Discovering Social Determinant of Health Risk Factors for Perinatal Morbidity Through Real World Data

Cheng Gao1, You Chen1,2

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.

Insights

Social vulnerability significantly impacts gestational diabetes and preterm birth risks. Addressing social determinants of health is crucial for improving maternal and infant outcomes in reproductive health.

Area of Science:

  • Perinatal health
  • Social epidemiology
  • Reproductive health

Background:

  • Gestational diabetes and preterm birth are major perinatal morbidities.
  • Clinical factors are known contributors, but social determinants of health are increasingly recognized as critical.
  • Understanding these social factors is vital for effective public health strategies.

Purpose of the Study:

  • To quantify the association between the Social Vulnerability Index (SVI) and perinatal morbidities.
  • To identify specific social determinants contributing to adverse pregnancy outcomes.
  • To inform targeted interventions for improved reproductive health.

Main Methods:

  • Multivariate logistic regression models were employed.
  • The Social Vulnerability Index (SVI) was used as a key exposure variable.
  • Associations between SVI themes and perinatal morbidities were analyzed.

Main Results:

  • Significant associations were found between SVI and both gestational diabetes and preterm birth.
  • Factors across all four themes of the SVI demonstrated significant links to these conditions.
  • This highlights the pervasive influence of social vulnerability on pregnancy outcomes.

Conclusions:

  • Social vulnerability is a significant predictor of gestational diabetes and preterm birth.
  • Interventions must address social determinants of health to reduce these perinatal morbidities.
  • Targeting SVI-related factors is essential for advancing reproductive health equity.

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
128
Dimensions of Health and Illness01:21

Dimensions of Health and Illness

The factors influencing the health-illness continuum can be internal or external and may or may not be under conscious control. They are related to the following eight human dimensions, and each dimension is interrelated to one other.
7.3K
Factors Affecting Illness01:18

Factors Affecting Illness

When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
For instance, risk factors are connected to illness,...
4.2K
Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
171
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
366