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Analysis For Prevention of Latelife Depression

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Spousal loss 30.39 27.27. High education 65.32 75.76. Poor or fair health 14.26 30.30 ... DSSI (p. social Support) ( =20) 44.89 60.61 1.69 24.79 56 ... – PowerPoint PPT presentation

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Title: Analysis For Prevention of Latelife Depression


1
Analysis For Prevention of Late-life Depression
  • Qin Yu
  • Department of Biostatistics and Computational
    Biology
  • University of Rochester

2
Research Context
  • Context Late-life depression is associated with
    substantial societal costs through its disease
    burden and prognosis.
  • Prevention may be an attractive means to generate
    health gains and reduce future costs.
  • Objectives To target high-risk groups for
    depression prevention such that maximum health
    gains are achieved.
  • Design Population-based cohort study over 4
    years.
  • Participants 745 community residents aged 65 to
    97 years.

3
Analysis Methods
  • Subjects and procedures
  • At baseline, 745 people were interviewed aged
    from 65 to 97 years. These people were followed
    for 4 years, all the predictors are recorded.
  • Two separate analyses
  • limited sample to those without initial major
    depression, among 617 people at risk, 33 became
    depressed this estimate is equal to 4.4 cases
    per 100 person-years
  • limited sample to those without initial major or
    minor depression, among 533 people at risk, 96
    became depressed.

4
Statistical Analysis
  • The analyses were conducted in several steps.
  • First Step
  • Calculation of the absolute and relative risk of
    becoming a case for subjects with or without
    being exposed to a certain risk indicator.
  • These risk indicators were dichotomized.

5
Statistical Analysis
  • Second step
  • The importance of risk indicators was
    quantified by computing the level of exposure to
    a risk factor in the population.
  • Four risk indicators
  • Exposure Rate (ER)
  • The incidence rate ratio (IRR)
  • Population attributable fraction (AF)
  • The number needed to be treated (NNT)




6
  • Table 1a. Exposure Status (in Percent) in the
    Group at Risk and the incident Group limited to
    those without initial major depression.

7
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8
  • Table 1b. Exposure Status (in Percent) in the
    Group at Risk and the incident Group limited to
    those without initial major or minor depression.

9
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10
Statistical Analysis
  • To summarize
  • We calculate the ER, IRR, AF, and NNT. Together,
    these indicators of impact allow us to select
    high-risk groups for which depression prevention
    is likely to be associated with the highest
    health benefit at the lowest cost.

11
Statistical Analysis
  • Third Step
  • High-risk group selection process was carried out
    by backward-stepping procedures
  • We selected the smallest set of risk indicators
    in which each risk indicator has a unique and
    significant contribution to the prediction of
    depression.

12
Results
  • Parsimonious Model
  • Table 3a Parsimonious model for the incident
    Group limited to those without initial major
    depression.

13
Results
  • Parsimonious Model
  • Table 3b Parsimonious model for the incident
    Group limited to those without initial major or
    minor depression.

14
Comments
  • The most promising risk indicator was selected
    from Table 3a and Table 3b, which was followed by
    consecutively selecting and adding risk
    indicators in such a way that the values for
    potential health benefit (IRR and AF) were kept
    as high as possible and the cost (ER and NNT) as
    low as possible.

15
Results
  • Selecting a smaller set of risk indicators
  • Limited sample to those without initial major
    depression

16
Results
  • Selecting a smaller set of risk indicators
  • Limited sample to those without initial major
    depression

17
Statistical Analysis
  • The fourth step analyses for joint exposures.
  • Assessed the potential health benefits when
    prevention efforts target people who are exposed
    to combination of multiple risk indicators.
  • Took SSD as the starting point because this risk
    indicator is associated with the highest IRR and
    AF values and has the lowest NNT and ER.

18
Results
Table 4a Additive effect of Joint Exposures
limited to the sample without initial major
depression.

19
Results
  • Table 4b Additive effect of Joint Exposures
    limited to the sample without initial major or
    minor depression.

20
Comments Quantifying the health gains
  • Consider the risk profile in Table 4a and 4b,
    e.g. in row 11 of table 4b, the risk of becoming
    depressed is a factor 1.39 higher in female with
    SSD and has received high education.
  • If the adverse effect of the joint exposure could
    be completely blocked, then the incidence rate in
    the population would be reduced by 20.4.
  • The current incidence rate of 4.4 cases per 100
    person-years would then become 4.4 x
    (1-20.4)3.5 cases per 100 person-years. In
    every 1 million older adults, this would
    represent roughly 9,000 prevented cases per year.

21
Comments Quantifying the health gains
  • Assuming that an intervention is 30 successful
    in avoiding new onsets, then 2,700 onsets will be
    avoided.
  • Assume the excess costs of minor and major
    depression as at least 1,045 per person per half
    year. Preventing 2,700 onsets would thus result
    in a cost saving of at least 2.82 million for
    every million elderly people in the population.
  • If the costs of the intervention do not exceed
    1,045 per avoided case, we can still use AF and
    NNT value to select interesting preventive
    interventions for further cost-effectiveness
    research.

22
Comments
  • We aimed to answer the question as to whether it
    would be possible to target depression prevention
    where it generates the best health benefits at
    the lowest cost.
  • This would also guide research efforts toward
    more promising research areas in preventive
    psychiatry.
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