System modeling of a health issue: the case of preterm birth in Ohio

Abstract

Preterm birth rate (PBR) stands out as a major public health concern in the U.S. However, effective policies for mitigating the problem is largely unknown. The complexities of the problem raise critical questions: Why is PBR increasing despite the massive investment for reducing it? What policies can decrease it? To address these questions, we develop a causal loop diagram to investigate mechanisms underlying high preterm rate in a community. Our boundary is broad and includes medical and education systems, as well as living conditions such as crime rate and housing price. Then, we built a simulation model and divided the population into two groups based on their chance of delivering a preterm baby. We calibrated the model using the historical data of a case study, Cuyahoga Ohio, from 1995 to 2017. Prior studies mostly applied reductionist approaches to determine factors associated with high preterm rate at the individual level. Our simulation model examines the reciprocal influences of multiple factors and investigates the effect of different resource allocation scenarios on the PBR. Results show that, in the case of Cuyahoga county with one of the highest rates of PBR in the U.S., estimated preterm birth rates will not be lower than the rates of 1995 during the next five years.