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Results Models including person level information (age, sex, and ICD-10 codes diagnostic recorded) and a range of area level information (such as socioeconomic deprivation and supply of health facilities) were most predictive of costs.
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Summarizing the conceptual model: multilevel factors including person, community, and state levels of influence are predictors of the odds of cancer diagnosed at an advanced stage.
In a first model, we included persons' area of residence and the control variables.
Age adjustments of the mortality rates were carried out applying a Poisson regression model including gender, person-years, age (1-year interval) and number of deaths in the analyses.
Persons receiving blood pressure and/or lipid lowering drugs were excluded in models including blood pressure and/or lipids.
For this model, the data were truncated to only include person time with active work history.
Social and economic context/community level Model 4, which includes person-centered access variables of mammography and transportation density, was the best model as indicated by the smaller AIC statistic.
Examples include person miles travelled, the number of person trips, and travel convenience.
This includes: Person named on the certificate.
These models included 1 197 989 participants and 22 982 760 person years at risk.
To account for correlation among the multiple measurements collected per person, our models included random intercepts for each subject.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com