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Resources allocation in healthcare for cancer: a case study using generalised additive mixed models
Monica Musio1, Erik A Sauleau, Nicole H Augustin
1Dipartimento di Matematica ed Informatica, Università di Cagliari, Cagliari, Italy. mmusio@unica.it
Geospatial Health
|December 18, 2012
Summary
This study introduces a novel method for cancer incidence monitoring to aid healthcare resource reallocation. Findings show increased cancer incidence over time, particularly in older populations, necessitating targeted resource allocation.
Area of Science:
- Epidemiology
- Health Services Research
- Biostatistics
Background:
- Population aging increases demand for specialized healthcare, especially for cancer care.
- Effective healthcare resource allocation requires accurate monitoring of cancer incidence trends.
- Understanding spatial and temporal variations in cancer incidence is crucial for public health planning.
Purpose of the Study:
- To develop and apply a statistical method for monitoring cancer incidence changes in space and time.
- To inform healthcare resource reallocation strategies for cancer care, particularly for the elderly.
- To analyze cancer incidence patterns across different age groups and geographic regions.
Main Methods:
- Utilized generalized additive mixed models with a Poisson response.
- Employed one-dimensional smooth functions for non-linear covariate effects.
- Modeled space-time interactions using scale-invariant smoothers.
- Analyzed cancer registry data from 1992-2007 in a French region.
Main Results:
- Observed a significant increase in cancer incidence over the study period.
- Identified a distinct spatial pattern of higher incidence in a central region for individuals over 70.
- Demonstrated the utility of the proposed method in revealing age-specific, spatio-temporal cancer trends.
Conclusions:
- The developed method effectively monitors cancer incidence, supporting evidence-based healthcare resource reallocation.
- Findings strongly support the need to re-allocate cancer care resources towards older populations in the identified high-incidence sub-region.
- This approach provides a framework for proactive public health interventions in response to demographic shifts and disease patterns.
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