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Spatial distribution of acute myeloid leukaemia in Denmark

Environmental Health volume 24, Article number: 21 (2025) Cite this article

Abstract

Background

Although various occupational and environmental exposures are suspected risk factors for acute myeloid leukaemia (AML), the aetiology of AML is largely unknown. We present an analysis of the spatial distribution of AML in Denmark on an unprecedented, detailed scale. Such investigations have the potential to uncover geographical areas of increased risk, which may in turn be tied to environmental or occupational exposures.

Methods

Individuals diagnosed with AML during 2000–2020 were obtained from the Danish National Acute Leukaemia Registry and assigned to a parish based on their residence six months prior to diagnosis. The incidence rate ratio (IRR) by parish was calculated as the ratio between the age- and sex-standardised incidence rate and the national incidence rate. The IRRs were smoothed using a spatial Poisson distributed generalised linear mixed model with a conditional autoregressive correlation structure. Parishes with a smoothed IRR > 1.10 with a posterior probability > 75% were considered to have an increased risk of AML.

Results

The study included 5,177 AML cases. The spatial model showed a homogeneous distribution of AML in Denmark with no parishes having an increased risk.

Conclusion

The study indicates that the risk of developing AML in Denmark is not affected by place of residence, suggesting that if an unknown environmental or occupational risk factor is present, it does not seem to be associated with specific areas.

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Introduction

Acute myeloid leukaemia (AML) is an aggressive haematologic cancer characterised by immature blast cell proliferation [1]. Though several somatic genetic alterations driving the different types of leukemogenesis have been identified, the aetiology of AML remains poorly understood [2]. Knowledge of aetiology may lay the foundation for future preventive actions which makes identification of risk factors for developing AML important.

Several factors have been associated with AML. Well-established risk factors include older age, male sex, Down’s syndrome, and prior treatment with chemotherapy or radiation [3,4,5,6,7]. Lifestyle factors such as smoking and obesity have also been associated with an increased risk of AML [8, 9]. However, for the majority of AML patients, no predisposing risk factors can be identified [2]. In this context, occupational and environmental factors have been suspected, in particular exposure to benzene [10, 11]. Environmental exposure to benzene has been explored by Talbott et al. who studied the effects of a gasoline leak in Pennsylvania and found an increased incidence rate of AML around the leak [12]. In addition, pesticides have been associated with an increased risk of AML in a meta-analysis [13]. The concerns about environmental and occupational factors as risk factors for developing AML have previously been explored by mapping the occurrence of AML in e.g., Northern Tunisia, Canada, and Brazil [14,15,16]. However, these studies are to some degree biased due to a lack of adjustment for a heterogenous age and sex distribution and the use of large geographical units.

In Denmark, health service utilisation is systematically recorded at an individual level in nationwide registries [17]. Furthermore, Denmark is structured into over 2,000 parishes with complete information on the age and sex distribution in each unit. These factors make it possible to study the spatial distribution of diseases in Denmark at a highly detailed level. In this study, we aim to investigate the spatial distribution of AML to identify possible parishes of increased risk which could prompt further investigation on environmental and occupational risk factors.

Methods

Data sources

We used the Danish National Acute Leukaemia Registry (DNLR) to identify incident AML cases. The DNLR contains clinical information on all adult Danish individuals with AML registered by the Danish haematological hospital departments since 2000. The registration completeness is nearly 100% [18].

Danish citizens are at birth or immigration assigned a personal identification number (CPR), which was used to link individual-level data from the DNLR to residential addresses from the Danish Civil Registration System; a national registry established in 1968 containing information about every Danish citizen from birth to death [19]. We used the residence six months prior to diagnosis to account for relocation to elder homes or nursing homes because of deteriorating health. Using the Danish Address Register [20], residential addresses were linked to one of 2,141 Danish parishes based on the parish division as of 2021. Population demographics from 2014 were used for standardisation and obtained from Statistics Denmark [21]. As the two parishes Høsterkøb and Birkerød belonged to a single parish in 2014, the two parishes were joined to enable standardisation, thus leaving a total of 2,140 parishes for analysis.

Coordinates describing the boundaries of the parishes for choropleth mapping were obtained from official sources [22].

Study cohort

The study cohort included individuals fulfilling the following criteria: 1) diagnosed with AML between 1 January 2000 and 31 December 2020; 2) ≥ 18 years old at diagnosis; and 3) residential address in Denmark six months prior to diagnosis. The patients were categorised into de novo, secondary, and therapy related AML. Secondary AML was defined as patients with a haematological disease prior to the AML diagnosis. Therapy related AML was defined as patients who had received chemo- and/or radiation therapy prior to the AML diagnosis.

Statistical methods

Baseline characteristics of the AML cohort were presented as counts and percentages. The national incidence rate was calculated as the total number of Danish AML cases over the study period divided by an estimated “total person-time at risk” based on the Danish population size in 2014 multiplied with a 21-year exposure period.

For each parish, we first calculated the parish-to-national incidence rate ratio (IRR) by dividing the observed number of cases by the expected number of cases under the national incidence rate. The expected number of cases were indirectly standardised with respect to sex and age using the following age groups: ≤40, 41–50, 51–60, 61–70, 71–80, 81–90, and > 90 years. The R-package PHEindicatormethods (v2.0.2) [23] was used for standardisation. Next, to obtain more stable estimates, we considered a fully Bayesian model framework to calculate smoothed parish-to-national IRRs by borrowing information from neighbouring parishes. Specifically, we used a spatial Poisson distributed generalised linear mixed model with a conditional autoregressive correlation structure as specified by the Leorux model [24]. The neighbourhood matrix was specified as a binary matrix taking the value 1 if two parishes shared a border, were connected by bridge, or, for parishes constituting a whole island, were connected by ferry. The spatial model was fitted using the R-package CARBayes (v6.1.1) [25] with default settings for priors. Inference was based on 6,000 Markov Chain Monte Carlo simulations obtained from three chains, a burnin of 100,000 samples, and thinning by 20. For model checking, Moran’s I statistic was calculated to check for residual autocorrelation using the R-package ape (v5.8) [26]. To identify any parishes with a higher-than-expected incidence rate, we calculated the posterior probability of the smoothed incidence rate being elevated with more than 10% compared to the national level, corresponding to a smoothed IRR above 1.10. Parishes with a posterior probability of more than 75% were considered to have an increased risk of AML. For a more detailed description of the modelling, see Appendix A in Christensen et al. [27].

As environmental and occupational factors may be less influential in the aetiology of secondary and therapy related AML, we performed a sensitivity analysis only including patients with de novo AML.

To investigate potential bias due to relocation, we examined the proportion of patients living in the same parish from six months prior to diagnosis and 15 years back in time.

All data were pseudo-anonymised and stored on a secured server governed by Statistics Denmark. All statistical analyses were conducted in R (v4.4.0) [28].

Results

Baseline characteristics

In total, 5,177 incident cases of AML from 2000 to 2020 were included in the study cohort. At the national level, the incidence rate of AML was 4.38 per 100,000 person-years. The majority of the patients were above 70 years at time of diagnosis, and the male-female ratio was 1.24:1. Characteristics of the cohort can be seen in Table 1.

Table 1 Characteristics of the population-based cohort with incident AML from 2000 to 2020
Full size table

Spatial distribution of AML across parishes

A total of 2,140 parishes were included with a median population size of 1,039 (interquartile range: 448-3,378) persons. The raw age- and sex-standardised IRRs are depicted in Supplementary Fig. 1. After fitting the spatial model, no residual autocorrelation was detected (Moran’s I statistic = 0.0051, p-value = 0.69). Figure 1 shows the smoothed IRRs of AML based on the fitted model. The map depicts a homogenous distribution of AML across the country with smoothed IRRs ranging from 0.86 to 1.19. A total of 40 parishes had a smoothed IRR above 1.10 with the elevated incidence rates corresponding to less than three extra predicted AML cases over 21 years.

Fig. 1
figure 1

Smoothed parish-to-national incidence rate ratios of acute myeloid leukaemia across 2,140 Danish parishes. Abbreviations: IRR, incidence rate ratio

Full size image

As shown in Fig. 2, the posterior probability of having a smoothed IRR above 1.10 did not exceed 75% for any of the parishes.

Fig. 2
figure 2

Posterior probability of the smoothed parish-to-national incidence rate ratios being higher than 1.10. The posterior probabilities are plotted against the population size of the parishes. The dashed line marks the 75% threshold used to identify parishes of increased risk. Abbreviations: IRR, incidence rate ratio

Full size image

Similar results were seen when excluding patients with secondary and therapy related AML (Supplementary Figs. 2 and 3).

The analysis of relocation showed that approximately 70% had lived in the same parish for 15 years (Supplementary Fig. 4).

Discussion

We present a comprehensive analysis of the spatial distribution of AML across Danish parishes during the period 2000–2020. We demonstrate a homogeneous spatial distribution of AML with smoothed IRRs ranging from 0.86 to 1.19. Though several parishes had a smoothed IRR above 1.10, none surpassed the 75% posterior probability threshold. Thus, no parishes of increased risk were identified. This indicates that no area-specific risk factors of AML are present in Denmark, though we cannot exclude the possibility of a uniformly distributed environmental or occupational risk factor being present across the country.

In our study, we defined a parish as having an increased risk of AML based on two thresholds; one defining when an elevated smoothed IRR is considered of health-related relevance, and one defining when the posterior probability is considered high enough to mark the parish as having an increased risk. The thresholds used in our study were 1.10 and 75%, respectively, which could be considered low. Given the low incidence of AML, the actual differences between the expected and predicted number of AML cases in the parishes were small. Even in the parishes with a smoothed IRR above 1.10, the difference only corresponded to less than three extra predicted AML cases over 21 years. However, the thresholds were chosen to not overlook potential parishes of increased risk.

To the best of our knowledge, this is the first study examining the spatial distribution of AML in Denmark at a detailed level. Maps and tables depicting age-standardised incidence rates across the Nordic countries and at different time periods are available in the NORDCAN database. The incidence rates are based on data from the Nordic cancer registries but are only presented at a regional level. In accordance with our findings, the NORDCAN data show similar rates of AML between the five Danish regions with age-standardised incidence rates ranging from 4.7 to 5.5 and 3.5 to 4.0 per 100,000 person-years for males and females ≥ 20 years, respectively, in the period 2000–2022 [29]. Spatial analyses of AML incidence have previously been made in other countries but most of the studies suffers from biases. A Brazilian study covering 62 municipalities in the state of Amazonas found 65.2% of all cases to be living in the capital region. However, this was accredited to the greater financial resources in the area, allowing for better diagnosis and reporting thereby leading to selection bias [16]. Other studies lack statistical power or have used large geographical units [14, 30,31,32,33,34,35,36,37,38]. In particular, the use of large geographical units carries the risk of ecological fallacy of blurring varying disease risks over smaller areas [39]. A study from Canada found a significantly elevated incidence rate over 300% higher than the national average in an industrial postal code, potentially related to oil refineries and chemical plants [15]. However, this study has been criticised for lack of age standardisation, large subunits with a median population of 18,000, rounding issues (could allegedly affect rates by up to 67%), and lack of correction for multiple testing [40,41,42]. In comparison, our analysis did not find increased risk of AML linked to the large Danish refineries in Fredericia and Kalundborg.

Compared to previous studies, our study has several strengths. Considering the small sizes of the Danish parishes, we were able to map the spatial distribution of AML at a much more detailed level, allowing for a more precise identification of potential areas of increased risk. However, due to the small population sizes of the parishes, even a small variation in the observed AML cases can lead to a high variability in the incidence rates, rendering the raw IRRs unstable. To obtain stable estimates, we used a statistical model to smoothen the IRRs by borrowing information from neighbouring parishes. Additionally, as our data are retrieved from population-based registries with a high level of completeness and accuracy throughout the study period, the results are unlikely to have been influenced by recording and selection bias.

In our study, we investigated the incidence rate for each parish over 21 years, providing an average estimate of the risk of AML for the time period. However, exposure to potential environmental or occupational risk factors could have changed during the period, meaning that we might have overlooked parishes having an increased risk only for a limited part of the period. Additionally, we mapped the spatial distribution of AML based on the residential addresses six months before date of diagnosis, but due to the time lag between exposure and cancer occurrence, an individual could have moved to a different parish between the two events. While it is difficult to estimate the latency period for AML, it has been found to vary according to the mutated gene and clonal complexity from a median time of a few years to approximately a decade [43]. Additionally, in Talbott et. al.’s study on gasoline spill, an increase in the incidence of AML was observed already after 4–10 years following the exposure [12]. In our study, approximately 70% lived in the same parish as they did 15 years prior to six months before date of AML diagnosis. Therefore, we believe any bias due to relocation after time of exposure to be minimal. Lastly, though we wanted to investigate the presence of potential environmental and occupational risk factors, spatial variations in IRRs could also be caused by other factors. We partly accounted for this by standardising the estimates with respect to age and sex and by performing a sensitivity analysis excluding patients with secondary and therapy related AML. However, as we did not have access to data on lifestyle factors such as smoking and body mass index, we were not able to account for any variation caused by these factors.

In conclusion, we found a homogenous spatial distribution of AML across the Danish parishes, indicating that the risk of developing AML in Denmark is not affected by place of residence.

Data availability

Research data from Statistics Denmark cannot be shared according to the GDPR.

Abbreviations

AML:

Acute myeloid leukaemia

CPR:

Personal identification number

DNLR:

Danish National Acute Leukaemia Registry

GDPR:

General Data Protection Regulation

IRR:

Incidence rate ratio

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Acknowledgements

Not applicable.

Funding

CT’s research was funded by the Novo Nordisk Foundation, grant no. NNF21SA0069373. RHJ’s, LDB’s, and MB’s research was funded by Health Insurance “danmark”, grant no. 2022-027. HSC was funded by the Danish National Research Foundation, grant no. DNRF148. The funders had no role in the design or writing of this article.

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Author notes

  1. Rikke Hedegaard Jensen and Christian Teglgaard contributed equally to this work and shared first authorship.

Authors and Affiliations

  1. Department of Haematology, Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark

    Christian Teglgaard, Lise Dueholm Bertelsen & Marianne Tang Severinsen

  2. Center for Clinical Data Science, Department of Clinical Medicine, Aalborg University and Research, Education, and Innovation, Aalborg University Hospital, Aalborg, Denmark

    Rikke Hedegaard Jensen, Lars Hernández Nielsen & Martin Bøgsted

  3. Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark

    Rikke Hedegaard Jensen, Lars Hernández Nielsen & Martin Bøgsted

  4. Center for Molecular Prediction in Inflammatory Bowel Disease, Department of Clinical Medicine, Aalborg University, Copenhagen, Denmark

    Heidi Søgaard Christensen

  5. Department of Clinical Medicine, Aalborg University, Aalborg, Denmark

    Lise Dueholm Bertelsen & Marianne Tang Severinsen

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Contributions

CT, LDB, LHN, MTS, and MB conceptualized and designed the work. LHN, HSC, and MB designed the statistical model. RHJ and CT analysed the data and wrote the manuscript. All authors reviewed the manuscript and approved the final version.

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Correspondence to Rikke Hedegaard Jensen.

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In Denmark, no ethical approval or informed consent are required for registry studies. However, registry studies involving person sensitive data must, according to the General Data Protection Regulation (GDPR), be registered by the data responsible. This project was registered at the North Denmark Region (Registration no. 2021-056).

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Supplementary Material 1

: Supplementary fig. 1: Age- and sex-standardised parish-to-national incidence rate ratios of acute myeloid leukaemia across 2,140 Danish parishes.

Supplementary Material 2

: Supplementary fig. 2: Smoothed parish-to-national incidence rate ratios of de novo acute myeloid leukaemia across 2,140 Danish parishes.

Supplementary Material 3

: Supplementary fig. 3: Posterior probability of the smoothed parish-to-national incidence rate ratios being higher than 1.10 for de novo acute myeloid leukaemia.

Supplementary Material 4

: Supplementary fig. 4: Percentage of acute myeloid leukaemia patients living in the same parish as six months before diagnosis.

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Jensen, R.H., Teglgaard, C., Nielsen, L.H. et al. Spatial distribution of acute myeloid leukaemia in Denmark. Environ Health 24, 21 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-025-01177-4

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Environmental Health

ISSN: 1476-069X