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Project Overview

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Health authorities traditionally report annual population-based counts and rates ... Actual incidence of lung cancer. 9. Solutions to the Prevalent Pool Effect ... – PowerPoint PPT presentation

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Title: Project Overview


1
Issues in estimating incidence from linked
hospital admissions data
Tim Churches and Baohui Yang Centre for
Epidemiology and ResearchNSW Department of
Health October 2008
2
Introduction why estimate incidence
  • Health authorities traditionally report annual
    population-based counts and rates of hospital
    admissions for particular diseases and conditions
  • Such metrics reflect both the underlying disease
    incidence rates in the community, and variations
    in hospital utilisation for those diseases and
    conditions
  • Cases of diseases are using defined as being
    incident when they are first diagnosed or
    detected
  • We are interested in variations in both hospital
    utilisation and incidence, but it is much better
    if they can be disentangled.
  • Estimation of disease incidence is required to
    properly assess the impact of disease prevention
    efforts, and to estimate prevalence and burden of
    disease

3
CVD hospital separation rates
4
Estimating incidence from hospitalisations
  • If the disease is acute and requires only one or
    few hospitalisations, then hospital admission or
    separation rates may approximate incidence rates
  • eg community-acquired pneumonia
  • For chronic diseases which require multiple
    hospitalisations, annual hospital admission or
    separation rates bear little or no relationship
    to underlying disease incidence rates
  • However, for some diseases, first hospitalisation
    for each person for the disease may be used as a
    proxy for incidence, if
  • almost all cases of the disease require hospital
    treatment as an admitted patient
  • hospital treatment is typically required soon
    after diagnosis
  • Linked data allows us to identify the first
    hospital admission for each person for the
    disease of interest, and thus estimate underlying
    incidence of diseases which meet these criteria

5
The Prevalent Pool Effect
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The Prevalent Pool Effect
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The Prevalent Pool Effect
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Apparent incidence of lung cancer
9
Actual incidence of lung cancer
10
Solutions to the Prevalent Pool Effect
  • Use a clearance period
  • initial years of linked data are ignored
  • how many years depends on condition
  • clearance period derived by visual inspection of
    trends
  • but no way to distinguish a true downward trend
    from the prevalent pool effect
  • wastes a large amount of data
  • only practical if many years of linked data are
    available

11
Backcasting methods
  • Developed by Brameld and Holman in WA
  • Kate J Brameld, C DArcy J Holman, David M
    Lawrence, and Michael ST Hobbs (2003). Improved
    methods for estimating incidence from linked
    hospital morbidity data. International Journal of
    Epidemiology 2003 32 617-624
  • Retrograde actuarial survival model to derive
    correction factors for over-ascertainment of
    first-time events
  • described two methods
  • a less accurate annual method
  • a method which provides individual-level
    correction factors
  • per-individual method is more accurate and quite
    easy to implement
  • based on the estimated probability that an
    apparently first-time admission in an individual
    may have actually been preceded by unobserved
    admissions

12
Brameld-Holman backcasting method theory and
assumptions
  • a is date of admission
  • b is date of previous admission
  • c is the start of the observation period
  • retrograde follow-up time l a b (for a
    previous admission),
  • or l a c (if censored)
  • use life-table to calculate the hazard of a
    previous admission at reverse time ?(t) and the
    corresponding survival function S(t)
  • Assumption is that for a chronic disease for
    which the retrograde hazard function is
    monotonically decreasing in reverse time, there
    is a point tf at which ?(tf ) 0 ie there is no
    further hazard of a previous admission prior to
    reverse-time tf

13
Brameld-Holman backcasting method theory and
assumptions
  • calculate a correction factor for
    over-ascertainment of incident cases due to the
    prevalent pool effect for an apparently first
    admission at time tj
  • for reverse times lt tf , Cj S(tf ) / S(tj )
  • for reverse-times gt tf, , Cj 1
  • The correction factors for each case, Cj, are
    summed to give an adjusted count by year of
    incident cases

14
Brameld-Holman backcasting method
implementation
  • fractional polynomial regression is used to fit a
    smooth curve to the life-table hazard function in
    order to extend it into the past
  • this curve is then inspected to determine the
    reverse time at which it approximates zero
  • a fractional polynomial curve is fitted to the
    survival and this is used to evaluate S(tf ) and,
    for each individual, S(tj ), and Cj is then
    calculated
  • We did all this in SAS using a custom fractional
    polynomial fitting macro written by Baohui Yang
  • Fractional polynomials are a convenient way of
    fitting non-linear regression curves, but other
    curve-fitting methods can be used

15
Issues in implementation of the method
  • Quite a long period of follow-up is need for many
    chronic diseases in order to determine when the
    hazard approaches zero.
  • We used 6 years of linked data
  • Sufficient for relatively acute chronic diseases
    such as lung cancer, where diagnosis is quickly
    followed by treatment and then either recovery of
    death
  • Insufficient of many other disease with a
    chronic, relapsing course, including breast
    cancer and even AMI
  • Hazard function for previous admission for these
    condition does not approximate zero at 6 years in
    the past
  • Some conditions have a pattern of remission and
    relapse, such as many (treated) leukaemias, and
    in these, the hazard function may be slightly U
    shaped

16
Hazard of previous admission for leukaemias
17
Hazard of previous admission for leukaemias
18
Modified backcasting method
  • Because of the difficulties in fitting a
    monotonically decreasing curve to hazard and
    survival functions that, at 6 years of
    reverse-time was not yet flat, we decided to omit
    this step and just use 6 years for tf
  • We found that the correction technique was robust
    to the selection of tf and this simplification
    made little difference to the results
  • There were still problems with conditions for
    which the survival function S(t) was still not
    very flat at the 6 years of reverse-time
    available to us.
  • The fractional polynomial regression fitting
    procedures needed to be constrained to only
    select monotonically decreasing polynomial
    expressions.

19
Results Survivorship for lung cancer
20
Incidence estimates for lung cancer
21
Incidence estimates for COPD
22
Incidence estimates for schizophrenia
23
Incidence estimates for ischaemic heart disease
24
Conclusions
  • Some form of adjustment (or use of a clearance
    period) is essential the prevalent pool effect
    is real and substantial
  • The Brameld-Holman backcasting incidence
    adjustment method works well if there is a
    sufficiently length of follow-up available
  • If not, great care needs to be taken
  • Anomalous corrections are possible
  • The method can be simplified by omitting the
    identification of tf
  • Other curve-fitting methods may give better
    results
  • Planners of record linkage facilities Important
    to have the longest times series of linked data
    as possible

25
References
  • Kate J Brameld, C DArcy J Holman, David M
    Lawrence, and Michael ST Hobbs. Improved methods
    for estimating incidence from linked hospital
    morbidity data, International Journal of
    Epidemiology 2003 32 617-624
  • Patrick Royston, Gareth Ambler and Willi
    Sauerbrei. The use of fractional polynomials to
    model continuous risk variables in epidemiology.
    International Journal of Epidemiology 1999 28
    964-974.
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