what percentage of todays most challenging chronic diseases is most likely due to lifestyle
Int J Environ Res Public Health. 2018 Mar; 15(3): 431.
An Empirical Study of Chronic Diseases in the Usa: A Visual Analytics Approach to Public Wellness
Wullianallur Raghupathi
1Gabelli School of Business organisation, Fordham University, New York, NY 10023, Usa; ude.mahdrof@ihtapuhgaR
Viju Raghupathi
2Koppelman Schoolhouse of Business organization, Brooklyn College of the City University of New York, Brooklyn, NY 11210, USA
Received 2018 Jan 12; Accepted 2018 Feb 27.
Abstruse
In this research we explore the current country of chronic diseases in the United states of america, using data from the Centers for Disease Control and Prevention and applying visualization and descriptive analytics techniques. V principal categories of variables are studied, namely chronic illness conditions, behavioral health, mental health, demographics, and overarching weather. These are analyzed in the context of regions and states within the U.S. to observe possible correlations between variables in several categories. There are widespread variations in the prevalence of various chronic diseases, the number of hospitalizations for specific diseases, and the diagnosis and mortality rates for different states. Identifying such correlations is key to developing insights that will aid in the creation of targeted management, mitigation, and preventive policies, ultimately minimizing the risks and costs of chronic diseases. As the population ages and individuals suffer from multiple conditions, or comorbidity, it is imperative that the various stakeholders, including the government, non-governmental organizations (NGOs), policy makers, wellness providers, and gild as a whole, address these agin effects in a timely and efficient manner.
Keywords: behavioral health, chronic disease, comorbidity, overarching condition, population health, preventive wellness
1. Introduction
A chronic condition "is a physical or mental wellness condition that lasts more than 1 year and causes functional restrictions or requires ongoing monitoring or treatment" [ane,2]. Chronic diseases are amid the most prevalent and plush health weather condition in the United States. Nearly half (approximately 45%, or 133 million) of all Americans suffer from at least 1 chronic disease [3,4,5], and the number is growing. Chronic diseases—including, cancer, diabetes, hypertension, stroke, heart affliction, respiratory diseases, arthritis, obesity, and oral diseases—can atomic number 82 to hospitalization, long-term inability, reduced quality of life, and death [half dozen,vii]. In fact, persistent conditions are the nation's leading cause of death and disability [6].
Globally, chronic diseases have affected the wellness and quality of life of many citizens [8,9]. In addition, chronic diseases have been a major driver of health care costs while also impacting workforce patterns, including, of course, absenteeism. Co-ordinate to the Centers for Illness Control, in the U.S. lonely, chronic diseases account for nearly 75 percent of aggregate healthcare spending, or an estimated $5300 per person annually. In terms of public insurance, treatment of chronic diseases comprises an fifty-fifty larger proportion of spending: 96 cents per dollar for Medicare and 83 cents per dollar for Medicaid [4,10,11,12]. Thus, the understanding, management, and prevention of chronic diseases are important objectives if, every bit a society, we are to provide better quality healthcare to citizens and improve their overall quality of life.
More than two thirds of all deaths are caused by one or more of these five chronic diseases: middle disease, cancer, stroke, chronic obstructive pulmonary affliction, and diabetes. Additional statistics are quite stark [five,13]: chronic diseases are responsible for seven out of 10 deaths in the U.S., killing more 1.vii million Americans each year; and more 75% of the $2 trillion spent on public and individual healthcare in 2005 went toward chronic diseases [5]. What makes treating chronic weather condition (and efforts to manage population health) particularly challenging is that chronic conditions often practice not be in isolation. In fact, today ane in 4 U.S. adults have 2 or more chronic conditions [5], while more than half of older adults accept three or more chronic conditions. And the likelihood of these types of comorbidities occurring goes up equally we age [5]. Given America'due south electric current demographics, wherein ten,000 Americans will turn 65 each day from now through the cease of 2029 [5], it is reasonable to expect that the overall number of patients with comorbidities will increase greatly.
Trends show an overall increment in chronic diseases. Currently, the summit ten health problems in America (not all of them chronic) are heart disease, cancer, stroke, respiratory disease, injuries, diabetes, Alzheimer'due south disease, flu and pneumonia, kidney disease, and septicemia [14,15,16,17,18]. The nation's crumbling population, coupled with existing risk factors (tobacco use, poor diet, lack of concrete activity) and medical advances that extend longevity (if not besides improve overall wellness), have led to the conclusion that these issues are only going to magnify if not finer addressed now [19].
A recent Milken Institute analysis adamant that handling of the seven most common chronic diseases coupled with productivity losses will price the U.S. economy more $ane trillion dollars annually. Furthermore, compared with other developed nations, the U.Due south. has ranked poorly on cost and outcomes. This is predominantly considering of our inability to finer manage chronic affliction. And still the same Milken assay estimates that modest reductions in unhealthy behaviors could preclude or delay 40 million cases of chronic illness per twelvemonth [11]. If we acquire how to finer manage chronic conditions, thus avoiding hospitalizations and serious complications, the healthcare system tin improve quality of life for patients and greatly reduce the ballooning cost burden we all share [10].
The success of population wellness and chronic disease management efforts hinges on a few key elements: identifying those at risk, having admission to the right data about this population, creating actionable insights about patients, and coaching them toward healthier choices. Methods such as data-driven visual analytics help experts analyze large amounts of data and proceeds insights for making informed decisions regarding chronic diseases [10,20]. According to the U.South.-based Institute of Medicine and the National Enquiry, the vision for 21st century healthcare includes increased attending to cognitive support in decision making [21]. This encompasses computer-based tools and techniques that aid comprehension and knowledge. Visualization techniques offering cerebral support by offer mental models of the information through a visual interface [22]. They combine statistical methods and models with advanced interactive visualization methods to assist mask the underlying complexity of large health data sets and make evidence-based decisions [23]. Chronic diseases are characterized by high prevalence amid populations, rising complexity rates, and increased incidence of people with multiple chronic conditions, to name a few. In this scenario, visualization tin can correspond association between preventive measures and disease control, summary health dimensions across diverse patient populations and, timeline of illness prevalence across regions/populations, to offer actionable insights for constructive population management and national development [24]. Additionally, visual techniques offering the ability to analyze data at multiple levels and dimensions starting from population to subpopulation to the private [25]. This paper addresses the challenge of agreement large amounts of data related to chronic diseases by applying visual analytics techniques and producing descriptive analytics. Our overall goal is to proceeds insight into the data and make policy recommendations.
Given that large segments of the U.S. population suffer from one or more chronic affliction weather, a data-driven arroyo to the analysis of the information has the potential to reveal patterns of clan, correlation, and causality. We therefore studied the variables extracted from a highly reliable source, the Centers for Affliction Control. Information for variables pertaining to several categories, namely chronic status ("status" is used interchangeably with "disease"), behavioral wellness, mental health, preventive wellness, demographics, overarching weather condition, and location for several years (typically 2012 to 2014). We analyzed relationships inside each category and across categories to obtain multi-dimensional views and insight into the data. The analytics provide insights and implications that suggest ways for the healthcare system to better manage population wellness.
This newspaper is organized as follows: Section 1 offers an introduction to the research, Department 2 discusses the methodology, Department 3 presents and discusses the visual charts and results, Section four contains the scope and limitations of the research, Section 5 describes the policy implications and future enquiry, and Department six presents our conclusions.
2. Materials and Methods
This study analyzes the characteristics of chronic diseases in the U.Due south. and explores the relationships between demographics, behavior habits, and other health weather condition and chronic diseases, thereby revealing information for public health practise at the state-specific level. In this information-driven study we use visual analytics [26], conducting primarily descriptive analytics [20] to obtain a panoramic insight into the chronic diseases information set pulled from the Centers for Illness Control and Prevention web site. The discipline of visual analytics aims to provide researchers and policymakers with better and more constructive ways to understand and analyze large data sets, while also enabling them to act upon their findings in real fourth dimension. Visual analytics integrates the analytic capabilities of the computer and the abilities of man analysts, thus inviting novel discoveries and empowering individuals to take command of the analytical process. It sheds low-cal on unexpected and hidden insights, which may atomic number 82 to beneficial and profitable innovation [27,28]. Driving visual analytics is the aim of turning information overload into opportunity; just as information visualization has changed our view on databases, the goal of visual analytics is to brand our fashion of processing data and data transparent and accessible for analytic discourse. The visualization of these processes provides the means for examining the actual processes and not only the results. Visual analytics applies such engineering science every bit business intelligence (BI) tools to combine man analytical skill with computing power. Clearly, this inquiry is highly interdisciplinary, involving such areas every bit visualization, data mining, data management, data fusion, statistics, and cognitive science, among others. One key understanding of visual analytics is that the integration of these diverse areas is a scientific discipline in its own correct [29,30].
Historically, automatic analysis techniques, such equally statistics and data mining, were developed independently of visualization and interaction techniques. I of the most important steps in the management of visual analytics research was the demand to motion from confirmatory data analysis (using charts and other visual representations to present results) to exploratory data analysis (interacting with the data), first introduced to the statistics research community by John Westward. Tukey in his book, Exploratory Data Analysis [31].
With improvements in graphical user interfaces and interaction devices, the enquiry community devoted its efforts to information visualization [27]. Somewhen, this community recognized the potential of integrating the user's perspective into the knowledge discovery and information mining process through effective and efficient visualization techniques, interaction capabilities, and knowledge transfer. This led to visual data exploration and visual data mining [29] and widened considerably the telescopic of applications of visualization, statistics, and data mining—the three pillars of analytics. In visual analytics is divers as "the scientific discipline of analytical reasoning facilitated by interactive human-machine interfaces" [29]. A more than current definition says "visual analytics combines automated analysis techniques with interactive visualizations for an constructive understanding reasoning and conclusion-making on the basis of very large and complex data sets" (both reported in [27]). In their book Illuminating the Path, Thomas and Cook define visual analytics as the science of analytical reasoning facilitated by interactive visual interfaces.
One application of visualization is descriptive analytics, the most commonly used and most well understood type of analytics. Information technology was the primeval to be introduced and the easiest by far to implement and understand in that it describes data "as is" without complex calculations. Descriptive analytics is more than data-driven than other models. Nearly health data analyses first with descriptive analytics, using data to understand past and current wellness patterns and trends and to make informed decisions [20]. The models in descriptive analytics categorize, characterize, aggregate, and classify data, converting it into data for understanding and analyzing business organization decisions, outcomes, and quality. Such data summaries can be in the form of meaningful charts and reports, and responses to queries using SQL. Descriptive analytics uses a significant amount of visualization. One could, for example, obtain standard and customized reports and drill down into the data, running queries to better empathize, say, the sales of a production [20]. Descriptive analytics helps answer such questions equally: How many patients with diabetes also accept obesity? Which of the chronic diseases are more prevalent in different regions of the country? What behavioral habits are correlated to the chronic diseases? Which groups of patients endure from more than one chronic status? Is in that location an association between health insurance (and lack thereof) and chronic diseases? What are cost trade-offs between chronic disease prevention and management? What are typical patient profiles for various chronic diseases?
This study concentrates on chronic condition indicators and related demographics, behavior habits, preventive health, and oral health factors. As mentioned, the information source for this written report is the Center for Disease Control and Prevention (CDC) [32]. The CDC's Division of Population Health offers a crosscutting fix of 124 indicators that were developed by consensus. Those indicators are integrated from multiple resources, with the assistance of the Chronic Disease Indicator spider web site, which serves equally a gateway to additional information and data sources. In this enquiry we downloaded secondary data for the United States from the CDC dataset, for the years 2012 to 2014. The data is for states, territories, and large metropolitan areas in the U.S., including the fifty states and Commune of Columbia, Guam, Puerto Rico, and the U.South. Virgin Islands. Data cleaning, integration, and transformation were conducted on the raw data set. The main categories of variables included—chronic condition, mental wellness, behavior habits, preventative health, and demographics. In addition, overarching conditions and location were besides studied. Table one summarizes the categories and variables.
Tabular array 1
Chronic diseases and related indicators.
| Category | Sub-Category | Variables (Measure) | Definition |
|---|---|---|---|
| Chronic condition | Diabetes | Diabetes (%) | Prevalence of diagnosed diabetes among adults aged ≥xviii years—2012–2014 |
| Hospital diabetes (number) | Hospitalization with diabetes equally diagnosis; 2010 and 2013 | ||
| Mortality diabetes (per 100,000) | Mortality rate due to diabetes listed equally crusade of death, 2010–2014 | ||
| Arthritis | Arthritis (%) | Prevalence of arthritis amid adults aged ≥18 years; 2013–2014 | |
| Off-white or poor health—arthritis (%) | Prevalence of fair or poor health among adults aged ≥xviii years with arthritis—2013–2014 | ||
| Obesity—arthritis (%) | Prevalence of Arthritis amid adults aged ≥eighteen years who are obese—2013–2014 | ||
| Asthma | Asthma (%) | Current asthma prevalence amid adults aged ≥eighteen years, through 2012–2014 | |
| Mortality—asthma (case per 100,000) | Asthma bloodshed charge per unit through 2010–2014 | ||
| Hospital—asthma (case per 100,000) | Hospitalizations for Asthma | ||
| Chronic Kidney Disease | Kidney (%) | Prevalence of chronic kidney disease among adults aged ≥18 years—2012–2014 | |
| Bloodshed—kidney (case per 100,000) | Mortality with end phase renal affliction, through 2010 to 2014 | ||
| Chronic Obstructive Pulmonary Disease | Pulmonary (%) | Prevalence of chronic obstructive pulmonary disease among adults anile ≥18 years, through 2012 to 2014 | |
| Infirmary—pulmonary (case per 100,000) | Hospitalization for chronic obstructive pulmonary disease as any diagnosis of 2010 and 2013 | ||
| Bloodshed—pulmonary (case per 100,000) | Bloodshed with chronic obstructive pulmonary illness as underlying cause among adults aged ≥45 years, through 2010 and 2014. | ||
| Mental health | Mental health | Mental—women (%) | The crude prevalence charge per unit of at least 14 recent mentally unhealthy days amongst women aged 18–44 years, through 2012 to 2014 |
| Postpartum (%) | The crude prevalence charge per unit of Postpartum depressive symptoms in 2011 | ||
| Mental (number) | The anile-adjusted mean of recently mentally unhealthy days among adults aged ≥18 years, through 2012 to 2014 | ||
| Behavioral Habits | Alcohol | Binge drink (%) | Rampage drinking prevalence among adults aged ≥18 years, through 2012 to 2014 |
| Heavy drink (%) | Heavy drinking among adults aged ≥eighteen years, through 2012 to 2014 | ||
| Nutrition, Physical Activeness, and Weight Status | Physical activity (%) | No leisure-time physical action amidst adults anile ≥18 years, through 2012 to 2014 | |
| Tobacco—smokeless (%) | Electric current smokeless tobacco use amongst adults aged ≥xviii years, through 2012 to 2014 | ||
| Tobacco (%) | Current smoking among adults aged ≥xviii years, through 2012 to 2014 | ||
| Obesity (%) | Obesity among adults aged ≥18 years, through 2012 to 2014 | ||
| Preventive wellness | Pneumococcal vaccination | Pneumonia—fume (%) | Pneumococcal vaccination among noninstitutionalized adults aged 18–64 years who smoke, through 2012 to 2014 |
| Pneumonia—heart (%) | Pneumococcal vaccination among noninstitutionalized adults aged xviii–64 years with a history of coronary centre affliction, through 2012 to 2014 | ||
| Pneumonia—asthma (%) | Pneumococcal vaccination amid noninstitutionalized adults aged eighteen–64 years with asthma, through 2012 to 2014 | ||
| Pneumonia—diabetes (%) | Pneumococcal vaccination among noninstitutionalized adults anile 18–64 years with diagnosed diabetes, through 2012 to 2014 | ||
| Immunization | Influenza—asthma (%) | Influenza vaccination amongst noninstitutionalized adults anile 18–64 years with asthma, through 2012 to 2014; | |
| Influenza—diabetes (%) | Influenza vaccination amidst noninstitutionalized adults aged xviii–64 years with diagnosed diabetes, through 2012 to 2014 | ||
| Influenza—heart (%) | Flu vaccination among noninstitutionalized adults aged 18–64 years with a history of coronary heart disease or stroke | ||
| Influenza (%) | Influenza vaccination among noninstitutionalized adults aged ≥xviii years, through 2012 to 2014 | ||
| Fume | Quit (number) | Quit attempts in the past yr among current smokers, through 2012 to 2014 | |
| Demographics | Gender | Gender (character) | Male and female |
| Ethnicity | Race (grapheme) | Race | |
| Location | State location | Location (graphic symbol) | 50 states and District of Columbia, Guam, Puerto Rico, Virgin Islands |
| Overarching Conditions | Overarching Conditions | Insurance (%) | Current lack of health insurance amongst adults aged eighteen–64 years, through 2012 to 2014 |
| Poor—cocky charge per unit (%) | Fair or poor self-rated health condition amid adults aged ≥xviii years | ||
| Sleep (%) | Prevalence of sufficient slumber amongst adults anile ≥xviii years |
3. 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. Nosotros would like to betoken out that since our sample includes adults aged 18 and over our results are applicable for adults in that age grouping.
Figure 1 models the average prevalence of diagnosed diabetes among adults anile ≥eighteen years in the menses 2012 to 2014. Puerto Rico leads the pack, followed by Mississippi.
Equally Figure two below shows, Puerto Rico has the highest number of citizens amidst adults anile ≥eighteen years, in fair or poor wellness with arthritis for the period 2013 to 2014. Puerto Rico is followed by Tennessee and Mississippi.
The electric current asthma prevalence among adults aged ≥xviii years for the menstruation 2012 to 2014 is indicated in Figure 3. Due west Virginia has a higher prevalence of the condition compared to other states.
With regard to end-stage renal illness, Effigy 4 shows that the condition is dispersed widely amongst various areas.
Stop-stage renal disease by region.
The average value for hospitalization for chronic obstructive pulmonary disease for all diagnoses between 2010 and 2013 is shown in Figure 5. Kentucky and West Virginia take higher hospitalizations compared to other states. Most of the areas are below 45 cases per 100,000.
Chronic obstructive pulmonary disease past state.
In exploring chronic conditions by location in the U.S., nosotros see that some conditions, such as diabetes, arthritis, and obstructive pulmonary diseases, are more prevalent in eastern states, while others, such every bit asthma, occur more often in northeastern states. For diabetes, listed every bit a crusade of death for the years 2010 to 2014, united states of america of Oklahoma and West Virginia had the relatively loftier average threshold of over 100 (age adjusted charge per unit per 100,000). In the case of asthma, West Virginia has the highest prevalence of the status (among adults), while Maryland, Massachusetts, and New York had the highest number of hospitalizations. With regard to chronic obstructive pulmonary disease, Kentucky and West Virginia had the most hospitalizations compared to other states. The majority of states are indeed below 45 cases per 100,000. With respect to arthritis amongst adults, a bulk of states average below 25%, with the exception of West Virginia, which averaged 34.15%. In summary, West Virginia ranks high in prevalence for most chronic atmospheric condition, such equally diabetes, asthma, chronic pulmonary disease, and arthritis when compared to all other states for the menstruum 2000 to 2014.
We looked at the distribution of chronic weather condition by gender and race to identify relevant trends and patterns (Figure six and Figure vii). Chronic conditions differ by gender. Women tend to have significantly college cases per 100,000 of hospitalizations for asthma. Whereas men tend to take a higher mortality rate from chronic obstructive pulmonary disease, diabetes, chronic kidney, and other conditions, as shown in Effigy 6.
Chronic condition by gender.
Chronic atmospheric condition by race.
We also examined all chronic weather past race (Figure vii) and found that non-Hispanic Blacks have higher mortality rate for pulmonary disease and asthma and a college hospitalization from diabetes. They are followed by Pacific Islander and American Indians. All categories of arthritis are adequately evenly distributed among Blackness, non-Hispanic, Multiracial, Whites, and other.
Females take a higher hospitalization rate for asthma (per 100,000), while in terms of bloodshed rate for chronic obstructive pulmonary disease, diabetes, and chronic kidney disease, males take the higher hospitalization rate. Again, American Indian or Alaskan Natives have higher mortality rate for chronic obstructive pulmonary affliction, diabetes, and kidney disease. They're followed past Blacks and not-Hispanics.
three.1. Mental Health by Gender and Race
Mental wellness is an important attribute of national healthcare impacting chronic diseases. We analyzed mental wellness past gender (Figure 8) and by race (Figure 9). When we examine how many days an individual feels "mentally unhealthy" for the years 2012 to 2014, women are more likely to have more unhealthy days than men, equally shown in Figure 8.
Simultaneously, multi-racial, non-Hispanic women in the age group 18 to 44 have a higher crude prevalence rate of at to the lowest degree 14 recent "mentally unhealthy" days. This group is followed by black non-Hispanics.
We and so studied behavioral habits in the data set up to gain insight into noticeable patterns, if in fact whatsoever exist.
iii.two. Behavioral Habits by Gender and Race
Figure 10 charts behavioral habits by gender.
Behavioral habits by gender.
As seen in the chart above, men display higher numbers in the booze categories of "binge drinking" and "heavy drinking", besides as in "current smokeless" tobacco use amid adults. In terms of engaging in "current smoking", "obesity", and "no leisure-time" physical activity, both men and women feel similar complications, that highlights the need for positive beliefs modification.
Figure 11 illustrates the analysis of behavioral habits by race.
Behavioral habits by race.
Figure 11 reveals that for the behavioral habits of "obesity" and "no leisure-fourth dimension" physical activeness among adults aged eighteen and over, the blackness not-Hispanic and Hispanic races have the highest frequency, while white non-Hispanics have the everyman. By and large, in most behavioral habits, the other non-Hispanics have the lowest frequency.
3.3. Preventive Health and Chronic Conditions
We analyzed the data to observe associations between demographics and preventive health. Every bit Figure 12 indicates, both men and women announced to engage in preventive health, though women have the edge. With regard to race, Blacks and Hispanics engage less in preventive health overall, every bit shown in Effigy xiii.
Preventive health by gender.
Preventive wellness by race.
While all chronic conditions are debilitating on the economy, for the sake of scope, we selectively analyze the influence of a few conditions such every bit diabetes and asthma. By 2034, the population with diabetes is expected to increase by 100% and the cost expected to increase by 53% [33]. Figure fourteen depicts the clan between diabetes and pneumococcal vaccination for diabetes.
Diagnosed diabetes ratio by pneumococcal vaccination ratio. (#: number).
Equally indicated in Figure 14, there is a significant negative human relationship between the average pneumococcal vaccination among diabetes patients and the average diagnosed diabetes ratio among the population (p < 0.0001). 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 15).
Mortality ratio for asthma and influenza vaccination for asthma.
Effigy fifteen shows a significant negative association (p < 0.0001): as the rate of influenza vaccination for asthma increases, the mortality ratio of asthma declines. Analysis of the above preventive wellness variables shows that resource and efforts dedicated to preventive healthcare offer promise. The importance of managing chronic diseases is also highlighted when nosotros examine the association betwixt behavioral habits and overarching weather condition.
iii.4. Behavioral Health and Overarching Weather condition
Overarching conditions represent situations or factors that directly or indirectly influence the area of study. In our research nosotros look at the influence of these weather on chronic diseases, behavioral wellness, and preventive health. The overarching conditions include lack of health insurance (%), self-rated health condition (good, fair, poor), and prevalence of sufficient sleep (%) for which information was available.
Nosotros explored the clan of self-assessed health statuses among adults with the behavioral habits of binge drinking and heavy drinking (Effigy xvi).
Rampage/heavy drink and poor self-rated health status.
In that location is a significant negative correlation (p < 0.0001) between binge drinking and self-assessment of wellness. That is to say that the lower the health self-assessment, the higher is the percentage of rampage drinking. A decrease of less than 1% (0.69%) in self-assessed wellness is associated with a 1% increase in binge drinking. Likewise, there is a pregnant negative clan (p < 0.0001) between self-assessment of health and percentage of heavy drinking. A decrease of one.half dozen% in cocky-assessed wellness is associated with a one% increase in heavy drinking. We tin 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 17).
Electric current smoking prevalence by presence of sufficient slumber among adults.
Figure 17 above shows a pregnant negative association (p < 0.0001) betwixt prevalence of current smoking and prevalence of sufficient slumber. When current smoking prevalence decreases by less than ane% (0.38%), the prevalence of sufficient sleep increases by 1%.
The relationship between poor self-rated health status and obesity is positive (Effigy 18). The college the prevalence of fair or poor self-rated wellness, the higher is the prevalence of obesity. When poor self-rated health increases by 1%, the prevalence of obesity increases by 0.468779%.
Obesity past poor self-rated wellness status.
Similarly, poor self-rated wellness has a positive association with electric current smoking, as indicated in Figure 19. Equally the prevalence of poor self-rated health increases by 1%, the prevalence of current smoking increases by 0.30425%.
Smoking by self-rated wellness status.
iii.v. Chronic Atmospheric condition and Overarching Weather condition
In the analysis of diverse chronic conditions, there are significant clusters of conditions amidst men and women, such equally the prevalence of asthma, with the women disposed to have a higher prevalence of asthma than men. Regarding such chronic atmospheric condition equally diabetes, there is a significant positive relationship (p < 0.001) between lack of health insurance and prevalence of diagnosed diabetes (Figure 20).
Current lack of health insurance by diagnosed diabetes.
Nosotros detect 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. Besides, for chronic kidney disease (Figure 21) at that place is a significant positive relationship (p < 0.0001) with lack of health insurance.
Lack of insurance past chronic kidney disease.
The relationship between lack of insurance and hospitalization for chronic pulmonary disease is positive and significant (p < 0.0001), as shown in Figure 22. An increase in the lack of insurance is associated with an increment in hospitalization for chronic pulmonary illness.
Lack of insurance by pulmonary disease.
3.6. Clan betwixt Chronic Conditions
We analyzed for whatsoever associations between unlike chronic conditions. Information technology is important to incorporate gender as a factor in the association and prevalence of chronic diseases, and then equally to develop customized plans for diagnoses and treatments. A linear trend model was developed for the human relationship between asthma and diabetes (Effigy 23).
The model in Figure 23 shows a meaning negative relationship (p < 0.01) between asthma and diabetes. We can see gender clusters for the prevalence of asthma. Women tend to take 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 accept 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 clan between diabetes and kidney affliction is shown in Figure 24.
Diabetes by kidney disease.
Effigy 24 shows a moderate, positive clan (p < 0.01) between prevalence of kidney disease and diabetes. As the prevalence of diagnosed diabetes increases by i%, the prevalence of chronic kidney illness increases past 0.09%. There are no obvious differences in gender hither.
The association between diabetes and chronic pulmonary disease is shown in Figure 25, and that between arthritis and asthma is shown in Effigy 26.
Diabetes by obstructive pulmonary illness.
In Figure 25, nosotros discover a significant positive clan betwixt diabetes and chronic pulmonary disease (p < 0.001).
When it comes to prevalence of arthritis and asthma, in that location conspicuously are clusters for men and women, every bit shown in Effigy 26. There is a positive association such that an increase of 1% in prevalence of arthritis is associated with a 0.4% increase in prevalence of asthma.
Figure 27 shows the clan between arthritis and chronic pulmonary affliction.
Arthritis past chronic obstructive pulmonary disease.
Although there are no divers clusters for men and women with regard to the prevalence of arthritis and chronic obstructive pulmonary disease, there is a significant positive clan (p < 0.0001), as Figure 27 illustrates. An increase of i% in prevalence of arthritis is associated with an increment of 0.3% in chronic obstructive pulmonary illness.
iii.7. Summary of Results
The visual analytics figures above offer insight into a representative cantankerous section of the data. They provide a bird'due south eye view of the dimensions and correlations of chronic diseases "conditions", behavioral health, and preventive wellness status in the U.Southward. In add-on, associations between mental health and chronic weather, preventive health and chronic conditions, and among chronic conditions themselves highlight the dynamics of coaction 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 direction, mitigation, and prevention of chronic conditions. By exploring these in the context of chronic conditions, we offer insight on resource allotment and prioritization of resources in mitigation and prospective eradication of chronic diseases at a national level. Overarching weather, including a lack of health insurance, influence the access to necessary health services, including preventive intendance. This lack of availability is associated with poor health and the prevalence of chronic diseases. Similarly, self-assessed health condition is a good indicator of overall health status, correlating with subsequent wellness service use, functional status, and bloodshed [36]. Poor mental health interferes with social functioning too 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 attain a healthy lifestyle and therefore should be addressed at individual, state, and national levels.
4. Scope and Limitations
Our research has a few limitations. Beginning, our study is cross-sectional and covers only the years 2012 to 2014, 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 fix of variables. Third, as population and public health have emerged every bit central disciplines in the contemporary wellness ecosystem, more scalable, macro-level, and drill-down studies would inform greater understanding of chronic diseases. 4th, one would assume that the quality of publicly available data is high and fault-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 take limitations in and of themselves.
5. Implications
This study has analyzed chronic atmospheric condition in conjunction with several demographic variables, including gender and race. There are widespread variations in the prevalence of diverse chronic diseases, the number of hospitalizations for specific diseases, and the diagnosis and bloodshed rates for dissimilar states. For some chronic diseases—such as diabetes, arthritis, and obstructive pulmonary —the prevalence in the east is college than in other regions, while, there is higher prevalence for other conditions, such as asthma, in the northeast. The due south and midwest also show their own prevalence of chronic diseases. Likewise, there are variations for hospitalization and mortality rates. In improver, there are gender differences related to chronic weather. For example, women tend to take higher cases per 100,000 for asthma-related hospitalizations. Men, on the other hand, announced to have higher bloodshed rates for chronic obstructive pulmonary disease, diabetes, chronic kidney, and others. Also, when we examined chronic conditions past race, we noticed that American Indian or Alaska Natives had higher mortality rates for chronic obstructive pulmonary affliction, diabetes, chronic kidney, and so on, followed by Black and non-Hispanic groups.
In add-on, the study analyzed demographics of mental health, behavior habits, and preventive health. The associations between behavioral health and chronic conditions and betwixt preventive health and chronic weather were also analyzed. In that location is a positive human relationship betwixt average female coronary centre disease mortality ratio and average female tobacco employ ratio. There is a negative relationship between the average pneumococcal vaccination amidst diabetes patients and the average diagnosed diabetes ratio among the population. Referring to the relationship between behavioral health and overarching conditions, the study constitute a negative correlation between historic period-adjusted prevalence percentage of fair or poor self-rated health condition among adults aged ≥eighteen years and binge drinking adults. The current smoking prevalence and sufficiency of sleep amongst adults is negatively related. The current lack of health insurance is negatively related to both prevalence of current smoking and that of electric current smokeless tobacco utilize. 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 condition. At that place are dissimilar negative or positive correlations between overarching weather condition and chronic atmospheric condition. For instance, in that location is a significant positive relationship betwixt the prevalence of a lack of health insurance and that of diagnosed diabetes. Merely the relationship between prevalence of a lack of health insurance and prevalence of asthma is negatively related.
Finally, we conducted analyses of the differences amidst chronic conditions. In that location are obvious clusters between men and women for asthma, although women tend to have a college prevalence of asthma than men. Overall, prevalence of asthma is negatively related to the prevalence of diabetes. 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 affliction, and asthma and chronic obstructive pulmonary.
6. Conclusions
The study makes multiple essential contributions to chronic disease analysis at the patient/doctor and the state levels. At the patient level, assay of chronic conditions and related behavioral factors allows patients to be proactive in managing their conditions every bit 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,twoscore], contributing to an ultimate care model that is increasingly personalized.
Similar to patients, physicians likewise have varying data needs in healthcare that need to be satisfied [41]. To physicians, information on chronic conditions and more than chiefly, associations betwixt multiple atmospheric condition and betwixt categories of healthcare, enable developing personalized treatment plans based on patient-specific profiles that integrate various symptoms with ecology and other health data [42]. Additionally, the array of information increases their power to guide patients in towards lifestyle medicine (making lifestyle changes in healthy diet, practise etc.) in the management of chronic diseases [43]. The road from sickness to health requires integrated efforts from physicians and patients—physicians tin coach and guide the patients but the ultimate cross-over to health lies in the patients' hands.
Whereas most studies on chronic diseases focus on specific chronic diseases and are somewhat limited, this written report offers comprehensive analysis over multiple categories of chronic diseases at the state-level. By utilizing visual analytics and descriptive analytics, our written report offers methods for gaining insight into the relationships between beliefs 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 tin can assistance make informed decisions on chronic diseases.
Going forward, more than theoretical and empirical enquiry is needed. Boosted studies can accost the relationship betwixt chronic disease conditions and other indicators, such as economic, fiscal, and social. While chronic illness management has become the focus in modern medicine as our population ages and medical costs go along to ascent, enquiry should focus on preventive and mitigating policies. The benefits of prevention and its potential to reduce costs and meliorate outcomes have received the attention of insurance companies, health care plans, and the U.Due south. Congress. Healthcare systems are now incentivized to reduce readmissions and physicians are encouraged to meet bear witness-based quality measures to provide the all-time outcomes for patients with chronic disease states.
Author Contributions
Both the authors contributed equally to the data analysis, pattern, and evolution of the manuscript.
Conflicts of Involvement
The authors declare no disharmonize of involvement.
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Articles from International Journal of Ecology Enquiry and Public Health are provided hither courtesy of Multidisciplinary Digital Publishing Institute (MDPI)
Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876976/
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