Linear association between number of modifiable risk factors and multiple chronic conditions: Results from the Behavioral Risk Factor Surveillance System
Introduction
Adults with multiple (≥ 2) chronic conditions (MCCs) account for about two-thirds of all healthcare costs in the U.S. (Anderson, 2010). MCCs are a major factor in the rise in Medicare spending (Thorpe et al., 2010), estimated to be responsible for 93% of those costs (Centers for Medicare and Medicaid Services, 2012). There is no standard definition of chronic conditions included in MCCs (Willadsen et al., 2016, Goodman et al., 2013) but chronic diseases, risk factors, mental health problems, and cognitive impairment can be among them (CMS: Centers for Medicare and Medicaid Services, n.d, U.S. Department of Health and Human Services, n.d). More attention is starting to be focused on MCCs as their contribution to health care costs is recognized (Centers for Medicare and Medicaid Services, 2012, Goodman et al., 2013, U.S. Department of Health and Human Services, n.d, Gupta, 2016). While a recent review (Willadsen et al., 2016) of 163 MCC studies found that 85% included risk factors (RFs) in their definitions of MCCs, apparently none studied possible associations between MCCs and RFs. Behavioral risk factors such as hypertension, obesity, and smoking have been shown to be associated with many separate chronic conditions (Brownson et al., 2010). Risk factors can also occur concurrently and are sometimes studied using composite measures. For example, Adams et al. (2016) and Liu et al. (2016) studied slightly different combinations of 5 RFs and both found that 92%–94% of all adults reported at least one RF. Addressing RFs collectively may help in understanding how they might predict MCCs, which in turn could inform clinical and public health practice.
Our objective for this current work was to study MCCs based on different definitions and their associations with composite measures of up to 9 risk factors. The chronic conditions chosen were asthma, arthritis, heart disease, stroke, chronic obstructive pulmonary disease (COPD), cognitive impairment, cancer other than skin, and chronic kidney disease. The RFs were current smoking, sedentary lifestyle, inadequate fruit and vegetable consumption, sleeping other than 7–8 h, and obesity. Because diabetes, hypertension, high cholesterol, and depression can be considered either chronic conditions (Willadsen et al., 2016, Goodman et al., 2013) or RFs (Brownson et al., 2010) they would be included in the study in each category. Prevalence rates of the composite measures plus their associations with each other would be studied. We would also test the hypothesis that there is a linear association between the number of RF's and MCC rates as was found for other outcomes (Adams and Grandpre, 2016). The hope was that results might aid in the development and targeting of integrated prevention programs addressing multiple RFs aimed at reducing rates of MCCs and lowering associated health care costs.
Section snippets
Data
We used publicly available (Behavioral Risk Factor Surveillance System (BRFSS) (Atlanta, Georgia), n.d.) Behavioral Risk Factor Surveillance System (BRFSS) data from 2013 in order to include sleep as a RF. The BRFSS is a large, representative, state-based telephone survey of non-institutionalized U.S. adults (Behavioral Risk Factor Surveillance System. Atlanta (GA), n.d.) and our data included 483,865 respondents ages ≥ 18 years in the 50 states and DC. In general, data have been shown to be
Results
Prevalence of the separate chronic conditions and risk factors and selected combinations are shown in Table 1 indicating that 71.5% of all adults reported one or more chronic condition and 96.4% reported at least one RF. Demographics of the MCC and risk factor measures are shown in Table 2, indicating the widespread prevalence of any RFs and demographic differences in MCC rates. Disabled adults and those reporting a cost barrier to health care were significantly more likely than those not
Discussion
Results of this study of MCCs show that for different combinations of chronic conditions and as many as 9 RFs, each additional risk factor, beginning with 1, increased the likelihood of MCCs up to > 80% for adults with all 9. Linear least squares regression lines for the unadjusted results (Fig. 1) for the MCC with 12 chronic conditions and 5 RFs and the MCC with 8 chronic conditions and all 9 RFs have virtually identical, relatively steep slopes. Both RFs and chronic conditions are very common
Conclusions
This study adds valuable information about MCCs with as many as 12 chronic conditions and 9 risk factors that was not available from earlier studies. Results found associations between composite measures of MCCs that could include arthritis, asthma, cancer, CVD, COPD, diabetes, kidney disease, cognitive impairment, diabetes, depression, high blood pressure and high cholesterol and the risk factors of smoking, sedentary lifestyle, inadequate fruit and vegetable consumption, sleeping other than
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Acknowledgments
This work was partially supported by Centers for Disease Control and Prevention (CDC) Grant/Cooperative Agreement # 1U58DP006069-01 (Ms. Adams & Dr. Grandpre). Funds supported data collection and analysis but CDC had no role in the study design or writing of the paper.
Conflict of interest
The authors declare there are no conflicts of interest.
References (31)
Results and their implications from comparing respondents and proxy responses for non-respondents with cognitive difficulties on a telephone survey
Disabil. Health J.
(2017 Jan)- et al.
Dose-response gradients between a composite measure of six risk factors and cognitive decline and cardiovascular disease
Prev. Med.
(2016 Oct) - et al.
A healthy lifestyle composite measure: significance and potential uses
Prev. Med.
(March 2016) - et al.
The projected effect of risk factor reduction on Alzheimer's disease prevalence
Lancet Neurol.
(2011 Sep) - et al.
Summary of the evidence on modifiable risk factors for cognitive decline and dementia: a population-based perspective
Alzheimers Dement.
(2015 Jun) - et al.
A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease
Alzheimers Dement.
(2014 Nov) Differences Between Younger and Older US Adults with Multiple Chronic Conditions
(2017)Chronic Care: Making the Case for Ongoing Care
(2010)- et al.
Patterns of chronic conditions and their associations with behaviors and quality of life, 2010
Prev. Chronic Dis.
(2015 Dec 17) Summary data quality report, centers for disease control and prevention, Atlanta, GA. August 15, 2014
Centers for disease control and prevention
Centers for disease control and prevention (CDC)
Chronic Disease Epidemiology and Control
Chronic Conditions Among Medicare Beneficiaries, Chartbook
Pre-existing Conditions and Medical Underwriting in the Individual Insurance Market Prior to the ACA
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