Laboratory

Population Health

At a Glance Projects Publications Team

Project

Methods, Models, and Measures in Population Health

Mikko Myrskylä, Christian Dudel, Daniel Schneider, Enrique Acosta, Jo Mhairi Hale; in Collaboration with Héctor Pifarré i Arolas (University of Wisconsin-Madison, La Follette School of Public Affairs, USA)

Detailed Description

To study population health, it is essential to have measures that accurately reflect the key dimensions of the underlying concept or process of interest and to have methods that allow us to model the underlying data structure. We work to develop new measures, methods, and models in order to extend the existing demographic toolbox for studying population health. For instance, in a paper recently published, we discussed the potential pitfalls of adjusting assessments of population health by population age structure, which under certain conditions can be misleading. We proposed a novel measure of population health that is not affected by this problem and demonstrated its usefulness in studying the racial/ethnic mortality gap in the United States. Other work in this project studies the inertia of population growth and statistical inference for time series models.

Publications

Hale, J. M.; Schneider, D. C.; Mehta, N. K.; Myrskylä, M.:

Intersectionality and opportunity-weighted cumulative (dis)advantage

MPIDR Working Paper WP-2023-040. (2023)    

Schneider, D. C.:

Statistical inference for discrete-time multistate models: asymptotic covariance matrices, partial age ranges, and group contrasts

MPIDR Working Paper WP-2023-041. (2023)    

Schneider, D. C.; Myrskylä, M.:

Statistical inference for discrete-time multistate models: extensions to Markov Chains with rewards

MPIDR Working Paper WP-2023-042. (2023)    

Ning, Y.; Lam, A. A.; Reilly, M.:

Estimating risk ratio from any standard epidemiological design by doubling the cases

BMC Medical Research Methodology 22:157, 1–15. (2022)    

Dudel, C.; Garbuszus, J. M.; Ott, N.; Werding, M.:

Income-(in)dependent equivalence scales and inequality measurement

German Economic Review 22:2, 235–255. (2021)    

Pifarré i Arolas, H.; Acosta, E.; Dudel, C.; Hale, J. M.; Myrskylä, M.:

U.S. racial/ethnic mortality gap adjusted for population structure

MPIDR Working Paper WP-2021-023. (2021)    

Preston, S. H.; Vierboom, Y. C.:

The changing age distribution of the United States

Population and Development Review 47:2, 527–539. (2021)    

Kripfganz, S.; Schneider, D. C.:

Response surface regressions for critical value bounds and approximate p-values in equilibrium correction models

Oxford Bulletin of Economics and Statistics 82:6, 1456–1481. (2020)    

Riffe, T.; Aburto, J. M.:

Lexis fields

Demographic Research 42:24, 713–726. (2020)    

Mehta, N. K.; Zheng, H.; Myrskylä, M.:

How do age and major risk factors for mortality interact over the life-course? Implications for health disparities research and public health policy

SSM-Population Health 8:100438, 1–10. (2019)    

Pifarré i Arolas, H.; Dudel, C.:

An ordinal measure of population health

Social Indicators Research 143:3, 1219–1243. (2019)    

Riffe, T.; Aburto, J. M.:

Lexis fields

MPIDR Working Paper WP-2019-001. (2019)    

Kröger, H.; Hoffmann, R.:

The association between CVD-related biomarkers and mortality in the Health and Retirement Survey

Demographic Research 38:62, 1933–2002. (2018)    

Bohk-Ewald, C.; Rau, R.:

Probabilistic mortality forecasting with varying age-specific survival improvements

Genus 73:1, 1–37. (2017)    

Pifarré i Arolas, H.; Dudel, C.:

An ordinal measure of population health

MPIDR Working Paper WP-2017-010. (2017)