INTRODUCTION

Reducing premature births has been an important public health agenda in the United States at least since the 1980s. Prematurity of infants is one of the critical causes of fetal death and medical complications in infants, e.g. , low oxygen level at birth and nervous system problems (1). Russell and colleagues estimated that the costs of hospitalization for infants with low birth weight (LBW) or preterm birth (PTB) would be six times higher than that of other infants without PTB or LBW (2). Moreover, the physical and mental health of newborn babies reproduce socioeconomic disadvantages throughout their lifetime (3).
As the premature birth rate has continuously been high despite advanced medical technologies and prenatal care,(4,5) several Prenatal Care Home Visiting (PCHV) programs have been implemented. A PCHV generally refers to a service in which nurses or trained counselors visit families with a pregnant woman to provide advice for antepartum care treatments or counseling services (6,7). The services mainly provide monitoring to keep mothers receiving appropriate care services and counseling focused on parenting curricula and reducing behavioral risk factors, such as smoking, alcohol/drug use, and mental health problems (7,8). Furthermore, receiving prenatal care on a regular basis is considered as a primary measure of preventing premature births because it becomes a starting point to detect risks at an earlier stage of gestation.(7) PCHV programs specifically target to improve the accessibility to prenatal care for mothers in underserved communities (6,7). Rich evidence finds that infants from mothers in a socially and economically disadvantaged group are exposed to a higher risk of prematurity (9–11).
With ambitions to reduce disparities in birth outcomes, several PCHV programs have been implemented in the United States, however, the outcomes have been mixed. Two well-known large-scale randomized experiments examined the effectiveness of the home visiting approach to providing prenatal care: Nurse-Family Partnership (NFP) and the Mothers and Infant Home Visiting Program Evaluation-Strong Start (MIHOPE-Strong Start). As an earlier attempt, NFP, as a nonprofit organization, has provided PCHV services for mothers in disadvantaged groups and established evidence-based practices built upon its foundational randomized trials in Elmira, Memphis, and Denver (12). Olds the founder of the program, reported that nurse-visited mothers in the group that received the PCHV service through the program showed fewer rates of preterm births, infections, diseases (e.g., pregnancy-induced hypertension and atrial blood pressures), and higher average birth weight, compared the other group that did not receive the service (8). On the other hand, the MIHOPE-Strong Start is a relatively recent attempt initiated by the Office of Planning, Research, and Evaluation (OPRE) of the Administration for Children and Families (ACF) in 2012 (7). Different from findings of Olds, this program has not produced statistically significant improvements in families’ prenatal behaviors and birth outcomes (7).
At its core, the implementation of PCHV stands on the assumption that mothers would seek out the service voluntarily if they need it,i.e. , self-efficacy theory (8). However, this may not hold true because individuals’ care demands are often not connected to the actual use of care services due to mothers’ preferences or lack of knowledge and information. For example, mothers who work full-time might be less likely to have a limited chance to use prenatal care services than mothers who work part-time. Although existing programs often narrow the target based on sociodemographic profiles of mothers, such an approach is still exposed to poor accuracy in screening mothers with high risks. Prematurity at birth is a complex clinical problem that occurs by various sociodemographic and behavioral risk factors (13,14). Therefore, identifying high-risk individuals with only a couple of sociodemographic factors may not be sufficient to account for the causal complexity of birth prematurity.
PCHV interventions may have a higher potential if the agency can selectively encourage mothers for whom a premature birth is highly likely, with higher precision that allows identifying high-risk individuals. To achieve this, this study explores the potential of applying machine learning (ML) techniques in predicting premature births by using the birth data from the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC). More specifically, this study compares the performances of ordinary linear regression (OLS) and Deep Neural Network (DNN). The application of ML has been increasing in the fields of medicine, including mortality (15), cancer (16), opioids (17), influenzas (18), and mental diseases (19). Different from conventional statistical models that express the outcome as a linear combination of inputs and weights, a DNN classifier improves predictive performance by employing non-linear functions and multiple layers that bridge inputs and outputs (20). By combining this approach with the US birth data that is publicly accessible to most of public agencies, this study shows that attempts to use a home visiting approach for improving birth outcomes among mothers with high risk can benefit from a predictive approach improved by ML.