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Abstract
Objectives To investigate whether regional variation changes with different beneficiary health insurance coverage types.
Design A cross-sectional study of the Health and Retirement Study (HRS) in 2018 was used.
Setting Medicare beneficiaries only covered by Medicare (group 1) are compared with those covered by Medicare and other health insurance (group 2). Outcomes included healthcare usage measures: (1) whether beneficiaries have a hospital stay and (2) the number for those with at least one stay; (3) whether beneficiaries have a doctor’s visit and (4) the number for those with at least one visit. We compared healthcare usage in both groups across the five regions: (1) New England and Mid-Atlantic; (2) East North Central and West North Central; (3) South Atlantic; (4) East South Central and West South Central; (5) Mountain and Pacific. We used logistic regression for binary outcomes and negative binomial regression for count outcomes in each group.
Participants We identified 8749 Medicare beneficiaries, of which 4098 in group 1 and 4651 in group 2.
Results Residents in all non-reference regions had a significantly lower probability of seeking a doctor’s visit in group 1 (OR with 95% CI 0.606 (0.374 to 0.982), 0.619 (0.392 to 0.977), 0.472 (0.299 to 0.746) and 0.618 (0.386 to 0.990) in the order of above regions, respectively), which is not significant in group 2. Residents in most non-reference regions (except South Atlantic) had a significantly fewer number of seeking a hospital stay in group 2 (incident rate ratio (IRR) with 95% CI 0.797 (0.691 to 0.919), 0.740 (0.643 to 0.865), 0.726 (0.613 to 0.859) in the order of above regions, respectively), which is not significant in group 1.
Conclusion Regional variation in the likelihood of having a doctor’s visit was reduced in Medicare beneficiaries covered by supplemental health insurance. Regional variation in hospital stays was accentuated among Medicare beneficiaries covered by supplemental health insurance.
- Health policy
- Health economics
- International health services
- Public health
- HEALTH SERVICES ADMINISTRATION & MANAGEMENT
Data availability statement
Data are available in a public, open access repository. Data are available in a public, open access repository (https://hrsdata.isr.umich.edu/data-products/rand-hrs-longitudinal-file-2018?_ga=2.258979978.1890758364.1616690587-360856504.1616690587)
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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- Health policy
- Health economics
- International health services
- Public health
- HEALTH SERVICES ADMINISTRATION & MANAGEMENT
Strengths and limitations of this study
This nationwide study provides a large sample size to explore the regional variation.
Our study was limited to general doctor’s visits and hospital stays and we could not study any other specific healthcare services.
We cannot identify these specific Medicare plans in our data, which limits our ability to assess the extent to which our estimated regional variations are driven by these different Medicare plans.
We combined nearby regions to increase the sample size in selected region classifications, and each region has many states, so these average estimates may mask variation across states within the same region.
Data were collected through a survey, which may lead to a recall bias.
Introduction
Equal access to healthcare is important to reduce health disparity.1 People should be given the same chance of getting appropriate treatment if they share the same type and degree of health need.2 The 2010 Patient Protection and Affordable Care Act (PPACA) was a substantial healthcare reform aiming to change the healthcare payment system and to improve quality of care while reducing cost.3 Since equal access is not the primary goal of this healthcare reform, the concern of important geographic variation in the use of healthcare services have been raised.4
Medicare aims to cover all elderly individuals who are over 65 years, as well as individuals less than 65 years of age with disabilities and renal disease. Medicare experienced many changes in the PPACA healthcare reform. Since Medicare is managed by the federal government with nearly the same standard across the nation, regional variation may be a primary factor for unequal access to healthcare. Individuals in some regions will have barriers to access necessary health resources. This unequal access to healthcare may be related to possible inefficiencies and inequality in the supply of healthcare. Since many Medicare beneficiaries are also covered by other health insurance, an interesting question arises, ‘does regional variation change across beneficiaries with different types of health insurance coverage?’. In the past few years, regional variations have been identified by some studies. These studies can be described as two types. The first type is to identify regional variations and the second type is to identify the factors related to regional variations. In terms of the first type studies, an evidence reveals that regional variation in imaging costs is greater than imaging usage.5 One study suggests that the usage of skilled nursing facility and hospital care among Medicare Advantage beneficiaries has greater regional variations than traditional Medicare beneficiaries.6 Another study suggests that the number of days of care per capita can be substantially different in two regions even though the two regions have similar per capita costs of care.7 Moreover, regional variation in Medicare spending and usage are substantial at the state level, even though state differences in demographic, demand and supply factors are controlled.8 In terms of the second type studies, socioeconomic characteristics have been proved to play a significant role in regional difference in admission rates and lengths of stay.9 Convenient public transportation can be used to address geographic barriers to healthcare in rural area.10 Some studies also suggest that regional variation is associated with bed availability, clinician workforce and races.11–13 However, these studies have some limitations. Many studies only explore regional variation in specific healthcare types, which cannot be extrapolated the results to other types of healthcare services. Moreover, many studies were conducted over decades ago, but Medicare has experienced important changes in recent years. Thus, these studies may be limited to reflect the current situation.
Therefore, it is necessary to revisit the question of regional variation in health usage among Medicare beneficiaries post-PPACA. Our new study bridges this research gap. We aim to identify (1) whether regional variation still exists among Medicare beneficiaries and (2) whether regional variation changes across Medicare beneficiaries with different types of health insurance coverage.
Method
Source of data
The HRS (Health and Retirement Study) is sponsored by the National Institute on Aging (grant number NIA U01AG009740) and is conducted by the University of Michigan. Data in our study are based on the HRS in 2018.14 HRS is a nationally longitudinal survey, which has been fielded every 2 years since 1992. This dataset concentrates on middle-aged and elderly individuals, which is representative of the middle-aged and elderly population over the country. It provides information on a broad array of domains including income and wealth; health, cognition and use of healthcare services; work and retirement; and family connections. The samples of HRS are drawn based on a multi-stage area probability design, involving geographical stratification, clustering and oversampling of certain demographic groups. HRS includes data for over 37 000 individuals over age 50 and 23 000 households in the USA.15
Study design
Figure 1 shows the flow chart for the analytic sample used in this study. There were 20 847 respondents in the 2018 HRS. There were 4221 participants with a missing value in residence region and these participants were excluded first. There were 7333 participants that had a missing value in Medicare coverage or not covered by Medicare and these participants were dropped as well. Additionally, we dropped 544 participants with missing value on demographic characteristics. The final analytic sample included 8749 HRS respondents with reported Medicare coverage. We separated Medicare beneficiaries into two mutually exclusive groups based on health insurance coverage type: (1) there were 4098 participants are only covered by Medicare (henceforth, group 1) and (2) there were 4651 participants are covered by both Medicare and supplemental health insurance (eg, Medicaid, VA/CHAMPUS and private health insurance) (henceforth, group 2). We did not exclude individuals who were covered by long-term care insurance from the Medicare-only group due to a large number of individuals with chronic diseases.
Flow chart for study participant from the 2018 Health and Retirement Study survey. EN, East North; ES, East South; S, South; WN, West North; WS, West South.
Dependent variables
We constructed four dependent variables. Two dummy variables for whether the individual had any hospital stay or doctor’s visit in the last 2 years. The other two variables measured the number of hospital stays for survey respondents with an inpatient visit in the previous 2 years and the number of doctor’s visits for those with an outpatient visit during the previous 2 years.
Independent variables
Our primary independent variable of interest was the Medicare beneficiaries’ region of residence, defined based on their reported state of residence: (1) New England Division and Middle Atlantic Division; (2) East North Central Division and West North Central Division; (3) South Atlantic Division; (4) East South Central Division and West South Central Division; (5) Mountain Division and Pacific Division.
Other variables
Other variables included patient demographic characteristics: gender, age, educational level, total household annual income per capita (PCI), employment status and chronic disease conditions. Specific, we used Pew’s study to categorise our income groups.16 We categories PCI into three groups: lower income (<$13 367), middle income ($13 367–$40 133) and upper income (>$40,133).
Statistical analysis
We compared characteristics of Medicare-only covered beneficiaries and beneficiaries with Medicare and supplemental insurance. Means and proportions were compared using χ2 tests. We modelled healthcare usage of Medicare beneficiaries using multivariate regression models. Logistic regressions were used to model binary outcomes (any hospital stay, any doctor’s visit in the past 2 years). The model specification is 
, α represents the intercept, p(x) represents the probability that individuals seek a doctor visit or a hospital stay and γθ represents individual-level demographic, socioeconomic and health characteristics. Negative binomial regressions were used to model count outcomes. To better reflect the variation of healthcare usage, we used the country map to visualise hospital stays and doctor visits. The model specification is 
, α represents the intercept, and γθ represents individual-level demographic socioeconomic and health characteristics.
In order to visualise the relative difference directly, we graphed event ratios instead of the exact events in the national map as figure 2 shows. We set the New England and Mid Atlantic region as the reference group (ie, event ratio=1). The event ratio for other regions was calculated as hospital stays (in other regions)/hospital stays (the New England and Mid Atlantic region) or doctor’s visits (in other regions)/doctor visits (the New England and Mid Atlantic region), separately. All our analyses are conducted with R V.4.1.1.
Average number ratio of hospital stays/doctor visits.
Patient and public involvement
We report no patient or public involvement in the design or implementation of the study.
Results
Demographic characteristics
Among individuals who were only covered by Medicare, 546, 885, 1,049, 755 and 863 individuals were in New England and Mid Atlantic regions, EN Central and WN Central regions, S Atlantic regions, ES Central and WS Central regions, and Mountain and Pacific regions, respectively. Among individuals who are both covered by Medicare and other health insurances, 720, 1093, 1151, 893 and 794 individuals are in each region category, respectively. ES and WS central regions had the highest percentage of individuals who were below age 65 (16.82%) and the lowest percentage of individuals who were over age 85 (11.39%). Mountain and Pacific regions had the lowest percentage of individuals who were below 65 years (8.23%) and the highest percentage of individuals who were over 85 years (12.86%) (table 1).
Descriptive statistics
Beneficiaries with less than a high school education were more concentrated in ES and WS central regions (29.93%) and less concentrated in EN and WN central regions (12.54%). Beneficiaries with a graduate degree were more concentrated in Mountain and Pacific regions (9.73%), but less concentrated in ES and WS central regions (5.83%). Considering the distribution of beneficiaries according to chronic diseases conditions reporting, ES and WS central regions had the highest percentage of individuals with more than one chronic disease (80.26%). Mountain and Pacific regions had the lowest percentage of individuals with more than one chronic disease (71.15%). ES and WS central regions had the highest percentage of lower-income (<$13 367) individuals (89.8%), while Mountain and Pacific regions had the lowest percentage of lower-income individuals (83.55%). In contrast, South Atlantic regions had the lowest percentage of upper-income (>$40 133) individuals (4.58%), while Mountain and Pacific regions had the highest percentage of upper income individuals (10.2%).
Among Medicare beneficiaries with supplemental insurances, there were significant variations in demographics across all residence regions (table 1). Considering the distribution of healthcare usage across regions, individuals living in the New England and Mid Atlantic regions had the highest number of hospital stays, while individuals living in the Mountain and Pacific regions had the lowest number of hospital stays (figure 2). Individuals living in the South Atlantic regions had the highest number of doctor’s visits, while individuals living in the East North and West North Central regions had the lowest number of doctor’s visits (figure 2).
ES and WS central regions had the highest percentage of individuals who were below 65 years (16.35%) and the lowest percentage of individuals who were over 85 years (10.41%) (table 1). EN and WN central regions had the lowest percentage of individuals who were below 65 years (12.08%) and the highest percentage of individuals who were over 85 years (16.1%). The percentage of individuals without a high school degree was highest in ES and WS central regions (25.08%) and lowest in EN and WN central regions (10.16%). Conversely, the percentage of people with a graduate degree was highest in Mountain and Pacific regions (12.22%) and lowest in ES and WS central regions (6.72%). The percentage of individuals with at least one chronic condition was highest in ES and WS central regions (81.63%) and lowest in Mountain and Pacific regions (71.91%). Considering annual household income per capita, the percentage of individuals with lower income was highest in ES and WS central regions (89.25%) and lowest in Mountain and Pacific regions (81.99%). The percentage of individuals with higher income was highest in Mountain and Pacific regions (9.45%) and lowest in ES and WS central regions (4.48%).
Logistic regression results
In terms of hospital stays, logistic regressions suggested that individuals living in Mountain and Pacific region were less likely to have a hospital stay than those residing in New England and Mid-Atlantic region among Medicare-only covered beneficiaries (OR=0.766, 95% CI 0.594 to 0.987). However, there were no significant differences in the probability of having a hospital stay across different regions among Medicare beneficiaries with supplemental insurances (table 2).
Logistic regression results
Age was significantly associated with hospital stays. Among Medicare-only covered beneficiaries, individuals aged over 85 were significantly more likely to have a hospital stay (OR=1.480, 95% CI 1.109 to 1.975), compared with individuals under 65 years. Among Medicare beneficiaries with supplemental insurance, individuals aged between 65 and 74 were less likely to have a hospital stay (OR=0.722, 95% CI 0.586 to 0.889). The results also suggested that education was not significantly related to hospital stays in both groups. The results also suggested that individuals with one chronic disease (OR=1.813, 95% CI 1.158 to 2.839) and with more than one chronic disease (OR=3.579, 95% CI 2.369 to 5.406) were more likely to have a hospital stay in group 1. In group 2, individuals with one chronic disease (OR=1.659, 95% CI 1.098 to 2.506) and with more than one chronic disease (OR=3.832, 95% CI 2.618 to 5.609) were also more likely to have a hospital stay. In terms of employment status, there were no significant differences in group 1. However, unemployment (OR=1.963, 95% CI 1.316 to 2.929) and retired (OR=1.609, 95% CI 1.181 to 2.192) individuals were more likely to have a hospital stay. In terms of household income, results suggested that only middle-income (≥13 367 and ≤$40 133) individuals (OR=0.618, 95% CI 0.447 to 0.854) were significantly less likely to have a hospital stay compared with lower-income individuals in group 1. However, there was no significant differences related to household income in group 2 (table 2).
In terms of doctor’s visit, logistic regressions suggested that individuals in EN Central and WN Central region (OR=0.606, 95% CI 0.374 to 0.982), S Atlantic region (OR=0.619, 95% CI 0.392 to 0.977), ES Central and WS Central region (OR=0.472, 95% CI (0.299 to 0.746)) and Mountain and Pacific region (OR=0.618, 95% CI (0.386 to 0.99)) were less likely to have a doctor’s visit than those residing in New England and Mid-Atlantic region among Medicare-only covered beneficiaries. However, there were no significant differences in the probability of having a doctor’s visit among Medicare beneficiaries with supplemental insurances (table 2).
There was no significant relationship between age and doctor’s visits in both groups. Females were more likely to have a doctor’s visit in both group 1 (OR=1.321, 95% CI (1.042 to 1.676)) and group 2 (OR=1.427, 95% CI (1.084 to 1.88)). Education was significantly related to doctor’s visits in both group 1 and group 2. In group 1, individuals with a high school degree (OR=2.142, 95% CI (1.627 to 2.821)), a college degree (OR=3.147, 95% CI (2.082 to 4.755)) and a graduate degree (OR=2.875, 95% CI (1.639 to 5.042)) were more likely to have a doctor’s visit, compared with individuals without a high school degree. In group 2, the results were similar. Individuals with a high school degree (OR=1.955, 95% CI (1.403 to 2.724)), a college degree (OR=2.712, 95% CI (1.677 to 4.384)) and a graduate degree (OR=5.095, 95% CI (2.25 to 11.535)) were more likely to have a doctor’s visit, compared with individuals without a high school degree.
Results suggested that individuals with one chronic condition (OR=2.438, 95% CI (1.558 to 3.815) in Medicare-only covered individuals and OR=2.925, 95% CI (1.72 to 4.974) in Medicare beneficiaries with supplemental insurance) and those with more than one chronic condition (OR=3.891, 95% CI (2.606 to 5.81) in Medicare-only covered individuals and OR=3.845, 95% CI (2.433 to 6.078) in Medicare beneficiaries with supplemental insurance were more likely to have a doctor’s visit. We did not notice significant associations between the outcome variables and employment status in both groups, and between the outcome variables and household income in group 2. However, middle-income (≥$13 367 and ≤$40 133) individuals were more likely to have a doctor’s visit (OR=2.44, 95% CI (1.054 to 5.648)) among Medicare beneficiaries with supplemental insurance, compared with lower-income individuals (table 2).
Negative binomial regression results
In terms of hospital stays, results suggested that there was no difference in the incident rate among different regions among Medicare-only covered beneficiaries. However, individuals in EN Central and WN Central region (IRR=0.797, 95% CI (0.691 to 0.919)), ES Central and WS Central region (IRR=0.740, 95% CI (0.634 to 0.865)) and Mountain and Pacific region (IRR=0.726, 95% CI (0.613 to 0.859)) had fewer incident rates of hospital stays than those residing in New England and Mid-Atlantic region in group 2 (table 3).
Negative binomial regression results
Individuals aged 65–74 years (IRR=0.802, 95% CI (0.672 to 0.957)), 75–84 years (IRR=0.781, 95% CI (0.658 to 0.927)) and over age 85 (IRR=0.785, 95% CI (0.646 to 0.954)) had significantly fewer incident rates of hospital stays in group 1, compared with individuals under 65 years. In group 2, the results were similar. Individuals who were aged 65–74 years (IRR=0.757, 95% CI (0.658 to 0.870)), 75–84 years (IRR=0.663, 95% CI (0.575 to 0.764)) and over age 85 (IRR=0.644, 95% CI (0.545 to 0.761)) had significantly fewer incident rates of hospital stays. In group 1, individuals with a high school degree had a significantly lower incident rate of hospital stays (IRR=0.824, 95% CI (0.721 to 0.943)), compared with individuals without a degree. In group 2, retired individuals (IRR=1.562, 95% CI (1.185 to 2.058)) had a higher incident rate of hospital stays, compared with individuals with a full-time job. However, we found that variables not significantly related to changes in the incident rate of hospital stays included chronic diseases, and household income in both groups, education in group 2, employment status in group 1 (table 3).
In terms of doctor’s visit, the results suggested that individuals in EN Central and WN Central region (IRR=0.743, 95% CI (0.668 to 0.826)), S Atlantic region (IRR=0.847, 95% CI (0.763 to 0.939)), ES Central and WS Central region (IRR=0.846, 95% CI (0.755 to 0.947)) and Mountain and Pacific region (IRR=0.806, 95% CI (0.722 to 0.900)) had lower incident rates of doctor’s visits than those residing in New England and Mid-Atlantic region in group 1. In group 2, results suggested that individuals in EN Central and WN Central region (IRR=0.884, 95% CI (0.797 to 0.981)) had a lower incident rate of doctor’s visits than individuals residing in New England and Mid-Atlantic region. However, individuals in S Atlantic region (IRR=1.157, 95% CI (1.043 to 1.283)) and Mountain and Pacific region (IRR=1.140, 95% CI (1.017 to 1.278)) had a higher incident rate of doctor’s visits than those residing in New England and Mid-Atlantic region in group 2 (table 3).
There was a significant relationship between age and doctor’s visits in both groups. Individuals who were aged 65–74 years (IRR=0.748, 95% CI (0.665 to 0.840)), 75–84 years (IRR=0.733, 95% CI (0.651 to 0.824)) and over age 85 (IRR=0.717, 95% CI (0.626 to 0.822)) had significantly lower incident rates of doctor’s visits in group 1, compared with individuals under 65 years. Individuals who were aged 65–74 years (IRR=0.719, 95% CI (0.646 to 0.801)), 75–84 years (IRR=0.686, 95% CI (0.614 to 0.767)) and over age 85 (IRR=0.781, 95% CI (0.686 to 0.890)) had significantly lower incident rates of doctor’s visits in group 2. In terms of education, individuals with a college degree (IRR=1.174, 95% CI (1.052 to 1.310)) and a graduate degree (IRR=1.230, 95% CI (1.073 to 1.411) in group 1; IRR=1.208, 95% CI (1.054 to 1.385) in group 2) had higher incident rates of doctor’s visit, compared with individuals without a degree. In terms of chronic disease, the results suggested that individuals with one chronic disease (IRR=1.712, 95% CI (1.450 to 2.021) in group 1; IRR=1.467, 95% CI (1.243 to 1.731) in group 2) and with more than one chronic disease (IRR=2.261, 95% CI (1.941 to 2.634) in group 1; IRR=2.262, 95% CI (1.939 to 2.639) in group 2) had more incident rate of doctor’s visits. In terms of employment status, the results were similar between group 1 and group 2. Unemployed individuals (IRR=1.706, 95% CI (1.363 to 2.135) in group 1; IRR=1.351, 95% CI (1.090 to 1.674) in group 2) and retired individuals (IRR=1.358, 95% CI (1.152 to 1.602) in group 1; IRR=1.283, 95% CI (1.089 to 1.513) in group 2) had more incident rate of doctor’s visits, compared individuals with a full-time job. Household income was not significantly related to incident rate of doctor’s visits in both groups (table 3).
Discussion
In our study, we used four health outcomes as the healthcare usage metrics: (1) the probability of hospital stay, (2) the probability of doctor’s visit, (3) the frequency of hospital stay and (4) the frequency of doctor’s visit. The regional variation is identified as the healthcare usage metrics are different among different regions even though we have controlled demographic, health and socioeconomic characteristics. Based on our results, our analysis has identified significant regional variation in healthcare usage among Medicare beneficiaries.
In terms of the logistic regression results in hospital stay, all ORs are not significant in both groups except Mountain and Pacific regions in group 1. In this case, we can conclude that regional variation does not exist most regions on the probability of a hospital stay. In terms of the logistic regression results in doctor’s visit, all ORs are significant in group 1, while all ORs are insignificant in group 2. Therefore, regional variation exists in group 1, while it does not exist in group 2. We can also conclude that if Medicare beneficiaries are covered by other health insurance, regional variation can be reduced and even eliminated on the probability of doctor visit.
In terms of the negative binomial regression results in hospital stay, all ORs are not significant in group 1, while all ORs are significant in group 2 except South Atlantic regions. In this case, regional variation exists in most regions in group 2, but it does not exist in group 1. Therefore, we can conclude that if Medicare beneficiaries are covered by other health insurance, regional variation can be reduced and even eliminated on the frequency of hospital stay. In terms of the negative binomial regression results in doctor’s visit, all ORs are significant in both groups except ES Central and WS Central regions in group 2. In this case, regional variation exists in most regions in both groups and the coverage of health insurance does not affect the frequency of doctor’s visits.
One potential explanation may be that narrow provider networks restricted access to care for Medicare beneficiaries.17–19 Compared with New England and Mid-Atlantic regions, Medicare plans in other regions may not provide large enough provider networks.18–20 Compared with Medicare beneficiaries with supplemental health insurance, Medicare-only beneficiaries are confronted with restrictions as an important barrier in healthcare access.17 21 Other barriers to access like lack of transportation may further restrict access to healthcare for certain Medicare beneficiaries.10 New England and Mid-Atlantic regions have better public transportations than other regions. Therefore, individuals in England and Mid-Atlantic regions may have less barrier to access healthcare usage. Bed availability and the number of physicians will also restrict healthcare usage.11 22 Moreover, physicians burn out are usually highly related to adverse health outcomes.23
We found that, compared with individuals with a full-time job, unemployed and retired individuals were more likely to have healthcare visits and also had a higher number of visits. These results are consistent with findings in other studies that show that individual’s health is negatively related to economic profiles.24 25 These studies also show reverse causality between lower health status and unemployment status. A potential reason is that poor health may cause longer unemployment spells.26 Some studies also suggest that ill workers are more likely to become unemployed.27–29 Moreover, this can also be a potential explanation for the regional variation estimated in healthcare usage: regions with different healthcare usage may differ in their population’s economic profiles. Unlike findings in previous studies, we found that household income was not significantly related to frequency of healthcare visits.30 31
Hospitalisation usually spends more than doctor visits. In order to control healthcare costs, we should concentrate on minimising hospital visit and stay. However, I think doctor visits are high correlated with hospital stays. Hospital stay usually means patients have some serious issues. However, some serious disease can be avoided by early detections. For example, if individuals have more frequencies to health examination, they can detect their diseases earlier and therefore they can avoid diseases becoming more serious. In this case, individuals have more doctor visits can avoid potential hospital stays. As we mentioned earlier, regional variation means individuals in some regions have more or less healthcare usages than other regions even though they have similar demographic, health and socioeconomic characteristics. In other words, there are some regional factors will restrict or encourage individuals to have doctor visits or hospital stays. If individuals’ needs of healthcare are restricted, they cannot get treatment in time and therefore cause much more healthcare costs in the future. If individuals’ health needs are encouraged, they will consume more health resources even though they do not really need them. This is a waste of healthcare resources. Therefore, the ideal situation is that individuals in different regions have similar healthcare usage if they have similar demographic, health and socioeconomic characteristics. If the regional variation exists, we also have to figure out a way to reduce or solve it. In our study, we have identified regional variations, and we also found that insurance coverage has impact on regional variation. In this case, adjusting insurance coverage could be one potential strategy to reduce regional variations.
Policy implications
There are several important implications of our research. First, regional variation broadly exists in Medicare beneficiaries. However, this variation is not in the same direction when considering different healthcare settings among different Medicare beneficiary groups. Second, although household income is not related to healthcare usage, employment status is significantly associated with healthcare usage. Unemployment and retired individuals seek more healthcare in both groups, especially in the outpatient setting. This suggests that unemployed individuals may need more care and potential assistance. Therefore, healthcare programmes and reforms should increase healthcare access for unemployed and retired individuals. Finally, Health insurance coverage plays a role in changing regional variation. For different subgroups, the government can adjust different health insurance coverage to reduce regional variation.
Limitations
There are some important limitations in this study. First, we combined nearby regions to increase the sample size in selected region classifications. Each region has many states, so these average estimates may mask variation across states within the same region. Second, Medicare has undergone substantial changes including the growth of Medicare Advantage and the introduction of numerous pay-for-performance and value-based programmes.32 33 We cannot identify these specific plans in the HRS which limits our ability to assess the extent to which our estimated regional variations are driven by these different Medicare plans. Third, data were collected through a survey, which may lead to a recall bias. Fourth, our study was limited to general doctor’s visits and hospital stays and we could not study any other specific healthcare services, due to data limitations. Finally, the sample weight this time is not available. Therefore, we cannot adjust our results by sampling weights, which leads to a potential selection bias. Notwithstanding these limitations, our study provides a general landscape of healthcare usage among Medicare beneficiaries.
Conclusion
Regional variation exists in healthcare usage for Medicare beneficiaries, and regional variation also changes in beneficiaries with different types of coverage. Specifically, Regional variation in the likelihood of having a doctor’s visit was reduced in Medicare beneficiaries covered by supplemental health insurance. Regional variation in hospital stays was accentuated among Medicare beneficiaries covered by supplemental health insurance. Further studies are needed to elicit the reasons explaining these variations.
Data availability statement
Data are available in a public, open access repository. Data are available in a public, open access repository (https://hrsdata.isr.umich.edu/data-products/rand-hrs-longitudinal-file-2018?_ga=2.258979978.1890758364.1616690587-360856504.1616690587)
Ethics statements
Patient consent for publication
Ethics approval
Not applicable.
Acknowledgments
Thanks for the critical comments and help from Dr Mariétou Ouayogodé and Dr Ying Cao.
References
Footnotes
Twitter @Luod1an
Contributors This article is finished by myself for my personal interests. DL is fully responsible for the work. DL conceived and designed the study. DL also conducted the data analysis, finished figures, and wrote the manuscript.
Funding The author has not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Map disclaimer The inclusion of any map (including the depiction of any boundaries therein), or of any geographic or locational reference, does not imply the expression of any opinion whatsoever on the part of BMJ concerning the legal status of any country, territory, jurisdiction or area or of its authorities. Any such expression remains solely that of the relevant source and is not endorsed by BMJ. Maps are provided without any warranty of any kind, either express or implied.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.