Traffic Injury Prevention
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Quantifying the Underestimated Burden of Road
Traffic Mortality in Mexico: A Comparison of Three
Approaches
Martha Híjar , Aruna Chandran , Ricardo Pérez-Núñez , Jeffrey C. Lunnen ,
Jorge Martín Rodríguez-Hernández & Adnan A. Hyder
To cite this article: Martha Híjar , Aruna Chandran , Ricardo Pérez-Núñez , Jeffrey C. Lunnen ,
Jorge Martín Rodríguez-Hernández & Adnan A. Hyder (2012) Quantifying the Underestimated
Burden of Road Traffic Mortality in Mexico: A Comparison of Three Approaches, Traffic Injury
Prevention, 13:sup1, 5-10, DOI: 10.1080/15389588.2011.631065
To link to this article: http://dx.doi.org/10.1080/15389588.2011.631065
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Traffic Injury Prevention, 13(S1):5–10, 2012
C 2012 Taylor & Francis Group, LLC
Copyright ISSN: 1538-9588 print / 1538-957X online
DOI: 10.1080/15389588.2011.631065
Quantifying the Underestimated Burden of Road
Traffic Mortality in Mexico: A Comparison
of Three Approaches
MARTHA HÍJAR,1 ARUNA CHANDRAN,2 RICARDO PÉREZ-NÚÑEZ,3
JEFFREY C. LUNNEN,2 JORGE MARTÍN RODRÍGUEZ-HERNÁNDEZ,1 and
ADNAN A. HYDER2
1
Centro de Investigación en Salud Poblacional del Instituto Nacional de Salud Pública, Cuernavaca, Mexico
Johns Hopkins International Injury Research Unit, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
3
Centro de Investigación en Sistemas de Salud del Instituto Nacional de Salud Pública, Cuernavaca, Morelos, Mexico
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2
Introduction: We present a novel multistep technique to estimate the actual burden of road traffic mortality in Mexico
during the time period 1999 to 2009 by comparing 3 approaches for redistribution of nonspecific (“garbage”) International
Classification of Diseases (ICD)-coded deaths.
Methods: Road traffic (RT) mortality data were extracted using a secondary analysis of the Mexican mortality databases
for the period 1999 to 2009. In an attempt to correct for underestimation due to inappropriately coded deaths, those deaths
assigned to nonspecific codes were redistributed utilizing 3 different adjustment methods. A comparison of the 3 adjustment
approaches (proportional, multiple imputation, and regression) is presented. A Poisson regression analysis was utilized to
model mortality trends in the raw data and the 3 estimates.
Results: After adjustments, the total number of RT deaths increased by 18 to 45 percent, showing significant underestimation when only the raw data are used. All 3 approaches showed statistically significantly higher RT mortality rates than
the crude figures. The proportional approach resulted in the highest RT mortality rate estimate of 23 per 100,000 in 2009
and showed a statistically significant positive increase of 1.5 percent per year across the decade. The 60+ age group and
pedestrians had the highest mortality rates of 40 and 10.3 per 100,000, respectively. Over the decade, there was an alarming
332 percent increase in the mortality rate for male motorcyclists.
Conclusion: Though efforts to improve coding should continue to be implemented, we present an additional and often
overlooked contribution to the underestimation of road traffic mortality: the ICD nonspecific codes. Improved estimates
of road traffic mortality are important in Mexico for policy change and decision making, highlighting the importance of
targeting road traffic deaths as a public health problem. The approach presented here may also be useful for estimating the
burden of other deaths with similar coding problems.
Keywords Road traffic mortality; Unintentional injury; Mexico; Adjusted disease burden
INTRODUCTION
affected the analysis of mortality due to unintentional injuries,
including estimates within the global burden of disease studies
to establish global mortality patterns (Bhalla et al. 2010). Bhalla
et al. (2010) documented recently that in many countries, over
20 percent of unintentional injury deaths are labeled as an unspecified external cause, using the International Classification
of Disease, 10th Revision (ICD-10) code X59, which represents
“exposure to an unspecified factor” (Bhalla et al. 2010). Other
commonly utilized nonspecific codes include V99 and Y85.9
(unintentional transport injury not otherwise specified; 20
percent of deaths in Georgia and Serbia were recently shown to
be allocated to these codes) and V89 (unspecified motor vehicle
accident, traffic), which is also utilized frequently in many
countries. Categories V87 and V88, other specified personal
It is widely recognized that a reliable estimate of the burden
of death by cause is essential for informing national and global
health priorities (Bhalla et al. 2010). However, a significant
proportion of mortality information, particularly from low- and
middle-income countries, tends to be misclassified; thus, the
burden of deaths from many diseases and injuries is significantly
underestimated and therefore considered to be of poor quality
(Naghavi et al. 2010). This is a problem that has particularly
Received 13 July 2011; accepted 7 October 2011.
Address correspondence to Ricardo Pérez-Núñez, PhD, Universidad #655,
Colonia Santa Marı́a Ahuacatitlán, Cerr los Pinos y Caminera CP 62100, Cuernavaca, Morelos, México. E-mail: [email protected]
5
6
Table I
HÍJAR ET AL.
Categories of corrections to reclassify nonspecifically coded road traffic deaths in Mexico, 1999 to 2009
Category
Existing code examples
Nonspecific codes
Adjustment
Total number of deaths
Communicable diseases
Noncommunicable diseases
Injuries
Intentional injuries
Unintentional injuries
Poisoning
Smoke
Transport
Etc.
Pedestrian
Motorcycle
Etc.
All R codes
Correction 1
Y32–34
Correction 2
X59
Correction 3
V87, V892, V899
Correction 4
Total number of injury deaths
Total number of unintentional injury deaths
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Total number of transport injury deaths
exposures and history, are less frequently but also often used as
nonspecific/garbage codes (Bhalla et al. 2010).
Interestingly, Bhalla et al. (2010) reported that Mexico has
good information systems to classify road traffic (RT) deaths,
although no further details were documented. However, in Mexico, it has been recognized that death certificates are not filled
out adequately. Analyzing only one city in 2003, only 12 percent of death certificates were useful for coding deaths using the
ICD-10 classification; therefore, reported data actually showed
an artificial decrease in RT mortality figures during the years
immediately following the introduction of the ICD-10 (LozanoAscencio et al. 2003). This phenomenon also occurred in other
countries such as Japan, Croatia, Germany, and Romania when
the ICD-10 was introduced as the official death coding method
(Lozano-Ascencio et al. 2003).
Previous efforts have been undertaken to adjust for the misclassification of deaths (Shahraz et al. 2008). In this study, we
have extended the work undertaken by other authors by introducing 3 potential methodologies for the adjustment of road
traffic mortality estimates from ICD-10–coded deaths by reallocating those deaths that were labeled with nonspecific codes.
This results in adjusted (higher and potentially more accurate)
road traffic mortality rate estimates. We also show mortality
rates disaggregated by age, sex, and road user type utilizing the
proportional adjustment method, highlighting subpopulations at
particularly high risk for RT deaths.
METHODS
Categorization of Deaths
Mexican death certificates are completed mainly by the health
sector (by health care providers in hospitals or forensic medical
examiners’ offices) and include basic information such as sociodemographic variables and ICD-10 diagnosis codes. Death
certificates from autopsies are completed by the forensic medical service; the information is entered into a database at the
state level and then transferred to a national database (Secretarı́a de Salud 2007). The mortality database is then compiled
and validated by the Instituto Nacional de Estadı́stica y Geografı́a (National Institute of Statistics and Geography; INEGI).
For this study, we extracted all RT mortality information for the
years 1999 to 2009.
In order to correct for the misclassification of RT deaths the
through the utilization of nonspecific codes, adjustments were
done to 4 categories of nonspecific codes using three different
statistical approaches (Table I). The first category was the “R”
codes or deaths of ill-defined or unknown cause; these codes
have been referred to as “garbage” codes because they are not
useful for public health analysis of cause-of-death data (Naghavi
et al. 2010). The second category was codes Y32-34, utilized
for deaths of undetermined intent. The third code was X59, a
widely used code for “accidental exposure to other and unspecified factors”; previous analysis has shown that this code was
overused in Mexico (Lozano 2003). Finally, the fourth category
is codes V87, V892, and V899 for those road traffic deaths in
which the victim’s mode of transportation was not specified.
Statistical Adjustments
Three different statistical techniques were utilized for the reallocation of deaths that were categorized using nonspecific codes.
1. Proportional categorization: Nonspecific codes were assigned to categories based on the observed proportion of appropriately coded observations within age–sex groups. This
assumes that the deaths in the nonspecific categories occur
in the same demographic proportions as the appropriately
coded deaths. The age groups used in this analysis were as
follows: <10 years, 10 to 19 years, 20 to 34 years, 35 to 59
years, and 60+ years.
2. Multiple imputation of missing values (Little 1992; Little and
Rubin 2002; Schafer 1997; Schafer and Graham 2002): Using the “uvis” module (Royston 2004) in Stata 10.1 (2009),
missing values (including nonspecific codes) were imputed
in the dependent variable based on multiple regression of
a dependent variable on a set of covariates, resulting in 10
regression coefficients sets that were then combined with
random draws from the conditional distribution of missing
observations, given the observed data and covariates. This assumes that those deaths coded with nonspecific values were
random and therefore similar to those deaths with appropriate codes. Briefly, the multiple imputation procedure follow
7
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these steps: (1) Using a multiple regression model, the vector of coefficients and residual variance are estimated from
observations with complete information; (2) a random draw
from the posterior distribution of β is provided, based on
a previously drawn variance; (3) point estimates and variances are calculated for each “missing” value based on the
coefficients, their uncertainty, and the covariates; and (4) the
results are combined (pooled) to create one overall estimate
and standard error (van Buuren et al. 1999).
3. Regression approach: nonspecifically coded data were assigned the code of the highest probability estimated after
multiple logistic regression analyses were performed on the
appropriately coded data (binary when the dependent variable was bivariate and multinomial when the dependent
variable was multivariate). Demographic covariates were inserted into a regression model to identify predictor parameters. The substituted value is that which is best predicted
based on the additional demographic variables. This assumes
that the group of nonspecifically coded deaths is similar to
the group of appropriately coded deaths.
The proportional approach has been traditionally used by global
burden of disease studies in order to reassign nonspecific codes
for public health analysis purposes (Naghavi et al. 2010).
The other 2 approaches rely on the use of additional demographic variables that were available in the Mexican mortality data sets. The available variables utilized for all 3 approaches were age categories, gender, medical insurance status,
employment status, level of schooling, marital status, whether
medical attention was received, and place of death. In the multiple imputation and regression approaches we also included
place of injury as a variable. Other variables that might have
been relevant such as place of residence and multiple causes of
death (i.e., associated causes) were not included because this
information was not available for all of the years under study.
These 3 approaches have been previously validated for Mexico by using a random sample of known cases taken from the
Mexican mortality database of 2004 (Shahraz et al. 2008). Another proposed method for reallocating nonspecifically coded
deaths is a consensus approach by an expert panel (Naghavi et
al. 2010); this approach was not employed in this study.
For this analysis, the ICD-10 codes considered as appropriately coded RT deaths were as follows: V01–V04 (0.1, 0.9);
V09 (0.2, 0.3, 0.9); V12–V15 (0.3–0.9); V19.4–V19.6, V19.9,
V20–V28 (.03–0.9); V29–V79 (0.4–0.9); V80.3–V80.9, V81.1,
V81.9, V82.1, V82.9, V83–V86 (0.0–0.3); V87.0–V87.8,
V89.2, V89.9. Although suggested by some authors (Naghavi
et al. 2010), we did not include deaths attributed to long-term
consequences of RT injuries. Instead, we followed the World
Health Organization’s (WHO) recommendation of considering
only deaths occurring within the first 30 days after a collision
(WHO 2009).
For the calculation of mortality rates, we used mid-year
Mexican population estimates from the Consejo Nacional de
Población (National Population Council; CONAPO) by sex and
Road traffic mortality rate per 100,000 inhabitants
ROAD TRAFFIC MORTALITY IN MEXICO
25
20
15
10
5
0
1999
2000
Not adjusted
2001
2002
2003
Proporonal
2004
2005
2006
2007
Mulple imputaon
2008
2009
Regression
Figure 1 Unadjusted and adjusted road traffic mortality rate per 100,000
population in Mexico 1999 to 2009 utilizing 3 ICD-10 code correction methods.
age groups from the years under study. All rates are expressed
per 100,000 population. To model mortality trends, a Poisson
regression analysis was employed using the raw data and the 3
adjusted estimates using bootstrap standard errors with 10,000
iterations.
RESULTS
From 1999 to 2009, there were a total of 5,338,322 registered
deaths, of which 402,847 were coded as unintentional injuries.
Of those, 169,932 were RT deaths. Between 1 and 2 percent
of the RT deaths registered in Mexico during the period under
study were coded as “R” (ill-defined/unknown), and in 2 to 3
percent of deaths the intent was not determined. In 19 to 28
percent of unintentional deaths, the external cause of death was
not specified, and in 18 to 45 percent of RT deaths the type of
road user was not specified.
Over the 10-year period, the proportion of deaths in most of
the nonspecific code categories remained the same; notably, the
assignation of deaths to codes V87–V892 and V899 increased
by 19.3 percent across the decade. Without any adjustment,
estimates from the Poisson regression model showed that the
crude road traffic mortality rate in Mexico has increased by an
average of 1.41 percent each year (95% confidence interval [CI]:
0.7, 2.1) during this period, increasing from 14.38 per 100,000 in
1999 to 16.60 per 100,000 in 2009 (Figure 1). There was a slight
but notable dip in the crude mortality rate (to ∼14 per 100,000)
in 2007 that did not appear in any of the adjustment approaches.
This is due to a higher number of nonspecific codes that appeared
that year (there was a 32.9%–44.6% RTI underestimation in that
year, which is on the higher end of the average, as shown in Table
II).
The different adjustment techniques show an underestimation of RT mortality between 18 and 45 percent, depending on
the method employed and the year under study (Table II). We
compared the relative percentage of underestimation by adjustment technique (Figure 2) for different indicators of RT mortality. For example, the raw data show that approximately 42.16
8
HÍJAR ET AL.
Table II Range of underestimation of road traffic injury mortality figures
using 3 different approaches by year, Mexico 1999 to 2009
Approach
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Proportional method
Multiple imputation method
Regression method
Maximum (%)
Minimum (%)
Average (%)
44.63
37.87
37.88
29.46
23.90
17.89
34.17
30.09
27.18
percent (95% CI: 41.65, 42.67) of unintentional injury deaths
are due to road traffic crashes (Figure 2). When the proportional
adjustment method is applied, this number increases to 53.68
percent (95% CI: 53.18, 54.18).
In general, all 3 approaches yield similar revised RT mortality figures. Of note, the discrepancy between approaches was
highest from 2004 to 2007. This is likely due to the higher numbers and differences in the relative distribution of nonspecific
codes that occurred during that time period.
Because the World Health Organization’s global burden of
disease studies traditionally use the proportional approach,
it provides the best numbers for comparison across studies/estimates. In this analysis, the proportional approach results
in slightly higher estimates of mortality and a positive trend that
showed an average of 1.15 percent increase in traffic fatalities
each year (95% CI: 0.9, 2.1) during this period. This approach
showed a maximum rate of 22 per 100,000 in 2009 (Figure 1).
Using the proportional adjustment approach, Figure 3 shows the
adjusted road traffic mortality rate disaggregated by age group.
By far, the highest mortality burden occurs in the elderly (60+
age group), with a mortality rate of more than 40 per 100,000
population. In 2009, this figure peaked at 46.7 per 100,000 population. This compares to an unadjusted RT mortality in the 60+
age group of 30.7 per 100,000.
Disaggregating the data by sex and road user category showed
that the road traffic mortality rate in males is nearly 3-fold
higher than in females; this increased rate in males is true
for all road user categories (Table III). In addition, the increased mortality burden in pedestrians is striking, constituting the main road user category affected in Mexico, with an
average mortality rate of 10.3 per 100,000 population in this
time period; male pedestrians had a mortality rate of 16.1 per
100,000 population. This is also higher than the unadjusted
male pedestrian mortality rate of 8.0 per 100,000. Worth noting is the substantial increase in mortality in both male motorcyclists and car occupants; the mortality rate increased 332.2
percent in the former and 30.6 percent in the latter during this
period.
Figure 2 Adjusted proportional burden of road traffic deaths in Mexico, 1999 to 2009: (A) percentage of total deaths due to injuries; (B) percentage of total
injury deaths that are from unintentional injuries; (C) percentage of unintentional injury deaths that are due to transport; and (D) percentage of transport injuries
that are road traffic deaths.
9
ROAD TRAFFIC MORTALITY IN MEXICO
<10
50
10-19
20-34
35-59
60 and more
Total
45
Rate per 100,000 inhabitants
40
35
30
25
20
15
10
5
0
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1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Figure 3 Road traffic mortality rate by age group in Mexico, adjusted using
the proportional approach, 1999 to 2009.
DISCUSSION
Comparing multiple adjustment techniques for reallocating
nonspecifically coded deaths, we estimated that the road traffic mortality rate in Mexico could be as high as 21.4 to 22.1
per 100,000 population, compared to the rate 16.6 per 100,000
yielded from the raw ICD-10–coded data. Adjustment techniques also highlight what has been shown in previous studies
in Mexico; the older age group (60+ years) and pedestrians are
particularly vulnerable to road traffic deaths (Hijar et al. 2001;
Hijar, Trostle, and Bronfman 2003; Hijar, Vazquez-Vela, and
Arreola-Risa 2003).
ICD-coded road traffic deaths in Mexico have a number of
quality and specificity problems. Although R codes were not
commonly employed and intent was defined in most of the cases
(∼2% nonspecifically coded), greater efforts are necessary in
terms of defining external causes and specifying victims’ modes
of transportation. Clearly, there are limitations to any method
used to estimate missing data. Therefore, the goal should be to
reduce the frequency of the utilization of these codes to less than
20 percent in order to conform to international standards.
This study documented that the already high burden of road
traffic deaths in Mexico reported using crude figures are potentially underestimated by up an average of 27.2 to 34.2 percent.
This would represent a substantial change in the road traffic mor-
tality burden in the country. Future studies should evaluate regional and city-level variations in coding in addition to any other
data collection inconsistencies that result in missing road traffic
deaths. By doing so, policy makers could detect and target specific states/municipalities that face particular challenges in terms
of filling out death certificates and/or coding mortality causes.
Comparing the 3 adjustment techniques, the proportional adjustment method yielded the highest rates for nearly all years and
risk categories. The fact that all adjusted results are statistically
different from the unadjusted (crude) data highlights the importance of implementing corrections to improve the quality of
existing information systems. In general terms, all 3 approaches
are robust; the results that each yields are similar. However,
it seems that although multiple imputation and regression approaches take into account more information available from the
database (Shahraz et al. 2008), which is preferable, they could
be difficult for health policy makers and municipal public health
officials to implement and replicate on a regular basis. On the
other hand, the proportional method would be easier to be systematized and potentially easier to explain to and be utilized by
nontechnical audiences.
Our analysis has several limitations. First, we were limited
by the variables available in the Mexican health sector database.
The adjustments would all be more robust if additional demographic and crash-specific information had been included.
Secondly, we did not attempt to quantify or account for the reasons for particular data being miscoded. It will be important for
future studies to analyze which types of deaths are most commonly miscoded and identify ways to correct such problems.
Finally, there are inherent limitations to these and all possible
methods for identifying and recoding miscoded data. We chose
to present these 3 methods because they are among the more
widely used and robust. Certainly the best solution would be
to identify ways to limit the number of nonspecifically coded
deaths in the Mexican database.
The mortality rate estimated for 2006 (20.8 per 100,000)
is actually quite similar to that which is officially reported in
the regional report the status of the road safety in the Americas
(Organización Panamericana de la Salud 2009). However, in
that study, Mexican authorities stated that RT deaths were only
those that occurred at the scene of a crash; for that reason
these figures were multiplied by an “adjustment factor” of
1.30 taken from what had been observed in previous studies
(WHO 2009). This was an assumption made in the consensus
Table III Average road traffic mortality rates (per 100,000), unadjusted vs. adjusted using the proportional approach, by sex and road user category, Mexico,
1999 to 2009
Male
Road user category
Pedestrians
Bicyclists
Motorcyclists
Car passengers
Other
All road traffic injuries
Female
Unadjusted
Adjusted
Unadjusted
Adjusted
8.0
0.5
0.6
5.3
9.5
23.8
16.1
0.6
1.3
10.6
3.3
31.9
2.3
0.0
0.1
1.6
2.6
6.5
4.5
0.1
0.2
3.1
0.9
8.7
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10
HÍJAR ET AL.
meeting by those who participated in the global status on road
safety survey in Mexico, because there was no documentation
of how RT deaths are counted. Personal communications with
relevant authorities revealed that deaths occurring up to a year
after a crash are still considered RT deaths in Mexico when the
external cause is known.
In conducting this study we noted that on average 32 percent
of deaths were reported to occur at a public or private medical facility. In addition, we found only 21 percent of all RT
deaths reported in 2006 in the Mexico country profile of the
PAHO regional report on the status of road safety were among
pedestrians, whereas this study documented the proportion of
pedestrian deaths was actually between 48.5 to 56.2 percent for
the same year. Similarly, the regional report documented that 68
percent of road users killed were vehicle passengers, whereas
our work estimated this percentage to be 35.6 to 42.9 percent.
These differences have potential implications in terms of health
planning and resource allocation for specific prevention strategies targeting the most vulnerable road user groups.
Accurate estimates of mortality due to road traffic crashes are
essential for proper public health prioritizing and implementation of targeted prevention initiatives. This study shows that the
actual burden of road traffic mortality in Mexico as reported by
nationally collected data is underestimated by 27 to 34 percent
due to inappropriate and nonspecific ICD-10 coding of deaths.
We recommend that reported ICD-10–coded data be adjusted
utilizing the proportional categorization approach in order to
more accurately reflect the significance of road traffic deaths
as a public health problem. Similar adjustment techniques may
be appropriate for other low- and middle-income countries that
likely face the same challenges in appropriate coding of deaths.
ACKNOWLEDGMENTS
We thank Dr. David M. Bishai for his suggestions and comments on this analysis and Dr. Rafael Lozano for his advice
on the implementation of the proportional approach. We also
thank Daniel Vera-López for his invaluable help in processing
data and Aarón Salinas-Rodrı́guez for his statistical advice. This
work was conducted as part of the Road Safety in 10 Countries
project funded by the Bloomberg Philanthropies.
REFERENCES
Bhalla K, Harrison JE, Shahraz S, Fingerhut LA. Availability and
quality of cause-of-death data for estimating the global burden of
injuries. Bull World Health Org. 2010;88:831–838C.
Hijar MC, Kraus JF, Tovar V, Carrillo C. Analysis of fatal pedestrian injuries in Mexico City, 1994–1997. Injury. 2001;32:279–
284.
Hijar M, Trostle J, Bronfman M. Pedestrian injuries in Mexico: a multi-method approach. Soc Sci Med. 2003;57:2149–
2159.
Hijar M, Vazquez-Vela E, Arreola-Risa C. Pedestrian traffic injuries
in Mexico: a country update. Inj Contr Saf Promot. 2003;10:37–
43.
Little R. Regression with missing X’s: A review. J Am Stat Assoc.
1992;87:1227–1237.
Little R, Rubin DB. Statistical Analysis with Missing Data. 2nd ed.
New York, NY: John Wiley; 2002.
Lozano R. Lesiones por vehı́culos en México: fuentes y sistemas de
información existentes. In: Hı́jar-Medina M, Vázquez-Vela E, eds.
Foro Nacional Sobre Accidentes de Tránsito en México. Enfrentando
los Retos a Través de Una Visión Intersectorial. Cuernavaca, México:
Instituto Nacional de Salud Pública; 2003:15–32.
Lozano-Ascencio R, Torres LM, Lara J, Santillán Á, González-Vilchis
JJ. Accidentes de Tráfico de Vehı́culo de Motor. Cambios Derivados
de la Implantación de la 10 Revisión de la CIE. Vol 6. Ciudad de
México, México: Secretarı́a de Salud; 2003.
Naghavi M, Makela S, Foreman K, O’Brien J, Pourmalek F, Lozano
R. Algorithms for enhancing public health utility of national causesof-death data. Popul Health Metr. 2010;8:1–14.
Organización Panamericana de la Salud. Informe Sobre el Estado de
la Seguridad Vial en la Región de las Américas. 1st ed. Washington,
DC: Organización Panamericana de la Salud; 2009.
Organización Panamericana de la Salud. Clasificación estadı́stica internacional de enfermedades y problemas relacionados con la salud.
Décima revisión. 10a ed. Washington, DC: OPS, 1995.
Royston P. Multiple imputation of missing values. Stata J.
2004;4:227–241.
Schafer JL. Analysis of Incomplete Multivariate Data. London, England: Chapman & Hall; 1997.
Schafer JL, Graham JW. Missing data: our view of the state of the art.
Psychol Methods. 2002;7(2):147–177.
Secretarı́a de Salud. Guı́a Para el Llenado de los Certificados de Defunción y Muerte Fetal. 3rd ed. Ciudad de México, México: Secretarı́a de Salud; 2007.
Shahraz S, Bhalla K, Naghavi M, Lozano R, Murray CJL. Improving the quality of injury statistics by using regression models to
redistribute ill-coded events. Paper read at: 9th World Conference
on Injury Prevention and Safety Promotion. March 15–18, 2008;
Merida, Mexico.
Stata 10.1 [computer program], College Station, Tex: Stata Press; 2009.
van Buuren S, Boshuizen HC, Knook DL. Multiple imputation of
missing blood pressure covariates in survival analysis. Stat Med.
1999;18:681–694.
World Health Organization. Global Status Report on Road Safety: Time
for Action. Geneva, Switzerland: World Health Organization; 2009.