Association between socioeconomic status and joint replacement of the hip and knee: a population-based cohort study of older adults in Tasmania.

BACKGROUND: A socioeconomic gradient exists in the utilisation of total hip replacements (THR) and total knee replacements (TKR) for osteoarthritis. However, the relations between socioeconomic status (SES) and time to THR or TKR is unknown. AIM: To describe the association between SES and time to THR and TKR. METHODS: One thousand and seventy-two older adults residing in Tasmania, Australia, were studied. Incident primary THR and TKR were determined by data linkage to the Australian Orthopaedic Association National Joint Replacement Registry. At baseline, each participant's area-level SES was determined using the Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD) from the Australian Bureau of Statistics' 2001 census data. The IRSAD was analysed in two ways: (i) categorised into quartiles, whereby quartile 1 represented the most socioeconomically disadvantaged group; and (ii) the cohort dichotomised at the quartile 1 cut-point. RESULTS: The mean age was 63.0 (±7.5) years and 51% were women. Over the median follow up of 12.9 (interquartile range: 12.2-13.9) years, 56 (5%) participants had a THR and 79 (7%) had a TKR. Compared with the most disadvantaged quartile, less disadvantaged participants were less likely to have a THR (i.e. less disadvantaged participants had a longer time to THR; hazard ratio (HR): 0.56; 95% confidence interval (CI) 0.32, 1.00) but not TKR (HR: 0.90; 95% CI 0.53, 1.54). However, the former became non-significant after adjustment for pain and radiographic osteoarthritis, suggesting that the associations may be mediated by these factors. CONCLUSIONS: The present study suggests that time to joint replacement was determined according to the symptoms/need of the participants rather than their SES.

Do Knee Pain Phenotypes Have Different Risks of Total Knee Replacement?

Pain is the main impetus for osteoarthritis (OA) patients to seek healthcare including joint replacement. The pain experience in OA is heterogeneous and affected by factors across multiple domains-peripheral, psychological, and neurological. This indicates the existence of homogenous subgroups/phenotypes within OA patients with pain. We recently identified three pain phenotypes using a wide spectrum of pain-related factors, including structural damage on magnetic resonance imaging (MRI), emotional problems, number of painful sites, sex, body mass index, education level and comorbidities (i.e., Class 1: high prevalence of emotional problems and low prevalence of structural damage (25%); Class 2: low prevalence of emotional problems and high prevalence of structural damage (20%); Class 3: low prevalence of emotional problems and low prevalence of structural damage (55%)). This study was to examine whether the total knee replacement (TKR) risk over 12 years was different among these three pain phenotypes. Data on 963 participants (mean age 62.8 ± 7.4 years) from a population-based cohort study were utilised. Data on socio-demographic, psychological and comorbidities were collected. MRI of the right knee structural pathology was performed. TKR history was ascertained by linking to the Australian Orthopedic Association National Joint Replacement Registry. Latent class analysis and the Cox proportional hazards model were applied for the analysis. During the follow-up period, 41 right and 44 left TKRs in 67 participants were identified. In multivariable analyses, participants in Class 1 and 2 had a higher risk of having a TKR (Class 1 vs. Class 3, HR (hazard ratio) 4.81, 95%CI (confidence interval) 2.33-9.93; Class 2 vs. Class 3, HR 9.23, 95%CI 4.66-18.30). These associations were stronger in the imaged right knee but were also significant in the left knee. Participants within distinct pain phenotypes have different risks of TKR, suggesting that the identified phenotypes reflect distinct clinical subgroups with different prognoses. The risk for TKR was higher in Class 1 than that in Class 3, suggesting that pain/emotional status is a stronger driver for TKR than structural damage, and that selecting patients for TKR needs to be optimized in clinical practice.

Identifying subgroups of community-dwelling older adults and their prospective associations with long-term knee osteoarthritis outcomes.

OBJECTIVES: To identify subgroups of community-dwelling older adults and to assess their longitudinal associations with long-term osteoarthritis (OA) outcomes. METHODS: 1046 older adults aged 50-80 years were studied. At baseline, body mass index (BMI), pedometer-measured ambulatory activity (AA), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) determined knee pain and information on comorbidities were obtained. Tibial cartilage volume and bone-marrow lesions (BMLs) were assessed using MRI at baseline and 10 years and total knee replacements (TKR) by data linkage to the Australian Orthopaedic Association National Joint Replacement Registry. Latent class analysis was used to determine participant subgroups, considering baseline BMI, AA, pain and comorbidities, and linear mixed-effects or log-binomial models were used to assess the associations. RESULTS: Three subgroups/classes were identified: subgroup 1 (43%): Normal/overweight participants with higher AA, lower pain and lower comorbidities; subgroup 2 (32%): Overweight participants with lower AA, mild pain and higher comorbidities; subgroup 3 (25%): Obese participants with lower AA, mild pain and higher comorbidities. Subgroup 3 had greater cartilage volume loss (ß - 60.56 mm3, 95% CI - 105.91, - 15.21) and a higher risk of TKR (RR 3.19, 95% CI 1.75, 5.81), compared to subgroup 1. Subgroup 2 was not associated with cartilage volume change (ß 13.06 mm3, 95% CI - 30.87, 57.00) or risk of TKR (RR 1.16, 95% CI 0.56, 2.36), compared to subgroup 1. Subgroup membership was not associated with worsening BMLs. CONCLUSIONS: Our findings suggest the existence of homogeneous subgroups of participants and support the utility of identifying patterns of characteristics/risk factors that may cluster together and using them to identify subgroups of people who may be at a higher risk of developing and/or progressing OA. Key Points • Complex interplay among characteristics/factors leads to conflicting evidence between ambulatory activity and knee osteoarthritis. • Distinct subgroups are identifiable based on ambulatory activity, body mass index, knee pain, and comorbidities. • Identifying subgroups can be used to determine those who are at risk of developing/progressing osteoarthritis.

Health in rural Sri Lanka: A cross-sectional survey of three rural districts

Introduction: Sri Lanka has a predominantly rural population. However, there is a dearth of research on health and socioeconomic issues in this group. Objective: To describe basic socioeconomic characteristics and health profile in a rural population. Methods: A descriptive cross-sectional household survey was conducted in 1950 households in three rural districts, selected by a three-stage stratified cluster sampling method. Results: The population pyramid showed an ageing population (dependency ratio of 50%). Only 39% had completed GCE (ordinary level). Unemployment rates were high (25% males, 76% females). Agriculture and related work were main occupations. Most lacked amenities (e.g. 61% households lacked a refrigerator) and practiced inappropriate methods of waste disposal (e.g. open burning by 72%). Household illnesses were frequent: episodes of acute illness within two weeks, injuries within past year and chronic illness were reported from 35.9%, 14.9% and 48.3% households. The prevalence of chronic diseases in adults >20 years were high: diabetes 13.5%, hypertension 16.7% and overweight/obesity 28.2%. Of the males, 22.1% smoked and 12.3% took alcohol. Almost 25% adults chewed betel. Reports of snake bite, dog bites and suicide/attempted suicide were seen in 15.5%, 9.7% and 3.0% households respectively. Conclusions: This study shows a unique clustering of health-related problems in rural Sri Lanka. This was characterized by demographic transition, burden from snake bites, chronic diseases and acute illnesses. There were resource limitations and low levels of education. Cohort studies and comparisons with urban areas will enable further elucidation of determinants of health and other issues in rural Sri Lanka.