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.
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.
Authors: Joseph Friedman; Alhelí Calderón-Villarreal; Ietza Bojorquez; Carlos Vera Hernández; David L Schriger; Eva Tovar Hirashima Journal: Ann Emerg Med Date: 2020-07-23 Impact factor: 5.721