Estimated Impact Of Medically Tailored Meals On Health Care Use And Expenditures In 50 US States

Abstract

Medically tailored meals (MTMs) can reduce health care use among high-risk patients with diet-related conditions. However, the potential impact of providing coverage for MTMs across fifty US states remains unknown. Using a population-based, open-cohort simulation model, we estimated state-specific one-year and five-year changes in annual hospitalizations, health care spending, and cost-effectiveness of MTMs for patients with diet-related diseases and limitations in activities of daily living, covered by Medicaid, Medicare, or private insurance. Assuming full uptake among eligible people, MTMs were net cost saving in the first year in forty-nine states, with the largest savings seen in Connecticut ($6,299 per patient). The exception was Alabama, where MTMs were cost-neutral. The number of treated patients needed to avert one hospitalization ranged from 2.3 (Maryland) to 6.9 (Colorado). These findings can inform state-level policy makers and health plans considering MTM coverage through state-specific strategies.

Poor diet is a leading determinant of disease burdens and health inequities.¹ Medically tailored meals (MTMs) are a “Food Is Medicine” intervention that can improve diet-related health outcomes, reduce financial strain and improve associated well-being, address disparities, and reduce health care spending.^2–4 MTMs are prepared, home-delivered meals, typically provided to people with complex health conditions and high acuity of care based on a referral from a medical professional or health plan. Registered dietitian nutritionists design MTMs on the basis of patients’ nutritional assessments and diagnoses. Existing MTM programs typically provide ten weekly meals for an average of eight months annually.⁵ Recipients often have comorbidities, limitations in instrumental activities of daily living (IADLs), nutritional risk, and high prior health care use.⁶

Research suggests that nationwide adoption of MTMs for patients with diet-sensitive conditions could reduce patients’ health care use and spending by public and private payers.⁶ However, MTM coverage remains limited, although several states are beginning to cover Food is Medicine interventions through Medicaid demonstration projects.^1,7 These states are important incubators for health care innovation.

US states vary considerably in demographics, disease prevalence, and health care use and spending, which may influence MTMs’ impact and cost-effectiveness. The impact of MTM coverage on health care use and spending by US state remains unknown. This study investigated the one-year and five-year effects of implementing MTMs in US states on hospitalizations, health care expenditures, and net costs among patients with diet-related diseases and ADL limitations under Medicaid, Medicare, and private insurance. By providing state-specific estimates, this analysis offers policy makers and health plans insights to guide MTM implementation decisions at the state level.

Study Data And Methods

Design, Setting, And Population

Adopting a previously published population-level, open-cohort policy simulation model,⁶ we estimated changes in annual hospitalizations and health care expenditures after the implementation of MTMs versus the status quo. The study sample combines 2010–19 state-specific data from Medical Expenditure Panel Survey (MEPS)⁸ to determine healthcare costs with state-specific data from the 2019 Behavioral Risk Factor Surveillance System to estimate the size of each state’s eligible population (see supplement table 1 in the online appendix).¹¹ We excluded 2020 MEPS data because of pandemic-related data collection changes.⁹ State-level restricted MEPS data were obtained from the Agency for Healthcare Research and Quality Data Center.¹⁰

Patient Eligibility

The analytic sample includes US adults older than age eighteen with Medicare, Medicaid, or private coverage who had one or more diet-sensitive conditions and IADL limitations. Diet-sensitive conditions in MEPS included diabetes, angina, coronary heart disease, heart attack, other heart disease, emphysema, stroke, cancer, and hypertension. To estimate the eligible number in each state, conditions in BRFSS were matched as closely as possible (supplement table 2). We selected these conditions based on previous MTM research.^3,12–16 IADL limitations include receiving help using the telephone, paying bills, taking medications, preparing meals, doing laundry, or going shopping.¹⁷

MTM Intervention And Effect Sizes

We modeled the impact of providing ten weekly MTMs for a mean of eight months,⁶ following Food is Medicine Coalition standards.¹⁸ Effect sizes were estimated via an inverse-variance weighted meta-analysis of eight published interventional studies on MTM provision, ^{3,12–14,19–22} all of which were quasi-experimental studies with comparators or randomized trials. The analysis suggested that MTM provision was associated with a 47 percent reduction in annual hospitalizations (95% confidence interval [CI]: 36.1, 57.9) and 19.7 percent reduction in annual health care expenditure (95% CI: 7.0, 32.4).

Intervention And Health Care Costs

Annual MTM program costs included clinical screening and program expenses. Screening expenses were based on 2024 Medicare reimbursement rates for a fifteen-minute medical nutritional therapy session with a registered dietitian, ranging from $33.50 (Mississippi) to $46.45 (Alaska).²³ A 2024 survey of MTM organizations within the Food is Medicine Coalition (six responses) estimated that the mean program expenses per meal was $11.15 (standard deviation: 1.71). The MTM meal costs represent fully burdened MTM-related expenditures, including labor; overhead; contracting; and direct expenses such as materials (food and packaging) and delivery.

We estimated state-specific health care expenditures among the eligible population using MEPS data, adjusted to 2024 US dollars. State-specific data were available for the twenty-nine most-populous states, representing 88 percent of the US population. For the remaining twenty-one states, we extrapolated annual hospitalizations and expenditures using regional averages (Northeast, South, Midwest, and West), excluding data from the twenty-nine most populous states.

Simulation Model

Input data for the simulation model programmed in R included state-level eligible populations and demographics, baseline annual hospitalizations and expenditures, MTM intervention effects, and MTM program costs (supplement table 3).¹¹ Model outputs included changes in annual hospitalizations and expenditures, program costs, and net policy costs evaluated from a health system perspective at one year (2024) and five years (2024–28). Five-year outcomes were simulated annually, incorporating trends in eligible populations and health care expenditures, using 2010–19 MEPS data. These trends were estimated using log-linear regression, stratified by regions. We assumed that eligible people received meals for eight months annually, as long as they remained qualified, with a summing of outcomes across years and applying 3 percent annual discounting of health care and intervention costs. This assumption was based on studies used to derive the effect size, which had an average duration of treatment around eight months per twelve months of observation.^3,12–14,22

Probabilistic Sensitivity Analyses

By randomly drawing from the input parameters’ range in a probabilistic sensitivity analysis, 1,000 Monte Carlo simulations accounted for uncertainty. The central estimate was the mean of 1,000 simulations, with the 95% uncertainty interval from the 2.5th and 97.5th percentiles, assuming a normal distribution.

Secondary Sensitivity Analyses

We performed secondary sensitivity analyses to assess the results’ robustness. First, we restricted the receipt of MTMs to people with diabetes or congestive heart failure. Second, we modeled 50 percent of eligible patients receiving MTMs, instead of 100 percent. Third, we varied the intervention effect size across the 2.5th 10th, 25th, 50th, 75th, 90th, and 97.5th percentiles of the effect size. Fourth, we conducted two threshold analyses to estimate minimum changes in the per meal cost and health care expenditures needed for cost-neutrality. Fifth, we evaluated annual discounting rates of 0 percent and 5 percent. Finally, we estimated sustained health benefits from MTMs, assuming that 15 percent of MTM recipients each year would not require MTMs the following year while maintaining reduced hospitalizations and expenditures.

Strengths and Limitations

Strengths of our study include the use of state-specific data on eligible patients, health care use, and expenditures, allowing us to generate findings relevant to local decision-makers. Effects of MTM treatment were derived from pooled interventional studies and program costs from contemporary insurance contracts between MTM providers and health care systems. Patient eligibility criteria were consistent with prior research and existing MTM programs. Our model incorporated an open cohort and probabilistic sensitivity analyses to jointly incorporate uncertainty and report a range of outcomes. One-way sensitivity analyses tested the influence of specific assumptions. We estimated one-year and five-year outcomes, providing a range of clinically relevant and policy-relevant time horizons.

However, our study also had limitations. Modeling MTMs for patients with severe comorbidities and IADL limitations might not generalize to healthier populations or less intensive nutritional interventions. Also, MTM effects may vary across disease states. However, the included conditions were selected on the basis of prior MTM research, which informed our effect size estimates. These estimates represent a reasonable averaged effect across the selected disease states.

The main analysis assumed full coverage of all eligible people to provide a best-case scenario. In practice, scaling MTM services to reach all eligible people would take time, making a 50 percent coverage sensitivity analysis an informative comparison. Although scaling could enhance program efficiency and reduce costs, it could also affect nutritional quality, necessitating empirical research for evaluation.

Although state-specific MEPS data were available for the twenty-nine most populous states, estimates for the remaining states were based on regional means. Smaller eligible populations in smaller states contributed to wider uncertainty intervals, influenced by sample size and variance. However, for policy makers, overall trends and expected benefits often can offer actionable insights despite some uncertainty.

Finally, although we could not jointly stratify results by state and payer because of limited sample sizes, MTMs were cost-effective in nearly all states across Medicare, Medicaid, and private payers, suggesting that substantial divergence by payer type in individual states is unlikely.

Study Results

Size And Characteristics Of The MTM-Eligible Population

Based on BRFSS state-specific data, an estimated 10.4 million US adults qualify for MTMs (exhibit 1). The number of eligible patients was generally proportional to the state population, with the largest number in California (n = 923,568) and the smallest in Connecticut (n = 89,554) (supplement table 4).¹¹ Nationally, the mean age was 67.8 (SD: 16.5); 62.7 percent of the study population were female; and 51.8 percent were non-Hispanic White adults, 22.3 percent were non-Hispanic Black adults, 10.6 percent were Hispanic adults, and 7.8 percent were non-Hispanic Asian adults. The median household income-to-poverty ratio was 1.8 (interquartile range: 1.0–3.4; supplement table 5),¹¹ and 67.3 percent of patients had Medicare or Medicaid (exhibit 1). The most common eligibility diagnoses were cardiovascular diseases (62.2 percent), diabetes (35.0 percent) and cancer (30.1 percent). Among the sample population, mean annual health care expenditures per person were $30,892 (SD: $45,403), and the annual number of hospitalizations per person was 0.53 (SD: 1.02), highlighting the high severity of illness and health care use.

Exhibit 1:

Sample description of people eligible to receive medically tailored meals in the US by eligibility criteria, based on MEPS (2010–19) and BRFSS (2019) data

Characteristic	Eligible for MTM based on MEPS, N = 6,977,352	Eligible for MTMs based on BRFSS, N = 10,358,159
Age (mean)	67.8	60.7
Sex (%)
Female	62.7	65.7
Male	37.3	34.3
Race (%)
Hispanic	10.6	12.8
Non-Hispanic Asian	7.8	n/a
Non-Hispanic Black	22.3	15.9
Non-Hispanic White	51.8	64.4
Other or multiple	7.6	6.8
Family income, percent of federal poverty level
Median	181.5	n/a
Mean	267.9	n/a
Region (%)
Midwest	21.1	20.5
Northeast	17.5	14.9
South	37.6	45.0
West	23.7	19.5
Insurancea (%)
Private	30.3	n/a
Medicare	55.5	n/a
Medicaid	11.8	n/a
Dual eligible	26.3	n/a
Disease diagnosisb (%)
Coronary heart diseasec	34.6	26.9
Other heart conditions	36.2	n/a
Aspirin for CVD prevention d		9.5
Stroke	28.9	20.0
Emphysema	12.2	n/a
Lung diseasese	n/a	33.8
Cancer	30.1	21.9
Diabetes	35.0	38.4
Hypertension	81.6	74.9
Annual hospitalizations (average number per person)	0.53	n/a
Annual health care expenditures ($)	30,892	n/a

SOURCE Authors’ analysis of data from the Medical Expenditure Panel Survey (MEPS, pooling data from 2010 to 2019) and the Behavioral Risk Factor Surveillance System (BRFSS, 2019). NOTES Eligibility for MTMs is based on the presence of diet-sensitive conditions and limitations in instrumental activities of daily living (details are in the text). Statistics presented are percentages of the totals listed in the column heads unless otherwise indicated in the row labels. A more detailed version of this table is in supplement table 1 and supplement table 5 in the appendix (see note 9 in text).

^aNot applicable.

^bThe totals for insurance types do not add up to 100 percent because uninsured people are not included in these calculations and some people are eligible for both Medicare and Medicaid and are counted in both categories.

^bTotals do not equal 100 percent because eligible people may have multiple comorbidities. Some condition-specific proportions differ between MEPS and BRFSS because of variations in survey design, condition definitions, and measurement methods. Details on sample sizes by condition are provided in Supplement Table 2.

^cCoronary heart disease includes patients with angina, myocardial infarction (heart attack), or coronary heart disease.

^dAspirin for CVD prevention is included in BRFSS column to approximate ‘Other heart conditions’ reported in the MEPS column.

^eThe BRFSS “Lung Diseases” category is based on self-reported diagnosis of COPD, emphysema, or chronic bronchitis and may capture a broader set of respiratory conditions than the emphysema-specific codes used in MEPS.

Projecting One-Year Outcomes

Assuming 100 percent uptake among eligible people, MTMs were projected to generate mean net health care cost savings across all states except Alabama (where MTMs were cost-neutral) (exhibit 2; supplement table 4 and supplement figure 2).¹¹ Nationally, after the intervention costs were accounted for, MTMs were estimated to save $23.7 billion (95% UI: −14.6, 64.9) (supplement table 4).¹¹ States with the largest annual net cost savings were Connecticut ($6,299 per patient; 95% UI: 15, 13,974), followed by Pennsylvania ($4,450; 95% UI: −845, 10,824) and Massachusetts ($4,331; 95% UI: −756, 10,148).

Exhibit 2. Estimated 1-year impact of medically tailored meal (MTM) treatment on per person health care costs, by US state.

Source/Notes: SOURCE Authors’ analysis of data from the Medical Expenditure Panel Survey (MEPS), pooling data from the period 2010–19. NOTES Each eligible person was assumed to have received meals for 8 months annually. The simulation model ran 1,000 Monte Carlo simulations, using inputs and their uncertainties from the pooled MEPS data, relative risks of annual hospitalizations, and annual percentage changes in health care expenditures associated with MTM receipt, screening costs, and meal costs. The results present the mean of 1,000 simulations, ranked by net per person cost savings.

MTMs would avert a meaningful number of hospitalizations across all states, despite variations (exhibit 3; supplement table 4).¹¹ The number of treated patients needed to avert one hospitalization annually was lowest in Maryland, at 2.3, followed by Massachusetts and Pennsylvania, each at 3.0. Nationally, MTMs were estimated to avert 2,607,200 hospitalizations (95% UI: 1,675,700, 3,558,100) annually (supplement table 4).¹¹

Exhibit 3. Estimated 1-year impact of medically tailored meal (MTM) treatment on hospitalizations, by US state.

Source/Notes: SOURCE Authors’ analysis of data from the Medical Expenditure Panel Survey (MEPS), pooling data from the period 2010–19. NOTES Each eligible person was assumed to have received meals for 8 months annually. The simulation model ran 1,000 Monte Carlo simulations, using inputs and their uncertainties from the pooled MEPS data, relative risks of annual hospitalizations, and annual percentage changes in health care expenditures associated with MTM receipt, screening costs, and meal costs. The results present the mean of 1,000 simulations, ranked by the number of patients needing to be treated.

The annual net per person health care cost savings for each state and averted hospitalizations were modestly correlated (r = 0.46), with meaningful variation (exhibit 4). Maryland had lower cost savings per MTM-treated patient ($3,248; 95% UI: −1,086, 8,352) while requiring the smallest number of treated patients needed to avert one hospitalization (2.3 patients). Conversely, Connecticut reported large cost savings per MTM-treated patient ($6,299) but required a modest number of treated patients to avert one hospitalization (4.1 patients).

Exhibit 4. Relationship between estimated 1-year impact of medically tailored meal (MTM) treatment on health care costs and on hospitalizations, by US state, with regional trends.

Source/Notes: SOURCE Authors’ analysis of data from the Medical Expenditure Panel Survey (MEPS), pooling data from the period 2010–19. NOTES Each eligible person was assumed to have received meals for 8 months annually. The simulation model ran 1,000 Monte Carlo simulations, using inputs and their uncertainties from the pooled MEPS data, relative risks of annual hospitalizations, and annual percentage changes in health care expenditures associated with MTM receipt, screening costs, and meal costs. The results present the mean of 1,000 simulations. Averted hospitalization is represented as the mean number of hospitalizations averted per person. Each dot represents a state.

States in the South and Midwest regions demonstrated a higher correlation between per person cost savings and averted hospitalizations, whereas states in the Northeast and West showed more variability (exhibit 4). In the five most populous states, between 142,400 and 211,200 hospitalizations would be averted, with total cost savings of $1.2–$2.8 billion (supplement figure 1).¹¹

Sensitivity Analyses With One-Year Projection

For MTMs serving people with diabetes in our one-year projections, the eligible population would decrease to 3.95 million nationally. MTMs would be net cost saving across all states (supplement table 6),¹¹ with an average savings of $3,403 (95% UI: −1,008, 8,149) per person, representing nearly 150 percent greater savings than in our base case. Per MTM-treated patient with diabetes, states with the largest net cost savings were Connecticut ($11,098; 95% UI: 1,262, 24,164), Washington ($8,702; 95% UI: −463, 19,757), and Pennsylvania ($8,620; 95% UI: 462, 18,982).

When the one-year analysis was restricted to patients with heart failure, 1.79 million people were MTM-eligible nationally. MTMs for those patients would be net cost saving across all states, with a mean savings of $3,989 (95% UI: −834, 9,109) per person nationally—nearly 174 percent greater than our base case (supplement table 7).¹¹ States with the largest net cost savings per MTM-treated patient were Connecticut ($9,279; 95% UI: 1,262, 18,845), Pennsylvania ($6,876; 95% UI: 21, 14,710), and Massachusetts ($6,686; 95% UI: 129, 13,901).

Assuming 50 percent uptake, MTMs were still estimated to avert 1,303,400 hospitalizations (95% UI: 830,900, 1,806,600) and reduce net costs by $11.8 billion (95% UI: −7.3, 32.5) nationally (supplement table 8).. For a one-year MTM intervention to be cost-neutral, the per meal cost would need to increase from $11.15 to $18.3; the effect size on health care expenditure reduction would need to decrease by around one-third, from 19.7 percent to 12.5 percent.

Projecting Five-Year Outcomes

Based on observed trends among MTM-eligible patients, the eligible population was estimated to increase annually by 2.4 percent (Northeast), 1.7 percent (Midwest), 2.4 percent (South), and 0.3 percent (West) over a five-year period. Inflation-adjusted health care expenditures would increase annually in our sample by 1.5 percent (Northeast), 5.6 percent (Midwest), 1.3 percent (South), and 1.4 percent (West) during these five years. In 2024 dollars with 3 percent discounting, five years of MTM intervention could prevent 10,792,000 (95% UI: 7,298,000, 14,333,000) hospitalizations (supplement table 9)¹¹ and gain $111.1 billion (95% UI: −44.3, 282.0) in net savings nationally. Total projected state-level findings for five years generally mirrored those observed in the one-year projection (supplement figure 1).¹¹

Sensitivity Analyses With Five-Year Projection

For 5.0 percent discounting and 0 percent discounting, five-year net cost savings were $101.0 billion (95% UI: −40.2, 256.2) and $128.8 billion (95% UI: −51.3, 327.0) (supplement table 9).¹¹ Assuming that MTM benefits were sustained into the second year for 15 percent of people (3 percent discounting), MTMs could generate net cost savings across all states, totaling $131.7 billion (95% UI: −23.4, 302.2; supplement table 9) nationally during the five-year projection period.

Discussion

Our simulation model estimated that state-level MTM coverage among Medicare, Medicaid, and privately insured patients with diet-sensitive conditions and IADL limitations would be associated with reductions in annual hospitalizations and health care expenditures for nearly all states. The overall net cost savings ranged from $6,299 per patient (Connecticut) to cost-neutrality (Alabama). The number of treated patients needed to avert one hospitalization in a year ranged from 2.3 (Maryland) to 6.9 (Colorado).

The variation in MTM outcomes by state can be attributed to eligible population sizes, baseline annual health care expenditures, annual hospitalizations, and state-specific health care management strategies. States with the largest net cost savings, such as Connecticut ($6,299), Pennsylvania ($4,450), and Massachusetts ($4,331), reported high baseline health care costs per capita.²⁴ Maryland was estimated to require the smallest number of treated patients to avert one hospitalization (2.3 patients), but had relatively lower net cost saving per MTM-treated patient ($3,248) than other similar states with higher costs, such as Massachusetts (3.0 patients and $4,331) and Pennsylvania (3.0 patients and $4,450). These findings were likely driven by population health and state-level factors in health care delivery. For example, Maryland hospitals operate under a unique global budget program, which sets fixed annual budgets for hospital operations.^25–27 Although this program discourages unnecessary admissions, it also limits the direct financial benefits that hospitals can realize from each averted hospitalization, in part because of Maryland’s higher baseline number of hospitalizations.

Overall, the modestly correlated state-level net cost saving and averted hospitalizations (r = 0.46) align with previous findings, suggesting potential inefficiency in health care practices in some states.^28,29 Although meal costs may vary by state, this variation is unlikely to have influenced the overall conclusion, as the cost-neutrality threshold ($18.30) is much higher than the highest reported per meal cost among major MTM organizations we surveyed. Compared with a previous analysis using only national MEPS data (6.3 million eligible people, $13.6 billion savings),⁶ this analysis using state-specific BRFSS data identified a higher number of eligible people (10.4 million) and thus greater estimated national cost savings ($23.7 billion) (supplement table 4).¹¹ This difference is attributable to a higher proportion of adults who reported limitations in IADLs in BRFSS (7.2 percent) compared with MEPS (3.2 percent) and BRFSS includes broader self-reported conditions that may overrepresent true diagnoses. For example, BRFSS includes a wider set of lung conditions—such as emphysema, chronic bronchitis, and chronic obstructive pulmonary disease—whereas MEPS captures only emphysema. Additionally, we added hypertension and angina among cardiovascular conditions as eligibility criteria in both datasets based on evolving indications for MTMs.^30–32

Most long-term MTM programs use eligibility criteria beyond specific diseases, often considering other factors linked to higher health care use. These decisions are typically made by clinical providers or social workers case by case. Among the eight MTM studies informing our effect sizes, one explicitly included disability as a criterion, reporting a greater effect size than found in other studies.¹² Other programs included criteria such as recent hospital discharge with home health or hospice care, indicating likely functional limitations. Because national data lack details on clinical acuity, we used IADL limitations as a proxy to identify higher-need patients. Discussions with major MTM providers confirmed this as a reasonable approach to identifying such patients.

Although the availability of MTM services remains uneven, interest in improving access to such programs has grown through a national network of nonprofit providers that provide training and technical assistance to new programs.³³ There has also been a proliferation of for-profit MTM providers.³⁴ We included published interventional studies that used nonprofit providers managing meal delivery through health systems, although some studies also involved commercial providers.³⁵ Similar evaluations of for-profit providers are critical to ensure that nutritional standards and health benefits are not diminished. For example, an analysis by STAT News identified the low nutritional quality of MTMs provided by a for-profit provider to several Medicaid programs.³⁶ In general, we would expect that the provision of less healthy meals would likely be less effective than our findings, given that the effects of MTMs on hospitalizations and health care use stem in part from improved diet quality.⁶

Most interventional evaluations have been quasi-experimental. There remains a need for large randomized controlled trials (RCTs). In a pilot RCT among 161 patients hospitalized with heart failure, MTMs reduced hospital readmissions by 46 percent at five months and 41 percent at nine months compared with controls.²¹ Conversely, a larger trial among nearly 2,000 patients who had recently been discharged with heart failure, diabetes, or chronic kidney disease¹⁹ found reductions in hospitalization only in patients with heart failure. However, the intervention was short (mean of 6.9 weeks), and longer periods may be necessary to affect hospitalizations. A recent RCT enrolling patients with HIV found an 89 percent reduction in hospitalizations after six months of receiving MTMs, underscoring the potential for greater impact with longer intervention periods.³⁷

Although cost analyses are relevant to payers’ coverage decisions, the primary goal of MTM programs is to provide high-quality medical care for patients with diet-sensitive chronic illnesses. These programs should not be viewed as cost containment strategies—a high bar that is unrealistic for most therapies, preventive services, and diagnostic testing used in health care today.³⁸ In addition, the estimated effects on hospitalizations and health care expenditures do not incorporate potential additional benefits of MTMs for quality of life, disease progression, caregivers’ well-being, and health equity.

Our findings support the implementation and evaluation of MTM programs in public and private health systems at the state level. As of January 2025, sixteen states had approved or proposed Medicaid Section 1115 waivers, enabling MTM treatment coverage and representing a major pathway to standardizing MTM treatment (Kathryn Garfield, Harvard University, personal communication, January 18, 2025). However, Section 1115 waivers often employ restrictive eligibility criteria and require cost-neutrality, and they require frequent renewal by the Centers for Medicare and Medicaid Services. In our study, 89.7 percent of patients were covered by Medicare or Medicaid, highlighting the importance of these programs in facilitating MTM access. Many Medicare Advantage plans are also starting to include MTMs to help improve chronic conditions and patient outcomes.³⁹

Conclusion

Our state-level simulation model estimated that coverage for MTMs in Medicare, Medicaid, and private insurance for patients with diet-sensitive conditions and IADL limitations could prevent hospitalizations in fifty states and be net cost saving in forty-nine states. These findings, including variations across states, can help inform state-level policy makers and health plans that are considering implementing MTMs in clinical care through state-specific strategies.

Biographies

BIOS for 2024–01307 (Deng)

Bio1: Shuyue Deng (sdeng01@tufts.edu), Tufts University, Boston, Massachusetts.

Bio2: Kurt Hager, University of Massachusetts, Worcester, Massachusetts.

Bio3: Lu Wang, Tufts University.

Bio4: Frederick P. Cudhea, Tufts University.

Bio5: John B. Wong, Tufts University and Tufts Medical Center, Boston, Massachusetts.

Bio6: David D. Kim, University of Chicago, Chicago, Illinois.

Bio7: Dariush Mozaffarian, Tufts University.