Health system adaptions to improve care for people living with non-communicable diseases during COVID-19 in low-middle income countries: A scoping review
Bibliographic record
Abstract
During the COVID-19 pandemic, access to health care for people living with non-communicable diseases (NCDs) has been significantly disrupted. Calls have been made to adapt health systems and innovate service delivery models to improve access to care. We identified and summarized the health systems adaptions and interventions implemented to improve NCD care and their potential impact on low- and middle-income countries (LMICs).We comprehensively searched Medline/PubMed, Embase, CINAHL, Global Health, PsycINFO, Global Literature on coronavirus disease, and Web of Science for relevant literature published between January 2020 and December 2021. While we targeted articles written in English, we also included papers published in French with abstracts written in English.After screening 1313 records, we included 14 papers from six countries. We identified four unique health systems adaptations/interventions for restoring, maintaining, and ensuring continuity of care for people living with NCDs: telemedicine or teleconsultation strategies, NCD medicine drop-off points, decentralization of hypertension follow-up services and provision of free medication to peripheral health centers, and diabetic retinopathy screening with a handheld smartphone-based retinal camera. We found that the adaptations/interventions enhanced continuity of NCD care during the pandemic and helped bring health care closer to patients using technology and easing access to medicines and routine visits. Telephonic aftercare services appear to have saved a significant amount of patients' time and funds. Hypertensive patients recorded better blood pressure controls over the follow-up period.Although the identified measures and interventions for adapting health systems resulted in potential improvements in access to NCD care and better clinical outcomes, further exploration is needed to establish the feasibility of these adaptations/interventions in different settings given the importance of context in their successful implementation. Insights from such implementation studies are critical for ongoing health systems strengthening efforts to mitigate the impact of COVID-19 and future global health security threats for people living with NCDs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".