Monik Jimenez

Mid-career Tenured Researcher, Mass General Brigham

3 active projects

Self-reported Stroke Validation

This study will examine the validity of self-reported stroke compared to stroke in the medical record. We will use data from participants who have provided electronic health records (EHR) and have also filled out a medical history survey. Specific questions…

Scientific Questions Being Studied

This study will examine the validity of self-reported stroke compared to stroke in the medical record. We will use data from participants who have provided electronic health records (EHR) and have also filled out a medical history survey. Specific questions we will ask:
1. How does self-reported stroke reflect stroke documented in the electronic health record?
2. How does the validity of self-reported stroke differ across sex?
3. How does the validity of self-reported across demographic factors such as age, race and ethnicity and level of education.
4. How does the validity of self-reported differ by age at the time the stroke occurred or continued treatment for stroke.
We hypothesize that self-reported stroke will demonstrate high validity compared to events identified in the electronic health record. However, there may be variability in the validity of self-reported stroke events, by demographics and socioeconomic factors.

Project Purpose(s)

  • Disease Focused Research (Stroke; cerebrovascular disease)
  • Population Health
  • Methods Development

Scientific Approaches

We will restrict our analysis to participants with EHR data who answered the medical conditions survey. We will use R statistical programing language to calculate standard measures of validity, including sensitivity, specificity, positive and negative predictive values. Unweighted kappa statistics with corresponding 95% CIs will be calculated to estimate inter-rater reliability. Metrics of the validity of self-reported stroke and EHR data will be stratified by sex, age, race and ethnicity, level of education, region of the US, regular source of medical care, age at the time of event, and continued stroke related treatment. However, subgroup analyses will depend on sufficient sample size across stratification factors. We will engage community partners to review our hypotheses, approach, and study limitations. One important limitation is that the EHR may not reflect strokes that occurred outside of the participants regular care system, or events that occurred earlier in life.

Anticipated Findings

We expect that self-reported stroke will show a high validity when compared to EHR data. However, there may be underreporting of stroke or other misclassification based on factors reflecting exposure to structural racism, such as race and ethnicity, access to care, health literacy, education, and region of the US. In addition, stroke events occurring earlier in a participant’s life may be misclassified by EHR ascertainment. To date few studies have examined the validity of self-reported stroke in the general population; therefore, this study will address a critical gap in knowledge for other survey-based population studies. In addition, assessing the validity of self-reported stroke is critical for future work examining stroke outcomes among participants of All of Us. These data can be used to support expanded work in stroke by establishing strong validity of self-reported stroke events or the creation of correction factors for future work using the self-reported stroke variables.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Geography

Research Team

Owner:

  • Monik Jimenez - Mid-career Tenured Researcher, Mass General Brigham

Collaborators:

  • Guohai Zhou - Other, Mass General Brigham

V5 data Demo - Uterine Fibroids

From this analysis, we hope to observe if there are differences in age, racial and risk distribution between both fibroids diagnostic cohorts. We also will determine the odds of this condition in both cohorts, based on modifiable risk factors such…

Scientific Questions Being Studied

From this analysis, we hope to observe if there are differences in age, racial and risk distribution between both fibroids diagnostic cohorts. We also will determine the odds of this condition in both cohorts, based on modifiable risk factors such as age, race and menopausal state and non-modifiable risk factors like obesity, use of hormones and smoking. These findings will be compared to findings from pre-existing data.

Project Purpose(s)

  • Disease Focused Research (Uterine fibroids or leiomyomas)
  • Population Health
  • Social / Behavioral
  • Methods Development
  • Other Purpose (“This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use”.)

Scientific Approaches

Participants were eligible if they were assigned female sex at birth and had both Electronic Health Records (EHR) data and Patient Provided Information (PPI). Two fibroids cohorts were created based on: 1. Presence of at least one SNOMED code for uterine fibroids in their Electronic Health Records 2. Presence of one ICD-9 or CPT code for pelvic imaging (e.g., pelvic ultrasound) and one diagnostic code for uterine fibroids Variables of interest were identified from the EHR and PPI and imported as concept sets into the notebook, otherwise they were created in the notebook. Data was analyzed in R software version 3.6.2, 2019.

Anticipated Findings

We anticipate that black females will have higher odds of fibroids compared to white women and that smokers will have lower odds of fibroids compared to non-smokers. While findings from this analysis are not novel, they validate existing knowledge and underscore the importance of the AoU data cohort in research. Furthermore, AoU cohort data represents females in the United States and Canada and, importantly, populations that are underrepresented in research.

Demographic Categories of Interest

  • Sex at Birth
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Research Team

Owner:

obesity_mansucript_rerun

National obesity prevention and intervention strategies may benefit from precision medicine approaches that incorporate integrated data on environments, social determinants of health, and genomic factors. We examined the quality and utility of the All of Us Research Hub Workbench for…

Scientific Questions Being Studied

National obesity prevention and intervention strategies may benefit from precision medicine approaches that incorporate integrated data on environments, social determinants of health, and genomic factors. We examined the quality and utility of the All of Us Research Hub Workbench for accelerating precision medicine by replicating methods from existing studies that examine the prevalence of obesity at the population level. We evaluated the measurements of obesity in the participant measurement (PM) data set and the electronic health record (EHR) data set using methods similar to the Ward et al. NEJM December 2019 publication that assessed prevalence of obesity in the US by state using BRFSS data.

Project Purpose(s)

  • Disease Focused Research (obesity)
  • Educational
  • Methods Development
  • Other Purpose (This work is the result of an All of Us Research Program Demonstration Project. Demonstration Projects are efforts by the All of Us Research Program designed to meet the goal of ensuring the quality and utility of the Research Hub as a resource for accelerating precision medicine. This work has been approved, reviewed, and overseen by the All of Us Research Program Science Committee and Data and Research Center to ensure compliance with program policy.)

Scientific Approaches

For this population-based cross-sectional study of All of Us Research Workbench participants, we excluded individuals with measurements obtained during pregnancy or inpatient visits and individuals from states with fewer than 100 participants. Physical measurements (PM) of height and weight at the time of program enrollment of 142,116 participants and measured weight and height extracted from electronic health records (EHR) of 40,885 individuals were used to calculate body-mass index (BMI). We did a complete case analysis for All of Us participants with known sex (male or female), race, income and education levels and estimated state-specific and demographic subgroup-specific prevalence of categories of BMI [obesity (BMI ≥30) and extreme obesity (BMI ≥ 35)] nationwide and for each state: overall and by subgroups, male and female. We examined the difference between EHR and PM calculated BMI by state.

Anticipated Findings

Using states with at least 100 participants, PM data included 142,116 individuals (mean [SD] age, 51.2 [16.6] and EHR data on height and weight included 40,885 individuals (mean [SD] age, 52.5 [16.5]. The median BMI for PM participants was 28.4 [24.4 to 33.7]; the median BMI for EHR was 29.0 [24.8 to 34.5]. The PM national prevalence for obesity (includes BMI>30 and BMI >35) and extreme obesity (BMI >35) were 41.2 % (95% Confidence Interval [CI], 40.9 to 41.4) and 20.8% (95% CI, 20.6 to 21.0), respectively, with large variations across states. Women had higher prevalence of extreme obesity than men in all selected states. Subgroups with extreme obesity (BMI, >35) prevalence greater than 25% included subgroup, N, prevalence %, (95% CI): Black NH, 8913, 28.9 (25.8 to 32.0) , individuals with income less than $25,000, 13,244, 25.1 (22.1 to 28.1); education of high school to some college, 17, 272, 26.1 (23.1 to 29.1) and the region of the South, 6,639, 25.3 (22.3 to 28.3).

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Education Level
  • Income Level

Research Team

Owner:

Collaborators:

  • Francis Ratsimbazafy - Other, All of Us Program Operational Use
  • Karthik Natarajan - Other, All of Us Program Operational Use
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