role of data in the decade of action road safety- 2011-2020
TRANSCRIPT
Role of Data in the Decade of Action
Road Safety: 2011-2020
Kavi Bhalla, PhD
Research Scientist
Department of Global Health and Population
Harvard School of Public Health
Why Road Traffic Injuries?
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Dea
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AUS AUT BELCAN CHE DEUDNK FIN FRAUK IRL ITAJPN NLD NORSWE USA
Roa
d in
jury
dea
ths
per m
illion
peo
ple
Why Road Traffic Injuries?
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1940 1950 1960 1970 1980 1990 2000 2010
Year
Dea
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per M
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AUS AUT BELCAN CHE DEUDNK FIN FRAUK IRL ITAJPN NLD NORSWE USA
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d in
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dea
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per m
illion
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Goal: A framework for injury metrics
• Funding: World Bank Global Road Safety Facility
Two grants (since 2006)• Build and implement a framework for estimating
national burden of road traffic injuries in 18 countries
• Adapt methods to Africa (ongoing)
• Research Team– Kavi Bhalla, Saeid Shahraz, Jerry Abraham, David Bartels,
Nicole DeSantis, and Pon-Hsiu Yeh
• External collaborators: GBD-Injury Expert Group
Talk OverviewEstimating the burden of road traffic injuries in:
1.Information-rich settings– E.g.: Mexico, Iran, Sri Lanka, Colombia ….
2.Information-poor settings: Africa– E.g.: Mozambique: Triangulating to national estimates
from multiple data sources
– Extending methods to other African countries
3.Global estimates of burden of road traffic injuries– GBD 2010 study
Information Rich Settings: MexicoNational burden of road injuries*
DEATHS
HOSPITAL ADMISSIONS
EMERGENCY ROOM VISITS
HOME CARE
Envelope from survey : further breakdown Using hospital registry (selected provinces)
Surveys:World Health Survey,ENSANUT
Envelope from survey : further breakdown Using Ministry of Health and IMSS Hospitals
Broken down by• age and sex groups• urban/rural• institutional care received• injury severity• victim mode (pedestrian, motorcycle, car occup, etc)• impacting vehicle• injuries (head, limb, etc)• time of day• type of roaddeath registration
* International Journal of Injury Control and Safety Promotion, Aug 2010
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Kaza
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Latv
ia
Slov
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Iran
Moz
ambi
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Spai
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USA
Swed
en UK
Net
herl
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00 0
00
Latin America Europe and Central Asia
High Income
Africa
Mid East & N. Africa
E. A
sia
& P
acifi
c
S.A
sia
"SUN" countries
Road injury deaths rates in 18 focus countries
www.globalburdenofinjuries.org
Country Reports Website
Journal Articles
Talk OverviewEstimating the burden of road traffic injuries in:1. Information-rich settings
– E.g.: Mexico, Iran, Sri Lanka, Colombia2. Information-poor settings: Africa
– E.g.: Mozambique: Triangulating to national estimates from multiple data sources
– Extending methods to other African countries3. Global estimates: Burden of road traffic injuries
– GBD 2010 study
Mozambique: Data Sources
DEATHS
NON-FATAL INJ2003 DHS – Trauma Module;
Maputo City Hospital Records
1. Mortuary data from Maputo city– Urban; Medico-legal deaths (injuries)– Retrospectively collected data; 10 years
2. Demographic Surveillance Site; Manhica– Rural; Causes of death from verbal autopsy
3. Post-census mortality survey (INCAM)– Nationally representative (~18000 deaths)– Verbal autopsy – “injury” is a cause
Triangulating to National Estimates
Estimating Injury Mortality– Urban Injury Mortality
• National Verbal Autopsy => Mortality envelope• Maputo Mortuary => Disaggregate envelope
– Rural Injury Mortality• National Verbal Autopsy => Mortality envelope• Manhica DSS => Disaggregate envelope
Inputs: Estimating Injury DeathsNational Verbal Autopsy Study Urban Mortuary (Maputo City)
Road injuryFall
DrowningFire
PoisoningSuicide
HomicideOther
Unspecified
0% 10% 20% 30% 40% 50%% of all Injuries
0
2
4
6
8
Urban Rural Total
%in
jury
dea
ths
Rural DSS Verbal Autopsy (Manhica)
0% 10% 20% 30% 40% 50%
Road Injury
Fall
Drowning
Burn
Cut
Firearm
Blunt Object
Explosives
Poisoning
Hanging
Intoxication
Strangling
Other
Unknown
% of all injuries
Unintentional
Suicide
Homicide
Unknown
“Injury Envelopes”
Mozambique
0
2000
4000
1975 1985 1995 2005
Road
Inju
ry d
eath
s
PoliceGBD (2002, 2004)Our triangulated estimate
Road Injury Deaths
* 95% CI shown
Mozambique
0
2000
4000
1995 1997 1999 2001 2003 2005 2007H
omic
ide
deat
hs
PoliceGBD (2002, 2004)Our triangulated estimate
0
2000
4000
1975 1985 1995 2005
Road
Inju
ry d
eath
s
PoliceGBD (2002, 2004)Our triangulated estimate
Road Injury Deaths Homicides
* 95% CI shown
Information Poor Settings:Triangulating to Estimates
Can we replicate this in other African countries?
Injury Data Sources: Sub-Saharan AfricaFocus Countries1.Ghana2.Burkina Faso3.Nigeria4.Uganda5.Mozambique6.Ethiopia7.Sudan
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Data Sources - DeathsSudan Ethiopia Ghana Burkina-
FasoNigeria Uganda
RURAL
URBANMORTUARY• Khartoum Teaching Hospital• Omdurman Teaching Hosp
NATIONAL• Census
RURALDSS SITES• Butajira
URBANMORTUARY• Menelik Hospital Fatal Injury Surveillance• Burial Records
RURALDSS SITES• Navrongo• Kintampo• Dodowa
URBANMORTUARY• Kumasi KATH MortuaryVITAL REG• Accra Births & Deaths Registry NATIONAL• DHS Maternal Mortality
RURALDSS SITES• Nouna• Sapone*• Banfora*• Kaya*URBANDSS SITES• Ouaga-dougou*
RURALDSS SITES• Zamfara*
URBANMORTUARY• Ibadan Teaching Hospital mortuary
NATIONALSURVEYS DHS Sibling mortality
RURALDSS SITES• Iganga• Rakai
URBANMORTUARY• Mulago Teaching Hospital mortuary• Kampala city mortuary
Data Sources:Non-fatal Injury- SurveysSudan Ethiopia Ghana Burkina-
FasoNigeria Uganda
NATIONAL- Sudan Household Health Survey 2010
NATIONAL• World Health Survey • Socio-Economic Survey of Disabled Population • Health & Nutrition Survey• Welfare Monitoring Survey
COMMUNITY• Jimma Injury Survey
NATIONAL• World Health Survey • DHS Maternal Mortality • Core Welfare Indicators Questionnaire• Living Standards Measurement Survey• Child Labour Survey COMMUNITY• Kumasi & Brong-Ahafo Injury Survey• Accra Injury Survey
NATIONAL• World Health Survey • Enquete Burkinabé Sur les Conditions de Vie des Mengages (CWIQ)
NATIONAL• Nigeria Injury Survey• Core Welfare Indicators Questionnaire• Living Standards Survey• General Household Survey
COMMUNITY• Lagos Household Survey
NATIONAL
• National Household Survey• Northern Uganda Baseline Survey
COMMUNITY• Kawempe & Mukono Community-based Injury Survey
Data Sources-Non-fatal Injury-HospitalSudan Ethiopia Ghana Burkina-
FasoNigeria Uganda
• Health Management Information System (HMIS)
• Black Lion hospital-based injury surveillance
• District Health Information System (DHIS)
• Health Management Information System (HMIS)
• Hospital morbidity tabulations
• Ministry of Health Hospital Statistics
• WHO Hospital-based Injury Surveillance
• ICCU Hospital-based Injury Surveillance
Themes: Census Data• Household mortality questions are common• Often ask if death was from injury
=> Can provide injury totals• Face validity tested in S. Africa
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-64
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-74
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-84
85+
Tota
l
(Inj
ury
deat
hs)/
(Tot
al d
eath
s)South Africa 2001: Injury death fraction, male
CensusVR
African countries with similar data: S. Africa 2001 & 2007, Sudan (North & South), Malawi, Lesotho, Mozambique, Ghana (2010)
Themes: Urban Mortuaries• Exist in most urban centers• Issues: Cause coding and catchment areas.
0%
20%
40%
60%
80%
% o
f all
inju
ry d
eath
s Ethiopia Ghana Mozambique
Nigeria Sudan Uganda
Zambia
Themes: Rural DSS Sites• Common in Africa; Causes of death via VA• Issues: External cause coding
0%2%4%6%8%
10%12%
(Inju
ry d
eath
s)/(
Tota
l dea
ths) DSS Sites, 1999-2001, Injury death fraction
Talk OverviewEstimating the burden of road traffic injuries in:1.Information-rich settings
– E.g.: Mexico, Iran, …2.Information-poor settings: Africa
– E.g.: Mozambique: Triangulating to national estimates from multiple data sources
– Extending methods to other African countries3.Global estimates: Burden of road traffic injuries
– GBD 2010 study
Global Mortality Model Sensible global
health priorities
TheoryRegionalestimates
Theoretical Input
Discussion PapersCase Definition
GBD “Sequelae” defsMultiple Injuries
Handling unspecifiedsRecurrent injuries
……
Real World DataDeath registersHospital records Verbal autopsy dataHealth surveys Mortuaries
Literature ReviewsGBD sequelae
Model: Burdenof non-fatal Injuries
GBD INJURY EXPERT GROUP
www.globalburdenofinjuries.org
Mortality data
Architecture of Global Injury Data
Non-fatal Injury Data Sources• Surveys
– Strength: Population incidence of road injuries
– Shortcoming: Poor measurement of sequelae
• Hospital Records– Strength: Precise (ICD) sequelae descriptions
– Shortcoming: Data not available from all regions; Denominator population is often not known
Household Surveys• For estimating incidence of hospitalized road injuries
Hospital Records• Individual Record Data• Information on external causes and medical diagnosis
Road Injuries Incidence:HOSPITALIZED(by region, age, sex)
Sequelae Incidence: HOSPITALIZED
(by sequelae, region, age, sex)
Sequelae Incidence: NOT HOSPITALIZEDlack of medical care
(by sequelae, region, age, sex)
Sequelae Incidence: NOT HOSPITALIZEDdo not need admission
(by sequelae, region, age, sex)
Surveys DISMOD
Covariates
Mapping:Road Injury Sequelae
Hospital data
Access to Care
Surveys
Probability of admissionin a high access to care setting
Model
NZ: Hospital data
Seq. Incidence: HOSPITALIZED
Seq. Incidence: NOT HOSPITALIZED
lack of medical care
Seq. Incidence:NOT HOSPITALIZED
do not need admission
Sequelae durations• % life long; excess mortality• duration of short term
Disability Weights(for three types of sequelae)
Australian HospitalRegistry
Model (contd)
GBD field studies
Burden of non-fatal of road injuries(YLDs)
Talk OverviewEstimating the burden of road traffic injuries in:1.Information-rich countries
– E.g.: Mexico, Iran, …2.Information-poor settings: Africa
– E.g.: Mozambique: Triangulating to national estimates from multiple data sources
– Extending methods to other African countries3.Global estimates: Burden of road traffic injuries
– GBD 2005 study
Conclusions
• Lots of Existing Data: even in Africa: HDSS, mortuaries, surveys, hospital, censuses, etc.
• Analysts Wanted: to develop methods for eliminating bias, triangulating to policy relevant statistics
• Emerging Research Field: Global Health Metrics: with unique methods, research community, and political stakeholders.
Thank You!Acknowledgements• Funding: World Bank Global Road Safety Facility
– Two grants over six years
• External collaborators: GBD-Injury Expert Group
• Research Team– Saeid Shahraz, Jerry Abraham, David Bartels, Nicole
DeSantis, and Pon-Hsiu Yeh
Find out more– www.globalburdenofinjuries.org
– email: [email protected]
Data Sources for GBD-Injury
Data Sources Availability1. Global Data Sources
a) Mortalityb) Health/Injury Surveyc) Hospital records
2. Data Sources in Africa
Detailed Information: www.GlobalBurdenofInjuries.org
Background• Closely associated with ongoing GBD-2010 study
– Earlier GBD studies used few African data sources
– GBD-Injury expert group• approximately 170 members
• www.globalburdenofinjuries.org
• Funder: World Bank Global Road Safety Facility
• History– Original Study: National Road Traffic Injury Estimates
– Vision• Should construct best estimates with all existing data sources
– 18 Focus countries
LATIN AMERICA EAST ASIA & PACIFIC
Brazil Mauritius
Colombia
Ecuador SOUTH ASIA
Mexico* Sri Lanka*
Argentina*
Uruguay MID. EAST & NORTH AFRICA
Iran*
EAST EUROPE & CENTRAL ASIA
Croatia SUB-SAHARAN AFRICA
Czech Republic Mozambique
Hungary
Kazakhstan HIGH INCOME COUNTRIES
Latvia Spain
Slovenia USA
Original Study: 18 Focus Countries
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Latin America Europe and Central Asia
High Income
Africa
Mid East & N. Africa
E. A
sia
& P
acifi
c
S.A
sia
"SUN" countries
Road injury deaths rates in focus countries
www.globalburdenofinjuries.org
Triangulating to National Estimates
• Original Method:– Deaths: using national death registration data
• adjust for completeness, redistribute unspecifieds
– Non-fatal injuries• Incidence from population surveys
• hospital data to estimate “sequelae” => convert to burden
• But, what to do about Mozambique?– There is no death registration in Africa!
Data Sources in Mozambique - Deaths
1. INCAM-2007: National Verbal Autopsy Study– ‘Total Injury’ death rates
2. One Urban Mortuary (Maputo City)– Medico-legal autopsies for unnatural deaths
– 10 years of retrospective data: 1993-2003
– age, sex, cause (intent and mechanism)
3. One Rural Demographic Surv. Site (Manhica)– Verbal Autopsy: 1999-2003
– age, sex, cause (intent and mechanism)
Constructing a national estimate
• Two Mozambique(s):– Urban Mozambique
• ‘Total Injury’ death rate (by age-sex) from INCAM• Further breakdown using Maputo city mortuary data
– Rural Mozambique• ‘Total Injury’ death rate from INCAM• Further breakdown using Manhica HDSS data
– National = Urban + Rural
Non-fatal Injury Incidence2003 Mozambique Demographic and Health Survey
7,61
2
2,08
7
1,50
0
1,30
1
968
927
563
332
15 3
0
1000
2000
3000
4000
5000
6000
7000
8000
9000TO
TAL
Falls
Stab
/cut
Burn
Stru
ck
Road
Inju
ry
Oth
er
Bite
Fire
arm
Chok
ing
Pois
onin
g
Sexu
al v
iole
nce
Min
e
Inju
ry ra
te p
er 1
00,0
00
Overview• Background
• Data sources in one country – Mozambique
• Data architecture in Africa– Censuses
– Mortuaries
– HDSS Sites
– Hospital data
⇒ Conclusion: Plenty of data: Analysts Wanted!
• Data Collection Process
Population Censuses
(Along with: Mike Levin and Steven Lwendo)
POPULATION CENSUS DATA• Household mortality questions are common in
African censuses.
• Usually intended for estimating maternal mortality
• Sometimes they ask if death was from injury
• Our Goal: Use census data to estimate total injury incidence by age, sex, urban/rural
SOUTH AFRICA - 2001
SUDAN 2008
MALAWI 2008
LESOTHO 2006
Censuses with Injury questions
Analyzed this far:
• South Africa – 2001
• South Africa – 2007 (large community survey)
• Sudan: South and North
• Lesotho 2006
• Malawi 2008
Hope to analyze:
• Ghana 2010 (In-field starting Sept 26)
• Mozambique INCAM survey (report available)
Validation: Are the census results sensible?
Using South African death registration data
Fraction of total deaths that are from injuries?
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0.700
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5-9
10-1
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-19
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425
-29
30-3
435
-39
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-49
50-5
455
-59
60-6
465
-69
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475
-79
80-8
485
+To
tal
(Inj
ury
deat
hs)/
(Tot
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eath
s)
South Africa 2001: Injury death fraction, male
CensusVR
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_64
65_6
970
_74
75_7
980
_84
85_8
990
_94
95+
(Acc
iden
t dea
ths)
/(To
tal d
eath
s)
Lesotho 2006-Accident death fraction
MALE
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Tota
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_04
05_0
910
_14
15 -
1920
-24
25 -
2930
-34
35 -
3940
-44
45 -
4950
-54
55 -
5960
-64
65 -
6970
-74
75 -
7980
-84 85
+
(Inj
ury
deat
hs)/
(Tot
al d
eath
s)N. Sudan, 2008, Injury Death Fraction
MALE
FEMALE
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
Tota
l 0
01_0
4
05_0
9
10_1
4
15_1
9
20_2
4
25_2
9
30_3
4
35_3
9
40_4
4
45_4
9
50_5
4
55_5
9
60_6
4
65_6
9
70_7
4
75_7
9
80_8
4
85+
(Inj
ury
deat
hs)/
(Tot
al d
eath
s)South Sudan 2008, Injury Death Fraction
MALE
FEMALE
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
01_
45_
910
_14
15-1
920
-24
25-2
930
-34
35-3
940
-44
45-4
950
-54
55-5
960
-64
65-6
970
-74
75-7
980
-84
85+
(Inj
ury
deat
hs)/
(Tot
al d
eath
s)
Malawi 2008, Injury Death Fraction
Urban-Female
Rural-Female
Conclusion about Censuses
• Face validity
• Can be a key source for getting accurate injury envelopes
Overview• Background
• Data sources in one country – Mozambique
• Data architecture in Africa– Censuses
– Mortuaries
– HDSS Sites
– Hospital data
⇒ Conclusion: Plenty of data: Analysts Wanted!
• Data Collection Process
Urban Mortuary Data
• Mortuary Data is very commonly available
• 7 countries => 7 mortuaries
Mortuary Data – 7 countriesCOUNTRY PERIOD No. of Cases
Sudan• Khartoum Mortuary• Omdurman Mortuary
• 6 years (2004-2009)• 4 months (in 2010)
~15,000255
Uganda(Kampala)
6 months (1 July -31 Dec 2007) 757
Ethiopia(Addis Ababa)
1 yr (1 Jul, 2006 -30 Jun 2007) 2114
Zambia (Lusaka)
13 months (Nov 2007-Dec 2008)
594
Ghana(Kumasi)
2 yrs (2005 - 2006) 1545
Nigeria(Ibadan)
3 yrs (2007-2009) 1045
Mozambique(Maputo city)
10 yrs (1994-2003) 12354
How complete is mortuary data?
• Medico-legal requirements vary dramatically across Africa– Nigeria: Family has a right to opt-out of medico legal
investigations.
– Zambia (Lusaka): Burial registration is strictly enforced
• Can we quantify completeness and quality?
Completeness Test for Sudan –Khartoum Mortuary
Compare 1. Number of injury deaths in Khartoum mortuary;
&
2. Number of injury deaths in Khartoum from 2008 Census
Completeness = (#1) / (#2)
MORTUARY CENSUS COMPLETENESS
MALE 1222 1347 91%
FEMALE 208 307 68%
OVERALL 1430 1654 86%
Completeness: Khartoum Mortuary Data
DEATHS BETWEEN AGES 15-49 YEARS
Quality of cause-of-death coding Sudan : Khartoum Mortuary
Mixed coding is a serious issue in existing mortuary records
Sudan : Omdurman Mortuary – prospectively collected data
0%
10%
20%
30%
40%
50%
60%
70%
80%
% o
f all
inju
ry d
eath
s
Ethiopia GhanaMozambique NigeriaSudan UgandaZambia
Summary Results : All 7 Mortuary Datasets
Demographic Surveillance Sites
Typical HDSS site• Example: Navrongo,
Ghana
• Established: 1993
• Community size: – 144,000 people
• Fairly rural
• Demographic and health monitoring: includes verbal autopsy on all deaths and regular morbidity surveys (including injuries)
DSS Sites (that do Verbal Autopsy)
0%
2%
4%
6%
8%
10%
12%(I
njur
y de
aths
)/(T
otal
dea
ths)
DSS Sites, 1999-2001, Injury death fraction
Household Surveys
for non-fatal injury incidence
(Work lead by: Saeid Shahraz)
Global Data Availability: Injury Surveys
Injury Surveys: Measurement Issues• Injury Involvement Questions
– Our Injury Definition: “resulting in one day disability”
• very rare, e.g. TASC
– What is usually measured:
– Common: without threshold (were you injured in the last month?)
– Common: Hospitalized e.g. WHS, LSMS, national health surveys
– Relatively rare: injury resulting in one day loss of school/work
• Recall period– 2 weeks, 1 month (very common), 3 month, 4 months, 5 months,
6 months, 7 months, 1 year (common)
Measurement Issues: Recall BiasesIn
jury
Rat
e, p
er 1
000
All road injuries (hospitalized and non-hospitalized)
Source: WHS
Last Month
2 to 3 monthsago
4 to 6 monthsago
6 to 12monthsago
Source: World Health Surveys: Aggregated data from 53 countriesmonth
inci
denc
e, p
er 1
000
Measurement Issues: Recall BiasesIn
jury
Rat
e
All road injuries (hospitalized and non-hospitalized)
Source: WHS
Hospitalized Road Inj
Substantial recall effects for the non-hospitalized cases
Last Month
2 to 3 monthsago
4 to 6 monthsago
6 to 12monthsago
inci
denc
e, p
er 1
000
month
Non-fatal road injury incidence (1 /2)
820 10 20 30 40 50 60 70
KAZAsia, Central-GEO
CHNAsia, East-CHN
INDBGDINDINDNPLPAKPAKIND
Asia, South-PAK
KHMVNMVNM
LKAMYSMYS
VNMPHL
VNMLAO
VNMLKA
MUSMMR
Asia, Southeast-THA
Australsia-NZL
Caribbean-DOM
SVKSVNCZE
HUNBIH
Europe, Central-HRV
RUSLVAUKREST
Europe, Eastern-RUS
ESPEurope, Western-NLD
Road injuries per 1000 persons
Non-fatal road injury incidence (2 /2)
830 10 20 30 40 50 60 70
Latin America, Andean-ECUMEXCOL
GTMLatin America, Central-MEX
URYBRAPRYIRNIRNARE
MARTUNTUR
North Africa / Middle East-SYRSub-Saharan Africa, Central-COG
ETHMOZUGAMWIKEN
COMZMBTZA
Sub-Saharan Africa, East-ETHZAFZAFKENZAF
NAMSWZ
Sub-Saharan Africa, Southern-ZWEBFA
GHANGAGHAGHANGANGANGANGA
CIVGHAGHASEN
NGATCDMLI
MRTSub-Saharan Africa, West-BFA
CANNorth America, High Income-USA
Road injuries per 1000 persons
Sub Saharan Africa - West
Sub Saharan Africa - East
Sub Saharan Africa - South
Sub Saharan Africa - Central
Conclusions about Surveys
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• Approximately 2% of the population has a non-fatal road injury every year
• Survey-based measurements are readily available
• Analytical corrections required for comparability (ongoing)
Plenty of Injury Data from Africa
Analysts Wanted!
Overview• Background
• Data sources in one country – Mozambique
• Data architecture in Africa– Censuses
– Mortuaries
– HDSS Sites
– Hospital data
⇒ Conclusion: Plenty of data: Analysts Wanted!
• Data Collection Process
Data Collection Process• Environmental Scan to Identify Sources
– Google Searches
– Lots of emails: GBD Injury Exp Gp has 160+ members
– Call for data in PLoS Medicine
– Systematic Literature Review
• Enabling data access: 3 levels of data sharing– Micro data (individual level records)
– Data tabulated to our specifications
– Published reports with most detailed tabulations
Country Visits• Prior to country visit
– Conduct data source scan
• During country visit– National Statistics Office
– University researchers
– Mortuaries and ED at main hospital
– Police (Traffic and Crime)
• What helps during visit– Being poor
– Having a country collaborator
– Having a plan that suggests high likelihood of getting to every other data source in the country
Thank you
89
• Find out more:– www.globalburdenofinjuries.org
– email: [email protected]
• Acknowledgements:– This research has been funded by a grant from the
World Bank Global Road Safety Facility
– The data sources analyzed in this project have been collected by other researchers and agencies.
www.globalburdenofinjuries.org
Injury Expert Group: Operations• Publications:
– strongly encouraged– Authorship: should reflect principles commonly
used in the academic community for large multi-center collaborative studies
– Example:
Authorship:- lists all those who contributed + “on behalf of the Global Burden of Disease Injury Expert group”
Mozambique Non-Fatal Injuries
Non-fatal injuries: DHS surveyNationally representative sample of 63,496 people
Non-fatal injuries: DHS survey
Non-fatal Injury Incidence (per population)
Nationally representative sample of 63,496 people
0.0
0.2
0.4
0.6
0.8
1.0P
edes
trian
Bik
e
TwoW
heel
er
Car
Thre
eWhe
eler
Bus
Truc
k
Van
Ani
mal
Rid
er
Tran
spor
t-non
-RTI
Dro
wni
ngs
Falls
Fire
s
Fire
arm
Poi
sons
ven
oms
bite
s
Oth
er u
nint
entio
nal
Frac
tion
of tr
ue p
oiso
ning
s in
Tes
t dat
a
Bayes
Proportional (age, sex)
Validation Results:fraction of poisonings assigned correctly
0.0
0.2
0.4
0.6
0.8
1.0Pe
dest
rian
Bike
TwoW
heel
er
Car
Thre
eWhe
eler
Bus
Truc
k
Van
Anim
alR
ider
Tran
spor
t-non
-RTI
Dro
wni
ngs
Falls
Fire
s
Fire
arm
Pois
ons
veno
ms
bite
s
Oth
er u
nint
entio
nalFr
actio
n of
true
dro
wni
ngs
in T
est d
ata
Bayes
Proportional (age,sex)
Validation Results:fraction of drownings assigned correctly
0.0
0.2
0.4
0.6
0.8
1.0P
edes
trian
Bik
e
TwoW
heel
er
Car
Thre
eWhe
eler
Bus
Truc
k
Van
Ani
mal
Rid
er
Tran
spor
t-non
-RTI
Dro
wni
ngs
Falls
Fire
s
Fire
arm
Poi
sons
ven
oms
bite
s
Oth
er u
nint
entio
nal
Frac
tion
of tr
ue fa
lls in
Tes
t dat
a Falls - Bayes
Falls - Proportional
Validation Results:fraction of falls assigned correctly
0.0
0.2
0.4
0.6
0.8
1.0P
edes
trian
Bik
e
TwoW
heel
er
Car
Thre
eWhe
eler
Bus
Truc
k
Van
Ani
mal
Rid
er
Tran
spor
t-non
-RTI
Dro
wni
ngs
Falls
Fire
s
Fire
arm
Poi
sons
ven
oms
bite
s
Oth
er u
nint
entio
nal
Frac
tion
of tr
ue c
ar o
ccup
ant i
n Te
st d
ata
Bayes
Proportional (age,sex)
Validation Results:fraction of car occup. assigned correctly
Other Key Themes• Household Surveys: Key Issues
– Recall biases, defining injury thresholds• Hospital Data
– only source for injury diagnosis– Key Issues:
• burden in the presence of multiple sequelae