| Objectives:(1)Evaluate the current quality of global road traffic injury mortality data and its impact on the epidemiological characteristics;(2)evaluate the current quality of global elderly fall mortality data and its impact on the epidemiological characteristics.Methods:(1)Design type: Longitudinal analysis based on the World Health Organization(WHO)Mortality Data Base(MDB).(2)Data source: Aggregated mortality data included in the WHO MDB between 1990 and 2019.(3)Indicators of data qualityCombining the availability of variable information in the database and relevant research literature,this study used data availability and coding quality of cause of death to briefly evaluate the quality of mortality data.(1)Data availability was measured by the number of countries/territories submitted mortality data for each year in the WHO Mortality Database,and the number of years in which mortality data were submitted for each country/territory during the study period.(2)Coding quality of road traffic injury mortality data was measured by the proportion of 5 categories of road traffic injury-related problematic codes(deaths with ill-defined and unknown cause,injury deaths with undetermined intent,unintentional injury deaths with unspecified cause,unintentional transport injury deaths with unspecified cause,unintentional road traffic deaths with unspecified cause).(3)Coding quality of elderly fall mortality data was measured by the proportion of 5 categories of elderly fall-related problematic codes(deaths with ill-defined and unknown cause,injury deaths with undetermined intent,unintentional injury deaths with unspecified cause,unintentional fall deaths with unspecified mechanism,unintentional fall deaths with unknown occurrence place).(4)Evaluation strategy for the impact of coding qualityBased on the purpose of using official data,the following evaluation strategies were developed to evaluate the impact of coding quality in this study.(1)Correct the road traffic injury-related problematic codes using the proportional redistribution method,and calculate the age-standardized mortality rates for road traffic injuries before and after correction,respectively.Calculate the ratio of age-standardized mortality rates before and after correction for each country/territory and each year,and the impacts of coding quality were categorized into six groups according to the maximum value of the ratio for each country/territory: very small(1.00-1.09),relatively small(1.10-1.19),small(1.20-1.29),large(1.30-1.39),relatively large(1.40-1.49),and very large(≥1.50);select two years with highest number of countries/territories reporting mortality data(2005 and2015),calculate and compare the magnitude of change in age-standardized mortality rates between 2005 and 2015 before and after correction for each country/territory;for countries/territories with mortality data for 5 or more years,calculate and compare the linear regression coefficients of year on age-standardized mortality rates for each country/territory before and after correction;for countries/territories with mortality data for 2 or more years after 2015,Predict and compare the monitoring results of road traffic injury fatality reduction targets in the United Nations Sustainable Development Goals(2015-2020)and the WHO Decade of Action for Road Safety(2021-2030)for each country/territory before and after correction.(2)Correct the elderly fall-related problematic codes using the proportional redistribution method,and calculate the age-standardized mortality rates for elderly fall before and after correction,respectively.Calculate the ratio of age-standardized mortality rates before and after correction for each country/territory and each year,and the impacts of coding quality were categorized into six groups according to the maximum value of the ratio for each country/territory: very small(1.00-1.09),relatively small(1.10-1.19),small(1.20-1.29),large(1.30-1.39),relatively large(1.40-1.49),and very large(≥1.50);select two years with highest number of countries/territories reporting mortality data(2005 and 2015),calculate and compare the magnitude of change in age-standardized mortality rates between 2005 and 2015 before and after correction for each country/territory;for countries/territories with mortality data for 5 or more years,calculate and compare the linear regression coefficients of year on age-standardized mortality rates for each country/territory before and after correction.(5)Statistical analysis(1)Calculate the number of countries/territories that submitted mortality data to the WHO for each year,and the number of years that each country/territory submitted mortality data to the WHO during the study period.(2)Use Kruskal-Wallis rank sum test to compare the differences in the proportion of 5 road traffic injury deaths related problematic codes among 4 income level(low-income,lower middle-income,upper middleincome,and high-income)countries/territories.(3)Calculate the ratio of age-standardized road traffic injury mortality rates before and after correction for each year;calculate the absolute and relative changes in age-standardized mortality rates between2015 and 2005 before and after correction;estimate the regression coefficients of year on age-standardized mortality rates and their statistical significance using linear regression model;calculate the average annual change rate of road traffic injury deaths after 2015 using the geometric mean method,and predict the magnitude of change in road traffic injury deaths from 2015-2020 and 2021-2030.(4)Use Kruskal-Wallis rank sum test to compare the differences in the proportion of 5 elderly fall deaths related problematic codes among 4income level countries/territories.(5)Calculate the ratio of age-standardized elderly fall mortality rates before and after correction for each year;calculate the absolute and relative changes in age-standardized mortality rates between 2015 and 2005 before and after correction;estimate the regression coefficients of year on agestandardized mortality rates and their statistical significance using linear regression model.Results:(1)Current quality of road traffic injury mortality data(1)124 of 194 WHO member states reported mortality data for at least one year to the WHO Mortality Database between 1990 and 2019.Mortality data were reported by 98% and 100% of member states in the European and the Americas regions respectively,with 76%,48% and 27%of member states in the Eastern Mediterranean,Western Pacific and SouthEast Asia regions respectively,and only 11% of member states in the African region.(2)In 102 countries/territories using ICD-10(4-digit version)and with World Bank income level,data on all-cause,injury,unintentional injury,unintentional transport injury,and unintentional road traffic injury death were reported for 1566,1566,1566,1550,and 1523 years for the whole population,respectively.Correspondingly,1.72%(27/1566),3.83%(60/1566),26.31%(412/1566),4.19%(65/1550),and 48.79%(743/1523)of the years had ≥20% of deaths with ill-defined and unknown cause,injury deaths with undetermined intent,unintentional injury deaths with unspecified cause,unintentional transport injury deaths with unspecified cause,unintentional road traffic deaths with unspecified cause,respectively.(3)The percentages of years with ≥20% of the 5 problematic codes in high-income countries/territories were 0.61%(6/979),2.25%(22/979),24.72%(242/979),1.35%(13/964),and 38.2%(364/953),respectively;and the percentages of years with ≥20% of the 5 problematic codes in upper middle-income countries/territories were 2.63%(12/456),3.51%(16/456),33.77%(154/456),9.01%(41/455),and 61.96%(272/439);for lower middle-income countries/territories,the percentages of years with ≥20% of the 5 problematic codes were 3.2%(4/125),16%(20/125),11.2%(14/125),8.8%(11/125),and 80.8%(101/125);for low-income countries/territories,the percentages of years with ≥20% of 5 problematic codes were 83.33%(5/6),33.33%(2/6),33.33%(2/6),0%(0/6),and 100%(6/6).With the exception of unintentional transport injury deaths with unspecified cause,the differences in the proportion of the other 4 problematic codes were statistically significant(P<0.05)between countries/territories with different income levels.(2)The impact of coding quality on the epidemiological characteristic of road traffic injury deaths(1)Of the countries/territories with population data provided by United Nations,96,88,84,90 and 95 countries/territories reported data on road traffic injury deaths for all road users,pedestrians,pedal cyclists,motorcyclists and occupants,respectively.32% of countries/territories(31/96)had a maximum ratio of age-standardized mortality rates ≥1.5 for all road users before and after correction,and 39%(34/88),26%(22/84),71%(64/90),and 72%(68/95)of countries/territories had a maximum ratio of age-standardized mortality rates ≥1.5 for pedestrians,pedal cyclists,motorcyclists,occupants before and after correction.(2)Of the countries/territories with population data provided by United Nations,55,51,46,54 and 54 countries/territories reported data on road traffic injury deaths for all road users,pedestrians,pedal cyclists,motorcyclists and occupants in 2005 and 2015,respectively.Compared with 2005,the direction of absolute change in the age-standardized mortality rates for all road users,pedestrians,pedal cyclists,motorcyclists,and occupants in 2015 changed in 2,3,2,4,and 7 countries/territories after correction,respectively;large changes(≥50%)in the relative change in age-standardized mortality rates for all road users,pedestrians,pedal cyclists,motorcyclists,and occupants in 2015 were observed in 2,2,3,13,and 6 countries/territories.(3)Of the countries/territories with population data provided by United Nations,85,81,78,83,and 85 countries/territories reported data on road traffic injury deaths for all road users,pedestrians,bicyclists,motorcyclists,and occupants for 5 years and above,respectively.After correction,the direction of the linear regression coefficients of year on agestandardized mortality rates for all road users,pedestrians,pedal cyclists,motorcyclists,and occupants changed in 6,2,2,9,and 10countries/territories,respectively;and the statistical significance of the linear regression coefficients for all road users,pedestrians,pedal cyclists,motorcyclists,and occupants changed in 5,0,4,12,and 13countries/territories.(4)Of the countries/territories with population data provided by United Nations,60 countries/territories reported data on road traffic injury deaths for at least 2 years after 2015.After correction,the direction of the average annual change rate in age-standardized mortality rate for all road users changed in 8%(5/60)of countries/territories,and the statistical significance of the average annual change rate changed in 13%(8/60)of countries/territories.Before correction,3 countries/territories(Oman,Peru and Chile)were predicted to achieve the road traffic injury deaths halving target by 2020,and 10 countries/territories(Georgia,Korea,Turkey,Croatia,Canada,Lithuania,Japan,Germany,Panama and Brazil)will meet the target by 2030;after correction,4 countries/territories(Oman,Peru,Chile and Brunei Darussalam)were predicted to achieve the target in 2020,and 5 countries/territories(Georgia,Korea,Lithuania,Japan and Brazil)will meet the target in 2030.(3)Current quality of elderly fall mortality data(1)During the study period,less than two-thirds(64%)of WHO member states reported mortality data to the WHO Mortality Database,of which only 15(12%)reported mortality data for the full 30 years.The highest number of countries/territories(124)reported mortality data in2009,and the lowest number of countries/territories(30)reported mortality data in 2019.(2)In 102 countries/territories using ICD-10(4-digit version)and with World Bank income level,data on all-cause,injury,unintentional injury,unintentional fall,and unintentional fall death with occurrence place were reported for 1566,1549,1541,1422,and 1384 years for the elderly population,respectively.Correspondingly,2.36%(37/1566),7.42%(115/1549),45.1%(695/1541),86.92%(1236/1422),and 86.27%(1194/1384)of the years had ≥20% of deaths with ill-defined and unknown cause,injury deaths with undetermined intent,unintentional injury deaths with unspecified cause,unintentional fall deaths with unspecified mechanism,unintentional fall deaths with unknown occurrence place,respectively.(3)The percentages of years with ≥20% of the 5 problematic codes in high-income countries/territories were 0.72%(7/979),3.84%(37/964),43.8%(420/959),88.22%(779/883),and 87.03%(738/848),respectively;and the percentages of years with ≥20% of the 5 problematic codes in upper middle-income countries/territories were 4.61%(21/456),11.84%(54/456),53.29%(243/456),84.38%(351/416),and 92.98%(384/413);for lower middle-income countries/territories,the percentages of years with 5problematic codes ≥20% were 3.2%(4/125),17.07%(21/123),25%(30/120),85.59%(101/118),and 56.78%(67/118);for low-income countries/territories,the percentages of years with 5 problematic codes ≥20%were 83.33%(5/6),50%(3/6),33.33%(2/6),100%(5/5),and 100%(5/5).The differences in the proportion of problematic codes in each of the 5categories were statistically significant(P<0.05)across countries/territories with different income levels.(4)The impact of coding quality on the epidemiological characteristic of elderly fall deaths(1)Of the countries/territories with population data provided by United Nations,94 countries/territories reported data on elderly fall deaths.62% of countries/territories(58/94)had a maximum ratio of agestandardized elderly fall mortality rates ≥1.5 before and after correction.(2)Of the countries/territories with population data provided by United Nations,55 countries/territories reported data on elderly fall deaths in 2005 and 2015.Compared with 2005,5 countries/territories had a change in the direction of the absolute change in the age-standardized mortality rate in 2015 after correction;and 6 countries/territories had a large change(≥50%)in the relative change in the age-standardized mortality rate in 2015.(3)Of the countries/territories with population data provided by United Nations,85 countries/territories reported data on elderly fall deaths for 5 years and above.After correction,the direction of the linear regression coefficients of year on age-standardized elderly fall mortality rates changed in 8 countries/territories,and the statistical significance of the linear regression coefficients changed in 14 countries/territories.Conclusions:(1)Availability and coding quality of road traffic injury and elderly fall mortality data in the WHO Mortality Database was poor between 1990 and 2019,with variations in data quality across countries/territories with different income levels.The availability of mortality data was best for countries/territories in the European and Americas regions and worst for countries/territories in the African region;the coding quality of cause of death was relatively good for high-income countries/territories and relatively poor for low-income countries/territories.The four categories of problematic codes(unintentional injury deaths with unspecified cause,unintentional road traffic deaths with unspecified cause,unintentional fall deaths with unspecified mechanism and unintentional fall deaths with unknown occurrence place)were commonly used in practice.(2)Coding quality has a significant impact on estimation of agestandardized mortality rates for road traffic injury and elderly fall;as well as on comparisons of the epidemiological characteristic across regions and time.The maximum ratio of pre-and post-correction age-standardized mortality rates for road traffic injury and elderly fall changed significantly in 31 countries/territories and 58 countries/territories.After correcting problematic codes,among countries/territories that reported mortality data in 2005 and 2015,the direction of absolute change in age-standardized mortality rates for road traffic injury and elderly fall changed in 2countries/territories and 5 countries/territories,the magnitude of relative change in age-standardized mortality rates for road traffic injury and elderly fall changed significantly in 2 countries/territories and 6countries/territories;among countries/territories that reported mortality data for 5 years and above,the direction of regression coefficients of year for road traffic injury and elderly fall changed in 6 countries/territories and8 countries/territories,and the statistical significance of regression coefficients of year for road traffic injury and elderly fall changed in 5countries/territories and 14 countries/territories;among countries/territories that reported 2 or more years after 2015,6countries/territories had a large change in the predicted outcome of progress towards the target for road traffic injury.(3)Global agencies plus each individual government should be aware of the importance of collecting and sharing high-quality mortality data,and take action to improve data availability and coding quality.For countries/territories with poor data availability,it is essential to provide resources to accelerate the development and improvement of vital statistics systems,and improve the openness of data;for countries/territories with poor coding quality,it is urgent to strengthen the number and professional capacity of staff,develop and promote automatic tools,establish and improve data quality audit systems,and take multiple measures to improve the quality of road traffic injury and elderly fall mortality data.41 figures,33 tables,165 references... |
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