Social risks (food insecurity, housing instability, financial strain, health insurance type) are associated with patients’ decisions to avoid/delay health care and increased utilization of the emergency department.
Objectives: The primary aim was to examine the association of social risks with avoiding/delaying health care after controlling for sociodemographic covariates, and the secondary aim was to examine the association of social risks with emergency department (ED) visits after controlling for avoiding/delaying health care and sociodemographic covariates.
Study Design: 2017 Ohio Medicaid Assessment Survey data were analyzed.
Methods: Descriptive, bivariate analysis and multiple weighted logistic regressions were conducted. First, weighted logistic regression assessed the association of aggregated social risk (food insecurity, housing instability, financial strain) and health insurance type with avoiding/delaying health care after controlling for sociodemographic covariates. Next, weighted logistic regression assessed the association of social risks with ED visits after controlling for avoiding/delaying health care and sociodemographic covariates.
Results: Among 39,711 respondents, 21.7% reported avoiding/delaying health care and 27.2% reported having at least 1 ED visit in the past year. Individuals with higher vs lower aggregated social risk had higher odds of avoiding/delaying health care (odds ratio [OR], 1.30; 95% CI, 1.26-1.34) and were more likely to have any ED visits (OR, 1.10; 95% CI, 1.07-1.13). Uninsured individuals compared with those with private insurance were more likely to avoid/delay health care (OR, 1.98; 95% CI, 1.73-2.26) and have higher likelihood of any ED visits (OR, 1.23; 95% CI, 1.06-1.42). Finally, individuals who reported avoiding/delaying getting health care were more likely to have higher odds of any ED visits (OR, 1.33; 95% CI, 1.23-1.45).
Conclusions: Social risks are important factors in patients’ decisions to avoid/delay health care and are associated with increased odds of any ED visits. To reduce ED visits, policy-level efforts need to be made to address these social challenges.
Am J Manag Care. 2021;27(3):115-121. https://doi.org/10.37765/ajmc.2021.88599
The Affordable Care Act expanded access to insurance coverage and health care services.1-4 However, many patients still report avoiding or delaying health care because of multiple reasons, including cost,5-7 lack of adequate insurance coverage,5,8 and low health literacy.7,9 In a report from the National Center for Health Statistics, approximately 31% of individuals aged 18 to 64 years reported avoiding/delaying health care for any reason.6 Avoiding/delaying health care is associated with poor health outcomes. Previous studies have associated avoiding/delaying health care with decreased rates of cancer screening,10 increased mortality among those with HIV,11 and increased health care costs that are avoidable.12 Avoiding/delaying health care may lead to the diagnosis of disease at a later stage, reduce survival rate, and result in health complications that could have been avoided.10-14
Health insurance, health care costs, patient-provider communication, and health literacy are health care system–related factors that are associated with health services utilization; however, factors referred to as social risks (also referred to as social determinants of health) have shown to be significant as well. Social risks include housing instability, food insecurity, transportation concerns, unemployment, financial instability, limited social resources, and lack of social networks. Social risks often present an additional challenge among individuals belonging to vulnerable population groups like those of lower socioeconomic class, racial or ethnic minorities, uninsured individuals, or older age cohorts. Currently, the full effect of the coronavirus disease 2019 (COVID-19) pandemic on health is still emerging; however, racial/ethnic minority groups are experiencing a disproportionate burden of worse health outcomes.15 Recent research results on health outcomes and COVID-19 highlight widening health disparities among racial/ethnic minorities due to exposure to poorer economic, social, and physical conditions that have contributed to increased infection rates and higher associated morbidity and mortality.15,16 This complex matrix of factors within the health care system, along with high social risks experienced by these vulnerable groups, are crucial factors that affect the decision-making process of seeking health care services by patients.7-9 Previous studies on social risks have shown the association of housing instability with increased emergency department (ED) utilization17-19 and decreased likelihood of using preventive services.20,21 Also, patients reporting food insecurity show increased needs for health services and often have to make the difficult decision of choosing between health care and basic survival requisites.22,23
Previous research has focused on avoiding or delaying health care from the lenses of patient motivation and health care system factors. There is limited research examining the relationship of avoiding/delaying care and ED visits with social risks (eg, food insecurity, housing instability, financial strain). Exploring these associations may provide important information on the challenges and missed opportunities that are presented outside the realm of the health care system. Thus, the objectives of this study are (1) to examine the association of social risks with avoiding/delaying health care after controlling for sociodemographic covariates, and (2) to examine the association of social risks with ED visits after controlling for avoiding/delaying health care and sociodemographic covariates.
A retrospective, cross-sectional study using the 2017 Ohio Medicaid Assessment Survey (OMAS) response data set was conducted. OMAS is a state-level representative survey of noninstitutionalized Ohioans. OMAS surveys changes in Ohio’s Medicaid, Medicaid-eligible, and non-Medicaid populations to assess health care access, health status, and health care use for Ohio’s current and potential Medicaid beneficiaries.24 OMAS adopts a complex design (multiple strata) and stratified random digit dual-frame (cell and landline) telephonic survey and uses validated questions from existing state and federal health surveys.24 Data are collected on health insurance type, health status (physical, mental, dental), diagnosis of selected health conditions, health utilization, health needs, perceptions of health care quality, access to health care, and health-related socioeconomic variables.
Social risks included in this study were aggregated social risk and health insurance type. Aggregated social risk was created by summing responses to questions in these 3 areas: (1) food insecurity (assessed by “In the past 12 months, has it gotten easier, harder, or stayed the same to buy food for family or household?”); (2) housing instability (assessed by “In the past 12 months, has it gotten easier, harder, or stayed the same to pay rent or mortgage?”); and (3) financial strain (assessed by “In the past 12 months, has it gotten easier, harder, or stayed the same to pay off any debt you had?”).24 Item responses for each question were coded as easier, stayed the same, and harder. Polychoric correlation was conducted to assess multicollinearity among the variables. Next, principal component analysis was done, and only 1 domain emerged with an Eigenvalue more than 1. Therefore, the questions assessing food insecurity, housing instability, and financial strain were combined to calculate the aggregated social risk. The second social risk measured health insurance type and had the following response options: dual (Medicare and Medicaid), Medicare only, Medicaid only, private, and uninsured.
Health care utilization was assessed by ED visits and avoiding/delaying health care. ED visits were assessed by the question “During the past 12 months, how many times were you a patient in a hospital emergency room?”24 Item responses ranged from 0 to 20, which were dichotomized as yes (1 or more visits) and no (0 visits). Avoiding/delaying of health care was assessed by the question “During the past 12 months, did you delay/avoid getting health care that you felt you needed?”24 The response category was categorical and coded as yes and no.
Covariates included in the study were age (19-34, 35-64, and > 64 years), gender (male and female), race (White, African American or Black, Asian American, and other), ethnicity (Hispanic and non-Hispanic), marital status (married, single, and widowed/separated/divorced), and patient-reported perception of health status (excellent, very good, good, fair, and poor). These variables were collected as categorical responses.24
Descriptive analyses were carried out comparing distributions of social risk factors and other covariates between male and female respondents using the χ2 test. Data analysis included survey weights to adjust for unequal probability of sample selection, and standard errors were adjusted for the complex design of the survey sample.24 Next, 2 separate weighted multiple logistic regressions were conducted. The first series of weighted logistic regression tested for the direct association of social risks (aggregated social risk and health insurance type) with avoiding/delaying health care. Next, association of social risks (aggregated social risk and health insurance type) with avoiding/delaying health care was done after controlling for sociodemographic factors (age, gender, race and ethnicity, marital status) and health status. The second series of weighted logistic regressions tested the direct association of social risks (aggregated social risk and health insurance type) with ED visits. Next, association of social risks (aggregated social risk and health insurance type) with ED visits was done after controlling for sociodemographic factors (age, gender, race, ethnicity, marital status) and health status. The final logistic regression model evaluated the association of social risks with ED visits after adjusting for avoiding/delaying health care, sociodemographic factors, and health status. All statistical analyses were performed using SAS version 9.3 (SAS Institute), and a P value of less than .05 was used to determine significance.
Sociodemographic characteristics of the sample by gender are provided in Table 1 [part A and part B]. There were 21,564 (54.3%) female and 18,147 (45.7%) male respondents. Of the total 39,711 respondents, 21.7% respondents reported delaying/avoiding health care, and 27.2% of the respondents reported having at least 1 visit to the ED in the past year. A higher proportion of women reported having government-sponsored insurance (Medicare, Medicaid, dual) vs men, who were more likely to report being uninsured or having private insurance. A significantly higher proportion of women compared with men reported finding it harder to buy food (21.9% vs 16.0%), pay for housing (19.0% vs 15.7%), and pay off debt (30.4% vs 23.6%). A higher proportion of women compared with men also reported avoiding/delaying health care (25.5% vs 20.4%) and having at least 1 ED visit in the past 12 months (30.5% vs 26.6%). Chi-square tests showed that these observed differences between men and women were highly significant (P < .0001). In addition, among individuals who reported avoiding/delaying health care, about 33.5% reported more difficulty paying for food, about 30.6% reported more difficulty paying for rent or mortgage, and about 45.2% reported more difficulty paying off their debt.
Results from the multiple logistic regression exploring the association of social risks with avoiding/delaying health care are shown in Table 2. Individuals with higher aggregated social risk had higher odds of avoiding/delaying health care (odds ratio [OR], 1.30; 95% CI, 1.26-1.34) and this association continued to be significant after controlling for all sociodemographic covariates. Individuals with Medicaid (OR, 1.27; 95% CI, 1.15-1.40) and who were uninsured (OR, 2.27; 95% CI, 2.00-2.58) were more likely to avoid/delay health care, and those who had Medicare were less likely (OR, 0.60; 95% CI, 0.54-0.67) to avoid/delay health care compared with individuals with private insurance. However, after controlling for sociodemographic covariates, only uninsured individuals (OR, 1.98; 95% CI, 1.73-2.26) continued to be more likely to avoid/delay health care compared with individuals with private insurance. Being female, single, and widowed/separated/divorced were associated with significantly higher odds of avoiding/delaying health care. Being older than 34 years and being African American or Asian American were associated with significantly lower odds of avoiding/delaying health care.
Results of the multiple logistic regression to explore the association of social risks with ED visits after controlling for avoiding/delaying health care and other covariates are shown in Table 3. Individuals with higher aggregated social risk reported greater odds of having at least 1 ED visit in the past 12 months (OR, 1.21; 95% CI, 1.17-1.24), and this association was consistently significant after controlling for other covariates. Individuals with health insurance types of Medicare (OR, 1.70; 95% CI, 1.55-1.87), Medicaid (OR, 3.79; 95% CI, 3.45-4.16), dual Medicare/Medicaid (OR, 3.35; 95% CI, 2.91-3.86), or who were uninsured (OR, 1.67; 95% CI, 1.46-1.91) were reported greater odds of any ED visits compared with individuals with private insurance. This association continued to be significant even after controlling for all the covariates. Individuals identifying as African American or “other” race group, or as being widowed/separated/divorced, reported greater odds of any ED visits. Finally, individuals who reported avoiding/delaying health care had greater odds of any ED visits (OR, 1.33; 95% CI, 1.23-1.45) compared with those who did not report avoiding/delaying health care.
The disproportionately worse health outcomes reported among racial/ethnic minorities in the current COVID-19 pandemic have made it increasingly apparent that research should look beyond biological and genetic factors as the major predictors of diseases. It is critical to examine social risk factors and their contribution to health disparities in studying societal patterns of illness and health. This investigation examined the association of social risks (health insurance type and aggregated social risk) with avoiding/delaying health care and its downstream association with ED utilization.
First, the primary hypothesis investigating the positive association of health insurance type and avoiding/delaying health care was confirmed in our study. Being uninsured led to avoiding or delaying health care, which is consistent with evidence from other studies.7,25-27 In addition, our secondary hypothesis investigating the positive association of health insurance type and ED visits was confirmed. Having government-sponsored health insurance, including Medicare, Medicaid, and dual, was associated with higher odds of any ED visits in this study, which is consistent with the evidence from previous studies.28,29 Being uninsured was also associated with having higher odds of any ED visits, albeit much lower than those with government-sponsored insurance. This is contrary to conventional understanding that uninsured individuals tend to use the ED at a much higher rate but consistent with findings of multiple recent research studies.30,31 This finding validates the direct association of comprehensive coverage (government insurance) with increased ED utilization observed in previous studies.30,31 More importantly, it highlights that the relationships between different insurance types and ED visits are complex and may be influenced by multiple factors, including characteristics of the population covered, the insurance coverage features, and availability of different types of care for the uninsured.32 This further emphasizes the financial and nonfinancial access barriers encountered by the uninsured. Research results have shown that individuals with insurance have better access to and increased utilization of health care services.33-36 Efforts need to be made to nudge uninsured patients toward utilizing safety-net facilities and gaining access to health insurance. On the other hand, utilization among those with Medicaid needs to be explored through the lenses of quality, access to primary care, and misaligned incentives of using the ED.
Second, our study found that social risks of food insecurity, housing instability, and financial strain were associated with higher odds of avoiding/delaying health care and any ED visits, similar to previous research findings.7,18,20 Studies show that poverty, underemployment or unemployment, and high housing costs are strongly associated with food insecurity,37-40 and individuals experiencing these high social risks are usually making difficult trade-offs, such as choosing between buying food and buying or paying for other items or needs, such as prescription medication,41,42 housing rent,43 and utilities.44,45 These competing priorities are significant and substantial barriers to access and utilization of necessary health care services.7,18,20,23 A report by JPMorgan Chase on financial strain shows that cash flow dynamics affect health care utilization.46 In their analysis of out-of-pocket expenditures, the authors observed that patients increased their out-of-pocket expenditures by about 60% in the first week after receiving their tax refunds.46 Considering these results, it may be worth exploring changes in the way flexible spending accounts and health savings accounts are replenished. Quarterly replenishment may nudge patients to use health care services in a more timely manner than a lump-sum yearly contribution. Similar nudges should be explored for individuals with government-sponsored health insurance.
In addition to our main research findings, significant gender differences with respect to social risks were observed. Higher proportions of women, compared with men, reported having government-sponsored health insurance (Medicare, Medicaid, dual) and reported hardships in paying for food, housing, and debt. Compared with their male counterparts, women also had higher odds of avoiding or delaying health care but no greater odds of ED visits. This observation is consistent with prior research, which indicated that after adjusting for health care needs and economic access, women had significantly fewer physician visits compared with men with similar health needs.47 Women often face more barriers to health services utilization due to responsibilities of childcare, other caregiving responsibilities, and employment.
Additionally, this study reaffirmed racial disparities in access to and utilization of health care services. Consistent with previous research, African Americans and Asian Americans reported lower odds of avoiding/delaying health care, but African Americans reported higher odds of any ED visits and Asian Americans had lower odds of any ED visits compared with White individuals.48,49 This disconnect between health utilization and ED visits raises critical questions around access, quality of received health care, timeliness of care, and distrust of the health care system among racial and ethnic minorities. Legacies of past abuses and discrimination experiences, such as the Tuskegee syphilis study, contribute to the continued mistrust in the health care system among racial and ethnic minorities.50 These observed disparities emphasize the need for interventions that create outpatient safety-net facilities in regions of need, especially to meet the needs of underserved racial and ethnic minorities. Furthermore, increasing awareness and engagement among minority communities can address issues pertaining to health utilization.
Finally, we found that individuals describing their health status as progressively worsening had higher odds of avoiding/delaying health care compared with those who reported excellent health status. In addition, individuals reporting progressively worsening health status also showed higher odds of any ED visits even after controlling for avoiding/delaying health care compared with those who reported excellent health status. This finding highlights that people who perceive themselves to be in excellent health promptly get health care when needed and subsequently utilize the ED less than those reporting their health status as less than excellent.
OMAS is a cross-sectional survey that is associated with inherent limitations regarding interpretation of the findings, as no causality in relationships can be ascertained. Additionally, measurement of social risks including food insecurity, housing instability, and financial strain were available for only the past year, which may lead to underestimation or overestimation based on the most recent experiences. Additionally, social determinants of health (food insecurity, housing instability, financial strain) are assessed using standard questionnaires. Although the questions asked in OMAS are not the standard way to measure these domains, they have been adapted from previously validated items like previous OMAS surveys, the Behavioral Risk Factor Surveillance System, the American Community Survey, the National Health and Nutrition Examination Survey, and the Medical Expenditures Panel Survey. Also, food insecurity, housing instability, and financial strain have been identified as “social risks” to account for the lack of consistency with respect to measurement of social determinants of health.
Social risks of health insurance type, food insecurity, housing instability, and financial strain are important factors in patients’ decisions of seeking or delaying health care and in utilizing the ED as their usual source of care. Policy makers should consider integrating health care and social resources to ensure the holistic health and well-being of individuals in the community. Payers and providers have an opportunity to measure, assess, and intervene strategically to enhance patients’ experiences inside and outside the realm of the health care matrix. This enhanced patient experience may generate improvements in health outcomes and reductions in health care costs.
Author Affiliations: College of Pharmacy and Pharmaceutical Sciences (PMP), and School of Population Health, College of Health and Human Services (SS, IT), University of Toledo, Toledo, OH.
Source of Funding: This research did not receive any external funding.
Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (PMP, SS, IT); acquisition of data (PMP, SS); analysis and interpretation of data (PMP, SS, IT); drafting of the manuscript (PMP, SS, IT); critical revision of the manuscript for important intellectual content (PMP, SS, IT); statistical analysis (PMP, SS); administrative, technical, or logistic support (PMP); and supervision (SS).
Address Correspondence to: Pranav M. Patel, PharmD, MS, College of Pharmacy and Pharmaceutical Sciences, University of Toledo, 3345 Airport Hwy, Apt 7A, Toledo, OH 43609. Email: email@example.com.
1. McMorrow S, Kenney GM, Long SK, Gates JA. Marketplaces helped drive coverage gains in 2015; affordability problems remained. Health Aff (Millwood). 2016;35(10):1810-1815. doi:10.1377/hlthaff.2016.0941
2. Courtemanche C, Marton J, Ukert B, Yelowitz A, Zapata D. Early impacts of the Affordable Care Act on health insurance coverage in Medicaid expansion and non-expansion states. J Policy Anal Manage. 2017;36(1):178-210. doi:10.1002/pam.21961
3. Sommers BD, Blendon RJ, Orav EJ. Both the ‘private option’ and traditional Medicaid expansions improved access to care for low-income adults. Health Aff (Millwood). 2016;35(1):96-105. doi:10.1377/hlthaff.2015.0917
4. Kirby JB, Vistnes JP. Access to care improved for people who gained Medicaid or marketplace coverage in 2014. Health Aff (Millwood). 2016;35(10):1830-1834. doi:10.1377/hlthaff.2016.0716
5. Collins SR, Rasmussen PW, Doty MM, Beutel S. Too high a price: out-of-pocket health care costs in the United States. findings from the Commonwealth Fund Health Care Affordability Tracking Survey. September-October 2014. Issue Brief (Commonw Fund). 2014;29:1-11.
6. Ward BW. Barriers to health care for adults with multiple chronic conditions: United States, 2012-2015. NCHS Data Brief. 2017;(275):1-8.
7. Tipirneni R, Politi MC, Kullgren JT, Kieffer EC, Goold SD, Scherer AM. Association between health insurance literacy and avoidance of health care services owing to cost. JAMA Network Open. 2018;1(7):e184796. doi:10.1001/jamanetworkopen.2018.4796
8. Galbraith AA, Soumerai SB, Ross-Degnan D, Rosenthal MB, Gay C, Lieu TA. Delayed and forgone care for families with chronic conditions in high-deductible health plans. J Gen Intern Med. 2012;27(9):1105-1111. doi:10.1007/s11606-011-1970-8
9. Smith KT, Monti D, Mir N, Peters E, Tipirneni R, Politi MC. Access is necessary but not sufficient: factors influencing delay and avoidance of health care services. MDM Policy Pract. 2018;3(1):2381468318760298. doi:10.1177/2381468318760298
10. Byrne SK. Healthcare avoidance: a critical review. Holist Nurs Pract. 2008;22(5):280-292. doi:10.1097/01.HNP.0000334921.31433.c6
11. Ohl M, Tate J, Duggal M, et al. Rural residence is associated with delayed care entry and increased mortality among veterans with human immunodeficiency virus infection. Med Care. 2010;48(12):1064-1070. doi:10.1097/MLR.0b013e3181ef60c2
12. Kraft AD, Quimbo SA, Solon O, Shimkhada R, Florentino J, Peabody JW. The health and cost impact of care delay and the experimental impact of insurance on reducing delays. J Pediatr. 2009;155(2):281-285.e1. doi:10.1016/j.jpeds.2009.02.035
13. Lund-Nielsen B, Midtgaard J, Rørth M, Gottrup F, Adamsen L. An avalanche of ignoring—a qualitative study of health care avoidance in women with malignant breast cancer wounds. Cancer Nurs. 2011;34(4):277-285. doi:10.1097/NCC.0b013e3182025020
14. Richards MA, Westcombe AM, Love SB, Littlejohns P, Ramirez AJ. Influence of delay on survival in patients with breast cancer: a systematic review. Lancet. 1999;353(9159):1119-1126. doi:10.1016/s0140-6736(99)02143-1
15. Abuelgasim E, Saw LJ, Shirke M, Zeinah M, Harky A. COVID-19: unique public health issues facing Black, Asian and minority ethnic communities. Curr Probl Cardiol. 2020;45(8):100621. doi:10.1016/j.cpcardiol.2020.100621
16. Yancy CW. COVID-19 and African Americans. JAMA. 2020;323(19):1891-1892. doi:10.1001/jama.2020.6548
17. Kushel MB, Gupta R, Gee L, Haas JS. Housing instability and food insecurity as barriers to health care among low-income Americans. J Gen Intern Med. 2006;21(1):71-77. doi:10.1111/j.1525-1497.2005.00278.x
18. Kushel MB, Perry S, Bangsberg D, Clark R, Moss AR. Emergency department use among the homeless and marginally housed: results from a community-based study. Am J Public Health. 2002;92(5):778-784. doi:10.2105/ajph.92.5.778
19. Martell JV, Seitz RS, Harada JK, Kobayashi J, Sasaki VK, Wong C. Hospitalization in an urban homeless population: the Honolulu Urban Homeless Project. Ann Intern Med. 1992;116(4):299-303. doi:10.7326/0003-4819-116-4-299
20. Kushel MB, Vittinghoff E, Haas JS. Factors associated with the health care utilization of homeless persons. JAMA. 2001;285(2):200-206. doi:10.1001/jama.285.2.200
21. Duchon LM, Weitzman BC, Shinn M. The relationship of residential instability to medical care utilization among poor mothers in New York City. Med Care. 1999;37(12):1282-1293. doi:10.1097/00005650-199912000-00011
22. Nelson K, Cunningham W, Andersen R, Harrison G, Gelberg L. Is food insufficiency associated with health status and health care utilization among adults with diabetes? J Gen Intern Med. 2001;16(6):404-411. doi:10.1046/j.1525-1497.2001.016006404.x
23. Gelberg L, Gallagher TC, Andersen RM, Koegel P. Competing priorities as a barrier to medical care among homeless adults in Los Angeles. Am J Public Health. 1997;87(2):217-220. doi:10.2105/ajph.87.2.217
24. Berzofsky M, Duffy T, Johnson C. 2017 Ohio Medicaid Assessment Survey: methodology report. 2018. Accessed June 11, 2019. http://grc.osu.edu/sites/default/files/inline-files/2017_OMAS_MethRept_10-18-2018_RTI.pdf
25. Weissman JS, Stern R, Fielding SL, Epstein AM. Delayed access to health care: risk factors, reasons, and consequences. Ann Intern Med. 1991;114(4):325-331. doi:10.7326/0003-4819-114-4-325
26. Christopher AS, McCormick D, Woolhandler S, Himmelstein DU, Bor DH, Wilper AP. Access to care and chronic disease outcomes among Medicaid-insured persons versus the uninsured. Am J Public Health. 2016;106(1):63-69. doi:10.2105/AJPH.2015.302925
27. Al Rowas S, Rothberg MB, Johnson B, et al. The association between insurance type and cost-related delay in care: a survey. Am J Manag Care. 2017;23(7):435-442.
28. Finkelstein AN, Taubman SL, Allen HL, Wright BJ, Baicker K. Effect of Medicaid coverage on ED use—further evidence from Oregon’s experiment. N Engl J Med. 2016;375(16):1505-1507. doi:10.1056/NEJMp1609533
29. Taubman SL, Allen HL, Wright BJ, Baicker K, Finkelstein AN. Medicaid increases emergency-department use: evidence from Oregon’s Health Insurance Experiment. Science. 2014;343(6168):263-268. doi:10.1126/science.1246183
30. Zhou RA, Baicker K, Taubman S, Finkelstein AN. The uninsured do not use the emergency department more—they use other care less. Health Aff (Millwood). 2017;36(12):2115-2122. doi:10.1377/hlthaff.2017.0218
31. Heintzman J, Gold R, Bailey SR, DeVoe JE. The Oregon experiment re-examined: the need to bolster primary care. BMJ. 2014;349:g5976. doi:10.1136/bmj.g5976
32. Sommers BD, Simon K. Health insurance and emergency department use—a complex relationship. N Engl J Med. 2017;376(18):1708-1711. doi:10.1056/NEJMp1614378
33. Finkelstein A, Taubman S, Wright B, et al; Oregon Health Study Group. The Oregon Health Insurance Experiment: evidence from the first year. Q J Econ. 2012;127(3):1057-1106. doi:10.1093/qje/qjs020
34. Card D, Dobkin C, Maestas N. The impact of nearly universal insurance coverage on health care utilization: evidence from Medicare. Am Econ Rev. 2008;98(5):2242-2258. doi:10.1257/aer.98.5.2242
35. Institute of Medicine. Coverage Matters: Insurance and Health Care. The National Academies Press; 2001.
36. Newhouse JP; Insurance Experiment Group. Free for All? Lessons From the RAND Health Insurance Experiment. Harvard University Press; 1993.
37. Rose D. Economic determinants and dietary consequences of food insecurity in the United States. J Nutr. 1999;129(suppl 2S):517S-520S. doi:10.1093/jn/129.2.517S
38. The U.S. Conference of Mayors’ report on hunger and homelessness: a status report on hunger and homelessness in America’s cities, December 2016. National Alliance to End Homelessness. December 2016. Accessed April 4, 2020. https://endhomelessness.atavist.com/mayorsreport2016
39. Nord M, Coleman-Jensen A, Gregory C. Prevalence of U.S. food insecurity is related to changes in unemployment, inflation, and the price of food. US Department of Agriculture Economic Research Service. June 2014. Accessed April 4, 2020. https://www.ers.usda.gov/webdocs/publications/45213/48166_err167_summary.pdf?v=0
40. Tuttle CJ, Beatty TKM. The effect of energy price shocks on household food security in low-income households. US Department of Agriculture Economic Research Service. July 2017. Accessed April 4, 2020. https://www.ers.usda.gov/webdocs/publications/84241/err-233.pdf?v=0
41. Biros MH, Hoffman PL, Resch K. The prevalence and perceived health consequences of hunger in emergency department patient populations. Acad Emerg Med. 2005;12(4):310-317. doi:10.1197/j.aem.2004.12.006
42. Sullivan AF, Clark S, Pallin DJ, Camargo CA Jr. Food security, health, and medication expenditures of emergency department patients. J Emerg Med. 2010;38(4):524-528. doi:10.1016/j.jemermed.2008.11.027
43. Cook JT, Frank DA, Levenson SM, et al. Child food insecurity increases risks posed by household food insecurity to young children’s health. J Nutr. 2006;136(4):1073-1076. doi: 10.1093/jn/136.4.1073
44. Nord M, Kantor LS. Seasonal variation in food insecurity is associated with heating and cooling costs among low-income elderly Americans. J Nutr. 2006;136(11):2939-2944. doi:10.1093/jn/136.11.2939
45. Frank DA, Neault NB, Skalicky A, et al. Heat or eat: the Low Income Home Energy Assistance Program and nutritional and health risks among children less than 3 years of age. Pediatrics. 2006;118(5):e1293-e1302. doi:10.1542/peds.2005-2943
46. Deferred care: how tax refunds enable healthcare spending. JPMorgan Chase & Co. December 2018. Accessed January 12, 2020. https://www.jpmorganchase.com/institute/research/healthcare/report-deferred-care
47. Cameron KA, Song J, Manheim LM, Dunlop DD. Gender disparities in health and healthcare use among older adults. J Womens Health (Larchmt). 2010;19(9):1643-1650. doi:10.1089/jwh.2009.1701
48. Chen J, Vargas-Bustamante A, Mortensen K, Ortega AN. Racial and ethnic disparities in health care access and utilization under the affordable care act. Med Care. 2016;54(2):140-146. doi:10.1097/MLR.0000000000000467
49. Brown LE, Burton R, Hixon B, et al. Factors influencing emergency department preference for access to healthcare. West J Emerg Med. 2012;13(5):410-415. doi:10.5811/westjem.2011.11.6820
50. Wells L, Gowda A. A legacy of mistrust: African Americans and the US healthcare system. Proceedings of UCLA Health. 2020;24:1-3.
Navigating Medicare's Part D Subsidy Program to Achieve Value-Based CareMay 26th 2023
On this episode of Managed Care Cast, we speak with the lead researcher from a study published in the May 2023 issue of The American Journal of Managed Care® about the impact of low-income subsidies on the uptake and equitable use of expensive orally administered antimyeloma therapy.
Health Equity Conversations: Managing Underserved Communities and Value-Based PaymentMay 23rd 2023
On this episode of Managed Care Cast, we feature several leaders in diversity, equity, and inclusion advancing health equity in their respective organization’s policy and practice initiatives.
How Do Primary Hospitals Enact Early Response to the Relaxation of COVID-19 Prevention and Control Measures? The Experience From Chengdu, ChinaJune 7th 2023
This article examines how primary hospitals in Chengdu, China, responded to the relaxation of COVID-19 prevention and control measures in December 2022.
2 Clarke Drive
Cranbury, NJ 08512