Background: Women diagnosed with breast cancer, their doctors, and their families, would find a valid estimate of her prognosis helpful in planning treatment and support. Assessing prognosis is complex as many factors influence it. Several predictive models have been produced, but none has been developed or tested on patients in New Zealand (NZ). Aim: We aimed to develop and validate a NZ predictive model (NZPM) for breast cancer, and compare its performance to a widely used UK-developed model, the Nottingham Prognostic Index (NPI). Methods: We developed a model to predict 10-year breast cancer-specific survival, using data collected prospectively in the largest population-based breast cancer registry in NZ (Auckland, 9182 patients), and assessed its performance in this data set (internal validation) and in an independent NZ population-based series of 2625 patients in Waikato (external validation). The data included all women with primary invasive breast cancer diagnosed from 1 June 2000 to 30 June 2014, with follow-up to death or to 31 December 2014. We used multivariate Cox proportional hazards regression to assess predictors and to estimate the probability of breast cancer mortality within 10 years, and therefore 10-year survival, for each patient. We assessed observed survival by the Kaplan-Meier method. We assessed discrimination by the C-statistic, and calibration by comparing predicted and observed survival rates for patients in 10 groups ordered by predicted 10-year survival. We compared this NZPM with the NPI in the validation data set. Results: The final NZPM used continuous variables of age, tumor size, and number of positive lymph nodes, and categorical variables of ethnicity, tumor stage, tumor grade, ER and PR receptors, HER2 status, and histologic type of tumor. Discrimination was good: C-statistics were 0.84 for internal validity and 0.83 for independent external validity. For calibration, for both internal and external validity, the predicted 10-year survival probabilities in 10 groups of patients, ordered by predicted survival, were all within the 95% confidence intervals (CI) of the observed Kaplan-Meier survival probabilities. The NZPM showed good discrimination even within the prognostic groups defined by the NPI. Conclusion: These results for the NZPM show good internal and external validity, transportability, potential clinical value, and its clear superiority over the NPI. Further research will assess other potential predictors, other outcomes, performance in specific subgroups of patients, and compare the NZPM to other models, which have been developed in other countries and have not yet been tested in NZ.
Background: Training courses in integrated management of childhood illness (IMCI) have been conducted for health workers for nearly one and half decades in Afghanistan. The objective of the training courses is to improve quality of care in terms of health workers communication skills and clinical performance when they provide health services for under-5 children in public healthcare facilities. This paper presents our findings on the effects of IMCI training courses on quality of care in public primary healthcare facilities in Afghanistan. Methods: We used a cross-sectional post-intervention design with regression-adjusted difference-in-differences (DiD) analysis, and included 2 groups of health workers (treatment and control). The treatment group were those who have received training in IMCI recently (in the last 12 months), and the control group were those who have never received training in IMCI. The assessment method was direct observation of health workers during patient-provider interaction. We used data, collected over a period of 3 years (2015–2017) from primary healthcare facilities, and investigated training effects on quality of care. The outcome variables were 4 indices of quality care related to history taking, information sharing, counseling/medical advice, and physical examination. Each index was formed as a composite score, composed of several inter-related tasks of quality of care carried out by health workers during patient-provider interaction for under-5 children. Results: Data were collected from 733 primary healthcare facilities with 5818 patients. Quality of care was assessed at the level of patient-provider interaction. Findings from the regression-adjusted DiD multivariate analysis showed significant effects of IMCI training on 2 indices of quality care in 2016, and on 4 indices of quality care in 2017. In 2016 two indices of quality care showed improvement. There was an increase of 8.1% in counseling/medical advice index, and 8.7% in physical examination index. In 2017, there was an increase of 5.7% in history taking index, 8.0% in information sharing index, 10.9% in counseling/medical advice index, and 17.2% in physical examination index. Conclusion: Conducting regular IMCI training courses for health workers can improve quality of care for under-5 children in primary healthcare facilities in Afghanistan. Findings from our study have the potential to influence policy and strategic decisions on IMCI programs in developing countries.
This study developed a systems-based approach (called FoodBack) to empower citizens and change agents to create healthier community food places. Formative evaluations were held with citizens and change agents in six diverse New Zealand communities, supplemented by semi-structured interviews with 85 change agents in Auckland and Hamilton in 2015-2016. The emerging system was additionally reviewed by public health experts from diverse organizations. A food environments feedback system was constructed to crowdsource key indicators of the healthiness of diverse community food places (i.e. schools, hospitals, supermarkets, fast food outlets, sport centers) and outdoor spaces (i.e. around schools), comments/pictures about barriers and facilitators to healthy eating and exemplar stories on improving the healthiness of food environments. All the information collected is centrally processed and translated into 'short' (immediate) and 'long' (after analyses) feedback loops to stimulate actions to create healthier food places. FoodBack, as a comprehensive food environment feedback system (with evidence databases and feedback and recognition processes), has the potential to increase food sovereignty, and generate a sustainable, fine-grained database of food environments for real-time food policy research.
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