Prevalence of chronic conditions and multimorbidity among healthcare workers in Zimbabwe: Results from a screening intervention
Bibliographic record
Abstract
The burden of non-communicable diseases (NCDs) in southern Africa is expanding and is superimposed on high HIV prevalence. Healthcare workers are a scarce resource; yet are vital to health systems. There are very limited studies on the burden of chronic conditions among healthcare workers in Africa, and none exploring multimorbidity (≥2 chronic conditions). We describe the epidemiology of infectious (HIV) and non-communicable chronic conditions, and multimorbidity, among Zimbabwean healthcare workers. Healthcare workers (≥18 years) in eight Zimbabwean provinces were invited to a voluntary, cross-sectional health-check, including HIV, diabetes, hypertension and mental health screening. Statistical analyses described the prevalence and risk factors for multimorbidity (two or more of HIV, diabetes, hypertension or common mental disorder) and each condition. Missing data were handled using multiple imputation. Among 6598 healthcare workers (July 2020-July 2022) participating in the health-check, median age was 37 years (interquartile range 29-44), 79% were women and 10% knew they were living with HIV. Half had at least one chronic condition: 11% were living with HIV, 36% had elevated blood pressure, 12% had elevated HbA1c and 11% had symptoms of common mental disorder. The overall prevalence of multimorbidity was 15% (95% CI: 13-17%); 39% (95% CI: 36-43%) among people aged 50 and older. Whilst most HIV was diagnosed and treated, other chronic conditions were usually undiagnosed or uncontrolled. Limiting our definition of multimorbidity to two or more screened conditions sought to reduce bias due to access to diagnosis, however, may have led to a lower reported prevalence than that found using a wider definition. Half of healthcare workers screened were living with a chronic condition; one in seven had multimorbidity. Other than HIV, most conditions were undiagnosed or untreated. Multisectoral action to implement contextually relevant, chronic disease services in Africa is urgently needed. Specific attention on health workers is required to protect and retain this critical workforce.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".