Information, consumer choice, and quality and quantity of primary care
Last registered on November 26, 2018

Pre-Trial

Trial Information
General Information
Title
Information, consumer choice, and quality and quantity of primary care
RCT ID
AEARCTR-0003599
Initial registration date
November 25, 2018
Last updated
November 26, 2018 2:18 PM EST
Location(s)
Region
Primary Investigator
Affiliation
University of Gothenburg
Other Primary Investigator(s)
PI Affiliation
Department of Economics, Lund University
PI Affiliation
VIVE – The Danish Center for Social Science Research
PI Affiliation
Department of Business Administration, Lund University
Additional Trial Information
Status
On going
Start date
2015-02-01
End date
2019-07-01
Secondary IDs
Abstract
In order to improve the quality of care, all regions in Sweden have implemented systems where individuals choose their primary health care provider. All providers are financed by public means and pre-set user fees, but may be privately or publicly run. For consumer choice systems to improve the functioning of health care, users must choose units that deliver care of relatively high quality. Demand side frictions related to switching present a possible obstacle to the functioning of this market: the gathering of information about and comparison of providers is costly in terms of time, and there are also small process costs related to notifying providers of the change. We study the effects of two interventions using two randomly drawn samples of the population in the region of Skåne. The first sample is representative of the adult population in the region (above 18 years of age in February 4th 2015) and the second includes all individuals that that moved to the region between mid-February and early May 2015. The treatment groups, 10,259 individuals in the population-representative sample and 3,454 in the sample of new residents, received a leaflet designed in collaboration with the regional health care authority by postal mail. The leaflet contained comparative information on, e.g., accessibility, quality, and available services of an individual’s current primary care provider and its three geographically closest competitors. By sending information directly to consumers, the experimental treatment reduced search costs and may also have improved understanding, as the information was presented differently on the leaflets compared to information available online. 7,700 of the treated in the population-representative sample, and all treated new residents, received a pre-paid choice form together with the leaflet. The small monetary and hassle costs associated with switching were therefore reduced. In a previous study, we showed that information and reduced switching costs increased the probability of switching provider. This study concerns a longer-term follow-up of treatment effects of the intervention. Primary outcomes relate to care consumption and process quality measures in the population-representative sample. Secondary outcomes include additional care consumption variables and long-term switching of providers.


External Link(s)
Registration Citation
Citation
Anell, Anders et al. 2018. "Information, consumer choice, and quality and quantity of primary care." AEA RCT Registry. November 26. https://doi.org/10.1257/rct.3599-1.0.
Former Citation
Anell, Anders et al. 2018. "Information, consumer choice, and quality and quantity of primary care." AEA RCT Registry. November 26. http://www.socialscienceregistry.org/trials/3599/history/37979.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
Part 1: In the first intervention, the regional health authority in the region of Skåne (located in the south-west of Sweden) - Region Skåne - provided 1 percent of the population that are over 18 years old with comparative information about the primary care center (PCC) that they were enrolled at and its three geographically nearest competitors. The information leaflet was sent by mail, and contained information about, among other things, opening hours, quality ratings, and special competences. The leaflet also contained information about a webpage, where comparisons to more PCCs can be made. An example of a leaflet is included in our previous study (Anell et al. 2018). The Part 1 intervention had two treatment arms: 25% of the treated received only information about primary care centers (info), while 75% of the treated received information along with a pre-paid form that could be used to register the change of provider (info&form). The control group received nothing.

Part 2: in the second intervention, the health authority sent similar information leaflets by postal mail to half of all new residents that had registered in the region during the three months following the first intervention. Half of all new residents were assigned to treatment, which in this intervention was one-armed and included only the info&form treatment. The control group received nothing.

The two interventions were implemented in 2015 (Intervention start dates: February 23 and June 12). These were followed up using data on provider choices up until October 2015. This is a pre-analysis plan for a medium-term follow up of these interventions for a period including October 2018. The experiments and the first follow-up was registered in 2015 with registration number AEARCTR-0000659.
Intervention Start Date
2015-02-23
Intervention End Date
2018-10-31
Primary Outcomes
Primary Outcomes (end points)
Primary health care consumption (quantity),
Measures of process quality of primary health care,
Emergency room visits
Primary Outcomes (explanation)
See analysis plan
Secondary Outcomes
Secondary Outcomes (end points)
See analysis plan
Secondary Outcomes (explanation)
See analysis plan
Experimental Design
Experimental Design
The premise for Part 1 was that the regional health authority has to treat all primary care centers equally, in order to be neutral in terms of competition on this market. Using the random number generator within Stata (which we use for all randomizations), we therefore first drew 11 percent of listed individuals over 18 from each of the 150 primary care centers. Then, then we randomly selected 9.0909091 percent of these 11 percent as the treatment group. The remaining share constituted the control group. Within the treatment group, we then randomly assigned 25 percent stratified per center to the group that would only receive information (info), and 75 percent to the group that would receive information and a pre-paid change form (info&form).

Part 2: The randomization procedure for this part was similar to the one described for Part 1. The second population included all individuals (above 18) who entered the enrollment register between February 4 and May 11, 2015. Of these 6,906 individuals, approximately 50% were assigned to treatment. To avoid spill-over effects within families, this intervention was cluster-randomized by residential address. The number of clusters were 6,059. The population was extracted from the enrollment register on May 11, the randomization took place on May 25 2015, and the leaflets were mailed out in the second week of June. In this part, all individuals in the treatment group received both a leaflet and a pre-paid form (i.e. info&form). 3,454 individuals were assigned to the treatment group and 3,452 to the control group.
Experimental Design Details
Randomization Method
Randomization performed using the random number generator in Stata 13.
Randomization Unit
The randomization unit is the individual in the population-representative sample (part 1) and the home address in the new residents sample (part 2).
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
Part 1: The total population-representative sample included 112,861 individuals, of which info = 2,559, info&form = 7,700, and control group = 102,602. Treatment was not clustered (randomization at individual level)

Part 2: The total number of new residents in the sample were 6,906 individuals, of which 3,454 treated and 3,452 controls. Randomization clustered by residential address (6,059 clusters).

The estimation samples in our planned analyses will deviate from the above for three reasons:
1. (Non-selective) Attrition. For the sample in Part 1, 137 individuals died or left the region before we extracted address information (for administrative reasons, address data was extracted after the randomization date) and an additional 146 individuals were de-registered from the region before the leaflets were mailed out in the last week of February. One individual chose to opt out from the study after randomization. For the sample in Part 2, we lack data for one individual for administrative reasons and 102 individuals died or left the region between randomization and intervention.
2. The analyses will exclude individuals who, at baseline, were registered at a PCC in a rural area.
3. The analyses of new residents will exclude recent immigrants.

With these restrictions, the size of the Part 1 sample equals 69,744 (info = 1,578, info&form = 4,751, control =63,415) and the Part 2 sample equals 3,157 (1,597 treated) individuals and 2,843 (1,418 treated) clusters

See analysis plan for more information.
Sample size: planned number of observations
The size of the relevant Part 1 sample equals 69,744 (info = 1,578, info&form = 4,751, control =63,415) and the size of the relevant Part 2 sample equals 3,157 (1,597 treated) individuals and 2,843 (1,418 treated) clusters. See "Planned number of clusters" for more info
Sample size (or number of clusters) by treatment arms
The size of the relevant Part 1 sample equals 69,744 (info = 1,578, info&form = 4,751, control =63,415) and the size of the relevant Part 2 sample equals 3,157 (1,597 treated) individuals and 2,843 (1,418 treated) clusters

See "Planned number of clusters" for more info
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We use a power of 80%, and a two-tailed p-value = 0.05 in all calculations. We have two continuous primary outcome measures (primary health care consumption and emergency room visits) and a range of binary outcome variables. For primary health care consumption, we include information about the baseline correlation between pre-treatment and post-treatment values 0.55, which is taken from pre-treatment data that we already have access to. Then we can find effects of 0.57 more contacts with primary care. For emergency room visits, we can find .053 more visits (given the estimated baseline correlation of 0.47). Our set of additional covariates should further decrease the minimum detectable effect size for both outcomes. For the binary measures, we do not have information about the baseline rate and correlation, and we will use analysis procedures suggested by e.g., Kling et al. (2007), which may increase our statistical power (see the analysis plan for more information). Using conservative assumptions of zero baseline correlation and no increase in power from the analysis procedure, we can find effects of e.g., 0.82 percentage points at a baseline rate of 5 percent, 1.84 percentage points at 50 percent, and 1.46 percentage points at a baseline rate of 80 percent.
Supporting Documents and Materials

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IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Regionala Etikprövningsnämnden Lund
IRB Approval Date
2018-02-07
IRB Approval Number
Dnr 2014/49, updated
IRB Name
Regionala Etikprövningsnämnden Lund
IRB Approval Date
2014-06-11
IRB Approval Number
Dnr 2014/49
Analysis Plan

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Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers