Historical trends in breast cancer presentation among women in Pakistan from join-point regression analysis.

Objective: Breast cancer prevalence in Pakistan is among the highest in Asian countries. Recent changes in the temporal trends of breast cancer are largely unknown and examination of these trends can provide a direction in national planning for future health programs. The aim of this study was to examine recent changes in breast cancer incidence in Pakistan. Methods: A total of 9,771 diagnosed breast cancer women were registered from 2004 to 2015 in three hospitals of Karachi. Join-point regression analysis was applied to assessing the age-standardized breast cancer presentation trends for each five-year age group. Age-specific average annual percentage changes were also calculated to assess the proportion of women with increased or decreased incidence of breast cancer. Results: Age-standardized rates of breast cancer steadily increased from 24.7 per 100,000 in 2004, to 45.4 per 100,000 in 2015. The analytical trend based on the join-point model showed an average percentage increase breast cancer prevalence of 5.4 (95% CI = 3.1, 7.8). Significantly higher prevalence rates were identified among women aged 40-44 years and 65-69 years with an average percentage change of 18.5 (95% CI = 2.3, 37.2) and 14.3 (95% CI = 2.2, 27.9) respectively in the four years from 2011 to 2015. Conclusion: The findings indicate the trend in age-standardized prevalence increased significantly in all age groups with noticeably larger increases observed among older and post-menopausal women from 2011 to 2015. The results warrant the need for more targeted interventions to high-risk groups and a sound foundation for cancer control program planning and policy development in Pakistan to reduce the increasing incidence of breast cancer.

Forecasting Total Fertility Rates For Urban And Rural Areas In Pakistan With A Coherent Functional Model.

OBJECTIVE: To project total fertility rates for urban and rural areas in Pakistan up to 2027 with 80% prediction intervals. METHODS: The secondary-data study was conducted from March 2020 to August 2020 at Data Bank in the Department of Statistics, University of Karachi, Pakistan, and comprised publicly available data of the Pakistan Demographic Survey from 1984 to 2007. Two statistical models, the functional time series model and the coherent functional model, were used to make the predictions about age-specific fertility rates. The forecasting performance of the models was compared through error measures. Data was analysed using R version 3.6.3. RESULTS: The predicted total fertility rate was 1.7 (80% prediction interval: 0.4-4.4) births per woman for urban areas and 2.2 (80% prediction interval: 0.6-5.3) births per woman for rural areas in 2027 using coherent functional model. The total fertility rate predicted by the functional time series model was 2.1 (80% prediction interval: 1.6- 2.6) births in urban areas and 2.7 (80% prediction interval: 1.7-4.1) births per woman in rural areas. The empirical comparison of forecast error measures obtained from the two models indicated that the coherent functional model performed better for forecasting total fertility rates for urban and rural areas in Pakistan. CONCLUSIONS: The projection of fertility rate obtained by the functional time series model and the coherent functional model described the future fertility behaviour of urban and rural populations.

Coherent mortality forecasting: the product-ratio method with functional time series models.

When independence is assumed, forecasts of mortality for subpopulations are almost always divergent in the long term. We propose a method for coherent forecasting of mortality rates for two or more subpopulations, based on functional principal components models of simple and interpretable functions of rates. The product-ratio functional forecasting method models and forecasts the geometric mean of subpopulation rates and the ratio of subpopulation rates to product rates. Coherence is imposed by constraining the forecast ratio function through stationary time series models. The method is applied to sex-specific data for Sweden and state-specific data for Australia. Based on out-of-sample forecasts, the coherent forecasts are at least as accurate in overall terms as comparable independent forecasts, and forecast accuracy is homogenized across subpopulations.

Forecasting age-related changes in breast cancer mortality among white and black US women: a functional data approach.

BACKGROUND: The disparity in breast cancer mortality rates among white and black US women is widening, with higher mortality rates among black women. We apply functional time series models on age-specific breast cancer mortality rates for each group of women, and forecast their mortality curves using exponential smoothing state-space models with damping. MATERIALS AND METHODS: The data were obtained from the Surveillance, Epidemiology and End Results (SEER) program of the US [1]. Mortality data were obtained from the National Centre for Health Statistics (NCHS) available on the SEER*Stat database. We use annual unadjusted breast cancer mortality rates from 1969 to 2004 in 5-year age groups (45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84). Age-specific mortality curves were obtained using nonparametric smoothing methods. The curves are then decomposed using functional principal components and we fit functional time series models with four basis functions for each population separately. The curves from each population are forecast and prediction intervals are calculated. RESULTS: Twenty-year forecasts indicate an overall decline in future breast cancer mortality rates for both groups of women. This decline appears to be steeper among white women aged 55-73 and black women aged 60-84. For black women under 55 years of age, the forecast rates are relatively stable indicating there is no significant change in future breast cancer mortality rates among young black women in the next 20 years. CONCLUSION: White women have smooth and consistent patterns in breast cancer mortality rates for all age-groups whereas the mortality rates for black women are much more variable. The projections suggest, for some age groups, black American women may not benefit equally from the overall decline in breast cancer mortality in the United States.