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[Transparency: can the effect of governmental surveillance be quantified?]
Anne-Margreet van Dishoeck1, Sandra F Oude Wesselink, Hester F Lingsma
1Erasmus Medisch Centrum, Afd. Maatschappelijke gezondheidszorg, Rotterdam, the Netherlands. a.m.vandishoeck@erasmusmc.nl
Nederlands Tijdschrift Voor Geneeskunde
|April 19, 2013
Summary
Existing data can measure the impact of Health Care Inspectorate (IGZ) surveillance on public health for clearly defined issues like pressure ulcers and suicide rates. However, establishing causality requires experimental designs, not just time series analysis.
Area of Science:
- Public Health
- Health Services Research
- Health Policy
Background:
- The Health Care Inspectorate (IGZ) conducts surveillance to improve public health.
- Measuring the impact of such surveillance on health outcomes is crucial for accountability and improvement.
- Existing data sources may offer a feasible approach to assess surveillance effectiveness.
Purpose of the Study:
- To determine if existing data can be utilized to quantify the impact of IGZ surveillance on public health outcomes.
- To explore the feasibility of using retrospective data and time series analysis to evaluate surveillance effects.
Main Methods:
- A retrospective, descriptive study design was employed.
- Data from the IGZ, external sources, and literature were analyzed for suicide, pressure ulcers, and medication errors.
- A time series design was used to assess trends before and after IGZ surveillance.
Main Results:
- Reliable quantification of pressure ulcer prevalence and suicide incidence was possible using IGZ data.
- A decline in both pressure ulcers and suicide rates was observed following IGZ surveillance.
- Causality could not be established due to the inability to control for external factors influencing these health problems.
Conclusions:
- IGZ data can define the extent of clearly defined health problems and observe trends using time series analysis.
- Establishing a causal link between IGZ surveillance and observed health trends necessitates experimental designs.
- Prospective randomized studies or stepped wedge designs are recommended for definitive causal inference.
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