Investigating Health Information Technology Usage by Sociodemographic Subpopulations to Increase Community Engagement in Healthcare: An Analysis of the Health Information National Trends Survey

It is well known that the US is plagued by health inequities: unjust differences in morbidity and mortality rates by sociodemographic factors. A potential method to address such inequities lies in utilizing health information technologies (HIT) to reach under-resourced populations and increase their involvement in healthcare. Previous researchers have done just this, using HIT tools to engage under-resourced communities and improve outcomes. However, it is unclear how HIT usage varies by sociodemographic characteristics. This study investigated this question through analysis of the Health Information National Trends Survey (HINTS) and proposed tailored HIT interventions for specific subpopulations. Internet, smartphone, and wearable device usage were analyzed by age, race/ethnicity, educational attainment, and income; purposes of HIT usage were assessed; and logistic regression models were conducted to determine associations between purposes of HIT usage and sociodemographic predictors. Results showed that Black/African American, Latinx, and Asian populations all had significantly increased use of health videos, while participants with low educational attainment had significantly decreased use of many HIT tools. Thus, this study highlights effective interventions for specific racial/ethnic populations and showcases a need for HIT tools inclusive towards low education populations to increase their engagement in healthcare and reduce inequities.

Stressful Life Changes Affect Nutrition-Related Health Outcomes Among US Army Soldiers (P18-070-19)

Objectives

This prospective cohort study aimed to determine if experiencing stressful life changes were associated with an earlier onset of adverse nutrition-related health outcomes among US Army Soldiers. An additional aim was to determine which stressful life changes were most associated with these outcomes and if there were gender differences in the magnitude of the associations.

Methods

Stressful life changes were changes in marital status, combat deployment or returning from deployment, relocation, adding a child, change in rank, change in occupation, or development of a physical limitation to duty. Nutrition-related health outcomes were hyperlipidemia diagnosis, substantial weight gain, or weight-related separation from the Army. Using longitudinal data from the Stanford Military Data Repository representing all active-duty Soldiers who were age 17–62 between 2011 and 2014 (n = 827,126), event history analysis was used to model the association of stressful life changes with nutrition-related outcomes.

Results

Marriage was found to raise the odds of substantial weight gain three months later by 1.24 times (95% CI: 1.16, 1.32) for men and 1.68 times (95% CI: 1.51, 1.89) for women. Developing a physical duty limitation raised the odds of hyperlipidemia two months later by 1.42 times (95% CI: 1.15, 0.75) for men and 1.83 times (95% CI: 1.01, 3.32) for women and the odds of substantial weight gain two months later by 3.16 times (95% CI: 2.89, 3.44) in men and 1.69 times (95% CI: 1.36, 2.08) in women.

Conclusions

Differences in the magnitude of associations show stressful life changes affect male and female Soldiers differently. Identifying times after stressful life changes when the risk of developing an undesirable health outcome is highest offers new possibilities for proactively addressing health. Findings could guide the timing of interventions to mitigate the effects of stress on health in military and civilian populations.

Funding Sources

None. Disclaimer: Authors’ views do not reflect official DoD or Army policy.

Use of large medical databases to study associations between diseases

We describe the use of a dataset of statistical medical records, the Oxford Record Linkage Study (ORLS), to identify diseases which occur together more commonly (association), or less commonly (dissociation), than their individual frequencies in the population would predict. We investigated some conditions known or suspected to enhance the subsequent risk of cancer, some conditions thought to be linked with schizophrenia, and some associations between conditions with a known autoimmune component. Diseases may occur in combination more often (or less often) than expected by chance because one predisposes to (or protects against) another or because they share environmental and/or genetic mechanisms in common. The investigation of such associations can yield important information for clinicians interested in potential disease sequelae, for epidemiologists trying to understand disease aetiology, and for geneticists attempting to determine the genetic basis of variation in disease course among individuals. We suggest that, through the use of datasets like the ORLS, it will be possible to 'map' comprehensively the phenomic expression of co-occurring diseases.