My Research Projects

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This presentation provides brief description of all the projects I did during my doctoral program.

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My Research Projects

  1. 1. Rajeshwari S. Punekar, M.P.H, Ph.D. Dual-title Doctorate in Health Policy and Administration Demography The Pennsylvania State University, University Park, PA
  2. 2. Research Projects <ul><li>Work disability associated with cancer survivorship and other chronic conditions </li></ul><ul><li>Why do some health centers provide more enabling services than others? </li></ul><ul><li>The dynamics behind the erosion of employment-based health insurance </li></ul><ul><li>Medical expenditure of U.S. cancer survivors </li></ul><ul><li>Mental health status and mental health services utilization of U.S. cancer survivors and their spouses </li></ul>
  3. 3. Work disability associated with cancer survivorship and other chronic conditions <ul><li>Objectives </li></ul><ul><ul><li>to quantify the increase in work disability attributable to cancer in a cohort of adult survivors. </li></ul></ul><ul><ul><li>to compare disability rates in cancer survivors to individuals with other chronic conditions. </li></ul></ul><ul><li>Role: Research Assistant </li></ul><ul><li>Data: Penn State Cancer Survivors Study (PSCSS) and Health and Retirement Study (HRS) </li></ul><ul><li>Sample: 647 cancer survivors from PSCSS and 5988 non-cancer adults from HRS between the ages of 55 and 65 </li></ul>
  4. 4. Work disability associated with cancer survivorship and other chronic conditions <ul><li>Statistical Methods: </li></ul><ul><ul><li>Chi-square test </li></ul></ul><ul><ul><li>Propensity score matching </li></ul></ul><ul><ul><li>Multivariate adjusted logistic regression </li></ul></ul><ul><li>Statistical Software: SAS </li></ul><ul><li>Results: </li></ul><ul><ul><li>Compared to other adults with no chronic conditions, cancer survivors attributed half of their disability to cancer. </li></ul></ul><ul><ul><li>Disability rates for cancer-free survivors were less than or similar to rates for other conditions. </li></ul></ul>
  5. 5. Why Do Some Health Centers Provide More Enabling Services Than Others? <ul><li>Objective </li></ul><ul><ul><li>To identify organizational and patient population characteristics associated with the provision of enabling services in community health centers </li></ul></ul><ul><li>Role: Co-investigator and Research Coordinator </li></ul><ul><li>Data: 2003-2004 Uniform Data System (UDS) </li></ul><ul><li>Sample: 841 community health centers </li></ul><ul><li>Statistical Methods </li></ul><ul><ul><li>One-year lagged Ordinary Least Squares multiple regression model </li></ul></ul><ul><li>Software: </li></ul><ul><ul><li>SAS (Data Analysis) </li></ul></ul><ul><ul><li>Microsoft Project (Project Management) </li></ul></ul>
  6. 6. Association between community health center, patient population, and enabling services *P,0.05, **P,0.01, ***P,0.001 GRE = Grants Revenue Per Encounter, NRE = Net Revenue Per Encounter, MCC = Managed Care Contracts, and FTE = Full-time Equivalent Staff -0.010 0.029** With private insurance 0.032* 0.016** HIV negative 0.181 -0.050 HIV positive 0.021** 0.012* Homeless 0.025** 0.024*** Migrant/seasonal workers 0.004 -0.005 Need Translation 0.006 0.002 Non-White -0.062* 0.001 Elderly 0.015 -0.015 Female Patient population (%) -0.508 0.209 Exurban -0.559 -0.851** Urban -0.010*** -0.007*** Staff caseload 0.008*** 0.694*** Number of FTE 0.036* 0.038*** Number of MCC 0.015* 0.006 NRE -0.012*** -0.002 GRE Health Center Attributes 5.067*** 6.398*** Intercept Volume of enabling services Scope of enabling services Covariates
  7. 7. The dynamics behind the erosion of employment-based health insurance <ul><li>Objectives </li></ul><ul><ul><li>to examine changes in the dynamics that underlie the downward trend of employment-based health insurance. </li></ul></ul><ul><li>Role: Assistant Data Analyst and Research Coordinator </li></ul><ul><li>Data: 2000-2006 Medical Expenditure Panel Survey (MEPS) </li></ul><ul><li>Sample: 207,052 adults between ages of 18 and 65 </li></ul>
  8. 8. The dynamics behind the erosion of employment-based health insurance <ul><li>Statistical Methods </li></ul><ul><ul><li>Conversion of multivariate data into univariate data i.e. converting data from wide format to long format. </li></ul></ul><ul><ul><li>Survival Analysis (Cox Regression model) </li></ul></ul><ul><li>Statistical Software: </li></ul><ul><ul><li>SAS </li></ul></ul><ul><ul><li>STATA </li></ul></ul><ul><ul><li>MS Project </li></ul></ul><ul><li>Results: (in progress) </li></ul><ul><ul><li>Less than 2% of the study sample enrolls and disenrolls from the employment-based health insurance coverage. </li></ul></ul><ul><ul><li>The duration of uninsured spells following the loss of employment-based insurance ranged from 1 month to 6 months. </li></ul></ul>
  9. 9. Medical Expenditure of U.S. cancer survivors <ul><li>Objectives </li></ul><ul><ul><li>To describe and analyze the annual medical expenditures of U.S. cancer survivors. </li></ul></ul><ul><li>Role: Assistant Data Analyst and Research Coordinator </li></ul><ul><li>Data </li></ul><ul><ul><li>1999-2004 National Health Interview Survey (NHIS) </li></ul></ul><ul><ul><li>2001-2006 Medical Expenditure Panel Survey (MEPS) </li></ul></ul><ul><li>Sample </li></ul><ul><ul><li>2,419 cancer survivors and 41, 985 persons with no history or diagnosis of cancer. </li></ul></ul>
  10. 10. Medical Expenditure of U.S. cancer survivors <ul><li>Statistical Methods </li></ul><ul><ul><li>Survey adjusted independent t-test </li></ul></ul><ul><li>Statistical Software: </li></ul><ul><ul><li>SAS </li></ul></ul><ul><ul><li>MS Project </li></ul></ul><ul><li>Results: </li></ul><ul><ul><li>Cancer survivors spend more on medical care compared to other (non-cancer) adults. </li></ul></ul>
  11. 11. Mental health status and mental health services utilization of U.S. cancer survivors and their spouses <ul><li>Doctoral thesis </li></ul><ul><li>Objectives </li></ul><ul><ul><li>To compare mental health status and the use of mental health services and prescribed psychotherapeutic medicines between the oncology population and the general (non-cancer) population. </li></ul></ul><ul><ul><li>To identify the significant predictors of mental health service utilization and prescribed psychotherapeutic medicine utilization among U.S. cancer survivors and their spouses. </li></ul></ul><ul><li>Data: </li></ul><ul><ul><li>1999-2004 National Health Interview Survey (NHIS) </li></ul></ul><ul><ul><li>2001-2006 Medical Expenditure Panel Survey (MEPS) </li></ul></ul>
  12. 12. Mental health status and mental health services utilization of U.S. cancer survivors and their spouses <ul><li>Sample: </li></ul><ul><ul><li>2,419 cancer survivors and 1,036 spouses of cancer survivors. </li></ul></ul><ul><li>Statistical Methods: </li></ul><ul><ul><li>Raking (Adjusting new weights so that the marginal totals were equal to the population totals) </li></ul></ul><ul><ul><li>Chi-square test </li></ul></ul><ul><ul><li>Survey design adjusted Ordinary Least Square regression </li></ul></ul><ul><ul><li>Survey design adjusted logistic regression </li></ul></ul><ul><li>Statistical Software: SAS </li></ul>
  13. 13. Mental Health Scores of U.S. adults (ages 25 to 85), 2001-2006 7 9 5 10 Depression a % % % % 5 4 4 6 Non-Specific Psychological Distress (K6) Scores a 52 52 51 49 Mental Component Scores of SF-12 a Non-cancer Cancer Non-cancer Cancer 65 and older 25-64 Adults 6 10 4 7 Depression a,b % % % % 3 3 3 4 Non-Specific Psychological Distress (K6) Scores a 53 53 52 50 Mental Component Scores of SF-12 a Non-cancer Cancer Non-cancer Cancer 65 and older 25-64 Spouses
  14. 14. Ambulatory Mental Health Visits by U.S. adults (age 25 to 85), 2001-2006 Cancer survivors between 25 and 64 years of age make ambulatory mental health visits significantly more often than their counterparts.
  15. 15. Usage of prescribed psychotherapeutic medicines by U.S. adults (ages 25 to 85), 2001-2006 Adult cancer survivors uses prescribed psychotherapeutic medications significantly more often than the non-cancer adults.
  16. 16. Usage of prescribed psychotherapeutic medicines by Spouses (ages 25 to 85), 2001-2006 Spouses of cancer survivors uses prescribed psychotherapeutic medications significantly more often than the spouses of non-cancer adults.
  17. 17. Tasks <ul><ul><li>Conducted literature review </li></ul></ul><ul><ul><li>Procured public use data files </li></ul></ul><ul><ul><li>Extracted variables required for further analysis from public use data files </li></ul></ul><ul><ul><li>Identified and rectified the discrepancies present in the compiled data </li></ul></ul><ul><ul><li>Created person-level and family-level expenditure variables. </li></ul></ul><ul><ul><li>Inflated expenditure variables using Personal Health Care Expenditure (PHCE) index </li></ul></ul><ul><ul><li>Created monthly public and private health insurance variables </li></ul></ul>
  18. 18. Tasks <ul><ul><li>Conducted trend analysis for the cross-sectional estimates of employment-based insurance and public insurance coverage </li></ul></ul><ul><ul><li>Investigated trends in enrollment, disenrollment, and the duration of uninsured spells following the loss of employment-based insurance </li></ul></ul><ul><ul><li>Interpreted the output of the data analysis </li></ul></ul><ul><ul><li>Wrote a report on the data analysis </li></ul></ul><ul><ul><li>Coordinated research meetings and presentations </li></ul></ul><ul><ul><li>Tracked the progress (and issues) of research projects </li></ul></ul>

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