Cause-specific mortality time series analysis

12 Pages·2017·0.43 MB·English

Cause-specific mortality time series analysis: a general
method to detect and correct for abrupt data
production changes
Grégoire Rey, Albertine Aouba, Gérard Pavillon, Rasmus Hoffmann, Iris Plug,
Ragnar Westerling, Eric Jougla, Johan Mackenbach
To cite this version:
Grégoire Rey, Albertine Aouba, Gérard Pavillon, Rasmus Hoffmann, Iris Plug, et al.. Cause-specific
mortality time series analysis: a general method to detect and correct for abrupt data production
changes. Population Health Metrics, 2011, 9 (1), pp.52. ￿10.1186/1478-7954-9-52￿. ￿inserm-00634738￿
HAL Id: inserm-00634738
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Reyetal.PopulationHealthMetrics2011,9:52
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RESEARCH Open Access
Cause-specific mortality time series analysis:
a general method to detect and correct for
abrupt data production changes
Grégoire Rey1*, Albertine Aouba1, Gérard Pavillon1, Rasmus Hoffman2, Iris Plug2, Ragnar Westerling3, Eric Jougla1
and Johan Mackenbach2
Abstract
Background: Monitoring the time course of mortality by cause is a key public health issue. However, several
mortality data production changes may affect cause-specific time trends, thus altering the interpretation. This paper
proposes a statistical method that detects abrupt changes ("jumps”) and estimates correction factors that may be
used for further analysis.
Methods: The method was applied to a subset of the AMIEHS (Avoidable Mortality in the European Union, toward
better Indicators for the Effectiveness of Health Systems) project mortality database and considered for six
European countries and 13 selected causes of deaths. For each country and cause of death, an automated jump
detection method called Polydect was applied to the log mortality rate time series. The plausibility of a data
production change associated with each detected jump was evaluated through literature search or feedback
obtained from the national data producers.
For each plausible jump position, the statistical significance of the between-age and between-gender jump
amplitude heterogeneity was evaluated by means of a generalized additive regression model, and correction
factors were deduced from the results.
Results: Forty-nine jumps were detected by the Polydect method from 1970 to 2005. Most of the detected jumps
were found to be plausible. The age- and gender-specific amplitudes of the jumps were estimated when they
were statistically heterogeneous, and they showed greater by-age heterogeneity than by-gender heterogeneity.
Conclusion: The method presented in this paper was successfully applied to a large set of causes of death and
countries. The method appears to be an alternative to bridge coding methods when the latter are not
systematically implemented because they are time- and resource-consuming.
Background changes that may bias the interpretation of time trends
The study of cause-specific mortality time series is one over a study period.
of the main sources of information for public health The production processes for mortality databases have
monitoring [1-3]. However, while demonstrative and been similar in many industrialized countries (particu-
striking use can be made of such trends when commu- larly in Western Europe) since the end of World War II.
nicating with the general public, many concerns relating When a death occurs, a medical certificate based on the
to the data production process have to be addressed. international form recommended by the World Health
More specifically, it is necessary to evaluate, and, if Organization (WHO) [4] is filled in by a physician. The
necessary, correct artifacts due to data production physician reports the causes of death that directly led or
contributed to the death on the death certificate. The
death certificate is then forwarded to a national (e.g.,
France) or regional (e.g., Germany) coding office, where
*Correspondence:[email protected]
1INSERM,CépiDc,LeKremlin-Bicêtre,France it is coded using the International Classification of
Fulllistofauthorinformationisavailableattheendofthearticle
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Diseases (ICD). The ICD has been regularly reviewed Concordance table and cause recombination
and improved since the end of the 19th century [5]. For The concordance table and cause recombination
each death, an underlying cause of death is selected in approach consists of determining the most consistent
compliance with the ICD rules. The underlying cause is cause categories, under medical consideration, for two
the most commonly used in statistical analyses. successive ICD revisions. Analysis of mortality using the
Underlying cause coding is a complex process and resulting categories is then theoretically influenced little
thus implies potential between-coder coding differences. by coding changes. This approach typically only works
These differences may produce coding discrepancies well when considering the coding of any particular
over time and space. This is why, in addition to ICD cause reported on the death certificate. It is often not
revisions, coding may induce variations in the causes of effective when considering changes in the rules for
death by period, region, or country. This situation has selecting the underlying cause of death, especially when
resulted in countries using increasingly automated cod- such rule changes favor the selection of one cause over
ing systems (ACS). another. It is often impossible to recombine codes to
Variations in mortality trends may also be related to fully account for these changes.
changes in death certification (death certificates, certifi- The method was used on French, Dutch, and Swedish
cation habits, diagnoses, etc.). However, the changes are data [19-21]. This approach, complex and time-consum-
often diffuse and take place over long periods of time, ing when it is applied to a single country, is even more
making them harder to take into account. Moreover, the difficult to use in the context of an international study
methods of analyzing actual historical variations in mor- [22].
tality by cause of death and taking into account data
production process changes are essentially related to Time series analysis
changes in the coding process. Of these methods, three The time series analysis method consists of looking for
main kinds can be distinguished: bridge coding, concor- sustainable jumps, evaluating their statistical signifi-
dance table and cause recombination, and time series cance and amplitude, and possibly smoothing the time
analysis-based methods. series by adjusting the data with correction factors.
The method is easy to document, even when the
Bridge coding volume of data considered is large (many countries,
The bridge coding method is used when there is a major many causes of death, etc.). Furthermore, the method
change in the coding process (ICD version change or is necessary when the time of the change in the data
switch from a manual to an automatic coding system). production process is unknown [23]. To the authors’
The method consists of coding a large set of death certi- knowledge, the detection of jumps in mortality data
ficates twice, applying the rules prevailing before and has rarely been undertaken [22,24], but, in particular
after the change. The ratios of numbers of death calcu- for Janssen et al’s work [22], has given rise to fruitful
lated by cause category before and after the change, international public health studies [25-28]. However,
called “comparability ratios,” generate information for the methods used in these studies did not take advan-
trend analyses and characterization of “jumps” in mor- tage of the recent development of automated jump
tality time series. However, analyses of long-period time detection methods in indexed data analysis (by time or
series do not necessarily use comparability ratios [1,6]. other variables) [29-31]. Interest in the automatic jump
Bridge coding analyses have been carried out in the detection method resides in its ability to avoid the sub-
United States and England and Wales for each ICD jectivity of visual detection or a priori selection of
change since the eighth version of the ICD (ICD-8) jump positions.
[7-12]. Bridge coding analysis was also used and gener- The aim of this paper is to propose a complement to
ated detailed results for the change from ICD-9 to ICD- a time series analysis method that was previously devel-
10 in some countries (Scotland, Sweden, Italy, Spain, oped by Janssen et al. [22], allowing detection of sus-
France, and Canada) [13-18]. However, to the authors’ tainable jumps attributable to changes in data
knowledge, most European countries have not imple- production and development of correction factors by
mented bridge coding to assess ICD changes. Compar- age and gender in order to enable subsequent epidemio-
ability ratios are heterogeneous between country, most logical analyses. The method is then applied to a wide
likely because of variations in intra-group composition range of different mortality time series: 13 causes of
of causes of death, reporting practices, and ICD coding death for each of six European countries participating in
interpretation. Therefore, it is unlikely that a compar- the AMIEHS (Avoidable Mortality in the European
ability ratio for one country can be inferred from the Union, toward better Indicators for the Effectiveness of
results of other countries. Health Systems) project http://amiehs.lshtm.ac.uk/.
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Methods codes was as follows:
General approach
The following step-by-step approach was adopted: • When the cause was included in the Eurostat 65
1. Given a list of selected causes of death, the ICD causes shortlist [32], the codes defined by Eurostat
codes to be considered were determined by nosologists were retained.
based on the correspondence table method, while main- • For other causes, two nosologists independently
taining the medical consistency of the list of codes for selected the optimal three-digit codes. Then, a final
the various ICD revisions. choice was made in order to minimize the coding-
2. An automated jump detection method was applied related jumps in cause of death-specific time series
to the mortality rate time series for each of the selected analysis. Table 2 shows the related codes.
causes of death.
3. For documented jumps (e.g., ICD changes), the
available comparability ratios were compared to the The automatic jump detection method
amplitude of the estimated jumps. For nondocumented The Polydect method [33] was applied to yearly log
jumps, general information feedback was requested from mortality rate time series for each country and cause of
the national data producers. death.
4. For documented or plausible jump positions, the Given that mortality analyses are often based on mul-
statistical significance of the between-age and between- tiplicative assumptions, log-linear generalized models
gender jump amplitude heterogeneity was evaluated by were used. Thus, the time series jump detection method
means of a regression model, and correction factors was applied to the log mortality rates.
were deduced from the results. Let O, be the number of deaths during year t, p be
t t
O
t
the number of person-years, and L =log be the
Mortality data t p
(cid:2) t(cid:3)
The mortality data were derived from the AMIEHS pro- log mortality rate time series. The occurrence of jumps
ject dataset. Six countries were included in the analysis: in the log mortality rate time series may be expressed as
Spain, France, the Netherlands, Germany, Sweden, and follows:
England and Wales (considered together). In order to
simplify presentation, Estonia, which is participating in log E(O) =log(p)
t t
the AMIEHS project, has not been considered in this
+g(t(cid:4))+ (cid:5)dt’·1(t>t’),
paper because Estonia used a specific coding system
until 1994. The study period is 1970 to 2005. While (cid:6)t’∈S
causes of death are usually coded with four-digit ICD
codes, the AMIEHS dataset only contains three-digit
codes for practical reasons. Some precision in the char-
acterization of causes has thus been lost.
Table 2ICD-8, ICD-9and ICD-10codes for the13
Generally, deaths are coded using the same ICD revi-
selected causes ofdeath
sionineachcalendaryear.ThedatesoftheICDrevisions
Cause ICD-8 ICD-9 ICD-10
usedbyeachEuropeancountryarepresentedintable1.
Cerebrovasculardisease 430-438 430-438 I60-I69
Conditionsoriginatingintheperinatal 760-779 760-779 P00-P96
Code allocation
period
For 13 causes of death selected in the AMIEHS project,
Congenitalheartdisease 746 745-746 Q20-
the method of allocating the ICD-8, ICD-9, and ICD-10 Q24
Heartfailure 428-429 428-429 I50-I51
Hodgkin’sdisease 201 201 C81
Table 1DatesofICD change andautomatic coding
Hypertension 400-404 401-404 I10-I13
system(ACS) implementation for sixofthe AMIEHS
European countries Ischemicheartdisease 410-414 410-414 I20-I25
Malignantcolorectalneoplasm 153-154 153-154 C18-C21
Country ICD-8 ICD-9 ICD-10 ACS
Malignantneoplasmofcervixuteri 180 180 C53
EnglandandWales 1968 1979 2001 1993
Malignantneoplasmoftestes 186 186 C62
France 1968 1979 2000 2000
Pepticulcer 531, 531, K25-K26
Germany 1968 1979 1998 2008
532 532
Netherlands 1969 1979 1996
Renalfailure 593, 584-586 N17-
Spain 1968 1980 1999 792 N19
Sweden 1969 1987 1997 1987 Rheumaticheartdisease 390-398 390-398 I00-I09
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In which g is a continuous function, S is the set of equal to 1000. A full description of this method is given
jump locations, and {d ,t Î S} are the corresponding elsewhere [33].
t
jump magnitudes. For a given jump i, the multiplicative factor MF
i
In this model, g, S, and {d , t Î S} are all assumed between before and after the jump period was calculated
t
unknown. as:
The method consists of three main steps:
MF =exp(d),
1. A left and right limit of E(L ) were estimated for i i
t
each point t using two local polynomial smoothers,
in which d is the amplitude of the jump.
i
denoted P(t) and P(t), fitted on [t - h, t) and (t, t + h],
l r
respectively, where h is the bandwidth for the estimation
Age- and gender-heterogeneity test
to be estimated in further steps. If t ∉ S, and the jumps
Age categories were defined as the tertile of the cause-
location are distant from at least h, then, given that g is
specific death counts.
continuous, we expect E(P (t)) = E(P(t)) = g(t). Else, if t
1 r Generally, when considering J different population
Î S, we expect E(P (t)) = g(t) and E(P(t)) = g(t)+d.
1 r t groups (age and gender), a generalized additive model
The noise sof the L process is estimated as:
t (GAM) with an overdispersed Poisson distribution is
used [34,35]. The model has the following form:
min L −P(t) 2, L −P(t) 2
t l t r
σˆ = (cid:7)t (cid:8)(cid:4) T−(cid:5)1 (cid:4) (cid:5) (cid:9) log E Ot,j =log pt,j
The polynomial kernel of the smoothers could, a +gj((cid:4)t)+(cid:4) (cid:5)(cid:5)dt’,j·1((cid:8)t>t’),(cid:9)
priori, be constant, linear, or quadratic, depending on (cid:6)t’∈S
the number of observations and the curvature level of
in which j is one of the J groups, g are continuous
j
the time series. Since, in the present case, the number
functions fitted by a thin plate penalized regression
of observations was not greater than 40, and the time spline, S is the set of jump locations, and {d , t ∉ S} are
t,j
series was expected to be quite stable, a linear kernel
the corresponding jump magnitudes for group j.
was selected.
S is supposed known, and the aim is to test for each t
2. Considering M(t) = Pr(t)-P1(t), jump points were Î S:
M(t)
defined as points where the signal-to-noise ratio
σˆ H0:dt,1 =···=dt,J
(cid:10) (cid:10)
was higher than a threshold Ca. (cid:10) (cid:10)
Backward variable selection was used to suppress, suc-
Ca was chosen such that, if t is not a jump point,
cessively, age and gender from the model if their respec-
P(cid:11)(cid:10)Mσˆ(t)(cid:10) >Cα(cid:12)≤α. The analytic calculation of Ca is tsiivgeniefifcfeacnttsaotnthtehe5%jumlevpela,muspilnitgudWeawlde’sretensot.t statistically
give(cid:10)n else(cid:10)where [33]. In the following steps, a was set to The MGCV (Multiple smoothing parameter estima-
10-5, a low value, in order to avoid as many as possible tion by Generalized Cross Validation) R package was
used for this purpose [36].
false positive jumps.
M(t)
Then, S= t: σˆ >Cα and dˆt =M(t),t∈Sˆ Correction factors
(cid:13) (cid:10) (cid:10) (cid:14) Correction factors were calculated for all confirmed
(cid:10) (cid:10) (cid:15) (cid:16)
were directly estimated. When several jumps were jumps.
detected in a time range less than the bandwidth, only The correction factors were calculated for use in sub-
the jump that maximized M(t) was retained. sequent analyses, not discussed in this article, with a
3. The bandwidth h was estimated by minimizing the log-linear model of general form:
Hausdorff distance [29], defined as:
log E(O) =
t
dH(S,Sˆ;h)= log((cid:4)pt)+c(cid:5)t+f(Xt),
max sup inf|t1−t2|,inf sup|t1−t2| , in which t is the year between T1 and T2 (respectively
(cid:13)t1∈Sˆ t2∈Sˆ t1∈Sˆ t2∈Sˆ (cid:14) equal to 1970 and 2005 in this study); ct is the correc-
tion factor and f(X) could be any function of indepen-
t
in which dH(S,Sˆ;h) was calculated through a boot- dent variables to be estimated.
strap procedure, setting B, the number of batches used, The correction factors were set so that the last values
of the corrected mortality rates were equal to the exact
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mortality rates, i.e., c =0. This choice was based on comparative ratios. Some coding changes were not
the supposed superioT2r quality and between-country detected by the jump detection method (Hodgkin’s dis-
comparability of the most recent year’s data. ease in England and Wales and Sweden and renal failure
The foregoing results in the following definition of the in England and Wales). However, none of the detected
correction factors c: jumps were found unrelated to a coding change.
t
For t Î [T , T ],
1 2
Corrected mortality rate time series
c = d − d Considering some of the most clear-cut time series, the
t t’ t’
t’∈(cid:6)S,t’<t (cid:6)t’∈S profile of the corrected time series is quite different
from that of the uncorrected series (Figure 1). It is note-
=− d .
t’
worthy that the corrected curves do not reduce the gen-
t’∈(cid:6)S,t’≥t eral trends at the jump positions, which would have
The estimate of c was then directly obtained from the been the case if constant rather than linear kernel
t
estimates of S and d detailed earlier. smoothing was chosen. Rather, they prolong the trends,
t
A corrected version of the log mortality rate was then even if the jump is in the opposite direction of the gen-
obtained as: eral trend.
Concerning hypertension in the Netherlands, we
Lcor =L −c
t t t observe a trend shift between the periods before and
after the corrected jumps. Such trend shift is not taken
into account by the current method.
Results
Jump detection
Estimates of jump amplitudes by age and gender
By applying the jump detection method to all of the
With regard to the jump amplitude heterogeneity test by
time series, a set of jumps was obtained (table 3). Most
age and gender, only 19 out of 47 jumps were not statis-
of the jumps detected were concomitant with a known
tically significantly heterogeneous (table 5). Five of the
coding change (ICD updates or change from a manual
jumps were heterogeneous by gender, 15 by age, and
to an automatic coding system). Some of the jumps (e.
eight by age and gender simultaneously. While the jump
g., for heart failure and rheumatic heart disease) were of
amplitudes are of the same order by gender, even when
great amplitude and almost systematically observed in
statistically heterogeneous, they are of different orders
each country. For the former East Germany, most of the
when considered by age group. This was particularly
changes were concomitant with the reunification of
marked for rheumatic heart disease and heart failure.
Germany.
The answers from data producers, contacted to deter-
Discussion
mine whether the jump was related to a data production
The originality of the methodology reported herein
issue, were consistent between countries. Most (exclud-
ing the “No answer,” 42 out of 44) of the detected mainly resides in its ability to detect jumps automati-
cally using the Polydect method, without a priori or
jumps were confirmed to be related to a coding change.
visual investigation for jump positions. In addition,
The three-digit coding constraint was given as an expla-
application of the method to a large dataset is less time-
nation for some jumps (rheumatic heart disease in
consuming and less human-dependent than any other
France, ischemic heart disease in Spain, etc.), especially
known method.
when countries chose specific codes (as in Spain for
Some methodological choices were made, such as the
malignant colorectal neoplasm). The 1990 and 1991
choice of a linear kernel smoother and the choice of the
jumps in East Germany were related to a complete
probability a of detecting fake jumps. Considering a
change of coding staff. However, most of the coding
constant kernel smoother or different values of a
changes are not documented by a literature reference.
slightly affected the final set of detected jumps and only
Given the large proportion of confirmed jumps, we
for time series in which the jump amplitudes were of an
decided to exclude from subsequent treatment the
order comparable to that of the overall noise of the time
jumps for which we received a negative answer from
series. Choosing a low value of a insured a better accu-
data producers.
racy in the jump’s amplitude estimation, which is more
It was possible to compare a few of the multiplicative
statistically stable when the jump is of much larger
factors with the comparative ratios generated by bridge
amplitude than the overall noise of the time series.
coding studies corresponding to ICD-9 to ICD-10
According to the visual inspection of time series graphs
changes (table 4). In particular, the large multiplicative
and comparable bridge coding results, a jump’s
factors (e.g., for rheumatic heart disease) had no related
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Table 3Jumps in the logmortality rate timeseries forthe 13selected causes ofdeath from1970to 2006identified
by the Polydect method
Country Underlyingcauseofdeath Yearofthe Multiplicative Dataproducer Proposedjump
jump factor1 confirmation cause2
Englandand Cerebrovasculardisease 2001 1.24 Yes ICD-10
Wales
Conditionsoriginatingintheperinatal 1986 0.09 Noanswer No
period
Conditionsoriginatingintheperinatal 2001 2.78 Yes ACS
period
Heartfailure 1979 3.59 Yes ICD-9
Heartfailure 1984 0.74 Yes No
Heartfailure 1993 1.61 Yes ACS
Renalfailure 1979 2.90 Yes ICD-9
Renalfailure 1993 2.33 Yes ACS
Rheumaticheartdisease 1979 0.63 Yes ICD-9
Rheumaticheartdisease 2001 0.77 Yes ICD-10
France Heartfailure 1979 1.99 Yes ICD-9
Hypertension 1979 0.71 Yes ICD-9
Hypertension 1998 1.26 Yes Deathcertificate
Malignantcolorectalneoplasm 1979 0.92 Yes ICD-9
Malignantcolorectalneoplasm 2000 0.94 No ICD-10
Pepticulcer 1979 0.74 Yes ICD-9
Pepticulcer 2000 0.63 Yes ICD-10
Rheumaticheartdisease 1979 0.36 Yes ICD-9
Rheumaticheartdisease 2000 1.54 Yes ICD-10
EastGermany Cerebrovasculardisease 1991 1.45 Yes Reunion
Conditionsoriginatingintheperinatal 1991 0.46 Yes Reunion
period
Heartfailure 1990 1.85 Yes Reunion
Heartfailure 1991 0.52 Yes Reunion
Hypertension 1991 0.51 Yes Reunion
Ischemicheartdisease 1991 1.43 Yes Reunion
Malignantcolorectalneoplasm 1991 1.21 Yes Reunion
Rheumaticheartdisease 1998 1.14 Yes ICD-10
WestGermany Heartfailure 1979 1.65 Yes IC-D9
Hodgkin’sdisease 1994 0.59 No No
Ischemicheartdisease 1979 0.87 Yes ICD-9
Rheumaticheartdisease 1979 0.51 Yes ICD-9
Netherlands Heartfailure 1979 1.54 Yes ICD-9
Heartfailure 1996 0.76 Noanswer ICD-10
Hypertension 1980 0.65 Yes No
Renalfailure 1979 0.75 Noanswer ICD-9
Rheumaticheartdisease 1992 5.40 Noanswer No
Rheumaticheartdisease 1996 0.13 Noanswer ICD-10
Spain Conditionsoriginatingintheperinatal 1975 2.94 Yes Definitionchange
period
Heartfailure 1980 1.41 Yes ICD-9
Ischemicheartdisease 1980 0.93 Yes ICD-9
Malignantcolorectalneoplasm 1980 0.78 Yes ICD-9
Renalfailure 1980 1.73 Yes ICD-9
Sweden Heartfailure 1987 5.55 Yes ICD-9
Malignantneoplasmoftestes 1974 1.33 Yes No
Malignantneoplasmoftestes 1979 0.74 Yes No
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Table 3 Jumps in the log mortality rate time series for the 13 selected causes of death from 1970 to 2006 identified
by the Polydect method(Continued)
Renalfailure 1987 0.69 Yes ICD-9
Renalfailure 1997 1.23 Yes ICD-10
Rheumaticheartdisease 1982 0.08 Yes Codingchange
Rheumaticheartdisease 1987 15.33 Yes ICD-9
1:multiplicativefactorofthemortalityratebetweentheperiodbeforeandafterthedetectedjump
2:newcoding,implementationofwhichmayhavecausedthejump
amplitude estimates were reliable enough to be used in When comparable, the multiplicative factors obtained
subsequent analyses. from bridge coding studies and time series methods
The codes used in this study to characterize the con- were similar [11,15-17,37,38].
ditions were not chosen to be used in all contexts. The purpose of this article is not to challenge bridge
Indeed, they were allocated with the constraint of being coding studies. However, bridge coding studies are not
comparable between three versions of the ICD and implemented in all countries, and it would be very diffi-
based on three-digit codes. Taking each ICD individually cult and costly to do so retrospectively for every data
would certainly have led us to select other codes. production change. The time series analysis methods
The method is designed to detect sustained jumps. proposed herein provide a reliable way of correcting
Therefore, it is not sensitive to the occurrence of one- data production changes affecting death count time
year outliers in time series data and it does not necessi- trends.
tate considering them separately, unlike other methods Given the indirect manner in which data production
[22]. changes are identified, the method necessitates feedback
However, the proposed method is not able to detect from data producers in order to confirm and explain the
and correct for nonabrupt data production changes. For plausibility of the changes. Without that additional
example, if a new death certificate form, impacting certi- information, the automatic method would blindly cor-
fication practice and final coding, slowly spread through rect any detected jumps, some of which may be related
the population (as was the case in France between 1997 to real abrupt and sustained variations in the mortality
and 1999), the impact on yearly death counts would risk. However, it is not always straightforward for a data
occur over several years. But, to the authors’ knowledge, producer to obtain a broad overview of past coding pro-
no general method is able to correct time-spread data cess methods in the producer’s country. The reasons for
production changes. the occurrence of some of the oldest jumps may have
Table 4Comparative ratios (CR) between bridge coding and multiplicative factors (MF)estimated bythe jump
detection method forICD-9to ICD-10 coding change
France EnglandandWales Sweden Spain
Underlyingcauseofdeath CR MF CR MF CR MF CR MF
Cerebrovasculardisease 0.97 1.00 1.13 1.13 1.03 1.00 - 1.00
Conditionsoriginatingintheperinatalperiod 1.03 1.00 - 2.24 1.02 1.00 - 1.00
Congenitalheartdisease - 1.00 - 1.00 - 1.00 - 1.00
Heartfailure - 1.00 - 1.00 - 1.00 - 1.00
Hodgkin’sdisease - 1.00 1.06 1.00 1.13 1.00 - 1.00
Hypertension - 1.00 - 1.00 - 1.00 - 1.00
Ischemicheartdisease 1.03 1.00 1.01 1.00 1.00 1.00 1.00 1.00
Malignantcolorectalneoplasm 0.97 0.94 1.01 1.00 0.99 1.00 1.00 1.00
Malignantneoplasmofcervixuteri 0.92 1.00 1.00 1.00 1.00 1.00 - 1.00
Malignantneoplasmoftestes - 1.00 0.99 1.00 - 1.00 - 1.00
Pepticulcer - 0.63 1.00 1.00 - 1.00 - 1.00
Renalfailure - 1.00 1.08 1.00 - 1.23 - 1.00
Rheumaticheartdisease - 1.54 - 0.77 - 1.00 - 1.00
CR:Comparativeratiosobtainedfrombridgecoding,calculatedastheratioofnumbersofdeathcalculatedbycausecategorybeforeandaftertheICDchange
MF:MultiplicativefactorofthemortalityratebetweentheperiodbeforeandaftertheICDchange,equal1.00whennojumpwasdetected
-:CRnotavailable
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England and Wales France
Spain The Netherlands
Figure1Examplesofcorrectedmortalityrate(per100,000people)timeseries.
been lost. Therefore, the decision to take into account 10 rule 3). However, the absence of high-amplitude
or not any detected jump that is not confirmed by the jumps is not sufficient to ensure the interpretability of
data producer will depend on the degree of confidence time trends. Time trends for some conditions like
that the jump is not attributable to a production change. hypertension, heart failure, and renal failure have to be
Some jumps are of great amplitude (e.g., rheumatic interpreted cautiously. Indeed, the approach chosen
heart disease). This may be observed when the cause was to only consider the underlying cause of death,
considered is highly likely to be the result of other and these specific causes may be selected as underly-
causes [10,23]. In that case, the death count time trend ing, due to lack of additional information about the
is very sensitive to changes in coding rules (e.g., ICD- real underlying cause on the death certificate. In these
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Table 5Multiplicative factor by ageand gender,if statistically heterogeneous, foreach detected jump
Country Underlyingcauseofdeath Year Gender2 Multiplicativefactor1
Agetertile3
1st 2nd 3rd
Cerebrovasculardisease 2001 Both 1.13
Conditionsoriginatingintheperinatalperiod 1986 Both 0.10
2001 Both 2.24
EnglandandWales Heartfailure 1979 Male 4.78 3.86 3.34
Female 4.22 3.41 2.95
1984 Both 0.60 0.72 0.77
1993 Both 1.83 1.58 1.37
Renalfailure 1979 Both 2.59
1993 Both 2.93 1.53 1.50
Rheumaticheartdisease 1979 Male 0.54 0.50 0.39
Female 0.82 0.75 0.59
2001 Both 0.87 0.94 0.75
Heartfailure 1979 Both 1.91
Hypertension 1979 Male 0.63
Female 0.70
1998 Both 1.14 1.10 1.25
France Malignantcolorectalneoplasm 1979 Both 0.91
Pepticulcer 1979 Both 0.75
2000 Both 0.61
Rheumaticheartdisease 1979 Male 0.39 0.32 0.22
Female 0.62 0.51 0.35
2000 Both 1.53
Cerebrovasculardisease 1991 Both 1.40
Conditionsoriginatingintheperinatalperiod 1991 Both 0.52
Heartfailure 1990 Both 1.71 1.46 1.27
1991 Both 0.72
EastGermany Hypertension 1991 Male 0.45 0.51 0.58
Female 0.52 0.58 0.67
Ischemicheartdisease 1991 Male 1.14 1.21 1.30
Female 1.22 1.30 1.40
Malignantcolorectalneoplasm 1991 Male 1.14 1.17 1.32
Female 1.04 1.07 1.20
Rheumaticheartdisease 1998 Male 0.92
Female 1.25
Heartfailure 1979 Both 1.60
WestGermany Ischemicheartdisease 1979 Male 0.98 0.92 0.76
Female 0.93 0.88 0.72
Rheumaticheartdisease 1979 Male 0.46
Female 0.69
Heartfailure 1979 Male 2.15 1.33 1.23
Female 1.86 1.15 1.06
1996 Both 0.64 0.77 0.82
Netherlands Hypertension 1980 Both 0.95 0.81 0.70
Renalfailure 1979 Both 0.41 0.72 0.63
Rheumaticheartdisease 1992 Both 0.89 2.76 5.32
1996 Both 0.42