Bio
David Goodman is a physician, health services researcher, and educator with a longstanding interest in the causes and consequences of health care variation. In his early years, his studies focused on the relationship of physician supply to population outcomes, and applied the findings to public policy development. More recently, he has developed a research portfolio that investigates unwarranted variation in the use of neonatal intensive care.
Goodman is one of the founding investigators of the Dartmouth Atlas of Health Care and has led multiple Atlas projects on such topics as end-of-life cancer care, post hospital discharge care, and care for infants and children. He has served on multiple journal editorial boards, and federal and Institute of Medicine committees, including a term as the chair of the Council on Graduate Medical Education. His research papers and editorials have been published in the New England Journal of Medicine, JAMA, Health Affairs, Pediatrics, and The New York Times.
He received a BA from the University of Vermont, an MD from the State University of New York Upstate Medical Center, and an MS in medical care epidemiology from Dartmouth College. He served his residency in pediatrics at The Johns Hopkins Hospital, and then practiced as a rural National Health Corps physician before joining the Dartmouth faculty.
Published Research
Daugaard C, Neergaard MA, Vestergaard AHS, Nielsen MK, Goodman DC, Johnsen SP
Eur J Public Health|2019 Nov 20
Phillips JD, Bostock IC, Hasson RM, Goodney PP, Goodman DC, Millington TM, Finley DJ
J Thorac Dis|2019 Mar
Health system factors and caesarean sections in Kosovo: a cross-sectional study.
Hoxha I, Fejza A, Aliu M, Jüni P, Goodman DC
BMJ Open|2019 Apr 11
Neonatal Intensive Care Variation in Medicaid-Insured Newborns: A Population-Based Study.
Goodman DC, Ganduglia-Cazaban C, Franzini L, Stukel TA, Wasserman JR, Murphy MA, Kim Y, Mowitz ME, Tyson JE, Doherty JR, Little GA
J Pediatr|2019 Jun
Caesarean section in uninsured women in the USA: systematic review and meta-analysis.
Hoxha I, Braha M, Syrogiannouli L, Goodman DC, Jüni P
BMJ Open|2019 Mar 3
Courses Taught
PH 139:
Epidemiology/Biostatistics: Part 1
(4)
The epidemiology component of this course introduces the basic principles of epidemiology, including formulation of the research question, choice of study subjects, measures of disease frequency, assessment of exposure and disease status, study design (cross-sectional studies, prospective and retrospective cohort studies, case-control studies, and clinical trials), measures of association between exposure and disease (risk ratio and risk difference measures) and causal inference. Taught as lectures and assigned exercises, this course stresses the practical applications of epidemiological techniques.
The biostatistical topics in this course include vital rates and ratios, probability concepts, discrete and continuous probability distributions, populations and samples, and introduction to the use of computers for statistical analysis.
Core Requirement for MPH and MS
Prerequisites: None
PH 144:
The Current Status of Practice Variation Research
(1)
This short course will focus on readings from the book, Tracking Medicine, by John E. (Jack) Wennberg. Wennberg reviews his and his colleagues' work on practice variations, beginning with small area variation in Vermont, extending through efforts to evaluate the reasons behind variation in surgical procedures, and concluding with the more recent work associated with overuse of supply sensitive care as described in the Dartmouth Atlas of Health Care. The course includes significant time for discussion and interaction.
Core Requirement for MPH and MS
Prerequisites: None
PH 140:
Biostatistics
(1.5 units)
This course develops skills at interpreting and appraising basic biostatistics, which is critical for evidence-based action. Exercises include interpreting published studies and replicating their analyses using statistical software, such as R. Students will conduct both descriptive and inferential statistics. Statistical techniques include summarizing a sample, conducting hypothesis tests (t-test, chi-squared, ANOVA), and describing relationships with correlation and multiple linear regression. Students will critically appraise measures and deeply understand how to interpret p-values and confidence intervals.