Developing a deprivation index to study geographical health inequalities in Ecuador

Peralta, Andrés; Espinel-Flores, Verónica; Gotsens, Mercè; Pérez, Glòria; Benach, Joan; Marí-Dell’Olmo, Marc

doi:10.11606/s1518-8787.2019053001410

Andrés Peralta

Agència de Salut Pública de Barcelona. Barcelona, Catalonia, Spain

Universitat Pompeu Fabra. Department of Political and Social Sciences. Health Inequalities Research Group, Employment Conditions Knowledge Network (GREDS-EMCONET). Barcelona, Spain

Johns Hopkins University - Pompeu Fabra University Public Policy Center, Barcelona, Spain

http://orcid.org/0000-0002-7617-108X

Verónica Espinel-Flores Correspondence: Verónica Espinel-FloresAgència de Salut Pública de Barcelona, Pl. Lesseps 1 08023 Barcelona, Spain Email: vespinelflo@gmail.com

Agència de Salut Pública de Barcelona. Barcelona, Catalonia, Spain

Universitat Pompeu Fabra. Department of Experimental and Health Sciences. Barcelona, Catalonia, Spain

http://orcid.org/0000-0001-9557-2105

Mercè Gotsens

Agència de Salut Pública de Barcelona. Barcelona, Catalonia, Spain

Institut d’Investigació Biomèdica (IIB Sant Pau). Barcelona, Spain

http://orcid.org/0000-0003-2952-2024

Glòria Pérez

Agència de Salut Pública de Barcelona. Barcelona, Catalonia, Spain

Universitat Pompeu Fabra. Department of Experimental and Health Sciences. Barcelona, Catalonia, Spain

Institut d’Investigació Biomèdica (IIB Sant Pau). Barcelona, Spain

CIBER Epidemiología y Salud Pública (CIBERESP), Spain

http://orcid.org/0000-0001-6653-3393

Joan Benach

Universitat Pompeu Fabra. Department of Political and Social Sciences. Health Inequalities Research Group, Employment Conditions Knowledge Network (GREDS-EMCONET). Barcelona, Spain

Johns Hopkins University - Pompeu Fabra University Public Policy Center, Barcelona, Spain

Universidad Autónoma Madrid. Transdisciplinary Research Group on Socioecological Transitions (GinTRANS2). Madrid, Spain

http://orcid.org/0000-0003-2285-742X

Marc Marí-Dell’Olmo

Agència de Salut Pública de Barcelona. Barcelona, Catalonia, Spain

Institut d’Investigació Biomèdica (IIB Sant Pau). Barcelona, Spain

CIBER Epidemiología y Salud Pública (CIBERESP), Spain

http://orcid.org/0000-0003-4345-4988

Correspondence: Verónica Espinel-FloresAgència de Salut Pública de Barcelona, Pl. Lesseps 1 08023 Barcelona, Spain Email: vespinelflo@gmail.com

Authors’ Contribution: Study conception, planning, analysis, interpretation and writing: VEF, AP. Study conception, insights for the analysis, and interpretation and writing: MG, MMDO, JB, GP. VEF and AP contributed equally to the methodological and conceptual elaboration of this article. This article forms part of the doctoral dissertations of VEF and AP, at Universitat Pompeu Fabra (UPF), Barcelona.

Conflict of Interest: The authors declare no conflict of interest.

Domains/Indicators		Numerator	Denominator	Survey Question
1. Education
	Literacy^a a Census 2010; .	Literate population	Population of 5 years or more.	19) Can you (...) read and write?
	Educational attainment^a a Census 2010; .	Population with secondary or university education (5–10 years).	Population of 24–65 years.	23) What is the highest level of instruction you attend or attended (...)?
2. Information and Communication
	Mobile phone access^a a Census 2010; .	Population that made use of a mobile phone in the last 6 months.	Population of 5 years or more.	20) In the last six months (...) have you used: a mobile phone…
	Internet access^a a Census 2010; .	Population that made use of the internet in the last six months.	Population of 5 years or more.	20) In the last six months (...) have you used: the internet…
3. Housing and Urban Environment
	Main cooking fuel^a a Census 2010; .	Households using adequate fuel for cooking (gas or electricity).	Total of households.	5) Which is the main fuel or energy that this home uses for cooking?
	Toilet for household use only^a a Census 2010; .	Households with exclusive use of a toillet.	Total of households.	3) The household toilet is of: Exclusive use of the household.
	Water Supply^a a Census 2010; .	Dwellings with piped water.	Total of households.	V8) Where does the water received in this home come from?
	Rubbish collection^a a Census 2010; .	Homes that eliminate garbage with a collector car.	Total of households.	V13) Mainly, how do you eliminate the garbage from the home:
	Toilet to sewer.	Households with toilet to sewer.	Total of households.	V9) The household toilet is connected to:
	Electricity supply^a a Census 2010; .	Households with electrical energy (any provider).	Total of households.	V10) The electric light (energy) service of the house comes mainly from:
	Dwellings overcrowding^a a Census 2010; .	Dwellings not overcrowded (3 or less people per bedroom).	Total of households.	14) Without considering the kitchen, bathroom and office, how many bedrooms does the household has? 16) How many persons (households) sleep and cook food?
	Piped into residence/dwelling^a a Census 2010; .	Dwellings with piped water inside.	Total of households.	V8) The water received in this house is:
	Compound indicator of Dwelling’s quality^a a Census 2010; : Walls, Floor, Ceiling	Dwellings in good condition (roof, walls and floors reported as “good”).	Total of households.	V2) The condition of the ceiling or covering of the dwelling is: V04) The condition of the walls of the dwelling is: V6) The condition of the floor of the dwelling is:
4. Work and Social Security
	Private insurance^a a Census 2010; .	Population with private insurance.	Total of population.	07) (...) has private health insurance?
	Women not payed working hours ratio^b b Life Conditions Survey 2013–2014. .	Weekly not payed work hours.	Total worked hours.	24) How many hours a week do you dedicate to the following home chores: (Total hours).
	Poverty perception^b b Life Conditions Survey 2013–2014. .	Population that considers not being poor.	Total of households.	4) According to your economic condition, do you consider your home to be:
5. Historical Discrimination
	Ethnicity^a a Census 2010; .	Population with white or mestizo self-identification	Total of the population.	16) How do you identify (...) according to your culture and customs?

Domains	Indicator	Minimum	Median	Maximum	Contribution (%)^* * Contribution, in percentage, of each variable in the construction of the index.
1. Education	1. Literacy.	72.99	89.75	97.63	6.60
1. Education	2. Educational attainment.	14.37	39.23	76.82	5.99
2. Information and Communication	3. Mobile phone access.	4.93	40.57	66.54	8.44
2. Information and Communication	4. Internet access.	2.75	11.59	44.48	8.89
3. Housing and Urban Environment	5. Main cooking fuel.	9.23	88.22	98.39	4.61
	6. Toilet for household use only.	25.07	75.31	94.27	3.58
	7. Water supply.	10.05	58.68	96.03	7.31
	8. Rubbish collection.	5.90	58.79	98.47	7.10
	9. Toilet to sewer.	0.52	32.17	90.91	6.88
	10. Electricity supply.	26.33	92.21	99.58	5.64
	11. Dwelling overcrowding.	31.97	75.78	92,28	6.05
	12. Pipes into residence/dwelling.	5.07	43.98	84.08	9.32
	13. Compound indicator of dwelling quality: walls, floor, ceiling.	7.55	20.17	57.81	6.49
4. Work and Social Security	14. Private insurance.	1.97	4.51	27.46	4.75
	15. Women’s unpaid working hours ratio.	38.49	44.76	52.80	1.49
	16. Poverty perception.	4.54	7.97	60.31	2.97
5. Historical Discrimination	17. Ethnicity.	4.08	80.96	99.45	3.87

Correlation	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	Index
1	1.00	0.73	0.66	0.62	0.47	0.27	0.61	0.64	0.57	0.42	0.45	0.64	0.55	0.46	-0.09	0.29	0.56	-0.76
2	0.73	1.00	0.62	0.76	0.42	0.22	0.49	0.69	0.56	0.19	0.29	0.60	0.76	0.70	0.01	0.25	0.14	-0.73
3	0.66	0.62	1.00	0.72	0.66	0.59	0.54	0.65	0.56	0.70	0.61	0.71	0.61	0.60	-0.12	0.43	0.53	-0.86
4	0.62	0.76	0.72	1.00	0.38	0.34	0.69	0.64	0.77	0.44	0.57	0.76	0.85	0.70	-0.44	0.55	0.34	-0.88
5	0.47	0.42	0.66	0.38	1.00	0.47	0.42	0.70	0.33	0.65	0.42	0.57	0.31	0.36	0.07	0.15	0.34	-0.64
6	0.27	0.22	0.59	0.34	0.47	1.00	0.33	0.42	0.25	0.69	0.62	0.46	0.27	0.30	-0.02	0.22	0.41	-0.56
7	0.61	0.49	0.54	0.69	0.42	0.33	1.00	0.64	0.73	0.59	0.57	0.83	0.50	0.35	-0.43	0.36	0.49	-0.80
8	0.64	0.69	0.65	0.64	0.70	0.42	0.64	1.00	0.58	0.50	0.37	0.72	0.55	0.53	-0.05	0.25	0.37	-0.79
9	0.57	0.56	0.56	0.77	0.33	0.25	0.73	0.58	1.00	0.45	0.61	0.78	0.57	0.39	-0.45	0.24	0.42	-0.78
10	0.42	0.19	0.70	0.44	0.65	0.69	0.59	0.50	0.45	1.00	0.68	0.66	0.23	0.27	-0.20	0.29	0.63	-0.70
11	0.45	0.29	0.61	0.57	0.42	0.62	0.57	0.37	0.61	0.68	1.00	0.65	0.43	0.27	-0.46	0.34	0.61	-0.73
12	0.64	0.60	0.71	0.76	0.57	0.46	0.83	0.72	0.78	0.66	0.65	1.00	0.66	0.47	-0.37	0.39	0.52	-0.91
13	0.55	0.76	0.61	0.85	0.31	0.27	0.50	0.55	0.57	0.23	0.43	0.66	1.00	0.71	-0.34	0.54	0.16	-0.76
14	0.46	0.70	0.60	0.70	0.36	0.30	0.35	0.53	0.39	0.27	0.27	0.47	0.71	1.00	-0.05	0.40	0.14	-0.65
15	-0.09	0.01	-0.12	-0.44	0.07	-0.02	-0.43	-0.05	-0.45	-0.20	-0.46	-0.37	-0.34	-0.05	1.00	-0.60	-0.27	0.36
16	0.29	0.25	0.43	0.55	0.15	0.22	0.36	0.25	0.24	0.29	0.34	0.39	0.54	0.40	-0.60	1.00	0.25	-0.51
17	0.56	0.14	0.53	0.34	0.34	0.41	0.49	0.37	0.42	0.63	0.61	0.52	0.16	0.14	-0.27	0.25	1.00	-0.58

[1] Correspondence: Verónica Espinel-FloresAgència de Salut Pública de Barcelona, Pl. Lesseps 1 08023 Barcelona, Spain Email: vespinelflo@gmail.com

Brasil

Brasil

Developing a deprivation index to study geographical health inequalities in Ecuador

ABSTRACT

OBJECTIVES:

METHODS:

RESULTS:

CONCLUSIONS: