Analysis of mortality metrics associated with a comprehensive range of disorders in Denmark, 2000 to 2018: A population-based cohort study.

BACKGROUND: The provision of different types of mortality metrics (e.g., mortality rate ratios [MRRs] and life expectancy) allows the research community to access a more informative set of health metrics. The aim of this study was to provide a panel of mortality metrics associated with a comprehensive range of disorders and to design a web page to visualize all results. METHODS AND
FINDINGS: In a population-based cohort of all 7,378,598 persons living in Denmark at some point between 2000 and 2018, we identified individuals diagnosed at hospitals with 1,803 specific categories of disorders through the International Classification of Diseases-10th Revision (ICD-10) in the National Patient Register. Information on date and cause of death was obtained from the Registry of Causes of Death. For each of the disorders, a panel of epidemiological and mortality metrics was estimated, including incidence rates, age-of-onset distributions, MRRs, and differences in life expectancy (estimated as life years lost [LYLs]). Additionally, we examined models that adjusted for measures of air pollution to explore potential associations with MRRs. We focus on 39 general medical conditions to simplify the presentation of results, which cover 10 broad categories: circulatory, endocrine, pulmonary, gastrointestinal, urogenital, musculoskeletal, hematologic, mental, and neurologic conditions and cancer. A total of 3,676,694 males and 3,701,904 females were followed up for 101.7 million person-years. During the 19-year follow-up period, 1,034,273 persons (14.0%) died. For 37 of the 39 selected medical conditions, mortality rates were larger and life expectancy shorter compared to the Danish general population. For these 37 disorders, MRRs ranged from 1.09 (95% confidence interval [CI]: 1.09 to 1.10) for vision problems to 7.85 (7.77 to 7.93) for chronic liver disease, while LYLs ranged from 0.31 (0.14 to 0.47) years (approximately 16 weeks) for allergy to 17.05 (16.95 to 17.15) years for chronic liver disease. Adjustment for air pollution had very little impact on the estimates; however, a limitation of the study is the possibility that the association between the different disorders and mortality could be explained by other underlying factors associated with both the disorder and mortality.
CONCLUSIONS: In this study, we show estimates of incidence, age of onset, age of death, and mortality metrics (both MRRs and LYLs) for a comprehensive range of disorders. The interactive data visualization site (https://nbepi.com/atlas) allows more fine-grained analysis of the link between a range of disorders and key mortality estimates.

Introduction

Mortality is arguably the most definitive measure of health. Within the field of epidemiology, mortality metrics are of the utmost importance and serve as a foundation for decision-making and prioritization of resources in the healthcare sector. A range of mortality-related metrics are available, such as age-specific mortality rates, standardized mortality ratios (SMRs), mortality rate ratios (MRRs), or case fatality rates. These “relative risk” types of mortality estimates are informative, but should be complemented with measures that examine premature mortality on an absolute scale. In particular, estimates looking at reduction in life expectancy for those experiencing a particular condition tend to be more widely understood by the general community and policy makers. Metrics that link health disorders with premature mortality can inform decision-making on the distribution of limited resources and be used in evaluations of the effectiveness of healthcare provision [1,2].

When looking at premature mortality, the Global Burden of Disease (GBD) studies measure years of life lost (YLLs) [3], which estimates the potential YLLs. The essential feature of this mortality metric is that it is based on the single primary cause of death. However, from a public health perspective, important clues related to prevention may lie many years “upstream” of the cause of death. There is a need to better understand the impact on life expectancy for nonfatal disorders (i.e., disorders that are rarely considered the primary cause of death), as early and more effective treatment of these disorders could reduce premature mortality.

While YLL focuses on age at death, other methods focus on remaining life expectancy at age at diagnosis, which allows us to explore the association between the onset of a broad range of conditions (fatal and nonfatal health outcomes) and subsequent life expectancy. Until recently, most studies linking disorders and life expectancy have applied assumptions related to age of onset of the disorder and/or age of the cohort for follow-up. For example, studies estimating life expectancy have assumed a fixed age of onset of 15 years old for those with mental disorders [4,5], 20 years old for those with type 1 diabetes [6], and 55 years old for those with colon cancer [7]. However, this simplifying assumption can bias estimates of life expectancy. Fortunately, advances in mortality metrics can now take into account the observed age of onset of the disorder of interest [8-10]. To date, this method has been applied mainly to mental disorders [9,11-14], indicating large reductions in life expectancy.

By examining both relative risk–based mortality measures and the more easily interpretable absolute measures of life expectancy, the research community can access a more informative set of mortality-related metrics. Some late-onset disorders among elderly are associated with large relative mortality risks but small reductions in life expectancy. Conversely, early-onset disorders in younger age groups may be associated with a greater number of life years lost (LYLs) even with modest relative mortality risks. When information on the prevalence and age of disorder onset is also provided, panels that link different types of mortality estimates can provide a richer and more nuanced understanding of the epidemiological landscape describing the association between disorders and mortality.

There is a large literature on mortality-related estimates; however, previous studies have tended to focus on (a) specific prior nonfatal disorders or risk factors and specific causes of death; or (b) a limited range of mortality-related estimates [3]. Furthermore, differences between study designs complicate direct comparison of mortality between different disorders. Thus, there is a need to harmonize mortality measures across a comprehensive range of health disorders and, for each of these disorders, to provide a broader panel of mortality-related estimates, combined with key epidemiological measures, such as incidence or age of onset. The aim of this study was to use the Danish population–based registers to provide a panel of mortality metrics associated with a comprehensive range of health disorders, covering 1,803 different health conditions. In light of a recent Danish study linking exposure to air pollution to increased mortality rates [15], we also undertook a planned sensitivity analysis in order to explore if exposure to air pollution influenced the strength of the association between these disorders and mortality.

Methods

A protocol was preregistered before having access to the data [16] (S2 Text), and a web page has been designed to visualize all results (http://nbepi.com/atlas).

Study population and follow-up

We designed a population-based cohort study including all 7,378,598 persons living in Denmark at any point between January 1, 2000 and December 31, 2018 (all individuals were included regardless of whether they had a hospital contact or not). Since 1968, the Danish Civil Registration System [17] has maintained information on all residents, including sex, date of birth, continuously updated information on vital status, and a unique personal identification number that can be used to link information from national registers.

Assessment of specific disorders based on ICD-10 classification

Specific disorders were identified through hospital contacts on or after January 1, 1995, allowing a period of at least 5 years to identify individuals with diseases diagnosed before the start of the follow-up. This information was obtained from the Danish National Patient Register [18], which contains data on all admissions to hospital inpatient facilities and visits to outpatient facilities (including visits to medical specialists), as well as emergency departments, since 1995 (including contacts from psychiatric departments, available through the Danish Psychiatric Central Research Register) [19]. The diagnostic system used during this period was the Danish modification of the International Classification of Diseases-10th Revision (ICD-10) [20]. For this study, we considered 19 overall chapters (e.g., A00-B99: Certain infectious and parasitic diseases), 207 subchapters (e.g., A00-A09: Intestinal diseases), and 1,538 3-character categories for certain disorders (e.g., A00: Cholera), making a total list of 1,764 specific categories. Additionally, we considered 39 general medical conditions (10 broad categories, 8 of which included 29 subcategories) based on combinations of several ICD-10 codes previously used in Danish health research [21,22]. Thus, the total list of disorders and related ICD-10 codes for which mortality-related metrics were estimated comprised 1,803 categories and is available in the Supporting information (S1 Table). For each individual in the study, the date of onset for each disorder was defined as the date of first contact (inpatient, outpatient, or emergency visit) for the specific disorder (different disorders developing within the same individual could have different dates of onset).

Mortality

Information on date and primary cause of death was obtained from the Danish Registry of Causes of Death [23]. All deaths were categorized in 2 widely used and nonoverlapping groups according to ICD-10 codes: external causes of death, which included suicide (X60-X84 and Y87.0), homicide (X85-Y09 and Y87.1), and accidents (V01-X59, Y10-Y86, Y87.2, and Y88-Y89), and natural causes of death, which included all other causes.

Statistical analysis

All individuals were followed up from birth, immigration to Denmark, or January 1, 2000, whichever came last, until death, emigration from Denmark, or December 31, 2018, whichever came first. All disorders were treated as time-varying factors (additional details in S1 Text). A list of all epidemiological measures reported in this study for each of the 1,803 disorders is available in Box 1. A description of the methods to estimate mortality rates and life expectancy is provided below, while specific details for all other measures are available in the Supporting information (S1 Text). All metrics were estimated for males and females separately and combined. Individuals with more than one diagnosis contributed information for each of their diagnoses; however, only information in relation to the specific disorder was considered in the estimates.

Box 1. Epidemiological measures reported for each of the 1,803 health disorders

Number of diagnosed: Number of individuals living in Denmark at some point between 2000 and 2018 diagnosed with the specific disorder at a hospital between January 1, 1995 and December 31, 2018.

Age at diagnosis: Median and IQR of age at diagnosis among those diagnosed with the specific disorder.

Number of deaths: Number of individuals who died between January 1, 2000 and December 31, 2018 after having received a diagnosis of the disorder.

Age at death: Median and IQR of age at death among those diagnosed with the specific disorder who died during the study period.

Incidence rates: Number of individuals diagnosed with the disorder for the first time per unit of time. In this study, incidence rates are reported per 10,000 person-years for each age group (0 to 5, 5 to 10, 10 to 15, …, and 95 to 100 years).

Mortality rates: Number of deaths per unit of time. In this study, mortality rates are reported per 10,000 person-years for those diagnosed with a specific disorder and for the entire population standardized to the same sex and age of those diagnosed with the disorder. Results are shown for all ages and for each age group (0 to 5, 5 to 10, 10 to 15, …, and 95 to 100 years).

MRRs: Represent the ratio of mortality rates between persons with and without a diagnosis of the specific disorder adjusted for age, sex, and birth date. In this study, we report MRRs for (i) all causes of death and separately for natural and external causes; (ii) males and females combined and separately; and (iii) overall MRRs as well as MRRs depending on age and time since the first diagnosis. Models including air pollution adjusted also for mean NO2 and PM2.5 during the year before start of follow-up.

Average life expectancy: Represents the average number of years a person is expected to live if age-specific mortality rates in a given period remain constant in the future. In this study, we report average remaining life expectancy at specific ages for individuals previously diagnosed with a specific disorder and for the general population.

LYLs after diagnosis: Represent differences in remaining life expectancy between individuals diagnosed with a specific disorder and the general population of same age and sex. In this study, we report LYLs for (i) all causes of death and separately for natural and external causes; and (ii) males and females combined and separately.

Mortality rates for the whole population and for those diagnosed with each disorder were calculated as the number of deaths divided by the total follow-up time in person-years. Standardized mortality rates for the whole population were calculated using the distribution of sex, age (5-year categories), and calendar time (2000 to 2004, 2005 to 2009, 2010 to 2014, and 2015 to 2018) of those diagnosed with each disorder. MRRs with 95% confidence intervals (CIs) were estimated for external and natural causes of death and for all-causes combined, comparing persons with and without each specific disorder using Cox proportional hazards models, with age as the underlying timescale, and adjusting for sex and birth date (using cubic splines with 4 knots). MRRs for all causes depending on age (5-year categories) and time since diagnosis (0 to 6 months, 6 to 12 months, 1 to 2 years, 2 to 5 years, 5 to 10 years, and 10+ years) were estimated including an interaction term with exposure in the regression models.

Differences in average life expectancy between the group of persons with a specific disorder and the general population were calculated as LYLs. The technical development of this method has recently been published [8,9], and a detailed account of how to implement it—with a specific R package—is available [10]. In brief, for each disorder, the expected residual lifetime was calculated at each possible age of diagnosis for the group of persons with a previous diagnosis and for the general population of same sex and age based on age-specific mortality rates. The main reason to compare those with a given disease to the general population—and not to persons without the disease—is that the number of LYLs at a given age, e.g., 45 years, is estimated using mortality rates at ages 45 years and beyond. By choosing persons without the disease as a comparison group, we would assume that someone who has not experienced the disease at age 45 would remain free of the disease until death. Although it might seem problematic to include persons with a disease in both the diseased and reference groups, this is analogous to SMRs, which compare mortality in a group of persons to the one in the general population. The difference between the estimate for those with a diagnosis and the general population was defined as differences in life expectancy at each possible age of diagnosis, and it requires the assumption that those diagnosed will experience the mortality rates of the diagnosed during the entire life (after diagnosis). A weighted average of all these age-specific estimates (weighted by the number of individuals diagnosed at each age) provided a summary measure of differences in life expectancy after disorder diagnosis. Finally, these differences were divided into natural and external causes of death using a competing risks model [24]. CIs for these estimates were obtained using nonparametric bootstrap with 500 iterations.

Our comprehensive and multifaceted approach provided us with the opportunity to explore the influence of candidate risk factors on mortality-related estimates. As air pollution is a prominent environmental health threat, we examined the potential confounding effect of air pollution on MRRs. The study population was linked with information on residential exposure to levels of nitrogen dioxide (NO2) and atmospheric particulate matter with a diameter of less than 2.5 micrometers (PM2.5) modeled using the multiscale and integrated air pollution model system [25,26] during the year before start of follow-up (specific details available in S1 Text). Models estimating MRRs for all-cause mortality were replicated with and without adjustment for mean NO2 and PM2.5, included in the models as continuous z-scores. The models did not adjust for socioeconomic characteristics, as data at the individual level could not be used.

Preregistered protocol, code and data availability, and visualization of results

This study is reported as per the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) guideline (checklist available in S2 Table). The protocol and analysis plan (S2 Text) were posted on Open Science Framework before having access to the data and are publicly available together with all programming code and summary outcomes [16]. Due to data protection laws, researchers have to apply to the Danish Health Data Authority to have access to the underlying person-level data.

We have developed an interactive web page to visualize all results from this study [27]. In order to simplify the presentation of results, we focus on the 39 general medical conditions [21,22], which cover 10 broad categories: circulatory, endocrine, pulmonary, gastrointestinal, urogenital, musculoskeletal, hematologic, mental, and neurologic conditions and cancer (see S3 Table for details). Individuals were considered to experience one of the broad categories if they were diagnosed with at least one of the disorders included in the category. Complete results for each of the 1,803 specific disorders are available on Open Science Framework [16] and on the web page (see S1 Fig for an overview of the main results for one specific disorder).

All analyses were performed on the secured platform of the Danish Health Data Authority. The Danish Data Protection Agency and the Danish Health Data Authority approved this study. According to Danish law, informed consent is not required for register-based studies. All data accessed were deidentified.

Results

A total of 7,378,598 persons (3,676,694 males and 3,701,904 females) were included in the study and followed up for 101.7 million person-years. The mean (standard deviation) age at entry to and exit from the study was 31.2 (24.4) and 44.9 (25.7) years, respectively. During the 19-year follow-up period, 1,034,273 persons (14.0%) died (509,032 males [13.8%] and 525,241 females [14.2%]), and 989,770 did so of natural causes and 44,503 of external causes. When focusing on the 39 selected disorders, the most prevalent were disorders of the circulatory system (n = 1,431,041), disorders of the neurological system (n = 1,283,880), and mental disorders (n = 1,128,977) (Fig 1). The median (interquartile range, IQR) age at diagnosis ranged from 34.0 (18.1 to 50.7) years for allergy to 76.2 (66.9 to 83.7) years for heart failure, whereas median (IQR) age at death for those diagnosed ranged from 53.5 (44.8 to 63.9) years for HIV/AIDS to 86.3 (79.7 to 91.4) years for hearing problems (Fig 1).

Fig 1

Number of diagnosed (left panel) and age at diagnosis and death among the diagnosed (right panel) for 39 selected conditions covering 10 broad categories. Estimates are available in S4 Table. IQR, interquartile range.

All-cause mortality

For 37 of the 39 disorders, mortality rates were larger and life expectancy shorter compared to the Danish general population (Fig 2). For these 37 disorders, MRRs ranged from 1.09 (1.09 to 1.10) for vision problems to 7.85 (7.77 to 7.93) for chronic liver disease, while reduction of life expectancy ranged from 0.31 (0.14 to 0.47) years (approximately 16 weeks) for allergy to 17.05 (16.95 to 17.15) years for chronic liver disease. The remaining 2 disorders were hearing problems, with reduced mortality rates (MRR = 0.93 [0.93 to 0.94]) and slightly longer life expectancy (LYL = −0.11 [−0.15 to −0.07] years; approximately 40 days); and migraine, with similar mortality rates (MRR = 1.00 [0.97 to 1.03]) and slightly longer life expectancy (LYL = −0.65 [−0.94 to −0.37] years). The adjustment for air pollution had very little impact on the estimates (i.e., the sign and the magnitude of the effect size did not vary, and the CIs between adjusted and unadjusted estimates were comparable; S2 Fig). The combination of different metrics is useful to show differences between disorders. For example, we observed similar increases in mortality rates in those with a disorder of the circulatory system (MRR = 2.90 [2.89 to 2.92]) and those with chronic pulmonary disease (MRR = 2.94 [2.92 to 2.95]), but the reduction in life expectancy was 8.27 (8.21 to 8.32) years in those diagnosed with the latter and 3.80 (3.78 to 3.82) years in those diagnosed with the former.

Fig 2

MRRs (left panel) and LYLs (right panel) for all causes of death for 39 selected conditions covering 10 broad categories. The red line indicates equal mortality in the 2 groups (MRR of 1; LYLs of 0). Estimates are not shown if they are based on less than 100 individuals diagnosed or less than 20 deaths; for LYLs, estimates are not shown if there were not enough individuals at older ages of follow-up. Estimates are available in S4 Table. CI, confidence interval; LYLs, life years lost; MRR, mortality rate ratio.

Cause-specific and sex-specific mortality

Estimates depending on natural and external causes of death are available in S3 Fig. MRRs due to natural causes of death were larger than those for external causes in 25 of the 39 disorders. For all disorders except mental disorders (LYL = 2.05 [2.00 to 2.10] years), chronic liver disease (LYL = 1.87 [1.75 to 1.99] years), and epilepsy (LYL = 1.21 [1.10 to 1.33] years), the reduction in life expectancy explained by external causes of death was less than 1 year compared to the general population (LYLs related to external causes in these disorders ranged from −0.41 [−0.42 to −0.40] years for cancers to 0.62 [0.55 to 0.69] years for chronic kidney disease). For most disorders, MRRs and LYLs were larger in males than in females or similar for both sexes (sex-specific estimates are available in S4 Fig).

MRRs depending on age and time since onset of the disorder

Age- and time-specific MRRs are available for the 10 broad categories of disorders in Figs 3 and 4. When looking at age-specific MRRs (Fig 3), generally, the largest MRRs were found in children and young adults. However, the decline in MRRs was not always linear with age, with some disorders (e.g., gastrointestinal or mental disorders) showing modest increases before declines in older adults. For most disorders, MRRs were largest in the first 6 months after diagnosis (Fig 4). For some disorders (e.g., cancer or musculoskeletal), there was a continuous decrease after diagnosis, while for some other disorders (e.g., circulatory or mental disorders), there was a decrease, which remained constant after the first 6 months post-diagnosis. Mortality rates 10 years after diagnosis remained higher for those with a diagnosis of any of the disorders, compared to those without the diagnosis: MRRs 10 years after diagnosis ranged from 1.27 (1.26 to 1.28) for disorders of the neurological system to 3.18 (3.16 to 3.21) for mental disorders.

Fig 3

Age-specific mortality rates ratios adjusting for age, sex, and birth date for 10 broad categories of conditions.

The red line indicates equal mortality in the 2 groups (MRR of 1). Estimates are available on Open Science Framework [16] and on the interactive web page [27]. CI, confidence interval; MRR, mortality rate ratio.

Fig 4

Mortality rates ratios depending on time since first diagnosis for 10 broad categories of conditions.

Estimates are adjusted for age, sex, and birth date. The red line indicates equal mortality in the 2 groups (MRR of 1). Estimates are available on Open Science Framework [16] and on the interactive web page [27]. CI, confidence interval; MRR, mortality rate ratio.

Estimates for all ICD-10 disorders

In addition to the 39 disorders discussed above, we have provided estimates for all 1,803 disorders on Open Science Framework [16] and on the interactive web page [27]. For example, Fig 5 shows the number of diagnosed, MRRs and LYLs for all 1,538 ICD-10 3-character categories of disorders as well as the overall results for each of the 19 chapters. When looking at overall ICD-10 chapters (Fig 5A), MRRs ranged from 0.63 (0.61 to 0.65) for Chapter XV (Pregnancy, childbirth and the puerperium) to 4.14 (4.12 to 4.15) for Chapter X (Diseases of the respiratory system), while LYLs ranged from 0.29 (0.24 to 0.35) years for Chapter VIII (Diseases of the ear and mastoid process) to 13.90 (12.97 to 14.93) years for Chapter XVI (Certain conditions originating in the perinatal period). When looking within ICD chapters (Fig 5B), there is considerable heterogeneity, with MRRs ranging from 0.50 (0.38 to 0.64) for ICD-10 code O83 (Other assisted single delivery; Chapter XV) to 152.31 (116.61 to 198.93) for P60 (Disseminated intravascular coagulation of newborn; Chapter XVI) and LYLs ranging from −3.37 (−3.48 to −3.24) years for M23 (Internal derangement of knee; Chapter XIII) to 26.95 (25.43 to 28.58) years for C74 (Malignant neoplasm of adrenal gland; Chapter II).

Fig 5

Number of diagnosed; MRRs; and LYLs for (a) 19 overall ICD-10 chapters, and (b) 1,538 3-character categories within each chapter. The red line indicates equal mortality in the 2 groups (MRR of 1; LYLs of 0). Estimates are not shown if they are based on less than 100 individuals diagnosed or less than 20 deaths; for LYLs, estimates are not shown if there were not enough individuals at older ages of follow-up. Estimates are available on Open Science Framework [16], but we encourage the reader to explore the estimates in more detail at the interactive web page [27]. The x-axes are different in figures (a) and (b). Chapters: (I) Certain infections and parasitic diseases; (II) Neoplasms; (III) Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism; (IV) Endocrine, nutritional and metabolic diseases; (V) Mental, behavioral and neurodevelopmental disorders; (VI) Diseases of the nervous system; (VII) Diseases of the eye and adnexa; (VIII) Diseases of the ear and mastoid process; (IX) Diseases of the circulatory system; (X) Diseases of the respiratory system; (XI) Diseases of the digestive system; (XII) Diseases of the skin and subcutaneous tissue; (XIII) Diseases of the musculoskeletal system and connective tissue; (XIV) Diseases of the genitourinary system; (XV) Pregnancy, childbirth and the puerperium; (XVI) Certain conditions originating in the perinatal period; (XVII) Congenital malformations, deformations and chromosomal abnormalities; (XIX) Injury, poisoning and certain other consequences of external causes; (XX) External causes of morbidity. CI, confidence interval; ICD-10, International Classification of Diseases-10th Revision; LYLs, life years lost; MRR, mortality rate ratio.

Discussion

Our study provides a comprehensive atlas of mortality-related estimates, based on high-quality Danish registers. To the best of our knowledge, this is the most detailed compendium of mortality-related estimates and the first to show reductions in life expectancy for a comprehensive range of disorders. The interactive data visualization site allows more fine-grained analysis of the association between a range of disorders and key mortality-related estimates. The discussion will focus on 3 key points, and when necessary, we will illustrate these points using the 39 conditions described above.

First, our findings are broadly consistent with related publications [3,28]. The majority of disorders were associated with both an increased mortality rate and a reduction in life expectancy of at least 1 year. However, important nuances were revealed when the 2 main mortality-related estimates are examined together. For example, we observed similar increases in mortality rates in those with a disorder of the circulatory system and those with chronic pulmonary disease; however, the reduction in life expectancy was double in those diagnosed with the latter compared to those diagnosed with the former. The discrepancy in those pairs of estimates is most likely explained by differences in age of onset. The median age of register-based onset for those diagnosed with chronic pulmonary disease was 55.7 years; thus, individuals with this disorder have more years of potential life lost compared to those diagnosed with a disorder of the circulatory system, with a median age of onset of 66.6 years.

Second, there were clear temporal signatures between the majority of disorders and time-dependent MRRs—the difference in mortality rates between those with and without a specific disorder peaks in the first 6 months after the diagnosis of the disorder, and it then decreases. For some disorders, there was a sharp decline in MRRs after the first 6 months, and then they were stable for the following 10 years. This may reflect a subgroup of individuals who delay seeking help until the disorder is more advanced. For other disorders, there was a stable decrease over time, suggesting that the disorder may remit over time (spontaneously or in response to optimal treatment). Finally, MRRs did not increase over time after onset for any of the selected 39 conditions; however, MRRs increased over time for few disorders (e.g., dementia; ICD-10 code F00) when looking at all 1,803 disorders. In summary, time-dependent MRRs provide interesting features of the association between specific disorders and mortality risks.

Third, it is important to note that a range of factors can influence mortality in those with specific health conditions. We do not propose a causal relationship between the health conditions and subsequent mortality. The observed associations could be explained by underlying factors that are associated both with morbidity and mortality (e.g., socioeconomic or environmental factors). Additionally, the onset of a health condition can have impacts on lifestyle, daily habits, and socioeconomic characteristics, which, in turn, might mediate the association with subsequent mortality. Although air pollution has been found to be associated both with mortality [15,29] and a wide range of health conditions [30,31], we found that the relationship between the disorders and mortality rates was not substantially altered in models adjusting for air pollution exposure. However, air pollutants were modeled at the residential address during only 1 year, and they might be poor indicators of the real individual accumulated exposure (which might occur at the work or educational place, for example). We plan to explore this issue in more detail in future studies.

This study has several strengths. First and foremost, all analyses are based on the same population, same time period, and same methodology, which allows comparison of results between different conditions. Additionally, we used an innovative metric of life expectancy, which can be more readily understood by the general community and that has several advantages. The “Life Years Lost” method uses the age of diagnosis and overcomes limitations from previous studies in which a fixed age of onset had to be assumed [4-7]. Additionally, it allows for the evaluation of the impact of particular disorders on premature mortality, regardless of how the individual died. A recent study using this method based on Danish registers [13] has shown that all mental and substance use disorders are associated with a reduction in life expectancy, although only a few of them are generally included as cause of death. Finally, it allows the total average reduction in life expectancy to be partitioned into specific types of causes of death; while we only used 2 broad categories (natural and external causes), the findings could be divided into narrower cause of death categories specifically selected for each health condition. We plan to explore these additional options in future studies.

While the study is based on a large sample including the entire Danish population and is based on complete Danish registers, it has several limitations. We used age at first diagnosis in the registers as a proxy for age of onset, which could introduce biases for disorders with delayed help-seeking. Individuals diagnosed with a specific disorder include only those diagnosed in hospitals, outpatient, and accident/emergency settings. The study does not include patients with disorders that more likely were treated by the general practitioner or who were not treated at all. While this limitation might have little impact on disorders such as cancer, psychotic disorders, or renal failure, the true prevalence and incidence of disorders like allergy, mild depression, or alcohol dependence might be underestimated, and their associated excess mortality overestimated since the mortality metrics are based on the subset of individuals with more severe diseases that are seen and treated in secondary care. In addition, this study did not include information on remission or other comorbid disorders; the group of individuals with a specific disorder can therefore be interpreted as persons who have had a diagnosis of the disorder regardless of whether they have other disorders or whether they have recovered afterward. Estimates of life expectancy are based on mortality rates from onset and onward; thus, they can be interpreted mostly for chronic conditions. Finally, information on mortality was also obtained from registers. Since date of death is considered to be accurate, all-cause mortality is not affected by potential misclassification. However, there could be some misclassification of the specific cause of death, given that only 5% of deaths in Denmark are examined by autopsy [32]. However, given that all deaths were classified into 2 broad categories (natural and external causes), misclassification is less likely. Finally, while our estimates may be reflective of high-income countries, it remains unknown the extent to which our findings generalize to other countries with different healthcare systems or levels of air pollution.

The analytic framework we presented can be used for a range of important public health research issues. For example, with the aging population, we can expect that some late-onset disorders (e.g., dementia) will increase over time. Additionally, the observed mortality estimates can be used for burden of disease modeling exercises.

In conclusion, we have presented a detailed atlas of disease mortality based on Danish hospital registers. Our study has provided a large amount of data, and we would like to emphasize the need for web-based data visualizations tools to make these data available to the broader public. With such interactive websites, it is possible for the reader to inspect specific conditions and have access to summary information of interest (e.g., age of onset, age of death, prevalence, incidence, mortality rates, life expectancy, etc.). We hope that this comprehensive study (with all summary data made available through an interactive data visualization site) can be used to generate future hypothesis-driven research.

Supplementary methods.

(PDF)

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Prespecified analysis plan.

(PDF)

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List of 1,803 disorders and related ICD-10 codes.

ICD-10, International Classification of Diseases-10th Revision.

(XLSX)

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GATHER checklist.

GATHER, Guidelines for Accurate and Transparent Health Estimates Reporting.

(PDF)

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List of 39 selected conditions covering 10 broad categories of disorders.

(PDF)

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For 39 selected conditions covering 10 broad categories: number of diagnosed, age at diagnosis, number of deaths among the diagnosed, age at death, MRRs and LYLs for all causes of death.

LYLs, life years lost; MRR, mortality rate ratio.

(PDF)

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MRRs for all causes of death for 39 selected conditions covering 10 broad categories with and without adjustment for air pollution during the year before start of follow-up.

All estimates are adjusted for age, sex, and birth date. MRR, mortality rate ratio.

(PDF)

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MRRs and LYLs for natural and external causes of death for 39 selected conditions covering 10 broad categories.

Estimates are not shown if they are based on less than 100 individuals diagnosed or less than 20 deaths; for LYLs, estimates are not shown if there were not enough individuals at older ages of follow-up. LYLs, life years lost; MRR, mortality rate ratio.

(PDF)

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Number of females and males diagnosed and sex-specific MRRs and LYLs for all causes of death for 39 selected conditions covering 10 broad categories.

Estimates are not shown if they are based on less than 100 individuals diagnosed or less than 20 deaths; for LYLs, estimates are not shown if there were not enough individuals at older ages of follow-up. LYLs, life years lost; MRR, mortality rate ratio.

(PDF)

Click here for additional data file.

Overview of the main results for mental disorders (ICD-10 F00-F99) available on the website http://nbepi.com/atlas [27].

ICD-10, International Classification of Diseases-10th Revision.

(PDF)

Click here for additional data file.

The red line indicates equal mortality in the 2 groups (MRR of 1). All estimates are adjusted for age, sex, and birth date. Estimates are available in S5 Table and on Open Science Framework [16]. MRR, mortality rate ratio.

(PDF)

Click here for additional data file.

The red line indicates equal mortality in the 2 groups (MRR of 1; LYLs of 0). Estimates are not shown if they are based on less than 100 individuals diagnosed or less than 20 deaths; for LYLs, estimates are not shown if there were not enough individuals at older ages of follow-up. Estimates are available in S6 Table and on Open Science Framework [16]. LYLs, life years lost; MRR, mortality rate ratio.

(PDF)

Click here for additional data file.

The red line indicates equal mortality in the 2 groups (MRR of 1; LYLs of 0). Estimates are not shown if they are based on less than 100 individuals diagnosed or less than 20 deaths; for LYLs, estimates are not shown if there were not enough individuals at older ages of follow-up. Estimates are available in S7 Table and on Open Science Framework [16]. LYLs, life years lost; MRR, mortality rate ratio.

(PDF)

Click here for additional data file.

16 Dec 2021

Dear Dr Plana-Ripoll,

Thank you for submitting your manuscript entitled "An analysis of mortality metrics associated with a comprehensive range of disorders: the Danish atlas of disease mortality" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.

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Kind regards,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine

10 Mar 2022

Dear Dr. Plana-Ripoll,

Thank you very much for submitting your manuscript "An analysis of mortality metrics associated with a comprehensive range of disorders: the Danish atlas of disease mortality" (PMEDICINE-D-21-05097R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also sent to three independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

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We look forward to receiving your revised manuscript.

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Associate Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

1. Title: Please revise your title according to PLOS Medicine's style. Your title must be nondeclarative and not a question. It should begin with main concept if possible. "Effect of" should be used only if causality can be inferred, i.e., for an RCT. Please place the study design ("A randomized controlled trial," "A retrospective study," "A modelling study," etc.) in the subtitle (ie, after a colon).

2. Line numbers: Please include line numbers running continuously throughout the manuscript with the revised version.

3. Abstract: Methods and Findings: Please include a few examples or broad summary of some of the 39 general medical conditions discussed. For example, as described in the Methods: “...ten broad categories: circulatory, endocrine, pulmonary,gastrointestinal, urogenital, musculoskeletal, hematologic, mental, and neurologic conditions and cancer…”

4. Abstract: Methods and Findings: Please mention which conditions correspond with these MRRs/LYLs given as examples: “For these 37 disorders, MRRs ranged from 1.09 (95%CI: 1.09-1.10) to 7.85 (7.77-7.93), while LYLs ranged from 0.31 (0.14-0.47) years (~16 weeks) to 17.05 (16.95-17.15) years.”

5. Abstract: Methods and Findings: In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

6. Abstract: Conclusions: Please address the study implications without overreaching what can be concluded from the data; the phrase "In this study, we observed ..." may be useful. Please avoid or temper assertions of primacy ("To the best of our knowledge, this atlas is the first....").

7. Author summary: At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

8. In text citations: Please place reference citations within square brackets, placed before the sentence punctuation, for example [1,2]. Where multiple references are listed, please do not include spaces within brackets.

9. Introduction: “A protocol was pre-registered before having access to the data and a webpage has been designed to visualize all results (https://nbepi.com/atlas).” Please move this information to the Methods.

10. Methods: “We designed a population-based cohort study including all 7,378,598 persons living in Denmark at any point between January 1, 2000 and December 31, 2018” Are more recent data available?

11. Methods: Statistical analysis: Please clarify if comorbidity status or level of education were taken into account in the analyses.

12. Methods: Given Reviewer 1’s comment about underlying social determinants of health, and the likelihood that these are geographically clustered, please explain if clustering by region or deprivation index was taken into account.

13. Methods: Please include a copy of the pre-specified analysis plan for the study as a supporting information file.

14. Methods: Please report your data according to GATHER (or according to the most relevant guideline for your study) and enclose a completed GATHER checklist as a supplementary document. Please add the following statement, or similar, to the Methods: "This study is reported as per the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) guideline (S1 Checklist)." Please see http://gather-statement.org/ for more information.

15. Results: Please clarify which subchapters are being described here: “When looking

within ICD chapters (Figure 5b), there is considerable heterogeneity, with MRRs ranging from 0.50 (0.38 to 0.64) to 152.31 (116.61 to 198.93); and LYLs ranging from -3.37 (-3.48 to -3.24) years to 26.95 (25.43 to 28.58) years.”

16. Discussion: Please present and organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion.

17. Figure 1 and Figure 2: If possible, please also provide these data in table format.

Comments from the reviewers:

Reviewer #1: General comments:

The study authors have created a fantastic resource exploiting the fantastic ability in Denmark to link health service records to death. This will be a resource for many users and particularly for burden of disease estimation. I only opened the website after reading the full paper and found it a much greater resource than I could have imagined from just reading the paper. It seems that you are 'under-selling' what you are giving the world! The least I would suggest you can do is provide an example for one particular condition showing all the results that you can find in the tool after clicking on that specific disease. I have a lot of more specific comment below, largely asking for more precise detail but a few more conceptual things and limitations that you have not spun out in enough detail.

Specific comments:

Abstract: I would not call life expectancy an 'absolute-risk mortality metric'

Abstract: how did you compute the 7.4 million persons living in Denmark 2000-2018? From a statement a bit further it seems that you conceptualise is as "anyone who lived in Denmark for some part of all of 2000-2018'

Abstract: you do not define LYL

Introduction: The statement "When looking at life expectancy, the Global Burden of Disease (GBD) studies measure Years of Life Lost (YLLs),3 which estimates the potential years of life lost." suggests that YLLs are a measure of life expectancy which it is not

Page 7 line 2: what do you mean with 'admissions to ….outpatient facilities'? Do you mean e.g. day surgeries or also consultations with medical specialists?

Page 8: you estimate your MRRs and difference in average life expectancy against population all-cause mortality rates (although I am a little confused by the contradicting statement on page 10: "mortality rates after diagnosis remained higher for those with a diagnosis of any of the disorders, compared to those without the diagnosis"). These population mortality rates also include the deaths from any condition of interest. Conceptually, I think you are using a counterfactual approach: "if a person had not become a case of a disease, how different would this person's risk of death and remaining life expectancy have been?". To do that correctly you would need to contrast people with a disorder with the rest of the population without the disorder. Most disorders will be a rare enough reason for death to make such comparisons a reasonable proxy for a true RR or difference in life expectancy but you also include disorders such as IHD and stroke that are highly prevalent/incident at oldest ages and then it would not be a very good proxy.

Results: when you present prevalence of aggregates of the 39 selected disorders, are you conceptualizing that as an individual experiencing any of the more specific disorders?

Discussion page 12 top: you mention dementia here but it is not one of the 39 chosen conditions that you concentrate on in this paper (…and see comment above: did you take the different spots in ICD where dementia is coded?)

Discussion page 12: what future study are you planning to further explore a potential impact of air pollution?

Discussion page 12: what do you mean with 'late-onset conditions'? For dementia that would read as onset at older ages but for cancer, that may be the case for some types of cancer but certainly not generalisable

Discussion page 12: the single sentence about COVID-19 seems a little gratuitous: either expand on the topic or leave it out.

Limitations: you do not mention one important limitation: a finding of excess mortality risk or reduced life span associated with a particular diagnosis may not be related to the disease per se but reflecting common underlying risks even if there is no evidence of a direct relationship. Many diseases are linked with upstream risks like poverty or poor education. These in turn cause many other more proximal risks to be more common. If the outcome of interest has a link to poverty it takes on the baggage of all other risks that are elevated with poverty leading to excess deaths that are not 'due to' the condition of interest but confounded by the excess baggage of risk factors this person carries. In other words, you may not be estimating a true counterfactual: the absence of a disease of interest may not take away the fact that someone is poor or has low education and therefore lots of other things predisposing to premature mortality.

Limitations: you mention that you only capture diagnoses from hospital encounters and that you may therefore be missing conditions for which people largely seek care with GPs or do not seek care. A consequence for those conditions is that you may be selecting more severe cases of the disease who are more likely to appear in your disease registry and hence overestimate their mortality risk.

Appendix: by choosing the whole F chapter as 'mental disorders' you partially include dementia but not cases coded to G30 and G31. It will depend on local coding practices which codes are preferred. There are more examples of disease that straddle different ICD chapters and certainly those that fall across multiple smaller ICD groupings you created.

Appendix: your list of ICD categories uses mainly the S and T chapters with 'nature of injury' codes but you add 'cause of injury' codes for suicide and violence. There would be overlap between those: e.g. someone getting assaulted (X59-Y09) with as a consequence a head injury (S00-S09): that would be one individual and one injury episode.

Reviewer #2: This study tried to estimate a wide range of health metrics in Denmark from 2000 to 2018 using a comprehensive population-based cohort including all residents of Denmark. Although some estimates produced in this study are useful for policy making and population health research, I don't think there is enough innovation and unique contribution for a original research paper. Most of the health metrics (the important ones) estimated in this study have already been estimated by IHME using a more comprehensive and rigorous approach. I believe that GBD studies also utilized this national cohort for their Danish estimates. I doubt this study make much additional contribution. In addition, I found one major assumption about remission made in this study particulary contraversial, which makes the results less reliable or useful.

Here are my specific comments

1. Please provide line numbers for easier reference

2. Data used in this study are not publicly available and some restrictions may apply

3. Page 5, Introduction: "There is a need to better understand the impact on life expectancy of non-fatal disorders…" GBD studies also produced years of lives lost due to disability (YLDs) and thus DALYs, which can reflect burden due to the 'upstream' causes or risk factors. Actually, when calculating the YLDs, the incidence/proportion of different stages of a condition (four stages of cancers) and their coresponding disablity weights were all carefully estimated to produce proper YLDs for each condition. So, I don't think this paragraph is well grounded.

4. Page 5, Introduction: "…have assumed a fixed age-of-onset of 15 years…" So, if age-of-onset is 15 years, does it mean the age of onset is 15 years old? It's a bit unclear. It's also really hard to believe that age of onset for mental disorders is 15 years old.

5. Page 6, Introduction: "limited range of mortality-related estimates (e.g. the GBD only presents YLLs)." That's not true. GBD studies actually produced a wide range of health metrics, including YLLs, YLDs, DALYs, all-cause and cause-specifc mortality rates, etc.

6. Page 7 and page 13: The authors ignored remission/recovery period for all diseases (one of their assumptions). This assumption does not make much sense for most infectious diseases that does not last long, do not affect health once fully recovered and can re-occur to the same person multiple times.

7. The auhtors found that air pollution has little effects on the mortality rates. However, since Denmak does not have severe air pollution issue, I don't think this finding is true for many developing countries where air pollution is moderate or poor.

Reviewer #3: This is a useful, innovative way to examine disease burden in high-income countries.

The MRRs are reasonably clear in methodology and presentation. However, the calculations for LYLs and the use of the method published earlier is not clear, and leaves the reader hanging. Further details of the calculations and a worked example in the appendix would be helpful. An appendix table showing the difference for say 39 conditions using the LYL used here versus a fixed LYL approach would be helpful- how does the new method alter priority setting?

As well, the main uncertainty here will not be sampling, which leads to the narrow CIs for most conditions, but two factors: (i) Misclassifications of the causes of death particularly at older ages, where COD data are less certain- so some exploration of the Danish death registry data for ill-defined contributions to COD by age would be helpful, and if possible stratifying the analyses into causes with low misclassification and those with high may be a useful appendix; (2) As mentioned, but not detailed in any sensitivity analyses, the variation in age at onset for conditions.

Minor point that the discussion results for COPD versus vascular should be in the results, and not presented in the discussion.

Finally, Figure 5 is way too complicated , it should either be simplified- say to the top 20 leading causes of death, or leading one in each ICD10 chapter.

Any attachments provided with reviews can be seen via the following link:

[LINK]

31 Mar 2022

Submitted filename: AtlasMortality_Cover letter and response to reviewers.pdf

Click here for additional data file.

5 May 2022

Dear Dr. Plana-Ripoll,

Thank you very much for re-submitting your manuscript "An atlas of mortality metrics associated with a comprehensive range of disorders. A Danish cohort study" (PMEDICINE-D-21-05097R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by three reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by May 12 2022 11:59PM.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

1. Title: Please revise the title to: “Analysis of mortality metrics associated with a comprehensive range of disorders in Denmark, 2000-2018: A population-based cohort study” or similar. Please update the title in the text as well as the manuscript submission system.

2. Abstract: Line 98: Please refer to an association between air pollution and MRRs, rather than an effect of air pollution.

3. Abstract: Methods and Findings: Line 107: Please make the sentence describing the limitations of the study more obvious. We suggest: “A limitation of the study is the possibility that the association between different disorders and mortality could be explained by underlying factors associated with both the disorder and mortality.” We suggest moving the statement about the adjustment for air pollution to the end of the results description.

4. Author summary: Please re-format as bulleted points (3-4 per section) rather than in paragraph format. We suggest:

Why Was This Study Done?

-There have been many studies related to mortality linked to particular disorders, but these studies have not covered a comprehensive range of disorders.

-Previous studies have traditionally focused on relative measures of mortality (e.g. mortality rate ratios) or crude estimates of life expectancy that do not incorporate variation in the age of onset of the disorder.

-Here the researchers address these issues in a comprehensive atlas of mortality estimates based on Danish registers.

What Did the Researchers Do and Find?

-Based on 7,378,598 persons living in Denmark in 2000-2018, the researchers used national registers to identify individuals diagnosed with 1,803 specific categories of disorders.

-For each of these disorders, a panel of epidemiological and mortality metrics was estimated, including incidence rates, age-of-onset distributions, mortality rate ratios (MRRs) and life years lost (LYLs).

-Within a set of 39 selected medical conditions, mortality rates were larger and life expectancy shorter for 37 conditions compared to the Danish general population.

-The researchers have prepared an interactive data visualization to optimize the interrogation of their findings (http://nbepi.com/atlas).

What Do These Findings Mean?

-This study allows a more fine-grained analysis of the associations between a comprehensive set of disorders and mortality-related estimates.

-These findings can guide health research and serve as a benchmark to evaluate future health interventions.

5. Introduction: Line 183-184: We suggest “Furthermore, differences between study designs complicates direct comparison of mortality between different disorders.” if accurate.

6. Methods: Analysis plan and Protocol: Line 195 and 276: Thank you for making the protocol and analysis plan publicly available. Please mention the included protocol and analysis plan in the supporting data files consistently throughout the text (e.g. S2_Text). Rather than including the web link in the text, we suggest including a reference for https://doi.org/10.17605/OSF.IO/ZAFHU.

7. Results: Line 345-347: Rather than including the web links for the data and interactive webpage in the text, we suggest including these in the reference list.

8. Results: Line 330-331: “Sex-specific estimates are available in supplementary S4 Figure.” If possible, please briefly summarize the key findings from the sex-specific analyses.

9. Author Contributions: Line 451: Please remove the “Authors’ Contributions” section from the main text and be sure all information is entered completely and accurately into the manuscript submission system.

10. References: Please check the formatting of each reference in the list. Please use the "Vancouver" style for reference formatting, and see our website for other reference guidelines https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references

11. Journal name abbreviations should be those found in the National Center for Biotechnology Information (NCBI) databases. For example, please use “Lancet” as the journal title for references 1 and 3. Please use Br J Psychiatry as the journal title for reference 2. For reference 10, “PLoS One” should be the title. Please check the formatting throughout.

12. Figures 2, 3, 4, 5 and Figure S2: In the legends, please note the meaning of the red dashed line.

13. Table S2: Thank you for including the GATHER checklist. Please do not refer to the page numbers, and instead please replace these with paragraph numbers per section (e.g. "Methods, paragraph 1").

14. Figures S2, S3, S4: If possible, please also present these data in table format (similar to the presentation of Table S4).

Comments from Reviewers:

Reviewer #1: I think the authors have done a good job at responding to comments.

A few minor remaining issues:

Reviewer 1, comment 3: spelling out an acronym is not same as defining

Reviewer 1, comment 6: I'm largely OK with explanation but would expect this to be discussed in limitation section of discussion rather than hidden somewhere in appendix text.

Reviewer 1, comment 8/14: you have not really addressed the issue of dementia codes that straddle two ICD chapters

Reviewer 2, comment about GBD: agree with most of the response. The statements on remission look a little odd; we have not used Dismod 2 for 15 years and I don't understand the reference to remission being a 'top-down process'

Reviewer #2: I think the authors have adequately answered the reviewers' questions and addressed our concerns. I am satisfied with the authors' responses and happy to accept this revised version. Congratulations!

Reviewer #3: The authors have addressed most of my concerns. I quibble about the still too complicated Figure 5, but defer to the authors and editors to make this decision.

Any attachments provided with reviews can be seen via the following link:

[LINK]

10 May 2022

Submitted filename: AtlasMortality letter - revision 2.docx

Click here for additional data file.

17 May 2022

Dear Dr Plana-Ripoll,

On behalf of my colleagues and the Academic Editor, Theo Vos, I am pleased to inform you that we have agreed to publish your manuscript "Analysis of mortality metrics associated with a comprehensive range of disorders in Denmark, 2000-2018: A population-based cohort study" (PMEDICINE-D-21-05097R3) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

Please also address the following editorial requests:

-Abstract Line 97: Please revise slightly to: “...differences in life expectancy between those with the specific condition and those in the general population (estimated life years lost (LYLs).”

-Abstract: Line 99: Please revise to: “...to explore potential associations with MRRs.”

-Introduction: Line 185-186: Please change “complicates” to “complicate” in this sentence.

-Methods Line 198 and Line 283: Please reference the webpage, rather than providing the link in the text.

-Methods: Line 220: In response to reviewer 1, comment 8/14, please add a sentence to the Methods (around line 220) that there do exist health conditions defined from combinations of ICD-10 classification, such as dementia. Because dementia is used as an example, please mention in the text the definition for dementia (e.g.“Dementia was included as different specific 3-level ICD-10 codes [include the codes here]”).

-Methods: Line 257: In response to reviewer 1, comment 6, please add the explanation from the Supporting Information Text here describing the use of the general population rather than those without the disease as the comparison group, as this is helpful to understanding the LYL metric: “The main reason to compare those with a given disease to the general population – and not to persons without the disease– is that the number of Life Years Lost at a given age, e.g. 45 years, is estimated using mortality rates at ages 45 years and beyond. By choosing persons without the disease as a comparison group, we would assume that someone who has not experienced the disease at age 45, would remain free of the disease until death. Although it might seem problematic to include persons with a disease in both the diseased and reference groups, this is analogous to widely used (and classic) standardized mortality ratios (SMRs), which compare mortality in a group of persons to the one in the general population. In any case, differences in life expectancy would be even larger if the comparison group were persons without the disease.”

Reference 5: Please change the journal title to PLoS One.

PRESS

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Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine