Jukka Kontto. Workshop: Projecting population health and care needs using population survey and register data. 17.9.2018.

1. METHODS FOR
POPULATION
HEALTH
PROJECTIONS
Jukka Kontto
Workshop on projecting population
health
17.9.2018
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2. PODDY-HEPO
 Work Package 2. To provide projections of the incidence,
prevalence and number of cases of major chronic disease
and disability measures under different scenarios in the whole
population and its sub-groups.
– Task 2.1. To assess and document the strengths and
weaknesses of available projection methods.
17-Sep-2018 Methods for population health projections / Jukka Kontto 2

3. LITTERATURE SEARCH & TOOLS
 Search was conducted using Web of Science
 11/2017 – 12/2017 (Additional searches later on)
 References imported to ProQuest RefWorks
 R-packages
– tm (text mining)
– kohonen (self-organising maps)
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4. FORECAST VS. PROJECTION
 Future prediction in general science can be divided into two
components: forecasting and projections
1. A forecast is an attempt to predict what will happen.
2. A projection is an attempt to describe what would happen,
given certain hypotheses
(Keyfitz 1972, Caswell 1989)
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5. SEARCH PROTOCOL
 Words
– forecast*, projecti*, projected, predict*, future
– obesity, smoking, diet, physical activity
– diabetes, cvd, chd, stroke, copd, cancer, mortality
– longitudinal, panel, cross-sectional, growth curve,
microsimulation, macrosimulation, machine learning
 Results sorted by times cited
 Based on the titles and on the abstracts, unrelated articles
were excluded
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6. INCLUDED CATEGORIES, EXAMPLES
 statistics probability
 public environmental occupational health
 multidisciplinary sciences
 social sciences mathematical methods
 mathematics interdisciplinary applications
 demography
 medicine general internal
 business finance
 health care sciences services
 cardiac cardiovascular systems
 oncology
 management
 medical informatics
 computer science interdisciplinary
applications
 geosciences multidisciplinary
 health policy services
 mathematical computational biology
 physiology
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7. EXCLUDED CATEGORIES, EXAMPLES
 environmental sciences
 economics
 ecology
 meteorology atmospheric sciences
 biodiversity conservation
 marine freshwater biology
 forestry
 fisheries
 infectious diseases
 gastroenterology hepatology
 surgery
 oceanography
 environmental studies
 plant sciences
 virology
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8. SEARCH RESULTS
 176 articles included
 Following tags were added manually:
– Statistical method
– Data sources
– Outcome
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9. SEARCH RESULTS
 Statistical method
– regression 47
– markov model 28
– microsimulation 13
 Data sources
– life-table data 50
– repeated cross-sectional 24
– register data 16
 Outcome
– mortality 63
– prevalence 44
– life years 22
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10. EXTRAPOLATION METHODS
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Hallstrom et al. Stroke incidence and survival in the beginning of the 21st century in southern Sweden:
comparisons with the late 20th century and projections into the future. Stroke 2008; 39(1):10-5.

11. REGRESSION METHODS
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Pandya et al. More Americans Living Longer With Cardiovascular Disease Will Increase
Costs While Lowering Quality Of Life. Health Aff 2013; 32(10):1706-14.

12. MULTI-STATE MODELS
 States e.g.:
– No CVD, CVD, death
– No diabetes, diabetes, death
 Markov assumption:
– Transition to the next state depends only on the current state
 Individual-level data
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13. EXAMPLE
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Sözmen et al. Estimating diabetes prevalence in Turkey in 2025 with and without possible
interventions to reduce obesity and smoking prevalence, using a modelling approach. Int J Public
Health 2015; 60 Suppl 1:S13-21.

14. MICROSIMULATION
 A way to model the behaviour and generate the life histories of
individual units
 Artificial cohort/population is simulated and different scenarios and
assumptions about risk factor changes can be tested
 The units of analysis are individuals
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15. PACSIM
 Population Ageing and Care Simulation (PACSim)
– simulates the survival and characteristics (disease and
associated risk factors) of a set of real individuals (the base
population) as they age over time
– estimates future prevalence, incidence, and life and health
expectancies
– Movements between states of each characteristic are determined
by applying age, sex and state-specific transition probabilities
derived from longitudinal data
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16. CONCLUSIONS
 Population projection methods for health outcomes is a broad
topic
 The majority of articles used methods with low data
requirements
 The more realistic predictions are required, the more complex
and laborious projections are needed
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17. WHAT’S NEXT?
 Concentrating on methods using individual-level data
 Additional search concerning machine learning methods
 Manuscript ready to submitted before 2019
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18. 17-Sep-2018 Methods for population health projections / Jukka Kontto 18
Thank you!
jukka.kontto@thl.fi