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. 2020 Jul 20;117(29-30):493–499. doi: 10.3238/arztebl.2020.0493

District-Level Life Expectancy in Germany

Roland Rau 1, Carl P Schmertmann 2
PMCID: PMC7588608  PMID: 33087229

Abstract

Background

Identifying regions with low life expectancy is important to policy makers, in particular for allocating resources in the health system. Life expectancy estimates for small regions are, however, often unreliable and lead to statistical uncertainties when the underlying populations are relatively small.

Methods

We combine the most recent German data available (2015–2017) with a Bayesian model that includes several methodological advances. This allows us to estimate male and female life expectancy with good precision for all 402 German districts and to quantify the uncertainty of those estimates.

Results

Across districts, life expectancy varies between 75.8 and 81.2 years for men and from 81.8 to 85.7 years for women. The spatial pattern is similar for women and men. Rural districts in eastern Germany and some districts of the Ruhr region have relatively low life expectancy. Districts with relatively high life expectancies cluster in Baden-Wuerttemberg and southern Bavaria. Exploratory analysis shows that average income, population density, and number of physicians per 100 000 inhabitants are not strongly correlated with life expectancy at district level. In contrast, indicators that point to particularly disadvantaged segments of the population (unemployment rate, welfare benefits) are better predictors of life expectancy.

Conclusions

We do not find a consistent urban–rural gap in life expectancy. Our results suggest that policies that improve living standards for poorer segment of the population are the most likely to reduce the existing differences in life expectancy.

Life expectancy in Germany is about 83.3 years for women and 78.5 years for men, according to the most recent data from the Federal Statistical Office (1). Internationally, Germany ranks 30th, trailing the leading countries for life expectancy by about 4 years for women and 3 years for men (2).

After only modest improvements since the 1970s, the former German Democratic Republic (referred to from here on as eastern Germany or the east) experienced a sudden increase in life expectancy after the reunification of Germany (3, 4). In contrast, life expectancy in the former Federal Republic of Germany (from here on, western Germany or the west) has increased steadily, at a rate comparable with other western industrialized countries. The current life expectancy for women no longer shows a difference between eastern Germany and western Germany, while there remains a gap of slightly over 1 year for men.

Comparisons of such large areas are certainly informative. Appropriate planning of health services, however, requires analysis at a finer geographic scale. Small-area estimates are therefore crucial in identifying marginalized regions. This is particularly important with regard to Art. 72 [2] of Germany’s constitution, which empowers the federal government to enact legislation to provide equivalent living conditions.

To improve the identification of such regions, we estimated—based on age-specific mortality rates—life expectancy at the district level for women and men in Germany.

Our article has two substantive goals. First, we wanted to provide reliable estimates for small areas by using methods that do not suffer from instability and high uncertainty when the underlying populations are small. Standard estimators are vulnerable to random statistical fluctuations when there are only a few deaths from which to estimate mortality in small populations. In addition, our statistical approach also produces estimates of the underlying uncertainty. Such estimates of uncertainty are valuable for assessing whether differences in life expectancy between two districts are real or merely the random outcome of small numbers of deaths in districts of modest size. To our knowledge, no such interval estimations of life expectancy at the district level in Germany have yet been published.

Second, we wanted to examine whether there are specific patterns of correlation between social and economic indicators at the district level and local life expectancy. It is well known that poor, disadvantaged, and less (formally) educated people have lower life expectancy than those with higher income, better economic prospects, or a university degree (5– 7). This is likely due not only to income and resources per se, but also to the higher prevalence of unhealthy lifestyles such as poor diet, smoking, and excessive drinking or to occupation-specific hazards among people with lower income or lower level of education (8, 9). By producing district-level estimates for life expectancy in Germany and investigating their correlations with social and policy variables, we hope to provide useful data and a solid foundation for future, more detailed, analyses.

Methods

Data

For each combination of district, sex, and age group (0, 1–4, 5–9, 10–14, …, 80–84, 85+) we had access to the resident population on 31 December in 2014, 2015, 2016, and 2017 and to the numbers of deaths in the calendar years 2015, 2016, and 2017. These data—the most recent available for estimation of mortality—are made available by the federal and state statistical offices via www.regionalstatistik.de (10, 11). We calculated the person-years lived by age group and sex in each calendar year as the mean of two consecutive end-of-year populations (e.g., the number of person-years lived in 2015 by women aged 50–54 years is the average for the women of this age group alive on 31 December 2014 and on 31 December 2015). In order to reduce random fluctuations due to small populations, we pooled the data on deaths and the person-years lived from the three available years (2015, 2016, 2017).

With the exception of the merger of the districts Göttingen and Osterode am Harz in 2016, the district boundaries did not change in the period 2015–2017. Consequently, our estimates for Göttingen and Osterode am Harz pertain to 2015 only.

Population sizes in German districts span two orders of magnitude. On 31 December 2017 the smallest district was Zweibrücken, with about 33 900 inhabitants, while Berlin, with about 3.6 million inhabitants, was the largest. Median district population size is about 150 000, and 50% of districts have between 100 000 and 240 000 inhabitants.

Annual death counts span slightly less than two orders of magnitude. In 2017, for example, there were 449 deaths in Zweibrücken and 34 339 deaths in Berlin. Slightly more than half of all districts had between 1200 and 2700 deaths in 2017.

Relational Bayesian model

We use a new relational model to estimate mortality rates by age and sex in each district. Relational models, such as the Brass logit model (12), have been widely employed for populations with sparse data, and were also partly used for estimation of life tables in Germany’s component states after the register-based census in 2011 (13). The relational TOPALS model that we used was proposed by de Beer (14), and has good statistical properties even in very small populations (15, 16).

Our approach estimates age-specific mortality rates for all districts and both sexes with a Bayesian model that links parameters across districts. The model produces probabilistic mortality rates at the district level, from which we can calculate a probability distribution for life expectancy e0 at the district level.

Based on the given death counts and person-years lived, this distribution assigns higher probability to e0 values that are more likely according to the model. Its median serves as the point estimate of life expectancy at the district level. We represent the uncertainty of our estimates by means of the interval between the 10th and 90th percentiles, which spans 80% of possible values for local e0. Mathematical and statistical details can be found online at http://german-district-mortality.schmert.net.

Results

Figures 1 displays estimates of life expectancy and of 80% probability intervals for each district and both sexes. We highlight the 10 largest cities and the districts with the highest and lowest life expectancies for women and men. Based on the estimated mortality rates for the years 2015–2017, men in Bremerhaven had the shortest life expectancy, while men living in the district surrounding Munich (Landkreis) could expect to live around 5 years longer. We found the lowest life expectancy for women in the Salzland district of Saxony–Anhalt, the highest in the Starnberg district, southwest of Munich. The eTable provides a list of all districts, arranged by federal state, with point estimates and 80% interval estimates. The light blue and light red bars in Figure 1 indicate these 80% probability intervals. Because of the larger populations these bars are narrower for the largest cities than for less densely populated districts. The width of the interval estimates varies from 0.14 years for Berlin to 0.55 years for Ansbach and Osterode am Harz; the median is 0.36 years.

Figure 1.

Figure 1

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Life expectancy at birth for men (blue) and women (red)

Estimated life expectancy in the 402 districts of Germany (including 80% uncertainty intervals between the 10% and 90% percentiles of probable values). The ten largest cities in Germany and the districts with the highest and lowest life expectancy for women and men were labeled.

eTable. Estimated life expectancy at birth for men and women in the 402 districts of Germany for the years 2015–2017 in alphabetical order by state. We also list the rank (1 = highest, 402 = lowest), the median of the a posteriori distribution, and the limits of the 80% prognosis intervals, i.e., 10th and 90th percentiles. Bold blue type indicates counties in the top 10% (rank ≤ 40); bold red type indicates counties in the bottom 10% (rank ≥ 363).

District Women Men
Rank Percentiles Rank Percentiles
10% 50% 90% 10% 50% 90%
(Median) (Median)
Baden-Württemberg
Alb-Donau-Kreis 47 84.43 84.59 84.77 32 79.89 80.06 80.23
Baden-Baden 28 84.52 84.73 84.94 106 78.98 79.23 79.46
Biberach 50 84.40 84.58 84.76 35 79.85 80.02 80.20
Böblingen 13 84.87 85.02 85.17 8 80.40 80.55 80.70
Bodenseekreis 14 84.81 84.99 85.18 10 80.31 80.49 80.67
Breisgau-Hochschwarzwald 5 85.15 85.33 85.50 2 80.72 80.89 81.05
Calw 71 84.24 84.42 84.61 60 79.57 79.75 79.94
Emmendingen 30 84.55 84.72 84.90 23 79.96 80.14 80.33
Enzkreis 36 84.50 84.69 84.86 21 79.97 80.15 80.32
Esslingen 11 84.90 85.03 85.17 9 80.38 80.51 80.64
Freiburg im Breisgau 9 84.88 85.06 85.25 70 79.47 79.66 79.85
Freudenstadt 127 83.80 83.98 84.17 97 79.15 79.35 79.55
Göppingen 38 84.50 84.66 84.83 33 79.90 80.06 80.22
Heidelberg 18 84.74 84.92 85.11 84 79.34 79.55 79.75
Heidenheim 25 84.59 84.78 84.98 22 79.95 80.15 80.36
Heilbronn, Landkreis 94 84.13 84.27 84.42 68 79.53 79.69 79.83
Heilbronn, Stadt 53 84.37 84.55 84.74 135 78.73 78.94 79.16
Hohenlohekreis 63 84.26 84.46 84.67 52 79.62 79.83 80.04
Karlsruhe, Landkreis 31 84.56 84.70 84.85 36 79.88 80.02 80.16
Karlsruhe, Stadt 51 84.41 84.57 84.74 133 78.80 78.96 79.13
Konstanz 27 84.58 84.74 84.91 31 79.89 80.06 80.23
Lörrach 74 84.23 84.40 84.57 67 79.52 79.69 79.87
Ludwigsburg 21 84.71 84.84 84.98 15 80.13 80.25 80.39
Main-Tauber-Kreis 54 84.36 84.55 84.73 41 79.75 79.96 80.15
Mannheim 151 83.69 83.85 84.00 317 77.72 77.89 78.05
Neckar-Odenwald-Kreis 130 83.77 83.96 84.15 107 79.02 79.22 79.41
Ortenaukreis 49 84.44 84.58 84.73 39 79.83 79.97 80.11
Ostalbkreis 55 84.38 84.54 84.70 48 79.71 79.86 80.02
Pforzheim 98 84.03 84.21 84.40 202 78.38 78.59 78.80
Rastatt 66 84.29 84.45 84.62 56 79.63 79.79 79.96
Ravensburg 39 84.49 84.65 84.82 27 79.95 80.11 80.27
Rems-Murr-Kreis 24 84.68 84.81 84.96 18 80.07 80.20 80.33
Reutlingen 32 84.55 84.70 84.86 24 79.97 80.13 80.29
Rhein-Neckar-Kreis 86 84.19 84.31 84.45 66 79.58 79.70 79.83
Rottweil 73 84.21 84.40 84.60 59 79.55 79.75 79.96
Schwäbisch Hall 69 84.25 84.43 84.61 62 79.55 79.73 79.92
Schwarzwald-Baar-Kreis 79 84.20 84.37 84.54 64 79.54 79.71 79.88
Sigmaringen 42 84.44 84.63 84.81 38 79.78 79.99 80.19
Stuttgart 4 85.27 85.40 85.55 44 79.78 79.92 80.07
Tübingen 12 84.83 85.02 85.21 7 80.39 80.58 80.77
Tuttlingen 46 84.40 84.60 84.80 49 79.65 79.85 80.07
Ulm 17 84.72 84.93 85.13 87 79.27 79.49 79.73
Waldshut 83 84.17 84.35 84.52 63 79.55 79.73 79.91
Zollernalbkreis 45 84.43 84.61 84.78 46 79.74 79.92 80.09
Bavaria
Aichach-Friedberg 44 84.42 84.62 84.81 30 79.86 80.06 80.27
Altötting 87 84.12 84.31 84.50 76 79.41 79.62 79.82
Amberg 261 83.13 83.36 83.59 198 78.34 78.60 78.84
Amberg-Sulzbach 250 83.21 83.40 83.60 185 78.49 78.70 78.90
Ansbach, Landkreis 191 83.51 83.68 83.85 142 78.74 78.92 79.10
Ansbach, Stadt 182 83.48 83.73 83.99 148 78.62 78.89 79.17
Aschaffenburg, Landkreis 72 84.24 84.41 84.58 43 79.74 79.92 80.11
Aschaffenburg, Stadt 158 83.60 83.81 84.02 113 78.93 79.16 79.39
Augsburg, Landkreis 82 84.21 84.36 84.53 54 79.67 79.82 79.99
Augsburg, Stadt 120 83.86 84.02 84.19 123 78.90 79.07 79.24
Bad Kissingen 129 83.78 83.97 84.15 98 79.15 79.34 79.55
Bad Tölz-Wolfratshausen 37 84.49 84.67 84.86 25 79.92 80.12 80.31
Bamberg, Landkreis 332 82.85 83.03 83.21 257 78.08 78.27 78.47
Bamberg, Stadt 322 82.89 83.10 83.31 264 78.02 78.25 78.48
Bayreuth, Landkreis 284 83.08 83.27 83.46 217 78.26 78.47 78.67
Bayreuth, Stadt 270 83.11 83.33 83.55 222 78.22 78.45 78.68
Berchtesgadener Land 43 84.43 84.63 84.83 37 79.78 80.00 80.21
Cham 294 83.05 83.23 83.41 208 78.35 78.55 78.73
Coburg, Landkreis 318 82.93 83.12 83.31 245 78.12 78.33 78.54
Coburg. Stadt 313 82.89 83.13 83.37 261 78.01 78.27 78.52
Dachau 35 84.51 84.69 84.87 19 80.00 80.19 80.39
Deggendorf 330 82.86 83.05 83.24 243 78.13 78.33 78.54
Dillingen a.d. Donau 97 84.01 84.22 84.43 82 79.36 79.57 79.78
Dingolfing-Landau 196 83.45 83.65 83.85 127 78.81 79.02 79.23
Donau-Ries 78 84.18 84.38 84.58 58 79.56 79.77 79.98
Ebersberg 15 84.78 84.97 85.16 11 80.29 80.49 80.69
Eichstätt 29 84.53 84.72 84.92 17 80.03 80.24 80.44
Erding 40 84.44 84.64 84.84 28 79.88 80.09 80.30
Erlangen 140 83.68 83.89 84.10 129 78.78 79.01 79.23
Erlangen-Höchstadt 118 83.83 84.02 84.21 93 79.24 79.43 79.62
Forchheim 197 83.46 83.64 83.83 144 78.72 78.92 79.12
Freising 57 84.34 84.53 84.72 40 79.78 79.97 80.16
Freyung-Grafenau 268 83.14 83.34 83.53 187 78.46 78.68 78.89
Fürstenfeldbruck 10 84.88 85.06 85.23 6 80.41 80.58 80.76
Fürth, Landkreis 103 83.92 84.12 84.31 91 79.26 79.46 79.66
Fürth, Stadt 257 83.17 83.38 83.58 219 78.25 78.46 78.66
Garmisch-Partenkirchen 33 84.50 84.69 84.90 34 79.84 80.04 80.27
Günzburg 105 83.92 84.10 84.29 86 79.31 79.50 79.69
Haßberge 152 83.64 83.84 84.03 108 79.01 79.22 79.43
Hof, Landkreis 335 82.84 83.01 83.20 277 77.96 78.15 78.35
Hof, Stadt 344 82.72 82.95 83.17 297 77.80 78.05 78.28
Ingolstadt 60 84.30 84.49 84.69 50 79.62 79.84 80.05
Kaufbeuren 84 84.11 84.34 84.57 71 79.40 79.66 79.91
Kelheim 253 83.20 83.39 83.59 175 78.57 78.77 78.99
Kempten 77 84.17 84.38 84.61 69 79.46 79.67 79.91
Kitzingen 141 83.69 83.89 84.10 105 79.03 79.24 79.45
Kronach 317 82.92 83.12 83.32 255 78.05 78.27 78.49
Kulmbach 298 83.01 83.21 83.42 228 78.18 78.41 78.63
Landsberg am Lech 23 84.63 84.83 85.03 13 80.14 80.33 80.53
Landshut, Landkreis 161 83.62 83.80 83.99 103 79.06 79.25 79.44
Landshut, Stadt 236 83.24 83.46 83.67 172 78.55 78.78 79.01
Lichtenfels 333 82.82 83.02 83.23 271 77.98 78.20 78.43
Lindau (Bodensee) 93 84.08 84.27 84.48 79 79.35 79.58 79.80
Main-Spessart 108 83.89 84.08 84.28 85 79.32 79.52 79.73
Memmingen, Stadt 88 84.07 84.31 84.56 74 79.38 79.63 79.89
Miesbach 16 84.75 84.95 85.15 12 80.22 80.44 80.65
Miltenberg 119 83.83 84.02 84.21 90 79.28 79.47 79.67
Mühldorf a. Inn 202 83.43 83.61 83.81 155 78.67 78.88 79.08
München, Landkreis 3 85.33 85.49 85.65 1 80.99 81.15 81.30
München, Stadt 2 85.41 85.53 85.65 3 80.72 80.83 80.92
Neu-Ulm 52 84.37 84.57 84.76 45 79.74 79.92 80.13
Neuburg-Schrobenhausen 65 84.25 84.46 84.67 47 79.69 79.91 80.12
Neumarkt i.d. Opf. 217 83.35 83.54 83.73 143 78.73 78.92 79.12
Neustadt a.d. Aisch-Bad Windsheim 186 83.51 83.71 83.92 140 78.71 78.93 79.14
Neustadt a.d. Waldnaab 315 82.93 83.13 83.32 230 78.19 78.40 78.60
Nürnberg, Stadt 254 83.26 83.39 83.52 262 78.14 78.26 78.38
Nürnberger Land 155 83.66 83.83 84.01 122 78.89 79.07 79.25
Oberallgäu 26 84.58 84.76 84.94 16 80.06 80.24 80.42
Ostallgäu 41 84.43 84.64 84.84 26 79.91 80.11 80.31
Passau, Landkreis 199 83.46 83.62 83.78 124 78.88 79.05 79.22
Passau, Stadt 210 83.35 83.58 83.79 149 78.64 78.89 79.12
Pfaffenhofen a.d. Ilm 61 84.27 84.49 84.69 42 79.71 79.93 80.15
Regen 338 82.78 83.00 83.21 263 78.04 78.26 78.48
Regensburg, Landkreis 171 83.60 83.78 83.93 104 79.08 79.25 79.42
Regensburg, Stadt 189 83.49 83.68 83.87 137 78.72 78.93 79.12
Rhön-Grabfeld 188 83.50 83.70 83.90 120 78.86 79.07 79.29
Rosenheim, Landkreis 48 84.42 84.59 84.76 29 79.91 80.07 80.23
Rosenheim, Stadt 59 84.28 84.50 84.72 53 79.60 79.83 80.07
Roth 157 83.64 83.82 84.02 117 78.92 79.11 79.31
Rottal-Inn 241 83.28 83.44 83.62 151 78.69 78.88 79.07
Schwabach 147 83.63 83.86 84.10 147 78.65 78.90 79.17
Schwandorf 163 83.63 83.80 83.97 259 78.07 78.27 78.46
Schweinfurt, Landkreis 68 84.24 84.43 84.61 119 78.89 79.09 79.31
Schweinfurt, Stadt 112 83.82 84.05 84.29 227 78.15 78.41 78.66
Starnberg 1 85.50 85.69 85.89 4 80.45 80.66 80.87
Straubing 203 83.40 83.60 83.82 301 77.77 78.03 78.27
Straubing-Bogen 148 83.67 83.86 84.04 207 78.34 78.56 78.75
Tirschenreuth 282 83.08 83.28 83.47 347 77.40 77.63 77.86
Traunstein 20 84.71 84.89 85.06 81 79.38 79.57 79.76
Unterallgäu 34 84.52 84.69 84.87 99 79.14 79.34 79.54
Weiden i.d. Opf. 184 83.50 83.72 83.94 292 77.80 78.07 78.33
Weilheim-Schongau 8 84.93 85.11 85.29 55 79.61 79.81 80.00
Weißenburg-Gunzenhausen 131 83.77 83.95 84.14 256 78.05 78.27 78.48
Wunsiedel i. Fichtelgebirge 336 82.82 83.00 83.19 386 76.94 77.16 77.37
Würzburg, Landkreis 19 84.71 84.90 85.07 61 79.54 79.73 79.92
Würzburg, Stadt 91 84.11 84.30 84.49 179 78.53 78.75 78.96
Berlin
Berlin 178 83.67 83.74 83.82 203 78.51 78.58 78.64
Brandenburg
Barnim 277 83.12 83.29 83.46 266 78.06 78.23 78.40
Brandenburg an der Havel 275 83.10 83.30 83.50 281 77.91 78.13 78.36
Cottbus 304 82.98 83.18 83.37 307 77.78 77.99 78.20
Dahme-Spreewald 215 83.39 83.55 83.72 209 78.36 78.53 78.70
Elbe-Elster 349 82.75 82.92 83.10 333 77.56 77.75 77.94
Frankfurt (Oder) 343 82.76 82.96 83.16 324 77.61 77.84 78.06
Havelland 325 82.92 83.07 83.24 295 77.88 78.05 78.22
Märkisch-Oderland 255 83.23 83.39 83.55 236 78.18 78.34 78.51
Oberhavel 251 83.24 83.40 83.55 237 78.18 78.34 78.51
Oberspreewald-Lausitz 329 82.90 83.06 83.23 325 77.65 77.83 78.01
Oder-Spree 230 83.33 83.49 83.65 235 78.20 78.36 78.53
Ostprignitz-Ruppin 324 82.91 83.09 83.27 308 77.79 77.98 78.18
Potsdam 169 83.61 83.78 83.96 165 78.64 78.82 79.01
Potsdam-Mittelmark 198 83.48 83.64 83.81 168 78.64 78.80 78.97
Prignitz 358 82.68 82.87 83.06 334 77.54 77.75 77.95
Spree-Neiße 341 82.82 82.99 83.16 320 77.68 77.86 78.04
Teltow-Fläming 309 82.98 83.15 83.32 293 77.89 78.07 78.25
Uckermark 354 82.72 82.89 83.05 340 77.50 77.69 77.87
Bremen
Bremen 174 83.63 83.77 83.91 342 77.53 77.68 77.82
Bremerhaven 391 82.24 82.46 82.68 402 75.57 75.82 76.06
Hamburg
Hamburg 125 83.91 84.00 84.10 156 78.78 78.88 78.97
Hesse
Bergstraße 145 83.74 83.88 84.04 111 79.04 79.21 79.36
Darmstadt 90 84.11 84.30 84.50 83 79.36 79.56 79.76
Darmstadt-Dieburg 99 84.03 84.18 84.33 77 79.45 79.60 79.74
Frankfurt am Main 109 83.94 84.07 84.21 112 79.08 79.20 79.33
Fulda 75 84.21 84.39 84.57 65 79.52 79.71 79.88
Gießen 162 83.64 83.80 83.95 132 78.81 78.97 79.13
Groß-Gerau 107 83.94 84.09 84.24 96 79.21 79.36 79.52
Hersfeld-Rotenburg 173 83.59 83.77 83.96 134 78.76 78.96 79.15
Hochtaunuskreis 6 85.05 85.22 85.39 5 80.48 80.65 80.80
Kassel, Stadt 208 83.41 83.59 83.77 216 78.31 78.49 78.67
Kassel, Landkreis 142 83.74 83.89 84.05 116 78.99 79.15 79.31
Lahn-Dill-Kreis 281 83.13 83.28 83.42 220 78.30 78.46 78.61
Limburg-Weilburg 243 83.26 83.43 83.62 205 78.38 78.57 78.75
Main-Kinzig-Kreis 110 83.94 84.07 84.21 92 79.32 79.45 79.59
Main-Taunus-Kreis 22 84.66 84.83 84.99 14 80.11 80.27 80.44
Marburg-Biedenkopf 220 83.37 83.54 83.70 183 78.56 78.73 78.89
Odenwaldkreis 134 83.72 83.92 84.12 109 79.00 79.21 79.42
Offenbach am Main, Stadt 143 83.69 83.89 84.08 126 78.83 79.04 79.25
Offenbach, Landkreis 56 84.38 84.53 84.68 51 79.69 79.84 79.99
Rheingau-Taunus-Kreis 81 84.20 84.36 84.54 57 79.61 79.78 79.95
Schwalm-Eder-Kreis 181 83.57 83.73 83.89 294 77.90 78.07 78.25
Vogelsbergkreis 111 83.87 84.06 84.24 223 78.23 78.44 78.64
Waldeck-Frankenberg 264 83.18 83.35 83.52 214 78.32 78.50 78.68
Werra-Meißner-Kreis 209 83.41 83.58 83.77 332 77.56 77.76 77.97
Wetteraukreis 92 84.12 84.27 84.42 178 78.60 78.75 78.91
Wiesbaden 58 84.37 84.52 84.69 164 78.66 78.83 79.00
Mecklenburg–West Pomerania
Ludwigslust-Parchim 367 82.64 82.79 82.94 345 77.49 77.65 77.82
Mecklenburgische Seenplatte 374 82.56 82.70 82.84 364 77.29 77.44 77.60
Nordwestmecklenburg 331 82.86 83.03 83.20 316 77.70 77.89 78.07
Rostock, Landkreis 369 82.61 82.77 82.92 348 77.45 77.61 77.78
Rostock, Stadt 249 83.24 83.40 83.57 274 77.99 78.16 78.34
Schwerin 233 83.30 83.48 83.68 380 76.99 77.23 77.45
Vorpommern-Greifswald 357 82.73 82.88 83.02 399 76.45 76.62 76.77
Vorpommern-Rügen 312 83.00 83.14 83.28 392 76.77 76.93 77.10
Lower Saxony
Ammerland 179 83.55 83.74 83.93 138 78.73 78.93 79.13
Aurich 389 82.33 82.50 82.66 362 77.28 77.46 77.63
Braunschweig 231 83.33 83.48 83.65 250 78.13 78.30 78.48
Celle 267 83.18 83.34 83.52 241 78.16 78.34 78.53
Cloppenburg 345 82.78 82.95 83.14 298 77.85 78.04 78.22
Cuxhaven 319 82.95 83.12 83.27 279 77.97 78.14 78.31
Delmenhorst 287 83.05 83.26 83.46 267 78.00 78.22 78.44
Diepholz 187 83.54 83.71 83.88 182 78.56 78.73 78.91
Emden 353 82.67 82.89 83.12 329 77.54 77.79 78.03
Emsland 288 83.10 83.26 83.40 229 78.25 78.40 78.55
Friesland 201 83.42 83.61 83.81 184 78.49 78.70 78.92
Gifhorn 218 83.37 83.54 83.73 190 78.47 78.65 78.85
Goslar 311 82.96 83.14 83.31 302 77.83 78.02 78.21
Göttingen 226 83.28 83.51 83.73 212 78.29 78.52 78.76
Grafschaft Bentheim 214 83.37 83.55 83.74 171 78.59 78.79 78.98
Hameln-Pyrmont 271 83.15 83.32 83.51 275 77.97 78.16 78.35
Hannover 149 83.75 83.86 83.96 181 78.63 78.74 78.84
Harburg 106 83.93 84.09 84.25 94 79.22 79.39 79.56
Heidekreis 274 83.11 83.30 83.48 247 78.12 78.32 78.52
Helmstedt 382 82.41 82.61 82.81 366 77.21 77.42 77.64
Hildesheim 320 82.97 83.12 83.26 309 77.83 77.98 78.14
Holzminden 292 83.02 83.24 83.44 283 77.90 78.12 78.35
Leer 364 82.66 82.82 82.99 318 77.72 77.88 78.07
Lüchow-Dannenberg 323 82.86 83.09 83.31 288 77.85 78.10 78.33
Lüneburg 269 83.17 83.34 83.51 218 78.28 78.47 78.65
Nienburg (Weser) 316 82.93 83.13 83.33 312 77.74 77.95 78.18
Northeim 305 82.99 83.18 83.35 280 77.94 78.14 78.33
Oldenburg (Old), Stadt 263 83.18 83.36 83.53 221 78.26 78.45 78.64
Oldenburg, Landkreis 235 83.29 83.47 83.66 189 78.47 78.66 78.86
Osnabrück, Landkreis 168 83.64 83.78 83.92 125 78.90 79.04 79.19
Osnabrück, Stadt 194 83.49 83.67 83.85 176 78.58 78.76 78.96
Osterholz 228 83.32 83.50 83.69 195 78.43 78.62 78.82
Osterode am Harz 352 82.65 82.89 83.14 330 77.51 77.79 78.05
Peine 326 82.88 83.07 83.26 303 77.82 78.02 78.21
Rotenburg (Wümme) 289 83.08 83.25 83.42 251 78.11 78.30 78.47
Salzgitter 363 82.62 82.83 83.03 344 77.45 77.67 77.88
Schaumburg 291 83.07 83.24 83.43 290 77.88 78.08 78.26
Stade 123 83.85 84.01 84.17 233 78.21 78.38 78.56
Uelzen 278 83.09 83.29 83.48 373 77.08 77.30 77.52
Vechta 128 83.79 83.97 84.16 215 78.28 78.49 78.69
Verden 167 83.61 83.78 83.96 289 77.89 78.09 78.29
Wesermarsch 237 83.26 83.45 83.66 358 77.31 77.54 77.76
Wilhelmshaven 276 83.09 83.29 83.49 384 76.96 77.19 77.40
Wittmund 234 83.26 83.47 83.69 351 77.36 77.61 77.85
Wolfenbüttel 204 83.43 83.60 83.78 343 77.48 77.68 77.87
Wolfsburg 156 83.63 83.82 84.02 321 77.64 77.86 78.08
North Rhine–Westphalia
Aachen 138 83.78 83.91 84.03 246 78.20 78.32 78.45
Bielefeld 124 83.85 84.01 84.18 141 78.77 78.92 79.09
Bochum 351 82.75 82.89 83.04 346 77.49 77.64 77.78
Bonn 62 84.29 84.47 84.64 80 79.41 79.57 79.74
Borken 225 83.37 83.52 83.66 177 78.62 78.76 78.90
Bottrop 365 82.62 82.82 83.01 336 77.52 77.73 77.93
Coesfeld 116 83.88 84.04 84.21 100 79.15 79.32 79.50
Dortmund 385 82.44 82.57 82.69 375 77.15 77.27 77.40
Duisburg 388 82.38 82.51 82.63 389 76.92 77.05 77.18
Düren 334 82.86 83.01 83.17 285 77.96 78.12 78.27
Düsseldorf 185 83.58 83.71 83.84 194 78.49 78.62 78.74
Ennepe-Ruhr-Kreis 355 82.75 82.88 83.02 323 77.69 77.84 77.97
Essen 381 82.49 82.61 82.74 376 77.14 77.26 77.38
Euskirchen 297 83.05 83.22 83.39 254 78.10 78.27 78.45
Gelsenkirchen 400 82.03 82.18 82.34 396 76.63 76.78 76.94
Gütersloh 114 83.89 84.04 84.20 101 79.11 79.26 79.42
Hagen 386 82.37 82.54 82.72 372 77.12 77.31 77.48
Hamm 362 82.66 82.83 83.01 338 77.52 77.71 77.88
Heinsberg 306 83.02 83.17 83.32 238 78.19 78.34 78.50
Herford 159 83.66 83.81 83.98 152 78.72 78.88 79.04
Herne 396 82.12 82.31 82.49 393 76.73 76.92 77.11
Hochsauerlandkreis 328 82.92 83.07 83.22 286 77.95 78.11 78.27
Höxter 146 83.69 83.87 84.05 131 78.79 78.98 79.17
Kleve 302 83.03 83.18 83.34 278 77.99 78.15 78.30
Köln 164 83.67 83.79 83.90 162 78.73 78.84 78.95
Krefeld 310 82.98 83.14 83.32 313 77.77 77.93 78.10
Leverkusen 132 83.78 83.95 84.13 121 78.89 79.07 79.25
Lippe 136 83.77 83.92 84.06 146 78.76 78.91 79.05
Märkischer Kreis 346 82.81 82.94 83.07 322 77.72 77.85 77.98
Mettmann 139 83.78 83.91 84.04 128 78.88 79.01 79.13
Minden-Lübbecke 160 83.66 83.81 83.96 166 78.67 78.82 78.97
Mönchengladbach 368 82.63 82.78 82.93 354 77.41 77.57 77.74
Mülheim an der Ruhr 238 83.28 83.45 83.63 242 78.16 78.34 78.52
Münster 64 84.30 84.46 84.63 72 79.49 79.65 79.82
Oberbergischer Kreis 245 83.28 83.43 83.57 210 78.37 78.52 78.68
Oberhausen 387 82.36 82.52 82.68 377 77.09 77.25 77.42
Olpe 327 82.87 83.07 83.25 276 77.95 78.16 78.35
Paderborn 117 83.87 84.03 84.18 102 79.09 79.25 79.41
Recklinghausen 360 82.76 82.87 82.98 331 77.65 77.76 77.87
Remscheid 300 82.98 83.19 83.39 300 77.82 78.04 78.25
Rhein-Erft-Kreis 193 83.55 83.68 83.81 150 78.76 78.88 79.01
Rhein-Kreis Neuss 195 83.53 83.66 83.79 160 78.71 78.84 78.97
Rhein-Sieg-Kreis 85 84.20 84.32 84.44 75 79.50 79.62 79.74
Rheinisch-Bergischer Kreis 70 84.26 84.42 84.58 73 79.49 79.64 79.80
Siegen-Wittgenstein 219 83.40 83.54 83.69 341 77.52 77.68 77.84
Soest 246 83.28 83.42 83.56 355 77.42 77.57 77.72
Solingen 265 83.17 83.35 83.52 369 77.20 77.39 77.58
Steinfurt 104 83.98 84.11 84.24 213 78.37 78.51 78.64
Unna 242 83.30 83.44 83.57 360 77.34 77.48 77.62
Viersen 126 83.85 84.00 84.15 258 78.11 78.27 78.42
Warendorf 76 84.24 84.39 84.54 169 78.65 78.80 78.97
Wesel 135 83.80 83.92 84.04 284 77.99 78.12 78.26
Wuppertal 248 83.27 83.41 83.56 374 77.13 77.29 77.43
Rhineland–Palatinate
Ahrweiler 180 83.55 83.73 83.91 136 78.74 78.93 79.12
Altenkirchen (Westerwald) 293 83.04 83.23 83.42 239 78.13 78.34 78.54
Alzey-Worms 222 83.34 83.52 83.72 159 78.65 78.85 79.04
Bad Dürkheim 229 83.32 83.50 83.68 174 78.59 78.78 78.97
Bad Kreuznach 224 83.35 83.52 83.69 191 78.45 78.63 78.80
Bernkastel-Wittlich 121 83.80 84.02 84.21 110 78.99 79.21 79.42
Birkenfeld 361 82.66 82.86 83.06 319 77.64 77.86 78.08
Cochem-Zell 244 83.21 83.43 83.67 201 78.34 78.59 78.83
Donnersbergkreis 256 83.18 83.38 83.58 199 78.37 78.60 78.81
Eifelkreis Bitburg-Prüm 177 83.53 83.74 83.96 145 78.68 78.92 79.14
Frankenthal (Pfalz) 221 83.31 83.54 83.77 186 78.43 78.68 78.94
Germersheim 166 83.60 83.79 84.00 118 78.89 79.10 79.32
Kaiserslautern, Stadt 286 83.05 83.26 83.47 252 78.07 78.29 78.51
Kaiserslautern, Landkreis 205 83.41 83.60 83.78 167 78.62 78.81 79.02
Koblenz 170 83.58 83.78 83.97 139 78.71 78.93 79.12
Kusel 260 83.16 83.37 83.57 204 78.35 78.57 78.79
Landau in der Pfalz 211 83.33 83.56 83.79 180 78.49 78.74 79.01
Ludwigshafen am Rhein 252 83.22 83.40 83.58 231 78.20 78.40 78.59
Mainz 95 84.05 84.23 84.42 89 79.28 79.47 79.66
Mainz-Bingen 100 83.96 84.14 84.32 78 79.41 79.58 79.76
Mayen-Koblenz 206 83.43 83.60 83.76 173 78.60 78.78 78.94
Neustadt an der Weinstraße 133 83.72 83.95 84.16 114 78.92 79.16 79.39
Neuwied 216 83.37 83.55 83.71 188 78.49 78.67 78.85
Pirmasens 347 82.71 82.94 83.16 299 77.78 78.04 78.27
Rhein-Hunsrück-Kreis 223 83.33 83.52 83.73 192 78.42 78.62 78.84
Rhein-Lahn-Kreis 272 83.14 83.32 83.50 225 78.21 78.41 78.61
Rhein-Pfalz-Kreis 102 83.96 84.13 84.31 88 79.30 79.48 79.67
Speyer 176 83.54 83.76 83.98 296 77.80 78.05 78.31
Südliche Weinstraße 96 84.04 84.23 84.41 170 78.59 78.80 79.00
Südwestpfalz 101 83.94 84.13 84.32 197 78.40 78.61 78.81
Trier 165 83.58 83.79 83.99 315 77.66 77.89 78.13
Trier-Saarburg 80 84.19 84.37 84.55 157 78.66 78.85 79.04
Vulkaneifel 122 83.80 84.01 84.24 232 78.14 78.38 78.63
Westerwaldkreis 190 83.52 83.68 83.84 310 77.80 77.97 78.15
Worms 144 83.67 83.88 84.09 282 77.89 78.12 78.37
Zweibrücken 137 83.69 83.91 84.16 269 77.94 78.20 78.48
Saarland
Merzig-Wadern 375 82.50 82.69 82.89 356 77.35 77.56 77.78
Neunkirchen 377 82.47 82.65 82.84 361 77.26 77.46 77.66
Saarbrücken 383 82.47 82.60 82.74 367 77.28 77.41 77.55
Saarlouis 340 82.83 82.99 83.15 311 77.79 77.96 78.13
Saarpfalz-Kreis 290 83.07 83.24 83.41 253 78.10 78.28 78.47
St. Wendel 308 82.96 83.16 83.35 268 77.99 78.21 78.41
Saxony
Bautzen 153 83.70 83.84 83.99 158 78.70 78.85 79.00
Chemnitz 227 83.36 83.50 83.66 211 78.37 78.52 78.68
Dresden 7 84.98 85.11 85.25 20 80.04 80.17 80.31
Erzgebirgskreis 240 83.32 83.44 83.58 226 78.27 78.41 78.55
Görlitz 259 83.22 83.37 83.52 272 78.04 78.20 78.35
Leipzig, Landkreis 183 83.56 83.72 83.88 193 78.45 78.62 78.78
Leipzig, Stadt 192 83.55 83.68 83.82 244 78.19 78.33 78.47
Meißen 89 84.14 84.30 84.47 95 79.19 79.36 79.53
Mittelsachsen 262 83.22 83.36 83.49 224 78.28 78.42 78.56
Nordsachsen 273 83.14 83.30 83.47 265 78.08 78.24 78.43
Sächsische Schweiz-Osterzgebirge 150 83.70 83.85 84.01 154 78.72 78.88 79.04
Vogtlandkreis 232 83.33 83.48 83.62 359 77.33 77.50 77.65
Zwickau 239 83.33 83.45 83.58 353 77.44 77.58 77.72
Saxony–Anhalt
Altmarkkreis Salzwedel 394 82.22 82.40 82.59 387 76.94 77.15 77.35
Anhalt-Bitterfeld 401 81.95 82.10 82.26 397 76.60 76.78 76.95
Börde 395 82.24 82.39 82.55 383 77.01 77.19 77.35
Burgenlandkreis 379 82.48 82.63 82.78 371 77.22 77.38 77.55
Dessau-Roßlau 376 82.50 82.68 82.85 363 77.24 77.45 77.64
Halle (Saale) 384 82.45 82.60 82.76 382 77.04 77.20 77.37
Harz 398 82.12 82.25 82.40 390 76.87 77.02 77.18
Jerichower Land 393 82.27 82.44 82.63 378 77.06 77.25 77.45
Magdeburg 370 82.61 82.77 82.93 365 77.26 77.42 77.60
Mansfeld-Südharz 397 82.09 82.26 82.42 391 76.84 77.02 77.19
Saalekreis 392 82.31 82.46 82.62 379 77.08 77.23 77.41
Salzlandkreis 402 81.62 81.77 81.92 400 76.22 76.38 76.54
Stendal 390 82.30 82.47 82.64 401 75.87 76.07 76.27
Wittenberg 356 82.72 82.88 83.04 398 76.44 76.62 76.81
Schleswig–Holstein
Dithmarschen 307 82.98 83.16 83.35 291 77.88 78.08 78.26
Flensburg 350 82.72 82.92 83.12 337 77.51 77.72 77.94
Hzgt, Lauenburg 207 83.43 83.60 83.76 196 78.45 78.62 78.80
Kiel 296 83.05 83.22 83.38 306 77.83 77.99 78.17
Lübeck 321 82.94 83.10 83.27 327 77.62 77.80 77.97
Neumünster 337 82.79 83.00 83.22 328 77.56 77.79 78.02
Nordfriesland 212 83.39 83.56 83.73 206 78.38 78.57 78.73
Ostholstein 200 83.46 83.62 83.78 200 78.42 78.59 78.76
Pinneberg 172 83.63 83.77 83.92 163 78.69 78.83 78.98
Plön 115 83.86 84.04 84.22 115 78.97 79.15 79.34
Rendsburg-Eckernförde 175 83.60 83.76 83.91 153 78.73 78.88 79.04
Schleswig-Flensburg 266 83.19 83.34 83.49 248 78.14 78.31 78.47
Segeberg 113 83.90 84.04 84.19 260 78.11 78.27 78.44
Steinburg 213 83.39 83.56 83.73 357 77.36 77.56 77.75
Stormarn 67 84.29 84.44 84.61 161 78.66 78.84 79.01
Thuringia
Altenburger Land 299 83.02 83.20 83.38 270 78.02 78.20 78.40
Eichsfeld 295 83.04 83.22 83.40 240 78.15 78.34 78.54
Eisenach 371 82.55 82.76 82.97 335 77.49 77.73 77.96
Erfurt 314 82.98 83.13 83.29 287 77.93 78.10 78.26
Gera 279 83.10 83.29 83.47 273 77.98 78.19 78.38
Gotha 373 82.54 82.71 82.88 339 77.51 77.69 77.87
Greiz 285 83.09 83.27 83.45 249 78.12 78.31 78.50
Hildburghausen 378 82.43 82.63 82.83 349 77.38 77.61 77.82
Ilm-Kreis 348 82.75 82.93 83.11 314 77.73 77.93 78.13
Jena 154 83.64 83.83 84.02 130 78.79 79.00 79.21
Kyffhäuserkreis 399 82.04 82.23 82.41 388 76.90 77.11 77.32
Nordhausen 366 82.60 82.79 82.98 326 77.60 77.81 78.02
Saale-Holzland-Kreis 283 83.09 83.28 83.47 234 78.16 78.37 78.57
Saale-Orla-Kreis 342 82.80 82.98 83.16 304 77.81 78.01 78.21
Saalfeld-Rudolstadt 339 82.83 82.99 83.15 305 77.82 78.00 78.19
Schmalkalden-Meiningen 380 82.46 82.62 82.78 350 77.43 77.61 77.79
Sömmerda 359 82.68 82.87 83.06 394 76.64 76.86 77.08
Sonneberg 301 83.00 83.19 83.38 385 76.94 77.16 77.40
Suhl 258 83.18 83.37 83.58 368 77.16 77.40 77.64
Unstrut-Hainich-Kreis 372 82.60 82.76 82.93 395 76.60 76.79 76.99
Wartburgkreis 280 83.11 83.28 83.46 370 77.19 77.38 77.57
Weimar 303 82.99 83.18 83.37 381 77.00 77.22 77.44
Weimarer Land 247 83.23 83.42 83.60 352 77.39 77.59 77.80
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Figures 2 and 3 give a good impression of the distribution of life expectancy across districts in Germany. eFigures 1 and 2 show the same maps for men and women with the rankings of the individual districts. District ranks should be interpreted cautiously due to overlapping interval estimates, but they indicate the approximate position among the 402 rural districts. An alphabetical list of districts by federal state is given in the eTable. Paler shades, denoting districts with lower life expectancy, are found more frequently in the east than in the west, especially for males. However, also in western Germany there are districts with comparably low life expectancy, especially districts in the Ruhr region, such as Dortmund or Gelsenkirchen.

Figure 2.

Figure 2

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Estimates of life expectancy for men in all 402 districts of Germany expressed as whole numbers, i.e., 75 stands for 75.00–75.99 years, 76 for 76.00–76.99 years, etc. For the estimated ranking of the districts, see eFigure 1.

Figure 3.

Figure 3

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Estimates of life expectancy for women in all 402 districts of Germany expressed as whole numbers, i.e., 81 stands for 81.00–81.99 years, 82 for 82.00–82.99 years, etc. For the estimated ranking of the districts, see eFigure 2.

eFigure 1.

eFigure 1

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Estimates of life expectancy for men in all 402 districts of Germany expressed as whole numbers, i.e., 75 stands for 75.00–75.99 years, 76 for 76.00–76.99 years, etc. The numbering of the districts shows the estimated ranking (1 = highest, 402 = lowest).

It is well known that economic development, local conditions, and the availability of medical services can play important roles in life expectancy (17, 18). We now go on to explore what correlations exist between district-level life expectancy and social and economic indicators from the INKAR database (INKAR, Indikatoren und Karten zur Raum- und Stadtentwicklung [Indicators and Maps for Spatial and Urban Planning]) (19).

For this purpose we used the following district-level indicators: population density (persons per km2), primary-care physicians per 100 000 inhabitants, gross domestic product (GDP) per capita, unemployment rate, child poverty (persons eligible under 15 years per 100 inhabitants under 15 years), housing subsidies (households that receive housing benefit per 1000 households), German Social Code II-based (“Hartz IV”) welfare benefits (persons eligible below age 65 years in %), and public assistance for the elderly (proportion of inhabitants ≥ 65 years in ‰) (“Grundsicherung im Alter”).

Figure 4 shows the strength of the relationships between life expectancies at the district level and each of the selected indicators. The bars in the figure represent standardized bivariate regression coefficients: the estimated changes in life expectancy with an increase of one standard deviation in the given indicator. For example, the standardized coefficient for the unemployment rate among men in the west is –0.6. The mean unemployment rate for men in the west was 5.40%, with a standard deviation of 2.47% (table). Thus a 2.47% difference in unemployment rates between two western districts corresponds to a life expectancy 0.6 years lower than in the district with the higher life expectancy.

Figure 4.

Figure 4

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Expected change in life expectancy (years) when indicator increases by 1 standard deviation

Standardized regression coefficients for social and economic indicators (see Table). For each of the four bars per indicator (men–west, men–east, women–west, women–east) the respective standard deviation was used.

GDP, Gross domestic product; GSC II, German Social Code II-based welfare benefit (“Hartz IV”)

Table. List of indicators used, their definitions, means and standard deviations (SD).

Indicator Definition East West
Mean SD Mean SD
Population density Inhabitants per km2 287.63 414.00 577.78 715.34
Physicians/person Primary-care physicians per 100 000 inhabitants 62.86 22.64 60.15 26.28
GDP per capita Gross domestic product in € 1000 per capita 27.05 5.11 37.62 16.80
Unemployment, women Unemployment rate among women in civilian occupations, in % 7.66 2.03 5.00 2.33
Unemployment, men Unemployment rate among men in civilian occupations, in % 8.52 2.16 5.40 2.47
Child poverty Non-working eligible persons < 15 years per 100 inhabitants < 15 years 16.26 5.63 12.37 7.27
Housing subsidy Households that receive housing subsidies per 1000 households 20.49 5.05 11.84 4.48
GSC II (“Hartz IV”) Persons (fit for work or not) eligible for German Social Code II-based welfare benefits as proportion of inhabitants < 65 years in % 11.38 3.47 7.40 4.35
Elder support Recipients of old-age basic income support per 1000 inhabitants ≥ 65 years 9.37 5.66 24.44 14.14
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Source: Indicators for the year 2016 from www.inkar.de (19).

Based thereon, authors’ estimates of means and standard deviations across districts

Several patterns emerge clearly from Figure 4. The selected indicators are more strongly correlated with life expectancy for men (left panel) than for women (right panel). Here we examine only aggregated cross-sectional data, but individual-level longitudinal studies of mortality often show that social, demographic, and economic conditions affect men more strongly than women (20).

The correlations between population density, physician density, and life expectancy are relatively low. It is interesting, however, that these correlations point in different directions in the east and the west: in eastern districts life expectancy rises slightly with increasing population density and physician density, while in western districts it declines slightly. Therefore only in eastern Germany does living in an urban area or a more densely populated region confer an advantage with regard to life expectancy.

The data in Figure 4 also demonstrate that economic indicators are much stronger predictors of life expectancy than population density or the number of primary-care physicians per 100 000 residents. This contradicts speculation in the popular press (21) that differences in the proximity to medical care could explain regional differences in health and mortality.

The positive relationship between GDP and life expectancy across countries has been well known since the 1970s (17). The same pattern is visible in our analysis of districts: higher GDP per capita coincides with higher life expectancy.

Correlations between life expectancy and population density, physician density, and GDP are relatively low, however, compared with economic indicators that focus on the most disadvantaged residents of a district. As seen from the bars in the lower section of Figure 4, indicators such as unemployment rate, housing subsidies, and other measure of public assistance have notably higher correlations (all negative) with district-level life expectancy. It is also interesting to note that local unemployment and public subsidies are more strongly related to lower life expectancies in western Germany.

In our opinion the simple correlations illustrated in Figure 4 clearly show it is not the average economic conditions in a region that influence life expectancy, but rather the circumstances of persons at the lower end of the socioeconomic spectrum.

Discussion

We estimate that life expectancy varies across German districts by more than 5 years for men (5.3 years), and by almost 4 years for women (3.9 years). To put this into an international context, the lowest life expectancy for men (75.8 years) corresponds roughly to that in Oman (ranked 53rd of 201 countries in UN estimates [2]), while the highest male life expectancy (81.2) is approximately equal to that in Australia (ranked 6th). For women, the district with the lowest life expectancy is comparable with the life expectancy of Czech women (81.8, ranked 46th). The highest female life expectancy in a German district is comparable with the life expectancy of women in South Korea (85.7, ranked 5th).

It is important to mention a few methodological limitations: Our estimates are based on the current population and death counts in each district, and could consequently be affected by selective migration. For instance, migration of particularly healthy people may reduce life expectancy in their home district and increase life expectancy in their new district. Neither our data nor our methods can control for this. In addition, models like ours that pool information across small units in order to lower variability tend to pull statistical outliers with small populations towards regional and national means. This is a classic bias–variance trade-off, and in some cases it may cause too much smoothing for a “real” outlier with a small population. Without a longer time series or additional information to identify such outliers, this is an unavoidable risk.

Although cross-sectional, aggregate data do not permit causal inferences, we explored bivariate correlations between district-level life expectancy and macrofactors often discussed in the media, such as population density or physician density. Our results indicate that these factors are notably less important than economic indicators that focus on poorer population segments, such as the unemployment rate or welfare payments.

Life expectancy summarizes the entire age profile of mortality in a single number. Thus, further analyses focusing specifically on infant and childhood mortality or mortality in higher age groups might yield different results. By providing all data, code, and results at http://german-district-mortality.schmert.net/ we encourage and enable other researchers to thoroughly understand our model, to reproduce our findings, to update them with new data, and to use them for independent analyses. Important examples of wider-reaching research initiatives would include more detailed examination of mortality by age and the integration of lifestyle factors such as smoking prevalence, alcohol consumption, or physical activity (22).

Key Messages.

  • We provide point and interval estimates for the life expectancy of men and women in all districts of Germany.

  • Life expectancy is generally highest in the southwest and in southern Bavaria; the lowest life expectancy is found for rural regions in the former GDR and in the Ruhr region.

  • The spatial patterns for women and men are very similar.

  • District-level socioeconomic indicators that highlight the situation of poorer segments of the population explain differences in life expectancy patterns better than gross domestic product (GDP) per capita, population density, or the density of physicians.

eFigure 2.

eFigure 2

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Estimates of life expectancy for women in all 402 districts of Germany expressed as whole numbers, i.e., 81 stands for 81.00–81.99 years, 82 for 82.00–82.99 years, etc. The numbering of the districts shows the estimated ranking (1 = highest, 402 = lowest).

Footnotes

Conflict of interest statement

The authors declare that no conflict of interest exists.

References

Articles from Deutsches Ärzteblatt International are provided here courtesy of Deutscher Arzte-Verlag GmbH

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