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Prepared for the International Conference on Health in the African Diaspora—ICHAD 2012 July, 2012

Afro-descendants in Latin America and challenges of measuring health gaps Luana Marques G. Ozemela Judith Anne Morrison Brittney Elise Bailey Josh Colston

Gender and Diversity Division (GDI) Inter-American Development Bank


Outline     

Motivation Data Used Methodology Findings Suggestions for future research


Motivation


Race impacts health outcomes

Indirect channels

Direct channels

• Higher exposure to health damaging factors • Social vulnerability • Aggravated consequences of illness on socio-economic condition

• Genetic predisposition or aggravated/difficult treatment • Deprivation of access to health care • Unequal treatment


Race health gaps have an economic cost  Negative impact on current and future generation’s health  Loss in current and future earnings/income

 Lower incentives to make health investments (fewer preventive consultations)  Higher rates of stress, depression, or other mental problems associated to poorer access to health care.  Higher pressure in public services such as public health care and social protection programs


Methodology and Data


Questions we wanted to explore 1. What is the magnitude of race and ethnic inequalities in health? 2. Are these inequalities persistent after controlling for differences in socio-economic condition? 3. Have race-related inequalities narrowed or widened over time?


Planned Approach 1. 2. 3. 4.

Define race category groups Define health outcome variables Define socio-economic condition variables Estimate race differences and calculate statistical significance of differences 5. Estimate differences and calculate statistical significance after controlling for socio-economic condition 6. For analysis overtime, repeat the previous steps using a comparable dataset (same race question and categories, health outcome and socio-economic condition variables)


Planned Variables  Self-assessed health status  Fertility

Administrative data

 Antenatal care  Infant mortality  Maternal mortality

+

 Chronic condition  Access to services

 Attained education

Sample survey data


Data used covers more than 95% of Afrodescendants living in LAC

* The analysis excluded Chile, Venezuela, Paraguay, Mexico, El Salvador, Bolivia and Argentina due to lack of microdata from Household Surveys including a question to identify the Afro-descendant populations. Source: Inter-American Development Bank (2012). “Ethnic Group Identification in Household Surveys Released Between 2000 and 2011� , anticipated technical note includes household survey and census data for 21 countries


Information actually available and used Brazil

Colombia

Two datasets with same race question  . and categories Variables Available

 .

Self-assessed health status

 .

Ecuador

Peru  .

Antenatal care Infant mortality

 .

Maternal mortality

 .

 .

 .

 .

 .

 .

 .

 .

 .

Prevalence of chronic condition Access to services Attained education

 .


Data Used     

Brazil – Pesquisa Nacional por Amostra de Domicilio Brazil –Administrative Data Colombia - Encuesta Nacional de Calidad de Vida Ecuador - Encuesta de Condiciones de Vida Peru - Encuesta Nacional de Hogares

Note: MICS (Multiple Indicator Cluster) , DHS (Demographic and Health) and national health surveys have not yet included afrodescendant categories.


Quality of race data in surveys % Afro-descendant population by country

Brazil

Ecuador

Peru

a b c d

Census

Latest Household Survey

50.8%

51.1%

• •

Parda (brown) Preta (black)

10.6%

8.5%

• • • •

Raizal del archipiélago Palenquero Negro Mulato (afrodescendiente)

7.2%

4.5%

• • •

Negro Mulato Afroecuatoriano

• • •

Negro Mulato Zambo

a

Colombia

Categories

b

c

d

n/a

1.9%

African Descendants

National Census (2010) / Pesquisa Nacional por Amostra de Domicilio (2009) National Census (2005) adjusted for pop growth/ Encuesta Nacional de Calidad de Vida (2010) National Census (2010) / Encuesta de Empleo, Desempleo y Subempleo (2010) National Census (2007) adjusted for pop growth / Encuesta Nacional de Hogares (2010)

Sample weights in use in all datasets


Findings


Ecuador


Access to health service Reason for not seeking service, % by race/ethnicity 50 45 40 35 30 25 20 15 10 5 0 Minor case (of sickness)

Lack time

Distance from Health Facility Blancos/Mestizos

Costs Afrodescendientes

Source: Encuesta Nacional de Condiciones de Vida (2006)

Ecuador

Poor service

Other


Differences after controlling for education 

The race gap between those who consider health costs as a primary reason for not seeking medical attention:  Urban areas: gap falls as education level increases  social class?  Rural areas: gap increases as education level increases  discrimination?

Costs as primary reason, by education level and geography 70 60

50 40 30 20 10 0 Urban

Urban- Primary

*Urban-Second. Blancos/Mestizos

Source: Encuesta Nacional de Condiciones de Vida (2006)

Ecuador

Rural Afrodescendientes

*Rural-Primary

Rural- Secondary


Fertility and Infant Mortality Rates Blanco, Mestizo

Negro, Mulato

Age-specific fertility rate

250.0 200.0 150.0 100.0 50.0 0.0 14-19

20-24

25-29

30-34 35-39 Age group

Blanco, Mestizo Births 223,727 Infant deaths 2,394

Ecuador

40-44

45-49

Negro, Mulato 17,484 111

Infant mortality rate

10.7

6.3

Total fertility rate

2.5

3.3

General fertility rate

75.9

102.8


Colombia


Self-Assessed Health Status White/Mestizo

Afro-descendants

70

60 50 40 30 20 10 0 Very Good Source: Encuesta de Calidad de Vida (2010)

Colombia

Good

Regular

Bad


Colombia Source: Encuesta de Calidad de Vida (2010)

Departments

CHOCO

ARCHIPIELAGO

VALLE DEL CAUCA

NARIテ前

BOLIVAR

PUTUMAYO

CESAR

14

12

10 80

70

8 60

6 50

40

4 30

2 20

10

0 0

% Afro-descendants

%White/Mestizo wih poor health

ANTIOQUIA

Total

LA GUAJIRA

CORDOBA

CAUCA

RISARALDA

META

BOGOTA D.C

AMAZONAS

CUNDINAMARCA

%Afro wih poor health

SANTANDER

MAGDALENA

SUCRE

ATLANTICO

ARAUCA

TOLIMA

CAQUETA

HUILA

CASANARE

NORTE DE SANTANDER

BOYACA

QUINDIO

CALDAS

% reporing poor health status

Self-Assessed Health Status, by state %Afro-descendants 100

90


Preventive Consultations (at least once yr) White/Mestizo

Afro-descendant

50 45 40 35 30 25 20 15 10 5 0 Physician

Dental

Source: Encuesta de Calidad de Vida (2010)

Colombia

Physician and Dental

None


State Name

CHOCO

ARCHIPIELAGO

VALLE DEL CAUCA

NARIテ前

BOLIVAR

%Afro with no consultation

PUTUMAYO

CESAR

ANTIOQUIA

LA GUAJIRA

CORDOBA

CAUCA

RISARALDA

META

%White/Mestizo with no consultation

BOGOTA D.C

AMAZONAS

CUNDINAMARCA

SANTANDER

MAGDALENA

SUCRE

ATLANTICO

ARAUCA

TOLIMA

CAQUETA

HUILA

CASANARE

NORTE DE SANTANDER

BOYACA

QUINDIO

CALDAS

% with no preventive consultations

Lack of preventive consultations, by State % Afro

100 100

90 90

80 80

70 70

60 60

50 50

40 40

30 30

20 20

10 10

0 0


Peru


Access to health services (2003) White/Mestizo

Afro-Peruano

80

Percentage of respondents

70 60 50 40 30 20 10 0 Costs

Distance to Trust Not Needed Unbelief in Home Health Facility medicine medication Reasons for not going to doctor to solve the problem

Source: ENAHO (2003)

Peru

No insurance


Access to health services (2010) White/Mestizo

80

Afro-Peruano

Percentage of respondents

70 60 50

40 30 20 10 0 Costs

Peru

Distance to Lack Time Trust Not Home No Health Needed Remedies insurance Facility Reasons for not going to doctor to solve problem

Source: ENAHO (2010)

Poor Service

Other


Fertility and Infant Mortality Rates Blanco, Mestizo

Negro, Mulato

Age-specific fertility rate

450 400 350 300

250 200 150 100 50 0 14-19

20-24

Births Infant deaths

30-34 35-39 Age group

40-44

45-49

Blanco, Mestizo Negro, Mulato 742,416 30,950 n/a n/a

Infant mortality rate

n/a

n/a

Total fertility rate

6.1

7.2

177.0

215.3

General fertility rate

Peru

25-29


Brazil


The debate on black-mixed disaggregation (from labor economics literature)  There is racial ambiguity among afro-descendants where “whiter” responses reflect individual’s economic ambitions (Lovell 1994)  Little difference in the socio-economic condition of blacks and mixed individuals (Silva 1978; Lovell 1998; Arcand & D’Hombres 2004; Ozemela 2012)  Severe discrimination is faced by blacks compared to mixed individuals (Arcand & D’Hombres 2004; Ozemela 2012)


Identification in live biths Live Births

Total

White

Preto + Mixed

Preto

Mixed

Sample Survey (Selfidentification)

2,576,803

1,108,656

1,459,744

201,478

1,258,266

Administrative data (identification by health personnel)

2,715,143

1,284,697

1,432,013

46,086

1,385,927

0.98

0.23

Ratio A/S

1.05

1.16

1.10

Sistema de Informações sobre Nascidos Vivos–2009 Pnad 2009

Aggregation is needed to avoid racial ambiguity in classification in Surveys and Administrative data.

Aggregation is problematic if one wishes to identify those mostly affected by discrimination in health.


Fertility and Infant Mortality Rates, PNAD Branco, asiatico

Preto

Pardo

Age-specific fertility rate

120.0

100.0 80.0 60.0

40.0 20.0 0.0 14-19

20-24

25-29

30-34

35-39

40-44

45-49

Age group

Births Infant deaths Infant mortality rate

Brazil

Total fertility rate General fertility rate

White and Black & Asian Black Mixed Mixed 1,110,413 202,140 1,263,673 1,465,813 10,830 1,656 15,167 16,823 9.8

8.2

12.0

11.5

1.4

1.7

1.7

1.7

42.5

52.6

54.3

54.1


Maternal mortality rate and education 800

Mortality Rate per 100 000 live births

700

600 White

Mixed

Black

500

400

300

200

100

0

< 3 years

4-7 years Years of Education

Source: DATSUS 2011

Brazil

> 8 years


Trends in marternal mortality Maternal Mortality Rate per 100 000 live births

450

Black

400 350 300

> 12 years education (black)

250 200 150

Mixed 100 < 3 years education (all races) 50 0 1990 Source: DATASUS

Brazil

White > 12 years education (all races) 2001

2010

2015


Suggestions for Future Research/Policy


Mapping Race Health Gaps in LAC

Coverage

• • • •

Quality

Aggregation

Indicators

Gaps

What are the determinants of the race health gaps? What territories are most affected? What is the role of discrimination in the access to health? What are the economic costs involved in the expansion in the access to health care of excluded populations? • What are the economic benefits?


Challenges with data 

Household surveys are one of the richest sources of information for national socio-economic statistics, however…

They lack health and ethnicity data  Limited information on basic health outcomes (antenatal care, access to health services, among others)  Difficult to find race/ethnicity and health data available in consecutive surveys making it difficult to draw comparisons or establish trends.

The quality of sample estimates need further attention or analyses drawn from indicators derived from HH surveys may be unreliable.

Afro-descendant sample size too small to capture certain health outcomes or to run multivariate analysis, in some cases

Disaggregation within race/ethnic groups need to be evaluated country by country.


Suggestions for Policy:  Improve coverage and quality of race data in existing sample surveys  Include afro-descendant categories in all health surveys such as MICS, DHS and national health surveys  Improve administrative records of health establishments to collect race/ethnicity data.  Develop a national system of indicators to monitor the Afrodescendant population’s health.  Evaluate the impact of universal health interventions on the health of the African descent  Develop surveys and pilot studies to monitor the compliance of health institutions with non-discriminatory standards of care


http://www.iadb.org The Inter-American Development Bank Discussion Papers and Presentations are documents prepared by both Bank and non-Bank personnel as supporting materials for events and are often produced on an expedited publication schedule without formal editing or review. The information and opinions presented in these publications are entirely those of the author(s), and no endorsement by the Inter-American Development Bank, its Board of Executive Directors, or the countries they represent is expressed or implied. This presentation may be freely reproduced.


Background Slides


Cause of death not investigated/disclosed Percentage of cases not investigated Region White

Black

Mixed

Total

North

26%

54%

33%

33%

Northeast

27%

39%

26%

28%

Southeast

8%

11%

10%

9%

South

6%

-

9%

6%

Center West

23%

38%

17%

22%

National

13%

25%

22%

19%

Source: MS/SVS/DASIS - Sistema de Informações sobre Mortalidade – SIM 2011

Brazil


Vulnerability to maternal mortality Death outside a health inditution

512

Race/color black

412

< 3 years education

120

Northeast

70

National

59

0

100

200

300

400

500

Maternal Mortality Rate (per 100 000 live births) Source: DATASUS 2011

Brazil

600


Maternal mortality ratio per 100,000 live births

Brazil and the top 8 worse Latin American countries 450 400 350 300

250 200 150 100

50 0

Brazil and the top 8 worse LAC countries Source: United Nations Statistics Division - Millennium Development Goals Indicators latest year available MS/SVS/DASIS - Sistema de Informações sobre Mortalidade – SIM 2011

Brazil


Maternal deaths in 2010 by race/color Total Number of Live Births

Total Number of Deaths

Region White

North

Black

Mixed

Total

White

Black

Mixed

Total

45 394

2 094

246 229

293 717

27

13

138

178

Northeast

136 071

9 315

634 987

780 373

96

64

383

543

Southeast

673 158

23 047

379 785 1 075 990

265

80

228

573

South

339 757

7 465

20 709

367 931

154

11

23

188

87 784

2 018

107 330

197 132

53

13

59

125

1 282 164

43 939

1 389 040 2 715 143

595

181

831

1 607

Center West National

Source: MS/SVS/DASIS - Sistema de Informações sobre Mortalidade – SIM Black = Preto Mixed = Pardo White = Branco

Brazil


Race-sensitive projects IDB (Afrodescendant populations’ health)  Loan to Rio de Janeiro State (Brazil): Youth at Risk – Sexual and reproductive health to prevent adolescent pregnancy

 Loan to Rio Grande do Sul State (Brazil): Youth Violence Prevention – Control and prevention of violence to curb the rise in homicide rates among youth

 Supporting the Health Sector Reform (Ecuador) – Race sensitive review program for training of health care personnel

 Loan to (Honduras): Social Protection Network Program – Development of a health services outreach model to expand access of geographically isolated populations


afro-descendants in brazil, colombia, ecuador and peru and the challenges of measuring health gap...