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Figure 10 charts behavioral habits by gender. Figure 11 illustrates the analysis of behavioral habits by race.

Ageing and health

By and large, in most behavioral habits, the other non-Hispanics have the lowest frequency. We analyzed the data to detect associations between demographics and preventive health. As Figure 12 indicates, both men and women appear to engage in preventive health, though women have the edge. With regard to race, Blacks and Hispanics engage less in preventive health overall, as shown in Figure While all chronic conditions are debilitating on the economy, for the sake of scope, we selectively analyze the influence of a few conditions such as diabetes and asthma.

The Hell of Chronic Illness - Sita Gaia - TEDxStanleyPark

Figure 14 depicts the association between diabetes and pneumococcal vaccination for diabetes. As the average pneumococcal vaccination among diabetes patients increases, the average diagnosed diabetes ratio decreases fewer cases of diabetes. Given the importance of asthma as another prevalent chronic condition, we decided to analyze the relationship between the mortality ratio and influenza vaccinations for asthma to determine the efficiency of preventive measures Figure Analysis of the above preventive health variables shows that resources and efforts dedicated to preventive healthcare offer promise.

The importance of managing chronic diseases is also highlighted when we examine the association between behavioral habits and overarching conditions. Overarching conditions represent situations or factors that directly or indirectly influence the area of study. In our research we look at the influence of these conditions on chronic diseases, behavioral health, and preventive health. We explored the association of self-assessed health statuses among adults with the behavioral habits of binge drinking and heavy drinking Figure That is to say that the lower the health self-assessment, the higher is the percentage of binge drinking.

A decrease of 1.

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We can surmise that reduced self-assessment of health has a stronger influence on heavy drinking than binge drinking among adults. Next, we looked at the association between current smoking prevalence and presence of sufficient sleep among adults Figure The relationship between poor self-rated health status and obesity is positive Figure The higher the prevalence of fair or poor self-rated health, the higher is the prevalence of obesity. Similarly, poor self-rated health has a positive association with current smoking, as indicated in Figure In the analysis of various chronic conditions, there are significant clusters of conditions among men and women, such as the prevalence of asthma, with the women tending to have a higher prevalence of asthma than men.

We notice in Figure 20 that the distribution of lack of health insurance is sparse compared to that of diagnosed diabetes among adults aged 18 and older. An increase in the lack of insurance is associated with an increase in hospitalization for chronic pulmonary disease. We analyzed for any associations between different chronic conditions. It is important to incorporate gender as a factor in the association and prevalence of chronic diseases, so as to develop customized plans for diagnoses and treatments.

A linear trend model was developed for the relationship between asthma and diabetes Figure We can see gender clusters for the prevalence of asthma. Women tend to have higher prevalence of asthma compared to men. Overall, prevalence of asthma is negatively related to the prevalence of diabetes. On average, a high prevalence of asthma is associated with a low prevalence of diabetes. In terms of gender differences our results are consistent with other studies that have shown that women are more prone to develop asthma.

Contributing factors include puberty, menstruation, pregnancy, menopause, and oral contraceptives [ 34 , 35 ]. There is potential for more research in this area. The association between diabetes and kidney disease is shown in Figure There are no obvious differences in gender here. The association between diabetes and chronic pulmonary disease is shown in Figure 25 , and that between arthritis and asthma is shown in Figure When it comes to prevalence of arthritis and asthma, there clearly are clusters for men and women, as shown in Figure Figure 27 shows the association between arthritis and chronic pulmonary disease.

The visual analytics figures above offer insight into a representative cross section of the data. In addition, associations between mental health and chronic conditions, preventive health and chronic conditions, and among chronic conditions themselves highlight the dynamics of interplay between these categories. This understanding is useful to policymakers in framing appropriate health policies. Preventive healthcare and mental health are both important elements in the management, mitigation, and prevention of chronic conditions.

By exploring these in the context of chronic conditions, we offer insight on allocation and prioritization of resources in mitigation and prospective eradication of chronic diseases at a national level. Overarching conditions, including a lack of health insurance, influence the access to necessary health services, including preventive care. This lack of availability is associated with poor health and the prevalence of chronic diseases.


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Similarly, self-assessed health status is a good indicator of overall health status, correlating with subsequent health service use, functional status, and mortality [ 36 ]. Poor mental health interferes with social functioning as well as health condition and should therefore be monitored in chronic disease mitigation. Experiencing activity limitation due to poor physical or mental health undermines efforts to achieve a healthy lifestyle and therefore should be addressed at individual, state, and national levels.

Our research has a few limitations. First, our study is cross-sectional and covers only the years to , the years for which data is available. Second, we included only a limited set of variables indicators from the large data repository on the CDC website. A more comprehensive study could draw from other sources and a larger set of variables. Third, as population and public health have emerged as key disciplines in the contemporary health ecosystem, more scalable, macro-level, and drill-down studies would inform greater understanding of chronic diseases.

Fourth, one would assume that the quality of publicly available data is high and error-free. Lastly, the study is limited to examining associations and correlations and does not investigate causality. Furthermore, we only apply visual analytics and descriptive analytics, which have limitations in and of themselves. This study has analyzed chronic conditions in conjunction with several demographic variables, including gender and race. For some chronic diseases—such as diabetes, arthritis, and obstructive pulmonary —the prevalence in the east is higher than in other regions, while, there is higher prevalence for other conditions, such as asthma, in the northeast.

The south and midwest also show their own prevalence of chronic diseases. Likewise, there are variations for hospitalization and mortality rates. In addition, there are gender differences related to chronic conditions.


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  7. For example, women tend to have higher cases per , for asthma-related hospitalizations. Men, on the other hand, appear to have higher mortality rates for chronic obstructive pulmonary disease, diabetes, chronic kidney, and others. Also, when we examined chronic conditions by race, we noticed that American Indian or Alaska Natives had higher mortality rates for chronic obstructive pulmonary disease, diabetes, chronic kidney, and so on, followed by Black and non-Hispanic groups. In addition, the study analyzed demographics of mental health, behavior habits, and preventive health.

    The associations between behavioral health and chronic conditions and between preventive health and chronic conditions were also analyzed. There is a positive relationship between average female coronary heart disease mortality ratio and average female tobacco use ratio. There is a negative relationship between the average pneumococcal vaccination among diabetes patients and the average diagnosed diabetes ratio among the population. The current smoking prevalence and sufficiency of sleep among adults is negatively related.

    The current lack of health insurance is negatively related to both prevalence of current smoking and that of current smokeless tobacco use. The relationship between obesity and poor self-rated health status is positively related. Similarly, current smoking prevalence has a strong, positive correlation with fair or poor self-rated health status. There are different negative or positive correlations between overarching conditions and chronic conditions.

    For instance, there is a significant positive relationship between the prevalence of a lack of health insurance and that of diagnosed diabetes. But the relationship between prevalence of a lack of health insurance and prevalence of asthma is negatively related. Finally, we conducted analyses of the differences among chronic conditions. There are obvious clusters between men and women for asthma, although women tend to have a higher prevalence of asthma than men. There is a moderate, positive correlation between prevalence of kidney and diabetes, which is akin to the positive correlation between the prevalence of chronic obstructive pulmonary disease and diabetes, arthritis and asthma, arthritis and chronic obstructive pulmonary disease, and asthma and chronic obstructive pulmonary.

    At the patient level, analysis of chronic conditions and related behavioral factors allows patients to be proactive in managing their conditions as well as modifying behavioral health. In this day and age, patients are eager to assimilate health information from various sources [ 37 , 38 ]. Being informed allows patients to self-monitor and seek appropriate and timely medical care [ 39 , 40 ], contributing to an ultimate care model that is increasingly personalized.

    Similar to patients, physicians too have varying information needs in healthcare that need to be satisfied [ 41 ]. To physicians, information on chronic conditions and more importantly, associations between multiple conditions and between categories of healthcare, enable developing personalized treatment plans based on patient-specific profiles that integrate various symptoms with environmental and other health data [ 42 ].

    Additionally, the array of information increases their ability to guide patients in towards lifestyle medicine making lifestyle changes in healthy diet, exercise etc. Whereas most studies on chronic diseases focus on specific chronic diseases and are somewhat limited, this study offers comprehensive analysis over multiple categories of chronic diseases at the state-level. By utilizing visual analytics and descriptive analytics, our study offers methods for gaining insight into the relationships between behavior habits, preventative health and demographics, and chronic conditions.

    Moreover, this study contributes in terms of the methodology of analytics used in the research. It demonstrates the efficacy of data-driven analytics, which can help make informed decisions on chronic diseases. Going forward, more theoretical and empirical research is needed. Additional studies can address the relationship between chronic disease conditions and other indicators, such as economic, financial, and social. While chronic disease management has become the focus in modern medicine as our population ages and medical costs continue to rise, research should focus on preventive and mitigating policies.

    The benefits of prevention and its potential to reduce costs and improve outcomes have received the attention of insurance companies, health care plans, and the U. Healthcare systems are now incentivized to reduce readmissions and physicians are encouraged to meet evidence-based quality measures to provide the best outcomes for patients with chronic disease states.

    Both the authors contributed equally to the data analysis, design, and development of the manuscript.

    Breadcrumb

    National Center for Biotechnology Information , U. Published online Mar 1. Author information Article notes Copyright and License information Disclaimer. Received Jan 12; Accepted Feb This article has been cited by other articles in PMC. Abstract In this research we explore the current state of chronic diseases in the United States, using data from the Centers for Disease Control and Prevention and applying visualization and descriptive analytics techniques. Keywords: behavioral health, chronic disease, comorbidity, overarching condition, population health, preventive health.

    Materials and Methods This study analyzes the characteristics of chronic diseases in the U. Table 1 Chronic diseases and related indicators. Open in a separate window. Results We use visualization and descriptive analytics to explore chronic conditions, preventive healthcare, mental health, and overarching conditions, with the objective of deciphering relationships and patterns that emerge from the visualization. Figure 1. Figure 2. Figure 3. Figure 4.

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    Figure 5. Figure 6. Figure 7. Mental Health by Gender and Race Mental health is an important aspect of national healthcare impacting chronic diseases. Figure 8. Figure 9. Behavioral Habits by Gender and Race Figure 10 charts behavioral habits by gender. Figure Preventive Health and Chronic Conditions We analyzed the data to detect associations between demographics and preventive health.

    Diagnosed diabetes ratio by pneumococcal vaccination ratio. Mortality ratio for asthma and influenza vaccination for asthma. Behavioral Health and Overarching Conditions Overarching conditions represent situations or factors that directly or indirectly influence the area of study. Current smoking prevalence by presence of sufficient sleep among adults. Chronic Conditions and Overarching Conditions In the analysis of various chronic conditions, there are significant clusters of conditions among men and women, such as the prevalence of asthma, with the women tending to have a higher prevalence of asthma than men.

    Association between Chronic Conditions We analyzed for any associations between different chronic conditions. Summary of Results The visual analytics figures above offer insight into a representative cross section of the data. Scope and Limitations Our research has a few limitations.

    Implications This study has analyzed chronic conditions in conjunction with several demographic variables, including gender and race. Author Contributions Both the authors contributed equally to the data analysis, design, and development of the manuscript. Conflicts of Interest The authors declare no conflict of interest. References 1.

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