Health insurance for the poor decreases access to HIV testing in antenatal care: Evidence of an unintended effect of health insurance reform in Colombia

Health Policy and Planning, Volume 29, No. 3, Year 2014

Interpretation

Prevention of mother-to-child transmission of HIV was added to standard antenatal care (ANC) in 2000 for Colombians enrolled in the two national health insurance schemes, the ‘subsidized regime’ (covering poor citizens) and the ‘contributory regime’ (covering salaried citizens with incomes above the poverty threshold), which jointly covered 80% of the total Colombian population as of 2007. This article examines integration of HIV testing in ANC through the relationship between ordering an HIV test with the type of health insurance, including lack of health insurance, using data from the nationally representative 2005 Colombia Demographic and Health Survey. Overall, health-care providers ordered an HIV test for only 35% of the women attending ANC. We regressed the order of an HIV test during ANC on health systems characteristics (type of insurance and type of ANC provider), women’s characteristics (age, wealth, educational attainment, month of pregnancy at first antenatal visit, HIV knowledge, urban vs. rural residence and sub-region of residence) and children’s characteristics (birth order and birth year). Women enrolled in the subsidized regime were significantly less likely to be offered and receive an HIV test in ANC than women without any health insurance (adjusted odds ratio = 0.820, P < 0.001), when controlling for the other independent variables. Wealth, urban residence, birth year of the child and the type of health-care provider seen during the ANC visit were significantly associated with providers ordering an HIV test for a woman (all P < 0.05). Our findings suggest that enrolment in the subsidized regime reduced access to HIV testing in ANC. Additional research is needed to elucidate the mechanisms through which the potential effect of health insurance coverage on HIV testing in ANC occurs and to examine whether enrolment in the subsidized regime has affected access to other essential health services. © 2013 The Author. All rights reserved. We used the ENDS 2005 for our analyses. The nationally representative sample of the ENDS 2005 included 38 143 women in 37 211 households. The selected households were located in 3935 clusters in 208 municipalities of 33 Colombian departments. The ENDS 2005 was a stratified, two-stage cluster sample survey. The household response rate was 88%, and the individual response rate from the selected households was 92%. Of the 38 143 women aged 13–49 years who were interviewed, 11 062 received ANC for a birth between the years 2000 and 2005. For this analysis, we analyzed data for only those women who responded to a survey question about whether an HIV test was ordered during their ANC visit. The selection process outlined in Figure 1 resulted in a final sample of 10 596 women. Statistical analysis was conducted using STATA version 11 (StataCorp, College Station, TX, USA). Sample selection. Our outcome variable was a binary indicator capturing whether an HIV test was ordered during an ANC visit of a woman’s most recent birth (for all births occurring between 2000 and 2005). In estimating the summary statistics (Tables 1–3), we used the standard Demographic and Health Survey sampling weights to account for the fact that the probabilities of being selected into the survey sample differed across different groups of women. Characteristics of sample Characteristics of HIV test order Characteristics of insurance types We conducted univariate and multivariate regression analysis. We included indicator variables for insurance type, capturing separately the two most common types of health insurance (contributive and subsidized, 26% and 34% of the sample, respectively), a category for all other forms of health insurance (8%), and the uninsured (31%). In the regression analysis, we controlled for potential confounders including types of health provider administering ANC visits (physician, nurse, midwife or other health provider) and the month of pregnancy where a woman attended her first antenatal visit. The analysis also controlled for other maternal characteristics including age, urban vs. rural residence, educational attainment, wealth (based on principal component analysis of data on household assets following the standard DHS protocol), knowledge of HIV, sub-region of residence, and child’s characteristics including birth year and birth order. These health system, maternal, and child characteristics were included in the analysis to control for potential confounders of any relationship between health insurance and the outcome variable. All values, confidence intervals and P values are based on standard errors that are adjusted for clustering at the level of the DHS cluster. In the regression estimation, we followed the recommendation by Dumouchel and Duncan (1983) and Deaton (1997) and estimated regression coefficients both with and without using sampling weights in the estimation. The differences between the two sets of regression coefficient estimates were small (all unweighted coefficient estimates were within ±10% of the weighted coefficient estimates), indicating that the regressions were homogenous across the groups of women with different probabilities to be included in the sample. In this case, both the weighted and the unweighted regression estimators are unbiased, but the unweighted estimator is more efficient. The regression results in Table 4 thus show the results of the unweighted regression estimations. Multiple logistic regression analysis of ordering an HIV test

AI Digest

Study Justification:

This study examines the unintended effect of health insurance reform in Colombia on access to HIV testing in antenatal care. The goal is to understand how the type of health insurance, specifically enrollment in the subsidized regime, affects the likelihood of receiving an HIV test during antenatal care. This research is important because it highlights potential barriers to HIV testing for pregnant women and identifies areas where improvements can be made in healthcare delivery.

Highlights:

– The study found that women enrolled in the subsidized regime were significantly less likely to be offered and receive an HIV test during antenatal care compared to women without any health insurance.
– Factors such as wealth, urban residence, birth year of the child, and the type of healthcare provider seen during the antenatal care visit were also associated with the likelihood of providers ordering an HIV test.
– These findings suggest that health insurance coverage, specifically enrollment in the subsidized regime, may have unintentionally reduced access to HIV testing in antenatal care.

Recommendations:

– Additional research is needed to understand the mechanisms through which health insurance coverage affects access to HIV testing in antenatal care.
– Further investigation should be conducted to determine if enrollment in the subsidized regime has impacted access to other essential health services.
– Policy makers should consider strategies to ensure equal access to HIV testing for all pregnant women, regardless of their health insurance coverage.

Key Role Players:

– Researchers and academics specializing in public health and healthcare delivery.
– Policy makers and government officials responsible for health insurance reform and healthcare provision.
– Healthcare providers, including physicians, nurses, midwives, and other healthcare professionals involved in antenatal care.

Cost Items for Planning Recommendations:

– Funding for additional research and data collection.
– Resources for conducting surveys and interviews to gather information on access to HIV testing and healthcare services.
– Budget allocation for implementing policy changes and interventions to improve access to HIV testing in antenatal care.
– Training and capacity building for healthcare providers to ensure they have the knowledge and skills to offer HIV testing to pregnant women.
– Monitoring and evaluation costs to assess the impact of policy changes and interventions on access to HIV testing.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on regression analysis using a nationally representative sample of women in Colombia. The study examines the relationship between health insurance type and the likelihood of receiving an HIV test during antenatal care. The analysis controls for various factors such as age, wealth, educational attainment, and urban vs. rural residence. However, the abstract does not provide information on the specific methodology used for the regression analysis, such as the model specification or the goodness-of-fit measures. To improve the evidence, the authors could provide more details on the regression analysis, including the model used, the statistical significance of the coefficients, and any diagnostic tests conducted to assess the validity of the model.

Abstract

We used the ENDS 2005 for our analyses. The nationally representative sample of the ENDS 2005 included 38 143 women in 37 211 households. The selected households were located in 3935 clusters in 208 municipalities of 33 Colombian departments. The ENDS 2005 was a stratified, two-stage cluster sample survey. The household response rate was 88%, and the individual response rate from the selected households was 92%. Of the 38 143 women aged 13–49 years who were interviewed, 11 062 received ANC for a birth between the years 2000 and 2005. For this analysis, we analyzed data for only those women who responded to a survey question about whether an HIV test was ordered during their ANC visit. The selection process outlined in Figure 1 resulted in a final sample of 10 596 women. Statistical analysis was conducted using STATA version 11 (StataCorp, College Station, TX, USA). Sample selection. Our outcome variable was a binary indicator capturing whether an HIV test was ordered during an ANC visit of a woman’s most recent birth (for all births occurring between 2000 and 2005). In estimating the summary statistics (Tables 1–3), we used the standard Demographic and Health Survey sampling weights to account for the fact that the probabilities of being selected into the survey sample differed across different groups of women. Characteristics of sample Characteristics of HIV test order Characteristics of insurance types We conducted univariate and multivariate regression analysis. We included indicator variables for insurance type, capturing separately the two most common types of health insurance (contributive and subsidized, 26% and 34% of the sample, respectively), a category for all other forms of health insurance (8%), and the uninsured (31%). In the regression analysis, we controlled for potential confounders including types of health provider administering ANC visits (physician, nurse, midwife or other health provider) and the month of pregnancy where a woman attended her first antenatal visit. The analysis also controlled for other maternal characteristics including age, urban vs. rural residence, educational attainment, wealth (based on principal component analysis of data on household assets following the standard DHS protocol), knowledge of HIV, sub-region of residence, and child’s characteristics including birth year and birth order. These health system, maternal, and child characteristics were included in the analysis to control for potential confounders of any relationship between health insurance and the outcome variable. All values, confidence intervals and P values are based on standard errors that are adjusted for clustering at the level of the DHS cluster. In the regression estimation, we followed the recommendation by Dumouchel and Duncan (1983) and Deaton (1997) and estimated regression coefficients both with and without using sampling weights in the estimation. The differences between the two sets of regression coefficient estimates were small (all unweighted coefficient estimates were within ±10% of the weighted coefficient estimates), indicating that the regressions were homogenous across the groups of women with different probabilities to be included in the sample. In this case, both the weighted and the unweighted regression estimators are unbiased, but the unweighted estimator is more efficient. The regression results in Table 4 thus show the results of the unweighted regression estimations. Multiple logistic regression analysis of ordering an HIV test

Materials

N/A

Innovations

Based on the provided information, it appears that the study is focused on the unintended effect of health insurance reform in Colombia on access to HIV testing in antenatal care. The study found that women enrolled in the subsidized health insurance regime were less likely to be offered and receive an HIV test during antenatal care compared to women without any health insurance.

To improve access to maternal health, including HIV testing in antenatal care, potential innovations and recommendations could include:

1. Strengthening health insurance coverage: The study highlights the need to examine the mechanisms through which health insurance coverage affects access to essential health services. Innovations could focus on improving the design and implementation of health insurance schemes to ensure that they effectively cover maternal health services, including HIV testing.

2. Targeted interventions for vulnerable populations: Given the finding that women enrolled in the subsidized health insurance regime had reduced access to HIV testing, targeted interventions could be developed to specifically address the barriers faced by this population. This could involve targeted outreach and education campaigns, as well as ensuring that health facilities in areas with a high proportion of subsidized insurance enrollees have the necessary resources and capacity to provide HIV testing.

3. Integration of HIV testing in antenatal care: To improve access to HIV testing, efforts could be made to integrate HIV testing into routine antenatal care services. This could involve training healthcare providers on the importance of offering HIV testing to all pregnant women, ensuring the availability of testing kits and resources, and implementing standardized protocols for HIV testing during antenatal care visits.

4. Community-based approaches: Innovations could focus on community-based approaches to improve access to maternal health services, including HIV testing. This could involve mobile clinics or outreach programs that bring healthcare services, including HIV testing, closer to communities, particularly in rural or underserved areas.

5. Quality improvement initiatives: To ensure that all pregnant women have access to high-quality maternal health services, quality improvement initiatives could be implemented. This could involve regular monitoring and evaluation of healthcare facilities to identify gaps in service provision, as well as providing training and support to healthcare providers to ensure they have the necessary skills and knowledge to offer comprehensive antenatal care, including HIV testing.

It is important to note that these recommendations are based on the information provided and may need to be further tailored and adapted to the specific context and needs of the population in Colombia.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health would be to address the unintended effect of health insurance reform in Colombia that decreases access to HIV testing in antenatal care for women enrolled in the subsidized regime. This can be done through the following steps:

1. Conduct additional research: Further investigation is needed to understand the mechanisms through which health insurance coverage affects HIV testing in antenatal care. This research should also examine if enrollment in the subsidized regime has impacted access to other essential health services.

2. Raise awareness and education: Implement educational campaigns to increase awareness among healthcare providers and pregnant women about the importance of HIV testing during antenatal care. Emphasize the benefits of early detection and prevention of mother-to-child transmission of HIV.

3. Improve healthcare provider training: Provide training and resources to healthcare providers to ensure they are knowledgeable about HIV testing guidelines and protocols. This will help ensure that all eligible women receive appropriate testing during antenatal care.

4. Strengthen healthcare infrastructure: Invest in improving healthcare infrastructure, particularly in rural areas, to ensure that antenatal care services, including HIV testing, are easily accessible to all pregnant women.

5. Policy review and adjustments: Review the current health insurance policies and identify any barriers or limitations that may be preventing women enrolled in the subsidized regime from accessing HIV testing during antenatal care. Make necessary adjustments to ensure equitable access to essential health services for all pregnant women.

By implementing these recommendations, it is possible to improve access to maternal health, specifically by addressing the unintended effect of health insurance reform that decreases access to HIV testing in antenatal care for women enrolled in the subsidized regime in Colombia.

AI Innovations Methodology

Based on the provided article, one potential innovation to improve access to maternal health is the implementation of targeted health insurance programs specifically designed to address the needs of pregnant women. This could involve creating a separate health insurance scheme that focuses on providing comprehensive coverage for maternal health services, including HIV testing during antenatal care.

To simulate the impact of this recommendation on improving access to maternal health, a methodology could be developed as follows:

1. Define the target population: Identify the specific group of pregnant women who would benefit from the targeted health insurance program. This could include low-income women, women without any health insurance, or those at higher risk for HIV transmission.

2. Collect baseline data: Gather data on the current access to maternal health services, including HIV testing, among the target population. This could involve conducting surveys or analyzing existing data sources, such as the nationally representative survey used in the article.

3. Design the intervention: Develop the details of the targeted health insurance program, including the coverage provided, eligibility criteria, and enrollment process. Consider factors such as affordability, accessibility, and the specific needs of pregnant women.

4. Simulate the impact: Use statistical modeling techniques to simulate the impact of the targeted health insurance program on access to maternal health services. This could involve creating a hypothetical scenario where the program is implemented and estimating the changes in the proportion of women receiving HIV testing during antenatal care.

5. Analyze the results: Evaluate the simulated impact of the targeted health insurance program on access to maternal health services. Assess the changes in the proportion of women receiving HIV testing and any potential disparities among different subgroups of the target population.

6. Refine and iterate: Based on the results of the simulation, refine the design of the targeted health insurance program if necessary. Repeat the simulation process to assess the impact of any modifications made.

By following this methodology, policymakers and healthcare providers can gain insights into the potential benefits of implementing targeted health insurance programs to improve access to maternal health services, such as HIV testing during antenatal care.

Author

Dima Health

Statistics:

Citations: 7

Identifiers:

DOI: 10.1093/heapol/czt021

ISSN: 02681080

Research Areas:

Community Interventions, Disparities, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Design:

Case-Control Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female