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Modeling Change in Health Status: Patterns over Time . Susan J. Henly, PhD, RN Methods Director Minnesota Center for Health Trajectory Research Seminar: September 24, 2008. Pain at bedtime over the 1 st post-op week.

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modeling change in health status patterns over time

Modeling Change in Health Status: Patterns over Time

Susan J. Henly, PhD, RN

Methods Director

Minnesota Center for Health Trajectory Research

Seminar: September 24, 2008

slide2

Pain at bedtime

over the 1st post-op week

Busch, S.E. (2002). Sleep patterns following an out-patient surgical procedure. Unpublished MS thesis. University of Minnesota, Minneapolis.

why study change in health status
Why study change in health status?
  • Within persons, health status varies over time
  • Accurate description of health status over time is essential to understanding health behaviors and illness responses
  • Intervention assumes that health status is malleable-- intra-individual change can be predicted and “controlled” (influenced by nursing actions)
  • Inter-individual differences in intra-individual change can be explained
some ideas about change
Some ideas about change

To be or cause to be different

  • To alter the course of
  • Naturalistic change
  • Experimentally induced change
operationalizing change
Operationalizing change

Increment: difference on 2 occasions

Rate: speed, velocity, pace

Pattern: form, shape, model

purpose
Purpose
  • Describe mathematical functions that can be used to model change in health status
  • Characterize intra-individual change using personalized functions
  • Recognize that variation in parameters of personalized functions represents inter-individual differences in change
  • Comment on formulation of hypotheses to explain inter-individual differences in change using parameters of personalized functions
about functions
About functions
  • A function is a rule that maps every point in a defined domain t with one and only one value in its range H
  • Function rules are defined by their parameters
  • Functions can be described using equations
  • Functions can be displayed in tables
  • Functions can be depicted by their graphs
the general function
The general function

Hi = fi (t)

For each person,

at each point in time,

for any given indicator of health status,

there is one and only one value for health status.

slide11

Hi (t): key features

  • Time is the primary “predictor” variable
  • Health status is “outcome”
  • Health as a function of time shows patterns of change
  • Each person follows their own pattern: everyone has their own set of parameters
constant functions h i t i
Constant functions: Hi (t) = κi

For person i,

κigives the function value at every time t

slide16

Linear functions: Hi (t) = π0i + π1i t

For person i,

π0igives the function value at t0 (intercept) and π1i gives the rate of change over time (slope). Note that selection of t0 is critical to scientific interpretation of the parameters.

quadratic and higher order polynomial functions
Quadratic and higher order polynomial functions
  • General polynomial form is:
  • H (t) = πntn + πn-1tn-1+ … + π1t + π0
  • Quadratic:
  • H (t) = π2t2 + π1t + π0
  • Cubic:
  • H (t) = π3t3 + π2t2+ π1t + π0
  • And so on with higher order functions of time
  • Polynomial Equation Grapher

http://www.math.umn.edu/~garrett/qy/Quintic.html

exploring sine functions for periodic change
Exploring sine functions for periodic change
  • Variations on the Sine Function
  • The website is:
  • http://www.analyzemath.com/trigonometry/sine.htm
slide21

Piece-wise functions (ex)

H1i (t) = κi, t < 0

H2i (t) = π0i + π1it, t ≥ 0

For person i,

κi gives the baseline value, π0igives the function value at the transition point, which is also the intercept in the example and π1i gives the rate of change over time (slope) after the transition. In this example, the time of transition is known. Sometimes, the transition point is itself a parameter to be estimated.

slide22

Piece-wise functions (ex)

H1i (t) = κi, t < 0

H2i (t) = π0i + π1it, t ≥ 0

statistical models for individual change
Statistical models for individual change
  • Longitudinal data on 3 or more occasions
  • Sensible metric for time
  • Theory about change
  • Graphs of individual cases to identify form of change
  • Personal parameters estimated to produce smoothed curves for each persons change pattern
  • Random coefficients in a mixed effects model
slide26

Describing and explaining patterns of change

  • Theory about change
  • Longitudinal ( ≥ 3 occasions of observation)
  • Measurement sensitive to individual change
  • Statistical models linking intra- and inter-individual change (mixed effects models)
nursing practice people and change
Nursing practice: people and change
  • Baby is off to a healthy start.
  • Patient is going downhill fast.
  • She recovered quickly after the nurse lifted her spirits.
  • He had a rocky post-op course.
  • When he exercised regularly, his glucose levels decreased and stabilized.
  • She reacted to her husband’s death with an intense sense of depression, but soon returned to her usual sunny self.
slide33

Person

TIME

Health

Nursing

Environment

Time for change

in the nursing metaparadigm

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