scholarly journals Defining ‘actionable’ high- costhealth care use: results using the Canadian Institute for Health Information population grouping methodology

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Maureen Anderson ◽  
Crawford W. Revie ◽  
Henrik Stryhn ◽  
Cordell Neudorf ◽  
Yvonne Rosehart ◽  
...  

Abstract Background A small proportion of the population consumes the majority of health care resources. High-cost health care users are a heterogeneous group. We aim to segment a provincial population into relevant homogenous sub-groups to provide actionable information on risk factors associated with high-cost health care use within sub-populations. Methods The Canadian Institute for Health Information (CIHI) Population Grouping methodology was used to define mutually exclusive and clinically relevant health profile sub-groups. High-cost users (> = 90th percentile of health care spending) were defined within each sub-group. Univariate analyses explored demographic, socio-economic status, health status and health care utilization variables associated with high-cost use. Multivariable logistic regression models were constructed for the costliest health profile groups. Results From 2015 to 2017, 1,175,147 individuals were identified for study. High-cost users consumed 41% of total health care resources. Average annual health care spending for individuals not high-cost were $642; high-cost users were $16,316. The costliest health profile groups were ‘long-term care’, ‘palliative’, ‘major acute’, ‘major chronic’, ‘major cancer’, ‘major newborn’, ‘major mental health’ and ‘moderate chronic’. Both ‘major acute’ and ‘major cancer’ health profile groups were largely explained by measures of health care utilization and multi-morbidity. In the remaining costliest health profile groups modelled, ‘major chronic’, ‘moderate chronic’, ‘major newborn’ and ‘other mental health’, a measure of socio-economic status, low neighbourhood income, was statistically significantly associated with high-cost use. Interpretation Model results point to specific, actionable information within clinically meaningful subgroups to reduce high-cost health care use. Health equity, specifically low socio-economic status, was statistically significantly associated with high-cost use in the majority of health profile sub-groups. Population segmentation methods, and more specifically, the CIHI Population Grouping Methodology, provide specificity to high-cost health care use; informing interventions aimed at reducing health care costs and improving population health.

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241994
Author(s):  
Shreya Banerjee ◽  
Indrani Roy Chowdhury

Objective The study attempts (a) to compute the degree of socio-economic inequity in health care utilization and (b) to decompose and analyze the drivers of socio-economic inequity in health care utilization among adults (20–59 years) in India during the periods 2014 and 2017–18. Data source The analysis has been done by using the unit level data of Social Consumption: Health (Schedule number 25.0), of National sample Survey (NSS), corresponding to the 71st and 75th rounds. Methods Odds ratios were computed through logistic regression analysis to examine the effect of the socio-economic status on the health seeking behaviour of the ailing adult population in India. Concentration Indices (CIs) were calculated to quantify the magnitude of socio-economic inequity in health care utilization. Further, the CIs were decomposed to find out the share of the major contributory factors in the overall inequity. Results The regression results revealed that socio-economic status continues to show a strong association with treatment seeking behavior among the adults in India. The positive estimates of CIs across both the rounds of NSS suggested that health care utilization among the adults continues to be concentrated within the higher socio-economic status, although the magnitude of inequity in health care utilization has shrunk from 0.0336 in 2014 to 0.0230 in 2017–18. However, the relative contribution of poor economic status to the overall explained inequities in health care utilisation observed a rise in its share from 31% in 2014 to 45% in 2017–18. Conclusion To reduce inequities in health care utilization, policies should address issues related to both supply and demand sides. Revamping the public health infrastructure is the foremost necessary condition from the supply side to ensure equitable health care access to the poor. Therefore, it is warranted that India ramps up investments and raises the budgetary allocation in the health care infrastructure and human resources, much beyond the current spending of 1.28% of its GDP as public expenditure on health. Further, to reduce the existing socio-economic inequities from the demand side, there is an urgent need to strengthen the redistributive mechanisms by tightening the various social security networks through efficient targeting and broadening the outreach capacity to the vulnerable and marginalized sections of the population.


2014 ◽  
Vol 2 (4) ◽  
pp. 52-58
Author(s):  
Samad Rouhani ◽  
Fatemeh Abdollahi ◽  
Reza Ali Mohammadpour ◽  
◽  
◽  
...  

2012 ◽  
Vol 3 (1) ◽  
pp. 13 ◽  
Author(s):  
Sebastian Kevany ◽  
Oliver Murima ◽  
Basant Singh ◽  
Daniel Hlubinka ◽  
Michal Kulich ◽  
...  

2009 ◽  
Vol 100 (3) ◽  
pp. 180-183 ◽  
Author(s):  
Mark Lemstra ◽  
Johan Mackenbach ◽  
Cory Neudorf ◽  
Ushasri Nannapaneni

2014 ◽  
Vol 105 (6) ◽  
pp. e431-e437 ◽  
Author(s):  
Sarah H. Manos ◽  
Yunsong Cui ◽  
Noni N. MacDonald ◽  
Louise Parker ◽  
Trevor J. B. Dummer

PEDIATRICS ◽  
1990 ◽  
Vol 85 (1) ◽  
pp. 114-118 ◽  
Author(s):  
Kelly Kelleher ◽  
Barbara Starfield

Reduction in medical care utilization is one criteria for assessing the impact of mental health treatment for children with psychosocial problems. This reduction has been termed the "offset" effect. Almost all published research concerning offset after mental health treatment concerns adults, and the few studies in pediatric populations are limited by methodologic problems. A study of health care utilization after mental health treatment for children was conducted. Mental health treatment for psychosocial problems was significantly associated with decreased use of medical care only for older children, after potentially confounding variables were controlled for. Furthermore, this decreased use was found only for nonmental health specialty care visits. No reduction in primary care visits occurred. Other factors such as previous patterns of use and the presence of other morbidity were stronger predictors of subsequent primary health care use than was mental health treatment. Mental health treatment does not have a major impact on the high utilization of most children with psychosocial problems in pediatric settings. Because the reasons for this may be particular morbidity patterns in these children, future studies should include some measure of case mix as a potentially important variable in assessment of mental health treatment effects.


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