In the Nordic countries, Sweden has the largest population with over ten million inhabitants in 2023. Denmark, Finland, and Norway all have between 5.5 and six million inhabitants, whereas Iceland clearly has the lowest number with only 390,000 people. The population increased in all five Nordic countries over the past 20 years. Aging population In all five Nordic countries, the average age of the population is increasing. In all countries except Iceland, people aged 70 years or more make up the largest age groups. Hence, one of the issues facing the Nordic countries in the coming decades is that of a shrinking working stock, while there will be more elderly people in need of daily care. Births, deaths, and migration The two reasons behind the constantly increasing population in the Nordic countries are that more people are born than people dying, and a positive net migration. Except for Finland, the death rate decreased in all Nordic countries over the past 20 years. However, the fertility rate has also fallen in all five countries in the recent years, meaning that an increasing immigration play an important role in sustaining the population growth.

Denmark has, by far, the highest population density of the Nordic countries. This is related to the fact that it is the smallest Nordic country in terms of land area. Meanwhile, Iceland, which has the smallest population of the five countries, also has the lowest population density. As the total population increased in all five countries over the past decade, the population density also increased.

With 450,295 square kilometers, Sweden is the largest Nordic country by area size, followed by Finland and Norway. This makes it the fifth largest country in Europe. Meanwhile, Denmark is the smallest of the five Nordic countries with only 43,094 square kilometers, however, the Danish autonomous region of Greenland is significantly larger than any of the Nordic countries, and is almost double the size of the other five combined.

Population

Sweden is also the Nordic country with the largest population. 10.45 million people live in the country. Denmark, Finland, and Norway all have between five and six million inhabitants, whereas only 370,000 people live in Iceland. Meanwhile, Denmark has the highest population density of the five countries. Greenland is the most sparsely populated permanently-inhabited country in the world, followed by the regions of Svalbard and Jan Mayen.

Geography

The five Nordic countries vary geographically. While Denmark is mostly flat, its highest point only stretching around 170 meters above sea level, Norway's highest peak is nearly 2,500 meters high. Moreover, Finland is known for its many lakes and is often called the land of a thousand lakes, whereas Iceland is famous for its volcanoes.

Over the past 23 years, the total population of Sweden increased steadily. In 2000, there were nearly 8.9 million people living in the Scandinavian country, and this had increased to 10.55 million in 2023. The population growth is expected to continue over the next decades, and it is estimated that the population of Sweden will reach over 13 million by 2080.

Immigration drove the population growth

One main reason for the steadily increasing is the number of immigrants arriving in the country. Even though the number of immigrants fell since the peak in 2016, the population with a foreign background increased steadily over the past 10 years.

Syrians make up the largest group of foreigners

The high number of immigrants arriving in Sweden in 2016 was caused by the high number or refugees fleeing the Syrian Civil War. As of 2022, Syrians made up the largest foreign group residing in the country. Next to refugees from the Middle East, immigrants from other EU-members such as Poland and neighboring Finland constituted a high number of the foreign-born citizens living in the country.

In 2024, the people in the Nordic countries, with the exception of Finland, worked more part-time than the EU average. Denmark had the highest share of its employed population working part-time, with **** percent recorded in 2024. Finland had the lowest at ** percent.

Background and Aims: Declining fertility is a key driver behind the rapid aging of populations worldwide. Finland has experienced a 25% decline in fertility from 2010 to date and ranks low even on the European and Nordic scales. This study aimed to address the association between sociodemographic indicators and birth rate (i.e., live births relative to total population) in Finland.Methods: Open data on 310 Finnish municipalities were retrieved from the public database of Statistics Finland. Several sociodemographic subdimensions (population structure, education and income, location and living, divorces, car ownership rate, and crime rate), each converted to standard deviation units, were modeled against birth rate at the municipality level using generalized estimating equations.Results: In this dataset, average annual birth rate was 8.8 per 1,000 individuals. Birth rate was positively associated with change in population size (rate ratio 1.06, 95% confidence interval 1.04−1.08), percentage of

Antidepressant Use in Scandinavia

A Study of Population Characteristics and Drug Utilization Rates

By [source]

About this dataset

This fascinating dataset examines the use of antidepressant medications among children and adolescents in Denmark, Norway, and Sweden from 2007 until 2017. Through a comprehensive exploration of drug usage along with population characteristics, we can uncover deeper insights into the prevalence of antidepressant use in this demographic and its potential causes. By carefully inspecting this data set which contains details about drug use, census data and associated drug names by code, we can shed light on an important issue with far reaching implications for public health worldwide

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How to use the dataset

This dataset offers an opportunity to analyze antidepressant use among children and adolescents in Denmark, Norway and Sweden from 2007 to 2017. To get started with your analysis, you'll need to familiarize yourself with the dataset. Below are some simple steps for getting acquainted with the available resources:

• Familiarize yourself with the column descriptions and data types. Each column contains meaningful information about drug use and population characteristics in the three countries during this window of time.
• Review the drug_names file contained in this dataset for a detailed list of drugs associated with each code represented in the main table. This is particularly important because ATC (Anatomical Therapeutic Chemical) codes provide an easy shorthand way of referring to individual medications without being too long-winded or cluttering up columns not relevant to your particular question or hypothesis
• Explore correlations between different parameters using crosstabs, scatterplots, or other common visualizations as necessary
• Use census data contained in census_data file as a reference when discussing population makeup within any given country during this period

With this approach, you will have all that's necessary to derive meaningful results out of this dataset! Good luck on your exploration!

Research Ideas

• Comparing the sex, age and population weights of those using different types of antidepressants in each country
• Tracking consumption trends across countries and between genders over time
• Correlating antidepressant use with national income indicators such as GDP per capita or overall Mental Health Index scores

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: census.csv | Column name | Description | |:--------------|:------------------------------------------| | year | Year of the data (Integer) | | sex | Gender of the population (String) | | age | Age group of the population (Integer) | | cnt | Number of people using the drug (Integer) | | country | Country of the population (String) |

File: drug_names.csv | Column name | Description | |:---------------|:------------------------------------------------------------------| | atc | Anatomical Therapeutic Chemical (ATC) code for the drug. (String) | | formalname | Formal name of the drug. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Number of persons in Sweden who have acquired Swedish citizenship by country of citizenship, sex, period and year

In the third quarter of 2020, the most popular subscription video-on-demand platform in Sweden was Netflix. The daily reach amounted to 25 percent, while the share of young people aged 9 to 19 years watching content on Netflix was even 40 percent. Second most viewed SVOD service among the total population in the Scandinavian country was Viaplay, with a daily reach of eight percent.

The dataset contains information about use of hypnotics in the Scandinavian countries, Denmark, Norway and Sweden. The categories of hypnotics described are: N05CH01 - Melatonine, N05CF* - Z drugs and R06AD* - H1 receptor antagonists.

The dataset consists of 2 files:

1. drug_use.csv
2. census.csv

Drug and census data were derived from the following national resources in the public domain:

Drug statistics data:

https://sdb.socialstyrelsen.se/if_lak/val.aspx (download date: 2020.06.15)

http://www.norpd.no/ (download date: 2020.12.06)

http://www.medstat.dk/ (download date: 2020.11.26)

Census data:

http://www.statistikdatabasen.scb.se (download date: 2020.06.12)

https://www.ssb.no/ (download date: 2020.06.15)

https://statistikbanken.dk (download date: 2020.06.12)

The source data owners take no responsibily for interpretation or analysis of data performed by third parties. Source data owners should be attributed when data are used. Consult data owners websites for details about attribution.

File descriptions:

drug_use.csv

This file contains aggregated information about the total number of unique users and the total amount of drug daily doses, by the categorical variables atc, year, country, sex and age. The categorical variables have the following valuesets

country (DK, NO, SE, SC), SC means Scandinavia and includes DK, SE and NO
year (2012 - 2019)
age (5-9, 10-14, 15-19, 20-24, 5-24)
sex (M,F, MF), MF means Male and Female

The variables prev_pr_1000 and ddd_pr_1000 are the results of dividing the variables n_users and ddd by the total population size (npop) in that country, year, agegroup and sex category. These census informations are also available in the census.csv file. Since ddd information is only available in Norway and Denmark, the ddd_pr_1000 for Scandinavia is based only on data from Denmark and Norway. The denominator for this calculation is in the variable called npop_excl_se.

census.csv

This file contains census data for each country grouped by sex and age in one year intervals. Use this file if you want to calculate population denominators for alternative aggregations of data in the drug_use.csv file

The NordChild study is a cross-sectional postal study among children aged 2-17 years from the five Nordic countries; Denmark, Finland, Iceland, Norway and Sweden. A random sample stratified for age and gender was drawn in all the Nordic countries. The total population included 10213 individuals in 1984, 10317 in 1996 and 7715 in 2011.

Iceland had the highest employment-to-population ratio among women over 15 years of age in the Nordic countries between 2012 and 2022. In 2022, more than two thirds of the country's female population was employed. Sweden had the second highest ratio until 2020, but its ratio dropped below Norway's in 2021. On the other hand, Finland had the lowest, with only slightly more than half of the female population in employment. Norway's ratio dropped significantly in 2014 after the fall of the oil price, on which the country's economy rely.

The use of genome-wide single nucleotide polymorphism (SNP) data has recently proven useful in the study of human population structure. We have studied the internal genetic structure of the Swedish population using more than 350,000 SNPs from 1525 Swedes from all over the country genotyped on the Illumina HumanHap550 array. We have also compared them to 3212 worldwide reference samples, including Finns, northern Germans, British and Russians, based on the more than 29,000 SNPs that overlap between the Illumina and Affymetrix 250K Sty arrays. The Swedes - especially southern Swedes - were genetically close to the Germans and British, while their genetic distance to Finns was substantially longer. The overall structure within Sweden appeared clinal, and the substructure in the southern and middle parts was subtle. In contrast, the northern part of Sweden, Norrland, exhibited pronounced genetic differences both within the area and relative to the rest of the country. These distinctive genetic features of Norrland probably result mainly from isolation by distance and genetic drift caused by low population density. The internal structure within Sweden (FST = 0.0005 between provinces) was stronger than that in many Central European populations, although smaller than what has been observed for instance in Finland; importantly, it is of the magnitude that may hamper association studies with a moderate number of markers if cases and controls are not properly matched geographically. Overall, our results underline the potential of genome-wide data in analyzing substructure in populations that might otherwise appear relatively homogeneous, such as the Swedes.

Patterns of genetic diversity have previously been shown to mirror geography on a global scale and within continents and individual countries. Using genome-wide SNP data on 5174 Swedes with extensive geographical coverage, we analyzed the genetic structure of the Swedish population. We observed strong differences between the far northern counties and the remaining counties. The population of Dalarna county, in north middle Sweden, which borders southern Norway, also appears to differ markedly from other counties, possibly due to this county having more individuals with remote Finnish or Norwegian ancestry than other counties. An analysis of genetic differentiation (based on pairwise Fst) indicated that the population of Sweden's southernmost counties are genetically closer to the HapMap CEU samples of Northern European ancestry than to the populations of Sweden's northernmost counties. In a comparison of extended homozygous segments, we detected a clear divide between southern and northern Sweden with small differences between the southern counties and considerably more segments in northern Sweden. Both the increased degree of homozygosity in the north and the large genetic differences between the south and the north may have arisen due to a small population in the north and the vast geographical distances between towns and villages in the north, in contrast to the more densely settled southern parts of Sweden. Our findings have implications for future genome-wide association studies (GWAS) with respect to the matching of cases and controls and the need for within-county matching. We have shown that genetic differences within a single country may be substantial, even when viewed on a European scale. Thus, population stratification needs to be accounted for, even within a country like Sweden, which is often perceived to be relatively homogenous and a favourable resource for genetic mapping, otherwise inferences based on genetic data may lead to false conclusions.

Changing climate and growing human impacts are resulting in globally rising temperatures and the widespread loss of habitats. How species will adapt to these changes is not well understood. The Northern Goshawk (Accipiter gentilis) can be found across the Holarctic but is coming under more intense pressure in many places. Studies of recent populations in Finland and Denmark have shown a marked decline in body size of Northern Goshawks over the past century. Here we investigate long-term changes to Norwegian populations of Northern Goshawk by including material from the Middle Ages. We measured 240 skeletons of modern Northern Goshawks from Norway, Sweden, Denmark and Finland, and 89 Medieval Goshawk bones. Our results show that Norwegian and Swedish female Goshawks have decreased in size over the past century, whilst males showed little decline. Medieval female Goshawks were larger than contemporary females. A decline in forest habitats and a concomitant shift towards smaller prey likely drove a shift to smaller body size in Northern Goshawks. Our study shows that significant body size changes in birds can occur over relatively short time spans in response to environmental factors, and that these effects can sometimes differ between sexes. Methods Modern comparative material To analyse changes in both modern and past populations of A. gentilis, metric data of the nominate Accipiter gentilis gentilis were collected from across the Nordic countries (Norway, Sweden, Denmark and Finland). The Northern Goshawk is a sedentary species, generally choosing to breed and winter in the same area. There are some exceptions to this in North America, Fennoscandia and Russia (Squires et al., 2020). However, individuals from Fennoscandia rarely migrate further than 300 Km (Squires et al., 2020). The nominate A. g. gentilis is distributed across Europe and east to the Urals, Caucasus, and Asia Minor, and southwards to NW Africa (Squires et al., 2020). The slightly larger subspecies Accipiter gentilis buteoides breeds in northern Fennoscandia and Siberia, wintering in south and central Eurasia (Ferguson-Lees & Christie, 2005). Goshawks found in northern Finland and northern Sweden likely represent the subspecies A. g. buteoides (Gladkov, 1941; Vaurie, 1965). Therefore, we excluded any material from these areas in our study. In addition, young Northern Goshawk from interior parts of Scandinavia are known to winter along the Norwegian coast in the north (Fransson & Petterson, 2001; Bakken et al., 2003). To avoid mixing of the larger subspecies A. g. buteoides within the modern comparative sample, we decided to use material from the south of Norway, Sweden and Finland.
Modern comparative skeletons (collected over the past c. 150 years from 1861 to 2015) have been measured from the University Museum of Bergen, the Natural History Museum of Denmark and the Finnish Museum of Natural History (see Appendix 1). The specimens were originally collected from Norway (n = 65), Sweden (n = 30), Denmark (n = 93) and Finland (n = 52) (see Fig. 2). We measured all skeletal elements, except for the vertebrae and ribs of 240 modern partial and complete Accipiter gentilis gentilis skeletons of which 103 were female and 137 were male. It is not clear how sex of the specimens was initially determined, however, we presume that for the majority it was through internal inspection. Accipiter gentilis are highly sexually dimorphic, with the female being larger than the male (i.e., reversed sexual size dimorphism). There is very little to no overlap between the sexes of Northern Goshawk (Kenward, 2006). As a result, it was possible to identify 5 modern specimens that were likely to have been mis-sexed at the time of collection. Wrongly identified sex can be common in Accipiter subspecies and especially in Accipiter gentilis, as the paired ovaries can sometimes be mistaken for testes (Storer, 1966). The specimens which were wrongly sexed have been reclassified as the correct sex based on our osteological analysis and included within this study. The museum numbers of mis-sexed specimens are as follows; B 4462 and KL 31303 (originally recorded as males but fall within the female size range and have been reclassified as females), B 9016, NHMD 306590 and NHMD 306670 (originally recorded as females but fall within the male size range and have therefore been reclassified as males). All specimens were measured by SJW using digital callipers. Measurements followed the conventions set out in Von den Driesch (1976). Three additional measurements were recorded: the smallest depth of the distal shaft of the humerus (KB) found in Kraft (1972), depth of the ulna proximal end (Tp) and height of the symphysis of the carpometacarpus (HS) taken from Otto (1981). Data suggest that Finnish populations of the nominate A. g. gentilis are slightly larger in their wing length and body mass than other Scandinavian populations (Tornberg et al., 2006), although the reasons for this remain unclear. It is possible that they represent a slightly more northern clinal population with bigger proportions. Alternatively, it may reflect a slightly more continental climate than Norway and Sweden (Tornberg pers. comm.). In addition, we noticed that the Danish specimens included here were on average smaller than the other Scandinavian specimens, again possibly due to clinal variation. ANOVA tests were used to detect statistical differences between the modern populations for each country. The results show that Norway and Sweden did not differ; Denmark was often statistically different to Norway, Sweden and Finland; Norway and Sweden rarely differed from Finland (full ANOVA results in Supporting Information File 1 (SIF1)). For these reasons, we have grouped Norway and Sweden together but kept Denmark and Finland separate when drawing comparisons. Archaeological material In general, A. gentilis is not frequent within the archaeological record for Norway (Walker et al., 2019). Morphologically, the osteology of A. gentilis is not easily confused with any other species, and we are confident of specimen identification. Despite this, all specimens were confirmed using the extensive modern comparative collections held at the University Museum of Bergen. In total 89 Medieval specimens were included within this study (Table 1). It is worth noting here that the 89 bones do not represent 89 individuals, although there is a possibility that some of these bones would have come from the same individual. The Medieval bones date to 1030–1537 Common Era (CE) and come from only 8 sites, all from the urban contexts of Oslo, Bergen and Trondheim (Table 1). Most Medieval bones come from female individuals and are likely to be linked to the practice of falconry (Walker et al., 2019). Most of the archaeological bones were limb elements, this may be due to taphonomic bias as they are more robust than for example cranial remains. However, this limited the skeletal elements that could be used for comparison. Within this paper, we focus on the humerus, ulna, carpometacarpus, femur, tibiotarsus and tarsometatarsus. Data analysis We first explored differences in size between groups using descriptive statistics in PAST 4.03 (Hammer et al., 2001). All data were tested for normality by looking at the variances and the Shapiro-Wilk test for normality (see SIF1). Principal Components Analysis (PCA) was used to establish which measurements were most responsible for the observed differences. Two separate PCAs were performed, one on the modern specimens only (including Norwegian, Swedish, Danish and Finnish modern specimens; Supporting Information File 2 (SIF2)) and a second with both modern and archaeological specimens (including Norwegian, Swedish, Danish and Finnish modern and Norwegian Medieval specimens; Supporting Information File 4 (SIF4)). To test for main and interaction effects of time, sex and country on greatest lengths (GL) of the humerus and femur of modern Northern Goshawks from Norway, Sweden and Denmark, we performed an Analysis of Covariance (ANCOVA) in R Statistical Software (v4.1.1; R Core Team, 2021), with the factor Time as the covariate and the factors Sex (2 levels), and Country (2 levels: Denmark and (Norway + Sweden) grouped together). For the humerus, n = 132, of which n = 57 for Norway & Sweden combined (43 males, 14 females), and n = 75 from Denmark (46 males, 29 females). For the femur, n = 172, with n = 83 for Norway & Sweden combined (55 males and 28 females) and n = 89 from Denmark (49 males, 40 females). Finnish modern specimens were excluded from the linear regression (Fig. 3) and the ANCOVA because, of the 52 Finnish specimens available, only 7 predate the year 2000, preventing a detailed look into the past century. To test for statistical differences between the mean greatest lengths of Norwegian Medieval specimens and modern Norwegian specimens of A. g. gentilis, we used a 10.000-iteration Fisher’s permutation test in R. The permutation test is somewhat similar to the bootstrap but differs from it in that a permutation test resamples without replacement. First, the sample means for each group and the difference between these means is computed. The data are then pooled and randomly permuted. The means and difference in mean for the permutated samples are computed. This process is then repeated n times for all possible permutations of the data, resulting in a frequency distribution of the mean difference. The 95% confidence interval and p-values can then be calculated. We considered p-values ≤ 0.05 statistically significant.

The population density in Sweden increased over the past 10 years, reaching 25.9 inhabitants per square kilometer in 2023. During that year, the population of Sweden reached 10.55 million.

Stockholm county had the highest population density

Sweden consists of 21 counties, administrative regions that primarily control public healthcare, public transport, and culture within the county. Among these, the most populated county is the capital region, Stockholm county, with a population density of 375 inhabitants per square kilometer in 2022. Stockholm county is followed by Skåne, with 129 inhabitants per square kilometer. The least populated county is Norrbotten, with only 2.6 inhabitants per square kilometer.

Land area of the Scandinavian countries

Though the population density in Sweden is increasing, the country still has a lot of surface area compared to its population. Of the Scandinavian countries, Sweden is the largest with a land area of over 447,000 square kilometers, but Norway is larger if the islands of Svalbard and Jan Mayen are taken into account.

The Nordic bike-sharing market, while a subset of a larger global trend, exhibits unique characteristics driven by strong environmental consciousness, robust public transportation infrastructure in major cities, and supportive government policies promoting sustainable transportation. The market's growth, exceeding a 6% CAGR (2019-2033), is fueled by increasing urbanization, rising fuel costs, and a growing preference for eco-friendly commuting options. Key players like Bycyklen (Denmark), Helsinki City Bikes (Finland), and Oslo City Bike (Norway) are strategically positioned to capitalize on this expansion, constantly innovating with features like integrated payment systems, improved app functionality, and the expansion of e-bike options within their fleets to cater to varied user needs and preferences. The market segmentation reveals a significant preference for dockless systems, reflecting a demand for greater convenience and flexibility. However, challenges remain, including seasonal variations in usage (with reduced ridership during colder months), the need for robust infrastructure to support increased numbers of bikes, and the potential for vandalism or theft. Overcoming these challenges through public-private partnerships, innovative anti-theft technologies, and targeted marketing campaigns will be crucial for sustained market growth. While precise market size figures for the Nordic region are not provided, we can extrapolate a reasonable estimate. Considering the global market size and the strong performance indicated by the 6%+ CAGR, a conservative estimate for the Nordic market in 2025 could be placed between $50 million and $100 million USD. This projection considers the relatively smaller populations of Nordic countries compared to larger global markets like China or the US. The future expansion will likely be driven by the introduction of advanced features, expansion into smaller towns and cities, and increasing integration with public transit systems. Growth will also be influenced by factors such as government subsidies, technological advancements in bike sharing systems (including battery technology and smart locking mechanisms), and evolving consumer behavior influenced by sustainability trends and urban planning initiatives. This in-depth report provides a comprehensive analysis of the Nordic bike-sharing industry, offering invaluable insights into market dynamics, growth drivers, and future trends. Covering the period 2019-2033, with a base year of 2025 and a forecast period spanning 2025-2033, this study is essential for businesses and stakeholders seeking to understand and capitalize on this rapidly evolving sector. The report leverages data from the historical period (2019-2024) to paint a clear picture of the current landscape and future projections, encompassing millions of users and substantial revenue streams. Key players like Smoove, Oslo City Bike, and others are analyzed for their market share and strategies. Key drivers for this market are: Government Policies to promote electric Vehicles Sales. Potential restraints include: High Cost of Capital Expenditure for Electric Vehicle Infrastructure. Notable trends are: E-Bike Rentalis Providing the Growth in Market.

In the Nordic countries, the largest number of people emigrated from Denmark in 2022. Nearly 70,000 people emigrated from Denmark that year, even though its neighbor Sweden has a population that is nearly twice as large. Iceland, on the other hand, had the lowest number of emigrants that year. Meanwhile, the highest number of immigrants over the last years arrived in Sweden.

Of the Nordic countries, Sweden has had the highest at-risk-of-poverty rate in the entire observed documented. In 2024, **** percent of Sweden's population lived at risk of poverty. Since 2015, Norway, Denmark, and Finland all have similar at-risk-of-poverty rates, around ** percent, although since 2022, Norway's rate has reached below ** percent.

Iceland had the highest employment rate of the Nordic countries from 2012 to 2022. In 2022, it was 83 percent among the adult population between 15 and 64 years in Iceland, whereas it was between 74 and 78 percent in the other four Nordic countries. The employment rate decreased in all five countries from 2019 to 2020 after the outbreak of COVID-19. The fall was most dramatic in Iceland, where the rate fell by four percentage points. Finland had the lowest employment rate of the countries. Moreover, the employment rates were higher in all five Nordic countries than the EU average.

The highest employment rates in Europe Comparing all European countries with each other, the Nordic countries had some of the highest employment rates in Europe in 2022. In the EU, the Netherlands had the highest employment rate.

Higher unemployment rates in Sweden and Finland Of the Nordic countries, Sweden had the highest unemployment rate in 2021. However, all five countries had an unemployment rate below eight percent. As of October 2022, Spain had the highest unemployment rate in the European Union, whereas Czechia had the lowest.