Inequity to the Utilization of Bariatric Surgery: a Systematic Review and Meta-Analysis
- 562 Downloads
This systematic review explores the sociodemographic factors associated with the utilization of bariatric surgery among eligible patients. Electronic databases were searched for population-based studies that explored the relationship between sociodemographic characteristics of patients eligible for bariatric surgery to those who actually received the procedure. Twelve retrospective cohort studies were retrieved, of which the results of 9 studies were pooled using a random effects model. Patients who received bariatric surgery were significantly more likely to be white versus non-white (OR 1.54; 95 % CI 1.08, 2.19), female versus male (OR 2.80; 95 % CI 2.46, 3.22), and have private versus government or public insurance (OR 2.51; 95 % CI 1.04, 6.05). Prospective cohort studies are warranted to further determine the relative effect of these factors, adjusting for confounding factors.
KeywordsBariatric surgery Obesity Sociodemographic disparities Inequity Utilization Systematic review Meta-analysis
We thank Christine Neilson for the literature search and ongoing library support, and Kevin Thorpe for statistical advice.
This systematic review is funded by the Department of Surgery, St. Michael’s Hospital. ACT is funded by a CIHR New Investigator Award in Knowledge Synthesis.
SKB collaborated in the development of the study protocol, study selection, data abstraction, methodology assessment, data management and entry, data interpretation, and writing of the final manuscript. JIR collaborated in the design of the study, development of the study protocol, study selection, data abstraction, methodology assessment, data management and entry, data interpretation, and writing of the final manuscript. ODR conceived of the study, collaborated in the design of the study, development of the protocol, data interpretation, and writing of the final manuscript. AC was responsible for the analysis, collaborated in data interpretation, assisted in writing of the manuscript, and critically reviewed the manuscript for important statistical content. DG collaborated in the development of the study protocol, study selection, data abstraction, and methodology assessment. ACT collaborated in data interpretation, assisted in the writing of the manuscript, and critically reviewed the manuscript for important methodological content. JKS collaborated in the design of the study, protocol development, and data interpretation. SAG collaborated in the interpretation of the results. JP collaborated in the design of the study, protocol development, and data interpretation. LGC collaborated in the design of the study, development of the study protocol, and data interpretation. TJ collaborated in the design of the study, development of the study protocol, data interpretation, and supervised the overall development, and writing of the study. All authors read and approved the final manuscript.
Conflict of Interest
The authors declare that they have no conflict of interest.
- 10.Hutter MM, Schirmer BD, Jones DB, Ko CY, Cohen ME, Merkow RP, et al. First report from the American College of Surgeons Bariatric Surgery Center Network: laparoscopic sleeve gastrectomy has morbidity and effectiveness positioned between the band and the bypass. Ann Surg. 2011;254(3):410–20.CrossRefPubMedCentralPubMedGoogle Scholar
- 20.Moher D, Shamseer L, Clarke M, Ghersi D, Liberti A, Petticrew M, Shekelle P, Stewart L. Reporting guidelines for systematic reiew protocols. In Madrid; 2011.Google Scholar
- 21.Health inequity in access to bariatric surgery: a protocol for a systematic review Jackson T, Zhang R, Glockler D, Pennington J, Reddigan J, Rotstein O, Smylie J, Perrier L, Conn LG. 2013. PROSPERO (CRD42013004920). http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42013004920
- 24.Scottish Intercollegiate Guidelines Network. Methodology checklist 3: cohort studies. In: SIGN 50: a guideline developers’ handbook. Edinburgh: The Network; 2004.Google Scholar
- 26.R Core Team. R. A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2014.Google Scholar
- 27.Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36(3):1–48.Google Scholar
- 41.Smedley BD, Stith AY, Nelson AR., editors. Unequal treatment: confronting racial and ethnic disparities in health care. National Academies Press; Washington, DC: 2003. Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care.Google Scholar
- 57.Dayaratna KD. Studies show: Medicaid patients have worse access and outcomes than privately insured. . 2012. Washington DC, Center for Health Policy Studies; The Heritage Foundation. Ref Type: Generic.Google Scholar
- 63.Bai A, Shukla VK, Bak G, Wells G. Quality assessment tools project report. Ottawa: Canadian Agency for Drugs and Technologies in Health; 2012.Google Scholar