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HSS Intro to Epidemiology

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					HSS4303B – Intro to Epidemiology
Jan 25, 2010 – Natural History of Disease
  International Culture & Development Week

• http://www.scdi-icdw.uottawa.ca/

• Today:
  – 2:30pm: Launch with Allan Rock, Tabaret Chapel
  – 4pm: “Casino capitalism”, UCU205 chaired by ME!
         The Midterm

Date       Pros   Cons
Feb 11


Feb 25
         The Midterm

Date       Pros                  Cons
Feb 11     -get it over with     -only a week away!
           -less material
           -more help
Feb 25     -more time to         -more material
           study (including      -Erin and I will not
           spring break)         be available during
                                 reading week



              Which will it be?!!!!
                The Abstract
• Due on Thursday at midnight
• Follow instructions carefully (including how to
  submit it!!!)
• Any issues thus far?
             Poster Assignment
•   I’ll be posting details soon
•   Seek out partners
•   I’ll be asking for names of teams soon
•   If you don’t know anyone in the class, let me
    know and I’ll see what I can do
                                  Tutorial
• Erin will be available on Thursday
• I will upload more exercises tonight (or
  tomorrow) for you to try before seeing her


(If you do a Google image search for “tutorial” these are the first two hits:)
                                    Review
• Mortality Rate (MR)
    – #deaths/# at risk
• Case Fatality Rate (CFR)
    – #deaths/#diagnosed
• Cause-specific mortality rate
    – #deaths from specific cause / # at risk
• Years of potential life lost (YPLL)
    – Expected lifespan – observed lifespan
• Disability assisted life year (DALY)
    – (years of life lost) +(years of productive life lost)
• Disability Adjusted Life Expectancy (DALES)
• Proportionate Mortality Ratio (PMR)
    – #deaths due to a cause / #deaths total
• Quality Adjusted Life Years (QALYs)
    – #years lived X quality index (0->1)
                                  Review
• Survival Rate (SR)
    – (# initial subjects - # subjects dead or censored) / (#initial subjects)
• Relative Survival Rate (RSR)
   – SR among subjects/ SR among total population
• Cause-Specific Survival Rate (CSS)
   – (#initial subjects - #subjects dead from specific cause) /
                     (#initial subjects)
                      Review
• Age-specific mortality rate
  – The mortality rate of a specific population within a
    specific age stratum
• Age-adjusted mortality rate
  – Total mortality rate for a population, after its age
    distribution has been adjusted to resemble a
    standard (reference) population
• Crude Mortality (Death) Rate
  – Un-adjusted total mortality rate
                        Review
• Standardized Mortality Ratio (SMR)
   – (#observed deaths per year) /
    (#expected deaths per year)
• Direct Standardization
   – Computes age-adjusted mortality rate by multiplying the
     age-specific rates from the test population by the age-
     specific populations from the reference
• Indirect Standardization
   – Computes age-adjusted mortality rate by multipling the
     age-specific rates from the reference population by the
     age-specific populations from the test population
   – SMR x (crude death rate in standard population)
                         Artefact
• (Artifact is the American
  spelling; both are
  acceptable)
• a spurious finding, such as
  one based on either a
  faulty choice of variables
  or an overextension of the
  computed relationship
Interpreting observed changes in mortality

• Changes in mortality
  – Artifactual
     • Problems with the numerator
     • Problems with the denominator
  – Real
     • Identify possible explanations
     • Develop a hypothesis
     Artifactual trends in mortality

1. Numerator     Errors in diagnosis
                 Errors in age
                 Changes in coding rules
                 Changes in classification


2. Denominator   Errors in counting population
                 Errors in classifying by demographic characteristics (e.g., age, race,
                 sex)
                 Differences in percentages of populations at risk
                      Cohort




From Latin “cohors”, it was the basic unit of the Roman Legion.
                     Cohort




Refers to a bunch of people who move together.
                    Cohort




Refers to a bunch of people who move through time together.
                    Cohort
• A group of people who share a particular
  experience or characteristic(s) over a period of
  time
  – Irish women born in 1950
  – Engineers who smoked between the ages of 25-30
  – HSS students in 3rd year
              Now…. An example
• Pertussis
  – Whooping cough
  – Highly contagious bacterial infection
  – Effective, well tolerate vaccine that lasts several
    years
  – One of the leading causes of vaccine-preventable
    deaths in the world
            Source: Wikipedia




Pertussis



            DALYs
                           Facts
• Beginning in 1990 Canada experienced a resurgence of
  pertussis.
• The mean annual incidence before 1990 was 3.8 cases per
  100 000 population which increased to 37.2 thereafter.
• The mean annual hospitalization rates increased from 2.7 per
  100 000 before 1990 to 5.2 afterward.
• The proportion of cases in 0- to 4-year-old children decreased,
  whereas it increased steadily in all other age groups
• Between 1990 and 1998 the median age of cases shifted from
  4.4 to 7.8 years.

                              The Pediatric Infectious Disease Journal:
                              January 2003 - Volume 22 - Issue 1 - pp 22-27
         So What’s Happening?
• “The sudden increase in pertussis incidence in
  Canada can be largely attributed to a cohort
  effect resulting from a poorly protective
  pertussis vaccine used between 1985 and
  1998.” –NTEZAYABO et al, 2003

• In other words, something that happened in
  the 80s to infants did not manifest till the 90s
  in older children, as the cohort moved
  through time
    Factors Around Cohort Effect
• Smoking behaviours differ between
  generations
• Income differs between generations
• Geopolitical circumstances (e.g. war) differ
• Health system issues may differ (e.g. infant
  health care)
• etc
                   Example
• In the UK, politicians often speak of the
  “cohort effect” in terms of a certain
  generation:
  – Brits born between 1925 and 1945 (centred
    around 1931) experienced more rapid
    improvements in mortality than generations born
    on either side (i.e., younger and older)

          WHY?
                  Cohort effect
• Cross sectional view
  – Identifies peculiarities and key messages from the
    data
  – Which age group has the highest rates of
    tuberculosis
• Cohort effect
  – Identifies groups with the trait or disease
    incidence
  – Group is followed over time to see if the trait
    develops or disease manifests
• Cross sectional view
  – Identifies peculiarities and key messages from the
    data
  – Which age group has the highest rates of
    tuberculosis
• Cohort view
  – Identifies groups with the trait or disease
    incidence
  – Group is followed over time to see if the trait
    develops or disease manifests
                       Cohort vs Cross-Sectional View (1900)

Table 4-14. Age-specific Death Rates per 100,000 from Tuberculosis (All Forms), Males, Massachusetts, 1880-1930

                                                                                                         Year
                                                            Age (yr)        1880        1890       1900        1910       1920   1930
                                                            0-4               760        578         309        309       108     41
                                                            5-9                43          49          31            21    24     11
                                                            10-19             126        115           90            63    49     21
                                                            20-29             444        361         288        207       149     81
                                                            30-39             378        368         296        253       164    115
                                                            40-49             364        336         253        253       175    118
                                                            50-59             366        325         267        252       171    127
                                                            60-69             475        346         304        246       172     95
                                                            70+               672        396         343        163       127     95




 Data from Frost WH: The age selection of mortality from tuberculosis in successive decades. J Hyg 30:91-96, 1939.




              Peak mortality occurred for the 30-39 years age group (Cross sectional view)
                                   Cohort effect
Table 4-15. Age-specific Death Rates per 100,000 From Tuberculosis (All Forms), Males, Massachusetts, 1880-1930


                                                          Year
                                 Age (yr)   1880   1890   1900   1910   1920   1930
                                 0-4        760    578    309    309    108     41
                                 5-9         43     49     31     21     24     11
                                 10-19      126    115     90     63     49     21
                                 20-29      444    361    288    207    149     81
                                 30-39      378    368    296    253    164    115
                                 40-49      364    336    253    253    175    118
                                 50-59      366    325    267    252    171    127
                                 60-69      475    346    304    246    172     95
                                 70+        672    396    343    163    127     95




            Follow the cohort and the peak mortality occurs for the 20-29 years old group
The History of Disease
               The History of Disease
Abdel Omran, 1971….

     In very very very broad terms, historians
     consider the history of human disease to
     have occurred in 3 phases:


 • Age of Pestilence and Famine
 • Age of Receding Pandemics
 • Age of Degenerative and Manmade Diseases


                 http://www.who.int/bulletin/archives/79%282%29159.pdf
      Age of Pestilence and Famine

•   High mortality rates
•   Wide swings in mortality rates
•   Little population growth
•   Very low life expectancy
     Age of Receding Pandemics

• Less frequent epidemics
• Less incident infectious disease
• A slow rise in degenerative disease
    Age of Degenerative and Manmade Diseases


•   Cancers
•   Obesity
•   Cardiovascular disease
•   Diseases associated with high SES and
    relatively bountiful food

• Most countries are here now
          Omran defined:
    The Epidemiologic Transition
• a human phase of development witnessed by
  a sudden and stark increase in population
  growth rates brought about by medical
  innovation in disease or sickness therapy and
  treatment, followed by a re-leveling of
  population growth from subsequent declines
  in procreation rates
  – Wikipedia
          Cf. Demographic Transition
1.   stage one, pre-industrial society, death rates
     and birth rates are high and roughly in balance
2.   stage two, that of a developing country, the
     death rates drop rapidly due to improvements
     in food supply and sanitation, which increase
     life spans and reduce disease
3.   stage three, birth rates fall due to access to
     contraception, increases in wages, urbanization,
     etc.
4.   stage four: there are both low birth rates and
     low death rates. Birth rates may drop to well
     below replacement level as has happened in
     countries like Germany, Italy, and Japan
5.   Stage five: sub-replacement fertility
Cf. Demographic Transition
Perfectly correlated to per capita alcohol consumption in these countries.
Epidemiologic transition from 1990 to 2020
Natural History of Disease
                refers to a description of the
                uninterrupted progression of
                a disease in an individual
                from the moment of
                exposure to causal agents
                until recovery or death
Natural history of a disease in a patient
Natural history of a disease in a patient




                                  Death
                                  Survival
An idealized depiction of the natural history of disease.
Natural history of coronary heart disease.
      Natural History of Disease
• …is not the same as the changing patterns of
  disease in a population
• E.g., the distribution of CHD over SES groups
  may change over time as a society changes….
• But the natural history of CHD will not change
“Pyramid” or “Iceberg” of Disease
-- SCREENING
                 Prognosis
• “the likely outcome of a disease”
• The important endpoint in the Natural History
  of Disease



                     “Petosiris to Nechepso”
                   Prognosis
• Identify the end points
  – Death
  – Survival with disability
  – Survival without disability
  – Relapse
• Delay the endpoints
• Improve the quality of life
• Measures of prognosis
          Measures of prognosis
1. Case-fatality ratio
2. Mortality rates
   – Person years
3. Five-year survival rate
4. Observed survival (rationale for life table)
5. Life table
   – Kaplan-Meier method for survival
6. Median survival time
7. Relative survival rate
        Measures of prognosis
       CFR
1. _______________
  – Is defined as the number of people who die of
    the disease divided by the number of people
    who have the disease
  – Is used mostly for diseases with shorter duration
    or acute conditions
  – Is less used for diseases with low mortality and
    long disease span
  – Alternate measure of prognosis need to be used
    for diseases with longer span
        Measures of prognosis
2. ______________ (person-years)
     Mortality Rate

  – Is defined as number of deaths divided by the
    person-years over which the group is observed
  – The unit of measure is person-years (individuals
    multiplied by the number of years the
    individuals are observed)
  – The risk for different individuals is assumed to
    be the same; for one person-year is equivalent
    to another
         Measures of prognosis
3. ______________ rate
   Five Year Survival

  •   Is the percentage of patients who are alive 5
      years after treatment begins or 5 years after
      diagnosis
  •   For cancer is used as a measure of treatment
      efficacy
  •   Is not effective in late diagnosis and when
      treatment is not effective
  •   Is not effective when the survival is less than five
      years
                 Next….
• Check website tomorrow (morning? Maybe?)
  for uploaded exercises
• Don’t forget to finish your abstracts!
• Next class: Kaplan Meier survival curves!

				
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