Access to healthcare services during the COVID-19 pandemic: a cross-sectional analysis of income and user-access across 16 economically diverse countries.

BACKGROUND: National health systems have different strengths and resilience levels. During the COVID-19 pandemic, resources often had to be reallocated and this impacted the availability of healthcare services in many countries. To date there have been few quantitative contemporary studies of inequalities in access to healthcare within and between countries. In this study, we aim to compare inequality within and between 16 economically diverse countries. METHODS: Online surveys were conducted on 22 150 adults in 16 countries across six continents in 2022. Quota sampling and post-stratification weighting was used to obtain an age, gender, geographically, and educationally representative sample. The study assesses the differences in challenges in access to healthcare during the pandemic (for GP, surgical/clinical and digital GP services) using country-specific expanded health-needs-adjusted Erreygers' concentration indices and compares these values between countries using a Spearman's rank correlation coefficient. RESULTS: Results show wide variation in income-related challenges in access within countries for different types of care. For example, Erreygers' concentration index for digital services in Colombia exhibited highly regressive inequality at 0·17, compared to Japan with an index of -0·15. Inequalities between countries were also evident, with Spearman rank coefficients of -0·69 and -0·65 (p-values of 0·003 and 0·006) for digital and surgical access, indicating that lower income countries had greater inequality in healthcare access challenges. CONCLUSION: During the pandemic, inequalities in challenges to accessing healthcare were greatest in low and middle-income countries. Digital technologies offer a reasonable means to address some of this inequality if adequate support is provided and accessible digital infrastructure exists.

Citizens from 13 countries share similar preferences for COVID-19 vaccine allocation priorities.

How does the public want a COVID-19 vaccine to be allocated? We conducted a conjoint experiment asking 15,536 adults in 13 countries to evaluate 248,576 profiles of potential vaccine recipients who varied randomly on five attributes. Our sample includes diverse countries from all continents. The results suggest that in addition to giving priority to health workers and to those at high risk, the public favors giving priority to a broad range of key workers and to those with lower income. These preferences are similar across respondents of different education levels, incomes, and political ideologies, as well as across most surveyed countries. The public favored COVID-19 vaccines being allocated solely via government programs but were highly polarized in some developed countries on whether taking a vaccine should be mandatory. There is a consensus among the public on many aspects of COVID-19 vaccination, which needs to be taken into account when developing and communicating rollout strategies.

The COVID-19 pandemic and health-related quality of life across 13 high- and low-middle-income countries: A cross-sectional analysis.

BACKGROUND: Most research on the Coronavirus Disease 2019 (COVID-19) health burden has focused on confirmed cases and deaths, rather than consequences for the general population's health-related quality of life (HRQoL). It is also important to consider HRQoL to better understand the potential multifaceted implications of the COVID-19 pandemic in various international contexts. This study aimed to assess the association between the COVID-19 pandemic and changes in HRQoL in 13 diverse countries. METHODS AND FINDINGS: Adults (18+ years) were surveyed online (24 November to 17 December 2020) in 13 countries spanning 6 continents. Our cross-sectional study used descriptive and regression-based analyses (age adjusted and stratified by gender) to assess the association between the pandemic and changes in the general population's HRQoL, measured by the EQ-5D-5L instrument and its domains (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression), and how overall health deterioration was associated with individual-level (socioeconomic, clinical, and experiences of COVID-19) and national-level (pandemic severity, government responsiveness, and effectiveness) factors. We also produced country-level quality-adjusted life years (QALYs) associated to COVID-19 pandemic-related morbidity. We found that overall health deteriorated, on average across countries, for more than one-third of the 15,480 participants, mostly in the anxiety/depression health domain, especially for younger people (<35 years old) and females/other gender. This translated overall into a 0.066 mean "loss" (95% CI: -0.075, -0.057; p-value < 0.001) in the EQ-5D-5L index, representing a reduction of 8% in overall HRQoL. QALYs lost associated with morbidity were 5 to 11 times greater than QALYs lost based on COVID-19 premature mortality. A limitation of the study is that participants were asked to complete the prepandemic health questionnaire retrospectively, meaning responses may be subject to recall bias. CONCLUSIONS: In this study, we observed that the COVID-19 pandemic was associated with a reduction in perceived HRQoL globally, especially with respect to the anxiety/depression health domain and among younger people. The COVID-19 health burden would therefore be substantially underestimated if based only on mortality. HRQoL measures are important to fully capture morbidity from the pandemic in the general population.

The comparative mortality of an elite group in the long run of history: an observational analysis of politicians from 11 countries.

This study aims to compare the mortality rate and life expectancy of politicians with those of the age and gender-matched general populations. This was an observational analysis of mortality rates of politicians (i.e. members of national parliaments with available data on dates of birth, death and election, gender, and life tables) in 11 developed countries. Politicians were followed from date of first election until either death or the last available year with life table data. Relative mortality differences were estimated using standardised mortality ratios (SMRs). Absolute inequalities were quantified as the difference in survival by deducting a population's remaining life expectancy from politicians' remaining life expectancy at age 45, estimated using Gompertz parametric proportional hazards models. We included 57,561 politicians (with follow-up ranging from 1816-2016 for France to 1949-2017 for Germany). In almost all countries politicians had similar rates of mortality to the general population in the early part of the twentieth century. Relative mortality and survival differences (favouring politicians) increased considerably over the course of the twentieth century, with recent SMRs ranging from 0.45 (95%CI 0.41-0.50) in Italy to 0.82 (95%CI 0.69-0.95) in New Zealand. The peak life expectancy gaps ranged from 4.4 (95% CI, 3.5-5.4) years in the Netherlands to 7.8 (95% CI, 7.2-8.4) years in the US. Our results show large relative and absolute inequalities favouring politicians in every country. In some countries, such as the US, relative inequalities are at the greatest level in over 150 years.

Eliciting risk preferences that predict risky health behavior: A comparison of two approaches.

Information on attitudes to risk could increase understanding of and explain risky health behaviors. We investigate two approaches to eliciting risk preferences in the health domain, a novel "indirect" lottery elicitation approach with health states as outcomes and a "direct" approach where respondents are asked directly about their willingness to take risks. We compare the ability of the two approaches to predict health-related risky behaviors in a general adult population. We also investigate a potential framing effect in the indirect lottery elicitation approach. We find that risk preferences elicited using the direct approach can better predict health-related risky behavior than those elicited using the indirect approach. Moreover, a seemingly innocuous change to the framing of the lottery question results in significantly different risk preference estimates, and conflicting conclusions about the ability of the indicators to predict risky health behaviors.

Development of a life expectancy table for individuals with type 1 diabetes.

AIMS/HYPOTHESIS: Tables reporting life expectancies by common risk factors are available for individuals with type 2 diabetes; however, there is currently no published equivalent for individuals with type 1 diabetes. We aimed to develop a life expectancy table using a recently published simulation model for individuals with type 1 diabetes. METHODS: The simulation model was developed using data from a real-world population of patients with type 1 diabetes selected from the Swedish National Diabetes Register. The following six important risk factors were included in the life table: sex; age; current smoking status; BMI; eGFR; and HbA1c. For each of 1024 cells in the life expectancy table, a synthetic cohort containing 1000 individuals was created, with other risk factors assigned values representative of the real-world population. The simulations were executed for all synthetic cohorts and life expectancy for each cell was calculated as mean survival time of the individuals in the respective cohort. RESULTS: There was a substantial variation in life expectancy across patients with different risk factor levels. Life expectancy of 20-year-old men varied from 29.3 years to 50.6 years, constituting a gap of 21.3 years between those with worst and best risk factor levels. In 20-year-old women, this gap was 18.9 years (life expectancy range 35.0-53.9 years). The variation in life expectancy was a function of the combination of risk factor values, with HbA1c and eGFR consistently showing a negative and positive correlation, respectively, with life expectancy at any level combination of other risk factors. Individuals with the lowest level (20 kg/m2) and highest level of BMI (35 kg/m2) had a lower life expectancy compared with those with a BMI of 25 kg/m2. Non-smokers and women had a higher life expectancy than smokers and men, respectively, with the difference in life expectancy ranging from 0.4 years to 2.7 years between non-smokers and smokers, and from 1.9 years to 5.9 years between women and men, depending on levels of other risk factors. CONCLUSIONS/INTERPRETATION: The life expectancy table generated in this study shows a substantial variation in life expectancy across individuals with different modifiable risk factors. The table allows for rapid communications of risk in an easily understood format between healthcare professionals, health economists, researchers, policy makers and patients. Particularly, it supports clinicians in their discussion with patients about the benefits of improving risk factors.

Population norms for quality adjusted life years for the United States of America, China, the United Kingdom and Australia.

Health economics uses quality adjusted life years (QALYs) to help healthcare decision makers. However, unlike life expectancy for which age- and sex-dependent national life tables are available, no general population norms exist to use as a benchmark against which to compare observed or modeled projections of QALYs in sub-populations or patients. We developed a 2-state Markov model to generate QALY population norms for the USA, UK, China and Australia. Annual age- and sex-specific probabilities of all-cause mortality were taken from life tables combined with general population country-specific age- and sex-specific health state utilities for the EQ-5D-3L (all countries); and SF-6D (Australia) multi-attribute utility instruments (MAUI). To validate our QALY benchmark model we found that the model closely predicted population life expectancies. Using EQ-5D-3L, undiscounted QALYs for males/females aged 18 years ranged 54.62/58.90 (USA), 55.55/60.21 (China), 57.11/60.16 (Australia), and 58.01/61.43 (UK) years. SF-6D benchmark QALYs for Australia were consistently lower than those generated from the EQ-5D-3L. The gap in undiscounted QALYs between the UK (highest) and the USA (lowest) was 2.53 QALYs in women and 3.39 QALYs in men aged 18 years. Our model's QALY population norms can be used for internal validation of future health economic models for the country-specific value sets for the instruments that we adopted, and when quantifying burden of disease in terms of QALYs lost due to illness compared to the general population. We have created a publicly available repository to continuously include QALY benchmarks that use country-specific value sets for other MAUIs and life expectancies.

Lessons from the pandemic on the value of research infrastructure.

The COVID-19 pandemic has shed a spotlight on the resilience of healthcare systems, and their ability to cope efficiently and effectively with unexpected crises. If we are to learn one economic lesson from the pandemic, arguably it is the perils of an overfocus on short-term allocative efficiency at the price of lack of capacity to deal with uncertain future challenges. In normal times, building spare capacity with 'option value' into health systems may seem inefficient, the costs potentially exceeding the benefits. Yet the fatal weakness of not doing so is that this can leave health systems highly constrained when dealing with unexpected, but ultimately inevitable, shocks-such as the COVID-19 pandemic. In this article, we argue that the pandemic has highlighted the potentially enormous option value of biomedical research infrastructure. We illustrate this with reference to COVID-19 response work supported by the United Kingdom National Institute for Health Research Oxford Biomedical Research Centre. As the world deals with the fallout from the most serious economic crisis since the Great Depression, pressure will soon come to review government expenditure, including research funding. Developing a framework to fully account for option value, and understanding the public appetite to pay for it, should allow us to be better prepared for the next emerging problem.

Association of technologically assisted integrated care with clinical outcomes in type 2 diabetes in Hong Kong using the prospective JADE Program: A retrospective cohort analysis.

BACKGROUND: Diabetes outcomes are influenced by host factors, settings, and care processes. We examined the association of data-driven integrated care assisted by information and communications technology (ICT) with clinical outcomes in type 2 diabetes in public and private healthcare settings. METHODS AND FINDINGS: The web-based Joint Asia Diabetes Evaluation (JADE) platform provides a protocol to guide data collection for issuing a personalized JADE report including risk categories (1-4, low-high), 5-year probabilities of cardiovascular-renal events, and trends and targets of 4 risk factors with tailored decision support. The JADE program is a prospective cohort study implemented in a naturalistic environment where patients underwent nurse-led structured evaluation (blood/urine/eye/feet) in public and private outpatient clinics and diabetes centers in Hong Kong. We retrospectively analyzed the data of 16,624 Han Chinese patients with type 2 diabetes who were enrolled in 2007-2015. In the public setting, the non-JADE group (n = 3,587) underwent structured evaluation for risk factors and complications only, while the JADE (n = 9,601) group received a JADE report with group empowerment by nurses. In a community-based, nurse-led, university-affiliated diabetes center (UDC), the JADE-Personalized (JADE-P) group (n = 3,436) received a JADE report, personalized empowerment, and annual telephone reminder for reevaluation and engagement. The primary composite outcome was time to the first occurrence of cardiovascular-renal diseases, all-site cancer, and/or death, based on hospitalization data censored on 30 June 2017. During 94,311 person-years of follow-up in 2007-2017, 7,779 primary events occurred. Compared with the JADE group (136.22 cases per 1,000 patient-years [95% CI 132.35-140.18]), the non-JADE group had higher (145.32 [95% CI 138.68-152.20]; P = 0.020) while the JADE-P group had lower event rates (70.94 [95% CI 67.12-74.91]; P < 0.001). The adjusted hazard ratios (aHRs) for the primary composite outcome were 1.22 (95% CI 1.15-1.30) and 0.70 (95% CI 0.66-0.75), respectively, independent of risk profiles, education levels, drug usage, self-care, and comorbidities at baseline. We reported consistent results in propensity-score-matched analyses and after accounting for loss to follow-up. Potential limitations include its nonrandomized design that precludes causal inference, residual confounding, and participation bias. CONCLUSIONS: ICT-assisted integrated care was associated with a reduction in clinical events, including death in type 2 diabetes in public and private healthcare settings.

Modelling the impact of multiple sclerosis on life expectancy, quality-adjusted life years and total lifetime costs: Evidence from Australia.

OBJECTIVES: To quantify life expectancy (LE), quality-adjusted life years (QALYs) and total lifetime societal costs for a hypothetical cohort of Australians with multiple sclerosis (MS). METHODS: A 4-state Markov model simulated progression from no/mild to moderate and severe disability and death for a cohort of 35-year-old women over a lifetime horizon. Death risks were calculated from Australian life tables, adjusted by disability severity. State-dependent relapse probabilities and associated disutilities were considered. Probabilities of MS progression and relapse were estimated from AusLong and TasMSL MS epidemiological databases. Annual societal (direct and indirect) costs (2017 Australian dollars) and health-state utilities for each state were derived from the Australian MS Longitudinal Study. Costs were discounted at 5% annually. RESULTS: Mean (95% confidence interval (CI)) LE from age 35 years was 42.7 (41.6-43.8) years. This was 7.5 years less than the general Australian population. Undiscounted QALYs were 28.2 (26.3-30.0), a loss of 13.1 QALYs versus the Australian population. Discounted lifetime costs were $942,754 ($347,856-$2,820,219). CONCLUSION: We have developed a health economics model of the progression of MS, calculating the impact of MS on LE, QALYs and lifetime costs in Australia. It will form the basis for future cost-effectiveness analyses of interventions for MS.

Evaluating the Ability of Economic Models of Diabetes to Simulate New Cardiovascular Outcomes Trials: A Report on the Ninth Mount Hood Diabetes Challenge.

OBJECTIVES: The cardiovascular outcomes challenge examined the predictive accuracy of 10 diabetes models in estimating hard outcomes in 2 recent cardiovascular outcomes trials (CVOTs) and whether recalibration can be used to improve replication. METHODS: Participating groups were asked to reproduce the results of the Empagliflozin Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME) and the Canagliflozin Cardiovascular Assessment Study (CANVAS) Program. Calibration was performed and additional analyses assessed model ability to replicate absolute event rates, hazard ratios (HRs), and the generalizability of calibration across CVOTs within a drug class. RESULTS: Ten groups submitted results. Models underestimated treatment effects (ie, HRs) using uncalibrated models for both trials. Calibration to the placebo arm of EMPA-REG OUTCOME greatly improved the prediction of event rates in the placebo, but less so in the active comparator arm. Calibrating to both arms of EMPA-REG OUTCOME individually enabled replication of the observed outcomes. Using EMPA-REG OUTCOME-calibrated models to predict CANVAS Program outcomes was an improvement over uncalibrated models but failed to capture treatment effects adequately. Applying canagliflozin HRs directly provided the best fit. CONCLUSIONS: The Ninth Mount Hood Diabetes Challenge demonstrated that commonly used risk equations were generally unable to capture recent CVOT treatment effects but that calibration of the risk equations can improve predictive accuracy. Although calibration serves as a practical approach to improve predictive accuracy for CVOT outcomes, it does not extrapolate generally to other settings, time horizons, and comparators. New methods and/or new risk equations for capturing these CV benefits are needed.

Development and Use of Prediction Models for Classification of Cardiovascular Risk of Remote Indigenous Australians.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death for Indigenous Australians. There is widespread belief that current tools have deficiencies for assessing CVD risk in this high-risk population. We sought to develop a 5-year CVD risk score using a wide range of known risk factors to further improve CVD risk prediction in this population. METHODS: We used clinical and demographic information on Indigenous people aged between 30 and 74 years without a history of CVD events who participated in the Well Person's Health Check (WPHC), a community-based survey. Baseline assessments were conducted between 1998 and 2000, and data were linked to administrative hospitalisation and death records for identification of CVD events. We used Cox proportional hazard models to estimate the 5-year CVD risk, and the Harrell's c-statistic and the modified Hosmer-Lemeshow (mH-L) χ2 statistic to assess the model discrimination and calibration, respectively. RESULTS: The study sample consisted of 1,583 individuals (48.1% male; mean age 45.0 year). The risk score consisted of sex, age, systolic blood pressure, diabetes mellitus, waist circumference, triglycerides, and albumin creatinine ratio. The bias-corrected c-statistic was 0.72 and the bias-corrected mH-L χ2 statistic was 12.01 (p-value, 0.212), indicating good discrimination and calibration, respectively. Using our risk score, the CVD risk of the Indigenous Australians could be stratified to a greater degree compared to a recalibrated Framingham risk score. CONCLUSIONS: A seven-factor risk score could satisfactorily stratify 5-year risk of CVD in an Indigenous Australian cohort. These findings inform future research targeting CVD risk in Indigenous Australians.

Estimation of annual probabilities of changing disability levels in Australians with relapsing-remitting multiple sclerosis.

BACKGROUND: Transition probabilities are the engine within many health economics decision models. However, the probabilities of progression of disability due to multiple sclerosis (MS) have not previously been estimated in Australia. OBJECTIVES: To estimate annual probabilities of changing disability levels in Australians with relapsing-remitting MS (RRMS). METHODS: Combining data from Ausimmune/Ausimmune Longitudinal (2003-2011) and Tasmanian MS Longitudinal (2002-2005) studies (n = 330), annual transition probabilities were obtained between no/mild (Expanded Disability Status Scale (EDSS) levels 0-3.5), moderate (EDSS 4-6.0) and severe (EDSS 6.5-9.5) disability. RESULTS: From no/mild disability, 6.4% (95% confidence interval (CI): 4.7-8.4) and 0.1% (0.0-0.2) progressed to moderate and severe disability annually, respectively. From moderate disability, 6.9% (1.0-11.4) improved (to no/mild state) and 2.6% (1.1-4.5) worsened. From severe disability, 0.0% improved to moderate and no/mild disability. Male sex, age at onset, longer disease duration, not using immunotherapies greater than 3 months and a history of relapse were related to higher probabilities of worsening. CONCLUSION: We have estimated probabilities of changing disability levels in Australians with RRMS. Probabilities differed between various subgroups, but due to small sample sizes, results should be interpreted with caution. Our findings will be helpful in predicting long-term disease outcomes and in health economic evaluations of MS.

Individualized Glycemic Control for U.S. Adults With Type 2 Diabetes: A Cost-Effectiveness Analysis.

Background: Intensive glycemic control in type 2 diabetes (glycated hemoglobin [HbA1c] level <7%) is an established, cost-effective standard of care. However, guidelines recommend individualizing goals on the basis of age, comorbidity, diabetes duration, and complications. Objective: To estimate the cost-effectiveness of individualized control versus uniform intensive control (HbA1c level <7%) for the U.S. population with type 2 diabetes. Design: Patient-level Monte Carlo-based Markov model. Data Sources: National Health and Nutrition Examination Survey 2011-2012. Target Population: The approximately 17.3 million persons in the United States with diabetes diagnosed at age 30 years or older. Time Horizon: Lifetime. Perspective: Health care sector. Intervention: Individualized versus uniform intensive glycemic control. Outcome Measures: Average lifetime costs, life-years, and quality-adjusted life-years (QALYs). Results of Base-Case Analysis: Individualized control saved $13 547 per patient compared with uniform intensive control ($105 307 vs. $118 854), primarily due to lower medication costs ($34 521 vs. $48 763). Individualized control decreased life expectancy (20.63 vs. 20.73 years) due to an increase in complications but produced more QALYs (16.68 vs. 16.58) due to fewer hypoglycemic events and fewer medications. Results of Sensitivity Analysis: Individualized control was cost-saving and generated more QALYs compared with uniform intensive control, except in analyses where the disutility associated with receiving diabetes medications was decreased by at least 60%. Limitation: The model did not account for effects of early versus later intensive glycemic control. Conclusion: Health policies and clinical programs that encourage an individualized approach to glycemic control for U.S. adults with type 2 diabetes reduce costs and increase quality of life compared with uniform intensive control. Additional research is needed to confirm the risks and benefits of this strategy. Primary Funding Source: National Institute of Diabetes and Digestive and Kidney Diseases.

Computer Modeling of Diabetes and Its Transparency: A Report on the Eighth Mount Hood Challenge.

OBJECTIVES: The Eighth Mount Hood Challenge (held in St. Gallen, Switzerland, in September 2016) evaluated the transparency of model input documentation from two published health economics studies and developed guidelines for improving transparency in the reporting of input data underlying model-based economic analyses in diabetes. METHODS: Participating modeling groups were asked to reproduce the results of two published studies using the input data described in those articles. Gaps in input data were filled with assumptions reported by the modeling groups. Goodness of fit between the results reported in the target studies and the groups' replicated outputs was evaluated using the slope of linear regression line and the coefficient of determination (R2). After a general discussion of the results, a diabetes-specific checklist for the transparency of model input was developed. RESULTS: Seven groups participated in the transparency challenge. The reporting of key model input parameters in the two studies, including the baseline characteristics of simulated patients, treatment effect and treatment intensification threshold assumptions, treatment effect evolution, prediction of complications and costs data, was inadequately transparent (and often missing altogether). Not surprisingly, goodness of fit was better for the study that reported its input data with more transparency. To improve the transparency in diabetes modeling, the Diabetes Modeling Input Checklist listing the minimal input data required for reproducibility in most diabetes modeling applications was developed. CONCLUSIONS: Transparency of diabetes model inputs is important to the reproducibility and credibility of simulation results. In the Eighth Mount Hood Challenge, the Diabetes Modeling Input Checklist was developed with the goal of improving the transparency of input data reporting and reproducibility of diabetes simulation model results.

Cost-effectiveness of artemisinin-naphthoquine versus artemether-lumefantrine for the treatment of uncomplicated malaria in Papua New Guinean children.

BACKGROUND: A recent randomized trial showed that artemisinin-naphthoquine (AN) was non-inferior to artemether-lumefantrine (AL) for falciparum malaria and superior for vivax malaria in young Papua New Guinean children. The aim of this study was to compare the cost-effectiveness of these two regimens. METHODS: An incremental cost-effectiveness analysis was performed using data from 231 children with Plasmodium falciparum and/or Plasmodium vivax infections in an open-label, randomized, parallel-group trial. Recruited children were randomized 1:1 to receive once daily AN for 3 days with water or twice daily AL for 3 days given with fat. World Health Organisation (WHO) definitions were used to determine clinical/parasitological outcomes. The cost of transport between the home and clinic, plus direct health-care costs, served as a basis for determining each regimen's incremental cost per incremental treatment success relative to AL by Day 42 and its cost per life year saved. RESULTS: In the usual care setting, AN was more effective for the treatment of uncomplicated malaria in children aged 0.5-5.9 years. AL and AN were equally efficacious for the treatment of falciparum malaria, however AN had increased anti-malarial treatment costs per patient of $10.46, compared with AL. AN was the most effective regimen for treatment of vivax malaria, but had increased treatment costs of $14.83 per treatment success compared with AL. CONCLUSIONS: Whilst AN has superior overall efficacy for the treatment of uncomplicated malaria in PNG children, AL was the less costly regimen. An indicative extrapolation estimated the cost per life year saved by using AN instead of AL to treat uncomplicated malaria to be $12,165 for girls and $12,469 for boys (discounted), which means AN may not be cost-effective and affordable for PNG at current cost. However, AN may become acceptable should it become WHO prequalified and/or should donated/subsidized drug supply become available.

Predicting the Long-Term Gains in Health-Related Quality of Life After Total Knee Arthroplasty.

BACKGROUND: We investigated the predictors of long-term gains in quality-adjusted life years (QALYs) from total knee arthroplasty (TKA) and the patient attributes that predicted cost-effective TKA. METHODS: Data on TKA patients (n = 570) from 2006 to 2007 were extracted from a single-institution registry. QALY gains over 7 years post surgery were calculated from health-related quality of life (HrQoL) scores measured preoperatively and annually postoperatively using the short-form health survey (SF-12) instrument. Multivariate linear regression analysis investigated the predictors of QALY gain from TKA from a broad range of preoperative patient characteristics and was used to predict QALY gains for each individual. Patients were grouped into deciles according to their predicted QALY gain, and the cost-effectiveness of each decile was plotted on the cost-effectiveness plane. Patient attribute differences between deciles were decomposed. RESULTS: After exclusions and dropout, data were available for 488 patients. The average estimated QALY gain over 7 years was 0.77 (95% confidence interval [CI] 0.70-0.83). Predictors significantly associated with smaller QALY gains were comorbidities (Charlson comorbidity index 3+ coefficient -0.54 CI -0.15 to -0.92), the absence of severe osteoarthritis in the ipsilateral knee (-0.51 CI -0.16 to -0.85), preoperative HrQoL (standardized coefficient -0.34 CI -0.26 to -0.43), the requirement for an interpreter (-0.24 CI -0.05 to -0.44), and age (-0.01 CI -0.01 to -0.02). The largest difference between cost-effective and non-cost-effective deciles was relatively high preoperative HrQoL in the non-cost-effective decile. CONCLUSION: TKA is likely to be cost-effective for most patients except those with unusually high preoperative HrQoL or a lack of severe osteoarthritis. The poorer outcomes for those requiring an interpreter requires further research.

Growth of linked hospital data use in Australia: a systematic review.

Objective The aim of the present study was to quantify and understand the utilisation of linked hospital data for research purposes across Australia over the past two decades. Methods A systematic review was undertaken guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 checklist. Medline OVID, PsycINFO, Embase, EconLit and Scopus were searched to identify articles published from 1946 to December 2014. Information on publication year, state(s) involved, type of data linkage, disease area and purpose was extracted. Results The search identified 3314 articles, of which 606 were included; these generated 629 records of hospital data linkage use across all Australian states and territories. The major contributions were from Western Australia (WA; 51%) and New South Wales (NSW; 32%) with the remaining states and territories having significantly fewer publications (total contribution only 17%). WA's contribution resulted from a steady increase from the late 1990s, whereas NSW's contribution is mostly from a rapid increase from 2010. Current data linkage is primarily used in epidemiological research (73%). Conclusion More than 80% of publications were from WA and NSW, whereas other states significantly lag behind. The observable growth in these two states clearly demonstrates the underutilised opportunities for data linkage to add value in health services research in the other states. What is known about the topic? Linking administrative hospital data to other data has the potential to be a cost-effective method to significantly improve health policy. Over the past two decades, Australia has made significant investments in improving its data linkage capabilities. However, several articles have highlighted the many barriers involved in using linked hospital data. What does this paper add? This paper quantitatively evaluates the performance across all Australian states in terms of the use of their administrative hospital data for research purposes. The performance of states varies considerably, with WA and NSW the clear stand-out performers and limited outputs currently seen for the other Australian states and territories. What are the implications for practitioners? Given the significant investments made into data linkage, it is important to continue to evaluate and monitor the performance of the states in terms of translating this investment into outputs. Where the outputs do not match the investment, it is important to identify and overcome those barriers limiting the gains from this investment. More generally, there is a need to think about how we improve the effective and efficient use of data linkage investments in Australia.