Economic burden of breast cancer in India, 2000-2021 and forecast to 2030

Background Breast cancer represents a significant public health concern in India, accounting for 28% of all cancer diagnoses and imposing a substantial economic burden. This study introduces a novel approach to forecasting the number of breast cancer cases (based on prevalence rates) and estimating the associated economic impact in India using the autoregressive integrated moving average (ARIMA) model. Methods Data on the prevalence of breast cancer in India from 2000 to 2021 were obtained from the Global Burden of Disease (GBD) database. This dataset provided annual estimates of the number of patients with breast cancer, serving as the basis for modeling future prevalence and estimating the economic burden. The ARIMA (Auto-Regressive Integrated Moving Average) model was employed to analyze and predict breast cancer prevalence in India up to the year 2030 (time-series forecasting). Data were visualized and checked for stationarity using the Augmented Dickey-Fuller (ADF) test. Using the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots, the appropriate parameters (p, d, q) were determined. Several ARIMA configurations were tested to identify the model with the best fit. The goodness-of-fit of the model was assessed using standard metrics such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The residuals were tested using the Box-Ljung test to confirm the absence of autocorrelation and verify that they followed a white noise distribution. Using the fitted ARIMA model, prevalence rates were forecasted from 2022 to 2030, with 95% confidence intervals to capture prediction uncertainty. Direct costs were calculated based on medical expenses for breast cancer patients, such as hospital visits, diagnostic tests, treatment costs, and follow-up care. A bottom-up approach was applied, which involves aggregating individual cost components from each stage of care to estimate the total direct burden of disease. A bottom-up approach was applied, which involves aggregating individual cost components from each stage of care to estimate the total direct burden of disease. Indirect costs were estimated using the human capital approach, which assesses productivity losses due to morbidity and premature mortality. The Disability-Adjusted Life Years (DALY) associated with breast cancer were also predicted using the ARIMA model. Results The results of coefficient of determination (0.99), mean absolute percentage error (69%), mean absolute error (5229), and root mean squared error (6451.2) showed that the ARIMA (0,2,0) model fitted well. Coefficient of determination (0.99) indicated that 99% of the variance in the data was explained by the model. Akaike information criterion (411.54) and Bayesian information criterion (412.53) indicated the ARIMA (0,2,0) model was reliable when analysing our data. The result of the relative error of prediction (2.76%) also suggested that the model predicted well. The number of patients with breast cancer from 2021 to 2030 was predicted to be about 1.25 million, 1.1.29 million,, 1.34 million, 1.39 million, 1.44 million, 1.48 million, 1.53 million, 1.58 million, 1.63 million, 1.68 million, and respectively. The total economic burden of breast cancer from 2021 to 2030 was estimated to be $8 billion, $8.72 billion, $9.05 billion, $9.84 billion, $10.20 billion, $11.07 billion, $11.49 billion, $12.44 billion, $12.91 billion, $13.95 billion, respectively is estimated to rise significantly. Conclusion Breast cancer prevalence and its economic impact are projected to grow substantially in India. Between 2021 and 2030, the number of breast cancer patients is expected to increase by approximately 0.05 million annually, with an annual increase rate of about 5.6%. The associated economic burden will also rise, averaging an additional $19.55 billion per year, underscoring the need for intensified healthcare and economic strategies to manage this growing challenge.