Impact of Socioeconomic Status on Adult Patients with Asthma: A Population-Based Cohort Study from UK Primary Care
Bibliographic record
Abstract
John Busby,1 David Price,2– 4 Riyad Al-Lehebi,5 Sinthia Bosnic-Anticevich,6– 8 Job FM van Boven,9 Benjamin Emmanuel,10 J Mark FitzGerald,11 Mina Gaga,12 Susanne Hansen,13,14 Mark Hew,15,16 Takashi Iwanaga,17 Désirée Larenas Linnemann,18 Bassam Mahboub,19,20 Patrick Mitchell,21 Daniela Morrone,22 Jonathan Pham,15,23 Celeste Porsbjerg,14 Nicolas Roche,24,25 Eileen Wang,26,27 Neva Eleangovan,2,3 Liam G Heaney28 1Centre for Public Health, Queen’s University Belfast, Belfast, Northern Ireland; 2Optimum Patient Care, Cambridge, UK; 3Observational and Pragmatic Research Institute, Singapore, Singapore; 4Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, UK; 5Department of Pulmonology, King Fahad Medical City, Riyadh, Saudi Arabia; 6Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia; 7Woolcock Institute of Medical Research, The University of Sydney, Sydney, Australia; 8Central Sydney Local Area Health District, Sydney, Australia; 9Department of Clinical Pharmacy & Pharmacology, Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; 10BioPharmaceuticals Medical, AstraZeneca, Gaithersburg, MD, USA; 11Department of Medicine, The University of British Columbia, Vancouver, Canada; 12 7th Respiratory Medicine Department and Asthma Centre, Athens Chest Hospital, Athens, Greece; 13Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark; 14Respiratory Research Unit, Bispebjerg University Hospital, Copenhagen, Denmark; 15Allergy, Asthma & Clinical Immunology Service, Alfred Health, Melbourne, Australia; 16Public Health and Preventive Medicine, Monash University, Melbourne, Australia; 17Center for General Medical Education and Clinical Training, Kindai University Hospital, Osakasayama, Japan; 18Directora Centro de Excelencia en Asma y Alergia, Hospital Médica Sur, Ciudad de México, Mexico; 19College of Medicine, University of Sharjah, Sharjah, United Arab Emirates; 20Rashid Hospital, Dubai Health Authority, Dubai, United Arab Emirates; 21Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; 22Personalized Medicine, Asthma and Allergy, Humanitas Clinical and Research Center, IRCCS, Rozzano, Italy; 23Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population & Global Health, The University of Melbourne, Melbourne, Australia; 24Respiratory Medicine, Cochin Hospital, APHP.Centre, Paris, France; 25University of Paris, Cochin Institute (UMR1016), Paris, France; 26Division of Allergy & Clinical Immunology, Department of Medicine, National Jewish Health, Denver, CO, USA; 27Division of Allergy & Clinical Immunology, Department of Internal Medicine, University of Colorado School of Medicine, Aurora, CO, USA; 28Wellcome-Wolfson Centre for Experimental Medicine, Queen’s University Belfast, Belfast, Northern IrelandCorrespondence: David PriceCentre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Polwarth Building, Foresterhill, Aberdeen, AB25 2ZD, UKTel +65 6962 3627Email dprice@opri.sgIntroduction: Asthma morbidity and health-care utilization are known to exhibit a steep socioeconomic gradient. Further investigation into the modulators of this effect is required to identify potentially modifiable factors.Methods: We identified a cohort of patients with asthma from the Optimum Patient Care Research Database (OPCRD). We compared demographics, clinical variables, and health-care utilization by quintile of the UK 2011 Indices of Multiple Deprivation based on the location of the patients’ general practice. Multivariable analyses were conducted using generalized linear models adjusting for year, age, and sex. We conducted subgroup analyses and interaction tests to investigate the impact of deprivation by age, sex, ethnicity, and treatment step.Results: Our analysis included 127,040 patients with asthma. Patients from the most deprived socio-economic status (SES) quintile were more likely to report uncontrolled disease (OR: 1.54, 95% CI: 1.16, 2.05) and to have an exacerbation during follow-up (OR: 1.27, 95% CI: 1.13, 1.42) than the least deprived quintile. They had higher blood eosinophils (ratio: 1.03; 95% CI: 1.00, 1.06) and decreased peak flow (ratio: 0.95, 95% CI: 0.94, 0.97) when compared to those in the least deprived quintile. The effect of deprivation on asthma control was greater among those aged over 75 years (OR = 1.81, 95% CI: 1.20, 2.73) compared to those aged less than 35 years (OR: 1.22, 95% CI: 0.85, 1.74; pinteraction=0.019). Similarly, socioeconomic disparities in exacerbations were larger among those from ethnic minority groups (OR: 1.94, 95% CI: 1.40, 2.68) than white patients (OR: 1.24, 95% CI: 1.10, 1.39; pinteraction=0.012).Conclusion: We found worse disease control and increased exacerbation rates among patients with asthma from more deprived areas. There was evidence that the magnitude of socioeconomic disparities was elevated among older patients and those from ethnic minority groups. The drivers of these differences require further exploration.Keywords: asthma, socioeconomic status, disparities
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".