Abstract P1-11-07: The relationship between breast cancer progression and workplace productivity in the US
Bibliographic record
Abstract
Abstract Background: A significant proportion of women with breast cancer leave employment due to their disease. Little is known about the effects of breast cancer progression on productivity among those who remain employed. We sought to determine the effect of disease progression on workplace productivity among women with breast cancer. Methods: By linking health insurance claims data to workplace productivity data, a longitudinal dataset of women with breast cancer was constructed. The study cohort consisted of commercially insured women aged 18 to 64 in the US who were treated for any type of breast cancer between 2005 and 2012. Disease stage was measured through diagnosis codes and treatments observed, to classify women into the following breast cancer groups in each 90-day quarter: local; locally advanced; other non-metastatic; metastatic, 1st line therapy; metastatic, 2nd line therapy; metastatic, ≥ 3rd line therapy; metastatic, end-of-life care. Progression was defined as movement to a more advanced disease stage. Workplace productivity was measured as employment status and total hours away from work per quarter. Covariates included employer industry, comorbidities, age, region of residence, and a time trend. Reduced workplace productivity was valued using average U.S. wages by industry. Kaplan Meier analysis was used to test whether women whose cancer progressed were more likely to drop out of our employment-based sample. Linear and Heckman models were used to measure the effect of disease progression on workplace hours missed. The Heckman model was used to correct for selection bias, given that healthier women may be more likely to remain in our employment-based dataset. Results: The study cohort included 6,409 women. Mean patient age was 52.0 years (SD: 7.7). The mean number of Charlson comorbidities was 0.52 per patient (SD: 2.9). The majority of our employment-based sample had non-metastatic breast cancer (90.7%). Breast cancer progression was associated with a lower probability of employment (hazard ratio = 0.65, P<0.01). Patients who left our employment-based dataset by the 12th quarter had a greater number of comorbidities (P<0.01) and missed a greater number of hours in the first two quarters (P<0.1), compared with those who remained. This indicated that patients leaving our employment-based sample were less healthy than those who stayed, supporting the use of the Heckman model. According to the Heckman results, progression was associated with increased workplace hours missed per quarter, both when comparing early versus late stage (P<0.001), and first-line versus later-line metastatic therapy (P<0.05). Linear results were similar. Using the Heckman results, the annual valuation of work missed per patient was $29,881 for patients without metastases and $34,141 for patients with, indicating that progression to metastatic cancer adds an additional $6,500 of lost work time, or about 14% of average US wages. Conclusions: Breast cancer progression leads to increased workplace hours missed, with greater hours missed among those with more advanced disease. Avoiding or delaying disease progression could bring productivity gains to the workplace in addition to the benefits to the patient. Support: This study was funded by Pfizer Inc. Citation Format: Yin W, Horblyuk R, Perkins JJ, Sison S, Smith G, Snider JT, Wu Y, Philipson TJ. The relationship between breast cancer progression and workplace productivity in the US. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P1-11-07.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".