TY - JOUR

T1 - Exploring the concurrent validity of the nationwide assessment of permanent nursing home residence in Denmark

T2 - A cross-sectional data analysis using two administrative registries

AU - Bebe, Anna

AU - Nielsen, Anni Brit Sternhagen

AU - Willadsen, Tora Grauers

AU - Søndergaard, Jens

AU - Siersma, Volkert

AU - Nicolaisdottir, Dagny Ros

AU - Kragstrup, Jakob

AU - Waldorff, Frans Boch

PY - 2017/8/29

Y1 - 2017/8/29

N2 - Background: Many register studies make use of information about permanent nursing home residents. Statistics Denmark (StatD) identifies nursing home residents by two different indirect methods, one based on reports from the municipalities regarding home care in taken place in a nursing home, and the other based on an algorithm created by StatD.The aim of the present study was to validate StatD’s nursing home register using dedicated administrative municipality records on individual nursing home residents as gold standard.Methods: In total, ten Danish municipalities were selected. Within each Danish Region, we randomly selected one municipality reporting to Stat D (Method 1) and one not reporting where instead an algorithm created by StatD was used to discover nursing home residents (Method 2). Method 1 means that municipalities reported to Stat D whether home care has taken place in a nursing home or in a private home. Method 2 is based on an algorithm created by Stat D for the municipalities where Method 1 is not applicable. Our gold standard was the information from the local administrative system in all ten selected municipalities. Each municipality provided a list with all individuals > 65 years living in a nursing home on January 1st, 2013 as well as the central personal number. This was compared to the list of individuals >65 living in nursing home facilities in the same ten municipalities on January 1st, 2013 retrieved from StatD.Results: According to the data received directly from the municipalities, which was used as our gold Standard 3821 individuals were identified as nursing home residents. The StatD register identified 6,141 individuals as residents. Additionally, 556 of the individuals identified by the municipalities were not identified in the StatD register.Overall sensitivity for the ten municipalities in the StatD nursing home register was 0.85 (95% CI 0.84-0.87) and the PPV was 0.53 (95% CI 0.52-0.54). The municipalities for which nursing home status was based on the StatD algorithm (method 2) had a sensitivity of 0.84 (95% CI 0.82-0.86) and PPV of 0.48 (95% CI 0.46-0.50). Both slightly lower than the reporting municipalities (method 1) where the sensitivity was 0.87(95% CI 0.85-0.88) and the PPV was 0.57 (95% CI 0.56-0.59).Additionally, the sensitivity and PPV of the Stat D register varied heavily among the ten municipalities from 0.51 (95% CI 0.43-0.59) to 0.96 (95% CI 0.95-0.98) and PPV correspondingly, from 0.14 (95% CI: 0.11-0.17) to 0.73 (95% CI 0.69-0.77).Conclusions: The overall PPV of StatD nursing home register was low and differences between municipalities existed. Even in countries with extensive nation-wide registers, validating studies should be conducted for outcomes based on these registers.

AB - Background: Many register studies make use of information about permanent nursing home residents. Statistics Denmark (StatD) identifies nursing home residents by two different indirect methods, one based on reports from the municipalities regarding home care in taken place in a nursing home, and the other based on an algorithm created by StatD.The aim of the present study was to validate StatD’s nursing home register using dedicated administrative municipality records on individual nursing home residents as gold standard.Methods: In total, ten Danish municipalities were selected. Within each Danish Region, we randomly selected one municipality reporting to Stat D (Method 1) and one not reporting where instead an algorithm created by StatD was used to discover nursing home residents (Method 2). Method 1 means that municipalities reported to Stat D whether home care has taken place in a nursing home or in a private home. Method 2 is based on an algorithm created by Stat D for the municipalities where Method 1 is not applicable. Our gold standard was the information from the local administrative system in all ten selected municipalities. Each municipality provided a list with all individuals > 65 years living in a nursing home on January 1st, 2013 as well as the central personal number. This was compared to the list of individuals >65 living in nursing home facilities in the same ten municipalities on January 1st, 2013 retrieved from StatD.Results: According to the data received directly from the municipalities, which was used as our gold Standard 3821 individuals were identified as nursing home residents. The StatD register identified 6,141 individuals as residents. Additionally, 556 of the individuals identified by the municipalities were not identified in the StatD register.Overall sensitivity for the ten municipalities in the StatD nursing home register was 0.85 (95% CI 0.84-0.87) and the PPV was 0.53 (95% CI 0.52-0.54). The municipalities for which nursing home status was based on the StatD algorithm (method 2) had a sensitivity of 0.84 (95% CI 0.82-0.86) and PPV of 0.48 (95% CI 0.46-0.50). Both slightly lower than the reporting municipalities (method 1) where the sensitivity was 0.87(95% CI 0.85-0.88) and the PPV was 0.57 (95% CI 0.56-0.59).Additionally, the sensitivity and PPV of the Stat D register varied heavily among the ten municipalities from 0.51 (95% CI 0.43-0.59) to 0.96 (95% CI 0.95-0.98) and PPV correspondingly, from 0.14 (95% CI: 0.11-0.17) to 0.73 (95% CI 0.69-0.77).Conclusions: The overall PPV of StatD nursing home register was low and differences between municipalities existed. Even in countries with extensive nation-wide registers, validating studies should be conducted for outcomes based on these registers.

KW - Nursing homes

KW - Nursing home admittance

KW - Nursing home entry

KW - Nursing home referral

KW - Nursing home placement

KW - Validation

KW - Validity

KW - Denmark

KW - Register data

KW - Population register

KW - Algorithm

KW - Epidemiology

U2 - 10.1186/s12913-017-2535-2

DO - 10.1186/s12913-017-2535-2

M3 - Journal article

C2 - 28851353

VL - 17

SP - 1

EP - 7

JO - BMC Health Services Research

JF - BMC Health Services Research

SN - 1472-6963

M1 - 607

ER -