IADR Abstract Archives
Comparative Analysis of Two Big Data Systems in Oral Health
Objectives: This study conducted a comprehensive comparative analysis of two major health datasets, namely “All of Us” and “BigMouth,” with the primary objective of identifying significant disparities in health-related factors, particularly focusing on the demographic variables of race, ethnicity, and gender.
Methods: An examination of these datasets was performed, encompassing a range of health domains, including behavioral health, systemic health, health literacy, and overall health status. Descriptive statistical analysis, Chi-square, odds ratio, 95% confidence interval, and Cohen’s D effect size calculations were employed to assess the significance of the observed differences.
Results: In the All of Us dataset, 80.6% of Hispanic or Latino respondents reported alcohol participation, compared to 16.8% in BigMouth. Females in the All of Us dataset reported alcohol participation at 87.9%, three times higher than BigMouth (26.0%). Females in the All of Us dataset had 8.8% type 2 diabetes prevalence, while BigMouth reported 21.6%. Health literacy patterns showed stark differences. Among Hispanic or Latino participants, the All of Us dataset had 49.2% with positive health literacy, compared to 3.2% in BigMouth. Moreover, All of Us revealed that 70.1% of Hispanic or Latino respondents reported satisfactory health status, while BigMouth showed a significantly higher percentage of 98.3%. All the above differences are tested for statistical differences by Cohen’s D calculations, and all had large effect sizes.
Conclusions: Differences may stem from recruitment methods, participant demographics, and healthcare access. To address such discrepancies, collaboration between datasets, standardized data collection methodologies, and inclusive recruitment strategies are imperative.
Methods: An examination of these datasets was performed, encompassing a range of health domains, including behavioral health, systemic health, health literacy, and overall health status. Descriptive statistical analysis, Chi-square, odds ratio, 95% confidence interval, and Cohen’s D effect size calculations were employed to assess the significance of the observed differences.
Results: In the All of Us dataset, 80.6% of Hispanic or Latino respondents reported alcohol participation, compared to 16.8% in BigMouth. Females in the All of Us dataset reported alcohol participation at 87.9%, three times higher than BigMouth (26.0%). Females in the All of Us dataset had 8.8% type 2 diabetes prevalence, while BigMouth reported 21.6%. Health literacy patterns showed stark differences. Among Hispanic or Latino participants, the All of Us dataset had 49.2% with positive health literacy, compared to 3.2% in BigMouth. Moreover, All of Us revealed that 70.1% of Hispanic or Latino respondents reported satisfactory health status, while BigMouth showed a significantly higher percentage of 98.3%. All the above differences are tested for statistical differences by Cohen’s D calculations, and all had large effect sizes.
Conclusions: Differences may stem from recruitment methods, participant demographics, and healthcare access. To address such discrepancies, collaboration between datasets, standardized data collection methodologies, and inclusive recruitment strategies are imperative.