ArticleBMC medical research methodology2011
Shared component modelling as an alternative to assess geographical variations in medical practice: gender inequalities in hospital admissions for chronic diseases.
Article in BMC medical research methodology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 23 citations in OpenAlex.
- Article
- An Offenders-Offenses Shared Component Spatial Model for Identifying Shared and Specific Hotspots of Offenders and Offenses: A Case Study of Juvenile Delinquents and Violent Crimes in the Greater Toronto Area.Journal of quantitative criminology · 2024Article
- Article
- Cancer mortality distribution in South Africa, 1997-2016.Frontiers in epidemiology · 2023Article
- Shared component modelling of early childhood anaemia and malaria in Kenya, Malawi, Tanzania and Uganda.BMC pediatrics · 2022Article
- Atlas VPM: two decades informing on unwarranted variations in health care in Spain.Research in health services & regions · 2022Article
- Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks.PLoS computational biology · 2020Article
- Spatial Variation in Lung Cancer Mortality and Related Men-Women Disparities in Iran from 2011 to 2014.Cancer management and research · 2020Article
- Decomposition of the US black/white inequality in premature mortality, 2010-2015: an observational study.BMJ open · 2019Observational
- Joint spatial mapping of childhood anemia and malnutrition in sub-Saharan Africa: a cross-sectional study of small-scale geographical disparities.African health sciences · 2019Article
- Application of SCM with Bayesian B-Spline to Spatio-Temporal Analysis of Hypertension in China.International journal of environmental research and public health · 2018Article
- Smoothed Temporal Atlases of Age-Gender All-Cause Mortality in South Africa.International journal of environmental research and public health · 2017Article
- Mapping Geographic Variation in Infant Mortality and Related Black-White Disparities in the US.Epidemiology (Cambridge, Mass.) · 2016Article
- Spatial analysis of gender variation in the prevalence of hypertension among the middle-aged and elderly population in Zhejiang Province, China.BMC public health · 2016Article
- Co-endemicity of Pulmonary Tuberculosis and Intestinal Helminth Infection in the People's Republic of China.PLoS neglected tropical diseases · 2016Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundSmall area analysis is the most prevalent methodological approach in the study of unwarranted and systematic variation in medical practice at geographical level. Several of its limitations drive researchers to use disease mapping methods -deemed as a valuable alternative. This work aims at exploring these techniques using - as a case of study- the gender differences in rates of hospitalization in elderly patients with chronic diseases.
methodsDesign and study setting: An empirical study of 538,358 hospitalizations affecting individuals aged over 75, who were admitted due to a chronic condition in 2006, were used to compare Small Area Analysis (SAVA), the Besag-York-Mollie (BYM) modelling and the Shared Component Modelling (SCM). Main endpoint: Gender spatial variation was measured, as follows: SAVA estimated gender-specific utilization ratio; BYM estimated the fraction of variance attributable to spatial correlation in each gender; and, SCM estimated the fraction of variance shared by the two genders, and those specific for each one.
resultsHospitalization rates due to chronic diseases in the elderly were higher in men (median per area 21.4 per 100 inhabitants, interquartile range: 17.6 to 25.0) than in women (median per area 13.7 per 100, interquartile range: 10.8 to 16.6). Whereas Utilization Ratios showed a similar geographical pattern of variation in both genders, BYM found a high fraction of variation attributable to spatial correlation in both men (71%, CI95%: 50 to 94) and women (62%, CI95%: 45 to 77). In turn, SCM showed that the geographical admission pattern was mainly shared, with just 6% (CI95%: 4 to 8) of variation specific to the women component.
conclusionsWhereas SAVA and BYM focused on the magnitude of variation and on allocating where variability cannot be due to chance, SCM signalled discrepant areas where latent factors would differently affect men and women.
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