Forecasts of Cancer and Chronic Patients: Big Data Metrics of Population Health

TL;DR

This paper presents a dynamical systems model for forecasting population health related to cancer and chronic diseases, enabling accurate short-term predictions and insights into preventive care impacts.

Contribution

It introduces a novel modeling approach that accurately forecasts disease prevalence and provides new metrics for evaluating preventive care effectiveness.

Findings

12-month forecast errors between 3% and 6%

Chronic conditions increase cancer risk similarly to diabetes

Model offers metrics for short-term evaluation of preventive programs

Abstract

Chronic diseases and cancer account for over 75 percent of healthcare costs in the US. Increased prevention services and improved primary care are thought to decrease costs. Current models for detecting changes in the health of populations are cumbersome and expensive, and are not sensitive in the short term. In this paper we model population health as a dynamical system to predict the time evolution of the new diagnosis of chronic diseases and cancer. This provides a reliable forecasting tool and a means of measuring short-term changes in the health status of the population resulting from preventive care programs. Twelve month forecasts of cancer and chronic populations were accurate with errors lying between 3 percent and 6 percent. We confirmed what other studies have demonstrated that diabetes patients are at increased cancer risk but, interestingly, we also discovered that all of the studied chronic conditions increased cancer risk just as diabetes did, and by a similar amount. The model(i)yields a new metric for measuring performance of preventive and clinical care programs that can provide timely feedback for quality improvement programs;(ii)helps understand "savings" in the context of preventive care programs and explains how they can be calculated in the short term, even though they materialize only in the long term and(iii)provides an analytic tool and metrics to infer correlations and derive insights on the effect of changes in socio-economic factors affecting population health on improving health and lowering costs of populations.