Measuring Health and Disease in Populations II

Chapter 3
Eric Delmelle
September 25 & 30 2025

Chapter Overview

  • Health Indicators and Indices: What are their characteristics and why are they important?
  • Sources and Evaluation of Health Data: Primary, secondary data, passive and active collection.
  • Disease Definition and Classification: How do we classify diseases? (ICD9, ICD10-codes)
  • Diagnostic Tests and Disease Surveillance: Is a test accurate? What about specificty and sensitivity? True Positive or False Negative?

1 Health Indicators and Indices

  • Health Indicators: Directly measurable variables reflecting a population’s health such as infant mortality rate and life expectancy.
  • Health Indices: Composite measures combining multiple indicators to give an overview of health status, like the Human Development Index.
  • See Box 3.1 and Box 3.3 for more details.

Evaluating Health Indicators

  • How are health indicators used to monitor and assess population health, informing public health decisions and policy-making?

Essential Qualities

  • Reliability/Reproducibility: Consistent results across studies and time.
  • Validity: Accurately measures what it is intended to measure.
  • Sensitivity: Detects changes or differences that are meaningful.

Practical Qualities

  • Acceptability: Acceptable to those who are assessed by the indicator.
  • Feasibility: Can be realistically collected and sustained.
  • Universality: Applicable across different settings and populations.

2 Sources and quality of health data

  • See Table 3.1 -sources of data to compilate indicators.
  • Health data sources are crucial for compiling health indicators.
  • Data can originate from individuals, healthcare providers, or be generated through health surveys and administrative databases.

Active or passive?

  • Active Data Sources: Require efforts to solicit and collect information, e.g., health surveys.
  • Passive Data Sources: Routinely submitted by other entities, e.g., vital statistics.

Primary and Secondary Data Sources

  • Primary Data Sources: Specifically collected for the purpose of health monitoring (e.g. disease registries and some health surveys).

    • Electronic Health Records (EHR): Detailed, real-time, patient-centered records.
    • Disease Registries: Used to estimate disease incidence and prevalence, like cancer registries.

  • Secondary Data Sources: Originally designed for other purposes but also used in health monitoring (e.g.administrative databases for Medicare and Medicaid).

Health Information System (HIS)

  • An organized set of activities whose purpose is to gather, maintain, and provide health-related information to improve health outcomes.

  • Components:

    • Disease registries
    • Utilization databases
    • National health surveys

Disease Registries and Notifiable Diseases

  • Disease Registries
    • Important for estimating disease incidence and prevalence.
    • Cover various conditions from cancer to communicable diseases.
  • Notifiable Diseases
    • Diseases that must be reported to national health authorities.
    • Managed by systems like the CDC’s National Notifiable Diseases Surveillance System.

Utilization of Health Data

  • Measuring Health Service Utilization
    • Utilization data from Medicare, Medicaid, and health maintenance organizations provide insights into health service use.
  • Health Surveys
    • Gather data on health behaviors, practices, and perceptions.
    • Vary by mode of interview and can be influenced by respondents’ perceptions and recall accuracy.
    • See Table 3.2 for pros and cons of survey type
    • Cross-section (versus longitudinal)
    • Likert scale!
    • Self-rated survey
      • Surprisingly good (dynamic)
    • QOL - see the one for Charlotte, NC

Linking health data

  • Enhanced Insights: Linking different data sources can provide a more comprehensive view of health outcomes.

  • Examples of Linked Data: Combining EHRs with national health surveys or insurance claim data.

  • Data Challenges

    • Data Integrity and Privacy: Managing the accuracy and confidentiality of linked data is paramount.
    • Analytical Complexity: The use of linked data often requires advanced statistical techniques to manage biases and variability.

3 Summary measures of population health

Integrating Mortality and Morbidity

  • Health indices like DFLE, DALE, DALY, HALE, QALY, and HLY combine measures of mortality with morbidity or disability into a single comprehensive figure.
  • These measures differ in how they account for morbidity:
    • Activities of daily living
    • Self-rated health
    • Activity limitations (institutional and non-institutional)

Quality-Adjusted Life Years (QALY)

  • Definition: A measure that combines the length of life with the quality of life in a single index number.

  • Calculation: Each year in perfect health is counted as one QALY, while years lived with illness or disability are adjusted according to the severity of the health condition.

Disability-Adjusted Life Years (DALY)

  • Definition of DALY: A measure that combines mortality (Years of Life Lost, YLL) and morbidity (Years Lived with Disability, YLD) into a single metric.
  • Why DALY Matters: Helps policymakers understand the burden of disease and prioritize interventions effectively.
  • Components of DALY:
    • YLL (Years of Life Lost): Premature death due to specific causes.
    • YLD (Years Lived with Disability): The impact of non-fatal health conditions on quality of life.

DALY: let’s dive in (1)

  • Mortality Contribution (YLL):
    • Leading causes of premature death: cardiovascular disease, cancer, infectious diseases.
    • How reductions in mortality affect overall DALY burden.
  • Morbidity Contribution (YLD):
    • Chronic diseases like diabetes, mental health disorders, and musculoskeletal conditions.
    • The role of disability weight in calculating burden.

DALY: let’s dive in (2)

  • Comparing YLL and YLD:
    • Some diseases cause high YLL (e.g., heart disease), while others contribute more to YLD (e.g., mental disorders, arthritis).
    • Policy implications: balancing treatment for high-mortality vs. high-disability conditions.
  • Intervention Strategies:
    • Preventative measures to reduce YLL (e.g., vaccinations, tobacco control policies).
    • Managing chronic conditions to reduce YLD (e.g., rehabilitation programs, mental health support).

DALY: let’s dive in (3)

  • Global and Local Comparisons:
    • How DALY burden varies by income level, access to healthcare, and social determinants.

DALY: takeaways

  • DALY as a Critical Metric: Essential for public health planning and resource allocation.
  • Balancing Mortality & Morbidity Interventions: Addressing both premature death and quality of life impairments.
  • Health Equity Considerations: Ensuring interventions reach vulnerable populations.
  • Data-Driven Public Health Strategies: Using DALY to guide policy decisions and optimize healthcare spending.

4 Understanding ICD-9 and ICD-10 Codes

  • What is ICD?
    • The International Classification of Diseases (ICD) is a standardized system for classifying diseases, conditions, and procedures.
    • Maintained by the World Health Organization (WHO), it is used for healthcare administration, epidemiology, and research.

Transitioning from ICD9 to ICD10

  • Transition from ICD-9 to ICD-10:
    • ICD-9 had ~13,000 codes, while ICD-10 expanded to ~68,000 codes.
    • Greater specificity and accuracy, especially in classifying complex diseases.
    • Improved tracking of public health trends and emerging diseases.
  • Key Differences Between ICD-9 and ICD-10:
    • Specificity: ICD-10 codes provide detailed descriptions, including laterality (left vs. right).
    • Combination Codes: ICD-10 combines multiple conditions into a single code (e.g., diabetes with kidney disease).
    • Expanded Code Structure: ICD-10 uses alphanumeric codes (A00-Z99) compared to the numeric structure of ICD-9.

Examples of ICD9 and ICD10 codes (1)

Examples of ICD9 and ICD10 codes (2)

Why are these codes important?

  • Disease Monitoring: ICD codes allow epidemiologists to track disease outbreaks (e.g., COVID-19 had its own ICD-10 codes: U07.1 for confirmed cases).

  • Healthcare Reimbursement: Ensures accurate billing and insurance claims.

  • Policy and Research: Facilitates international comparisons of health data.

In-class exercise

  • Let’s look at this patient. The principal diagnosis (ICD9 code is V3000; look it up)

  • These are secondary diagnoses - look them up - 76502, 77181, 7707, 77212, 77081, 7470, 7455, 77182, 2760, 76522, 7742, 769, 04110, 7793, 6910, 75432, 4019, 0416, 0413, 9999, 2768.

  • What can we tell about this patient?

5 Sensitivity and Specificity

  • Accurately diagnosing and classifying diseases is critical for effective treatment and disease tracking.
  • Errors in classification—whether due to poor coding, faulty diagnostic tests, or reporting mistakes—can significantly impact health outcomes and policy decisions.

Sensitivity: Avoiding False Negatives

  • Sensitivity measures how well a test identifies people who actually have a disease.
  • A highly sensitive test minimizes false negatives, meaning fewer sick people go undiagnosed.
  • If a test has low sensitivity, it will fail to detect many cases, allowing diseases to spread unnoticed.

Example: COVID-19 Testing Sensitivity Issues

  • Early PCR tests for COVID-19 had sensitivity rates of 70-80%.
  • Up to 30% of infected individuals received false-negative results.
  • False negatives led to infected individuals unknowingly spreading the virus, worsening the pandemic.

Specificity: Avoiding False Positives

  • Specificity measures how well a test excludes people who do not have the disease.
  • A highly specific test minimizes false positives, meaning fewer healthy people are mistakenly diagnosed as sick.
  • If a test has low specificity, people might receive unnecessary treatments for diseases they don’t actually have.

Example: False Positives in Cancer Diagnosis

  • Low specificity cancer screenings sometimes identify benign tumors as malignant.
  • This leads to unnecessary biopsies and emotional distress.
  • False positives in COVID-19 antibody tests led some people to believe they were immune when they had never actually been infected.

Why Sensitivity and Specificity Matter

  • False negatives can delay treatment and increase transmission of infectious diseases.
  • False positives can lead to unnecessary medical interventions and public panic.
  • Balancing both sensitivity and specificity is crucial in public health decisions.

In-class exercise

Diagnostic Test

  • Sensitivity: 75% (correctly detects 75% of sick students, 25% false negatives).
  • Specificity: 90% (correctly identifies 90% of healthy students, 10% false positives).

Distribute Test Results

  • 75% of sick students get a Positive test = 6

  • 25% of sick students get a Negative test = 2

  • 90% of healthy students get a Negative test.

  • 10% of healthy students get a Positive test.

  • Each student announces their test result (“I tested positive” or “I tested negative”).

Discussion and debrief

  • Ask sick students to stand and check if they were correctly diagnosed.
  • Ask healthy students who tested positive how they feel about misdiagnosis.
  • How did it feel to be wrongly diagnosed?
  • What are the real-world consequences of false negatives?
  • What are the consequences of false positives?
  • How do public health officials handle these testing errors?

Conclusion: Why This Matters

  • Sensitivity and specificity are never perfect.
  • False negatives can lead to disease spread.
  • False positives waste resources and create unnecessary fear.
  • Multiple tests over time improve diagnosis accuracy.