How old is too old for medicaid

These programs are managed by each state rather than the federal government. Contact your state's Medicaid program to report your issue. Ask a real person any government-related question for free. They'll get you the answer or let you know where to find it. Medicaid is a federal and state health insurance program for people with a low income.

To be eligible, the child's family must have an income that is: Too high to qualify for Medicaid Too low to afford private coverage Medicaid and CHIP program names are different in each state. What help is available through Medicaid? Medicaid provides free or low-cost medical benefits to eligible: Adults with a low income Children Pregnant women People who are age 65 or over People with disabilities Am I eligible for Medicaid?

Fraction of people with at least two ADL impairments who are single upper panel and in couples lower panel after age 75, by age, cohort and permanent income. However, once people are in the dataset, they stay in the dataset as long as they are alive, including when institutionalised.

Because of this sample design, it is important to mention two things. First, the set of people that we initially observe at each age tends to be healthier than the representative population of the same age, and this selection is especially pronounced at older ages when the probability of being sick and in a nursing home or hospital is higher.

Second, as people in the same cohort age, their health tends to revert to the mean to some extent, thus lessening this initial selection problem. As a result of these features of the survey design, our cohort outcomes are different not only because of cohort effects, but also because of the differential selection by age and over time. Because Medicaid recipiency depends on income, assets and health in that it provides health insurance, in this section we show some key facts on Medicaid recipiency, net worth and health by age, cohort, marital status and permanent income.

In Online Appendix B, we show that the results by education are very similar to those by permanent income. It is important to distinguish between couples and singles for several reasons. First, important differences in wealth, income and health have been documented between couples and singles in the US data.

Second, the death of a spouse has been associated with spikes in medical expenditures and large drops in assets for the surviving spouse. First, there is a big gap in Medicaid recipiency between the people in the bottom PI tercile and the people in the two higher PI terciles.

The fraction of people receiving Medicaid in the lower PI tercile starts higher at age 76, at 33 per cent, compared with under 3 per cent for the singles in the second and third PI terciles, and it grows fast with age, reaching 60 per cent for those who survive to age Second, the fraction of survivors receiving Medicaid in the second and third PI terciles also rises significantly, going from 3 per cent at age 76 to 25 per cent at age 99 for those in the second PI tercile, and from 1 per cent at age 76 to 10 per cent at age 99 for those in the third PI tercile.

These findings confirm those by De Nardi, French and Jones a , even though they used different PI bins and different cohorts. Thus, although Medicaid, as intended, is a programme that mainly helps the elderly poor, even the elderly in the top two PI groups often receive benefits if they live long enough. The fraction of people in couples in the lowest PI tercile receiving Medicaid at age 76 is 15 per cent, which is less than half of the corresponding fraction for singles in the lowest PI tercile; however, this number climbs fast as the survivors age, reaching 60 per cent, as for singles.

Finally, the fraction of individuals in couples in the two highest PI terciles who are receiving Medicaid is lower than the corresponding terciles for singles and well below the fraction for singles at all ages. The first thing to notice compared with the Medicaid graphs that we have just discussed is that people in the lowest income tercile have the highest Medicaid recipiency and the lowest assets.

Similarly, median assets tend to be higher for people with higher permanent income for each cohort and age. Because net worth is only measured at the household level, we plot the net worth of male individuals to avoid duplicating the same family unit. There are several things worth noticing. First, couples tend to start out in our sample with more household assets than their single counterparts. Second, with the exception of those in the lowest PI tercile, couples also tend to hold more assets as they age.

Thus, although couples do not start out with twice as much in assets as singles, those in the two highest income terciles who survive with their spouse to very old ages have almost twice the assets of the surviving singles. In contrast, couples with low permanent income seem to rely on government transfers as much as singles once they reach a very advanced age.

Online Appendix C reports the graphs for median wealth when the main residence is excluded from net worth. They show that median liquid assets of those in the lowest PI tercile are zero at age 76 and remain at zero for both couples and singles. In contrast, the liquid assets of those in the highest PI tercile start out high at age 76, remain substantial at very advanced ages, and exhibit less decumulation by couples than by singles.

Although health is not, per se, a criteria to be eligible for Medicaid, Medicaid provides good and services to the unhealthy based on various health measures.

For instance, to be eligible for a Medicaid nursing home, an individual needs to satisfy criteria based on ADL impairments, and high medical expenses are required to be medically eligible. Hence, we also describe the evolution of health after age 75 for our subgroups. To do so, we look at three different measures of health and we mainly report results on ADLs in this section. We report more results on other health measures in Online Appendix D. The ADL variable that we use is based on indicators of difficulties performing six basic tasks: eating, dressing, walking across a room, getting in and out of bed, bathing and using the toilet.

It shows that the fraction of people with ADLs at age 76 is under 10 per cent for all PI terciles, and that it increases fast by age, surpassing 50 per cent for those who survive past age A similar pattern holds for people in couples lower panel. In this section, we first analyse the probability of receiving Medicaid in the context of a descriptive multivariate analysis.

Then, we use the regressions results to study the implications of Medicaid rules, other observable factors, and their interactions in determining Medicaid eligibility. To study the probability of receiving Medicaid, and its determinants in the context of a descriptive multivariate analysis, we estimate a logistic probability model, with a binary dependent variable equal to 1 if the individual is covered by Medicaid, and zero otherwise.

We include a broad set of explanatory variables to identify the main factors influencing the probability of receiving Medicaid. Descriptive statistics of the variables used in the analysis are shown in Online Appendix E. In Table 1 , we present the average marginal effects for each variable included. These are computed by leaving all the other explanatory variables at their observed values, starting in column 1 with a specification that includes all the variables just described.

As the estimated specification includes many interactions terms, in the table we report the average marginal effects for the variables included, while in Online Appendix E we report the complete table of the coefficients. The results in column 1 show that the PI percentile our measure of permanent income has a large impact on the probability of receiving Medicaid and one additional percentile reduces this probability, on average, by 0.

This may be surprising because, in many circumstances, an individual with a home can be eligible for Medicaid, whereas an individual with more than a small amount of liquid assets is not eligible. Predictors of Medicaid recipiency: average marginal effects resulting from logistic estimates.

Clustered standard errors at the individual level are given in parentheses. Among the variables capturing health, reporting poor or fair health increases the probability of receiving Medicaid by about 1.

Having two or more ADL impairments increases the probability by 6. The dummy capturing current residency in a nursing home also has a large and positive effect, on average, increasing the probability of receiving Medicaid by 15 percentage points.

As for the other factors affecting Medicaid recipiency, older age, conditional on the included covariates, increases the probability of receiving Medicaid, with an average marginal effect of about 0.

As for family structure, we find that being a single woman increases the probability of receiving Medicaid by about 3 percentage points, on average, relative to all other family structures. Being white reduces this probability by 4 percentage points, on average, while the number of children has a positive although small effect, with the probability of receiving Medicaid increasing by 0.

Census division turns out to be a significant predictor. We also include the education level, which has a significant and negative effect, even conditional on permanent income and wealth. For instance, having a college degree reduces the probability of receiving Medicaid by almost 7 percentage points.

Its marginal effect is precisely estimated and indicates, for example, that an increase of days in a stay increases the probability of receiving Medicaid by 2 percentage points, on average. The effect and significance of all other variables are unchanged when using the number of days in a nursing home rather than being in a nursing home. To better quantify our results, we start by showing the average predicted probability of receiving Medicaid, plotted as a function of the variables that capture the rules governing eligibility: permanent income and wealth.

Then, we look at the other observable factors and their interactions with various Medicaid rules, including health. To be more precise, we report the average predicted probabilities as a function of that variable alone, with all other characteristics held constant. More specifically, we take our sample of people and we apply their own other observable characteristics and regression coefficients when one variable e.

PI is changed from the lowest to the highest level. Then, at each PI level, we compute the average probability of receiving Medicaid, integrating over all other characteristics other than the one that we are considering.

The vertical bars refer to the 95 per cent confidence interval. We use estimates from the specification shown in column 2 of Table 1 ; figures plotted using coefficients from column 1 are virtually identical.

Subsequent points are computed in a similar way. The figure shows that the average predicted probability of receiving Medicaid is a negative function of PI, ranging from 28 per cent at the first percentile, declining fast as PI increases, and reaching 2 per cent at the highest PI percentile. The average marginal effect of PI is equal to 0. Increasing PI from the first to the tenth percentile, for example, reduces the probability of receiving Medicaid by 6.

The effect is small but still sizeable even at the upper end of the distribution, where an increase of PI from the 90 th to the top percentile reduces the probability of receiving Medicaid by half a percentage point. The average predicted probability of receiving Medicaid turns out to be 16 per cent for housing wealth equal to zero, and then declines gradually with wealth to 2 per cent.

The marginal effect, which on average is about 0. Effect of PI upper panel and of housing wealth lower panel on the probability of receiving Medicaid. Note : Average predicted probability of Medicaid receipt, using the estimates in Table 1 , holding all other variables at their observed values.

In the upper panel, the probability of receiving Medicaid is plotted as a function of the number of nights spent in a nursing home over the previous two years. The average predicted probability of receiving Medicaid is, on average, 13 per cent when the number of nights is equal to zero, and this grows to 38 per cent when the number of nights is , or two years.

In the lower panel, the probability of receiving Medicaid is plotted as a function of age. The average probability is also increasing in age, increasing from about 12 per cent at age 72 to 19 per cent at age Effect of the number of nights in a nursing home upper panel and age lower panel on the probability of receiving Medicaid. Our estimates have shown that the interactions between PI percentile, health and other characteristics are quantitatively important.

Especially at low income percentiles, the effect of this variable is sizeable, increasing the predicted probability of receiving Medicaid from 26 to 35 per cent. Although, at the upper end of the permanent income distribution, its effect is much smaller in absolute terms e. Thus, it implies that the probability of receiving Medicaid doubles in the presence of two or more ADLs.

Effect of ADLs upper panel and the number of nights in a nursing home lower panel on the probability of receiving Medicaid, as a function of PI percentile. The average predicted probability when the number of nights spent in a nursing home is zero goes from 27 per cent at the lowest PI percentile to 2 per cent at the highest PI percentile.

When the number of nights spent in a nursing home is , the average predicted probability of receiving Medicaid increases to 32 per cent at the lowest PI percentile and to 22 per cent at the 30 th PI percentile. For stays that are as long as two years, the average predicted probability increases dramatically, reaching 37 per cent at the lowest PI percentile, 44 per cent at the 30 th percentile and 13 per cent at the 80 th percentile. Hence, longer stays in a nursing home substantially increase the probability of receiving Medicaid.

This effect is especially large between the 2 nd and 8 th PI deciles. We also show the average predicted probabilities by permanent income and variables capturing other factors that influence the probability of receiving Medicaid.

The marginal effect of being a single woman, relative to the reference category of being a married man, is the difference between the two functions. As is apparent from the figure, being a single woman statistically significantly raises the probability of receiving Medicaid, with respect to the reference category, but only in the three lowest PI deciles. In the lowest PI percentile, the probability of receiving Medicaid is 0. In general, you should apply for Medicaid if you have limited income and resources.

You must match one of the descriptions below:. Apply for Medicaid if you think you are pregnant. You may be eligible if you are married or single. If you are on Medicaid when your child is born, both you and your child will be covered. Apply for Medicaid if you are the parent or guardian of a child who is 18 years old or younger and your family's income is limited, or if your child is sick enough to need nursing home care, but could stay home with good quality care at home.

If you are a teenager living on your own, the state may allow you to apply for Medicaid on your own behalf or any adult may apply for you. Many states also cover children up to age Apply if you are aged 65 years old or older , blind, or disabled and have limited income and resources. Apply if you are terminally ill and want to get hospice services.