Assessing the roles of demographic, social, economic, environmental, health-related, and political factors on risk of osteoporosis diagnosis among older adults

Modelling Adverse Events in Patients Receiving Chronic Oral Corticosteroids in the UK

BACKGROUND

Oral corticosteroids (OCS) are effective anti-inflammatory agents used across a range of conditions. However, substantial evidence associates their use with increased risks for adverse events (AEs), causing high burden on healthcare resources. Emerging biologics present as alternative agents, enabling the reduction of OCS use. However, current modelling approaches may underestimate their effects by not capturing OCS-sparing effects. In this study, we present a modelling approach designed to capture the health economic benefits of OCS-sparing regimens and agents.

METHODS

We developed a disease-agnostic model using a UK health technology assessment (HTA) perspective, with discounting of 3.5% for costs and outcomes, a lifetime horizon, and 4-week cycle length. The model structure included type 2 diabetes mellitus, established cardiovascular disease, and osteoporosis as key AEs and drivers of morbidity and mortality, as well as capturing transient events. Quality-adjusted life-years (QALYs), life-years, and costs were determined for OCS-only and OCS-sparing treatment arms. Outcomes were determined using baseline 50% OCS-sparing, considering several OCS average daily doses (5, 10, 15 mg).

RESULTS

A treatment regimen with 50% OCS dose-sparing led to lifetime incremental cost savings per patient of £1107 (95% confidence interval £1014-£1229) at 5 mg, £2403 (£2203-£2668) at 10 mg, and £19,501 (£748-£51,836) at 15 mg. Patients also gained 0.033 (0.030-0.036) to 0.356 (0.022-2.404) QALYs dependent on dose. The benefits of OCS sparing were long-term, plateauing after 35-40 years of treatment.

CONCLUSIONS

We present a modelling approach that captures additional long-term health economic benefits from OCS sparing that would otherwise be missed from current modelling approaches. These results may help inform future decision making for emerging OCS-sparing therapeutics by comparing them against the cost of such treatments.

Act now - serious pathology of the spine is affected by health inequalities

Early diagnosis of serious spinal pathology is the key to optimise patient outcomes, yet early diagnosis can be adversely affected by health inequalities. In this paper we consider the impact of health inequalities on the incidence and outcome for serious spinal musculoskeletal (MSK) pathologies. Health inequalities can be experienced by people grouped around a range of factors. These include socio-economic factors, the environmental conditions in which people live, protected characteristics such as ethnicity, and socially excluded groups such as people who are homeless. These factors can affect people's exposure to health risks and their opportunities to lead healthy lives. A person's behaviour is a key determinant of their health status. 'Risky' health behaviours include smoking, poor diet, harmful alcohol consumption and lack of exercise, and are more common in these groups. Importantly, socio-economic factors combined with health behaviours influence the health inequalities a person may experience. The most significant social and economic factors influencing poor MSK health are poverty, education, employment, environment, and food ethos. These determinants of health not only predispose people living in deprivation to having benign MSK conditions at a younger age and with worse outcomes, they are also risk factors of more serious MSK pathologies.

Assessment of causal association between the socio-economic status and osteoporosis and fractures: a bidirectional Mendelian randomization study in European population

The correlation between socio-economic status (SES) and bone-related diseases garners increasing attention, prompting a bidirectional Mendelian randomization (MR) analysis in this study. Genetic data on SES indicators (average total household income before tax, years of schooling completed, and Townsend Deprivation Index at recruitment), femoral neck bone mineral density (FN-BMD), heel bone mineral density (eBMD), osteoporosis, and five different sites of fractures (spine, femur, lower leg-ankle, foot, and wrist-hand fractures) were derived from genome-wide association summary statistics of European ancestry. The inverse variance weighted method was employed to obtain the causal estimates, complemented by alternative MR techniques, including MR-Egger, weighted median, and MR-pleiotropy residual sum and outlier (MR-PRESSO). Furthermore, sensitivity analyses and multivariable MR were performed to enhance the robustness of our findings. Higher educational attainment exhibited associations with increased eBMD (β: .06, 95% confidence interval [CI]: 0.01-0.10, P = 7.24 × 10-3), and reduced risks of osteoporosis (OR: 0.78, 95% CI: 0.65-0.94, P = 8.49 × 10-3), spine fracture (OR: 0.76, 95% CI: 0.66-0.88, P = 2.94 × 10-4), femur fracture (OR: 0.78, 95% CI: 0.67-0.91, P = 1.33 × 10-3), lower leg-ankle fracture (OR: 0.79, 95% CI: 0.70-0.88, P = 2.05 × 10-5), foot fracture (OR: 0.78, 95% CI: 0.66-0.93, P = 5.92 × 10-3), and wrist-hand fracture (OR: 0.83, 95% CI: 0.73-0.95, P = 7.15 × 10-3). Material deprivation appeared to increase the risk of spine fracture (OR: 2.63, 95% CI: 1.43-4.85, P = 1.91 × 10-3). A higher FN-BMD level positively affected increased household income (β: .03, 95% CI: 0.01-0.04, P = 6.78 × 10-3). All these estimates were adjusted for body mass index, type 2 diabetes, smoking initiation, and frequency of alcohol intake. The MR analyses show that higher educational levels is associated with higher eBMD, reduced risk of osteoporosis and fractures, while material deprivation is positively related to spine fracture. Enhanced FN-BMD correlates with increased household income. These findings provide valuable insights for health guideline formulation and policy development.

Psychological Disorders Linked to Osteoporosis Diagnoses in a Population-Based Cohort Study of Middle and Older Age United States Adults

BACKGROUND AND OBJECTIVES

Although it is well established that psychological disorders and osteoporosis risk are linked, how the relationship manifests is not. This study examines depressive symptoms and a history of psychological problems as potential risk factors for osteoporosis diagnosis, adjudicating between 4 theoretical models rarely tested together. We analyze these models across multiple domains (i.e., demographic, socioeconomic, and health-related), while accounting for bone mineral density (BMD) scans, which have been shown to improve health equity across sex and racial/ethnic identities.

RESEARCH DESIGN AND METHODS

Data from the 2012-2016, nationally representative, population-based, cohort Health and Retirement Study (N = 18,224-18,359) were used to estimate 4 logistic regression models with the outcome of osteoporosis diagnosis. Approximately 50% of the sample identified as female and 50% as male, while about 81% identified as White/European American, 11% as Black/African American, and 8% as another race/ethnicity. The key independent variables were depressive symptoms-measured using two common scales-and a history of psychological problems.

RESULTS

A history of psychological problems and one depressive symptoms measure were associated with the odds of osteoporosis diagnosis in the presence of other known risk factors for osteoporosis.

DISCUSSION AND IMPLICATIONS

Support for the theoretical models was limited. Evidence suggests possible directionality; a history of psychological distress may be a risk factor for osteoporosis, though we cannot rule out the other direction. Public health professionals and healthcare providers should consider a history of psychological problems as a risk factor for osteoporosis when deciding whether to recommend a BMD scan.

Development of a diverse osteoporosis screening tool for older US adults from the health and retirement study

Existing osteoporosis screening tools have limitations, including using race as a predictor, and development on homogeneous samples. This biases risk assessment of osteoporosis in diverse populations and increases health inequities. We develop a tool that relies on variables easily learned during point-of-care, known by individuals, and with negligible racial bias. Data from the 2012-2016 waves of the population-based cohort Health and Retirement Study (HRS) were used to build a predictive model of osteoporosis diagnosis on a 75 % training sample of adults ages 50-90. The model was validated on a 25 % holdout sample and a cross-sectional sample of American individuals ages 50-80 from the National Health and Nutrition Examination Survey (NHANES). Sensitivity and specificity were compared across sex and race/ethnicity. The model has high sensitivity in the HRS holdout sample (89.9 %), which holds for those identifying as female and across racial/ethnic groups. Specificity is 57.9 %, and area under the curve (AUC) is approximately 0.81. Validation in the NHANES sample using empirically measured osteoporosis produced relatively good values of sensitivity, specificity, and consistency across groups. The model was used to create a publicly-available, open-source tool called the Osteoporosis Health Equality (& Equity) Evaluation (OsteoHEE). The model provided high sensitivity for osteoporosis diagnosis, with consistently high results for those identifying as female, and across racial/ethnic groups. Use of this tool is expected to improve equity in screening and increase access to bone density scans for those at risk of osteoporosis. Validation on alternative samples is encouraged.

Association between air pollutants with calcaneus ultrasound T-score change in a large Taiwanese population follow-up study

Exposure to ambient air pollution has been associated with increased rates of mortality and morbidity and a shorter life expectancy. Few studies have evaluated the associations between air pollution and change in calcaneus ultrasound T-score (∆T-score). Therefore, in this longitudinal study, we explored these associations in a large group of Taiwanese participants. We used data from the Taiwan Biobank database and Taiwan Air Quality Monitoring Database, which contains detailed daily data on air pollution. We identified 27,033 participants in the Taiwan Biobank database who had both baseline and follow-up data. The median follow-up period was 4 years. The studied ambient air pollutants included particulates of 2.5 μm or less (PM_2.5), particulates of 10 μm or less (PM₁₀), ozone (O₃), carbon monoxide (CO), sulfur dioxide (SO₂), nitric oxide (NO), nitrogen dioxide (NO₂), and nitrogen oxide (NO_x). Multivariable analysis showed that PM_2.5 (β, -0.003; 95% confidence interval (CI), -0.004 to -0.001; p < 0.001), PM₁₀ (β, -0.005; 95% CI, -0.006 to -0.004, p < 0.001), O₃ (β, -0.008; 95% CI, -0.011 to -0.004; p < 0.001), and SO₂ (β, -0.036; 95% CI, -0.052 to -0.020; p < 0.001) were negatively associated with ∆T-score, and that CO (β, 0.344; 95% CI, 0.254, 0.433; p < 0.001), NO (β, 0.011; 95% CI, 0.008 to 0.015; p < 0.001), NO₂ (β, 0.011; 95% CI, 0.008 to 0.014; p < 0.001), and NO_x (β, 0.007; 95% CI, 0.005 to 0.009; p < 0.001) were positively significantly associated with ∆T-score. Furthermore, PM_2.5 and SO₂ (β, -0.014; 95% CI, -0.016 to -0.013; p < 0.001) and PM₁₀ and SO₂ (β, -0.008; 95% CI, -0.009 to -0.007; p < 0.001) had synergistic negative effects on ∆T-score. In conclusion, we found that high PM_2.5, PM₁₀, O₃, and SO₂ were associated with a rapid decline in T-score, whereas high CO, NO, NO₂, and NO_x were associated with a slow decline in T-score. Furthermore, PM_2.5 and SO₂ and PM₁₀ and SO₂ had synergistic negative effects on ∆T-score, causing an acceleration in T-score decline. These findings may be helpful when developing policies on air pollution regulation.

Childhood Family Environment and Osteoporosis in a Population-Based Cohort Study of Middle-to Older-Age Americans

Demographic and early-life socioeconomic and parental investment factors may influence later-life health and development of chronic and progressive diseases, including osteoporosis, a costly condition common among women. The "long arm of childhood" literature links negative early-life exposures to lower socioeconomic attainment and worse adult health. We build on a small literature linking childhood socioeconomic status (SES) and bone health, providing evidence of whether associations exist between lower childhood SES and maternal investment and higher risk of osteoporosis diagnosis. We further examine whether persons identifying with non-White racial/ethnic groups experience underdiagnosis. Data from the nationally representative, population-based cohort Health and Retirement Study (N = 5,490-11,819) were analyzed for participants ages 50-90 to assess these relationships. Using a machine learning algorithm, we estimated seven survey-weighted logit models. Greater maternal investment was linked to lower odds of osteoporosis diagnosis (odds ratio [OR] = 0.80, 95% confidence interval [CI] = 0.69, 0.92), but childhood SES was not (OR = 1.03, 95% CI = 0.94, 1.13). Identifying as Black/African American (OR = 0.56, 95% CI = 0.40, 0.80) was associated with lower odds, and identifying as female (OR = 7.22, 95% CI = 5.54, 9.40) produced higher odds of diagnosis. There were differences in diagnosis across intersectional racial/ethnic and sex identities, after accounting for having a bone density scan, and a model predicting bone density scan receipt demonstrated unequal screening across groups. Greater maternal investment was linked to lower odds of osteoporosis diagnosis, likely reflecting links to life-course accumulation of human capital and childhood nutrition. There is some evidence of underdiagnosis related to bone density scan access. Yet results demonstrated a limited role for the long arm of childhood in later-life osteoporosis diagnosis. Findings suggest that (1) clinicians should consider life-course context when assessing osteoporosis risk and (2) diversity, equity, and inclusivity training for clinicians could improve health equity. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Health Insurance Coverage as a Social Determinant of Osteoporosis Diagnosis in a Population-Based Cohort Study of Older American Adults

Social determinants of health theoretical frameworks identify health insurance coverage as a determinant of older adults' osteoporosis diagnoses, which results in health inequities. In this research, we used the longitudinal Health and Retirement Study dataset of older United States adults, sampled biennially from 2012 to 2016. Logistic regressions estimated odds of osteoporosis diagnosis with and without a bone scan and/or hip fracture, holding insurance type, and health and demographic factors constant. Results were validated using the National Health and Nutrition Examination Survey. Probable underdiagnosing is present in older adults identifying as Black/African American and as males without a bone scan, regardless of fracture status, potentially as products of structural racism and sexism. Models including a bone scan show a reduction in disparities. These findings suggest having a bone scan is still crucial for addressing health inequities in older adults, and remedying barriers to accessing a scan is paramount.

Structural Inequity and Socioeconomic Status Link to Osteoporosis Diagnosis in a Population-Based Cohort of Middle-Older-Age Americans

Socioeconomic status (SES) is an important social determinant of health inequities that has been linked to chronic conditions, including osteoporosis, but research tends to focus on socioeconomic disadvantage rather than how socioeconomic advantage may facilitate these inequities. This study accounts for structural inequities and assesses the relationship between early-life and later-life SES, and risk of osteoporosis diagnosis. Data come from the nationally representative, population-based cohort Health and Retirement Study and include individuals ages 50 to 90. The outcome variable is osteoporosis diagnosis. Logistic regression models of the relationship between SES and osteoporosis diagnosis are estimated, accounting for demographic, health, and childhood variables. Higher levels of childhood and adult SES link to lower odds of osteoporosis diagnosis. Structural inequities in income and underdiagnosis of osteoporosis among persons identifying as Black/African American were detected. Accounting for bone density scan access, inequities in osteoporosis diagnosis appear to stem from barriers to accessing health care due to financial constraints. The important role of SES and evidence of structural inequities leading to underdiagnosis suggest the critical importance of clinicians receiving Diversity, Equity, and Inclusion training to reduce health inequities.