Health disparities in respiratory care continue to disproportionately affect low-income individuals, people of color, and those living in areas with poor air quality. With new requirements from NCQA, HEDIS, and CMS, maintaining equal access to quality care has become of even greater importance for healthcare systems.
Propeller Health and ResMed presented several abstracts at the ATS 2023 International Conference in Washington D.C. on May 19-24, many of which examined the economic impact of respiratory diseases and how poor air quality contributes to disproportionate rates of asthma and COPD among vulnerable patient populations. Read brief summaries of each abstract below or click the links to view them online.
The economic and health burden of COPD in North America: Forecasting through 2050
Summary: COPD is the third leading cause of death worldwide1 and incurs $50 billion in direct and indirect costs annually,2 making it a critical global health challenge. Using an open cohort model and country-specific data, researchers estimate that by the year 2050 in North America:
- The total cumulative direct cost of COPD will be $2.5 trillion
- The total cumulative indirect costs of COPD will be $1.7 trillion
- The total cumulative number of COPD exacerbations will reach 791 million
These estimates highlight the importance of preventing and mitigating healthcare and societal COPD costs. Policymakers, healthcare systems and providers, and patients must work together to develop strategies to address this growing issue, particularly among high-risk populations.
Evaluation of machine learning models to project global indoor and outdoor air pollution through 2050
Summary: Air pollution is linked to morbidity and mortality among people with respiratory diseases like asthma and COPD. Estimating long-term levels of air pollution is crucial for projecting the health cost of air pollution and the benefits of reducing long-term exposure levels. To address this, researchers sought to determine the prediction accuracy of machine learning (ML) algorithms in projecting future air pollution estimates from historic trends. The results showed that regression-based ML is a suitable approach in projecting the long-term global trajectories of various air pollutants, long-term household air pollution (HAP) from solid fuels, ambient ozone (O3), and ambient particulate matter pollution (PM2.5). By accurately predicting future levels of air pollutants, government and healthcare industry leaders can better understand the impact of poor air quality and make informed decisions to reduce air pollution exposure.
Projecting global indoor and outdoor air pollution through 2050
Summary: Air pollution is a significant public health concern worldwide, with chronic respiratory conditions particularly vulnerable to its harmful effects. Using trends in historic air pollution data from the Institute for Health Metrics and Evaluation and machine learning models, researchers sought to project global long-term air pollution exposure through 2050. The results showed that the proportion of the global population exposed to long-term household air pollution (HAP) is estimated to decrease from 2020 to 2050. Conversely, ambient ozone pollution (O3) and ambient particulate matter pollution (PM2.5) are projected to increase by 2050. Thus — despite a projected decrease in household air pollution from solid fuels — ambient air pollution will continue to be a global problem. The study highlights the importance of adopting novel technologies and strategies to improve the monitoring and reduction of air pollution to protect public health and the environment. Further research is crucial to reduce respiratory morbidity and mortality, particularly in vulnerable populations.
Sociodemographic predictors of digital health data sharing and concern with remote care in asthma and COPD patients
Summary: The COVID-19 pandemic accelerated the adoption of remote healthcare, but there is a need to better understand patient comfort and behaviors around digital platforms — especially among vulnerable populations. Using Propeller’s digital therapeutic platform, researchers aimed to identify sociodemographic predictors associated with health data sharing and concern with remote care among patients with asthma and COPD. On average, males and asthma patients over 60 years of age had lower concerns about remote care. In both asthma and COPD, patients with household income levels under $50,000 had higher concerns with remote care. No significant differences in concern were reported among patients with lower education levels or public insurance. By understanding the concerns and behaviors of different populations, we can start to better tailor remote care programs and support more equitable access for patients with chronic respiratory conditions.
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