METHODS: Focus group discussions were conducted with cancer patients who were diagnosed at least 1 year prior to recruitment, and either had paid work, were self-employed, currently unemployed, or currently retired (N = 66).
RESULTS: Three main themes were identified: (1) loss of income: While some participants were entitled for a 1-year cancer-specific sick leave, many other participants recounted having insufficient paid sick leave, forcing them to take prolonged unpaid leave to complete treatment; (2) dealing with side effects of cancer and its treatment: The need for workplace accommodations was highlighted including flexible working hours, lighter workloads, and dedicated rest areas to enable patients to cope better; (3) Discrimination and stigma at workplace: Some participants mentioned being passed over on a promotion, getting demoted, or being forced to resign once their cancer diagnosis was disclosed, highlighting an urgent need to destigmatize cancer in the workplace.
CONCLUSION: In settings with limited employment protection policies, a cancer diagnosis severely impacts the working experiences of patients, leading to financial loss. Urgent interventions and legislative reforms are needed in these settings to address the unmet employment needs of cancer survivors.
IMPLICATIONS FOR CANCER SURVIVORS: This study may facilitate planning of local solutions to fulfill the unmet employment needs following cancer, such as return-to-work navigation services.
METHODS: This multi-center, cross-sectional, descriptive survey was conducted at 54 study sites in seven Asia-Pacific countries. A modified Likert-scale questionnaire was used to determine the importance of each element in the ICF among research participants of a biomedical study, with an anchored rating scale from 1 (not important) to 5 (very important).
RESULTS: Of the 2484 questionnaires distributed, 2113 (85.1%) were returned. The majority of respondents considered most elements required in the ICF to be 'moderately important' to 'very important' for their decision making (mean score, ranging from 3.58 to 4.47). Major foreseeable risk, direct benefit, and common adverse effects of the intervention were considered to be of most concerned elements in the ICF (mean score = 4.47, 4.47, and 4.45, respectively).
CONCLUSIONS: Research participants would like to be informed of the ICF elements required by ethical guidelines and regulations; however, the importance of each element varied, e.g., risk and benefit associated with research participants were considered to be more important than the general nature or technical details of research. Using a participant-oriented approach by providing more details of the participant-interested elements while avoiding unnecessarily lengthy details of other less important elements would enhance the quality of the ICF.
METHODS: Using Singapore Malaysia Hospital-Based Breast Cancer Registry, clinical information was retrieved from 7064 stage I to III breast cancer patients who were diagnosed between 1990 and 2011 and underwent surgery. Predicted and observed probabilities of positive nodes and survival were compared for each subgroup. Calibration was assessed by plotting observed value against predicted value for each decile of the predicted value. Discrimination was evaluated by area under a receiver operating characteristic curve (AUC) with 95 % confidence interval (CI).
RESULTS: The median predicted probability of positive lymph nodes is 40.6 % which was lower than the observed 43.6 % (95 % CI, 42.5 %-44.8 %). The calibration plot showed underestimation for most of the groups. The AUC was 0.71 (95 % CI, 0.70-0.72). Cancermath predicted and observed overall survival probabilities were 87.3 % vs 83.4 % at 5 years after diagnosis and 75.3 % vs 70.4 % at 10 years after diagnosis. The difference was smaller for patients from Singapore, patients diagnosed more recently and patients with favorable tumor characteristics. Calibration plot also illustrated overprediction of survival for patients with poor prognosis. The AUC for 5-year and 10-year overall survival was 0.77 (95 % CI: 0.75-0.79) and 0.74 (95 % CI: 0.71-0.76).
CONCLUSIONS: The discrimination and calibration of CancerMath were modest. The results suggest that clinical application of CancerMath should be limited to patients with better prognostic profile.
DESIGN: Retrospective study.
METHODS: Based on the mean deviation (MD) of the Humphrey Field Analyzer (HFA), the 152 subjects were categorized into mild (MD > - 6 dB, 100), moderate (MD - 6 to - 12 dB, 26), and severe (MD
PURPOSE: Osteoporosis self-assessment tool for Asians (OSTA) is a convenient screening algorithm used widely to identify patients at risk of osteoporosis. Currently, the number of studies validating OSTA in Malaysian population is limited. This study aimed to validate the performance of OSTA in identifying subjects with osteoporosis determined with DXA.
METHODS: This cross-sectional study recruited 786 Malaysians in Klang Valley, Malaysia. Their bone health status was assessed by DXA and OSTA. The association and agreement between OSTA and bone mineral density assessment by DXA were determined by Pearson's correlation and Cohen's kappa, respectively. Receiver operating characteristics (ROC) curves were used to determine the sensitivity, specificity, and area under the curve (AUC) for OSTA.
RESULTS: OSTA and DXA showed a fair association in the study (r = 0.382, κ = 0.159, p
METHODS: The bone health status of Malaysians aged ≥40 years was assessed using CM-200 and DXA. Sensitivity, specificity, area under the curve (AUC) and the optimal cut-off values for risk stratification of CM-200 were determined using receiver operating characteristic (ROC) curves and Youden's index (J). Results: From the data of 786 subjects, CM-200 (QUS T-score 0.05). Modified cut-off values for the QUS T-score improved the performance of CM-200 in identifying subjects with osteopenia (sensitivity 67.7% (95% CI: 62.8-72.3%); specificity 72.8% (95% CI: 68.1-77.2%); J = 0.405; AUC 0.702 (95% CI: 0.666-0.739); p < 0.001) and osteoporosis (sensitivity 79.4% (95% CI: 70.0-86.9%); specificity 61.8% (95% CI: 58.1-65.5%); J = 0.412; AUC 0.706 (95% CI: 0.654-0.758); p < 0.001). Conclusion: The modified cut-off values significantly improved the performance of CM-200 in identifying individuals with osteoporosis. Since these values are device-specific, optimization is necessary for accurate detection of individuals at risk for osteoporosis using QUS.