Health services use in people with chronic diseases during the pandemic: Results from the iCARE study
Bibliographic record
Abstract
Abstract Background COVID-19 containment measures, such as stay-at-home orders and transportation shutdowns, might have created barriers to healthcare access and impacted service utilization worldwide. This cross-sectional study aims to explore how COVID-19 impacted health service utilization in individuals with chronic health conditions across the globe. Methods We included a convenience sample of the global iCARE study (www.icarestudy.com) from March, 2020 to January, 2021. Logistic regressions were used to test association between difficulties getting non-COVID-19 related care and presence of physical health conditions, individuals' sex and age as well as countries' economy, with appropriate time adjustments. Stratified analysis was conducted for three continents (Europe, Asia and America). Results The study included 28,340 individuals from 33 countries (female (71%), mean age=43.4, low and middle-income country (LMIC) (31%)). Overall, 30% of people with chronic diseases had difficulties getting non-COVID related care, compared to 24% of individuals with no conditions. Individuals with chronic conditions were around 1.6 times more likely to have trouble in healthcare access in Europe, America, and Asia. Older age was associated with fewer difficulties in healthcare access (in Asia and America). Females, compared to males, had around 30% and 50% higher odds of difficulties in access (in America and Europe, respectively). Finally, individuals from LMICs reported higher difficulties in getting care (OR = 1.64; 95% CI = 1.50-1.79). Conclusions We observed decreased service utilization in people with chronic diseases during the pandemic, with certain disparities across continents and country income. Future research should assess indirect health effects of the pandemic (i.e., morbidity and mortality of specific diseases). Public health efforts should be directed towards improving the resilience of health systems, healthcare access and delivery in emergency situations. Key messages Globally, around 1 in 3 individuals with chronic health conditions reported decreased healthcare utilization during the pandemic. COVID-19 impacts on healthcare utilization were more pronounced in low and middle-income countries, compared to high-income countries.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".