Clinical Epidemiology: The Essentials - 3rd Edition - Notes

  • Clinical Epidemiology: Science of making predictions about individual patients by counting clinical events in similar patients, using strong scientific methods, while avoiding systematic error and chance.
  • Clinical epidemiology seeks to answer clinical questions and guide decision-making with evidence, situating individual care within the larger population.
  • Clinicians focus on individual patients, taking personal responsibility for their welfare, and may be reluctant to categorize patients or express risk as probabilities.
  • Traditional clinical training emphasizes the mechanisms of disease through basic sciences, assuming disease processes can predict course and treatment.
  • Knowledge of disease biology should be tested by clinical research due to environment factors affecting outcomes.
  • Personal experience as a guide to clinical decision-making is limited by the subtlety and long-term relationships characterizing chronic diseases.
  • Understanding clinical epidemiology is crucial for interpreting clinical information, making it a basic science alongside anatomy, pathology, biochemistry, and pharmacology.
  • Key Realities of Clinical Science:
    • Uncertainty in diagnosis, prognosis, and treatment must be expressed as probabilities.
    • Individual patient probability is best estimated by past experience with similar groups.
    • Clinical observations risk systematic errors from variable skills/biases.
    • All observations are influenced by chance.
    • Clinicians should use sound scientific principles to reduce bias and estimate chance.
  • Modern society requires clinical care based on research, judged by outcomes at an affordable cost, relating individuals to larger groups to optimize resource allocation.
  • Basic purpose of clinical epidemiology: Foster methods leading to valid conclusions while avoiding being misled.
  • Clinical Questions (Table 1.1):
    • Abnormality: Is the patient sick or well?
    • Diagnosis: How accurate are tests?
    • Frequency: How often a disease occurs.
    • Risk: What factors increase disease risk?
    • Prognosis: What are disease consequences?
    • Treatment: How does treatment change the course?
    • Prevention: Does intervention on well people keep disease from arising?
    • Cause: What conditions lead to disease?
    • Cost: How much does illness care cost?
  • Health Outcomes (Table 1.2): clinical events of primary interest to patients and caregivers, examined directly in humans.
    • Death: A bad and untimely outcome.
    • Disease: Symptoms, signs and laboratory abnormalities.
    • Discomfort: Symptoms like pain, nausea, dyspnea, itching and tinnitus.
    • Disability: Impaired ability to perform usual activities.
    • Dissatisfaction: Emotional reactions to the illness.
    • Destitution: Financial cost of illness to the individual patients.
  • Biologic outcomes cannot properly be substituted for clinical ones without direct evidence that the two are related, as seen in HIV treatment (Table 1.3).
  • Clinical science relies on quantitative measurements to improve confirmation, communication, and error estimation, counting clinical outcomes like death, symptoms, or disability.
  • Populations are groups of people in a defined setting or with a certain characteristic, while a sample is a subset of the population selected for clinical research.
  • Important considerations regarding bias:
    • Selection bias: Comparisons made between patients differing in determinants of outcome, not the main factors under study.
    • Measurement bias: When methods of measurement are dissimilar among groups of patients.
    • Confounding bias: When two factors "travel together", distorting the effect of one by the effect of the other (Fig 1.1).
  • Herpesvirus case with potential Confounding factors: Increased sexual activity and association to human papillomavirus (HPV).
  • Chance (random variation): divergence of an observation on a sample from the true population value, which is reduced through statistics.
  • Distinguishing between Bias and Chance:
    • Bias can be prevented or corrected; chance cannot be eliminated, but its influence can be reduced by proper design and statistics.(Fig 1.2)
  • Blood pressure measurement by intraarterial cannula has the “true” pressure, while sphygmomanometer is prone to error (bias).
  • Internal validity: The degree to which the results of a study are correct for the sample of patients being studied.
  • External validity (generalizability): The degree to which the results of an observation hold true in other settings. (Fig 1.3)
  • Sampling bias: Occurs when patient sample is systematically different from population for the research question or clinical use , over representing the serious end of the disease.
  • Clinical epidemiology helps to foster understanding of clinical evidence strengths/weaknesses, increased efficiency in acquiring sound information during colleague interactions through strong studies.
  • The book is organized around clinical questions encountered during patient care throughout the natural history of a disease (Fig 1.5).