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Center on Aging and Health University of Texas Pan American Elena Bastida Shelton Brown, III Jose Pagan

Health Insurance, Health Care and Socio Economic Disparities along the US Mexico Border: Evidence from the Border Epidemiologic Study of Aging. Center on Aging and Health University of Texas Pan American Elena Bastida Shelton Brown, III Jose Pagan . Sample .

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Center on Aging and Health University of Texas Pan American Elena Bastida Shelton Brown, III Jose Pagan

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  1. Health Insurance, Health Care and Socio Economic Disparities along the US Mexico Border: Evidence from the Border Epidemiologic Study of Aging Center on Aging and Health University of Texas Pan American Elena Bastida Shelton Brown, III Jose Pagan

  2. Sample • Three data waves completed. Data were drawn from a random sample of households in Hidalgo and Starr Counties. Sample is age stratified, where at least one household member was at least 45 years or older and of Mexican origin. A sub-sample was later drawn for Waves 3 and 4 to include households with at least one member over the age of 37. • Baseline: 1,299 Households, n = 2,082 (includes respondent and spouse), 1996 -1997 • Wave 3: 1,048 households, n = 1,491 (respondent and spouse). 2001- 2002 • Wave 4 currently in progress.

  3. Where we are

  4. Demographic Characteristics

  5. Demographic Characteristics

  6. Demographic Characteristics

  7. Demographic Characteristics

  8. *** p<.1%, **p<1% and *p<5%, + Includes private & public/gov. programs

  9. *** p<.1%, **p<1% and *p<5% + Includes private & public/gov. programs

  10. Sample Size based on 700 completed interviews 2005

  11. Table 4: Predictors of Insurance *** p<1%, **p<5% and *p<10% , RC – Reference category , + Includes private & public/gov. programs

  12. Table 5: Predictors of Mexican Health Care Utilization *** p<1%, **p<5% and *p<10% , RC – Reference category

  13. Table 6: Predictors of Unpaid Medical Bills *** p<1%, **p<5% and *p<10% , RC – Reference category

  14. IV estimates (using bivariate probit) ***: p<.01; **: p<.05

  15. IV equation (from bivariate probit) ***: p<.01; **: p<.05

  16. Probit Model (Employed Adults under 65) ***: p<.01; **: p<.05

  17. Probit Model (Adults Ages 65+ with Medicare Part A) ***: p<.01; **: p<.05

  18. Summary of Findings • Household Income and Education are significant predictors of Insurance and they remain significant predictors when controlling for country of birth, age, gender, length of residence in the US, physical health and disability. • Age is a significant predictor of insurance, the Medicare eligible population being the most protected. • Men are twice (1.7) as likely to be insured than women regardless of age. • Those who self rate their health as very good are less likely to have insurance when compared to those who self rate their health as poor. • Those who report no difficulties with activities of daily living are also less likely to have insurance than those indicating at least some difficulty.

  19. Summary of Findings • Those reporting household incomes between $7200 and $30,000 are more likely to utilize health care services in Mexico than either those with incomes under $7200 or above $30,000. • Those who subjectively rate their health as excellent are two times less likely to utilize Mexican health care than those who indicate poor health. • The uninsured are seven times more likely to utilize Mexican health care than the insured. • Absence of any form of insurance coverage, private or public, is the best and most significant predictor of Mexican heath care utilization and this finding persists when other predictors are controlled.

  20. Summary of Findings • Income between $7200 and $15,000 is a significant predictor of unpaid medical bills. • The $15,000 to $30,000 income group becomes a significant predictor in Model 2. Those with incomes between $7200 and $15,000 are four times more likely to have unpaid medical bills; while those with incomes between $15,000 and $30,000 are two and a half times more likely to have unpaid medical bills. • Age is also a significant predictor with those aged 50 – 64 three times more likely to have unpaid medical bills when compared to the 65 and older. • As may be expected, Subjective Health Assessment is also a significant predictor of unpaid medical bills with those indicating excellent and very good health about 7 times less likely to report unpaid medical bills than those indicating poor health. • Insurance is also a significant predictor of unpaid medical bills with the uninsured almost two times less likely to have unpaid medical bills.

  21. Main Results from Probit Models (Based on Wave 4) • Employed adults under the age of 65 who utilize medical care in Mexico are less likely to have private health insurance coverage than those who do not rely on Mexican medical care. • Adults 65 years of age and older with Medicare Part A who utilize medical care in Mexico are less likely to purchase Medicare Part B than those who do not rely on Mexican medical care.

  22. Conclusion • The South Texas border region has perhaps the highest rate of uninsurance in the U.S. • BESA sample = 31% • U.S. Census Bureau estimates = 36% • About two-thirds of uninsured border residents report that they do not have health insurance coverage because they can obtain low cost care in Mexico. • Use of medical care in Mexico is negatively correlated with having private health insurance coverage (employed adults under age 65) or Medicare Part B (Medicare Part A recipients). • Border uninsurance seems to be related to both: • lack of access to health insurance coverage (e.g., low income; no employer-sponsored coverage) • the availability of alternatives to health care in both Mexico and the U.S.

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