Understanding the impact of the COVID‑19 pandemic on healthcare services for adults during three waves of COVID‑19 infections: A South African private sector experience
Bibliographic record
Abstract
BACKGROUND: Since the onset of the COVID‑19 pandemic, healthcare resources have been repurposed to focus on COVID‑19. Resource reallocation and restrictions to movement that affected general access to care may have inadvertently resulted in undue disruptions in the continuum of care for patients requiring non-COVID‑19 healthcare services. OBJECTIVES: To describe the change in pattern of health service use in the South African (SA) private sector. METHODS: We conducted a retrospective study of a nationwide cohort of privately insured individuals. An analysis of claims data was performed for non-COVID‑19 related healthcare services provided from April 2020 to December 2020 (year 1 of COVID‑19) and April 2021 to December 2021 (year 2 of COVID‑19) relative to the same period in 2019 prior to the COVID‑19 pandemic in SA. Over and above plotting the monthly trends, we tested for statistical significance of the changes using a Wilcoxon test given the non-normality of all the outcomes. RESULTS: Between April and December 2020, relative to the same period in 2021, and also relative to the same period in 2019, we found a 31.9% (p<0.01) and a 16.6% (p<0.01) reduction in emergency room visits, respectively; a 35.9% (p<0.01) and 20.5% (p<0.01) reduction in medical hospital admissions; a 27.4% (p=0.01) and 13.0% (p=0.03) reduction in surgical hospital admissions; a 14.5% (p<0.01) and 4.1% (p=0.16) reduction in face-to-face general practitioner consultations for chronic members; a 24.9% (p=0.06) and 5.2% (p=0.54) reduction in mammography for female members; a 23.4% (p=0.03) and 10.8% (p=0.09) reduction in Pap smear screenings for female members; a 16.5% (p=0.08) and 12.1% (p=0.27) reduction in colorectal cancer registrations and an 18.2% (p=0.08) and 8.9% (p=0.07) decrease in all oncology diagnoses. Uptake of telehealth services throughout the healthcare delivery system increased by 5 708% in 2020 compared with 2019, and 36.1% for 2021 compared with 2020. CONCLUSION: A significant reduction in emergency room visits, hospital admissions and utilisation of primary care services was observed since the start of the pandemic. Further research is required to understand if there are long-term consequences of delayed care. An increase in the use of digital consultations was observed. Research on their acceptability and effectiveness may open new modalities of care, which may have cost- and time-saving benefits.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".