Introduction
TB remains one of the most relevant infectious diseases globally with around 10 million
new infections per year [1].
In Germany there has been a constant decline in incidence until 2014 when a slight
reversal in trend occurred in incident cases [2]. Similar to other Western European countries incident cases are unequally distributed
in Germany among and within the federal states with highest incidence in the major
urban settings [3]
[4]. While in the federal state of North Rhine-Westphalia the incidence stood at 6.7/105 the city of Cologne over the last decade had an average 49 % higher incidence of
10.0/105.
In 2014, WHO has launched the “End TB Strategy” with the aim to reduce incident cases
in low incidence high resource countries like Germany to 1/100,000 by 2035 and 0.1/100,000
by 2050 [5].
To reach these targets, major steps in reducing incidence would be necessary in these
settings. To better understand the situation and to gain information to optimize TB
control in Cologne, the 4th largest city in Germany, we analyzed the temporal and spatial distribution of incident
TB cases in this city from 2006 to 2015.
In line with the consensus statement for TB control in big cities in the EU [3] and for better planning of the local TB control, this study specifically analyses
the pattern of case distribution within the city boundaries, identifies the areas
with the highest densities of incident TB cases and changes in distribution over one
decade.
Methods and Setting
Cologne is located in the federal state of North Rhine-Westphalia (NRW) in Germany.
This state counts around 1,100 cases of incident TB annually of which around 10 %
occur within Cologne [2]. The city as a whole can be seen as a large-scale hotspot of TB incidence in NRW
and even in Germany, as already noted by Kistemann et al. (2002) [6].
Data about Cologne such as the number, age and sex distribution of inhabitants were
retrieved from open sources of the municipal statistical office, broken down to the
administrative sub-units [7]
[8]. Population data were directly available for the year 2005 and 2011 – 2014, data
for the years 2006 – 2010 were interpolated. Since data for 2015 were not yet available,
we used the same numbers as for 2014 [9].
The analysis was based on the local TB register of the municipal health authority
of Cologne which captures all notifiable characteristics of incident cases. Comorbidities
such as HIV are not part of the notification. All available data of notified incident
cases between 2006 and 2015 – collected mandatorily under the federal infection prevention
legislation – were retrieved, geographically identified by address and transferred
into an Excel® data sheet.
Cologne covers an area of 405 km² and had 1.07 million inhabitants in 2015. Administratively
it is subdivided in 9 districts and 86 sub-districts which served as observational
units in the analysis. Because of considerable differences in the number of inhabitants
very small units were merged with neighboring units to reduce the level of difference.
Following data cleaning and control 1,038 incident TB cases were identified, out of
which 967 (93.2 %) could be geographically located using Google Earth Pro (GEP) Version
7.1.5.1557 [10] and thus were available for the spatio-temporal analysis. The distribution over
the whole city is depicted in [Fig. 1].
Fig. 1 Overview of incident cases.
Further processing and statistical analysis were done using QGIS 2.8.4 [11], ArcGIS 10.3.1 [12] and R 3.2.3 [13].
As a result, the TB incidences for each sub-district as well as the absolute case
numbers were available for analysis and comparison.
A descriptive analysis was done with regards to age, sex, country of birth and type
of tuberculosis. In the spatial analysis differences in incidence between the sub-districts
were tested for spatial autocorrelation (global Moran’s I) and tested by chi-square
test. For further spatial analysis of the distribution of the incident cases we performed
optimized hot spot and a cluster-outlier analysis, a tool to identify spatial outliers
[14]. We also analyzed temporal differences of incidence density per sub-district (adjusted
image differencing) for the whole period as well as separated for the time periods
2006 – 2010 and 2011 – 2015.
Ethical consideration
Only data collected under the German infection prevention and control act was used
in a pseudonymized format accessible only for authorized personnel. Personal informed
consent therefore was not required. A degree of fuzziness in not ion the geographical
figure was applied to avoid direct localisation of addresses.
Results
Between 2006 and 2015 a total of 1038 incident cases were notified and 967 (93.2 %)
could be geographically located. Among the 71 cases without geographical location
39 were known as homeless. For the others the address was partly incomplete. This
number corresponds with an average incidence rate of 10.0 per 100,000 inhabitants
and a range over time between 8.80 in the year 2012 and 13.50 in the year 2006 without
a clear downward trend after 2006. Meanwhile the national TB incidence slightly decreased
from 2006 until 2013 and then rose again in 2014 and 2015 (see [Fig. 2]).
Fig. 2 Comparison of TB incidence rates in Cologne, North Rhine-Westphalia and Germany.
Data source: Survstat (2016), Amt für Stadtentwicklung und Statistik (2015a) and Municipal
Health Authority Cologne.
Of the 1038 incident TB cases notified and registered by the municipal public health
authority 63.6 % were males, mean age was 46.3 years (IQR 31 – 61) with more than
45 % of the cases in the age group between 25 and 55 years. Over the whole period
56 % of the incident cases consisted of contagious pulmonary disease (26 % were positive
in sputum microscopy and culturally confirmed, 30 % only culturally positive). The
remainder were either non-contagious pulmonary cases (11 %) or had extrapulmonary
disease (33 %). Resistance testing was done for all cases with culturally confirmed
diagnosis (87 %). Any resistance was found in 64 cases (6.2 %) most of which were
INH (44) related. MDR-Tb was diagnosed in 11 cases (1 %). No XDR was notified during
this period.
The rate of patients born in Germany (40 %) in relation to patients born in foreign
countries (60 %) was constant with a slight increase to 68 % in 2015. Foreign born
cases originated from 89 different countries with Turkey contributing 14 %. Comparing
the type of diagnosed TB under the aspect of the country of birth, it is noticeable
that the percentage of contagious (i. e. smear positive and/or culture positive) lung
TB is higher among patients born in Germany. Regarding the TB of other organs than
the lung, the affected patients were much more often born in a foreign country ([Fig. 3]). Based on individual history, contact data and follow up, the vast majority of
cases appear unrelated to each other likely as a result of progression from LTBI to
active disease.
Fig. 3 Comparison of the group of diagnosis by country of birth. Data source: Municipal
Health Authority Cologne (n = 381).
The incidence analysis of the sub-districts showed a spatial range from < 5 to > 20
per 100,000. [Fig. 4] shows the incidence rates of each sub-district for the whole period as well as for
the intervals 2006 – 2010 and 2011 – 2015. The distribution is inhomogeneous across
the city, but there are no clear trends identifiable. However, some of the sub-districts
stand out because of higher or lower incidences.
Fig. 4 Incidences of the TB cases in Cologne regarding the years 2006 – 2015 (a), 2006 – 2010 (b) and 2011 – 2015 (c).
In two city sub-districts (Godorf/Hahnwald, Weiss) not a single TB incident case was reported in the period of the study. The highest
incidences in contrast were detected in Riehl (21.6), Kalk (19.5) and Mülheim (18.6). They are almost twice as high as the average incidence of the city.
Most of the sub-districts show incidences between 5 and 15, and those with lower incidences
more often lie in the outer urban area.
Comparing the two time intervals 2006 – 2010 and 2011 – 2015 some differences can
be noted. In general, the incidences in the second interval seem to be more heterogeneous.
While during the first years there were 16 sub-districts with an incidence below 5
and none with an incidence above 20, in the second interval there are 23 sub-districts
with an incidence below 5 and 3 above 20: Riehl (24.83), Kalk (21.09) and Dünnwald (20.08).
The chi-square test suggests that the incident TB cases are not homogeneously distributed
over the city (p < 0.01).
The density analysis for absolute numbers of cases and the cluster analysis reveal
statistically significant, identifiable hot spots. Global Moran’s I hints towards
a middle grade clustering (p < 0.01) ([Fig. 5]).
Fig. 5 Density Map (a) and Cluster-Outlier Analysis (b) of the incident TB cases in Cologne 2006 – 2015.
Discussion
We analysed the distribution of all notified incident TB cases in one of the largest
cities of Germany over a period of 10 years of which 93 % could geographically be
located, hence providing a good picture on the spatial and temporal distribution of
incident TB in this urban setting. The German infection prevention legislation clearly
stipulates the notification process and in line with the RKI reports we assume that
notification numbers should be almost equal to the true incidence.
Regarding the spatial dimension of the TB incidence our analysis points towards an
inhomogeneous distribution across the sub-districts of the city. As a result, there
are areas with TB incidence significantly below the average as well as areas with
a TB incidence well above the average. The former are often found at the outskirts
of Cologne. This may be caused by the less urbanized environment and a change in housing
conditions [15]. Analysing sub-districts not following this pattern could provide additional insights
into the local TB dynamics. However, it must be emphasized, that the number of incident
TB cases on this scale is low. Under these circumstances a qualitative approach would
be preferable for further investigations.
On the other hand, the city sub-districts with the highest TB incidences over time
are generally located closer to the city centre. Taking a closer look on the located
TB cases it can be observed, that small scale patterns and hotspots occur independent
from the sub-districts’ borders. The reasons therefore could lie in the urban settlement.
The ‘Rhine port’ in the southwest, as an example for an extremely low population density,
can be recognised as a light area on the density map regarding the sub-districts Deutz and Mülheim. Areas with high TB incidence rates mostly occur in areas with a high population
density as to be found in the northern parts of Mülheim. However, the sub-districts’ inner structure varies and small scale patterns can
often explain the inhomogeneity.
One of the sub-districts with the highest TB incidence is Kalk. Over time the number of TB cases there remains relatively constant and on the scale
of the TB cases there is only little spatial variation within this sub-district. Kalk as well as Mülheim have been classified as ‘Sozialraumgebiet’ – a term describing areas with a recognised
need to improve general housing and living conditions [16]. Further, Kalk is known as an area of the city with high unemployment rates [17]. Without being able to draw causal association from the data here, the observation
is in line with the known association between low social status and higher TB incidence.
Riehl on the contrary is not one of the sub-districts with lower social and housing conditions
but has an almost equally high TB incidence as Kalk. This fact could be linked to certain facilities located in Riehl, such as a large retirement home and a refugee accommodation, because both elderly
persons and refugees have higher incidences of TB.
The TB incidence in most industrial countries concerns mainly the underprivileged
population [18]. While our data for Cologne seem to support that high TB incidences in some parts
of the city can partly be related to social economic factors, a much closer inspection
of the particular cases is necessary. Our data do not allow drawing a causal association
between TB incidence rates to socio-economic variables or vice versa. Considering
the low numbers of cases the spatial analysis of the single TB cases on a small scale
level including a more detailed assessment of socio-economic conditions is needed
for further analysis.
Regarding the increasing numbers of refugees and displaced persons arriving in Europe
and in Germany [19], our analysis cannot present any changes echoing that development because of the
time period under analysis, but can rather serve as a baseline for follow-up comparative
studies looking at the effect of migration.
Analysing the temporal dynamics of TB incidence, a high variation without a clear
upward or downward trend can be noticed. In the meantime, TB incidence is slightly
rising in the whole of Germany [2]. Compared to NRW or Germany the TB incidence in Cologne is relatively high and can
be seen as a hotspot at a large scale.
On the small scale the sub-districts Mülheim, Kalk and Riehl show constantly the highest TB incidences per year and thereby pose local hotspots.
Although our data show some clustering, these observations should be considered with
caution avoiding the conclusion of simplified causal relations. Such analysis is only
possible in larger studies like from the Rotterdam region 1995 – 2006 and is not the
focus of our work [20]. Yet in a follow-up study those high incidence sub-districts could be further examined
regarding their commonalities and differences. Furthermore, these results suggest
the monitoring of high TB areas. Besides they provide an opportunity for the city
of Cologne to develop practical approaches to achieve a local reduction of TB incidence
rates and adequate provision of services, as suggested by the European Consensus statement
and in line with the WHO End TB strategy [3].
Conclusion
The incident TB cases in Cologne between 2006 and 2015 have been digitalised, spatially
located and analysed. The resulting maps deliver detailed insights into distribution
and development of TB clusters in the city. TB incidence in Cologne and other big
cities is higher than in the rest of Germany and inhomogeneously distributed within
the city. Urban districts and sub-districts show different patterns of TB incidence
with relatively higher rates in the quarters near to the city centre.
As a combined result of analysing the TB incidence rates and the TB cases within sub-districts
different TB hotspots could be identified.
This study presents a method to monitor the distribution and development of incident
TB cases per administrative units and sub-units of a city, thereby revealing hotspots
of TB incidence. The granular presentation of the Tb cases in Cologne allows better
understanding of the local TB dynamics and targeted interventions for local prevention
and control.
Authors contribution
TK and LMP developed the concept and LMP conducted the main analysis, FN, NF supported
the retrieval, analysis and interpretation of the data, LMP wrote the first draft
of the manuscript, FN revised the draft with inputs by GAW, NF, TK. All authors read
and endorsed the final version,
