain, IR (per 105 PY (95%CI)) of ADD decreased by 72% from 2010 (51,003 (49,199-52,855)) to 2020 (14,571 (14,109-15,043)). In the Netherlands, IR (per 105 PY (95%CI)) of ADD decreased by 77% from 2009 (21,151 (14,967-29,031)) to 2020 (4763 (4176-5409)). Subjects aged ≥65-79 years and men (in the UK and the Netherlands) initiated more frequently an ADD. CONCLUSIONS: Treatment of dementia remains highly heterogeneous. Further consensus in the pharmacological management of patients living with dementia is urgently needed.

Characteristics and outcomes of COVID-19 patients with and without asthma from the United States, South Korea, and Europe.

Objective: Large international comparisons describing the clinical characteristics of patients with COVID-19 are limited. The aim of the study was to perform a large-scale descriptive characterization of COVID-19 patients with asthma.Methods: We included nine databases contributing data from January to June 2020 from the US, South Korea (KR), Spain, UK and the Netherlands. We defined two cohorts of COVID-19 patients ('diagnosed' and 'hospitalized') based on COVID-19 disease codes. We followed patients from COVID-19 index date to 30 days or death. We performed descriptive analysis and reported the frequency of characteristics and outcomes in people with asthma defined by codes and prescriptions.Results: The diagnosed and hospitalized cohorts contained 666,933 and 159,552 COVID-19 patients respectively. Exacerbation in people with asthma was recorded in 1.6-8.6% of patients at presentation. Asthma prevalence ranged from 6.2% (95% CI 5.7-6.8) to 18.5% (95% CI 18.2-18.8) in the diagnosed cohort and 5.2% (95% CI 4.0-6.8) to 20.5% (95% CI 18.6-22.6) in the hospitalized cohort. Asthma patients with COVID-19 had high prevalence of comorbidity including hypertension, heart disease, diabetes and obesity. Mortality ranged from 2.1% (95% CI 1.8-2.4) to 16.9% (95% CI 13.8-20.5) and similar or lower compared to COVID-19 patients without asthma. Acute respiratory distress syndrome occurred in 15-30% of hospitalized COVID-19 asthma patients.Conclusion: The prevalence of asthma among COVID-19 patients varies internationally. Asthma patients with COVID-19 have high comorbidity. The prevalence of asthma exacerbation at presentation was low. Whilst mortality was similar among COVID-19 patients with and without asthma, this could be confounded by differences in clinical characteristics. Further research could help identify high-risk asthma patients.[Box: see text]Supplemental data for this article is available online at https://doi.org/10.1080/02770903.2021.2025392 .

Impact of the COVID-19 pandemic on eating disorders diagnoses among adolescents and young adults in Catalonia: A population-based cohort study.

OBJECTIVE: To describe the impact of the COVID-19 pandemic on trends in incidence rates (IR) of diagnoses of eating disorders (ED) among adolescents and young adults. METHODS: Population-based cohort study using primary care records of people aged 10-24 years between January, 2016 and December, 2021 in Catalonia, Spain. IRs were calculated monthly and grouped by the different stages of the COVID-19 pandemic in Catalonia: (1) the pre-lockdown (January, 2016-February, 2020), (2) lockdown (March-June, 2020) and, (3) post-lockdown (July, 2020-December, 2021) periods. Incidence rate ratios (IRR) relative to the corresponding periods in 2018-2019 were calculated. RESULTS: A total of 1,179,009 individuals were included. The IR was 9.2 per 100,000 person-months (95% confidence intervals [CI]: 8.9-9.5) during the pre-lockdown period. It decreased during the lockdown period (6.3 per 100,000 person-months [5.5-7.3]), but substantially increased during the following period (19.4. per 100,000 person-months [18.7-20.1]). While large reductions in IRs were observed for both sexes during the lockdown period (IRR 95% CI: 0.65 [0.54-0.78] in females and 0.46 [0.29-0.71] in males), substantial increases during the post-lockdown period were limited to females, and were particularly pronounced among those aged 10-14 and 15-19 years (2.50 [2.23-2.80] and 2.29 [2.07-2.54], respectively). DISCUSSION: The COVID-19 pandemic has resulted in a substantial increase in ED diagnoses, primarily driven by higher rates among adolescent females. PUBLIC SIGNIFICANCE: This population-based cohort study demonstrated a substantial increase in incidence rates of eating disorders in primary care following the end of lockdown in Catalonia, Spain, with adolescent girls seen to be most affected.

Association between outpatient follow-up and incidence of revision after knee and hip replacements: a population-based cohort study.

BACKGROUND: Follow-up visits 5 or 7 years after surgery were recommended for people having primary hip or knee replacement. The benefits of this practice to patients and the healthcare system, however, have not yet been specifically examined. The aim of this study was to investigate the association between long-term follow-up outpatient hospital visits and revision rates for patients who undergo primary knee or hip replacement surgery. METHODS: Cohorts were identified for patients undergoing knee or hip replacement surgery using medical records from primary care practices within the UK Clinical Practice Research Datalink (CPRD) GOLD dataset linked to hospital records from the English Hospital Episodes Statistics (HES) data. Two groups of patients were compared in terms of revision and mortality rates: those with at least one long-term (between five and 10 years since primary surgery) follow-up visit at the orthopaedic department ('Follow-up' group), and those without ('No follow-up' group). RESULTS: A total of 9856 (4349 in the Follow-up group) patients with knee replacement and 10,837 (4870 in the Follow-up group) with hip replacement were included in the analysis. For knee replacement, the incidence of revision was 3.6% for those followed-up and 0.6% for those not followed-up. An adjusted regression model confirmed the difference in the hazard ratio (HR) for revision was statistically significant (HR: 5.65 [95% CI 3.62 to 8.81]). Mortality at 4 years was lower for the Follow-up (17%) compared to the No follow-up group (21%), but this difference was not statistically significant (HR: 0.95 [0.84 to 1.07]). For hip replacement, the incidence of revision rates were 3.2 and 1.4% for the follow-up and not follow-up groups, respectively, the difference being statistically significant (HR: 2.34 [1.71 to 3.20]). Mortality was lower for the Follow-up (15%) compared to the No follow-up group (21%), but the difference was not statistically significant (HR: 0.91 [0.81 to 1.02]). CONCLUSION: Patients attending follow-up orthopaedic consultations show a higher risk of revision surgery compared to those who are not followed-up. A cause for this difference could not be identified in this study but a likely explanation is that surgeons play an effective role as ultimate arbitrators when identifying patients to be included in long-term follow-up lists.

Socioeconomic Inequalities in COVID-19 Vaccination and Infection in Adults, Catalonia, Spain.

Evidence on the impact of the COVID-19 vaccine rollout on socioeconomic COVID-19-related inequalities is scarce. We analyzed associations between socioeconomic deprivation index (SDI) and COVID-19 vaccination, infection, and hospitalization before and after vaccine rollout in Catalonia, Spain. We conducted a population-based cohort study during September 2020-June 2021 that comprised 2,297,146 adults >40 years of age. We estimated odds ratio of nonvaccination and hazard ratios (HRs) of infection and hospitalization by SDI quintile relative to the least deprived quintile, Q1. Six months after rollout, vaccination coverage differed by SDI quintile in working-age (40-64 years) persons: 81% for Q1, 71% for Q5. Before rollout, we found a pattern of increased HR of infection and hospitalization with deprivation among working-age and retirement-age (>65 years) persons. After rollout, infection inequalities decreased in both age groups, whereas hospitalization inequalities decreased among retirement-age persons. Our findings suggest that mass vaccination reduced socioeconomic COVID-19-related inequalities.

Cancer and the risk of coronavirus disease 2019 diagnosis, hospitalisation and death: A population-based multistate cohort study including 4 618 377 adults in Catalonia, Spain.

The relationship between cancer and coronavirus disease 2019 (COVID-19) infection and severity remains poorly understood. We conducted a population-based cohort study between 1 March and 6 May 2020 describing the associations between cancer and risk of COVID-19 diagnosis, hospitalisation and COVID-19-related death. Data were obtained from the Information System for Research in Primary Care (SIDIAP) database, including primary care electronic health records from ~80% of the population in Catalonia, Spain. Cancer was defined as any primary invasive malignancy excluding non-melanoma skin cancer. We estimated adjusted hazard ratios (aHRs) for the risk of COVID-19 (outpatient) clinical diagnosis, hospitalisation (with or without a prior COVID-19 diagnosis) and COVID-19-related death using Cox proportional hazard regressions. Models were estimated for the overall cancer population and by years since cancer diagnosis (<1 year, 1-5 years and ≥5 years), sex, age and cancer type; and adjusted for age, sex, smoking status, deprivation and comorbidities. We included 4 618 377 adults, of which 260 667 (5.6%) had a history of cancer. A total of 98 951 individuals (5.5% with cancer) were diagnosed, and 6355 (16.4% with cancer) were directly hospitalised with COVID-19. Of those diagnosed, 6851 were subsequently hospitalised (10.7% with cancer), and 3227 died without being hospitalised (18.5% with cancer). Among those hospitalised, 1963 (22.5% with cancer) died. Cancer was associated with an increased risk of COVID-19 diagnosis (aHR: 1.08; 95% confidence interval [1.05-1.11]), direct COVID-19 hospitalisation (1.33 [1.24-1.43]) and death following hospitalisation (1.12 [1.01-1.25]). These associations were stronger for patients recently diagnosed with cancer, aged <70 years, and with haematological cancers. These patients should be prioritised in COVID-19 vaccination campaigns and continued non-pharmaceutical interventions.

Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network.

BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. RESULTS: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. CONCLUSIONS: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom.

BACKGROUND: A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity. METHODS: We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status. RESULTS: We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8-40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0-33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity. CONCLUSION: We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.

Risk of depression, suicide and psychosis with hydroxychloroquine treatment for rheumatoid arthritis: a multinational network cohort study.

OBJECTIVES: Concern has been raised in the rheumatology community regarding recent regulatory warnings that HCQ used in the coronavirus disease 2019 pandemic could cause acute psychiatric events. We aimed to study whether there is risk of incident depression, suicidal ideation or psychosis associated with HCQ as used for RA. METHODS: We performed a new-user cohort study using claims and electronic medical records from 10 sources and 3 countries (Germany, UK and USA). RA patients ≥18 years of age and initiating HCQ were compared with those initiating SSZ (active comparator) and followed up in the short (30 days) and long term (on treatment). Study outcomes included depression, suicide/suicidal ideation and hospitalization for psychosis. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate database-specific calibrated hazard ratios (HRs), with estimates pooled where I2 <40%. RESULTS: A total of 918 144 and 290 383 users of HCQ and SSZ, respectively, were included. No consistent risk of psychiatric events was observed with short-term HCQ (compared with SSZ) use, with meta-analytic HRs of 0.96 (95% CI 0.79, 1.16) for depression, 0.94 (95% CI 0.49, 1.77) for suicide/suicidal ideation and 1.03 (95% CI 0.66, 1.60) for psychosis. No consistent long-term risk was seen, with meta-analytic HRs of 0.94 (95% CI 0.71, 1.26) for depression, 0.77 (95% CI 0.56, 1.07) for suicide/suicidal ideation and 0.99 (95% CI 0.72, 1.35) for psychosis. CONCLUSION: HCQ as used to treat RA does not appear to increase the risk of depression, suicide/suicidal ideation or psychosis compared with SSZ. No effects were seen in the short or long term. Use at a higher dose or for different indications needs further investigation. TRIAL REGISTRATION: Registered with EU PAS (reference no. EUPAS34497; http://www.encepp.eu/encepp/viewResource.htm? id=34498). The full study protocol and analysis source code can be found at https://github.com/ohdsi-studies/Covid19EstimationHydroxychloroquine2.

Threshold for Computer- and Robot-Assisted Knee and Hip Replacements in the English National Health Service.

OBJECTIVES: To estimate threshold prices for computer- and robot-assisted knee and hip replacement. METHODS: A lifetime cohort Markov model provided the framework for analysis. Linked primary care and inpatient hospital records informed estimates of outcomes under current practice. Outcomes were estimated under a range of hypothetical relative improvements in quality of life if unrevised and in revision risk after computer or robot-assisted surgery. Threshold prices, a price at which the net health benefit from funding the intervention would be zero, for these improvements were estimated for a cost-effectiveness threshold of £20 000 per additional quality-adjusted life-year (QALY) gained. RESULTS: For average patient profiles under current knee and hip replacement practice, lifetime QALYs were 10.3 (9.9 to 10.7) and 11.0 (10.6 to 11.4), with costs of £6060 (£5947 to £6203) and £6506 (£6335 to £6710) for knee and hip replacement, respectively. A combined 50% relative reduction in risk of revision and 5% improvement in postoperative quality of life if unrevised would, for example, result in QALYs increasing to 10.9 (10.4 to 11.3) and 11.6 (11.2 to 12.0), and costs falling to £5880 (£5816 to £5956) and £6258 (£6149 to £6376) after knee and hip replacement, respectively. These particular improvements would have an associated threshold price of £11 182 (£10 691 to £11 721) for knee replacement and £12 134 (£11 616 to £12 701) for hip replacement. The 50% reduction in revision rate alone would have associated threshold prices of £1094 (£788 to £1488) and £1347 (£961 to £1842), and the 5% improvement in quality of life alone would have associated threshold prices of £9911 (£9476 to £10 296) and £10 578 (£10 171 to £10 982). CONCLUSIONS: At current prices, computer- and robot-assisted knee and hip replacement will likely need to lead to improvements in patient-reported outcomes in addition to any reduction in the risk revision.

Lifetime risk of knee and hip replacement following a diagnosis of RA: findings from a cohort of 13 961 patients from England.

OBJECTIVE: To estimate the lifetime risk of knee and hip replacement following a diagnosis of RA. METHODS: The analysis was undertaken using routinely collected data from the English NHS. Diagnosis of RA was identified using primary care records, with knee and hip replacement observed in linked hospital records. Parametric survival models were fitted for up to 15 years of follow-up, with age, sex, Charlson comorbidity score, socioeconomic status, BMI and smoking status included as explanatory variables. A decision model was used to combine and extrapolate survival models to estimate lifetime risk. RESULTS: The number of individuals with a diagnosis of RA and included in the study was 13 961. Lifetime risk of knee replacement and hip replacement was estimated to be 22% (95% CI: 16, 29%) and 17% (95% CI: 11, 26%) following a diagnosis of RA for the average patient profile (non-smoking women aged 64 with no other comorbidities, BMI of 27 and in the top socioeconomic quintile). Risks were higher for younger patients. CONCLUSION: The lifetime risk of knee and hip replacement for individuals with a diagnosis of RA is approximately double that of the general population. These findings allow for a better understanding of long-term prognosis and healthcare resource use, and highlight the importance of timely diagnosis and effective treatment.

The effect of rheumatoid arthritis on patient-reported outcomes following knee and hip replacement: evidence from routinely collected data.

OBJECTIVES: To compare outcomes of total knee replacement (TKR) and total hip replacement (THR) for individuals with RA and OA. METHODS: We performed a cohort study using routinely collected data. Oxford Knee Score, Oxford Hip Score, and EuroQol 5-dimension 3-level (EQ-5D-3L) questionnaires were collected before and 6 months after surgery. Multivariable regressions were used to estimate the association between diagnosis and post-operative scores after controlling for pre-operative scores and patient characteristics. RESULTS: Study cohorts included 2070 OA and 142 RA patients for TKR and 2030 OA and 98 RA patients for THR. Following TKR, the median Oxford Knee Score was 37 [interquartile range (IQR) 29-43] for OA and 36 (27-42) for RA while the median EQ-5D-3L was 0.76 (0.69-1.00) and 0.69 (0.52-0.85), respectively. After THR, the Oxford Hip Score was 42 (IQR 36-46) for OA and 39 (30-44) for RA while the EQ-5D-3L was 0.85 (0.69-1.00) and 0.69 (0.52-1.00), respectively. The estimated effect of RA, relative to OA, on post-operative scores was -0.05 (95% CI -1.57, 1.48) for the Oxford Knee Score, -0.09 (-0.13, -0.06) for the EQ-5D-3L following TKR, -1.35 (-2.93, -0.22) for the Oxford Hip Score, and -0.08 (-0.12, -0.03) for the EQ-5D-3L following THR. CONCLUSION: TKR and THR led to substantial improvements in joint-specific scores and overall quality of life. While diagnosis had no clinically meaningful effect on joint-specific outcomes, improvements in general quality of life were somewhat less for those with RA, which is likely due to the systemic and multijoint nature of rheumatoid disease.

Modelling cost-effectiveness of tenofovir for prevention of mother to child transmission of hepatitis B virus (HBV) infection in South Africa.

BACKGROUND: International sustainable development goals for the elimination of viral hepatitis as a public health problem by 2030 highlight the need to optimize strategies for prevention, diagnosis and treatment of hepatitis B virus (HBV) infection. An important priority for Africa is to have affordable, accessible and sustainable prevention of mother to child transmission (PMTCT) programmes, delivering screening and treatment for antenatal women and implementing timely administration of HBV vaccine for their babies. METHODS: We developed a decision-analytic model simulating 10,000 singleton pregnancies to assess the cost-effectiveness of three possible strategies for deployment of tenofovir in pregnancy, in combination with routine infant vaccination: S1: no screening nor antiviral therapy; S2: screening and antiviral prophylaxis for all women who test HBsAg-positive; S3: screening for HBsAg, followed by HBeAg testing and antiviral prophylaxis for women who are HBsAg-positive and HBeAg-positive. Our outcome was cost per infant HBV infection avoided and the analysis followed a healthcare perspective. RESULTS: Based on 10,000 pregnancies, S1 predicts 45 infants would be HBV-infected at six months of age, compared to 21 and 28 infants in S2 and S3, respectively. Relative to S1, S2 had an incremental cost of $3940 per infection avoided. S3 led to more infections and higher costs. CONCLUSION: Given the long-term health burden for individuals and economic burden for society associated with chronic HBV infection, screening pregnant women and providing tenofovir for all who test HBsAg+ may be a cost-effective strategy for South Africa and other low/middle income settings.

Ten-year patient-reported outcomes following total and minimally invasive unicompartmental knee arthroplasty: a propensity score-matched cohort analysis.

PURPOSE: For patients with medial compartment arthritis who have failed non-operative treatment, either a total knee arthroplasty (TKA) or a unicompartmental knee arthroplasty (UKA) can be undertaken. This analysis considers how the choice between UKA and TKA affects long-term patient-reported outcome measures (PROMs). METHODS: The Knee Arthroplasty Trial (KAT) and a cohort of patients who received a minimally invasive UKA provided data. Propensity score matching was used to identify comparable patients. Oxford Knee Score (OKS), its pain and function components, and the EuroQol 5 Domain (EQ-5D) index, estimated on the basis of OKS responses, were then compared over 10 years following surgery. Mixed-effects regressions for repeated measures were used to estimate the effect of patient characteristics and type of surgery on PROMs. RESULTS: Five-hundred and ninety UKAs were matched to the same number of TKAs. Receiving UKA rather than TKA was found to be associated with better scores for OKS, including both its pain and function components, and EQ-5D, with the differences expected to grow over time. UKA was also associated with an increased likelihood of patients achieving a successful outcome, with an increased chance of attaining minimally clinically important improvements in both OKS and EQ-5D, and an 'excellent' OKS. In addition, for both procedures, patients aged between 60 and 70 and better pre-operative scores were associated with better post-operative outcomes. CONCLUSION: Minimally invasive UKAs performed on patients with the appropriate indications led to better patient-reported pain and function scores than TKAs performed on comparable patients. UKA can lead to better long-term quality of life than TKA and this should be considered alongside risk of revision when choosing between the procedures. LEVEL OF EVIDENCE: II.

A cost-effectiveness analysis of optimal care for diabetic foot ulcers in Australia.

In addition to affecting quality of life, diabetic foot ulcers (DFUs) impose an economic burden on both patients and the health system. This study developed a Markov model to analyse the cost-effectiveness of implementing optimal care in comparison with the continuation of usual care for diabetic patients at high risk of DFUs in the Australian setting. The model results demonstrated overall 5-year cost savings (AUD 9100·11 for those aged 35-54, $9391·60 for those aged 55-74 and $12 394·97 for those aged 75 or older) and improved health benefits measured in quality-adjusted life years (QALYs) (0·13 QALYs, 0·13 QALYs and 0·16 QALYs, respectively) for high-risk patients receiving optimal care for DFUs compared with usual care. Total cost savings for Australia were estimated at AUD 2·7 billion over 5 years. Probabilistic sensitivity analysis showed that optimal care always had a higher probability of costing less and generating more health benefits. This study provides important evidence to inform Australian policy decisions on the efficient use of health resources and supports the implementation of evidence-based optimal care in Australia. Furthermore, this information is of great importance for comparable developed countries that could reap similar benefits from investing in these well-known evidence-based strategies.