Macrolevel Analysis of Labour Productivity Losses Associated With Breast Cancer Among Women in 47 African Countries
Bibliographic record
Abstract
Background: Breast cancer remains one of the major diseases affecting women in the world. Relative to high‐income settings, women in low‐income settings such as Africa are less likely to be diagnosed with breast cancer and are more likely to die when they are affected by the disease. Apart from the negative health consequences of breast cancer, it could also reduce the labour productivity (LP) of the affected persons, at both the micro‐ and macrolevels. Nonetheless, empirical evidence on LP effects of breast cancer are scant and mostly focused on the microlevel and, hence, do not provide broader insights into the productivity losses associated with the disease. This study, to the best of our knowledge, therefore, provides the first cross‐country macrolevel empirical evidence of the effect of breast cancer (among women) on LP in Africa. Methods: The study uses data on 47 African countries spanning the period 1992–2021. Disability‐Adjusted Life Years (DALYs) associated with breast cancer in women is used as the baseline measure of breast cancer, while Years Lived with Disability (YLDs) and deaths associated with the disease in women are used as robustness measures. The system Generalised Method of Moments (GMM) regression is used as the main estimation technique, while two other estimators are used for robustness purposes. Results: Our analysis reveals a negative statistically significant association between breast cancer DALYs and LP. Specifically, we find a percentage increase in breast cancer DALYs to be associated with a 0.27% and 0.87% fall in LP in the short‐ and long‐run periods, respectively, at the 1% level of significance. The findings are robust using the other measures of breast cancer and different estimation techniques. Conclusion: There is a need to enhance measures towards breast cancer prevention and control in Africa such as timely diagnosis, all‐inclusive management of breast cancer, health promotion geared towards early detection and the creation of dependable referral systems to significantly reduce its associated LP losses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".