- Title:
- Data for thesis research on the impact of PE Ownership on healtchare affordability and quality
- Requested data:
- - Metrics for the quality of facilities (primarily dental and nursing homes, but other types of care too): Staff Hours Per Patient Day and Overall Ratings
Private Equity Ownership:
- This variable is indicated as a binary variable capturing whether a nursing home is owned by a private equity firm at the time of analysis, over several years to be able to identiy the year where a facility was bought by a PE fund
If possible, patient Characteristics:
- Includes demographic information such as age, race, gender, marital status, morbidity indicators, insurance, mortality rates (also on a facility level)
Facility Characteristics:
- Variables such as facility age, location, and financials
- Requested format:
- CSV
- Requested period:
- 01/01/2019 - 00:00 till 12/27/2024 - 00:00
- Usage:
- I am currently writing my thesis on the impact of PE Ownership on healtchare affordability and quality. After having read the EY Onderzoek Private Equity in de Zorg, I would like to improve their research by having more data and potentially adding an instrumental variable, the difference in distances from the patient's residence to the closest PE-owned and non-PE-owned facility. EY reports havign had issues in the gathering of data which I hereby highlight: For the quantitative analysis, a of datasets with data on indicators that can give more insight into the
effects of Private Equity on accessibility, affordability or quality of care were used. Unfortunately, the
declaration data from Vektis was not available for this study.
Requests for relevant data from, among others, CBS, IGJ, Patient Federation Netherlands, professional
associations (GGZ Nederland, VGN, KNGF, NVDA, ActiZ, Keurmerk Fysiotherapie, KNMT) and health
insurers also failed to yield data sources useful for this study. An important prerequisite for being able to
use the dataset for this was the possibility, based on Chamber of Commerce numbers, to split the data
into data on healthcare institutions with PE participation and healthcare institutions without PE
participation. C.1 Dataset: NZa merger decisions
A dataset was created based on the published care-specific concentration test decisions as published on
the website at puc.overheid.nl/na. For this purpose, a search for merger decisions published on
overheid.nl by the Dutch Healthcare Authority (https://puc.overheid.nl/nza/) on 2 January 2024 was
carried out. This gave a total of 1,432 hits over the past 10 years. Of the 1,432 hits, eight related to a
'withdrawal decision'; these were not included further in the analysis. A dataset was created where the title
of the published decision was split into 'organisation 1' and 'organisation 2', where 'organisation 1' refers
to the party taking an interest in 'organisation 2'50
. Based on this dataset, a grouping of the number of
merger decisions by 'organisation 1' was made. In doing so, where this appeared simple from the naming,
different notations (e.g. BV versus B.V.) were corrected to avoid unnecessary duplicate groupings.
C.2 Dataset: EY Barometer Dutch Healthcare
The dataset used, among others, for the EY Barometer Dutch Healthcare, which
4,181 healthcare institutions includes with the last update on 17-12-2023, forms the basis for the
quantitative analyses of the year 2022, with 2021 as the comparison year. When the categories 'youth
aid' and 'other' are excluded, 3,069 healthcare institutions remain, divided among the different
subsectors of healthcare institutions. The 'other' subsector generally, but not exclusively, includes
services such as ambulance services, crisis care, addiction care, medical diagnostics and regional
health and safety regions.
Further subdivisions have been within the subsectors, which can be filtered and selected. Primary care
includes, but is not limited to, physiotherapy, other paramedical care (other than physiotherapy),
rehabilitation centres, dietetics, occupational therapy and GP care. The Nursing, Care and Home Care
(VVT) subsector includes district nursing, nursing home care and home care. The hospital category
(HMO) is composed of general and categorical hospitals, as well as university medical centres.
This dataset was compiled in part from data in filed financial statements. The analysis showed that a
substantial number of annual accounts are not publicly available or have not been filed with the Chamber
of Commerce. This is partly caused by the lack of general annual accountability within a number of
subsectors
- Impact:
- I believe having such data would allow me to reach reliable conclusions about the relationship between PE ownership and healthcare quality and affordability, which has been a topic of great debate in Dutch politics, and potentially shed light as to whether PE activity in fields related to care should eb more or less regulated.
