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Address correspondence to: Brandy J. Lipton, PhD, San Diego State University, School of Public Health, 5500 Campanile Drive, Hardy Tower Room 11, San Diego, CA 92182
Many low-income children do not receive regular vision care despite the fact that all state Medicaid programs cover these services. The primary objective of this study was to examine whether children were more likely to have at least one past-year eye doctor visit when their parents gained Medicaid vision benefits. Other indicators of child vision care access (ie, unmet needs for glasses and difficulty seeing) and eye doctor visits among Medicaid-enrolled parents were also assessed.
Methods
Difference-in-differences regression analysis leveraged within-state changes to Medicaid adult vision benefits. Study samples included 17,345 children with a Medicaid-enrolled parent and 12,219 parents with Medicaid coverage interviewed during the 2000 to 2013 National Health Interview Survey.
Results
Providing Medicaid adult vision coverage was associated with a 5.4 percentage point increase (P = .009) in having at least one past-year eye doctor visit among parents and a 2.8 percentage point increase (P = .01) in this measure among children. These estimates represent increases of 22% and 14%, respectively, relative to unadjusted parent and child visit rates over the study period. These effects appeared to be concentrated among older children ages 12 to 17. Estimates for the other measures of child access to vision care were not statistically significant.
Conclusions
Providing adult vision benefits was associated with having at least one past-year eye doctor visit among low-income children, and may help to reduce income-based disparities in children's receipt of vision care. This research adds to the limited evidence base on the role of public policy in increasing access to vision services.
All state Medicaid programs cover vision exams and eyeglasses for children, but income-based disparities in the use of these services persist. This study finds that providing parental Medicaid vision benefits may increase the use of vision care among low-income children.
Low-income children are more likely to have unmet vision care needs than their higher income peers, with one study finding a more than sixfold difference in unmet needs for glasses.
The impact of providing vision screening and free eyeglasses on academic outcomes: evidence from a randomized trial in title I elementary schools in Florida.
Dudovitz RN, Sim MS, Elashoff D, et al. Receipt of corrective lenses and academic performance of low-income students. (in press). Acad Pediatr. doi: 10.1016/j.acap.2020.01.001.
suggesting that these disparities may have important and lasting consequences on health and human capital development. The prevalence of uncorrected refractive error is 10% to 11% among children ages 12 and older, higher than most other age groups.
Large disparities and high rates of baseline need present an opportunity for policy intervention, however there is little research examining the potential for public health insurance policy to increase access to vision care among children.
While all state Medicaid programs cover eye exams and glasses for children through the Early and Periodic Screening, Diagnostic, and Treatment (EPSDT) benefit, many eligible children do not receive regular and complete screenings suggesting that direct costs are not the only barrier to receipt of recommended care.
For example, lack of parental awareness, transportation costs, and difficulty in taking time off from work or school, among other factors, may prevent a child from visiting an eye doctor. One qualitative study found that a parent's belief that a child did not need glasses was one of several key factors in explaining why children did not obtain needed vision correction.
Use of eyeglasses among children in elementary school: perceptions, behaviors, and interventions discussed by parents, school nurses, and teachers during focus groups.
States are not required to cover vision benefits for adults, and have added and dropped these benefits over time. According to recent data from the Kaiser Family Foundation, 33 states covered exams and glasses for adults in 2018, though some of these coverage policies were restrictive.
Kaiser Family Foundation Medicaid benefits: eyeglasses and other visual aids.
State Health Facts.2020; (Available at: https://www.kff.org/medicaid/state-indicator/eyeglasses/?currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D. Accessed September 9, 2020)
For example, some states limited coverage to postcataract surgery lenses or other medical necessities.
The fact that Medicaid vision benefits are required for children but optional for adults enables study of the connection between parent and child receipt of vision services. Past research demonstrates that vision benefits are associated with increased vision care use among Medicaid-enrolled adults, but it is unknown whether the addition of a benefit for adults is associated with increases in eye care visits among their children.
The effect of Medicaid adult vision coverage on the likelihood of appropriate correction of distance vision: evidence from the National Health and Nutrition Examination Survey.
Adult Medicaid benefit generosity and receipt of recommended health services among low-income children: the spillover effects of Medicaid adult dental coverage expansions.
For example, studies have found that extending public health insurance eligibility to parents is associated with an increased likelihood of coverage among already eligible children.
Adult Medicaid benefit generosity and receipt of recommended health services among low-income children: the spillover effects of Medicaid adult dental coverage expansions.
While none of these studies examine vision care, they provide support for the notion that covering vision services for adults may increase vision care use among their children. This study adds to the extremely limited evidence base on the role of public policy in increasing vision care access among low-income children.
Methods
Study Design and Data Sources
This study used a difference-in-differences design that leveraged within-state changes in adult vision coverage policies during 2000 to 2013. The analysis period captured a number of state-level changes to vision benefits, ending prior to implementation of most Affordable Care Act (ACA) provisions in 2014. This selection avoided most confounding changes due to the ACA's Medicaid expansion.
Policies were linked with a restricted version of the National Health Interview Survey (NHIS), a nationally representative, repeated cross-sectional US household survey. Interviews are conducted with each member of selected households, and within each household, one adult and one child are sampled to complete a more detailed survey that includes a question about past-year eye doctor visits.
Vision coverage policies were obtained from the Kaiser Family Foundation, and are described in previous research.
The effect of Medicaid adult vision coverage on the likelihood of appropriate correction of distance vision: evidence from the National Health and Nutrition Examination Survey.
States were considered as providing benefits if they covered both an exam and glasses for correction of refractive error since glasses account for the majority of the costs of refraction.
During the study period, 13 states changed their policies according to this definition including California, Florida, Idaho, Massachusetts, Michigan, Missouri, Nevada, New Mexico, North Carolina, Oregon, Texas, Utah, and Washington (Table 1). Appendix Figure S1 shows child eye doctor visit rates over time in 2 larger states (California and Florida). States that did not change their vision coverage policies during the study period were included in the analysis as a control group. Since states changed their policies at different times, states with changes early and late in the study period also served as controls for each other. Significant variation in the timing of policy changes can bias difference-in-differences estimates.
As shown in Table 1, 6 of the 13 states changed their policies between 2009 and 2011.
Table 1State Vision Coverage Policies, 2000–2013
Never covered vision (17 states)
AZ, CO, DE, GA, KY, LA, MD, ME, MT, OK, PA, SC, TN, VT, VA, WV, WY
Always covered vision (21 states)
AL, AK, AR, CT, DC, HI, IL, IN, IA, KS, MN, MS, NE, NH, NJ, NY, ND, OH, RI, SD, WI
Dropped vision (6 states)
CA (7/1/2009), ID (7/1/2011), NM (1/1/2010), NC (10/1/2011), OR (1/1/2010), WA (7/1/2011)
Dropped and added vision (7 states)
FL (dropped 7/1/2003, added 7/1/2006), MA (dropped 10/1/2002, added 10/1/2006), MI (dropped 10/1/2008, added 10/1/2012), MO (added 7/1/2003, dropped 7/1/2005, added 7/1/2006), NV (dropped 7/1/2008, added 7/1/2009), TX (dropped 9/1/2004, added 9/1/2006), UT (dropped 2/1/2003, added 7/1/2005, dropped 7/1/2006)
Notes: Author's analysis of data from the Kaiser Family Foundation. States are considered as providing vision coverage if they cover both exams and glasses for correction of refractive error for nonelderly, nondisabled, and nonpregnant adults. States that cover these services after cataract surgery or for other medical necessities only are not considered as providing coverage. One state (ME) covered one pair of glasses per lifetime during the study period, but was not considered as providing coverage given the restrictive nature of the coverage.
While states both added and dropped coverage during the study period, more of these changes were coverage drops. Figure 1 shows the percentage of children with a parent with Medicaid vision coverage, a measure of exposure to the policy. This percentage declined from about 78% to 54% over the study period, demonstrating that more parents lost than gained vision coverage.
Figure 1Percentage of children with a parent with Medicaid adult vision coverage, NHIS 2000–2013.
Notes: Author's analysis of 2000-2013 National Health Interview Survey. Estimates represent the weighted percentage of children in the main analysis sample residing in a state that offered coverage of adult eye exams and eyeglasses for at least 6 months of the year prior to the NHIS interview date.
This study was deemed nonhuman subjects research after an initial screening by San Diego State University's Institutional Review Board (IRB), and therefore was not subject to IRB review.
Study Population
The main analysis sample included children ages 1 to 17 with complete demographic and eye doctor visit information who could be linked with a nonelderly, Medicaid-enrolled parent aged 22 to 64 and therefore ineligible for age-based EPSDT benefits in the past year. Only sampled children and adults responded to the questions about eye care use. However, Medicaid status was available for all adults, and therefore a child could be included in the sample even if the sampled adult was not her parent. Families who reported receipt of supplemental security income in the past year were excluded since vision benefits can differ by eligibility category. The final sample included 17,345 children, with the vast majority (94%) enrolled in Medicaid and CHIP. The sample for the parent analysis, defined analogously, included 12,219 adults. Only about 3% of the parent sample reported CHIP enrollment and therefore parent Medicaid coverage is referenced throughout the text.
Appendix Table S1 describes the characteristics of the child sample overall and by whether the child's parent had Medicaid vision coverage at the time of the interview. Overall, about half of the sample was male and nearly all (96%) were US citizens. The average age was about 8 years and most (83%) resided in an urban area. About 37% were non-Hispanic white, 29% were non-Hispanic black, 29% were Hispanic, and 5% were another race. Most characteristics were similar across states with and without vision coverage at a given point in time.
Outcomes and Covariates
The main outcome was an indicator for whether a child had visited an eye doctor in the past year (ie, “optometrist, ophthalmologist, or…someone who prescribes eyeglasses”). Secondary outcomes included indicators for unmet needs for glasses due to cost and difficulty seeing with usual vision correction among children, and past-year eye doctor visits among parents. Parent eye doctor visits were assessed to provide context for the child results.
The primary independent variable was an indicator for adult vision coverage equal to one in states that covered an exam and glasses versus an exam only or neither service for at least 6 months of the year prior to the NHIS interview date. Most states that covered exams also covered glasses during the study period, and no states covered glasses but not exams.
Other covariates included demographic controls, state-level policy and population variables, and state and year indicators. Demographic controls for the child analysis included sex, age, race/ethnicity, citizenship, urban area, the number of minor children in the household, and maternal characteristics including citizenship, education, employment, and marital status. Demographic controls for the parent analysis included sex, age, race/ethnicity, citizenship, education, marital status, urban area, and the number of minor children. State-by-year controls in all analyses included the maximum temporary assistance for needy families benefit for a family of four, state earned income tax credit as a proportion of the federal benefit, number of primary care providers per capita, unemployment rate, and maximum supplemental nutrition assistance program benefit for a family of four. These controls were obtained from the University of Kentucky Center for Poverty Research and Area Health Resources File.
Statistical Analysis
Multivariable regression models controlled for the adult vision coverage indicator and the individual demographic and state-level variables described above. The analysis leveraged all state-level changes by assuming that adding and dropping vision coverage had symmetric effects. The state-level variables controlled for specific time-varying characteristics and state indicators accounted for fixed characteristics that could be correlated with both vision coverage policies and outcomes. Year indicators accounted for secular trends in each outcome over time. Similar to most research using difference-in-differences methods,
linear probability models allowed for interpretation of estimates as percentage point effects. These models perform well relative to logit models under a range of sample size and error distribution assumptions.
However, marginal effects estimates from logit models for key estimates are provided in the appendix. Model errors were clustered at the state level to account for serial correlation in the policy variable.
Several sensitivity and placebo tests were conducted to assess the robustness of the results. First, an event history model was estimated to assess whether changes in the rate of having a past-year eye doctor visit preceded state vision coverage policy changes. Significant estimates in the period before a policy change would indicate that the main analysis could be biased. This analysis included 6 states that changed their vision coverage policies once during the study period (Calif, Idaho, NM, NC, Ore, and Wash) and states with no policy change as a control group. Since these 6 states all changed their coverage policies between 2009 and 2011, this analysis also limited the extent of differences in the timing of policy changes and therefore served as an additional check on the robustness of the results.
An additional test compared linear yearly trends in 8 states that changed their coverage policies after July 2008 (Calif, Idaho, Mich, Nev, NM, NC, Ore, Wash) to states without a change using 2000 to 2008 data. The appendix describes these analyses in more detail.
Second, results for low-income children without a coresiding Medicaid-enrolled parent and low-income parents not enrolled in Medicaid were examined as a placebo test. A significant association between providing vision benefits and changes in outcomes for these groups would suggest that factors other than Medicaid vision coverage that affect all low-income families may have contributed to estimates. For example, if Medicaid vision benefits are correlated with the availability of vision services at community health centers, or with school vision screening policies, then placebo estimates could be significant. Third, because the sample was defined based on parent Medicaid enrollment, the association between providing vision coverage and parent participation in Medicaid was examined. A significant association would indicate possible changes to sample composition coincident with changes in vision benefits, which could bias estimates. A related check examined the association between providing vision benefits and changes in observable parent characteristics. Several other sensitivity tests were examined including using an alternative multilevel vision coverage indicator (ie, no coverage, exam only coverage, and coverage of exams and glasses), controlling for Medicaid payment rates to ophthalmologists, accounting for the effects of early state Medicaid expansions under the ACA, limiting the child sample to children enrolled in Medicaid and CHIP, eliminating families who were likely subject to more generous pregnancy benefits, and using logit models instead of linear probability models.
All statistical analyses were conducted using Stata software, version 15.1. Statistical significance was a 2-tailed α strictly less than .05.
Results
Income-Based Disparities in Child Eye Doctor Visit Rates
Figure 2 plots unadjusted eye doctor visit rates for all NHIS children by income group (up to 250% FPL, 250% to 400% FPL, and greater than 400% FPL). Visit rates for the lowest income children increased from about 17% to 24% during the study period. However, the difference in the visit rate for the lowest relative to the highest income children remained about seven percentage points in 2013. Income-based disparities at the end of the study period were larger among older children ages 12 to 17 (10 percentage point difference in 2013) (Appendix Figure S2).
Figure 2Percentage of children with a past-year eye doctor visit by income category, NHIS 2000–2013.
Notes: Author's analysis of 2000-2013 National Health Interview Survey data. Samples include all children with eye doctor visit information. Estimates are weighted.
gaining vision coverage was associated with a 5.4 percentage point (P = .009) increase in the likelihood that a Medicaid-enrolled parent had a past-year eye doctor visit. Relative to the unadjusted parent visit rate over the study period (24%), this estimate represented a 22% change. Among children with a Medicaid-enrolled parent, providing vision benefits was associated with a 2.8 percentage point (P = .01) increase in having at least one past-year eye doctor visit, or a 14% change. The estimate for children ages 1 to 11 was positive but statistically insignificant. Among children ages 12 to 17, providing coverage was associated with a 6.1 percentage point increase (P = .009) in at least one visit, representing a 20% increase. The larger estimate among older children may be related to a higher prevalence of refractive error among this group, higher baseline visit rates (Appendix Figure S2), or because their parents have characteristics that make them more likely to respond to vision benefits. For example, the difference-in-differences estimate for parents at least age 40 is 8.5 percentage points (P = .002), as shown in Appendix Table S2. Exploratory analyses suggested that effects were also concentrated among female children and children with married mothers (Appendix Table S2).
Table 2Regression Estimates of the Association Between Changes in Medicaid Adult Vision Coverage, Having a Past-Year Eye Doctor Visit, and Related Outcomes, Parents and Children, NHIS 2000–2013
Low-income parent and child samples included those in families with incomes up to 250% of the federal poverty level. Children did not have a coresiding parent with Medicaid coverage. Parents did not have Medicaid coverage.
Low-income parent and child samples included those in families with incomes up to 250% of the federal poverty level. Children did not have a coresiding parent with Medicaid coverage. Parents did not have Medicaid coverage.
Responses to unmet needs for eyeglasses were only available for children ages 2–17.
All children (ages 2–17)
2.9
−1.0 (−2.6 to 0.6)
.23
16,111
Children ages 2–11
1.7
−0.4 (−1.5 to 0.6)
.39
10,897
Children ages 12–17
5.6
−2.2 (−5.3 to 1.0)
.17
5,213
Trouble seeing with correction
All children
3.3
0.2 (−0.7 to 1.1)
.61
17,322
Children ages 1–11
2.8
−0.2 (−1.2 to 0.9)
.78
12,108
Children ages 12–17
4.8
1.3 (−1.5 to 4.0)
.36
5,214
Notes: This table presents difference-in-differences estimates from linear multivariable regression models of outcomes on the Medicaid adult vision coverage indicator, individual demographic characteristics, state-by-year variables, and state and year indicators. The “difference-in-differences” estimate is the coefficient on the adult vision coverage indicator. Regression estimates are in terms of percentage points and 95% confidence intervals are in parentheses. Unadjusted rates and regression estimates are weighted and model errors are clustered at the state level. Sample sizes are unweighted.
Low-income parent and child samples included those in families with incomes up to 250% of the federal poverty level. Children did not have a coresiding parent with Medicaid coverage. Parents did not have Medicaid coverage.
† Responses to unmet needs for eyeglasses were only available for children ages 2–17.
Placebo estimates among low-income parents and children not expected to be directly affected by Medicaid policies were small and statistically insignificant. For example, the estimate for low-income children without a Medicaid-enrolled parent was 0.3 percentage points (P = .71).
Regression Estimates for Other Child Outcomes
Unadjusted rates of unmet needs for glasses due to cost and difficulty seeing with usual vision correction were only about 3% (Table 2). Similar to having a past-year eye doctor visit, rates for both of these measures were higher among children ages 12 to 17. Difference-in-differences estimates were not statistically significant for either outcome overall or for either age group. Point estimates for unmet needs for glasses were in the expected direction and larger among children ages 12 to 17 relative to 1 to 11. Estimates for reported difficulty seeing, however, did not follow a similar pattern.
Pre-Trends and Sensitivity Analysis
Figure 3 shows difference-in-differences estimates from the event history analysis. Estimates for the period preceding vision coverage policy changes were not statistically significant. The postpolicy variable, which aligns with the indicator used in the main analysis, suggested a significant 3.7 percentage point (P = .02) increase in the likelihood that a child had a past-year eye doctor visit. The comparison of linear yearly trends during 2000 to 2008 for states that ultimately changed their policies versus those that did not also did not suggest a significant difference in trends (P = .54 for the difference).
Figure 3Event history estimates of the association between changes in Medicaid adult vision coverage and having a past-year child eye doctor visit, NHIS 2000–2013.
Notes: Each estimate represents a regression coefficient for the indicated period relative to 3 or more years prior to a policy change. Bars represent 95% confident intervals. Estimates are in terms of percentage points and are results from a single linear multivariable regression model that controlled for individual demographic characteristics, state-by-year variables, and state and year indicators. Estimates were weighted and model errors were clustered at the state level. The sample includes children in the main analysis sample, except those in states that changed their vision coverage policies multiple times during the analysis period (FL, MA, MI, MO, NV, TX, and UT). Included states with a single coverage drop are CA, ID, NM, NC, OR, and WA. States without a policy change were included as a control group.
Estimates of the association between gaining vision benefits and parent participation in Medicaid were generally small and statistically insignificant (Appendix Table S3), and most parent observable characteristics were not significantly associated with Medicaid vision benefits (Appendix Table S4). The results were also robust to various methods of accounting for early state Medicaid expansions under the ACA (Appendix Table S5). Replacing the binary vision coverage indicator with controls for exam only and exam and glasses coverage (vs no coverage) suggested similar effects of coverage of both exams and glasses for parents (P <.10) and children ages 12 to 17 (P <.05), though estimates for all children were smaller and no longer statistically significant (Appendix Table S6). Results were similar when considering only children with Medicaid and CHIP coverage, excluding households likely subject to more generous pregnancy benefits, including only the 13 states with a vision coverage policy change, including a control for payment rates to ophthalmologists, excluding the state-by-year controls, and including state-specific linear trends (Appendix Table S7). Finally, marginal effects estimates from logit models were similar to those shown in the main text (Appendix Table S8).
Discussion
This study found that providing adult vision coverage was associated with an increase in the likelihood that a child had a past-year eye doctor visit, with effects being concentrated among older children ages 12 to 17. Several mechanisms could explain these findings. First, parents and children may be able to make an appointment with the same provider on the same day when a parent gains vision benefits. Second, parent-provider interaction may increase awareness of child vision care needs. Third, prior research suggests that many parents are unaware of vision coverage for publicly insured children,
and changes to adult vision coverage may bring awareness or salience to these benefits.
Results for unmet needs for glasses and difficulty seeing with usual vision correction were small and statistically insignificant. Unmet needs for glasses specifically referred to cost-related barriers. In fact, the direct costs of children's eye doctor visits were unchanged by the presence or absence of an adult vision benefit. These results may imply that perceived affordability is unlikely to explain the observed increase in the rate of past-year child eye doctor visits.
The fact that this study did not find a reduction in reported trouble seeing may be because this analysis lacked the power to detect smaller effects (ie, only a portion of the 2.8 percentage point increase in past-year eye doctor visits would be likely to result in improved vision). However, it is also noteworthy that reported rates were much lower than estimates based on vision examination data.
By the end of the study period, about 54% of children in the sample had a parent with Medicaid vision benefits. If all states provided adult vision benefits, the results of this study suggest that children's eye doctor visit rates would also increase. Given the relatively modest difference-in-differences estimate for children (2.8 percentage points), visit rates would only be expected to increase by about 1.3 percentage points (=2.8 × 0.46) at the national level. Figure 1 is consistent with this analysis as eye care use trended upward among low-income children during a period of substantial declines in the percent of parents with Medicaid vision benefits.
However, many low-income parents are not eligible for Medicaid and providing vision benefits to this group could reduce income-based disparities in child eye care use substantially. For example, the 2.8 percentage point difference-in-differences estimate for children accounts for 37% of the 7 percentage point gap in past-year eye doctor visit rates between the highest and lowest income children observed at the end of the study period. Among children ages 12 to 17, the 6 percentage point difference-in-differences estimate accounts for 60% of the 10 percentage point gap at the end of the study period.
Put another way, this study's findings suggest that about 0.5 child eye doctor visits result from each additional parent visit (=2.8/5.4). An increase in the percent of low-income parents with a past-year visit from 24% to 50% (ie, the percent of US adults with a refractive error)
would be expected to result in a 14 percentage point increase in the rate of child eye doctor visits (ie, from 21% to 35%). Therefore, efforts to increase parent visits through outreach and mitigation of nonmonetary barriers to eye care receipt in states that offer Medicaid vision benefits may also have substantial impacts on children.
This study had several limitations. Similar to other observational studies, it was not possible to exclude all possible factors that may have been correlated with both vision coverage policies and outcomes. However, the analysis included a variety of time-varying state-level policy variables with likely impacts on low-income families and also assessed the robustness of the results to the inclusion of state-specific trends. Further, estimates for low-income parents and children who were not directly affected by Medicaid vision coverage policies were small and statistically insignificant, suggesting little influence of omitted factors that affect all low-income families similarly. An additional limitation is that all outcomes were self-reported and may have been subject to recall error and other inaccuracies. Child outcomes were proxy-reported by an adult respondent, which may lead to additional biases if respondents did not have complete information about the child's health. These biases may be worse for unmet needs for vision correction and vision trouble versus eye doctor visits since a parent or guardian would most likely facilitate a provider visit.
Despite these limitations, this research provides some of the first evidence at the national level of the potential for public health insurance policy to improve access to vision care among low-income children. Findings suggest that the effects of increasing a parent's access to vision services may extend to children. Providing vision benefits to parents may present an opportunity for families to learn more about the benefits available to publicly insured children. A provider's recommendation that a child visit an eye doctor or the convenience of a combined parent and child visit may also play a role in the observed connection between adult vision benefits and child vision care use. Investigating the relative importance of these channels to determine the most effective approaches to increasing the use of recommended care among low-income children is an important area for future research.
Acknowledgments
This research was supported in part by funding from the William T. Grant Foundation, New York, NY (Officer's Research Grant #188400). Dr Lipton was also supported by a Fellowship from San Diego State University's Center for Health Economics & Policy Studies, San Diego, CA. The sponsors had no role in the design or conduct of this research.
The impact of providing vision screening and free eyeglasses on academic outcomes: evidence from a randomized trial in title I elementary schools in Florida.
Dudovitz RN, Sim MS, Elashoff D, et al. Receipt of corrective lenses and academic performance of low-income students. (in press). Acad Pediatr. doi: 10.1016/j.acap.2020.01.001.
Use of eyeglasses among children in elementary school: perceptions, behaviors, and interventions discussed by parents, school nurses, and teachers during focus groups.
Medicaid benefits: eyeglasses and other visual aids.
State Health Facts.2020; (Available at: https://www.kff.org/medicaid/state-indicator/eyeglasses/?currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D. Accessed September 9, 2020)
The effect of Medicaid adult vision coverage on the likelihood of appropriate correction of distance vision: evidence from the National Health and Nutrition Examination Survey.
Adult Medicaid benefit generosity and receipt of recommended health services among low-income children: the spillover effects of Medicaid adult dental coverage expansions.
Low-income parent and child samples included those in families with incomes up to 250% of the federal poverty level. Children did not have a coresiding parent with Medicaid coverage. Parents did not have Medicaid coverage.
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