Estimation of Transition Probabilities from a Large Cohort (> 6000) of Australians Living with Multiple Sclerosis (MS) for Changing Disability Severity Classifications, MS Phenotype, and Disease-Modifying Therapy Classifications.

BACKGROUND: Multiple sclerosis (MS) is a chronic autoimmune/neurodegenerative disease associated with progressing disability affecting mostly women. We aim to estimate transition probabilities describing MS-related disability progression from no disability to severe disability. Transition probabilities are a vital input for health economics models. In MS, this is particularly relevant for pharmaceutical agency reimbursement decisions for disease-modifying therapies (DMTs). METHODS: Data were obtained from Australian participants of the MSBase registry. We used a four-state continuous-time Markov model to describe how people with MS transition between disability milestones defined by the Expanded Disability Status Scale (scale 0-10): no disability (EDSS of 0.0), mild (EDSS of 1.0-3.5), moderate (EDSS of 4.0-6.0), and severe (EDSS of 6.5-9.5). Model covariates included sex, DMT usage, MS-phenotype, and disease duration, and analysis of covariate groups were also conducted. All data were recorded by the treating neurologist. RESULTS: A total of N = 6369 participants (mean age 42.5 years, 75.00% female) with 38,837 person-years of follow-up and 54,570 clinical reviews were identified for the study. Annual transition probabilities included: remaining in the no, mild, moderate, and severe states (54.24%, 82.02%, 69.86%, 77.83% respectively) and transitioning from no to mild (42.31%), mild to moderate (11.38%), and moderate to severe (9.41%). Secondary-progressive MS was associated with a 150.9% increase in the hazard of disability progression versus relapsing-remitting MS. CONCLUSIONS: People with MS have an approximately 45% probability of transitioning from the no disability state after one year, with people with progressive MS transitioning from this health state at a much higher rate. These transition probabilities will be applied in a publicly available health economics simulation model for Australia and similar populations, intended to support reimbursement of a plethora of existing and upcoming interventions including medications to reduce progression of MS.

Exploring the workplace factors and their influence on the employment outcomes for people with multiple sclerosis.

BACKGROUND: High unemployment rate of people with multiple sclerosis (PwMS) is associated with substantial economic costs. Whilst the impact of MS symptoms and other disease-related factors on employment outcomes of PwMS has been assessed, limited evidence exists on the impacts of workplace factors. OBJECTIVE: To investigate the most common individual and group workplace factors associated with unemployment or a perceived risk of unemployment in PwMS, and to identify patient subgroups that are more susceptible to changes in employment status due to such factors. METHODS: Data from the Australian MS Longitudinal Study (AMSLS) on employment status and workplace factors were used. Fifteen workplace factors were classified under four groups: organisational, commuting, moving around at work, and equipment usage factors. Participants answered 'Yes' to each factor if it related to their unemployment and/or perceived risk of becoming unemployed and a group factor was considered "Yes" if at least one individual factor within it was answered as "Yes". The proportions of "Yes" responses were calculated for both individual and group factors. Total number of individual factors was calculated and descriptive analyses and ordered logistic regression were used to summarize the total number of factors affecting each participant, and their association with participants' occupations, sex, disability severity and disease duration. RESULTS: Common workplace factors influencing employment were organisational (39.8 % perceived risk, 44.0 % lost employment), commuting (28.9 % perceived risk) and equipment usage difficulty (30.9 % lost employment). Common individual factors included inflexible working conditions, lack of suitable work, commuting difficulties, architectural barriers, and requirement to stand for long periods to use equipment. Professionals, blue-collar workers, and those with moderate/severe disability were more likely to report a higher number of workplace factors risking their employment. CONCLUSIONS: Workplace factors undermine PwMS employment, with variations among subgroups based on occupation and disability severity. Understanding these barriers is crucial for supporting PwMS in the workforce.

The Hidden Toll of Psychological Distress in Australian Adults and Its Impact on Health-Related Quality of Life Measured as Health State Utilities.

BACKGROUND: Psychological distress (PD) is a major health problem that affects all aspects of health-related quality of life including physical, mental and social health, leading to a substantial human and economic burden. Studies have revealed a concerning rise in the prevalence of PD and various mental health conditions among Australians, particularly in female individuals. There is a scarcity of studies that estimate health state utilities (HSUs), which reflect the overall health-related quality of life in individuals with PD. No such studies have been conducted in Australia thus far. OBJECTIVE: We aimed to evaluate the age-specific, sex-specific and PD category-specific HSUs (disutilities) in Australian adults with PD to inform healthcare decision making in the management of PD. METHODS: Data on age, sex, SF-36/SF6D responses, Kessler psychological distress (K10) scale scores and other characteristics of N = 15,139 participants (n = 8149 female individuals) aged >15 years were derived from the latest wave (21) of the nationally representative Household, Income and Labor Dynamics in Australia survey. Participants were grouped into the severity categories of no (K10 score: 10-19), mild (K10: 20-24), moderate (K10: 25-29) and severe PD (K10: 30-50). Both crude and adjusted HSUs were calculated from participants' SF-36 profiles, considering potential confounders such as smoking, marital status, remoteness, education and income levels. The calculations were based on the SF-6D algorithm and aligned with Australian population norms. Additionally, the HSUs were stratified by age, sex and PD categories. Disutilities of PD, representing the mean difference between HSUs of people with PD and those without, were also calculated for each group. RESULTS: The average age of individuals was 46.130 years (46% male), and 31% experienced PD in the last 4 weeks. Overall, individuals with PD had significantly lower mean HSUs than those likely to be no PD, 0.637 (95% confidence interval [CI] 0.636, 0.640) vs 0.776 (95% CI 0.775, 0.777) i.e. disutility: -0.139 [95% CI -0.139, -0.138]). Mean disutilities of -0.108 (95% CI -0.110, -0.104), -0.140 (95% CI -0.142, -0.138), and -0.188 (95% CI -0.190, -0.187) were observed for mild PD, moderate PD and severe PD, respectively. Disutilities of PD also differed by age and sex groups. For instance, female individuals had up to 0.049 points lower mean HSUs than male individuals across the three classifications of PD. There was a clear decline in health-related quality of life with increasing age, demonstrated by lower mean HSUs in older population age groups, that ranged from 0.818 (95% CI 0.817, 0.818) for the 15-24 years age group with no PD to 0.496 (95% CI 0.491, 0.500) for the 65+ years age group with severe PD). Across all ages and genders, respondents were more likely to report issues in certain dimensions, notably vitality, and these responses did not uniformly align with ageing. CONCLUSIONS: The burden of PD in Australia is substantial, with a significant impact on female individuals and older individuals. Implementing age-specific and sex-specific healthcare interventions to address PD among Australian adults may greatly alleviate this burden. The PD state-specific HSUs calculated in our study can serve as valuable inputs for future health economic evaluations of PD in Australia and similar populations.

The economic burden of myalgic encephalomyelitis/chronic fatigue syndrome in Australia.

Objective This study aimed to estimate costs of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) to patients, government and Australian society. Methods Australian ME/CFS patients and their carers were recruited using convenience sampling. Patients completed an online retrospective cost diary, providing ME/CFS-related direct medical, non-medical and indirect costs. Informal care costs were collected directly from carers. Data from the Pharmaceutical Benefits Scheme and Medicare Benefits Schedule were linked to participant survey data. Annual per patient and total societal costs were estimated, broken down by category and presented in 2021 AUD. Factors associated with higher costs were investigated using generalised linear models. Results One hundred and seventy five patients (mean age 49 years s.d. 14, 79.4% female) completed the cost diary. Estimated total annual societal costs of ME/CFS in Australia ranged between $1.38 and $10.09 billion, with average annual total costs of $63 400/patient. Three-quarters of these costs were due to indirect costs ($46 731). Disability severity was the key factor associated with higher costs, particularly for indirect costs (being 2.27-fold higher for severe disability than no/mild disability). Conclusions ME/CFS poses a significant economic burden in Australia, owing mainly to high indirect and informal care costs.

Estimating the disutility of relapse in relapsing-remitting and secondary progressive multiple sclerosis using the EQ-5D-5L, AQoL-8D, EQ-5D-5L-psychosocial, and SF-6D: implications for health economic evaluation models.

BACKGROUND AND AIMS: Relapses are an important clinical feature of multiple sclerosis (MS) that result in temporary negative changes in quality of life (QoL), measured by health state utilities (HSUs) (disutilities). We aimed to quantify disutilities of relapse in relapsing remitting MS (RRMS), secondary progressive MS (SPMS), and relapse onset MS [ROMS (including both RRMS and SPMS)] and examine these values by disability severity using four multi-attribute utility instruments (MAUIs). METHODS: We estimated (crude and adjusted and stratified by disability severity) disutilities (representing the mean difference in HSUs of 'relapse' and 'no relapse' groups as well as 'unsure' and 'no relapse' groups) in RRMS (n = 1056), SPMS (n = 239), and ROMS (n = 1295) cohorts from the Australian MS Longitudinal Study's 2020 QoL survey, using the EQ-5D-5L, AQoL-8D, EQ-5D-5L-Psychosocial, and SF-6D MAUIs. RESULTS: Adjusted mean overall disutilities of relapse in RMSS/SPMS/ROMS were - 0.101/- 0.149/- 0.129 (EQ-5D-5L), - 0.092/- 0.167/- 0.113 (AQoL-8D), - 0.080/- 0.139/- 0.097 (EQ-5D-5L-Psychosocial), and - 0.116/- 0.161/- 0.130 (SF-6D), approximately 1.5 times higher in SPMS than in RRMS, in all MAUI. All estimates were statistically significant and/or clinically meaningful. Adjusted disutilities of RRMS and ROMS demonstrated a U-shaped relationship between relapse disutilities and disability severity. Relapse disutilities were higher in 'severe' disability than 'mild' and 'moderate' in the SPMS cohort. CONCLUSION: MS-related relapses are associated with substantial utility decrements. As the type and severity of MS influence disutility of relapse, the use of disability severity and MS-type-specific disutility inputs is recommended in future health economic evaluations of MS. Our study supports relapse management and prevention as major mechanisms to improve QoL in people with MS.

Long-term exposure to low concentrations of air pollution and decline in lung function in people with idiopathic pulmonary fibrosis: Evidence from Australia.

BACKGROUND AND OBJECTIVE: Little is known about the association between ambient air pollution and idiopathic pulmonary fibrosis (IPF) in areas with lower levels of exposure. We aimed to investigate the impact of air pollution on lung function and rapid progression of IPF in Australia. METHODS: Participants were recruited from the Australian IPF Registry (n = 570). The impact of air pollution on changes in lung function was assessed using linear mixed models and Cox regression was used to investigate the association with rapid progression. RESULTS: Median (25th-75th percentiles) annual fine particulate matter (<2.5 µm, PM2.5 ) and nitrogen dioxide (NO2 ) were 6.8 (5.7, 7.9) µg/m3 and 6.7 (4.9, 8.2) ppb, respectively. Compared to living more than 100 m from a major road, living within 100 m was associated with a 1.3% predicted/year (95% confidence interval [CI] -2.4 to -0.3) faster annual decline in diffusing capacity of the lungs for carbon monoxide (DLco). Each interquartile range (IQR) of 2.2 µg/m3 increase in PM2.5 was associated with a 0.9% predicted/year (95% CI -1.6 to -0.3) faster annual decline in DLco, while there was no association observed with NO2 . There was also no association between air pollution and rapid progression of IPF. CONCLUSION: Living near a major road and increased PM2.5 were both associated with an increased rate of annual decline in DLco. This study adds to the evidence supporting the negative effects of air pollution on lung function decline in people with IPF living at low-level concentrations of exposure.

The influence of immortal time bias in observational studies examining associations of antifibrotic therapy with survival in idiopathic pulmonary fibrosis: A simulation study.

Background: Immortal time bias (ITB) has been overlooked in idiopathic pulmonary fibrosis (IPF). We aimed to identify the presence of ITB in observational studies examining associations between antifibrotic therapy and survival in patients with IPF and illustrate how ITB may affect effect size estimates of those associations. Methods: Immortal time bias was identified in observational studies using the ITB Study Assessment Checklist. We used a simulation study to illustrate how ITB may affect effect size estimates of antifibrotic therapy on survival in patients with IPF based on four statistical techniques including time-fixed, exclusion, time-dependent and landmark methods. Results: Of the 16 included IPF studies, ITB was detected in 14 studies, while there were insufficient data for assessment in two others. Our simulation study showed that use of time-fixed [hazard ratio (HR) 0.55, 95% confidence interval (CI) 0.47-0.64] and exclusion methods (HR 0.79, 95% CI 0.67-0.92) overestimated the effectiveness of antifibrotic therapy on survival in simulated subjects with IPF, in comparison of the time-dependent method (HR 0.93, 95% CI 0.79-1.09). The influence of ITB was mitigated using the 1 year landmark method (HR 0.69, 95% CI 0.58-0.81), compared to the time-fixed method. Conclusion: The effectiveness of antifibrotic therapy on survival in IPF can be overestimated in observational studies, if ITB is mishandled. This study adds to the evidence for addressing the influence of ITB in IPF and provides several recommendations to minimize ITB. Identifying the presence of ITB should be routinely considered in future IPF studies, with the time-dependent method being an optimal approach to minimize ITB.

The relative contribution of co-morbidities to health-related quality of life of people with idiopathic pulmonary fibrosis using the Assessment of Quality of Life-8-Dimension multi-attribute utility instrument.

PURPOSE: Little is known about the impact of co-morbidities on health-related quality of life (HRQoL) for people with idiopathic pulmonary fibrosis (IPF). We aimed to investigate the relative contribution of co-morbidities to HRQoL of people with IPF. METHODS: N = 157 participants were recruited from the Australian IPF Registry (AIPFR). Health state utilities (HSUs), and the super-dimensions of physical and psychosocial scores were measured using the Assessment of Quality of Life-8-Dimensions (AQoL-8D). The impact of co-morbidities on HRQoL was investigated using linear regression and general dominance analyses. RESULTS: A higher number of co-morbidities was associated with lower HSUs (p trend = 0.002). Co-morbidities explained 9.1% of the variance of HSUs, 16.0% of physical super-dimensional scores, and 4.2% of psychosocial super-dimensional scores. Arthritis was associated with a significant reduction on HSUs (ß = - 0.09, 95% confidence interval [CI] - 0.16 to - 0.02), largely driven by reduced scores on the physical super-dimension (ß = - 0.13, 95% CI - 0.20 to - 0.06). Heart diseases were associated with a significant reduction on HSUs (ß = - 0.09, 95% CI - 0.16 to - 0.02), driven by reduced scores on physical (ß = - 0.09, 95% CI - 0.16 to - 0.02) and psychosocial (ß = -0.10, 95% CI - 0.17 to - 0.02) super-dimensions. CONCLUSIONS: Co-morbidities significantly impact HRQoL of people with IPF, with markedly negative impacts on their HSUs and physical health. A more holistic approach to the care of people with IPF is important as better management of these co-morbidities could lead to improved HRQoL in people with IPF.

Validation of the EQ-5D-5L and psychosocial bolt-ons in a large cohort of people living with multiple sclerosis in Australia.

BACKGROUND: Multiple sclerosis (MS) is an inflammatory, neurodegenerative disease of the central nervous system which results in disability over time and reduced quality of life. To increase the sensitivity of the EQ-5D-5L for psychosocial health, four bolt-on items from the AQoL-8D were used to create the nine-item EQ-5D-5L-Psychosocial. We aimed to externally validate the EQ-5D-5L-Psychosocial in a large cohort of people with MS (pwMS) and explore the discriminatory power of the new instrument with EQ-5D-5L/AQoL-8D. METHODS: A large representative sample from the Australian MS Longitudinal Study completed the AQoL-8D and EQ-5D-5L (including EQ VAS) and both instruments health state utilities (HSUs) were scored using Australian tariffs. Sociodemographic/clinical data were also collected. External validity of EQ-5D-5L-Psychosocial scoring algorithm was assessed with mean absolute errors (MAE) and Spearman's correlation coefficient. Discriminatory sensitivity was assessed with an examination of ceiling/floor effects, and disability severity classifications. RESULTS: Among 1683 participants (mean age: 58.6 years; 80% female), over half (55%) had moderate or severe disability. MAE (0.063) and the distribution of the prediction error were similar to the original development study. Mean (± standard deviation) HSUs were EQ-5D-5L: 0.58 ± 0.32, EQ-5D-5L-Psychosocial 0.62 ± 0.29, and AQoL-8D: 0.63 ± 0.20. N = 157 (10%) scored perfect health (i.e. HSU = 1.0) on the EQ-5D-5L, but reported a mean HSU of 0.90 on the alternative instruments. The Sleep bolt-on dimension was particularly important for pwMS. CONCLUSIONS: The EQ-5D-5L-Psychosocial is more sensitive than the EQ-5D-5L in pwMS whose HSUs approach those reflecting full health. When respondent burden is taken into account, the EQ-5D-5L-Psychosocial is preferential to the AQoL-8D. We suggest a larger confirmatory study comparing all prevalent multi-attribute utility instruments for pwMS.

Maternal choices and preferences for screening strategies of gestational diabetes mellitus: A exploratory study using discrete choice experiment.

Aims: This study aimed to investigate maternal preferences for gestational diabetes mellitus (GDM) screening options in rural China to identify an optimal GDM screening strategy. Methods: Pregnant women at 24-28 gestational weeks were recruited from Shandong province, China. A discrete choice experiment (DCE) was conducted to elicit pregnant women's preferences for GDM screening strategy defined by five attributes: number of blood draws, out-of-pocket costs, screening waiting-time, number of hospital visits, and positive diagnosis rate. A mixed logistic model was employed to quantify maternal preferences, and to estimate the relative importance of included attributes in determining pregnant women's preferences for two routinely applied screening strategies ("one-step": 75 g oral glucose tolerance test [OGTT] and "two-step": 50 g glucose challenge-test plus 75 g OGTT). Preference heterogeneity was also investigated. Results: N = 287 participants completed the DCE survey. All five predefined attributes were associated with pregnant women's preferences. Diagnostic rate was the most influential attribute (17.5 vs. 8.0%, OR: 2.89; 95%CI: 2.10 to 3.96). When changes of the attributes of "two-step" to "one-step" strategies, women's uptake probability from full "two-step" to "one-step" significantly increased with 71.3% (95%CI: 52.2 to 90.1%), but no significant difference with the first step of "two-step" (-31.0%, 95%CI: -70.2 to 8.1%). Conclusion: Chinese pregnant women preferred the "one-step" screening strategy to the full "two-step" strategy, but were indifferent between "one-step" and the first step of "two-step" strategies.

A gender-specific assessment of tobacco use risk factors: evidence from the latest Pakistan demographic and health survey.

BACKGROUND: The high prevalence of tobacco use in Pakistan poses a substantial health and economic burden to Pakistani individuals, families, and society. However, a comprehensive assessment of the key risk factors of tobacco use in Pakistan is very limited in the literature. A better understanding of the key risk factors of tobacco use is needed to identify and implement effective tobacco control measures. OBJECTIVES: To investigate the key socioeconomic, demographic, and psychosocial determinants of tobacco smoking in a recent large nationally representative sample of Pakistani adults. METHODS: N = 18,737 participants (15,057 females and 3680 males) from the 2017-18 Pakistan Demographic Health Survey, aged 15-49 years, with data on smoking use and related factors were included. Characteristics of male and female participants were compared using T-tests (for continuous variables) and χ2-tests (for categorical variables). Multivariable logistic regression models were used to identify gender-specific risk factors of tobacco use. The Receiver Operating Characteristic Curve test was used to evaluate the predictive power of models. RESULTS: We found that the probability of smoking for both males and females is significantly associated with factors such as their age, province/region of usual residence, education level, wealth, and marital status. For instance, the odds of smoking increased with age (from 1.00 [for ages 15-19 years] to 3.01 and 5.78 respectively for females and males aged 45-49 years) and decreased with increasing education (from 1.00 [for no education] to 0.47 and 0.50 for females and males with higher education) and wealth (from 1.00 [poorest] to 0.43 and 0.47 for richest females and males). Whilst the odd ratio of smoking for rural males (0.67) was significantly lower than that of urban males (1.00), the odds did not differ significantly between rural and urban females. Finally, factors such as occupation type, media influence, and domestic violence were associated with the probability of smoking for Pakistani females only. CONCLUSIONS: This study identified gender-specific factors contributing to the risk of tobacco usage in Pakistani adults, suggesting that policy interventions to curb tobacco consumption in Pakistan should be tailored to specific population sub-groups based on their sociodemographic and psychosocial features.

Resource utilization and disaggregated cost analysis of bariatric surgery in the Australian public healthcare system.

OBJECTIVES: To present a comprehensive real-world micro-costing analysis of bariatric surgery. METHODS: Patients were included if they underwent primary bariatric surgery (gastric banding [GB], gastric bypass [GBP] and sleeve gastrectomy [SG]) between 2013 and 2019. Costs were disaggregated into cost items and average-per-patient costs from the Australian healthcare systems perspective were expressed in constant 2019 Australian dollars for the entire cohort and subgroup analysis. Annual population-based costs were calculated to capture longitudinal trends. A generalized linear model (GLM) predicted the overall bariatric-related costs. RESULTS: N = 240 publicly funded patients were included, with the waitlist times of ≤ 10.7 years. The mean direct costs were $11,269. The operating theatre constituted the largest component of bariatric-related costs, followed by medical supplies, salaries, critical care use, and labour on-costs. Average cost for SG ($12,632) and GBP ($15,041) was higher than that for GB ($10,049). Operating theatre accounted for the largest component for SG/GBP costs, whilst medical supplies were the largest for GB. We observed an increase in SG and a decrease in GB procedures over time. Correspondingly, the main cost driver changed from medical supplies in 2014-2015 for GB procedures to operating theatre for SG thereafter. GLM model estimates of bariatric average cost ranged from $7,580 to $36,633. CONCLUSIONS: We presented the first detailed characterization of the scale, disaggregated profile and determinants of bariatric-related costs, and examined the evolution of resource utilization patterns and costs, reflecting the shift in the Australian bariatric landscape over time. Understanding these patterns and forecasting of future changes are critical for efficient resource allocation.

Healthcare resource utilisation and predictors for critical care unit admissions after primary bariatric surgery in an Australian public hospital setting: an exploratory study using a mixed-methods approach.

Objectives The aim of this exploratory study was to investigate resource use and predictors associated with critical care unit (CCU) admission after primary bariatric surgery within the Tasmanian public healthcare system. Methods Patients undergoing primary bariatric surgery in the Tasmanian Health Service (THS) public hospital system between 7 July 2013 and 30 June 2019 were eligible for inclusion in this study. The THS provides two levels of CCU support, an intensive care unit (ICU) and a high dependency unit (HDU). A mixed-methods approach was performed to examine the resource use and predictors associated with overall CCU admission, as well as levels of HDU and ICU admission. Results There were 254 patients in the study. Of these, 44 (17.3%) required 54 postoperative CCU admissions, with 43% requiring HDU support and 57% requiring more resource-demanding ICU support. Overall, CCU patients were more likely to have higher preoperative body mass index and multimorbidity and to undergo sleeve gastrectomy or gastric bypass. Patients undergoing gastric banding were more likely to require HDU rather than ICU support. Total hospital stays and median healthcare costs were higher for CCU (particularly ICU) patients than non-CCU patients. Conclusions Bariatric surgery patients often have significant comorbidities. This study demonstrates that patients with higher levels of morbidity are more likely to require critical care postoperatively. Because this is elective surgery, being able to identify patients who are at increased risk is important to plan either the availability of critical care or even interventions to improve patients' preoperative risk. Further work is required to refine the pre-existing conditions that contribute most to the requirement for critical care management (particularly in the ICU setting) in the perioperative period. What is known about the topic? Few studies (both Australian and international) have investigated the use of CCUs after bariatric surgery. Those that report CCU admission rates are disparate across the contemporaneous literature, reflecting the different healthcare systems and their associated incentives. In Australia, the incidence and utilisation of CCUs (consisting of HDUs and ICUs) after bariatric surgery have only been reported using Western Australian administrative data. What does the paper add? CCU patients were more likely to have a higher preoperative body mass index and multimorbidity and to undergo a sleeve gastrectomy or gastric bypass procedure. Just over half (57%) of these patients were managed in the ICU. Sleeve gastrectomy patients had a higher incidence of peri- and postoperative complications that resulted in an unplanned ICU admission. Hospital length of stay and aggregated costs were higher for CCU (particularly ICU) patients. What are the implications for practitioners? The association of increased CCU (particularly ICU) use with multimorbidity and peri- and postoperative complications could enable earlier recognition of patients that are more likely to require CCU and ICU support, therefore allowing improved planning when faced with increasing rates of bariatric surgery. We suggest streamlined clinical guidelines that anticipate CCU support for people with severe and morbid obesity who undergo bariatric surgery should be considered from a national perspective.

Systematic Review and Meta-Analysis of Health State Utility Values for Osteoarthritis-Related Conditions.

OBJECTIVE: Health state utility values (HSUVs) are a key input in health economic modeling, but HSUVs of people with osteoarthritis (OA)-related conditions have not been systematically reviewed and meta-analyzed. Our objective was to systematically review and meta-analyze the HSUVs for people with OA. METHODS: Searches within health economic/biomedical databases were performed to identify eligible studies reporting OA-related HSUVs. Data on study design, participant characteristics, affected OA joint sites, treatment type, HSUV elicitation method, considered health states, and the reported HSUVs were extracted. HSUVs for people with knee, hip, and mixed OA in pre- and posttreatment populations were meta-analyzed using random effects models. RESULTS: A total of 151 studies were included in the systematic review, and 88 in meta-analyses. Of 151 studies, 56% were conducted in Europe, 75% were in people with knee and/or hip OA, and 79% were based on the EuroQoL 5-dimension instrument. The pooled mean baseline HSUVs for knee OA core interventions, medication, injection, and primary surgery treatments were 0.64 (95% confidence interval [95% CI] 0.61-0.66), 0.56 (95% CI 0.45-0.68), 0.58 (95% CI 0.50-0.66), and 0.52 (95% CI 0.49-0.55), respectively. These were 0.71 (95% CI 0.59-0.84) for hip OA core interventions and 0.52 (95% CI 0.49-0.56) for hip OA primary surgery. For all knee OA treatments and hip OA primary surgery, pooled HSUVs were significantly higher in the post- than the pretreatment populations. CONCLUSION: This study provides a comprehensive summary of OA-related HSUVs and generates an HSUV database for people with different affected OA joint sites undergoing different treatments to guide HSUV choices in future health economic modeling of OA interventions.

Impact of remoteness on patient outcomes for people with multiple sclerosis in Australia.

BACKGROUND: Little is known about whether living in remote areas is associated with worse health outcomes in Australians with MS. OBJECTIVES: To evaluate whether living in remote areas was associated with worse health outcomes, employment outcomes and different disease modifying therapy (DMTs) utilisation among Australians with MS. METHODS: We included 1,611 participants from the Australian MS Longitudinal Study. Level of remoteness (major cities, inner regional, outer regional, remote, and very remote Australia) was determined using postcode. Data were analysed using linear, log-binomial, log-multinomial and negative binomial regression. RESULTS: Living in more remote areas was not associated with substantial worse health/employment outcomes. There was a consistent pattern of those living in inner regional areas having worse outcomes, but the effect sizes were relatively small with no clear dose-response relationships with increasing remoteness. Those living in more remote areas were less likely to use high efficacy DMTs. Adjusting for age, disease duration, and education level only marginally reduced the associations. CONCLUSIONS: There is no large inequity in health outcomes in the Australian MS population due to remoteness. However, modest and consistent differences in health outcomes and DMTs treatment are likely to have a substantial cumulative impact at an individual level.

Does health-related quality of life differ between people with relapse onset and progressive onset Multiple Sclerosis?

BACKGROUND: Multiple Sclerosis (MS) can be categorised as relapse onset MS (ROMS) and progressive onset MS (PROMS). We aimed to examine if health-related quality of life (in terms of health state utilities [HSUs] and dimensional scores) differed by onset type, in which health dimensions the differences were most pronounced, and whether these differences remained when stratified by disability severity. METHODS: We estimated HSUs and the unique composite 'super-dimension' and 'individual dimension' scores (crude, age, sex, disease duration and disease modifying therapies use adjusted; and stratified by onset type and disability severity) for a sample of 1577 participants in the Australian MS Longitudinal Study, using the Assessment of Quality of Life (AQoL)-8D. RESULTS: Adjusted mean overall HSU of PROMS was 0.55, 0.07 lower than ROMS. Adjusted mean physical and psychosocial super-dimension scores for PROMS were 0.51 and 0.28, 0.07 and 0.06 lower than for ROMS, respectively. For the individual health dimensions, the largest difference was seen in independent living (-0.12), followed by relationships (-0.07), and self-worth (-0.07). Whilst HSUs and dimensional scores were negatively associated with increasing disability severity in both onset types, estimates by disability severity did not differ between the two cohorts. CONCLUSIONS: Our study provides a comprehensive assessment of the effects of MS onset type on the overall and disability-severity specific HRQoL scores using a detailed preferentially sensitive AQoL-8D instrument. While overall HRQoL was substantially lower in PROMS than in ROMS, the mean HRQoL values for each disability level did not differ by onset type, indicating that future health economic models can use the same HSU inputs for both onset types.

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.

The impact of comorbidities on health-related quality of life of people with osteoarthritis over 10 years.

OBJECTIVE: To investigate the impact of total number and patterns of comorbidities on health-related quality of life (HRQoL) and identify the most prevalent and influential comorbidity patterns in people with OA over 10 years. METHODS: Participants from the Tasmanian Older Adult Cohort aged 50-80 years, with self-reported OA and data on comorbidities and HRQoL were included. Participants were interviewed at baseline (n = 398), 2.5 (n = 304), 5 (n = 269) and 10 years (n = 191). Data on the self-reported presence of 10 chronic comorbidities were collected at baseline. HRQoL was assessed using the Assessment of Quality of Life-4-Dimensions. The long-term impacts of the number and of the nine most prevalent combinations of cardiovascular (CVD), non-OA musculoskeletal (Ms), metabolic and respiratory comorbidities on HRQoL over 10 years were analysed using linear mixed regressions. RESULTS: Compared with comorbidity-free OA participants, the health state utility (HSU) of those with 2 or ≥3 comorbidities was respectively -0.07 and -0.13 units lower over 10 years, largely driven by reduced scores for independent living, social relationships and psychological wellness. Comorbidity patterns including 'CVD+Ms' were most influential, and associated with up to 0.13 units lower HSU, mostly through negative impacts on independent living (up to -0.12), psychological wellness (up to -0.08) and social relationship (up to -0.06). CONCLUSION: Having more comorbidities negatively impacted OA patients' long-term HRQoL. OA patients with CVD and non-OA musculoskeletal conditions had the largest HSU impairment, and therefore optimal management and prevention of these conditions may yield improvements in OA patients' HRQoL.

Cross-sectional and temporal differences in health-related quality of life of people with and without osteoarthritis: a 10-year prospective study.

OBJECTIVE: To describe the impact of OA on health-related quality of life (HRQoL) in the forms of health state utilities (HSUs) and health-dimension scores, and to compare the longitudinal changes in HRQoL for people with and without OA, using an Australian population-based longitudinal cohort. METHODS: Participants of the Tasmanian Older Adult Cohort with data on OA diagnosis and HRQoL were included [interviewed at baseline (n = 1093), 2.5 years (n = 871), 5 years (n = 760) and 10 years (n = 562)]. HRQoL was assessed using the Assessment of Quality of Life four-dimensions and analysed using multivariable linear mixed regressions. RESULTS: Compared with participants without OA, HSUs for those with OA were 0.07 (95% confidence interval: 0.09, 0.05) units lower on average over 10 years. HSUs for participants with knee and/or hip OA were similar to those with other types of OA at the 2.5 year follow-up and then diverged, with HSUs of the former being up to 0.09 units lower than the latter. Those with OA had lower scores for psychological wellness, independent living and social relationships compared with those without OA. Independent living and social relationships were mainly impacted by knee and/or hip OA, with the effect on the former increasing over time. CONCLUSION: Interventions to improve HRQoL should be tailored to specific OA types, health dimensions, and times. Support for maintaining psychological wellness should be provided, irrespective of OA type and duration. However, support for maintaining independent living could be more relevant to knee and/or hip OA patients living with the disease for longer.

Estimating the relative contribution of comorbidities in predicting health-related quality of life of people with multiple sclerosis.

BACKGROUND: Little is known about the relative contribution of comorbidities in predicting the health-related quality of life (HRQoL) of people with Multiple Sclerosis (PwMS). OBJECTIVE: To determine the associations between the number of and individual comorbidities and HRQoL and estimate the relative contribution of different comorbidities on HRQoL. METHODS: Cross-sectional analysis of data on self-reported presence of 30 comorbidities and HRQoL from the Australian MS Longitudinal Study (AMSLS) participants (n = 902). HRQoL was measured using the Assessment of Quality of Life-8 Dimensions (AQoL-8D). Linear regression and general dominance analysis were used. RESULTS: Higher number of comorbidities was associated with lower HRQoL (p trend p < 0.01). Comorbidities accounted for 18.1% of the variance in HRQoL. Mental health and musculoskeletal disorders were the strongest contributors to lower HRQoL. Of individual comorbidities, systemic lupus erythematosus (SLE) [ß = - 0.16 (- 0.27, - 0.05)] and depression [ß = - 0.15(- 0.18, - 0.13)] were most strongly associated with overall HRQoL, depression [ß = - 0.14(- 0.16, - 0.11)] and anxiety [ß = - 0.10 (- 0.13, - 0.07)] with psychosocial HRQoL, and SLE [ß = - 0.18 (- 0.29, - 0.07)], rheumatoid arthritis [ß = - 0.11 (- 0.19, - 0.02)] and hyperthyroidism [ß = - 0.11 (- 0.19, - 0.03)) with physical HRQoL. CONCLUSION: Comorbidities potentially make important contributions to HRQoL in PwMS. Our findings highlight groups of and individual comorbidities that could provide the largest benefits for the HRQoL of PwMS if they were targeted for prevention, early detection, and optimal treatment.