Abstract
Aims:
This study provides an off-the-shelf catalogue of prevalence rates of eight health-related risk factors (self-perceived stress, loneliness, sleep quality, obesity/body mass index, smoking, physical exercise, alcohol consumption, and fruit intake) for 199 chronic conditions, disease groups and socioeconomic covariates in Denmark.
Methods:
The study population comprised a randomised sample of Danish residents aged 16 years and older (n=56,988). Data were derived from a linkage of three national health surveys (2010, 2013) and seven national health and sociodemographic registers. Means and prevalence rates, including sex and age-standardised estimates, were presented.
Results:
The most prevalent health risks were insufficient fruit intake (93.8%), most stressed quartile (20.5%), smoking daily (17.2%), physical inactivity (16.6%), obesity (15.0%), very bothered by sleep (9.7%), drinking more than recommended (8.7%), and ‘often feels lonely’ (5.5%). Chronic conditions with the highest mean numbers of health risks were mental disorders (disease group F; mean = 2.6), diseases of the digestive system (K; mean = 2.2), neurological diseases (G; mean = 2.1), musculoskeletal-related diseases (M; mean = 2.0), and respiratory-related diseases (J; mean = 1.9). In comparison, people without a chronic condition had a mean of 1.6 health risks. Marked socioeconomic disparities were also observed, with more risks among groups with lower education and income.
Conclusions:
Keywords
Introduction
Multiple health risk factors (MHRFs) involving single or clusters of health risk factors have been linked to increased mortality and morbidity from numerous preventable chronic diseases [1–8] and to decreased health-related quality of life (HRQoL) [9–12]. MHRFs commonly comprise behavioural health behaviour factors such as obesity, smoking, physical inactivity, alcohol use and unhealthy dietary choices [3,13,14] and increasingly also psychosocial-related health risks [15] such as perceived stress [16,17], loneliness [18–20] and sleep quality [21,22].
Behavioural MHRFs have been considered one of the key preventable risk factors connected with chronic disease burden; they add cumulatively to the growth of cardiovascular diseases, diabetes mellitus, arthritis, chronic obstructive pulmonary disease (COPD), and some types of cancers with poor prognosis [1–3,23]. Manifestation and causal mechanisms of these chronic conditions are predominantly connected to four biologically measurable parameters: body mass index (BMI), blood pressure, glucose blood levels, and cholesterol [24]. It is broadly acknowledged that the co-occurrence or the combined effect of two or more MHRFs seems to decrease life expectancy [25,26]. According to the latest World Health Organization (WHO) estimates, 41 million deaths or nearly three-quarters of deaths globally, are ascribed to ‘non-communicable’ or chronic diseases [27]. Among the major risk factors contributing to these deaths are tobacco use (7.2 million annually), physical inactivity (4.1 million), unhealthy diet (3.3 million) and excess alcohol use (1.6 million) [1,27].
Besides the individual disease burdens of increased morbidity, mortality and decreased HRQoL, MHRFs also pose increased societal and individual economic costs from chronic diseases [3,28,29]. Hence, efforts to prevent or reduce the prevalence of MHRFs by promoting, facilitating and upholding a healthier health behaviour are crucial from a societal perspective, as these most likely are more cost-effective than actual disease treatment [26]. Nonetheless, before developing appropriate intervention programmes, it is essential to estimate the burden and the characteristics of the MHRFs of the target population – for instance, specific chronic conditions.
As the importance of MHRFs is well documented, there is a high volume of literature describing behavioural risk factors. For instance, the WHO regularly monitors global, regional, and national global burden of disease studies [13]. In Europe, the Survey of Health, Ageing and Retirement in Europe (SHARE) found a high prevalence of obesity and more physical inactivity for people aged over 50 years, etc. [3,17]. However, using an elderly population might not be optimal if early prevention is targeted in interventions. Many other studies exist – and countless countries, including Denmark, regularly monitor national public health (health profiles), including health risk factors [14,15,30].
Despite the well-documented links between MHRFs and chronic disease, one common limitation across studies is that they often only report the prevalence and characteristics of risk factors for a limited number of chronic conditions. However, detailed knowledge of MHRFs is fundamental and valuable for tailoring and targeting populations for all chronic conditions, ensuring the best possible prevention and treatment. Moreover, existing population studies on chronic conditions often focus primarily on behavioural risk factors and rarely include psychosocial risk factors such as stress, loneliness, or sleep quality, although their impact on health is well documented. For instance, social isolation has long been known to be a major risk factor for morbidity and mortality – comparable in size to obesity, inactive health behaviours, and feasibly even smoking [19]. A recent study also confirmed that stress and loneliness had an even more significant impact on the HRQoL than the classic reported behavioural risk factors [9,10]. Finally, estimates are usually provided within different methodological frameworks, making reliable comparisons more complex [31]. Hence, comparable estimates are needed for a more comprehensive set of chronic conditions and a broader spectrum of health risks, including psychosocial risk factors, for population targeting, management, and intervention use.
The current study aims to accommodate some of the earlier limitations by providing a descriptive off-the-shelf catalogue (i.e. here a collection of table prevalence estimates that are readily available) of the prevalence of eight common MHRFs (obesity/BMI groups, smoking, physical exercise, alcohol consumption, fruit intake, loneliness, self-perceived stress, and sleep quality) for 199 chronic diseases and socioeconomic variables based on a Danish sample. Using a uniform, methodological framework for reliable comparisons, we also provide mean MHRFs for the 199 chronic conditions to identify the conditions with the most considerable MHRF-related treatment and prevention potential and typically associated risk factors. All the MHRFs under examination in this study are modifiable, which means that interventions and health behaviour changes can potentially reduce or mitigate their impact on individuals’ health. Finally, a supplemental, separate analytical paper provides adjusted regression models on the number of MHRFs of the eight health risk factors and to answer which of those have an actual effect along with a health profile of the population with a high number of risk factors for treatment targeting and prevention [32].
The current study is part of a series of studies which has provided extensive off-the-shelf catalogues of disease prevalence and sociodemographic characteristics [33,34], mean and prevalence rates on multimorbidity [35,36], socioeconomic disparities [37,38] and HRQoL [9,10,31] on more than 199 chronic conditions for adults aged over 16 years on a Danish representative population. The aim has been to provide health administrators, health professionals and decision makers reliable, easily accessible estimates for prevention, treatment, planning and policymaking. However, until now, estimates of MHRFs for conditions have not been using the same uniform methodology and data, enabling comparable estimates across this unprecedented number of 199 chronic conditions. We expect the catalogue to provide information to those interested in differentiating treatment in clinical practice and policymakers interested in identifying and allocating resources towards the conditions with the largest disparities and needs.
Methods
Survey data
The randomised, cross-sectional National Health Profile Survey (NHPS) provides healthcare professionals and decision makers with Danish nationally representative health profiles comprising more than 90 health indicators; full details of the NHPS can be found elsewhere [15]. In summary, the surveys are collected every fourth year, and informed consent is given. The recipients are individually identified based on a civil registration number. It is distributed electronically to a private online mailbox or by letter post to a large, random sample of Danish residents. The NHPS included several measures of disease burden, 18 self-reported chronic conditions, health-related quality of life, behavioural risk factors (smoking, exercise, alcohol, and fruit intake), BMI, social networks, and Cohen’s Perceived Stress Scale, sleep and several others [15]. Notably, the samples consist of core questions identical for all five regions and the National Institute of Public Health (NIPH) and optional questions that may differ across the regions. The population surveys are managed by the NIPH. In Denmark, the five regions are responsible for providing most healthcare services, while the 98 municipalities are responsible for a range of health services, including prevention, rehabilitation, and long-term care. Six samples were collected every fourth year: a national sample collected by NIPH and a regional sample for each of the five regions. The six random subsamples were mutually exclusive within the sampling year. Of these, three subsamples, one national (NIPH) and two regional (both from the North Denmark Region), were pooled for use in the present and a series of earlier studies [9,10,31] from two subsequent waves in early 2010 and early 2013 [15,39–41]. The subsamples were chosen as they only contained the necessary optional questions. The three survey samples had an overall response rate of 60.2% from 56,988 respondents.
Sampling weighting
Statistics Denmark provided sampling weights to account for the stratified design and non-response. To ensure the regional skewed samples were nationally representative, these were weight standardised to fit the national average on age, sex, and educational achievements [15,42]. All survey subsamples were compared with the total survey sample and the whole national population on average age, sex proportions, and proportions in five educational achievement categories to evaluate their representativeness (see supplemental Table I online).
Register data
To obtain information on sociodemographic variables on sex, age, ethnicity, place of residence, income, and educational achievement, each individual in the survey was linked by their unique ten-digit civil registration number to the Danish Civil Registration System [43], Danish registers on personal income and transfer payments [44], and the Danish Population's Education Registe' [45]. To obtain a more reliable and larger number of chronic conditions than the 18 self-reported conditions from the survey, each respondent from the NHPS was also linked to four national health registers. The health registers contained information from somatic [46] and psychiatric [47] hospital contacts, primary healthcare visits [48], and prescribed medicines [49]. Further details on the methodology and linkage between the samples and registers can be found elsewhere [10,31].
Identifying the chronic conditions and disease groups in registers
The algorithms used to identify the chronic conditions in the health registers stemmed from earlier work that defined 199 chronic conditions based on information from those mentioned above national public health registers [31,33,34,50,51]. A ‘chronic condition’ was defined following previous studies as a ‘condition had lasted or was expected to last 12 or more months and resulted in functional limitations and/or the need for functional limitations and/or the need for ongoing medical care’ [52–54]. From this definition, medical experts identified and grouped International Classification of Diseases, version 10 (ICD-10) codes into 199 chronic conditions [50,51]. The methods used and the content of registers are described in detail elsewhere [31,50,51].
Furthermore, to provide a reader-friendly overview of the 199 conditions, we also grouped the 199 chronic conditions into WHO’s 14 ICD-10 letter based disease groups [55] as defined below:
B: Viral hepatitis and HIV disease;
C: Malignant neoplasms;
D: In situ, benign and neoplasms of uncertain or unknown behaviour and diseases of the blood and blood-forming organs, etc.;
E: Endocrine, nutritional and metabolic diseases;
G: Diseases of the nervous system;
H: Diseases of the eye and adnexa and diseases of the ear and mastoid process;
I: Diseases of the circulatory system;
J: Diseases of the respiratory system;
K: Diseases of the digestive system;
L: Diseases of the skin and subcutaneous tissue;
M: Diseases of the musculoskeletal system and connective tissue;
N: Diseases of the genitourinary system;
Q: Congenital malformations, deformations and chromosomal abnormalities;
F: Mental and behavioural disorders.
See Table I or the referenced literature [33,51] for details of diagnosis codes of the 14 disease groups.
Overview of mean health risk factors (MHRFs) and obesity rates by disease groups, sex, age, and prescribed medications.
Two-year inclusion times. n/a: not available. All means weighted. n is not weighted. Sex and age standardisation (Std.) are in brackets. n/a: not available. Percentages are all calculated without missing values. See online Supplemental Table II for estimates with missing values.
Health risks
The eight health risk factors were measured dichotomously. They were given a value of one if they had: a BMI greater than 30, daily smoking, excessed drinking recommendations, a low physical activity level, often lonely, very bothered by sleep; and zero if they did not have the health risk and the most stressed quartile on Cohen’s Perceived Stress Scale (defined as scoring 18–40 on the 0–40 scale, corresponding to approximately the 20% most stressed respondents in the general population. This cut-off has been applied in the Danish national health profile reports and previous studies [9,10,56,57], in which it has identified groups with clearly poorer health outcomes and lower health-related quality of life [39,58–60]).
Socioeconomic and other variables
Socioeconomic variables were measured chronologically and ranked as described in the following: educational achievement based on the International Standard Classification of Education, ISCED2011 [37,38] (no education/training except primary school, students or in training, short education like high school or equivalent, middle education like bachelor degree or equivalent, higher education at postgraduate level or above), ethnicity (Danish, other western, non-western) defined on the basis of the country of birth and citizenship of the individuals and parents as recorded in the national registers consistently with earlier research and practice [15,61,62], family equalised income quartiles, socioeconomic status (retired due to age, free earlier retirement, early retirement health reasons, sick leave and other leave, unemployed social benefits longer term, ordinary unemployed minimum 6 months, in training or education, employed, and others not in the workforce), partnership status (have/not) and children (no children home, having children living home under 15 years of age). The number of chronic conditions (NCC) was measured by comparing the order number of chronic conditions from 0 to 7+.
Missing values
We used self-reported survey data and register-based variables to impute missing values. Although most register variables (for instance, all the 199 chronic conditions, age, and sex) had no missing values, some register-based variables (‘educational achievement’, ‘family equalised income’, ‘partnership’, ‘children living at home’) had a few missing values. We used self-reported auxiliary information from the NHPS (e.g. respondents’ self-reports about educational achievement and others in the NHPS survey data) to replace/impute the missing register values. Hence, we used a value confirmed by the respondents. For all the remaining variables with missing values, for example, ‘BMI’, ‘smoker’, ‘alcohol intake’, ‘exercise’, ‘fruit intake’, ‘loneliness’, and ‘stress’, missing values were included as a separate category named ‘missing’. The total sample size was 56,988 respondents; however, for BMI the sample was slightly smaller (n=5032) due to missing data. Further details on numbers and handling missing values can be found elsewhere [10,31].
Statistical analysis
The summed number of the eight health risk factors and BMI were calculated for each respondent, whereafter the group means of the MHRFs were computed for each of the 199 chronic conditions, disease groups, NCCs and socioeconomic variables. The chronic conditions, disease groups and five common medicines were further ranked from 1 to 227, with one being the condition with the highest mean number of MHRFs. The percentage of the eight health risks was calculated for each of the 199 chronic conditions, NCCs, and socioeconomic variables. A total mean score of Cohen's Perceived Score was calculated and presented in Supplemental Table II online along with BMI and mean MHRFs with standard errors (SE) or standard deviations (SD) not shown in the main tables.
Percentages were calculated without missing survey responses in all the main tables. However, for transparency, all eight health risk factors had percentages calculated with the missing categories and the complete response categories in the Supplemental Table II spreadsheet to provide the full information. Furthermore, all tables provide calculated information on MHRFs, BMI, and stress means without missing values. A particular focus on BMI was given during analysis and presentation due to an ongoing, increasingly public and scientific interest in overweight as a global challenge [63–65]. This also includes a focus on all BMI groups (BMI <18; 18–25; 26–30; 30–35; 35+) in Supplemental Table II.
All estimates were weighted, and regression-based direct standardised prevalence estimates were calculated as referenced [66,67] for the eight health risks and sub-grouped covariates based on the national percentage of sex and age on 1 January 2013. Data management and statistical analysis were done using SAS 9.4 and Stata version 16 from Statistics Denmark’s remote, secured research servers.
Results
Consistent with earlier findings [9,10], the pooled samples were largely identical to the actual whole population regarding sex, age, educational achievements, and other parameters. The supporting information Supplemental Table I provides further sample details and comparisons.
Figure 1 provides a basic overview of the right-skewed distribution of the mean number of MHRFs for the population and people with one or more chronic conditions. It indicates that people with chronic conditions more commonly have three or more health risks than the average population.
Histogram showing the distribution of health risks among respondents (N and %).
Figure 2 shows disparities in mean MHRFs across the different chronic conditions, disease groups, and medicines based on rankings from the highest to lowest mean number of MHRFs. Generally, people with chronic conditions had more MHRFs than the average population and people without chronic disease.
Ranking of 199 chronic conditions, medicines, and disease groups by number of multiple health risk factors (MHRFs) with 1.96 standard error confidence interval (CI). The dotted black line at 1.8 represents the average MHRFs for the entire national population. The blue dotted line at 1.6 represents the average MHFRs for individuals with no chronic conditions.
Table I elaborates on the mean numbers of MHRFs and obesity proportions in overall disease groups, comorbidities, and medicines for sex and age groups. The highest mean MHRFs are found within disease groups B (viral hepatitis and HIV disease) and F (mental and behavioural disorder), medicines, and with higher numbers of chronic conditions (NCCs) or comorbidity. Overall, there were no or small sex and age differences across all variables. Varying sex differences are found within disease groups B, F, N, obesity, and psychiatric medicines. Moreover, disease groups B and F, people with four or more comorbidities, and most of the five medicines showed a decline in mean MHRFs with increasing age.
Somehow, different trends were found for the proportions of obesity. While there was a slight, overall significant sex difference, the age differences were commonly inverse U-shaped, with smaller proportions of obesity for the younger and the elderly. Moreover, the highest proportions compared with the population mean of 15.0 were found among people with five or more NCCs, people on antipsychotic and anxiety medicine, and disease groups N (diseases of the genitourinary system), L (diseases of the skin and subcutaneous. tissue), E (endocrine, nutritional and metabolic diseases), I (diseases of the circulatory system), G (diseases of the nervous system), F (mental and behavioural disorders), ranging from 20.3 to 28.1% obesity.
Table II provides the prevalence of the eight health risks and the mean BMI for the population on all 199 chronic conditions, disease groups, medicines, NCCs and socioeconomic variables. Given the extent of the results, discussing all study findings in detail is not feasible. Instead, the focus will be on general trends and patterns observed. This includes the overall prevalence, the 10 and 50 conditions with the highest rank of mean MHRFs and obesity. We recommend that the reader extract further results for the specific conditions and health risks of interest.
MHRFs, BMI means and prevalence of the eight health risks across 199 chronic conditions. disease groups and socioeconomic variables. Presented as percentages with corresponding 95% confidence intervals [CI].
Two-year inclusion times; ** NCC: Number chronic conditions; a: complex defined conditions; (SE): Standard Error; n/a: not available.
All data is weighted. STD: Sex and age standardisation estimates. Percentages are all calculated without missing values. See online Supplemental Table II for estimates with missing values.
Overall, the ranked proportions from high to low for the eight health risks were: fruit (93.8%), stress (20.5%), smoking daily (17.2%), physical inactivity (16.6%), obesity (15.0%), very troubled by sleep (9.7%), drinking more than recommended (8.7%), and ‘often feels lonely’ (5.5%).
Across socioeconomic groups, clear gradients were observed in the number and distribution of MHRFs. Respondents with lower educational attainment and income had higher mean numbers of health risks and higher prevalences of several behavioural and psychosocial risks. For example, individuals with no formal education or only primary schooling had a mean MHRF score of 2.1 compared with 1.5 among those with postgraduate education. Similarly, the lowest income quartile had higher proportions of obesity (30%) and daily smoking and reported stress than the highest income quartile (12%). Comparable gradients were also seen across categories of labour market attachment (Table II and Supplemental Table II).
Focusing on the top 50 out of the 199 chronic conditions (excluding disease groups and medicines) with the highest mean MHRFs, means range from 3.4 to 2.4. Among the top 50 chronic conditions, 22 were psychiatric conditions (F), nine were musculoskeletal-related diseases (M), six were circulatory system-related disease (I), four were a neurological disease (G), three were a respiratory-related disease (J), and two were diseases of the digestive system (K). In addition, two were infectious diseases (viral hepatitis (B18) and HIV disease (B20–24)), and one was a retinal disorder (H36).
Table III names and ranks the top 50 individual chronic conditions, categorised across disease groups with the ICD-10 codes of the individual chronic conditions in parentheses (moreover, rank 1 means it is having the highest numbers of mean MHRFs, rank 2 the second highest, etc.).
The top 50 chronic conditions with the highest rank of mean MHRFs across disease groups (each disease group is ranked from highest mean MHRFs to lowest).
The top 10 conditions with the highest number of MHRFs were: emotionally unstable personality disorder (F603; mean=3.4), phobic anxiety disorders (F40; mean=3.3), mood (affective) disorders (F340, F348–F349, F38–F39; mean=3.3), schizophrenia (F20; mean=3.2), mental and behavioural disorders due to use of alcohol (F10; mean=3.2), bipolar affective disorder (F30–F31; mean=3.2), other anxiety disorders (F41; mean=3.1), disorders of adult personality and behaviour (F61–F69; mean=3.1), systemic sclerosis (M34; mean=3.1), and specific personality disorders (F602, F604–F609; mean=3.1).
Among those 50 chronic conditions (excluding disease groups and medicines) with the highest proportion of obesity ranging from 45.6% to 23.4%, 13 had musculoskeletal-related diseases (M), 10 had a psychiatric diagnosis (F), seven had a circulatory system-related disease (I), six had a neurological disease (G), three had an endocrine, nutritional and metabolic disease (E), three had cancers, two had a disease of the digestive system (K), two had an eye or ear disease (H), one had certain disorders involving the immune mechanism (D80–D89, 27.6%), one had a chronic renal failure (N18, 27.9%), one had bronchitis (J40–J42, 26.8%), and one had psoriasis (L40, 26.8%). Table IV shows the top 50 conditions in details categorised across disease groups.
The top 50 chronic conditions with the highest proportion of obesity across disease groups (each disease group is ranked from highest to lowest proportions).
The top 10 conditions with the highest obesity rates were: sleep disorders (G47; 45.6%), fibromyalgia (M797; 38.6%), pulmonary heart diseases (I26–I28; 37.5%), diabetes type 2 (E11; 37.3%), schizophrenia (F20; 36.5%), ventral hernia (K43; 36.1%), mental retardation (F70–F79; 35.7%), emotionally unstable personality disorder (F603; 35.1%), bipolar affective disorder (F30–F31; 34.5%), and post-traumatic stress disorder (F431; 32.4%).
Discussion
The current study is the first and most comprehensive descriptive attempt to estimate the burden of MHRFs associated with 199 chronic conditions using a comparable, uniform methodology. The study aimed to provide health administrators, health professionals, and decisions makers with a descriptive off-the-shelf catalogue of the eight health risk factors (obesity/BMI, smoking, physical exercise, alcohol consumption, fruit intake, loneliness, self-perceived stress, and sleep quality) for future prevention, treatment, and healthcare planning. However, it was not the aim to provide an exhaustive summary of the results. Hence, we only elaborated on a small fraction of possible examples. For example, we examined the mean MHRFs, BMI and prevalence associations of eight health risk factors across 14 disease groups, five common medicines and 199 chronic conditions, including differences in sex, age groups, educational achievements, socioeconomic status, positions, etc. Overall, the ranked proportions from high to low for the eight health risks were: insufficient fruit intake (93.8%), most stressed quartile (20.5%), smoking daily (17.2%), physical inactivity (16.6%), obesity (15.0%), very troubled by sleep (9.7%), drinking more than recommended (8.7%), and often feels lonely (5.5%). The mean number of MHRFs also increased with higher multimorbidity (from mean 1.6 to mean 2.5). Sex and age differences were less common, although differences in MHRFs existed across ages for higher multimorbidity and some conditions. Differences in mean MHRFs across conditions ranged from mental conditions (disease group F; mean=2.6), diseases of the digestive system (disease group K; mean=2.2), neurological disease (disease group G; mean=2.1), musculoskeletal-related diseases (disease group M; mean=2.0), and related respiratory diseases (disease group J; mean=1.9). Furthermore, the prevalence of the 50 conditions with the highest proportion of obesity (BMI >30) ranged from 23.4% to 45.6% compared with a population average of 15% (Table IV). In summary, we provided two comprehensive catalogues (Table II and Supplemental Table II) that can be used to explore how the means and associations of eight health risk factors are distributed across the 199 chronic conditions and sociodemographic variables. Table I provides an overview of disease groups, sex and age differences, etc.
The observed clustering of multiple health risk factors among people with chronic conditions aligns with previous international studies showing similar co-occurrence patterns [8,9,24,25]. As in other population-based research, obesity, smoking, and physical inactivity were among the most prevalent risks, while loneliness and sleep problems were less frequent [13,14]. Moreover, the social gradients found in the present study are consistent with established evidence that lower socioeconomic position is associated with a higher burden of behavioural risks [68,69] as well as heterogeneous socioeconomic obesity patterns [70]. Together, these findings reinforce that multimorbidity and health risks are intertwined within both biological and social dimensions, supporting the need for integrated and equity-oriented prevention strategies.
The findings also correspond well with the latest Danish disease burden report [71], which identified smoking, alcohol use, physical inactivity, and poor diet as leading contributors to chronic disease burden. The present study broadens this perspective by including psychosocial health risks, such as stress, sleep problems, and loneliness, that were not covered in the national report but appear equally important in the context of chronic conditions. In line with the report’s observation on the cumulative effects of multiple concurrent risks (p. 292), our results show that people with chronic illnesses often present with four or more co-occurring health risks.
The catalogues and future use
The above-highlighted data extracts are not exhaustive but merely a few of the numerous possible data extracts. Thus, the primary aim of the current study was to deliver detailed off-the-shelf catalogues for health administrators and health professionals to explore their specific diseases of interest.
In detail, we see three primary potential uses of the catalogue. First, it supports and informs frontline healthcare professionals of possible MHRF concerns to be considered in interventions for specific or clusters of chronic conditions. Although socioeconomic disparities within health behaviours have long been used to differentiate and personalise treatments broadly [68], healthcare systems worldwide are organised to provide services to patients with single diseases [72,73]. Although individual screening and interventions targeting health behaviour factors such as obesity, smoking, alcohol, and diet to some extent [8], the approach is still fragmented and less systematic when it comes to addressing multimorbidity. Additionally, there seems to be a lack of systemic integration of prior knowledge regarding MHRFs in specific chronic diseases. However, newer approaches and guidelines are emerging [74]. Specifically, crucial social health risks such as stress, loneliness, and sleep are even less systemically treated in chronic conditions. In summary, future interventions should consider differentiating based on the specific diseases and clusters associated with MHRFs. This can be informed by evidence from sources such as the current catalogue. For instance, the catalogue can be used to identify and prioritise the specific conditions or clusters of conditions with the highest proportions of obesity (Table II and Supplemental Table II) for intervention. This could make future treatments more data evidence based and MHRFs directly rooted in medical practice.
Second, we suggest that the catalogues be used to identify patients with chronic conditions for treatment based on high-risk groups of MHRFs (e.g. high rates of MHRFs) for secondary and tertiary prevention. However, as MHRFs are only one aspect of the disease burden, prioritisation should be done in combination with other aspects such as overall disease prevalence and sociodemographic characteristics [33], multimorbidity rates [35,69], socioeconomic disparities [37], and health-related quality of life [9,10,31].
Third, we suggest that the information provided on MHRFs and associations with chronic conditions be further used in policy development and implementation of services by healthcare professionals to model future healthcare systems, prevention and healthcare services, and population management. We advocate those chronic conditions be seen more holistically, encompassing clusters of health risk factors and related diseases instead of treating single health risk factors or conditions. We further propose that health services are planned systemically to threaten, first and foremost, prevalent clusters of health risk factors and prevalent diseases to support the most effective use of sparse resources. We suggest that the current catalogues be used to identify further relevant clusters of MHRFs and chronic conditions within medical specialities. For example, the detailed spreadsheet in Supplemental Table II provides comprehensive data on MHRFs for all 199 chronic conditions that can be applied to identify clusters of health risk factors associated with conditions (see also related paper for further profiling [32]). As healthcare systems are presently mainly set up to treat single health risk factors and single conditions [72], future changes in healthcare systems need to address and integrate the real-world norm of MHRFs and multimorbidity [35].
Strengths and limitations
One first strength of this study is the data, that is, the application of data from multiple, high-quality registers and surveys.
A second strength is the use of a comparable, uniform methodology as recommended by WHO and scholars [2,75–78]; that is, the use of medically defined algorithms employed on the unique data and the large number of chronic diseases encompassed. This allows for reliable comparisons across a significant number of chronic diseases.
A third strength is the identified variation in the means and proportions across various factors and diseases. The data clearly show disparities across health risk factors, chronic conditions and sociodemographic variables. The more variation, the higher the potential for treatment, as differences show potential. Contrary to many other studies, we have not limited health risks to the classic four behavioural health risks but also included a social aspect of health risks. This and allied information could prove essential evidence in planning future healthcare interventions across different chronic conditions, targeting distinct issues dependent on the condition.
Finally, using raw descriptive statistics ensures that variated details are not reduced using classic statistical methods like factor analysis, latent class analysis, or correspondence analysis [35]. Hence, the data from the present study can be used to identify the most detailed differences for concrete interventions.
There are, however, also certain methodological limitations in the present study. Several limitations have been discussed, including limitations in the definition of ‘chronic’ as chronic does not necessarily mean forever [50,79,80] and limitations using registered data identifying chronic conditions as not all patients have their diseases registered [31,33,50]. In addition, as the age of the data is more than 10 years old, newer estimates would most likely be different from the current estimates as health behaviours are known to change over time. Newer health profiles confirm this. For example, obesity rates have increased steadily across the past decades, while smoking rates have decreased [14,30]. However, those health risk factors are not earlier reported on 199 specific chronic conditions, but are typical on overall populations; hence, the exact differences within chronic conditions are difficult to assess. Nevertheless, we expect the same trends to be seen across the different chronic conditions with varying strength, and most likely less for people with a higher number of chronic conditions as these groups often tend to have lower treatment adherence [8,81]; which is why we expect them to respond less to positive health behaviour changes. However, little is known about the differences in the implementation and adherence to health behaviour changes in people with multimorbidity disease.
Additionally, there are other ways to measure health risk factors. For instance, fruit intake may not be the best indicator of food habits as more than 90% of the population did not fulfil the recommendations. Food choice, however, is difficult to measure, and the choices of all health risk factors were limited by the questions used in the national health profiles. Nevertheless, other questions or cut-off points for the eight different health risks could have been used, which would have provided slightly different estimates. The health risk factors included in this study were selected to align with previous research and national reporting, enabling comparisons at the population level. However, alternative choices of risk factor definitions or cut-offs might have influenced the conclusions.
Moreover, the variable ‘socioeconomic status’ used in this study refers to register-based labour market status categories (e.g. employed, unemployed, retired, in education, or outside the workforce) and should be interpreted as such, rather than as a composite socioeconomic index.
Finally, a limitation is the descriptive and cross-sectional nature of the study. Consequently, the observed associations between health risk factors and chronic conditions should not be interpreted as causal, and the directionality of these relationships cannot be established.
Implications for research
The current study is based on a high-quality sample of register-based chronic conditions and health risk factors for Danish residents aged 16 years and older (n=56,988). This comprehensive catalogue of large variations in the significance of health risk factors across the different chronic conditions provided comprehensive comparisons for healthcare planners and clinicians who need to know their detailed disease populations as a first step to identify treatment potentials, optimising treatments by differentiating and targeting patients according to their health risk factors. Common clusters of chronic conditions and health risk factors could generate interaction effects. Hence, future research should analyse clusters of conditions and health risk factors to identify possible disease development and treatment trajectories.
Conclusions
The present study provides an off-the-shelf catalogue of MHRFs means and prevalence, illustrating the specific disease proportions for eight common health risks across 199 chronic conditions, disease groups, and socioeconomic variables based on a representative nationwide, Danish population sample. The findings underline that having numerous health risk factors is the rule and not the exception and that MHRFs are a fundamental precondition to transcending population disease burdens, affecting all future treatments. However, current treatment practices rarely systematically consider MHRFs and multimorbid diseases, and if done, mainly for a limited number of MRHFs. We argue that there is a need to address MHRFs and multimorbidity by using reliable, real-world evidence of MHRFs and the multimorbid disease burden as provided here for others to use. We further propose that future research should identify MHRF clusters and clusters of multimorbid disease states. To the best of the authors’ knowledge, the current study provides the most wide-ranging descriptive study of the MHRFs of an unprecedented number of more than 199 chronic conditions. Finally, our focus on modifiable MRHFs underscores our commitment to identifying actionable strategies for improving public health outcomes and reducing the burden of preventable diseases.
Supplemental Material
sj-pdf-1-sjp-10.1177_14034948251404090 – Supplemental material for Catalogue of eight psychological and behavioural health risks and related disparities among 199 chronic conditions in Denmark
Supplemental material, sj-pdf-1-sjp-10.1177_14034948251404090 for Catalogue of eight psychological and behavioural health risks and related disparities among 199 chronic conditions in Denmark by Michael Falk Hvidberg, Anne Frølich, Pia Ryom and Sanne Lykke Lundstrøm in Scandinavian Journal of Public Health
Supplemental Material
sj-xlsx-2-sjp-10.1177_14034948251404090 – Supplemental material for Catalogue of eight psychological and behavioural health risks and related disparities among 199 chronic conditions in Denmark
Supplemental material, sj-xlsx-2-sjp-10.1177_14034948251404090 for Catalogue of eight psychological and behavioural health risks and related disparities among 199 chronic conditions in Denmark by Michael Falk Hvidberg, Anne Frølich, Pia Ryom and Sanne Lykke Lundstrøm in Scandinavian Journal of Public Health
Footnotes
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Danish National Health Survey was funded by the Capital Region, Region Zealand, the South Denmark Region, the Central Denmark Region, the North Denmark Region, the Ministry of Health and the National Institute of Public Health, University of Southern Denmark.
Data availability
Data sharing is protected by the European General Data Protection Regulation (GDPR). Due to the protection of patient privacy and Danish data protection law restrictions, the combined dataset used in the current study can only be made available via a trusted third party, Statistics Denmark. This state organisation holds and controls the data used for this study. Danish scientific organisations can be authorised to work with data provided by Statistics Denmark. Such organisations can provide data access to individual researchers inside and outside Denmark. Requests for data can be sent to Statistics Denmark: 
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Ethical Approval
Declaration and approval to conduct the study were obtained from the Danish Data Protection Agency and the Secretariat for Research Processing Records, Data and Development Support, Region Zealand (REG-142-2021). Informed consent was obtained when conducting the surveys, and participants gave consent by responding to the survey as described in Christensen et al. [15] and Pedersen et al. []. Statistics Denmark anonymised the data before they were available on their secured server.
Supplemental material
Supplemental material for this article is available online.