Ethnic-racial inequity in health insurance in Colombia: a cross-sectional study.

OBJECTIVE: Characterize the relationship between ethnic-racial inequity and type of health insurance in Colombia.
METHODS: Cross-sectional study based on data from the 2019 Quality of Life Survey. We analyzed the type of health insurance (contributory, subsidized, or none) and its relationship to ethnic-racial status and predisposing variables (sex, age, marital status), demographic variables (area and region of residence), and socioeconomic variables (education, type of employment, income, and unmet basic needs) through simple and multivariate regression analyses. The association between ethnic-racial status and type of health insurance was estimated using odds ratios (OR) and their 95% confidence intervals, through a multinomial logistic model.
RESULTS: A statistically significant association was found between ethnic-racial status and type of health insurance. In comparison with the contributory system, the probabilities of being a member of the subsidized system were 1.8 and 1.4 times greater in the indigenous population (OR x 1.891; 95%CI: 1.600-2.236) and people of African descent (OR = 1.415; 95%CI: 1.236-1.620), respectively (p <0.01) than in the population group that did not identify as belonging to one of those ethnic-racial groups.
CONCLUSIONS: There is an association between ethnic-racial status and type of insurance in the contributory and subsidized health systems in Colombia. Ethnic-racial status is a structural component of inequity in access to health services and heightens the disadvantages of people and population groups with low socioeconomic status.

In Latin America, certain ethnic-racial groups have been systematically exposed to multiple forms of material and social deprivation that generally manifest as low socioeconomic status (2). Health inequity occurs when there are unjust, avoidable, or remediable inequalities between social groups (3). The scientific literature shows that ethnic-racial status plays a key role in the different manifestations of inequity that affect health (3-7). There is also consensus that socioeconomic position and type of health insurance are social determinants of health that influence access to and quality of health services in some ethnic-racial groups (3, 8). Several authors have shown that having or not having health insurance is the main factor in health access disparities between different ethnic-racial groups (8-10).

In Colombia, the 1991 Constitution reaffirmed the multi-ethnic and multicultural nature of the country. According to the 2005 Census, 10.4% of respondents self-identified as people of African descent, 3.2% as indigenous, 0.012% as Romani or Gypsy, and 86.1% did not identify as part of any of these ethnic-racial groups. The people included in these minorities have not had equal access to power, prestige, and resources (11, 12), and are characterized as having higher mortality, lower chances of survival when faced with disease (13), worse indicators of self-perceived health (14), worse health status (14, 15), insufficient health insurance (16), and less access to medical services (17-19).

Given that health insurance is a component of health service access (20), in 1993 the Colombian government passed Law 100 (21) to ensure that the most vulnerable populations have access to health services through what is called the subsidized regime (22). The other insurance system in Colombia, called contributory, is only for individuals with employment contracts or the ability to pay. Individuals who cannot afford to pay and are not yet affiliated with the subsidized regime are considered associate members (22). It follows then that a person’s type of health insurance is associated with their social position and ability to pay.

Studies indicate that there are major differences in the use of health services among insured and uninsured individuals (23), as well as in populations insured through the contributory and subsidized regimes (24). The subsidized insurance system is associated with poor medical care (25) and reduced access to preventive primary care services and specialized consultations (26), a worse state of health and longer wait times to receive care (27), a higher incidence of poverty-related infectious and communicable diseases including malaria, and a higher risk of dying before the age of 5 (26). There are also extreme inequalities in sexual and reproductive health care (26).

Socio-economic deprivation is the main barrier to having adequate health insurance (16, 22, 26) and full access to health services (27). However, there is insufficient literature that analyzes inequity due to ethnic-racial status based on socioeconomic position and affiliation with different health insurance systems. There are even fewer studies on improving welfare systems in Latin America and subsequently insuring a significant part of the vulnerable population currently associated with the subsidized system (28). The objective of this study is to characterize the relationship between ethnic-racial inequity and type of health insurance in Colombia.

MATERIALS AND METHODS

A cross-sectional study was conducted, based on data from Colombia’s 2019 National Quality of Life Survey (ECV-2019) carried out by the National Statistics Department (DANE) (29).

The ECV-2019 is a primary source of direct and representative information on the country’s households that is based on a probabilistic sample of 75,780 households selected using a multi-stage, stratified, and conglomerate design, according to 2005 census data. Of the 289,432 people aged 18 or older who were surveyed, 8.6% self-identified as people of African descent, 4.7% as indigenous, 0.03% as Romani or Gypsy, and 86.8% were individuals who did not identify with any of these ethnic-racial groups. Due to the low weighting of Romani or Gypsy populations, it was decided to exclude them from the study, and people with missing information were also eliminated, resulting in a final sample of 98,818 individuals. For calculation purposes, each observation was weighted by the inverse probability of being sampled in the ECV-2019 (29).

Variables and measurements

Type of health insurance was used as a dependent variable in this study: a) affiliation with the contributory regime, b) affiliation with the subsidized regime, and c) not affiliated with either of those two regimes.

Independent variables were selected from ECV-2019 survey questions about the individual factors that predispose and enable an individual to use health services, based on the Aday and Anderson model for accessing these services (20) and the social determinants of health proposed by Solar and Irwing (3).

Ethnic-racial status is considered a structural component of social determinants and is therefore a mediator of access to health services (3). This variable was based on answers to the question: “According to your culture, people, or physical traits, you are or identify as: (a) Indigenous; b) Gypsy (Romani); c) Raizal from the Colombian archipelago of San Andres, Providencia and Santa Catalina d) Palenquero from San Basilio; e) Black, Mulatto, Afro-descendant, Afro-Colombian; f) None of the above”. For analysis purposes, these six categories were reduced to three: 1) indigenous: people who self-identified as such, 2) Afro-descendants: those who self-identified as Raizal, Palenquero, Black, Mulatto, Afro-Colombian, or Afro-descendant, and 3) those with no self-reported ethnicity (no-SRE), i.e. people who answered “None of the above.” The self-reported ethnicity model used by DANE was used, which independently shows only ethnic-racial minorities and not people of European, Asian, or other descent who do not identify as part of the specific minorities mentioned.

The structural determinants or predisposing factors for accessing health services were sex, age (completed years), and marital status (in a formal or consensual marital union, or no marital union).

Demographic factors that enable an individual to access health services included the area of residence (municipal capital: the urban center where the mayor’s office is located; populated area: a corregimiento [subdivision of a municipality], inspección de policía [smaller subdivision of a municipality], hamlet, parish, or agricultural area), and the region in which it is located.

Socio-economic position, which covers different aspects of social stratification (3), was estimated by the following variables: years of education (school years completed); type of employment (formal or informal); and employment income, quantified as the number of current monthly legal minimum wages. In 2019 the monthly minimum wage was $828,116 Colombian pesos (30), equivalent to US$254.82.

The variable “unsatisfied basic needs” (UBNs) was used to describe material circumstances, as stated: (a) inadequate housing; (b) homes with inadequate services; (c) households with school non-attendance; (d) critically overcrowded households; (e) households with high economic dependence (3) (Table 1).

TABLE 1.

Operationalization of the variables used in the Quality of Life Survey, Colombia, 2019

Type of variable	Variables	Operationalization
Dependent variable	Type of health insurance	Contributory regime Subsidized regime No insurance
Ethnic-racial status	Self-reported ethnicity/racea	Indigenous Afro-descendantb No self-reported ethnicity
Structural determinants	Sex	Male Female
	Age	Years of age completed
	Marital status	In a marital union (formal or consensual) No marital union
Demographic factors	Area of residence	Urban Rural populated area
	Region of residence	Bogotá Atlantic Eastern Central Pacific (not including Valle del Cauca department) Antioquia Valle del Cauca San Andrés and Providencia Orinoco-Amazon
Socioeconomic position	Years of education	Years of schooling completed
	Type of employment	Formal Informal
	Employment income in terms of current legal monthly minimum wagesc	More than 3 Between 2 and 2.99 Between 1 and 1.99 Between 0 and 0.99
Material circumstances	Unmet basic needs (UBNs)d	Lives in a household with no UBNs Lives in a household with some UBNs

Prepared by the authors.

The self-reported ethnicity model used by Colombia’s National Statistics Department was followed, which combines people of European, Asian, or other descent who do not identify as part of any ethnic-racial minorities.

A person who self-identifies as Raizal from the Colombian archipelago of San Andres, Providencia, and Santa Catalina; a Palenquero from San Basilio; Black, Mulatto, Afro-Colombian, or Afro-descendant.

Legal monthly minimum wage in 2019, in Colombian pesos: $828,116 (30), equivalent to US$254.82; the exchange rate as of January 1, 2019 was $3,249.75 pesos per U.S. dollar (31).

Inadequate housing, housing with inadequate services, households with school non-attendance, critically overcrowded households, households with high economic dependence.

Statistical analysis

A descriptive analysis was applied to the different category variables presented in the form of frequency tables; the chi-squared test (χ2) was used to measure the association between discontinuous variables and the Student’s t-test was used to compare averages in the continuous values.

Self-reported ethnicity/race

Employment income in terms of current legal monthly minimum wages

Unmet basic needs (UBNs)

For the multifactorial analysis of the association between type of health insurance and ethnic-racial status, a multinomial logistic model with regression of race-specific intercepts (32) was applied. Five models were estimated to test the robustness of the association between ethnic-racial status and the type of health insurance. After selecting the variables using a simple analysis by ethnic-racial status, sequential adjustments were made for structural determinants, sociodemographic characteristics, social position variables, and material conditions; odds ratios (OR) and their 95% confidence intervals (95%CI) were calculated.

The presence of a linear association between two or more independent variables, particularly due to the multidimensionality of ethnic-racial status and its interaction with other social variables (33), was investigated using the variance inflation factor (VIF) test: VIF values ≤3.0 would indicate that multicolinearity is not a problem for estimating the model, although the general threshold is 10 (34).

As goodness-of-fit measures, we calculated the adjusted R2 indicator, which measures the contribution of independent variables in the explanation of the dependent variable, from 0 (independent variables do not explain the dependent variable) to 1 (full explanation of the dependent variable), and the Wald test, which reflects the statistical significance of the variables or set of variables involved in the model; we concluded that these variables are important for explaining the dependent variable if p <0.05 (34).

Statistical analysis was carried out using Stata version 16™ (35).

Ethical considerations

The study employed a secondary database anonymized by DANE that meets all ethical requirements for human research according to international standards.

RESULTS

Descriptive analysis

Significant differences in the type of health insurance were observed based on ethnic-racial status (Table 2). The population with no self-reported ethnicity (no-SRE) had the highest level of insurance under the contributory regime (56.6%), followed by Afro-descendants (36.7%), and indigenous people (17.8%) (p <0.001 among the three groups). An inverse relationship was observed in terms of affiliation with the subsidized regime: indigenous people showed the highest percentage (77.1%), followed by Afro-descendants (57.0%), and the no-SRE group (36.1%) (p <0.001 between the three groups). Paradoxically, in the uninsured population, people with no SRE had a slightly greater (7.3%) but statistically significant presence in the three groups (p <0.001), compared to Afro-descendants (6.3%) and indigenous people (5.1%).

TABLE 2.

Sociodemographic characteristics of the study sample, Colombia, 2019

Variable	No self-reported ethnicity (n = 81,370)	Afro-descendant (n = 9,929)	Indigenous (n = 7,519)	p
Health insurance regimen (%)
Contributory	56.6	36.7	17.8	<0.001d
Subsidized	36.1	57.0	77.1
Unaffiliated	7.3	6.3	5.1
Sex (%)
Female	40.7	39.9	35.1	<0.001d
Male	59.3	60.1	64.9
Marital status (%)
In a marital union	57.9	57.5	63.5	<0.001d
No marital union	42.1	42.5	36.5
Area of residence (%)
Municipal capital	83.5	69.6	34.0	<0.001d
Rural populated area	16.5	30.4	66.0
Geographical region (%)
Bogotá	20.8	3.3	2.1	<0.001d
Atlantic	17.8	29.3	34.5
Eastern	21.4	1.3	2.4
Central	11.1	2.1	10.3
Pacific (not including Valle del Cauca department)	4.5	24.6	34.9
Antioquia	13.8	11.3	2.2
Valle del Cauca	8.1	26.7	6.4
San Andrés	0.1	0.8	0.0
Orinoco-Amazon region	2.4	0.6	7.2
Type of employment (%)
Informal	57.4	67.1	81.7	<0.001d
Formal	42.6	32.9	18.3
Job income in current legal monthly minimum wages (%)b
Between 0 and 0.99	55.8	70.0	82.1	<0.001d
Between 1 and 1.99	29.0	23.6	14.0
Between 2 and 2.99	5.7	3.1	2.1
More than 3	9.5	3.3	1.8
Unsatisfied basic needs (UBNs)c (%)
Lives in a household with no UBNs	91.6	82.0	61.7	<0.001d
Lives in a household with some UBNs	8.4	18.0	38.3
Years of education (average)	10.4	9.1	7.5	<0.001e
Age (years, average)	39.9	39.4	39.1	<0.001f 0.4273g

Prepared by the authors.

The self-reported ethnicity model used by Colombia’s National Statistics Department was followed, which combines people of European, Asian, or other descent who do not identify as part of any ethnic-racial minorities.

98,818 people were studied based on a randomized sample representative of Colombia from the 2019 National Quality of Life Survey. Observations were weighted by the inverse probability of being sampled.

Legal monthly minimum wage in 2019, in Colombian pesos: $828,116 (30), equivalent to US$254.82; the exchange rate as of January 1, 2019 was $3,249.75 pesos per U.S. dollar (31).

Inadequate housing, housing with inadequate services, households with school non-attendance, critically overcrowded households, households with high economic dependence.

Chi-squared test for the frequency distribution of each variable.

No self-reported ethnicity vs. indigenous and no self-reported ethnicity vs. Afro-descendants, using the Student’s t-test to compare averages.

No self-reported ethnicity vs. Afro-descendants and no self-reported ethnicity vs. indigenous people, using the Student’s t-test to compare averages.

Indigenous people vs. Afro-descendants, using the Student’s t-test to compare averages.

Job income in current legal monthly minimum wages (%)

Unsatisfied basic needs (UBNs) (%)

0.4273

Some predisposing factors did not show significant percentage point differences between certain ethnic-racial groups, although due to the size of the groups they were statistically significant among the three groups (p < 0.001): the percentage of men and the population in a marital union was slightly higher among indigenous peoples (63.5%), compared to Afro-descendants and those classified as having no SRE (57.5% and 57.9%, respectively). The average ages also showed some numerical similarity among the different groups (no-SRE: 39.9 years; Afro-descendants: 39.4 indigenous people: 39.1), but with statistically significant differences between the no-SRE group and indigenous people and between the no-SRE group and Afro-descendants.

As for the factors considered to be enabling (area and region of residence), statistically significant differences (p < 0.001) were observed in the populations analyzed. The largest percentage in rural populated areas were indigenous peoples (66.0%), followed by Afro-descendants (30.4%), and people with no SRE (16.5%). The majority of indigenous peoples and Afro-descendants lived in the Pacific (not including the Valle del Cauca department), Atlantic, and Orinoco-Amazon regions, which are considered to have the highest levels of poverty and marginality in Colombia: 76.6% - indigenous peoples, 54.5% - Afro-descendants, and only 24.7% for people with no SRE (p < 0.001 among the three groups).

With respect to indicators of socio-economic position, although the percentage of informal workers was high overall, indigenous people had the highest rate of informality (81.7%), followed by Afro-descendants (67.1%), and individuals with no SRE (57.4%) (p <0.001 between the three groups). Indigenous people also accounted for the highest percentage of those earning less than one current legal monthly minimum wage (82.1%), followed by Afro-descendants (70.0%), and the no-SRE group (55.7%) (p <0.001 between the three groups). In addition, the indicator ‘average years of education’ was lowest in indigenous people (7.5), followed by Afro-descendants (9.1), and the no-SRE group (10.4) (p <0.001 between the three groups).

The indicators of material conditions reflected more disadvantages in indigenous people (38.3% with some UBNs), followed by Afro-descendants (18.0%), and the no-SRE group (8.4%) (p <0.001 between the three groups).

Multifactorial analysis

No multicolinearity was found between any of the variables (average VIF = 2.63); only a few categories associated with the region of residence had values above 3.0, but they did not exceed the significance threshold (data not shown). Adjustment measures showed an increase in the adjusted R2 value to account for the inclusion of blocks of variables associated with social determinants of health, thus suggesting their importance to an explanation of health insurance. All estimated models were globally significant according to the Wald test (Table 3).

TABLE 3.

Association between ethnic-racial status and type of health insurance in the study sample, Colombia, 2019

Ethnic-racial status	Block 1b		Block 2c		Block 3d		Block 4e		Block 5f
	Subsidized	None	Subsidized	None	Subsidized	None	Subsidized	None	Subsidized	None
	OR (95%CI)		OR (95%CI)		OR (95%CI)		OR (95%CI)		OR (95%CI)
No self-reported ethnicity (reference)	1	1	1	1	1	1	1	1	1	1
Indigenous	6,787 (5,999-7,679) ***	2,218 (1,746-2,818) ***	6,779 (5,995-7,667) ***	2,081 (1,637-2,645) ***	2,100 (1,839-2,398) ***	1,393 (1,074-1,808) **	2,033 (1,728-2,392) ***	1,401 (1,060-1,852) **	1,891 (1,600-2,236) ***	1,202 (0,899-1,607)
Afro-descendant	2,428 (2,230-2,644) ***	1,333 (1,105-1,607) ***	2,445 (2,245-2,664) ***	1,297 (1,075-1,565) ***	1,573 (1,420-1,744) ***	1,216 (0,995-1,487) *	1,479 (1,296-1,689) ***	1,148 (0,918-1,436)	1,415 (1,236-1,620) ***	1,056 (0,839-1,329
Adjusted R2	0.0204	0.0204	0.0329	0.0329	0.143	0.143	0.375	0.375	0.381	0.381
Wald test	1,330,053	1,330,053	1,960,673	1,960,673	7,740,287	7,740,287	9,259,562	9,259,562	9,280,180	9,280,180
p	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001

Prepared by the authors.

The self-reported ethnicity model used by Colombia’s National Statistics Department was followed, which combines people of European, Asian, or other descent who do not identify as part of any ethnic-racial minorities. The individual significance of each variable was determined by using the Wald test, with the contributory regime as the reference variable; 95%CI: 95% confidence interval;*** p <0.01; ** p <0.05; * p <0.1.

98,818 people were studied based on a randomized sample representative of Colombia from the 2019 National Quality of Life Survey. Observations were weighted by the inverse probability of being sampled.

Adjusted for ethnic-racial status.

Adjusted for ethnic-racial status and structural determinants (see Table 1).

Adjusted for ethnic-racial status, structural determinants, and demographic factors (see Table 1).

Adjusted for ethnic-racial status, structural determinants, demographic factors, and socioeconomic position (see Table 1).

Adjusted for ethnic-racial status, structural determinants, demographic factors, and socioeconomic position (see Table 1).

The probability of being affiliated with the subsidized insurance regime compared to affiliation with the contributory regime was higher in indigenous people (1.8 times) (OR = 1.891; 95%CI: 1.600-2.236) and Afro-descendants (1.4 times) (OR = 1.415; 95%CI: 1.236-1.620) than in the no-SRE group (p <0.01). Compared to the no-SRE group, indigenous people and Afro-descendants were 20.2% (OR = 1.202; 95%CI: 0.899-1.607) and 5.6% (OR = 1.056; 95%CI: 0.839-1.329) more likely, respectively, of not being affiliated with any health insurance than to being affiliated with the contributory regime, although the differences were not statistically significant (Table 3).

There was also no observed significant statistical association between ethnic-racial status and type of health insurance, after controlling for four blocks of variables related to the social determinants of health. This association was only significant when comparing the subsidized regime and the contributory regime (p <0.01), which indicates significant inequity due to ethnic-racial status in the population affiliated with the subsidized regime compared to the contributory regime. This reflects significant differences in access to health services and health outcomes among indigenous peoples and Afro-descendants in Colombia; this difference was not observed in the population with no health insurance. This association proved highly significant (p <0.01) based on the five models analyzed, which suggests good model specification and robustness of the effects of ethnic-racial status on type of health insurance.

Sociodemographic factors (area and region of residence) showed greater variation in their association with ethnic-racial status and type of health insurance in indigenous peoples (OR from 6.779 to 2.100) than in Afro-descendants (OR from 2. 445 to 1.573) (Table 3, Block 3).

DISCUSSION

Several authors have investigated the effect of belonging to an ethnic-racial group on health inequalities (4-7). These results, which focus on inequity due to ethnic-racial status in the type of health insurance in Colombia based on 2019 data, show the effect of ethnic-racial status as a structural component of inequity on access to health services (8-10, 16). Even when socio-economic factors are taken into account, indigenous and Afro-descendant populations were more likely to be affiliated with the subsidized regime than the contributory regime, compared to the no-SRE group. In this respect it should be emphasized that ethnic-racial differences between not having insurance and having insurance in the contributory regime were not statistically significant.

After Law 100 was enacted in 1993 (21), programs to improve health system access for vulnerable populations focused on expansion of the subsidized regime. In fact, indigenous communities are particularly prominent in terms of affiliation in the subsidized regime, while other ethnic groups are not explicitly mentioned. Similarly, the Integrated Social Security System has provided for extending coverage to those sectors with insufficient financial capacity, where the indigenous population is one of the priority groups. This has led in part to the disappearance of the association that existed between ethnic-racial status and the probability of not having health insurance. However, the increased likelihood of disadvantaged ethnic-racial groups being affiliated with the subsidized regime could be creating persistent situations of inequity in health service access and health outcomes based on ethnic-racial status in Colombia.

The impact was greater on the indigenous population than on Afro-descendants and the no-SRE group. In fact, indigenous peoples are lagging farther behind in terms of the social determinants of health that could influence access to health services (12). This could be due to a higher incidence of the historical accumulation of inequalities related to health inequity for the most disadvantaged ethnic-racial groups (4-7), despite the positive effect of the universal insurance coverage strategy.

Of the set of socioeconomic factors used to control for the effect of ethnic-racial status on type of health insurance, the area and region of residence showed the greatest variation in inequity due to ethnic-racial status with respect to access to health (27). It should be remembered that ethnic-racial groups, especially indigenous groups, are over-represented in the regions with the most poverty and marginality in Colombia (12).

Block 1

Block 2

Block 3

Block 4

Block 5

These results are novel because although it was known that ethnic-racial status was associated with the probability of having or not having health insurance (8-10, 16), it was not specifically known how significant this actually was in terms of the probabilities of being affiliated with different health insurance regimes. This is particularly novel in the context of improving welfare systems in Latin America based primarily on the universalization of the subsidized regime for vulnerable people (28). It is known that the segmentation between health insurance regimes can lead to disadvantages in accessing health services and the quality of services provided to the subsidized population, and that this may lead to significant inequity in terms of access to health for the most disadvantaged ethnic-racial groups in Colombia (16-19). Most research studies on ethnic-racial status and health inequalities hypothesize that racism and discrimination are factors associated with reduced access to health services and worse health outcomes in the most disadvantaged ethnic-racial groups (4-10, 16, 36); this could be associated with the current structural discrimination in Colombia. Structural discrimination refers to the “… range of policies and practices that contribute to a systematic disadvantage for members of certain groups” (37).

Nonetheless, it is difficult to capture the cumulative and structural consequences of racism and discrimination through the multifactorial regression analysis used in this study, based on cross-sectional data and adjusting for ethnic-racial status due to socio-economic factors (32, 37). However, the empirical evidence accumulated in Colombia shows that ethnic-racial status has a significant effect on factors associated with the social determinants of health (11, 12, 16, 38-40), which could support the hypothesis of cumulative disadvantages and the creation of a vicious cycle of cumulative disadvantages as a factor associated with ethnic-racial inequity in terms of the type of health insurance in Colombia (36).

Several limitations need to be taken into account when interpreting these results. First, it is not possible to establish a causal relationship between the variables studied, particularly the effects that could lead to the historical disadvantages accumulated over generations and the life course of the most disadvantaged ethnic-racial groups. Similarly, the effects of omitted variable bias could not be taken into account when developing the model, which could lead to overestimating or underestimating ethnic-racial discrepancies in the type of health insurance. Furthermore, the reference group consisted of people who did not consider themselves to be indigenous or of African descent but who could still be quite heterogeneous from an ethnic-racial standpoint (for example, including whites and mestizos with greater access to resources, privileges, and power); this could lead to underestimating the association between ethnic-racial status and the type of health insurance in Colombia. However, the use of such a voluminous, nationally representative database with abundant individual information on the social determinants of health strengthens the analysis and the robustness of its conclusions.

Conclusions

There is a statistically significant association between ethnic-racial status and the type of health insurance (subsidized or contributory) in Colombia. These results confirm that ethnic-racial status is a structural component of inequity in terms of accessing health services in the country. In the context of the significant segmentation of health insurance regimes in Colombia, ethnic-racial status heightens the disadvantages of individuals and population groups with low socioeconomic status, resulting in less access to and enjoyment of the right to health for the most disadvantaged ethnic-racial groups.

Recommendations

Once greater health insurance coverage has been achieved through the subsidized regime, access to health and the quality of services must be improved, especially in regions with a larger presence of more disadvantaged ethnic-racial groups where health service delivery is poor. Specific policies and actions that introduce improvements in various areas such as education, labor markets, and housing should be implemented to enhance opportunities to improve the socio-economic status of all ethnic-racial groups in Colombia, thereby increasing equity.

Longitudinal and life-course studies are required to analyze how the effects of discrimination can accumulate across different domains and generations and create a vicious cycle of cumulative disadvantages in accessing health services in Colombia and other countries. In addition, more research is needed to define the true role of health insurance and other social determinants with respect to the ethnic-racial discrepancies that impede access to the health system in Colombia.

Disclaimer.

Authors hold sole responsibility for the views expressed in the manuscript, which may not necessarily reflect the opinion or policy of the Pan American Journal of Public Health and/or the Pan American Health Organization.