ABSTRACT
Background
Information on chronic kidney disease (CKD) epidemiology is essential for healthcare provision. Moreover, variation in CKD occurrence and prognosis may reveal unwarranted inequalities in healthcare. Therefore, we examined the nationwide incidence, prevalence, and mortality of laboratory-confirmed CKD in Denmark and assessed variation according to urbanicity.
Methods
This nationwide cohort study utilized population-based laboratory data on estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio (uACR) and included all adult (≥18 years) Danish residents without prevalent CKD on 1 January 2019. We estimated crude and standardized incidence rates, prevalences, and mortality rates of CKD overall and according to urbanicity categories. We standardized by age and sex, and by age, sex, diabetes, hypertension, and outpatient eGFR and uACR testing frequency.
Results
The study included 4 157 059 adult Danish residents of whom 281 985 developed incident CKD during the 5-year study period. The CKD incidence rate was 14.5 [95% confidence interval (CI), 14.4–14.5] per 1000 person-years, and the CKD prevalence was 9.5% (95% CI, 9.5%–9.5%). The 5-year mortality was 24.9% (95% CI, 24.5%–25.3%). The crude CKD incidence, prevalence, and mortality varied across urbanicity categories; however, differences were largely accounted for by standardizing for age and sex.
Conclusions
Chronic kidney disease affected nearly one in ten adults in Denmark, and one in four individuals with incident CKD died within 5 years. The occurrence and prognosis of CKD varied across urbanicity categories and was predominantly related to differences in demographics. This emphasizes the need for a targeted allocation of healthcare resources according to population demographics.
Keywords: chronic kidney disease, mortality, occurrence, population health, prognosis
Graphical Abstract
Graphical Abstract.
KEY LEARNING POINTS.
What was known:
International estimates of chronic kidney disease (CKD) occurrence vary widely, and data are predominantly based on hospital diagnoses, use of kidney replacement therapy, or laboratory data from specific geographical regions within countries.
No studies have reported the nationwide occurrence and prognosis of CKD and assessed variation using complete population-based laboratory information. Such information may reveal unwarranted inequalities in healthcare provision, which could aid in optimizing clinical care.
This study adds:
This nationwide cohort study used population-based laboratory data on estimated glomerular filtration rate and urine albumin-creatinine ratio tests to assess CKD occurrence and prognosis in Denmark.
The CKD incidence rate in Denmark was 14.5 (95% CI, 14.4–14.5) per 1000 person-years, while the CKD prevalence was 9.5% (95% CI, 9.5%–9.5%), and the 5-year mortality after incident CKD was 24.9% (95% CI, 24.5%–25.3%).
CKD occurrence and prognosis varied across urbanicity categories; however, the variation was to a large extent explained by differences in demographics.
Potential impact:
The present study demonstrates the potential for utilizing population-based laboratory data for nationwide monitoring of CKD incidence, prevalence, and mortality.
The level of unwarranted variation in CKD occurrence and prognosis in Denmark was low.
The variation in crude CKD occurrence emphasizes the need for targeted allocation of healthcare resources according to differences in population demographics.
INTRODUCTION
Chronic kidney disease (CKD) is a growing global health challenge and among the leading causes of morbidity and mortality worldwide [1, 2]. Moreover, CKD is associated with substantial costs both in terms of loss of quality of life and life-years for the individual and in terms of healthcare expenditures (e.g. kidney replacement therapy) [3]. While mortality from other non-communicable diseases has been steadily declining, age-standardized CKD mortality is rising, suggesting that efforts for improving CKD prevention, identification, and management are warranted [2, 4].
Appropriate care for individuals with CKD encompasses timely diagnostic workup, regular follow-up, lifestyle interventions, and pharmaceutical interventions including the use of nephroprotective medications [5–8]. Information on a population’s state of health, including information on CKD occurrence and prognosis, is essential for directing effective strategies for prevention, detection, and healthcare management [9]. Contemporary estimates of CKD occurrence vary widely and are predominantly based on hospital diagnoses, use of kidney replacement therapy, or laboratory data from specific geographical regions within countries [10–12]. Notably, there is a lack of nationwide data on laboratory-confirmed CKD. In this context, the Danish healthcare data with laboratory information from both primary care and hospitals offer a unique opportunity to assess the incidence, prevalence, and prognosis of CKD in a nationwide population-based setting.
Variation in clinical care practice and healthcare system performance within various medical areas is well documented [13]. Denmark boasts a universal healthcare system with tax-funded and universally available healthcare for all residents [14]. Even so, variability in kidney care has been reported, including differences in active waiting times for transplantation and follow-up after acute kidney injury [15, 16]. Variation in CKD occurrence and prognosis may be related to urbanicity through differences in demographics, lifestyle, comorbidities, and socioeconomic status, but also through differences in the availability and quality of healthcare [11, 17–19]. Importantly, variation that is unrelated to differences in population characteristics is considered unwarranted and may signal underlying quality problems requiring further examination and intervention [13, 20–22]. Thus, examination and reporting of unwarranted variation is of great importance to ensure appropriate and equal healthcare provision. Therefore, this study examined the nationwide incidence, prevalence, and mortality of CKD in Denmark and furthermore assessed variation according to urbanicity.
MATERIALS AND METHODS
Setting
The Danish National Health Services provides tax-supported healthcare for the Danish population (5.9 million in 2023 [23]) including hospital and primary care [14]. All nephrology care in Denmark is centralized at public hospitals with no private nephrology services. The Danish public sector is structured into three administrative levels: the national level (central government), the regional level (five regions), and the local level (98 municipalities) [14]. This structure implies that health legislation, strategies, and guidelines are made at the national level; the regions govern the publicly funded hospitals and provide reimbursement to private practicing general practitioners (GPs); and the municipalities are responsible for social and community care including rehabilitation, home care, and elderly care.
All Danish residents are assigned a unique civil registration number, which is used in healthcare registries and allows for unambiguous individual-level data linkage [24–26]. This study is based on data from the Register of Laboratory Results for Research [27] linked with data from the Danish Civil Registration System [28], the Danish National Patient Registry [29], the Danish National Prescription Registry [30], and the National Health Service Registry [31], which are all population-based registries. The study was registered at the Danish Data Protection Agency through registration at Aarhus University (record number 2016–051-000001/812). According to Danish legislation, ethical approval or informed consent from participants is not required for registry-based, observational studies.
Study populations
This nationwide cohort study examined CKD incidence among all adult (≥18 years) Danish residents without prevalent CKD on 1 January 2019. Follow-up on incident CKD and subsequent all-cause mortality started on 1 January 2019 and ended at outcome occurrence or end of the study period on 31 December 2023. Included individuals were required to have at least 3 years of continuous residency in Denmark before start of the study and were censored in the case of emigration from Denmark during follow-up. Mortality was estimated among individuals with incident CKD during the study period, while CKD prevalence was estimated in the entire adult population alive at the end of the study period.
Chronic kidney disease
From the Register of Laboratory Results for Research, we identified all laboratory measurements of plasma creatinine (pCr) and urine albumin-creatinine ratio (uACR) recorded from 1 January 2016 through 31 December 2023. Measurements of pCR and uACR performed during 1 January 2016 to 31 December 2018 were used to determine CKD status at the start of the study. The Register of Laboratory Results for Research contains all routinely collected laboratory results in primary and hospital care settings with nationwide coverage from 1 October 2015 [32]. We excluded tests obtained during emergency room visits or hospital admissions. We calculated the estimated glomerular filtration rate (eGFR) for all remaining pCr measurements using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2009 formula without correction for race [33]. Chronic kidney disease was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) CKD definition [5]. Thus, all individuals with at least two outpatient measurements of eGFR <60 ml/min/1.73 m2 or uACR >30 mg/g at least 90 days apart were categorized as having CKD. The date of the second qualifying test was defined as the CKD index date.
Urbanicity
Individuals were classified into five categories of urbanicity based on the municipality of residence at the time of inclusion (Supplementary Fig. S1). This classification uses a composite of the number of residents in the largest city in the municipality and the availability of jobs [34]. The categories include capital municipalities (>200 000 available jobs), metropolitan municipalities (<200 000 available jobs and >100 000 residents in the largest city), provincial municipalities (<200 000 available jobs and 30 000–100 000 residents in the largest city), commuter municipalities (40 000–200 000 available jobs and <30 000 residents in the largest city), and rural municipalities (<40 000 available jobs and <30 000 residents in the largest city) [34].
Covariates
We collected data on residency, age, sex, and vital status from the Danish Civil Registration System and data on primary and secondary diagnoses within 10 years prior to index from the Danish National Patient Registry, which includes all nonpsychiatric admissions since 1977 and outpatient visits and emergency room visits since 1995 [26, 29]. Cardiovascular disease covered diagnoses or procedures related to atherosclerotic heart disease, heart failure, cardiac arrhythmias, and valvular heart disease. From the Danish National Prescription Registry, we collected data on all prescription drugs dispensed at community pharmacies or used at nursing homes within 1 year prior to index [30]. We identified type 1 and type 2 diabetes, smoking, alcohol abuse, and hypertension using recorded hospital diagnoses or relevant redeemed prescriptions. Comorbidity was summarized using the Charlson Comorbidity Index (CCI) score based on hospital diagnoses [35, 36]. Codes used for defining covariates are presented in Supplementary Table S1 Kidney function at index was categorized by the most recent outpatient measurement of eGFR and uACR according to the KDIGO categories [5]. Healthcare utilization covered hospitalizations, outpatient visits, and contacts with GPs (online, telephone, and physical) in addition to measurements of eGFR and uACR. Hospitalizations and outpatient visits, including contacts with nephrology departments, were identified using the Danish National Patient Registry, while contacts with GPs were retrieved from the National Health Service Registry, which holds information on services provided by private practicing healthcare providers [31].
Statistical analysis
We assessed characteristics among individuals without CKD on 1 January 2019, individuals with incident CKD during 2019–2023, and individuals with prevalent CKD on 31 December 2023. Categorical variables were presented as counts with proportions and continuous variables as medians with interquartile range (IQR). We estimated crude and standardized CKD incidence rates per 1000 person-years among individuals without CKD on 1 January 2019; crude and standardized all-cause mortality rates per 1000 person-years among persons with incident CKD during the study period; and crude and standardized prevalences of CKD on 31 December 2023. Rates were computed as the number of events divided by the number of person-years, and prevalences were computed as the number of individuals with CKD divided by the number of residents in the population. The weights used for standardization were computed using multinomial logistic regression models [37, 38]. As independent variables, we included age and sex in the first model and age, sex, diabetes, hypertension, and outpatient eGFR and uACR testing frequency in the second model. The weight of the individual resident was set to the inverse of the estimated probability of residing in a municipality with the observed urbanicity category. The weights were stabilized using the marginal distribution of urbanicity categories. Cumulative incidences (risks) of CKD were estimated using the Aalen-Johansen estimator treating death as a competing risk, while mortality after incident CKD was estimated using the Kaplan–Meier estimator [39, 40]. Confidence intervals (CIs) were computed using bootstrapping with 200 samples. In addition to overall CKD incidence, we estimated the incidence of CKD stages A2, A3, and G3a-G5 separately, using the same methods as for overall CKD (Supplementary Methods). Statistical analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC, USA), and graphical illustrations were developed using R version 4.3.3 (R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria; www.R-project.org).
RESULTS
Population characteristics
The overall study population included 4 157 059 adult Danish residents without CKD on 1 January 2019 (Table S2). The median age was 48 years (IQR, 33–62 years) and 50.6% were females. The proportion of female residents increased slightly with the level of urbanicity, while the median age and prevalence of morbidities was higher in rural municipalities than in capital municipalities. Additionally, there was a tendency toward more outpatient visits and outpatient eGFR tests with decreasing urbanicity.
From 2019 to 2023, 281 985 individuals developed incident CKD (Table 1). The median age at the time of CKD was 74 years (IQR, 65–80 years) and 52.8% were females. Frequent comorbidities included hypertension (60.3%), cardiovascular disease (29.5%), and diabetes (23.6%). Similar to the underlying population without CKD, the proportion of females increased with the level of urbanicity, while age, CCI score, and healthcare utilization in the year before CKD development showed little variation across urbanicity categories. Nonetheless, the capital municipalities displayed a lower proportion of individuals with a CCI score of 0, more hospitalizations, and fewer measurements of eGFR and uACR in the year before CKD than the remaining urbanicity categories. The proportion of individuals without a uACR measurement in the prior year was high across all urbanicity categories (36.2%–49.3%).
Table 1:
Characteristics of individuals with incident CKD during 2019–2023.
| All of Denmark | Capital municipalities | Metropolitan municipalities | Provincial municipalities | Commuter municipalities | Rural municipalities | |
|---|---|---|---|---|---|---|
| Number of individuals | 281 985 (100) | 60 672 (21.5) | 30 208 (10.7) | 67 044 (23.8) | 52 682 (18.7) | 71 379 (25.3) |
| Female | 148 946 (52.8) | 33 078 (54.5) | 16 090 (53.3) | 35 452 (52.9) | 27 387 (52.0) | 36 939 (51.8) |
| Age (yr), median (IQR) | 74 (65–80) | 74 (65–80) | 73 (65–80) | 73 (65–80) | 74 (66–80) | 74 (66–80) |
| Age category | ||||||
| <40 yr | 4 750 (1.7) | 1 078 (1.8) | 714 (2.4) | 1 193 (1.8) | 697 (1.3) | 1 068 (1.5) |
| 40–59 yr | 38 524 (13.7) | 8 802 (14.5) | 4 226 (14.0) | 9 460 (14.1) | 7 091 (13.5) | 8 945 (12.5) |
| 60–79 yr | 168 143 (59.6) | 35 362 (58.3) | 17 792 (58.9) | 39 895 (59.5) | 31 997 (60.7) | 43 097 (60.4) |
| ≥80 yr | 70 568 (25.0) | 15 430 (25.4) | 7 476 (24.7) | 16 496 (24.6) | 12 897 (24.5) | 18 269 (25.6) |
| Kidney function | ||||||
| eGFR (ml/min/1.73 m2), median (IQR) | 58 (54–78) | 58 (53–76) | 59 (54–81) | 58 (54–80) | 58 (54–78) | 58 (54–75) |
| eGFR category (ml/min/1.73 m2) | ||||||
| eGFR ≥90 | 36 767 (13.0) | 7 529 (12.4) | 4 578 (15.2) | 9 286 (13.9) | 6 688 (12.7) | 8 686 (12.2) |
| eGFR 60–89 | 60 254 (21.4) | 12 123 (20.0) | 6 875 (22.8) | 15 150 (22.6) | 11 732 (22.3) | 14 374 (20.1) |
| eGFR 45–59 | 162 706 (57.7) | 35 550 (58.6) | 16 681 (55.2) | 37 761 (56.3) | 30 240 (57.4) | 42 474 (59.5) |
| eGFR 30–44 | 17 765 (6.3) | 4 302 (7.1) | 1 620 (5.4) | 3 865 (5.8) | 3 252 (6.2) | 4 726 (6.6) |
| eGFR 15–29 | 3 074 (1.1) | 773 (1.3) | 315 (1.0) | 655 (1.0) | 544 (1.0) | 787 (1.1) |
| eGFR <15 | 714 (0.3) | 177 (0.3) | 81 (0.3) | 153 (0.2) | 115 (0.2) | 188 (0.3) |
| Missing eGFR | 705 (0.3) | 218 (0.4) | 58 (0.2) | 174 (0.3) | 111 (0.2) | 144 (0.2) |
| uACR (mg/g), median (IQR) | 41 (15–79) | 44 (25–87) | 41 (14–78) | 39 (13–76) | 40 (14–77) | 40 (15–79) |
| uACR category (mg/g) | ||||||
| <30 mg/g | 51 715 (18.3) | 8 082 (13.3) | 6 248 (20.7) | 13 949 (20.8) | 10 673 (20.3) | 12 763 (17.9) |
| 30–300 mg/g | 101 503 (36.0) | 20 498 (33.8) | 11 899 (39.4) | 25 463 (38.0) | 19 370 (36.8) | 24 273 (34.0) |
| >300 mg/g | 10 102 (3.6) | 2 186 (3.6) | 1 133 (3.8) | 2 378 (3.5) | 1 891 (3.6) | 2 514 (3.5) |
| Missing uACR | 118 665 (42.1) | 29 906 (49.3) | 10 928 (36.2) | 25 254 (37.7) | 20 748 (39.4) | 31 829 (44.6) |
| Comorbidities | ||||||
| Hypertension | 169 936 (60.3) | 35 427 (58.4) | 17 705 (58.6) | 40 862 (60.9) | 31 429 (59.7) | 44 513 (62.4) |
| Diabetes | 66 631 (23.6) | 14 820 (24.4) | 7 044 (23.3) | 15 600 (23.3) | 12 089 (22.9) | 17 078 (23.9) |
| Cardiovascular disease | 83 111 (29.5) | 17 140 (28.3) | 8 313 (27.5) | 20 410 (30.4) | 15 565 (29.5) | 21 683 (30.4) |
| Cancer | 51 601 (18.3) | 11 271 (18.6) | 5 363 (17.8) | 12 346 (18.4) | 9 903 (18.8) | 12 718 (17.8) |
| COPD | 18 488 (6.6) | 4 265 (7.0) | 1 704 (5.6) | 4 288 (6.4) | 3 317 (6.3) | 4 914 (6.9) |
| Chronic liver disease | 4 962 (1.8) | 1 324 (2.2) | 608 (2.0) | 1 057 (1.6) | 852 (1.6) | 1 121 (1.6) |
| Connective tissue disease | 13 110 (4.6) | 2 740 (4.5) | 1 393 (4.6) | 3 044 (4.5) | 2 539 (4.8) | 3 394 (4.8) |
| Obesity | 18 144 (6.4) | 3 690 (6.1) | 1 668 (5.5) | 4 361 (6.5) | 3 311 (6.3) | 5 114 (7.2) |
| Markers of smoking | 23 791 (8.4) | 5 776 (9.5) | 2 201 (7.3) | 5 347 (8.0) | 4 197 (8.0) | 6 270 (8.8) |
| Alcohol abuse | 8 904 (3.2) | 2 412 (4.0) | 953 (3.2) | 1 932 (2.9) | 1 451 (2.8) | 2 156 (3.0) |
| CCI score | ||||||
| 0 | 128 625 (45.6) | 26 405 (43.5) | 14 239 (47.1) | 30 911 (46.1) | 24 143 (45.8) | 32 927 (46.1) |
| 1–2 | 107 978 (38.3) | 23 775 (39.2) | 11 435 (37.9) | 25 381 (37.9) | 20 212 (38.4) | 27 175 (38.1) |
| ≥3 | 45 382 (16.1) | 10 492 (17.3) | 4 534 (15.0) | 10 752 (16.0) | 8 327 (15.8) | 11 277 (15.8) |
| Healthcare utilization in the prior year | ||||||
| Hospitalizations | ||||||
| 0 | 209 153 (74.2) | 43 747 (72.1) | 23 050 (76.3) | 49 849 (74.4) | 39 461 (74.9) | 53 046 (74.3) |
| 1 | 44 899 (15.9) | 10 179 (16.8) | 4 522 (15.0) | 10 558 (15.7) | 8 253 (15.7) | 11 387 (16.0) |
| ≥2 | 27 933 (9.9) | 6 746 (11.1) | 2 636 (8.7) | 6 637 (9.9) | 4 968 (9.4) | 6 946 (9.7) |
| Outpatient hospital visits | ||||||
| 0 | 83 737 (29.7) | 18 640 (30.7) | 9 139 (30.3) | 19 310 (28.8) | 15 878 (30.1) | 20 770 (29.1) |
| 1–2 | 66 032 (23.4) | 12 808 (21.1) | 7 299 (24.2) | 15 997 (23.9) | 12 521 (23.8) | 17 407 (24.4) |
| ≥3 | 132 216 (46.9) | 29 224 (48.2) | 13 770 (45.6) | 31 737 (47.3) | 24 283 (46.1) | 33 202 (46.5) |
| GP visits | ||||||
| 0–4 | 56 930 (20.2) | 11 912 (19.6) | 5 696 (18.9) | 13 802 (20.6) | 10 805 (20.5) | 14 715 (20.6) |
| 5–9 | 94 803 (33.6) | 19 959 (32.9) | 9 813 (32.5) | 22 708 (33.9) | 17 854 (33.9) | 24 469 (34.3) |
| ≥10 | 130 252 (46.2) | 28 801 (47.5) | 14 699 (48.7) | 30 534 (45.5) | 24 023 (45.6) | 32 195 (45.1) |
| Outpatient eGFR tests | ||||||
| 0 | 705 (0.3) | 218 (0.4) | 58 (0.2) | 174 (0.3) | 111 (0.2) | 144 (0.2) |
| 1–3 | 180 336 (64.0) | 41 363 (68.2) | 18 800 (62.2) | 42 445 (63.3) | 33 910 (64.4) | 43 818 (61.4) |
| ≥4 | 100 944 (35.8) | 19 091 (31.5) | 11 350 (37.6) | 24 425 (36.4) | 18 661 (35.4) | 27 417 (38.4) |
| Outpatient uACR tests | ||||||
| 0 | 118 665 (42.1) | 29 906 (49.3) | 10 928 (36.2) | 25 254 (37.7) | 20 748 (39.4) | 31 829 (44.6) |
| 1 | 107 489 (38.1) | 19 741 (32.5) | 12 423 (41.1) | 27 518 (41.0) | 21 724 (41.2) | 26 083 (36.5) |
| ≥2 | 55 831 (19.8) | 11 025 (18.2) | 6 857 (22.7) | 14 272 (21.3) | 10 210 (19.4) | 13 467 (18.9) |
| Contact with a nephrology department within 6 months after CKD |
9 049 (3.2) | 2 001 (3.3) | 997 (3.3) | 2 530 (3.8) | 1 506 (2.9) | 2 015 (2.8) |
Abbreviations: CCI, Charlson comorbidity index; COPD, chronic obstructive pulmonary disease; ER, emergency room; GP, general practitioner; yr, years.
Values are n (%) unless indicated otherwise.
Incidence of chronic kidney disease
The overall crude CKD incidence rate in Denmark during 2019–2023 was 14.5 (95% CI, 14.4–14.5) per 1000 person-years, which was equivalent to a 5-year risk of 6.9% (95% CI, 6.8%–6.9%) (Table 2 and Fig. 1). The highest rate was found in the rural municipalities [18.0 (95% CI, 17.9–18.1) per 1000 person-years] and the lowest rates in the capital municipalities [11.5 (95% CI, 11.4–11.5) per 1000 person-years] and metropolitan municipalities [11.5 (95% CI, 11.4–11.6) per 1000 person-years] (Table 2). The age and sex standardized model showed less variation with incidence rates ranging from 13.6 (95% CI, 13.9–14.2) to 15.0 (95% CI, 14.9–15.1) per 1000 person-years with additional standardization for diabetes, hypertension, and eGFR and uACR testing frequency only having little impact (Table 2 and Fig. 1). As for the overall analyses, incidence rates of specific CKD stages increased with decreasing urbanicity, but differences were largely removed by age-and-sex-standardization. The most common incident CKD stages were G3a and A2 with incidence rates of 9.8 (95% CI, 9.8–9.8) and 7.0 (95% CI, 7.0–7.0) per 1000 person-years, respectively (Table S3).
Table 2:
Incidence, mortality, and prevalence of CKD.
| Incidence of CKD | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Crude | Age and sex standardized | Fully standardizeda | ||||||||||
| At risk, N | Events | Follow-up, 1 000 person-years | CKD incidence rate (95% CI) per 1 000 person-years | At risk, weighted, N | Events | Follow-up, 1 000 person-years | CKD incidence rate (95% CI) per 1 000 person-years | At risk, weighted, N | Events | Follow-up, 1 000 person-years | CKD incidence rate (95% CI) per 1 000 person-years | |
| All of Denmark | 4 157 059 | 281 985 | 19 510 | 14.5 (14.4–14.5) | ||||||||
| Capital municipalities | 1 129 959 | 61 056 | 5 320 | 11.5 (11.4–11.5) | 1 129 973 | 71 753 | 5 288 | 13.6 (13.5–13.7) | 1 129 789 | 76 039 | 5 275 | 14.4 (14.3–14.5) |
| Metropolitan municipalities | 559 862 | 30 380 | 2 648 | 11.5 (11.4–11.6) | 559 832 | 37 187 | 2 629 | 14.1 (14.0–14.3) | 559 740 | 36 884 | 2 629 | 14.0 (13.9–14.2) |
| Provincial municipalities | 945 761 | 66 664 | 4 446 | 15.0 (14.9–15.1) | 945 801 | 65 212 | 4 450 | 14.7 (14.5–14.8) | 945 752 | 64 144 | 4 451 | 14.4 (14.3–14.5) |
| Commuter municipalities | 666 694 | 52 474 | 3 120 | 16.8 (16.7–16.9) | 666 794 | 46 906 | 3 138 | 14.9 (14.8–15.1) | 666 783 | 46 945 | 3 137 | 15.0 (14.9–15.1) |
| Rural municipalities | 854 783 | 71 411 | 3 976 | 18.0 (17.9–18.1) | 854 916 | 60 089 | 4 017 | 15.0 (14.9–15.1) | 854 836 | 58 500 | 4 023 | 14.5 (14.4–14.6) |
| Mortality among individuals with CKD | ||||||||||||
| Crude | Age and sex standardized | Fully standardized | ||||||||||
| At risk, N | Events | Follow-up, person-years | Mortality rate (95% CI) per 1 000 person-years | At risk, weighted, N | Events | Follow-up, person-years | Mortality rate (95% CI) per 1 000 person-years | At risk, weighted, N | Events | Follow-up, person-years | Mortality rate (95% CI) per 1 000 person-years | |
| All of Denmark | 281 985 | 36 283 | 645 226 | 56.2 (55.8–56.7) | ||||||||
| Capital municipalities | 60 672 | 8 050 | 140 856 | 57.2 (56.1–58.2) | 60 672 | 8 020 | 140 882 | 56.9 (55.7–58.2) | 60 657 | 7 817 | 139 397 | 56.1 (54.8–57.4) |
| Metropolitan municipalities | 30 208 | 3 783 | 67 829 | 55.8 (54.3–57.3) | 30 208 | 3 831 | 67 765 | 56.5 (54.8–58.3) | 30 221 | 3 957 | 68 278 | 58.0 (56.1–59.8) |
| Provincial municipalities | 67 044 | 8 306 | 152 456 | 54.5 (53.6–55.3) | 67 044 | 8 408 | 152 328 | 55.2 (54.1–56.3) | 67 043 | 8 621 | 152 670 | 56.5 (55.4–57.6) |
| Commuter municipalities | 52 682 | 6 600 | 121 001 | 54.5 (53.4–55.7) | 52 682 | 6 641 | 120 928 | 54.9 (53.5–56.3) | 52 686 | 6 770 | 121 069 | 55.9 (54.6–57.3) |
| Rural municipalities | 71 379 | 9 544 | 163 084 | 58.5 (57.6–59.5) | 71 379 | 9 387 | 163 308 | 57.5 (56.4–58.6) | 71 385 | 9 144 | 163 381 | 56.0 (54.9–57.0) |
| Prevalence of CKD | ||||||||||||
| Crude | Age and sex standardized | Fully standardized | ||||||||||
| Population, N | Prevalent CKD, N | CKD prevalence, % (95% CI) | Population, weighted, N | Prevalent CKD, N | CKD prevalence, % (95% CI) | Population, weighted, N | Prevalent CKD, N | CKD prevalence, % (95% CI) | ||||
| All of Denmark | 4 807 630 | 456 083 | 9.5 (9.5–9.5) | |||||||||
| Capital municipalities | 1 340 969 | 98 008 | 7.3 (7.3–7.3) | 1 341 035 | 121 127 | 9.0 (9.0–9.1) | 1 340 788 | 131 985 | 9.8 (9.8–9.9) | |||
| Metropolitan municipalities | 656 281 | 49 841 | 7.6 (7.5–7.6) | 656 338 | 61 907 | 9.4 (9.4–9.5) | 656 219 | 60 434 | 9.2 (9.1–9.3) | |||
| Provincial municipalities | 1 089 364 | 109 238 | 10.0 (10.0–10.1) | 1 089 367 | 105 202 | 9.7 (9.6–9.7) | 1 089 361 | 102 985 | 9.5 (9.4–9.5) | |||
| Commuter municipalities | 764 457 | 83 030 | 10.9 (10.8–10.9) | 764 428 | 73 160 | 9.6 (9.5–9.6) | 764 454 | 73 393 | 9.6 (9.5–9.7) | |||
| Rural municipalities | 956 559 | 115 966 | 12.1 (12.1–12.2) | 956 652 | 93 690 | 9.8 (9.7–9.8) | 956 601 | 89 598 | 9.4 (9.3–9.4) | |||
The fully standardized models included age, sex, diabetes, hypertension, and outpatient eGFR and uACR testing frequency.
Figure 1:
Cumulative incidence of CKD with 95% CIs. 1The fully standardized model includes standardization for and age, sex, diabetes, hypertension, and outpatient eGFR and uACR testing frequency.
All-cause mortality
Among individuals with incident CKD, the overall mortality rate was 56.2 (95% CI, 55.8–56.7) per 1000 person-years, which was equivalent to a 5-year mortality of 24.9% (95% CI, 24.5%–25.3%) (Table 2 and Fig. 2). The lowest mortality rates were found in provincial [54.5 (95% CI, 53.6–55.3) per 1000 person-years] and commuter [54.5 (95% CI, 53.4–55.7) per 1000 person-years] municipalities, while rural municipalities had the highest rate [58.5 (95% CI, 57.6–59.4) per 1000 person-years] (Table 2). As for CKD incidence, the variation in mortality was largely explained by differences in age and sex (Table 2 and Fig. 2).
Figure 2:
Cumulative mortality with 95% CIs among individuals with incident CKD. 1The fully standardized model includes standardization for and age, sex, diabetes, hypertension, and outpatient eGFR and uACR testing frequency.
Prevalence of chronic kidney disease
On 31 December 2023, 456 083 individuals had prevalent CKD and the overall CKD prevalence was 9.5% (95% CI, 9.5%–9.5%), with the prevalence across urbanicity categories ranging from 7.3% (95% CI, 7.3%–7.3%) in capital municipalities to 12.1% (95% CI, 12.1%–12.2%) in rural municipalities (Table 2 and Fig. 3). Similar to the variation in CKD incidence and mortality, the variation in CKD prevalence was largely explained by differences in age and sex (Table 2).
Figure 3:
Prevalence of CKD on 31 December 2023. The white lines indicate municipality borders.
Among individuals with prevalent CKD, 92% had an eGFR measurement within the prior year, and the distribution of eGFR categories was similar across urbanicity categories (Table S4). The proportion of individuals with a uACR measurement within the prior year ranged from 46.7% in the capital municipalities to 63.6% in metropolitan municipalities. Moreover, individuals in the capital municipalities had a higher CCI score and were more often missing an eGFR measurement within the prior year than in the other urbanicity categories (Table S4).
DISCUSSION
This nationwide cohort study used population-based laboratory data on pCr and uACR measurements to assess CKD occurrence and prognosis in Denmark. By the end of 2023, nearly one in ten adults in Denmark had prevalent CKD, and among individuals with newly diagnosed CKD, one in four died within 5 years. Chronic kidney disease incidence and prevalence varied across urbanicity categories, while the variation in mortality after incident CKD was less prominent. The observed variation was markedly reduced when adjusting for differences in sex and age, while additional adjustment for diabetes, hypertension, and eGFR and uACR testing frequency had limited effect.
This study is the first to assess CKD occurrence and prognosis with complete nationwide laboratory information from primary care and hospitals. Similar to our overall CKD prevalence, a study from the Northwest of Ireland reported a CKD prevalence of 11.8% [41], while a Swedish study covering the region of Stockholm reported a prevalence of 6.1% [42]. However, estimates of CKD prevalence vary widely across European countries. Brück et al. compared CKD prevalence across 13 European countries and found crude prevalences that varied from 3.7% in Norway to 19.4% in Northeast Germany [11]. This variation persisted after accounting for differences in age and sex and was consistent across high-risk populations, e.g. individuals with hypertension and diabetes, which suggests that factors such as climate, diet, public health policies, genetic factors, etc. may be important for variation between European countries [11].
In addition to country-to-country variation, national variation in CKD prevalence has been reported in specific regions of Ireland and Germany [41, 43]. In Ireland, the prevalence of CKD ranged from 10.4% to 13.5% based on eGFR measurements, while the prevalence of CKD in two small cross-sectional surveys in Germany ranged from 3.1% to 5.9% based on eGFR measurements and from 8.8% to 20.2% based on uACR measurements. In contrast to our findings, these variations in CKD prevalence were not explained by differences in CKD risk factors including age, sex, diabetes, and hypertension [41, 43].
Overall, the five Danish regions are considered homogeneous in terms of sociodemographic and health-related characteristics [44]. Nonetheless, the Capital Region of Denmark has a lower proportion of elderly and fewer prescription drug users than the other regions [44]. This was reflected in the characteristics of the population without CKD in our study. Reflective of the strong association and shared underlying risk factors of cardiovascular-, metabolic-, and kidney diseases, the most frequent comorbidities in individuals with incident CKD included hypertension, cardiovascular disease, and diabetes [45, 46]. Individuals with incident CKD from the capital municipalities had a slightly higher CCI score and more hospitalizations within the last year than the other urbanicity categories. The higher frequency of hospitalizations may contribute to the slightly higher CCI score as differences in surveillance may affect the registration of hospital diagnoses. However, it may also reflect that individuals with incident CKD from the capital municipalities actually have more comorbidities, especially since the frequency of hospitalizations among individuals with prevalent CKD was higher in the capital municipalities, while hospitalizations among the population without CKD did not differ across urbanicity categories.
The overall low proportion of individuals with a uACR measurement confirms a low level of surveillance in general and highlights a potential area of improvement in CKD care with the current KDIGO CKD guidelines recommending annual assessment of albuminuria [5]. In a universal healthcare system, this lack of surveillance is likely related to the inadequate awareness and silent nature of CKD in the early stages, thus leaving the condition unrecognized by both physicians and patients [47]. This highlights the need for increased disease awareness, which could be aided by new technologies including risk prediction tools [48].
The nationwide population-based design and the availability of laboratory data from primary care and hospitals are important strengths of this study [32, 48, 27]. However, the study has limitations that need to be considered. First, the study relied on routinely collected clinical data and the use of uACR measurements among individuals without prevalent CKD was low. Thus, we may have underestimated the overall incidence and prevalence of CKD and overlooked variations in undiagnosed CKD related to urbanization. By contrast, repeated episodes of acute kidney injury with eGFR <60 ml/min/1.73 m2 may have qualified as CKD if separated by >90 days and could have caused an overestimation of CKD occurrence. To minimize this risk, we excluded measurements performed during emergency room visits or hospital admissions as these could have been performed during acute illness. However, as the clinical indication for prescribing an eGFR or uACR measurement is not provided in the laboratory databases, we cannot ensure that this did not influence our results. Second, the frequency of eGFR and uACR measurements may affect the time of CKD detection and thus the characteristics of individuals with incident CKD [48]. Outpatient eGFR testing of individuals without prevalent CKD was more common as the level of urbanicity decreased. This is likely related to differences in demographics and morbidities and may increase the sensitivity for detecting CKD in rural municipalities, which could facilitate the inclusion of individuals with less severe CKD. Even so, the age and eGFR of individuals with incident CKD were overall similar across urbanicity categories. Moreover, the differences in crude incidence rates were mitigated by standardization for age and sex, while the addition of diabetes, hypertension, and outpatient eGFR and uACR testing frequency had little effect. Finally, while the validity of definitions for covariates used in this study is generally considered high, information on specific variables including alcohol abuse and smoking was only available through surrogate markers, which could have led to misclassification [29].
In a nationwide population-based setting, we have shown that CKD is common and associated with high mortality. Additionally, we found variation in CKD incidence and prevalence across urbanicity categories in Denmark, which was to a large extent explained by differences in demographics. Importantly, the variation in crude incidence and prevalence rates across urbanicity categories reflects the actual burden of CKD on the healthcare system. Thus, there is a need for a prioritized resource allocation to facilitate a high quality of care in rural areas, where the burden of CKD is higher. Finally, the present study demonstrates the potential of utilizing population-based laboratory data for nationwide monitoring of CKD incidence, prevalence, and mortality. Acknowledging the increasing global prevalence of CKD, this supports the implementation of surveillance programs for CKD occurrence and prognosis that could facilitate healthcare planning and resource allocation.
Supplementary Material
Contributor Information
Simon Kok Jensen, Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark.
Michael Bertelsen, Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Søren Viborg Vestergaard, Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Mette Nørgaard, Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Reimar Wernich Thomsen, Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Henrik Birn, Department of Biomedicine, Aarhus University, Aarhus, Denmark; Department of Renal Medicine, Aarhus University Hospital, Aarhus, Denmark.
Uffe Heide-Jørgensen, Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Christian Fynbo Christiansen, Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; CONNECT - Center for Clinical and Genomic Data, Aarhus University Hospital, Aarhus, Denmark.
FUNDING
The study was supported by the Karen Elise Jensen Foundation and the Independent Research Fund Denmark (grant number 0134-00407B).
AUTHORS' CONTRIBUTIONS
All authors participated in conceptualizing and designing the study. S.K.J. and M.B. reviewed the literature. S.K.J., M.B., and C.F.C. directed the analyses, which were performed by U.H. All authors participated in the discussion and interpretation of the data. S.K.J. organized the writing and wrote the initial draft. All authors critically reviewed the manuscript for intellectual content and approved the final version for submission.
DATA AVAILABILITY STATEMENT
The data underlying this article cannot be publicly shared according to Danish law. Researchers from certified Danish research institutions can request access to the databases used in this study by emailing the Danish Health Data Authority (forskerservice@sundhedsdata.dk).
CONFLICT OF INTEREST STATEMENT
S.K.J., M.B., S.V.V., M.N., R.W.T., U.H., and C.F.C. have no personal conflicts of interest to declare regarding this study. The departments of Clinical Epidemiology, Biomedicine, and Renal Medicine are involved in studies with funding from various companies in the form of research grants to and administered by Aarhus University or Aarhus University Hospital. None of these grants are related to the present study. H.B. has received a research grant from Glaxo Smith Kline (GSK) and Vifor Pharma (paid to institution) and has received consultancy fees and/or speaker honorariums from AstraZeneca, Boehringer Ingelheim, Bayer, GSK, Alexion, Novo Nordisk, Netdoktor.dk, and BestPractice.dk as well as support for attending meetings from Novartis and AstraZeneca.
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Associated Data
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Supplementary Materials
Data Availability Statement
The data underlying this article cannot be publicly shared according to Danish law. Researchers from certified Danish research institutions can request access to the databases used in this study by emailing the Danish Health Data Authority (forskerservice@sundhedsdata.dk).