Erasmus University Rotterdam (EUR)
Browse
TEXT
01 identify sample_new.do (4.54 kB)
TEXT
02 link covariates.do (28.57 kB)
TEXT
02.2 charlson icd10.do (7.65 kB)
TEXT
03 prepare nursing home characterstics.do (4.15 kB)
TEXT
04 match sample to agbcodes.do (1.69 kB)
TEXT
04.1 agb link address_export.do (3.76 kB)
TEXT
05 merge, clean, select_export.do (5.61 kB)
TEXT
06 nursing home mortality 2016-2019b.do (4.42 kB)
TEXT
06 nursing home mortality 2019b.do (4.37 kB)
TEXT
06 nursing home zzp.do (0.54 kB)
TEXT
10 descriptives - nursing homes.do (4.16 kB)
TEXT
10 descriptives - survival figure.do (0.9 kB)
TEXT
10 descriptives.do (1.54 kB)
TEXT
20 predicting mortality.do (1.82 kB)
TEXT
21 describe predictions.do (4.45 kB)
TEXT
22 excess per nursing home.do (2.92 kB)
TEXT
22.1 cause specific mortality per nursing home.do (2.05 kB)
TEXT
22.1b define other main causes.do (1.63 kB)
TEXT
23 export excess variation across nursing homes.do (2.16 kB)
TEXT
24 export excess variation other mortality.do (2.27 kB)
1/0
31 files

Determinants of excess mortality rates in nursing homes (Code)

software
posted on 2023-09-28, 11:22 authored by Marlies BarMarlies Bar, Judith BomJudith Bom

This research is based on our own calculations using non-public microdata from Statistics Netherlands. Under certain conditions, these microdata are accessible for statistical and scientific research. For further information, contact microdata@cbs.nl.

Our study population consists of all individuals who lived in a nursing home in the Netherlands on January 1st in 2020 or 2021. To predict the individual level probability of dying in 2020 and 2021 had there not been a COVID-19 pandemic, we construct a second sample consisting of individuals who lived in a nursing home on January 1st in the years 2016-2019. We select nursing home residents who are i) at least 65 years old; ii) with complete information on individual level characteristics, which excludes individuals whose nursing home admission was before the year 2015 as data on hospitalizations is less reliable prior to 2015; and iii) who are eligible for a permanent stay, which excludes those who receive palliative and rehabilitation care.

For all these nursing home residents we use data at the individual level consisting of information on: long-term care use and care profile (Dutch: Zorgzwaarte pakket) from CAK; the reason for eligibility for long-term care from CIZ; date and cause of death from death registries; income and wealth data from tax registries; date of birth and death, sex, and place of residence from municipal records; medicine use based on health insurance data; date and diagnose of inpatient hospital admissions from Dutch Hospital Data and healthcare expenditures from Vektis.

We use individual-level information on nursing home provider codes and addresses to link individuals to the nursing home facility they were admitted to. The data at the nursing home resident level is linked to publicly available data on nursing home organizations from the annual reports and on facilities from Dutch Healthcare Institute.

Funding

ZonMw - project number 10430252210012

History

Usage metrics

    Erasmus School of Health Policy & Management

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC