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?

0

50

100

150

200

250

300

350

400

1940 1950 1960 1970 1980 1990 2000 2010

Year

Dea

ths

per M

illion

Peo

ple

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?

0

50

100

150

200

250

300

350

400

1940 1950 1960 1970 1980 1990 2000 2010

Year

Dea

ths

per M

illion

Peo

ple

AUS AUT BELCAN CHE DEUDNK FIN FRAUK IRL ITAJPN NLD NORSWE USA

Roa

d in

jury

dea

ths

per m

illion

peo

ple

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

0

10

20

30

40

50A

rgen

tina

Braz

il

Colo

mbi

a

Ecua

dor

Mex

ico

Uru

guay

Mau

riti

us

Sri L

anka

Croa

tia

Czec

h Re

publ

ic

Hun

gary

Kaza

khst

an

Latv

ia

Slov

enia

Iran

Moz

ambi

que

Spai

n

USA

Swed

en UK

Net

herl

ands

Dea

th ra

te p

er 1

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

1

2

3

4

5

6

7

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

0.0

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+

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: kavi_bhalla@harvard.edu

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

0

10

20

30

40

50A

rgen

tina

Braz

il

Colo

mbi

a

Ecua

dor

Mex

ico

Uru

guay

Mau

riti

us

Sri L

anka

Croa

tia

Czec

h Re

publ

ic

Hun

gary

Kaza

khst

an

Latv

ia

Slov

enia

Iran

Moz

ambi

que

Spai

n

USA

Swed

en UK

Net

herl

ands

Dea

th ra

te p

er 1

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 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?

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.700

1-4

5-9

10-1

415

-19

20-2

425

-29

30-3

435

-39

40-4

445

-49

50-5

455

-59

60-6

465

-69

70-7

475

-79

80-8

485

+To

tal

(Inj

ury

deat

hs)/

(Tot

al d

eath

s)

South Africa 2001: Injury death fraction, male

CensusVR

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Tota

l 001

_04

05_0

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_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

l 001

_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

84

• 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: kavi_bhalla@harvard.edu

• 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