Social Determinants Influencing Access to Home Delivery of Medication During the COVID-19 Pandemic for Cape Town Residents Living With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic disrupted routine healthcare services, disproportionately affecting people living with chronic conditions such as type 2 diabetes (T2D). In response, the Western Cape Government Health implemented home delivery of medication (HDM) via community health workers (CHWs) to maintain continuity of care. This study aimed to evaluate the association between socioeconomic factors and access to HDM among T2D patients in Cape Town, South Africa, during the pandemic, with a focus on equity and health system responsiveness. METHODS: A descriptive cross-sectional survey was conducted via telephone interviews with 267 patients receiving care at 4 public primary care facilities. Sociodemographic, economic, and treatment-related variables were collected. Fisher's exact test and multivariable logistic regression were used to assess the associations between these variables and access to HDM. RESULTS: Language, marital status, employment, access to piped water, distance from the clinic, and duration of diabetes were significantly associated with access to HDM. IsiXhosa-speaking and unmarried participants were less likely to receive HDM, while unemployed individuals and those with longer diabetes duration were more likely to benefit. Geographic and infrastructural barriers further limited access, suggesting that HDM implementation may have inadvertently excluded vulnerable groups. CONCLUSION: While HDM was a valuable innovation during the pandemic, its uneven reach highlights the persistence of health inequities. Language, social support, and geographic location emerged as key barriers. These findings underscore the need for inclusive, community-informed service design and the critical role of CHWs in delivering equitable, person-centred care. Future interventions should prioritise co-design with communities and address structural barriers to ensure equitable access to healthcare during crises and beyond.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".