# Presentation: 868109

## Health Insurance among Hispanics in the United States: National Health Interview Survey, 1998-2000 

**Health Insurance among Hispanics in the United States: ****National Health Interview Survey, 1998-2000**** **

**Debra L. Blackwell, Ph.D., NCHS/DHIS**
**LaJeana Howie, MPH, NCHS/OAEHP**

## Background

**	****Levels of non-coverage in 1997: **
**	****	****33% of Hispanic persons   **
**	****	****12% of non-Hispanic white persons**
**	****	****19% of non-Hispanic black persons****  **
**Source: 1997 NHIS Summary Statistics report on the U.S. Population**
** **

**Notes:**
The 1997 Summary Statistics report on the United States population, which was based on the National Health Interview Survey, revealed many striking findings, one of which was that 33% of Hispanic persons in the United States were without health insurance coverage, compared with 12% of non-Hispanic white and 19% of non-Hispanic black persons.

## Research Questions

**Do all Hispanic subgroups have the same level of non-coverage?  **
**What characteristics are associated with a lack of health insurance coverage?  **
**Who is most likely to be uninsured?**

**Notes:**
This led us to pose the following research questions.

Do all Hispanic subgroups exhibit similar percentages of non-coverage?

If we move beyond a frequency table analysis to a multivariate analysis, what characteristics are most strongly associated with non-coverage?  

And who is most likely to be uninsured?  

## National Health Interview Survey, 1998-2000

**Three years of data**
**	****	****Adults 18-64 years of age**
**Files used:**
**	****	****Person files –> Spouse file  **
**	****	****Family files**
**Analyses were weighted.**
**Sudaan was used for variance estimation.**

**Notes:**
Today we will focus on preliminary findings regarding working-age adults, ages 18-64 years.  

We use the Person files and Family files from the 1998-2000 NHIS.  In addition to using characteristics describing the person, we also use the Person files to create a Spouse file containing characteristics defining spouses, if one exists.  So far, all our models utilize variables from the public use files.  

All analyses are appropriately weighted.  Because we are using three years of data, we divide the weight variable for each observation by 3.  In addition, SAS-calleable Sudaan is used to adjust for the NHIS’ complex sampling design. 

## Subgroups included in Analyses

**	****Mexican-Americans**
**	****Puerto Ricans**
**	****Cubans**
**	****Other Latin Americans**
**	****Multiple or non-specified Hispanics**
**	****Non-Hispanic blacks**
**	****Non-Hispanic whites  **
**	****    **

**Notes:**
Race is based on the respondent’s description of his or her racial background (or, in the case of proxy responses, the family respondent’s description of each family member’s racial background).  Respondents are also asked whether they are of Hispanic origin.  Since race and ethnicity information are obtained separately during the course of the interview, Hispanics may be of any race.  For our analysis, any respondent who is of Hispanic origin is coded as Hispanic regardless of their race.  

Regarding Hispanic subgroups, we distinguish between Mexican-Americans, Puerto Ricans, Cubans, other Latin Americans, and multiple or non-specified Hispanics.
1998 NHIS – Other Latin Americans were coded to a single “Other Latin American” category;
1999-2000 NHIS – This “Other Latin American” category is further refined, and distinguishes between Hispanic persons who are from the Dominican Republic, Central or South America, and then “Other Latin American, type not specified”.  We combined these three categories into one category in order to make the 1999-2000 NHIS comparable with the 1998 NHIS, for merging purposes.  

The final Hispanic subgroup includes persons coded in the data as “Multiple Hispanic”, “Other Spanish”, or “Hispanic/Spanish, non-specific type”.  Each of these categories is quite small numerically, so we combined them into a single category for analytic purposes.  Note that a small number of Hispanics with unknown origin group were excluded from the Hispanic subgroup analyses. 

## Health Insurance Coverage Status

**   ****Not covered**
**   ****Covered**
**   ****Refused, Not ascertained, Don’t know**
** **

**Notes:**
The 1998-2000 NHIS health insurance section in the Person file contains a variables called NOTCOV (or FHI.070), which indicates coverage status as used in NCHS’ annual publication Health US.  

	1 = Not covered;
	2 = Covered;
	7-9 = Refused, Not ascertained, Don’t know

NOTCOV is based on the FHI recodes (e.g., PRIVATE, MEDICARE, MEDICAID, OTHERPUB, OTHERGOV, MILITARY, etc).  Private plans that provide only extra cash while hospitalized, or pay for only one type of service – such as nursing home care, accidents, or dental care – are not considered as coverage.  Additionally, persons with Indian Health Service coverage only are considered uninsured. 

Our analyses consist of logistic regressions, in which we are predicting the probability of not having health insurance coverage.  By necessity, the 7-9 cases are not included in our analyses.  

## Non-insurance by subgroup type, NHIS, 1998-2000Adults 18 years of age and over, age-adjusted percents

**Non-insurance by subgroup type, ****NHIS, 1998-2000****Adults 18 years of age and over, age-adjusted percents**

**Notes:**
These are age-adjusted percents from our frequency table analysis showing the extent of non-coverage among Hispanic subgroups relative to non-Hispanics.  We age-adjust our percents to control for the young age structure of the Hispanic population; these percents use the 2000 non-Hispanic white population age structure as the standard.  Among working-age adults, men are more likely than women to lack health insurance coverage regardless of race/ethnicity.  And clearly, Mexican-American and Other Latin Americans are more likely to lack coverage than persons in the other Hispanic subgroups, or non-Hispanics.  Specifically, 38% of Mexican-American men and 38% of Other Latin American men lacked coverage, compared with 13% of non-Hispanic white men.  Among women, 34% of Mexican-American women and 35% of Other Latin American women lacked coverage, compared with 11% of non-Hispanic white women.

## Immigrant-Citizenship status

**	**
**Not US born, not US citizen (6%)**
**Not US born, naturalized US citizen (5%) **
**Unknown (1%)**
**US born, US citizen (88%) ****– ****baseline**
**	**
** **

**Notes:**
We control for a number of different characteristics in our models.  Two of the more important controls are immigrant-citizenship status, and employment status.  The NHIS obtains information regarding each respondent’s country of origin and citizenship status.  We form an indicator that distinguishes respondents born in one of the 50 states or the District of Columbia (who are thus citizens) from foreign-born naturalized citizens and foreign-born persons who are not citizens of the US.  We also include a category for those respondents whose immigrant status and/or citizenship is unknown.  The percents on the slide indicate the breakdown of NHIS respondents into the four categories.  

## Employment status 

**Full-time/workplace insurance offered**
**Full-time/workplace insurance not offered**
**Full-time/workplace insurance unknown**
**Part-time/workplace insurance offered**
**Part-time/workplace insurance not offered**
**Part-time/workplace insurance unknown**
**Employment status unknown**
**Not employed ****– ****baseline**

**Notes:**
Regarding employment status, we create an indicator based on three variables in the NHIS: the respondent’s employment in the week prior to the interview, the number of hours worked during that week, and whether the respondent’s workplace offered insurance coverage.  We take the three variables together and create what amounts to 8 dummy variables.  Note also that while we form 8 variables, only 7 are included in our model: the 8th variable indicating unemployed persons is the baseline or reference category and is held out of the model for that reason.  

## Variables in adult model

** **

**	****Subgroup type ****	****	****	****Age**
**	****Sex ****	****	****	****	****Immigrant-citizenship status**
**	****Health status ****	****	****	****Family’s health **
**	****Family income****	****	****	****Poverty status****	**
**	****Region****	****	****	****	****MSA****	**
**	****Marital status****	****	****	****Family size**
**	****Education (own or spouse’s)****	****Home tenure status **
**	****Employment status**
**	****Spouse’s workplace offers insurance coverage**
**	**

**Notes:**
This is a list of the various characteristics that we control for in our model.  Our AGE variable consists of three categories: persons 18-30 years of age, 31-45 years of age, and 46-64 years of age.  HEALTH STATUS refers to the person’s own health and has five categories: excellent, very good, good, fair, and poor.  In addition to individual health status, we include a dummy variable indicating those persons living in families with 1 or more family members in fair or poor health.  POVERTY STATUS is based on family income and family size using the Census Bureau's poverty thresholds.  We include three categories in our models:  "Poor" persons are defined as below the poverty threshold.  "Near poor" persons have incomes of 100%  to less than 200% of the poverty threshold.  "Not poor" persons have incomes that are 200% or greater than the poverty threshold.  REGION consists of four categories: Northeast, Midwest, South, and West.   MSA refers to Metropolitan Statistical Area.  We distinguish between Large MSAs, or central cities, smaller MSAs that are not central cities, and non-MSAs.   

## Results 

_**Characteristic**_**	****	****	**_**Odds Ratio**_**	****            ****	**_**C.I.**__**        **_
**Male****	****	****	****	****    1.29****	****    ****	****    (1.25, 1.33)**
**Baseline: Females**
**18-30 years of age****	****	****    1.83****	****    ****	****    (1.72, 1.94)**
**31-45 years of age ****	****   ****	****    1.67****	****    ****	****    (1.59, 1.76)**
**Baseline: Persons 46-64 years of age**
**Divorced/Separated****	****    ****	****    1.31****	****    ****	****    (1.22, 1.39)**
**Living with a partner ****	****    1.91****	****    ****	****    (1.77, 2.06)**
**Baseline: Married persons**

**Notes:**
Again, keep in mind that we are predicting the probability of NOT having insurance coverage.  

Males were 1.3 times more likely than females to lack health insurance coverage.

Younger adults were more likely to lack health insurance coverage than older adults.  Adults aged 18-30 years of age were 1.8 times more likely to lack insurance than adults 46-64 years of age, the baseline or reference group.  

Regarding marital status, we had additional categories for widowed and never married persons, but they proved to be statistically indistinguishable from married persons, who served as the baseline category. 

_**Characteristic**_**	****	**_**Odds Ratio**_**	****             **_**C.I.**__**     **_
**Mexican-American****	****    ****	****    1.53****	****    ****	****    (1.41, 1.66)**
**Other Latin American****	****    1.32****	****    ****	****    (1.17, 1.49)**
**Mult/Non-sp Hispanic****	****    1.41 ****	****    (1.24, 1.61)**
**Non-Hispanic black****	****    ****	****    1.16                 (1.09, 1.23)**
**Baseline: Non-Hispanic white adults**
**Language of Interview – **
**Spanish****	****	****                 1.45                (1.31, 1.61)**
**Baseline: English**** **
**Not US born/not citizen****	****    1.91****	****    ****	****    (1.77, 2.06)**
**Baseline: US born persons**

**Notes:**
In our full model, Mexican-Americans were 1.5 times more likely than non-Hispanic white adults to lack coverage.  Note that Cuban and Puerto Rican adults were statistically no different than non-Hispanic whites with respect to the likelihood of having coverage.  

Similarly, if Spanish was the language used during the interview, this increased the odds of being without health insurance coverage by almost 1.5 times relative to interviews when only English was spoken.  

Also, not being US born and not being a citizen resulted in a sizable increase in the odds of not having health insurance coverage.  Persons not born in the US who were naturalized citizens were no more likely than US born adults to lack coverage.  

_**Characteristic**_**	****	**_**Odds Ratio**_**	****             **_**C.I.**__**     **_
_**   **_
**Less than HS education****	****    2.46****	****      ****	****    (2.29, 2.65)**
**GED****	****	****       ****	****	****    2.90****	****                (2.60, 3.25)**
**HS Graduate****	****	****    ****	****    2.20 ****	****    (2.05, 2.36)**
**Some college****	****	****    ****	****    1.51                 (1.41, 1.62)**
**Baseline: Persons with a college degree**

**Notes:**
Clearly, educational attainment is strongly associated with health insurance coverage status.  High school dropouts were nearly 2.5 times more likely than the college educated to lack coverage; even persons with some college were 1.5 times more likely to be without coverage than the college educated. 

_**Characteristic**_**	****	**_**Odds Ratio**_**	****             **_**C.I.**_
**Near poor ****	****	****	****    1.40****	****    ****	****    (1.30, 1.51)**
**Baseline: Not poor persons**
**Less than $20,000****	****	****    2.70****	****    ****	****    (2.41, 3.03)**
**$20,000-$34,999   ****	****	****    2.55****	****    ****	****    (2.36, 2.76)**
**$35,000-$54,999****	****	****    1.73 ****	****    (1.60, 1.87)**
**Baseline: Persons w/ family incomes equal/above $55,000**

**Notes:**
Similarly, income was strongly associated with health insurance coverage status.  Persons living in a family with a combined income of less than $20,000 were 2.7 times more likely to lack coverage than persons with a family income greater than or equal to $55,000.  

Somewhat surprisingly, poverty status added less insight to our models.  Persons in “near poor” families – that is, persons in families with incomes of 100%  to less than 200% of the poverty threshold – were 1.4 times more likely to lack coverage than “not poor” persons (those 200% or greater than the poverty threshold).  “Poor” persons –  those strictly below the poverty line –  were not any more likely to lack coverage than “not poor” persons.

_**Characteristic**_**	****	**_**Odds Ratio**_**	****             **_**C.I.        **_
**Person in fair health****	****    ****	****    0.81****	****    ****	****   (0.75, 0.88)**
**Person in poor health****	****    0.51****	****    ****	****   (0.45, 0.58)**
**Baseline: Persons in excellent health**
**1+ family members in **
**fair/poor health****	****	****    1.23   ****	****    (1.12, 1.36)**
**Baseline: Persons in families whose members are in good/very good/excellent health**

**Notes:**
Persons in fair or poor health were more likely to be insured relative to persons in excellent health.  However, note that if one is living in a family with one or more persons in fair or poor health, one is somewhat more likely to be uninsured. 

_**Characteristic**_**	****	**_**Odds Ratio**_**	****             **_**C.I.        **_
**Emp FT, wrkplace ins**
**offered****	****	**** ****	****    0.23****	****    ****	****      (0.22, 0.25)**
**Emp FT, wrkplace ins **
**not offered****	**** ****	****	****    2.59****	****    ****	****      (2.46, 2.73)**
**Emp PT, wrkplace ins**
**offered****	****	**** ****	****    0.43****	****    ****	****      (0.39, 0.47)**
**Emp PT, wrkplace ins **
**not offered****	**** ****	****	****    1.47****	****    ****	****      (1.39, 1.56)**
**Baseline: Non-employed persons**
** **

**Notes:**
Not surprisingly, employment status and whether or not one’s workplace offers insurance coverage were strongly related to coverage status.  Persons working full or part time in a workplace that offered health insurance coverage were considerably more likely than non-employed persons to have health insurance coverage, while persons working in a workplace that did not offer health insurance coverage were more likely to be without it.  

_**Characteristic**_**	****	****	**_**Odds Ratio**_**	****             **_**C.I.        **_
**Spouse’s wrkplace **
**offers ins****	****	****  ****	****   0.34****	****   ****	**** (0.32, 0.36)**
**Spouse’s wrkplace**
**does not offer ins****	****  ****	****   1.50****	****   ****	**** (1.42, 1.59)**
**Baseline: Unmarried persons**
**South region****	****	****    ****	****   1.65   ****	**** (1.54, 1.77)**
**West region****	****	****	****   1.22****	****    ****	**** (1.12, 1.32)**
**Baseline: Persons residing in the Northeast**

**Notes:**
If the respondent was married and his or her spouse was employed in a workplace that offered health insurance coverage, again, he or she was considerably more likely to have coverage than unmarried persons.  However, if the spouse’s workplace did not offer health insurance benefits, one is 1.5 times more likely to lack coverage than unmarried persons.  

As with previous research, we found that persons living in the South and West regions of the United States were more likely to lack health insurance than persons living in the Northeast.  

_**Characteristic**_**	****	****	**_**Odds Ratio**_**	****             **_**C.I.        **_
**Mexican male, emp FT,**
**no wrkplace ins****	****  ****	****   1.86****	****    ****	**** (1.64, 2.11)**
**Baseline: All other persons (except Otr LA males, working FT, no wrkplace ins offered)**
**Otr LA male, emp FT,**
**no wrkplace ins ****	****    ****	****   1.65****	****    ****	**** (1.35, 2.02)**
**Baseline: All other persons (except Mexican-American males, working FT, no wrkplace ins offered)**
** **

**Notes:**
Finally, we are just starting to consider interaction terms, so these are preliminary findings.  Mexican males and Other Latin American males who were employed full time in a workplace that did not offer health insurance were more likely to lack coverage than all other persons.  

## Conclusions

**Cubans and Puerto Ricans**
**	****no more likely than non-Hispanic white adults to lack health insurance coverage**** **
**Mexican Americans, Other Latin Americans, and Hispanics who did not specify a particular origin or specified multiple Hispanic origins **
**	****significantly more likely than non-Hispanic whites to lack coverage****  **
**Introduction of relevant controls does not “explain away” this effect.**

**Notes:**
To conclude, Cubans and Puerto Ricans were no more likely than non-Hispanic white adults to lack health insurance coverage. 

Mexican Americans, Other Latin Americans, and Hispanics who did not specify a particular origin or specified multiple Hispanic origins were significantly more likely than non-Hispanic whites to lack coverage  

Introduction of relevant controls does not “explain away” this effect, which has proven to be robust in all our models.  The most potent predictors of health insurance coverage were the employment status/insurance offered in the workplace variables.

Regarding our future research plans, in addition to fleshing out interaction effects, we intend to drop the 1998 data and replace it with the 2001 data when they become available later this year, in order to obtain more information about Hispanic subgroups.

## Where to get more info about the NHIS....

***	******NHIS web-site:***
**	****http://www.cdc.gov/nchs/nhis.htm**
***	******To join the NHIS listserve: ***
**	****go to http://www.cdc.gov/subscribe.html**
**	****and follow instructions.**