Food Insecurity, Healthcare Utilization, and High Cost: A Longitudinal Cohort Study

ABSTRACTObjectives: Reducing utilization of high-cost healthcare services is a common population health goal. Food insecurity—limited access to nutritious food owing to cost—is associated with chronic disease, but its relationship with healthcare utilization is understudied. We tested whether food insecurity is associated with increased emergency department (ED) visits, hospitalizations, and related costs.

Study Design: Retrospective analysis of a nationally representative cohort.

Methods: Adults (≥18 years) completed a food insecurity assessment (using 10 items derived from the US Department of Agriculture Household Food Security Module) in the 2011 National Health Interview Survey and were followed in the 2012-2013 Medical Expenditures Panel Survey. Outcome measures were ED visits, hospitalizations, days hospitalized, and whether participants were in the top 10%, 5%, or 2% of total healthcare expenditures.

Results: Of 11,781 participants, 2056 (weighted percentage, 13.2%) were in food-insecure households. Food insecurity was associated with significantly more ED visits (incidence rate ratio [IRR], 1.47; 95% CI, 1.12-1.93), hospitalizations (IRR, 1.47; 95% CI, 1.14-1.88), and days hospitalized (IRR, 1.54; 95% CI, 1.06-2.24) after adjustment for demographics, education, income, health insurance, region, and rural residence. Food insecurity was also associated with increased odds of being in the top 10% (odds ratio [OR], 1.73; 95% CI, 1.31-2.27), 5% (OR, 2.53; 95% CI, 1.51-3.37), or 2% (OR, 1.95; 95% CI, 1.09-3.49) of healthcare expenditures.

Conclusions: Food insecurity is associated with higher healthcare use and costs, even accounting for other socioeconomic factors. Whether food insecurity interventions improve healthcare utilization and cost should be tested.

Am J Manag Care. 2018;24(9):399-404Takeaway Points

• Food insecurity is an important risk factor for use of emergency department and inpatient healthcare services.
• Those with the highest healthcare costs are often food-insecure.
• Improving healthcare use for those with the highest costs may require addressing needs such as food insecurity along with medical needs.

A disproportionately large share of healthcare costs is generated in the course of care for a small proportion of patients, which is often due to emergency department (ED) visits, inpatient hospitalizations, or long lengths of hospital stay.^1,2 The desire to improve use of these services has prompted investigations into risk factors for high use and costs. Programs targeting patients with particularly high total healthcare costs³ (eg, those in the top 10%, 5%, or 1%) typically focus on clinical conditions or factors internal to the healthcare system, such as care coordination.² Because the impact of these programs can be modest,² there has recently been increased interest in addressing modifiable social determinants of health with the intention of reducing healthcare utilization and cost (eg, through the Accountable Health Communities model proposed by CMS).⁴

One particular area of focus is food insecurity, defined as lacking “access to enough food for an active, healthy life for all household members.”⁵ Food insecurity affects 12.7% of American households as of 2015⁵ and has been associated with increased prevalence of illness, as well as worsened chronic disease management.^6-11 It is hypothesized that food insecurity increases healthcare utilization and cost by making it more difficult to follow a healthy diet (exacerbating diet-dependent conditions, such as type 2 diabetes and congestive heart failure); forcing competing demands between food and other necessities, such as medications or transportation; and reducing the cognitive bandwidth necessary to manage chronic illness.^9,12 Prior studies in both Canadian¹³ and American^14-16 contexts have found that food insecurity is associated with higher average healthcare costs. However, several of these studies had limitations, including cross-sectional¹³ and ecological designs,^15,16 and none focused on the most relevant group for population health management—those with the highest healthcare use. For population health management, the extremes of the distribution, rather than the mean, may be most relevant.

To address these issues, we tested the hypothesis that food insecurity is associated with higher utilization of ED services, inpatient hospital admissions, and length of stay and an increased risk of being in top percentiles (10%, 5%, and 2%) of total healthcare expenditures, accounting for socioeconomic covariates.

METHODS

Data Source and Study Sample

Data for this study were obtained from the 2011 National Health Interview Survey (NHIS)¹⁷ and the 2012-2013 Medical Expenditure Panel Survey (MEPS). The 2012-2013 panel of MEPS is drawn from respondents to the 2011 NHIS to be nationally representative, and the responses were linked by anonymized identification number.¹⁸ We included all adults (≥18 years at time of NHIS completion) with information on food security status (nonresponse rate for food insecurity items, <1%)¹⁹ in our analysis. Interviews were conducted by trained interviewers in English or Spanish.^17,18

The Human Research Committee at Partners Healthcare exempted this study from human subjects review, as it made use of deidentified data.

Food Insecurity

Food insecurity was assessed at the household level with a 30-day lookback period in the NHIS using a 10-item food insecurity instrument derived from the US Department of Agriculture Household Food Security Module.²⁰ An example item asked whether the respondent and their household often, sometimes, or never worried about whether “food would run out before [they] had money to get more.” An affirmative response to 3 or more items indicated food insecurity, in accord with standard scoring practices for this instrument.²⁰ Owing to sample size limitations, we did not further subdivide the food-insecure category into low versus very low food security. These data came from the 2011 NHIS.

Healthcare Utilization and Expenditures

Information on healthcare expenditures and use that occurred in 2012 and 2013 was taken from MEPS. Because they are often the focus of programs to reduce healthcare utilization,² we evaluated the number of ED visits not resulting in a hospital admission, the number of inpatient hospital admissions, and the number of days spent as a hospital inpatient. To provide context, we also examined the medical conditions associated with use of these services and outpatient service use. Because a disproportionate share of total healthcare costs is attributable to a small number of people with the highest costs, it is important to understand other parts of the distribution of healthcare expenditures besides the mean. Therefore, we examined those in 3 commonly used thresholds: those in the top 10% of expenditures, those in the top 5%, and those in the top 2%.¹ Using the top 2% rather than the top 1% gives more stable estimates owing to the larger sample sizes. For consistency, we used the Consumer Price Index to convert all expenditures to 2015 dollars.

Other Measures

We considered several factors that may confound the relationships between food insecurity and healthcare utilization and expenditures. We used data from the 2011 NHIS to determine the participant’s age (at time of NHIS interview), gender, race/ethnicity (categorized as non-Hispanic white, non-Hispanic black, Hispanic, or other), education (less than high school diploma, high school diploma, or greater than high school diploma), income expressed as a percentage of federal poverty level (which accounts for household size), and health insurance (private, Medicare, other public insurance [which includes Medicaid, Medicare/Medicaid dual-eligibility, and coverage through the Department of Veterans Affairs], or no health insurance). Because area of residence is associated with variation in healthcare expenditure and use, we used data from MEPS to assess Census region of residence (Northeast, Midwest, South, or West) and urban versus rural residence. We also assessed the presence of 4 common conditions (heart disease [coronary heart disease, angina pectoris, myocardial infarction, or other heart disease], diabetes, respiratory illness [asthma, emphysema, or chronic bronchitis], and hypertension) using data from MEPS.²¹

Statistical Analysis

When developing our analysis plan, we relied on a published conceptual model of food insecurity and poor health that recognizes that food insecurity may be associated with poor health both by increasing the prevalence of chronic conditions and by making these conditions more difficult to manage once present.¹² Therefore, our main analytic strategy was to adjust for sociodemographic covariates that may confound the association between food insecurity and poor health, but not to adjust for clinical characteristics that may mediate the association between food insecurity and poor health. However, because healthcare systems often implement disease-specific management programs (eg, a diabetes management program), we conducted sensitivity analyses that adjusted for 4 conditions commonly targeted by disease management programs: heart disease, diabetes, respiratory illness, and hypertension. This helped ensure that any differences observed were not solely due to different burden of common chronic disease and helped provide evidence regarding the association between food insecurity and healthcare use within levels of a specific condition (eg, whether those with food insecurity and diabetes have greater healthcare use than those with diabetes but without food insecurity). We used zero-inflated negative binomial regression for our utilization analyses because many participants have zero healthcare utilization for a given type (eg, no ED visits).²² We conducted both unadjusted analyses and analyses adjusted for the covariates described above. To help understand differences in use and cost, we also analyzed whether participants had a routine source of care (using logistic regression) and their outpatient utilization (using zero-inflated negative binomial regression).

To examine whether food insecurity was associated with being in the top 10%, 5%, and/or 2% of expenditures, we conducted additional analyses. We tested unadjusted associations using χ² tests. Next, we constructed adjusted logistic regression models that included age (both linear and quadratic), gender, race/ethnicity, education, income, health insurance, region of residence, and rural versus urban residence.

To determine the difference in the mean expenditures between those with and without food insecurity by insurance coverage, we fit a generalized linear regression using a gamma distribution and log link and calculated the marginal differences, adjusting for other covariates.^23,24 The gamma regression approach was selected on the basis of a modified Park test as the distribution best fit to the healthcare expenditures data.²⁴

Finally, although sample size limitations prevented detailed exploration of the diagnoses associated with ED and inpatient use, we conducted exploratory analyses focused on the top 20 conditions responsible for ED visits and the top 20 responsible for inpatient admissions, which, when combined, yielded 30 total condition categories.²⁵

Analyses were conducted using SAS version 9.4 (SAS Institute; Cary, North Carolina) and Stata SE 14.1 (StataCorp; College Station, Texas). All analyses accounted for survey design information (sampling strata and weights).

RESULTS

The study included 11,781 adults. Of these, 13.2% (n = 2056; percentage is weighted) belonged to households that reported food insecurity in 2011. Those in food-insecure households were more likely to be younger, racial/ethnic minorities, and poorer compared with those in food-secure households (Table 1).

Among study participants, unadjusted utilization was highly right-skewed, with most participants having no utilization (eAppendix Table 1; eAppendix Figure 1A-1C [eAppendix available at ajmc.com]). In zero-inflated negative binomial models, adjusted for age, age squared, gender, race/ethnicity, education, income, health insurance, region, and living in a rural area, food insecurity was associated with significantly more ED visits (incidence rate ratio [IRR], 1.47; 95% CI, 1.12-1.93) (Table 2). Similarly, food insecurity was associated with more inpatient hospitalizations (IRR, 1.47; 95% CI, 1.14-1.88) and greater number of days hospitalized (IRR, 1.54; 95% CI, 1.06-2.24). The adjusted differences for those with Medicare were: difference in ED visits, 0.42 visits (P = .01); difference in inpatient admissions, 0.25 admissions (P = .01); and difference in days hospitalized, 1.93 days (P = .04). (P values represent comparison between predicted values for those with and without food insecurity when insurance type is Medicare.) The adjusted differences for other public insurance, which includes Medicaid and dual eligibility, were 0.39 visits (P <.0001), 0.10 admissions (P = .005), and 0.62 days (P = .03), respectively (full models in eAppendix Tables 2-4).

In zero-inflated negative binomial models that additionally included high-priority clinical conditions, results were generally similar to those of analyses without clinical conditions, with no qualitative or significant changes in the results. Food insecurity was associated with more ED visits (IRR, 1.41; 95% CI, 1.12-1.78), inpatient admissions (IRR, 1.28; 95% CI, 1.01-1.61), and days hospitalized (IRR, 1.61; 95% CI, 1.12-2.31) (full models in eAppendix Tables 5-7).

We next examined use of outpatient services and having a usual source of care. In models adjusted for age, age squared, gender, race/ethnicity, education, income, health insurance, region, and living in a rural area, we found that participants with food insecurity had higher rates of outpatient visits (IRR, 1.30; 95% CI, 1.11-1.52) but were less likely to report having a usual source of care (odds ratio [OR], 0.69; 95% CI, 0.51-0.93) (full models in eAppendix Tables 8-9).

Examining high-cost participants, those in the top 10% had expenditures of $26,201 or greater over the 2-year period, those in the top 5% had expenditures of $42,116 or greater, and those in the top 2% had expenditures of $68,314 or greater. In unadjusted, likely confounded analyses, participants from food-insecure households were not more likely to be in the top 10% (11.6% of food-insecure participants in top 10% vs 9.8% of food-secure; P = .09) but were more likely to be in the top 5% (6.9% vs 4.7%, respectively; P = .01) or top 2% (3.0% vs 1.9%; P = .03) of expenditures. In the fully adjusted models, food insecurity was associated with increased odds of being in the top 10% (OR, 1.73; 95% CI, 1.31-2.27), top 5% (OR, 2.53; 95% CI, 1.51-3.37), or top 2% (OR, 1.95; 95% CI, 1.09-3.49) of expenditures (full models in eAppendix Tables 10-12).

In gamma regression modeling, with total cost as the outcome and adjusting for the same factors, the mean annual cost difference between a food-insecure and a food-secure Medicare beneficiary was $5527.06 (95% CI, $2552.39-$8501.73; P <.0001), and that difference between a food-insecure and a food-secure participant with public health insurance other than Medicare was $1826.40 (95% CI, $797.69-$2855.11; P = .001) (full model in eAppendix Table 13).

Exploratory logistic regression models examining the clinical conditions associated with ED and inpatient use are presented in the eAppendix (eAppendix Figure 2). Point estimates suggest that visits related to diabetes and respiratory illnesses were more common in those with food insecurity, but, owing to small sample sizes, CIs were wide and often crossed 1.

DISCUSSION

In this study of nationally representative healthcare utilization and expenditure data in adults, we found that food insecurity was associated with significantly greater healthcare utilization, including ED visits and inpatient admissions, which are common targets of programs to reduce healthcare use. We further found that food insecurity was associated with increased odds of subsequently being a high-cost healthcare user. We found that the higher use of ED and inpatient services in food-insecure participants occurred despite higher use of outpatient services.

The findings in this study are consistent with and extend those in previous work.^26,27 Higher ED and inpatient service use despite higher outpatient service use suggests that, rather than food insecurity being associated with less healthcare access, the healthcare system, as currently structured, may be unable to address the factors that worsen health for these patients. Fragmentation of care, as indicated by food-insecure participants being less likely to report having a usual source of care, may help explain this finding. A single-center study of food-insecure patients with diabetes had a similar finding.²⁸ Two prior studies have estimated the total burden of food insecurity on healthcare costs in the United States, but they relied on ecological methods in which the healthcare costs of individuals experiencing food insecurity were not assessed.^15,16 A prior cross-sectional study of food insecurity and healthcare costs based in Canada¹³ found that mean costs are higher in those who experience food insecurity, and a longitudinal study based in the United States reached a similar conclusion.¹⁴ The study presented here used individual-level, nationally representative, longitudinal data to delve further into these findings by examining the distribution of healthcare costs. We found that very high use by a few participants greatly affects total healthcare expenditures and thus may be an important focus of interventions.

This study has several important implications. The longitudinal nature of these data may be particularly useful for care management programs. The ability to target resources to those likely to generate high healthcare costs in the subsequent 2 years is highly relevant for population health management efforts. Newly validated tools such as brief 2-item screeners for food insecurity may help accomplish this in clinical settings.^29,30 Programs that assess for unmet needs in clinical care and then link patients to community resources that help meet these needs may be 1 strategy to aid patients.^31-33 We also think it is important to note that ED visits or inpatient admissions are not necessarily to be avoided in all situations. Often, they represent appropriate care.^34-36 But given the clear association between food insecurity and these types of healthcare use, which are often disruptive to patients and represent worsening of clinical conditions, interventions to determine whether addressing food insecurity can help alter healthcare use in a way beneficial to both patients and the healthcare system are warranted.

Limitations

This study should be interpreted in light of several important limitations. We cannot exclude the possibility of unmeasured confounding, and the associations between food insecurity and healthcare use and cost cannot be viewed as causal. Because food insecurity is often episodic, the single assessment, with just a 30-day lookback period, used in NHIS may have resulted in misclassifying some participants who did experience food insecurity during the study period as food-secure. This would tend to reduce the magnitude of observed associations. Finally, given the low number of ED visits or inpatient admissions for any given clinical condition and lack of clinic detail about the health service use, we were not able to investigate the causes of the observed differences in great depth in this study, and we were not able to adjust for the specific diagnoses associated with the ED visit or admission.

CONCLUSIONS

Food insecurity is closely associated with higher use of ED visits, higher inpatient admissions, and having high healthcare costs. Although we do not yet know whether addressing food insecurity helps improve these outcomes, food insecurity indicates a group at high risk. Further work to integrate interventions on social determinants of health, such as food insecurity, into routine care may be an important step toward improving health and healthcare for vulnerable populations.&ensp;

Acknowledgments

The authors gratefully acknowledge Bianca Porneala, MS, of the Division of General Internal Medicine at Massachusetts General Hospital, for assistance with formatting the data set for analysis.Author Affiliations: Division of General Internal Medicine (SAB, JBM), and Diabetes Population Health Unit (SAB), Massachusetts General Hospital, Boston, MA; Harvard Medical School (SAB, JBM), Boston, MA; Division of General Internal Medicine, University of California, San Francisco (HKS), San Francisco, CA; Center for Vulnerable Populations at Zuckerberg San Francisco General Hospital & Trauma Center (HKS), San Francisco, CA; Department of Medicine, Center for Population Health Sciences, and Center for Primary Care and Outcomes Research, Stanford University (SB), Palo Alto, CA; Center for Primary Care, Harvard Medical School (SB), Boston, MA.

Source of Funding: This project was supported with a grant from the University of Kentucky Center for Poverty Research through funding by the US Department of Agriculture (USDA), Economic Research Service and the Food and Nutrition Service, agreement number 58-5000-3-0066. The opinions and conclusions expressed herein are solely those of the authors and should not be construed as representing the opinions or policies of the sponsoring agencies. Dr Berkowitz’s role in the research reported in this publication was supported, in part, by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health (NIH) under award number K23DK109200. Dr Meigs was supported, in part, by NIDDK under award number K24DK080140. Dr Basu’s role was supported, in part, by the National Heart, Lung, and Blood Institute under award number K08HL121056 and the National Institute on Minority Health and Health Disparities under award number DP2 MD010478. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Prior Presentation: A working paper that included preliminary versions of results included in the manuscript was presented to the USDA’s Economic Research Service on December 9, 2016, and submitted to the funder to solicit feedback, as per grant requirements, but was not for peer review or publication. The manuscript is not under peer review at any other journal.

Author Disclosures: Dr Seligman is a senior medical advisor and lead scientist for Feeding America, has given legislative testimony regarding food insecurity, and regularly speaks about the public health burden of food insecurity at meetings and conferences. The remaining 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 (SAB, HKS, JBM, SB); acquisition of data (SAB); analysis and interpretation of data (SAB, JBM, SB); drafting of the manuscript (SAB); critical revision of the manuscript for important intellectual content (HKS, JBM, SB); statistical analysis (SAB); obtaining funding (SAB); and supervision (HKS, JBM).

Address Correspondence to: Seth A. Berkowitz, MD, MPH, Division of General Internal Medicine and Diabetes Population Health Research Center, Massachusetts General Hospital/Harvard Medical School, 50 Staniford St, 9th Fl, Boston, MA 02114. Email: SABerkowitz@partners.org.REFERENCES

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