This program can be used to analyze cause-specific regional Swedish mortality data at county or municipality level using the Statistics Sweden API (API documentation in English). The database accessible via the API covers deaths from 1969 to 1996 (a database covering cause-specific deaths at the county level occurring since 1997 is publicly accessible via The National Board of Health and Welfare but cannot be used with the API). Data are retrieved in the JSON-stat format and saved into Pandas DataFrames using the pyjstat library. HTTP requests are made with requests, and diagrams are plotted with matplotlib.
Possible values for regions, age groups, sexes and causes of death can be retrieved by metadata(morturl) which sends a GET request to the mortality table.
If the cause of death given contains a hyphen, it is assumed to be a chapter
rather than a single cause of death (the agg:DödsorsakKapitel level). The
following multi-cause chapters are supported:
| Chapter | Description |
|---|---|
| 1-2 | Infections |
| 3-16 | Tumors |
| 17-18 | Endocrine disorders |
| 20-21 | Mental disorders |
| 23-28 | Circulatory disorders |
| 29-32 | Respiratory disorders |
| 33-35 | Digestive disorders |
| 36-39 | Genitourinary disorders |
| 44-45 | Ill-defined causes |
| 46-52 | External causes |
##Examples Save data on deaths from circulatory disorders in Västmanland County for the whole period in a dictionary, and plot a smoothed diagram showing the time trend for proportion of deaths due to this cause group for females and males in age intervals above 70 years:
pardict = catot_yrsdict('19', '23-28')
propplotyrs(**pardict, startage = '70-74', endage = '90+')Save data on deaths from circulatory disorders in all municipalities in Norrbotten County for the period 1981--86, and make a scatterplot of female vs male proportion of all deaths due to this cause group during the period in the age intervals between 75--79 and 85--89 years:
pardict = catot_mapdict(munis_incounty('25', metadata(morturl)), '23-28', 1981, 1986)
propscatsexes(**pardict, startage = '75-79', endage = '85-89')Note that data for single years and narrow age bands are often not very useful due to the small numbers of deaths, especially at the municipality level.
Using cartopy, it is also possible to plot maps showing regions with a lower or higher proportion of deaths from a given cause.
The script has been adapted to work with the shapefiles available (under a
CCZero license) from National Archives of Sweden. You may download a ZIP
archive with these files and
unzip it in the directory naddata under the seregmort directory. One of the Excel
metadata files is included in this
repository in CSV format (under naddata); this file is used to translate the
geographical codes used by Statistics Sweden into the unit codes used in the
shapefiles.
Plot a map of the proportion of female deaths due to circulatory disorders in
all municipalities in Västernorrland County during the period 1981--86 in the
age intervals between 75--79 and 85--89 years (note that
naddata/2504/__pgsql2shp2504_tmp_table.shp is default shapefile path):
pardict = catot_mapdict(munis_incounty('22', metadata(morturl)), '23-28', 1981, 1986)
propmap(**pardict, startage = '75-79', endage = '85-89', sex = '2') By default, maps are plotted with three percentiles with different shades. You
can call the functions fourp and fivep to plot with four or five
percentiles instead. To plot a map of proportion of deaths due to ischemic
heart disease for all counties and all ages during the period 1981--86 among
males with five percentiles:
pardict = catot_mapdict(allregions('county', metadata(morturl)), '25', 1981, 1986)
propmap(**pardict, startage = '0', endage = '90+', sex = '1', percfunc = fivep)There is limited support for visualizing mortality rates by using population size in the denominator (based on data from a population table. However, this is difficult to implement fully, because the tables use differing age formats and (more importantly) because the population table uses a newer regional division. Currently, it should work for age groups between 5--9 and 85--89 years and regions which have not changed since 1996. For example, to plot a map of male mortality rates from circulatory disorders in the municipalities in Västerbotten County during the period 1981--86 in the age intervals between 50--54 and 70--74 years:
pardict = capop_mapdict(munis_incounty('24', metadata(morturl)), '23-28', 1981, 1986)
propmap(**pardict, startage = '50-54', endage = '70-74', sex = '1')The capop_mapdict wrapper can also be used with propscatsexes for drawing
scatterplots, as in the example with catot_mapdict. The capop_yrsdict
wrapper can be used with propplotyrs.
The unchanged_regions function can be used to selection those counties which
have remained unchanged. To plot a map of female mortality rates from circulatory
disorders in all counties, except for the changed ones in what is nowadays
Skåne and Västra Götaland County, during the period 1981--86 in the age intervals
65--69 and 70--74 years:
pardict = capop_mapdict(unchanged_regions('county', metadata(morturl)), '23-28', 1981, 1986)
propmap(**pardict, startage = '65-69', endage = '70-74', sex = '2')Death rates may be influenced by the age structure of a population, which may
give misleading results, especially if wide age bands are compared. Because of
this, it is possible to calculate average death rates over 5-year age
intervals, by setting the argument mean to True, for example:
pardict = capop_mapdict(unchanged_regions('county', metadata(morturl)), '23-28', 1981, 1986)
propmap(**pardict, startage = '15-19', endage = '70-74', sex = '2', mean = True)