Modeling Breast Cancer Incidence Rates: A Comparison between the Components of Functional Time Series (FTS) Model Applied on Karachi (Pakistan) and US Data

Abstract

Several studies showed that the breast cancer incidence rates are higher in high-income (developed) countries, due to the link of breast cancer with several risk factors and the presence of systematic screening policies. Some of the authors suggest that lower breast cancer incidence rates in low-income (developing) countries probably reflect international variation in hormonal factors and accessibility to early detection facilities. Recent studies showed that the breast cancer increased rapidly among women in Pakistan (a developing country) and it became the first malignancy among females of Pakistan. Although, the incidence rates may contain important evidence for understanding and control of the disease; however in Pakistan, the breast cancer incidence data have never been available in the last five decades since independence; rather, only hospital-based data are available. In this study, we intend to apply Functional Time Series (FTS) models to the breast cancer incidence rates of United State (developed country), and to see the difference between various components (age and time) of Functional Time Series (FTS) models applied independently on the breast cancer incidence rates of Karachi (Pakistan) and US. Past studies have already suggested that the incidence of US breast cancer cases was expected to increase in the coming decades. A progressive increase in the number of new cases is already predetermined by the high birth rate that occurred during the middle part of the century, and it will lead to nearly a doubling in the number of cases in about 4 decades. We also obtain 15 years predictions of breast cancer incidence rates in United States and compare them with the forecasts of incidence curves for Karachi. Development of methods for cancer incidence trend forecasting can provide a sound and accurate foundation for planning a comprehensive national strategy for optimal partitioning of research resources between the need for development of new treatments and the need for new research directed toward primary preventive measures.

Predicting Breast Cancer Incidence Rates Among White and Black Women in the United States: An Application of FTS Model

“…The patterns for whites are already discussed in [12]. The breast cancer incidence rates among black women increased during 1973-1999, and this increase is quite sharp from 1999 to 2000 for all age-groups.…”

“…These models werealso applied to the breast cancer mortality in Australia [7] and incidence rates by [9][10][11]. [12] made an attempt to compare various components of FTS models (in the form of age and time) by applying them independently to the breast cancer incidence data of Karachi (Pakistan) and US (White females). Here, we are going to apply these models to the age-specific incidence rates of breast cancer among black and white women in the United States.…”

“…These models are previously applied to the breast cancer mortality rates in Australia [7], to the French mortality and Australian fertility rates [6], to the breast cancer mortality among black and white women in the United States [8][9] and to the breast cancer incidence rates in Karachi [10][11][12]. The performance of these models with the most common age-period-cohort models of modeling mortality is compared in [13].…”

Diagnostic delay in breast cancer diagnosis: the role of health practitioners

“…Every ninth woman in Pakistan has a substantial lifetime risk of being diagnosed with breast cancer [5]. Moreover, the highest age-standardized incidence rate of breast cancer has put Pakistan first on the list of Asian countries [6]. Undoubtedly, breast cancer has significantly surged the death tolls in the country, mainly attributed to delayed referrals of patients to healthcare facilities and, ultimately, late diagnosis with advanced stages of breast cancer [7].…”