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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases
|
|---|---|
| Published in |
BMC Health Services Research, May 2018
|
| DOI | 10.1186/s12913-018-3128-4 |
| Pubmed ID | |
| Authors |
Eline F. de Vries, Richard Heijink, Jeroen N. Struijs, Caroline A. Baan |
| Abstract |
To indicate inefficiencies in health systems, previous studies examined regional variation in healthcare spending by analyzing the entire population. As a result, population heterogeneity is taken into account to a limited extent only. Furthermore, it clouds a detailed interpretation which could be used to inform regional budget allocation decisions to improve quality of care of one chronic disease over another. Therefore, we aimed to gain insight into the drivers of regional variation in healthcare spending by studying prevalent chronic diseases. We used 2012 secondary health survey data linked with claims data, healthcare supply data and demographics at the individual level for 18 Dutch regions. We studied patients with diabetes (n = 10,767) and depression (n = 3,735), in addition to the general population (n = 44,694). For all samples, we estimated the cross-sectional relationship between spending, supply and demand variables and region effects using linear mixed models. Regions with above (below) average spending for the general population mostly showed above (below) average spending for diabetes and depression as well. Less than 1% of the a-priori total variation in spending was attributed to the regions. For all samples, we found that individual-level demand variables explained 62-63% of the total variance. Self-reported health status was the most prominent predictor (28%) of healthcare spending. Supply variables also explained, although a small part, of regional variation in spending in the general population and depression. Demand variables explained nearly 100% of regional variation in spending for depression and 88% for diabetes, leaving 12% of the regional variation left unexplained indicating differences between regions due to inefficiencies. The extent to which regional variation in healthcare spending can be considered as inefficiency may differ between regions and disease-groups. Therefore, analyzing chronic diseases, in addition to the traditional approach where the general population is studied, provides more insight into the causes of regional variation in healthcare spending, and identifies potential areas for efficiency improvement and budget allocation decisions. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 1 | 50% |
| Netherlands | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 46 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 15% |
| Researcher | 7 | 15% |
| Student > Master | 5 | 11% |
| Professor > Associate Professor | 4 | 9% |
| Student > Bachelor | 3 | 7% |
| Other | 8 | 17% |
| Unknown | 12 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 8 | 17% |
| Economics, Econometrics and Finance | 5 | 11% |
| Social Sciences | 5 | 11% |
| Medicine and Dentistry | 4 | 9% |
| Psychology | 3 | 7% |
| Other | 4 | 9% |
| Unknown | 17 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 May 2018.
All research outputs
#3,652,465
of 23,047,237 outputs
Outputs from BMC Health Services Research
#1,613
of 7,721 outputs
Outputs of similar age
#71,888
of 326,458 outputs
Outputs of similar age from BMC Health Services Research
#59
of 214 outputs
Altmetric has tracked 23,047,237 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,721 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 326,458 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 214 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.