Evidence map›Paper›PMID 30382828›Full record

ArticleBMC geriatrics2018

Regional variation in healthcare spending and mortality among senior high-cost healthcare users in Ontario, Canada: a retrospective matched cohort study.

Sergei Muratov, Justin Lee, Anne Holbrook, Andrew Costa, J Michael Paterson, Jason R Guertin, Lawrence Mbuagbaw, Tara Gomes, Wayne Khuu, Jean-Eric Tarride

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC geriatrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Sergei MuratovDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada. muratos@mcmaster.ca.ORCID 0000-0002-4105-4609
Justin LeeDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Anne HolbrookDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Andrew CostaDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
J Michael PatersonInstitute for Clinical Evaluative Sciences (ICES), Toronto, ON, Canada.
Jason R GuertinDépartement de Médecine Sociale et Préventive, Faculté de Médecine, Université Laval, Quebec City, QC, Canada.
Lawrence MbuagbawDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Tara GomesInstitute for Clinical Evaluative Sciences (ICES), Toronto, ON, Canada.
Wayne KhuuInstitute for Clinical Evaluative Sciences (ICES), Toronto, ON, Canada.
Jean-Eric TarrideDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.

Funding

CIHR
6 · The paper itself

Abstract

backgroundSenior high cost health care users (HCU) are a priority for many governments. Little research has addressed regional variation of HCU incidence and outcomes, especially among incident HCU. This study describes the regional variation in healthcare costs and mortality across Ontario's health planning districts [Local Health Integration Networks (LHIN)] among senior incident HCU and non-HCU and explores the relationship between healthcare spending and mortality.

methodsWe conducted a retrospective population-based matched cohort study of incident senior HCU defined as Ontarians aged ≥66 years in the top 5% most costly healthcare users in fiscal year (FY) 2013. We matched HCU to non-HCU (1:3) based on age, sex and LHIN. Primary outcomes were LHIN-based variation in costs (total and 12 cost components) and mortality during FY2013 as measured by variance estimates derived from multi-level models. Outcomes were risk-adjusted for age, sex, ADGs, and low-income status. In a cost-mortality analysis by LHIN, risk-adjusted random effects for total costs and mortality were graphically presented together in a cost-mortality plane to identify low and high performers.

resultsWe studied 175,847 incident HCU and 527,541 matched non-HCU. On average, 94 out of 1000 seniors per LHIN were HCU (CV = 4.6%). The mean total costs for HCU in FY2013 were 12 times higher that of non-HCU ($29,779 vs. $2472 respectively), whereas all-cause mortality was 13.6 times greater (103.9 vs. 7.5 per 1000 seniors). Regional variation in costs and mortality was lower in senior HCU compared with non-HCU. We identified greater variability in accessing the healthcare system, but, once the patient entered the system, variation in costs was low. The traditional drivers of costs and mortality that we adjusted for played little role in driving the observed variation in HCUs' outcomes. We identified LHINs that had high mortality rates despite elevated healthcare expenditures and those that achieved lower mortality at lower costs. Some LHINs achieved low mortality at excessively high costs.

conclusionsRisk-adjusted allocation of healthcare resources to seniors in Ontario is overall similar across health districts, more so for HCU than non-HCU. Identified important variation in the cost-mortality relationship across LHINs needs to be further explored.

Indexed as

Patient Acceptance of Health CareAgedAged, 80 and overCohort StudiesDelivery of Health CareFemaleHealth Care CostsHealth ExpendituresHumansMaleMortalityOntarioRetrospective StudiesSocioeconomic FactorsHealthcare expendituresMortalitySenior high-cost usersSmall area variation

Identifiers

PMID30382828
PMCPMC6211423

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.