Objective
Productivity‐adjusted life‐years (PALYs) offers a novel approach for quantifying the productivity burden of chronic conditions at the population level over the working lifespan. This study ...was undertaken to estimate the productivity burden of knee osteoarthritis (KOA) among working‐age Australians, defined as lost PALYs and lost gross domestic product (GDP).
Methods
A static life‐table model was constructed to simulate the experiences of working Australians (between the ages of 15–64 years) with KOA and those without KOA, with follow‐up to 65 years (retirement age), a 1‐year cycle length, and an annual discount rate of 5%. KOA prevalence data were obtained from the 2019 Global Burden of Diseases, Injuries, and Risk Factors study. Demographic and mortality data were sourced from the Australian Bureau of Statistics. Health utilities and productivity indices were derived from published sources. Population‐level losses in years of life, quality‐adjusted life‐years (QALYs), and PALYs attributable to KOA were estimated by comparing estimates in the KOA cohort to the no KOA cohort.
Results
In 2019, a total of 913,539 working‐age Australians were estimated to have KOA, with an overall prevalence of 5.5% (4.5% in men and 6.5% in women). By retirement age, KOA was associated with 39,602 excess deaths, 125,651 years of life lost, 1,938,059 QALYs lost, and 1,943,287 PALYs lost. The economic impact of lost productivity due to KOA amounted to 424 billion Australian dollars in lost GDP.
Conclusion
Our modeling demonstrates a significant economic burden of KOA among the working Australian population, with marked productivity loss. Our findings highlight the need for public health funding and scalable population‐level strategies for effective KOA prevention and support to maintain productive working.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Comprehensive national joint replacement registries with well-validated data offer unique opportunities for examining the potential future burden of hip and knee osteoarthritis (OA) at a population ...level. This study aimed to forecast the burden of primary total knee (TKR) and hip replacements (THR) performed for OA in Australia to the year 2030, and to model the impact of contrasting obesity scenarios on TKR burden.
De-identified TKR and THR data for 2003-2013 were obtained from the Australian Orthopaedic Association National Joint Replacement Registry. Population projections and obesity trends were obtained from the Australian Bureau of Statistics, with public and private hospital costs sourced from the National Hospital Cost Data Collection. Procedure rates were projected according to two scenarios: (1) constant rate of surgery from 2013 onwards; and (2) continued growth in surgery rates based on 2003-2013 growth. Sensitivity analyses were used to estimate future TKR burden if: (1) obesity rates continued to increase linearly; or (2) 1-5% of the overweight or obese population attained a normal body mass index.
Based on recent growth, the incidence of TKR and THR for OA is estimated to rise by 276% and 208%, respectively, by 2030. The total cost to the healthcare system would be $AUD5.32 billion, of which $AUD3.54 billion relates to the private sector. Projected growth in obesity rates would result in 24,707 additional TKRs totalling $AUD521 million. A population-level reduction in obesity could result in up to 8062 fewer procedures and cost savings of up to $AUD170 million.
If surgery trends for OA continue, Australia faces an unsustainable joint replacement burden by 2030, with significant healthcare budget and health workforce implications. Strategies to reduce national obesity could produce important TKR savings.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Understanding population-level trends in osteoarthritis (OA) is critical for planning health services and disease prevention initiatives.
To examine trends in the burden of hip, knee, hand and other ...OA related conditions in Australia from 1990 to 2019 and consider the OA burden in the context of other common conditions associated with older age.
Global Burden of Disease Study 2019 data for Australia on OA prevalence, OA-related years lived with disability (YLDs) and OA-related YLDs attributable to high body mass index (BMI) were sourced for 1990-2019. Age-standardised YLD data for ischaemic heart disease, stroke, dementia, type 2 diabetes and chronic obstructive pulmonary disease were obtained for comparison.
Overall, 3.20 million Australians were estimated to have OA in 2019, with substantial growth in the prevalence of hip (+171%), knee (+126%), hand (+110%) and other types of OA (+130%) from 1990 to 2019. Age-standardised prevalence rates reflect the contribution of population ageing. Concomitant growth in OA-related YLDs was also evident; knee OA and hand OA demonstrated the highest disease burden in 2019 (59 684 and 41 893 YLDs respectively). The proportion of knee OA burden attributable to high BMI was 36% in 2019. In 2019, age-standardised YLD rates were higher for OA (313 per 100 000 population) than other common conditions (range: 47 per 100 000 (ischaemic heart disease) to 284 per 100 000 (type 2 diabetes)).
OA is an increasingly prevalent, impactful condition with a high non-fatal disease burden relative to other health conditions. Growth in OA populations and OA-related disability underscore the need for enhanced investment in prevention and management.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
To determine the availability and readiness of health facilities to provide family planning, antenatal care and basic emergency obstetric and newborn care in Nepal in 2021. Secondary objectives were ...to identify progress since 2015 and factors associated with readiness.
This is a secondary analysis of cross-sectional Nepal Health Facility Survey (NHFS) data collected in 2015 and 2021. The main outcome measures were availability and readiness of family planning, antenatal care, and basic emergency obstetric and newborn care services. Readiness indices were calculated using WHO-recommended service availability and readiness assessment (SARA) methods (score range 0 to 100%, with 100% indicating facilities are fully prepared to provide a specific service). We used independent t-tests to compare readiness indices in 2015 and 2021. Factors potentially associated with readiness (rurality setting, ecological region, managing authority, management meeting, quality assurance activities, and external supervision) were explored using multivariable linear regression.
There were 940 and 1565 eligible health facilities in the 2015 and 2021 surveys, respectively. Nearly all health facilities provided family planning (2015: n = 919 (97.8%); 2021: n = 1530 (97.8%)) and antenatal care services (2015: n = 920 (97.8%); 2021: n = 1538 (98.3%)) in both years, but only half provided delivery services (2015: n = 457 (48.6%); 2021: n = 804 (51.4%)). There were suboptimal improvements in readiness indices over time: (2015-21: family planning 68.0% to 70.9%, p<0.001, antenatal care 49.5% to 54.1%, p<0.001 and basic emergency obstetric and newborn care 56.7% to 58.0%, p = 0.115). The regression model comprising combined datasets of both NHFSs indicates facilities with regular management meetings and/or quality assurance activities had significantly greater readiness for all three indices. Similarly, public facilities had greater readiness for family planning and basic emergency obstetric and newborn care while they had lower readiness for antenatal care.
Readiness to deliver family planning, antenatal care and basic emergency obstetric and newborn care services in Nepal remains inadequate, with little improvement observed over six years.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Objective
To forecast the prevalence and direct health care costs of osteoarthritis (OA) and rheumatoid arthritis (RA) in Australia to the year 2030.
Methods
An epidemiologic model of the Australian ...population was developed. Data on the national prevalence of OA and RA were obtained from the Australian Bureau of Statistics (ABS) 2014–2015 National Health Survey. Future prevalence was estimated using ABS population projections for 2020, 2025, and 2030. Available government data on direct health care expenditure for OA and RA were modeled to forecast costs (in Australian $) for the years 2020, 2025, and 2030, from the perspective of the Australian public health care system.
Results
The number of people with OA is expected to increase nationally from almost 2.2 million in 2015 to almost 3.1 million Australians in 2030. The number of people with RA is projected to increase from 422,309 in 2015 to 579,915 in 2030. Health care costs for OA were estimated to be over $2.1 billion in 2015; by the year 2030, these are forecast to exceed $2.9 billion ($970 for every person with the condition). Health care costs for RA were estimated to be over $550 million in 2015, including $273 million spent on biologic disease‐modifying antirheumatic drugs. Health care costs for RA are projected to rise to over $755 million by the year 2030.
Conclusion
OA and RA are costly conditions that will impose an increasing health care burden at the population level. These projections provide tangible data that can be used to map future health service provision to expected need.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Research involving more representative samples is needed to extend our understanding of the broader impact of obesity in hip or knee joint disease (arthritis and OA) beyond clinical settings. ...Although population-based research has been conducted in the United States, how these findings translate to other countries is unclear. Using a national approach, this study explored associations between obesity and the burden of hip and knee joint disease in Australia (in terms of prevalence, pain, stiffness, function, Health-Related Quality of Life (HRQoL) and disease severity).
A random sample of 5000 Australians (≥ 39 years) from the federal electoral roll was invited to complete a mailed questionnaire to identify doctor-diagnosed hip arthritis, hip OA, knee arthritis and knee OA and evaluate the burden of these conditions. Validated questionnaires included the WOMAC Index, Assessment of Quality of Life instrument and Multi-Attribute Prioritisation Tool. Body Mass Index (BMI) was classified into underweight/normal weight (≤ 24.99 kg/m2), overweight (25-29.99) or obese (≥ 30). Multiple logistic regression was used to estimate odds of arthritis and OA, with demographic and socioeconomic variables included in the models. Associations between BMI and other variables were investigated using analysis of covariance, with adjustment for age and sex.
Data were available from 1,157 participants (23%). Overweight participants had increased odds of knee arthritis (adjusted OR (AOR) 1.87, 95%CI 1.14-3.07) and knee OA (AOR 2.11, 95%CI 1.07-4.15). Obesity was associated with higher prevalence of hip arthritis (AOR 2.18, 95%CI 1.17-4.06), knee arthritis (AOR 5.47, 95%CI 3.35-8.95) and knee OA (AOR 7.35, 95%CI 3.85-14.02). Of those with arthritis or OA, obese individuals reported more pain (for hip arthritis, hip OA and knee OA), greater stiffness (for hip arthritis, knee arthritis and knee OA), worse function (all diagnoses), lower HRQoL (for hip arthritis and hip OA) and greater disease severity (all diagnoses).
This national study has demonstrated that the odds of arthritis and OA was up to 7 times higher for obese individuals, compared with those classified as underweight/normal weight. Concurrent obesity and joint disease had a marked impact on several key aspects of wellbeing, highlighting the need for public health interventions.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In the last 2 years, our group has collaboratively published PALY estimates for a range of highly prevalent health conditions and risk factors, including diabetes mellitus 6-8, coronary heart disease ...9, 10, hypertension 11, smoking 12, 13, occupational-related hearing loss 14, pneumococcal disease 15, familial hypercholesterolaemia 16, epilepsy 17 and migraine 18. ...hypertension was estimated to cause the loss of > 609,000 PALYs in Australia, but optimal blood pressure control would save > 340,000 PALYs or $76 billion in GDP 11. ...far, PALY estimates have been produced for Australia 6, 9, 11, 14, 16-18, Bangladesh 8, China 7, Indonesia 10, 19, Japan, Korea 20, Malaysia 13 and Thailand 15. A dynamic model (accounting for future migration, births and deaths, and changes in the working age population) was most recently used to examine the impact of coronary heart disease and potential PALY gains if all new cases could be prevented over a 10-year period 9. ...it offers a novel, but now well-tested, approach to quantifying the population-level impact of disease on productivity (arising from unemployment, days off work, reduced efficiency at work and premature death) and the broader economy.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Emerging evidence suggests that people with arthritis are reporting increased physical pain and psychological distress during the COVID-19 pandemic. At the same time, Twitter's daily usage has surged ...by 23% throughout the pandemic period, presenting a unique opportunity to assess the content and sentiment of tweets. Individuals with arthritis use Twitter to communicate with peers, and to receive up-to-date information from health professionals and services about novel therapies and management techniques.
The aim of this research was to identify proxy topics of importance for individuals with arthritis during the COVID-19 pandemic, and to explore the emotional context of tweets by people with arthritis during the early phase of the pandemic.
From March 20 to April 20, 2020, publicly available tweets posted in English and with hashtag combinations related to arthritis and COVID-19 were extracted retrospectively from Twitter. Content analysis was used to identify common themes within tweets, and sentiment analysis was used to examine positive and negative emotions in themes to understand the COVID-19 experiences of people with arthritis.
In total, 149 tweets were analyzed. The majority of tweeters were female and were from the United States. Tweeters reported a range of arthritis conditions, including rheumatoid arthritis, systemic lupus erythematosus, and psoriatic arthritis. Seven themes were identified: health care experiences, personal stories, links to relevant blogs, discussion of arthritis-related symptoms, advice sharing, messages of positivity, and stay-at-home messaging. Sentiment analysis demonstrated marked anxiety around medication shortages, increased physical symptom burden, and strong desire for trustworthy information and emotional connection.
Tweets by people with arthritis highlight the multitude of concurrent concerns during the COVID-19 pandemic. Understanding these concerns, which include heightened physical and psychological symptoms in the context of treatment misinformation, may assist clinicians to provide person-centered care during this time of great health uncertainty.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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