Impact of Unmet Social Needs, Scarcity, and Future Discounting on Adherence to Treatment in Children With Asthma: Protocol for a Prospective Cohort Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Asthma is one of the most prevalent chronic diseases of childhood and disproportionately affects children with lower socioeconomic status. Controller medications such as inhaled corticosteroids significantly reduce asthma exacerbations and improve symptoms. However, a large proportion of children still have poor asthma control, in part owing to suboptimal adherence. Financial barriers contribute to hindering adherence, as do behavioral factors related to low income. For example, unmet social needs for food, lodging, and childcare may create stress and worry in parents, negatively influencing medication adherence. These needs are also cognitively taxing and force families to focus on immediate needs, leading to scarcity and heightening future discounting; thus, there is the tendency to attribute greater value to the present than to the future in making decisions. OBJECTIVE In this project, we will investigate the relationship between unmet social needs, scarcity, and future discounting as well as their predictive power over time on medication adherence in children with asthma. METHODS This 12-month prospective observational cohort study will recruit 200 families of children aged 2 to 17 years at the Asthma Clinic of the Centre Hospitalier Universitaire Sainte-Justine, a tertiary care pediatric hospital in Montreal, Canada. The primary outcome will be adherence to controller medication, measured using the proportion of prescribed days covered during follow-up. Exploratory outcomes will include health care use. The main independent variables will be unmet social needs, scarcity, and future discounting, measured using validated instruments. These variables will be measured at recruitment as well as at 6- and 12-month follow-ups. Covariates will include sociodemographics, disease and treatment characteristics, and parental stress. Primary analysis will compare adherence to controller medication, measured using the proportion of prescribed days covered, between families with versus those without unmet social needs during the study period using multivariate linear regression. RESULTS The research activities of this study began in December 2021. Participant enrollment and data collection began in August 2022 and are expected to continue until September 2024. CONCLUSIONS This project will allow the documentation of the impact of unmet social needs, scarcity, and future discounting on adherence in children with asthma using robust metrics of adherence and validated measures of scarcity and future discounting. If the relationship between unmet social needs, behavioral factors, and adherence is supported by our findings, this will suggest the potential for novel targets for integrated social care interventions to improve adherence to controller medication and reduce risk across the life course for vulnerable children with asthma. CLINICALTRIAL ClinicalTrials.gov NCT05278000; https://clinicaltrials.gov/ct2/show/NCT05278000 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/37318
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.009 |
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".