Reinventing the cottage hospital : Did implementation of municipal acute bed units reduce the demand for hospital admissions?

Transcription

1 Reinventing the cottage hospital : Did implementation of municipal acute bed units reduce the demand for hospital admissions? Terje P. Hagen and Jayson O. J. Swanson Department of Health Management and Health Economics

2 Introduction The Coordination Reform (2009) pointed to three primary challenges in the Norwegian health services: Patients needs for coordinated services are not being sufficiently met. In the services there is too little initiative aimed at limiting and preventing disease. Population development and the changing range of illnesses among the population.

3 Main reform elements: Municipal co-financing of treatment in the state owned hospitals internal medicine departments and out-patients clinics was implemented from January The reform implied that 20 per cent of the hospital running costs as measured by the DRG-system was to be covered by the municipalities. Municipal payment for patients ready for discharge was also implemented from January This element implied that the municipalities were charged 4000 Norwegian Crowns (app. 500) per day from the day the patient was deemed ready for discharge. Implementation of municipal acute bed units (MAUs) that happened steadily from the summer of MAUs are intermediate units or community hospitals set up to reduce hospital admissions, in particular for elderly patients.

4 This main aim of the paper is to evaluate the effects of the implementation of the Norwegian MAUs. We re in in particular interested in the effects of implementation of MAUs on the number of hospital admittances for the higher age groups.

5 Municipal acute bed units (MAUs) The MAUs is one of the measures of the coordination reform and the government s aim is that all municipalities shall have a MAU running from 2016 either organized as a municipal or an inter-municipal service. The MAUs are funded partly by a matching grant from the central state to the municipalities and partly by transfers of resources from the regional health authorities to the municipalities.

6 Initially the MAUs were regarded as a service for patient with the following characteristics: Stable patient with known diagnosis where the main problem was an acute disease that could be evaluated and treated by primary care methods or patients with a worsening condition with need of adjustment of the treatment. Stable patients with unknown diagnoses in need of observation and medical evaluations. Typical patients expected to be admitted to the MAUs were elderly patients with pneumonia, urinary tract infections, other infections, gastroenteritis, chronic obstructive pulmonary disease (COPD), diabetes, heart failure and dehydration.

7 The model DH mt = S mt + D mt + MAU mt + h + m + y + t + u - DH mt were variables describing demand for hospital services from each municipality m at time t (month), - S was a vector of municipal supply variables such as nursing homes and GPs - D was a vector of municipal demand factors such as the number of elderly, number of disabled and number of dead - MAU was a dummy variable taking the value of 1 if the municipality had implemented municipal acute units and 0 if not. Alternatively: Dummies for organization of the MAUs - Fixed effects: Municiplilties or hospital districts - Variables standardized by 1000 inhabitants

8 Results NFR, referansegruppa, m2 2010m4 2010m6 2010m8 2010m m m2 2011m4 2011m6 2011m8 2011m m m2 2012m4 2012m6 2012m8 2012m m m2 2013m4 2013m6 2013m8 2013m m m2 2014m4 2014m6 2014m8 2014m m12 YearMonth

9 Organization Most MAUs co-located with: Local acute centers Nursing homes Organization 40% of the MAUs covers one municipality 60% cover 2 or more municipalities

10 Dependent variables (December 2013) Variable name Mean Std.dev Min Max Admittances per 1000 inhab Admittances years per 1000 inh Acute admittances years per 1000 inhabitants

11 Independent variables (2014) Variable name Mean Std.dev Min Max Beds in nursing homes per 1000 inhabitants (NURSINGHOMES) Number of GPs per 1000 inhabitants (GPs) Number of the population aged 80 years and above per 1000 inhabitants (SHPOP80+) Number of dead per 1000 inhabitants (DEATHRATE) Number of disabled per 1000 inhabitants (DISABELED) Travel time (km to hospital) MAU

12 Dependent variable: Acute admittances age group 80-89/population Variable name Est. (sig) Est. (sig) Intercept Nursinghomes -0.01*** -0.02*** GPs -0.01** -0.01** SHPOP *** 0.22*** Deathrate 0.22*** 0.23*** Disabled Traveltime -0.01* -0.01* MAU -0.04*** - MAU Host ** MAU Guest *** MAU Alone -0.05*** Fixed effects for hospital districts Yes Yes *, **, *** = p<0.1, 0.05, 0.01

13 Other age groups years and 90 years and above Also negative effects, but weaker (in general insignificant) Total population No effects, indicating that the number of admittances in lower age groups are increasing Avoidable hospital admittances Not analyzed yet

14 Conclusion A small negative effect of MAU on acute admittances for the higher age groups, in particular for the age group years Cost effectiveness: Not yet known Quality: Not yet known

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