A Trend Analysis of Home-Delivered Meal Programs in the US

Abstract

Introduction/Objectives:

Home-delivered meals improve diet quality and increase independence in homebound individuals. Yet, home-delivered meal service data at a US national level remains scarce.

Methods:

Based on NHANES (National Health and Nutrition Examination Surveys) data from 23 000 participants (2009-2023), we performed a secular trend analysis and reviewed the prevalence of home-delivered meals from community programs, “Meals on Wheels,” and other government programs in adults aged ≥60 years. A special focus was put on sociodemographic characteristics and physical disabilities of home-delivered meal recipients.

Results:

Up to 4% of the older US population reported home-delivered meals in the 2021 to 2023 NHANES cycle – the highest weighted proportion in all examined cycles. This constitutes a 2-fold increase compared to 2009/2010. Home-delivered meal recipients were more likely to live alone and in poverty. Very low food security increased the odds for receiving home-delivered meals by 5.09 (CI: 3.11-8.34). Vision problems and difficulties doing errands alone emerged as the leading causes of disability associated with home-delivered meals (OR: 2.65 (CI: 1.70-4.15) and OR: 3.90 (CI: 2.59-5.88), respectively with a P-value <.001). Increasing age and decreasing household size were also significant predictors.

Conclusions:

Results suggest an increasing prevalence of home-delivered meals among adults aged ≥60, particularly among those in precarious, food-insecure situations. Given the rapidly aging population and the desirable cost-benefit ratio of home-delivered meals, policymakers and public health strategies may consider expanding such programs to address the increasing demand.

Keywords

community meals home-delivered meals nutrition programs healthy aging public health

Introduction

The population of older Americans is rapidly growing, and so is the need for healthcare services for older adults.¹ Instead of institutionalization, many older US adults prefer to “age in place.”^2,3 Comprehensive social and nutrition services are thus essential.³ Several home- and community-based services, including home-delivered meal programs, are currently funded by the Older Americans Act.^3,4

Home-delivered meals are essential for homebound individuals (e.g., due to illness and physical disabilities).³ Such meals may improve diet quality and enable their recipients to stay more independent in their own home.^5-8 Recipients may also benefit from the social contacts and human interactions associated with home-delivered meals.³

Thomas and Mor⁶ emphasized that providing more home-delivered meals could effectively keep older adults out of nursing homes, which may decrease healthcare costs. Despite proven benefits, a review by Sahyoun and Vaudin³ emphasized that additional research on home-delivered meal programs is required. This applies in particular to health outcomes and service data at a national level.³ This call for additional research and data is reinforced by the ongoing debate about proposed budgetary cuts to the US Department of Health and Human Services, which would significantly cut or eliminate key programs (including home-delivered meals) under the Older American Act.^9,10

In light of this debate, we performed a national secular trend analysis and reviewed the prevalence of home-delivered meals from community programs, “Meals on Wheels,” and other government programs in the US-based National Health and Nutrition Examination Surveys (NHANES) from 2009 to 2023 in adults aged 60 years or older. An additional focus was put on sociodemographic characteristics of home-delivered meal recipients to identify factors associated with an increased likelihood of reporting home-delivered meals at a national level.

Materials and Methods

Study Population

The NHANES is a national survey that measures the health and nutrition of adults and children in the United States.^10,11 It is the only US-based national health survey that includes health examinations and laboratory measurements for participants of all ages. Since 1999, NHANES has been conducted without interruptions, collecting data from approximately 5000 participants yearly in communities across the United States. Due to its complex, multistage, stratified, clustered, and probability sampling design, NHANES allows for representative assessments at a national level. NHANES data is frequently used as the basis for shaping public health policy and designing population-based health programs and services. To the best of our knowledge, data related to home-delivered meals in the NHANES has not been analyzed in the form of a secular trend analysis so far. The STROBE Guidelines for reporting observational studies were followed.¹² Written informed consent was obtained from all NHANES participants, and study procedures were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board.¹³

Outcome

The primary outcome of interest was the self-reported delivery of community/government meals to NHANES participants’ homes. We thus used data from the NHANES diet behavior and nutrition section.¹⁴ Participants aged 60 years or older were asked: “In the past 12 months, did you receive any meals delivered to your home from community programs, “Meals on Wheels,” or any other programs?” The pre-defined variable categories (yes/no) were not modified. However, we excluded a total of 4 participants who refused to answer (n = 2) or replied with “don’t know” (n = 2).

Covariates/Predictors

Based on a thorough literature research prior to the analysis,^1,3,6,8,15 we selected the following covariables/potential predictor variables for receiving meals from community/government programs: age (continuous variable), sex (categorical variable, 2 levels: male and female), race/ethnicity (categorical variable, 5 levels: Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Race), marital status (categorical variable, 3 levels: married/living with partner, widowed/divorced/separated, never married), education status (categorical variable, 5 levels: less than 9th grade, 9−11th grade, high school graduate, some college or associate degree, college graduate or above), household size/number of persons in the household (continuous variable, ranging from 1 to 7 persons), food security level (categorical variable, 4 levels: full, marginal, low and very low food security), and poverty level (categorical variable, 2 levels: below and above poverty level). For the latter, we used the family income to poverty ratio, whereby a ratio <1 was considered the poverty threshold for this analysis.

Other covariables taken into account were the country of birth (categorical variable, 2 levels: inside or outside of the USA), citizenship status (categorical variable, 2 levels: citizen by birth or naturalization vs not a citizen of the US), annual household income (categorical variable, 3 levels: <20 000$, ≥20 000$ but below 75 000$ and ≥75 000$), self-assessed diet quality (categorical variable, 3 levels: poor, good or fair, excellent or very good), history of active duty in the US armed forces (categorical variable, 2 levels: yes vs no), health insurance coverage (categorical variable, 2 levels: yes vs no), lack of insurance in the past 12 months (categorical variable, 2 levels: yes vs no), and occupation-related data (categorical variable, 3 levels: working at a job or business, looking for work, not working at a job or business?). We selected these variables because previous studies suggested potential associations with home-delivered meals.^1,3,6,8

Part I: Secular Trend Analysis

In a first instance, we performed a secular trend analysis. The crude predicted probabilities for receiving home-delivered meals in individuals aged 60 or older were estimated for each examined NHANES cycle (2009/2010 through 2021-2023). Data from the incomplete 2019-2020 NHANES cycle was not used here, as it was not deemed generalizable to the U.S. civilian non-institutionalized population.¹⁶ Predicted probabilities by age were plotted for the general US population and for males and females only. Pairwise comparisons were performed across the various NHANES cycles using Stata’s post-estimation contrast operator.

Part II: Predicted Probabilities Analysis

In a second step, we constructed a defined subpopulation with complete data on all covariates and predictors (Supplemental Figure 1). Participants with missing data on any variable of interest were excluded. Binomial logistic regression was used to ascertain the effects of various sociodemographic predictors on the likelihood that participants reported home-delivered meals. The model techniques for this approach are described in detail below.

Part III: Sub-Analysis – Associations with Disabilities

In a final step, we performed a second (smaller) subpopulation analysis in which we investigated the effects of various disabilities (including self-reported hearing, vision, walking, and concentration difficulties) on the likelihood that participants reported home-delivered meals. Data for this analysis were drawn from the disabilities module, which was only included in the following 3 NHANES cycles: 2013/2014, 2015/2016, and 2017/2018. We performed part III separately to avoid compromising the overall sample size in the predictor analysis (part II).

Statistical Analysis

The statistical analysis was performed in Stata 18 (StataCorp, 2023. Stata Statistical Software: Release 18. College Station, TX: StataCorp LLC). Stata’s “. svyset” and “. svy” commands accounted for the complex NHANES survey design characteristics. A 12-year sample weight for household interview data was constructed for the secular trend analysis (part I). The sociodemographic predictor analysis was restricted to 2009-2018; thus, a 10-year sample weight was constructed for this analysis (part II).

Unconditional subclass analyses were performed to compare sociodemographic data between participants reporting home-delivered meals and participants denying them. Categorical variables were described with their weighted proportions and 95% confidence interval (CI). Data presentation standards for weighted proportions were taken into account.^17,18 Continuous variables were described with their mean and 95% CI in parentheses.

We ran a design-adjusted version of the Pearson Chi-square test to examine potential associations between sociodemographic variables and home-delivered meals. Between-group differences were compared using Stata’s lincom command (in the case of proportions) or regression analyses followed by adjusted Wald tests (in the case of continuous variables).

Logistic regression models predicting the likelihood of reporting home-delivered meals were constructed using a widely used model-building technique by Heeringa et al¹⁹ for applied survey data analysis.¹⁹ All variables (sex, race/ethnicity, marital status, education level, food security level, poverty status) were entered as categorical variables into the regression model, except for age and household size. Odds ratios (OR) from the multivariate logistic regression model were displayed in a coefficient plot.²⁰ Using Stata’s margins command, we calculated the average predicted values for the included sociodemographic predictors. Results were then visualized in a series of margins plots.²¹

Results

The final sample for the secular trend analysis (part I) comprised n = 23 000 unweighted observations. Based on these data, Figure 1 shows predicted probabilities for receiving home-delivered meals in individuals aged 60 or older for each NHANES cycle. Approximately 4% of the sample were expected to report home-delivered meals in the 2021 to 2023 NHANES cycle. A significant difference was found in the general population when comparing the predicted probabilities of 2021/2023 versus 2009/2010 (contrast: +0.021, P = .005) and versus 2013/2014 (contrast: +0.014, P = .034), indicating an increase in participants reporting home-delivered meals. This trend was found in both sexes although females were overall more likely than males to report home-delivered meals.

Figure 1.

Predicted probabilities for receiving home-delivered meals (HDMs) in individuals aged 60 or older by NHANES cycle.

For a more in-depth analysis of predicted probabilities and factors associated with an increased likelihood of home-delivered meals, we restricted the study to the NHANES cycles of 2009/2010 to 2017/2018 (part II). The sub-analysis included a large set of covariates, and the final sample comprised n = 7889 unweighted observations. Supplemental Figure 1 depicts a participant inclusion flowchart for this analysis. Table 1 illustrates sample characteristics by home-delivered meal status. Reporting home-delivered meals was not independent of sex, race/ethnicity, age, marital status, educational level, and age. Participants reporting home-delivered meals tended to be female, of higher age, unmarried/widowed or divorced, and with a lower educational level. Those with a low annual household income (<20 000$) and living in food insecurity/below poverty threshold tended to report home-delivered meals more frequently.

Table 1.

Sample Characteristics by Home-delivered Meals (HDMs) Status: NHANES 2009 to 2018.

Variables	HDMs: no (n = 7572)	HDMs: yes (n = 317)	P-value
Sex			P = .043 ^a
Male	45.61% (44.52-46.70)	38.52% (32.11-45.36)*
Female	54.39% (53.30-55.48)	61.48% (54.64-67.89)*
Ethnicity/race			P < .001 ^a
Mexican American	3.45% (2.58-4.60)	6.32% (3.30-11.75)^c
Non-Hispanic White	78.84% (76.25-81.21)	67.43% (59.64-74.37)*
Non-Hispanic Black	8.33% (7.04-9.84)	13.85% (9.65-19.48)*
Other Hispanic	3.26% (2.56-4.13)	5.15% (3.06-8.54)
Other race	6.12% (5.15-7.25)	7.25% (4.22-12.17)
Age (years)	69.75 (69.52-69.99)	74.16 (73.36-74.97)	P < .001 ^b
Education level			P < .001 ^a
Less than 9th grade	6.00% (5.21-6.89)	20.92% (15.55-27.54)*
9-11th grade	9.58% (8.42-10.88)	14.87% (10.20-21.16)*
High school graduate/GED	24.32% (22.57-26.16)	29.49% (24.19-35.40)
Some college or AA degree	29.82% (28.02-31.68)	26.72% (21.08-33.23)
College graduate or above	30.28% (27.77-32.92)	8.01% (4.85-12.95)*
Marital status			P < .001^a
Living with a partner/married	64.93% (63.16-66.66)	32.66% (24.56-41.94)*
Divorced/separated/widowed/	31.22% (29.59-32.91)	61.74% (52.91-69.86)*
Never married	3.85% (3.35-4.41)	5.60% (3.19-9.66)
Citizenship status			P = .238^a
US citizen by birth or naturalization	97.59% (97.08-98.02)	96.36% (92.64-98.23)
Not a citizen of the US	2.41% (1.98-2.92)	3.64% (1.77-7.36)^c
Language of NHANES interview			P < .001 ^a
English	97.14% (96.22-97.84)	93.18% (88.32-96.10)*
Spanish	2.86% (2.16-3.78)	6.82% (3.90-11.68)*
Born in the United States			P = .794^a
Yes	88.27% (86.60-89.76)	88.87% (83.20-92.80)
No	11.73% (10.24-13.40)	11.13% (7.20-16.80)
Ratio of family income to poverty			P < .001 ^a
<1 (below the poverty threshold)	9.05% (8.03-10.19)	30.11% (23.68-37.44)*
≥1 (above the poverty threshold)	90.95% (89.81-91.97)	69.89% (62.56-76.32)*
Annual household income			P < .001 ^a
<$20 000	15.17% (13.50-17.00)	55.46% (47.33-63.32)*
≥$20 000 & <$75 000	51.90% (49.29-54.51)	41.22% (33.46-49.43)*
≥$75 000	32.93% (30.14-35.85)	3.32% (1.44-7.46)*,^c
Adult food security category			P < .001 ^a
Full food security	86.23% (84.81-87.55)	60.19% (52.37-67.53)*
Marginal food security	5.78% (5.00-6.69)	11.26% (7.14-17.32)*
Low food security	4.71% (4.10-5.42)	13.73% (10.33-18.03)*
Very low food security	3.27% (2.68-3.98)	14.81% (10.40-20.67)*
Number of people in the household	2.15 (2.11-2.20)	1.70 (1.58-1.82)	P < .001 ^b
Covered by health insurance?			P = .421^a
Yes	99.43% (99.14-99.62)	99.75% (98.17-99.96)
No	0.57% (0.38-0.86)	0.25% (0.04-1.83)^c
Time when no insurance in the past?			P = .088^a
No	97.86% (97.42-98.22)	96.30% (93.24-98.01)
Yes	2.14% (1.79-2.58)	3.70% (1.99-6.76)^c
Income from retirement/survivor pension			P = .085^a
Yes	43.09% (40.83-45.38)	35.09% (27.16-43.94)
No	56.91% (54.62-59.17)	64.91% (56.06-72.84)
Served in the US armed forces			P = .995^a
Yes	21.14% (19.78-22.57)	21.12% (15.97-27.40)
No	78.86% (77.43-80.22)	78.88% (72.60-84.03)
Self-perceived diet quality/health			P < .001 ^a
Excellent or very good	42.20% (40.40-44.01)	28.44% (23.27-34.24)*
Good or fair	54.67% (52.96-56.38)	65.93% (60.29-71.15)*
Poor	3.13% (2.65-3.69)	5.63% (3.15-9.87)
Type of work done last week			P < .001 ^a
Working at a job or business	28.79% (27.33-30.30)	5.91% (3.39-10.21)*
Looking for work	0.83% (0.61-1.13)	0.83% (0.23-2.97)^c
Not working at a job or business	70.38% (68.86-71.85)	93.26% (88.97-95.96)*

Based on n = 7889 participants. Continuous variables are shown as mean (95%-CI). Categorical variables are shown as weighted proportions (95%-CI). All weighted proportions can be considered reliable, as per the recent NCHS Guidelines, except for those marked with a “^c” symbol.

Based on Stata’s design-adjusted Rao–Scott test.

Regression analyses followed by adjusted Wald tests.

The “*” symbol denotes significant between-group differences in the weighted proportions.

Significant p-values shown in bold.

A multivariate logistic regression model was built to ascertain the effects of sex, race/ethnicity, age, civil status, education level, household size (number of persons), poverty status, and food security level on the likelihood that participants received home-delivered meals. Figure 2 shows a coefficient plot, plotting the obtained odds ratios (OR) from the final model based on n = 7889 observations. Increasing age was associated with an increased likelihood of reporting home-delivered meals (OR: 1.10 (CI: 1.07-1.12), whereas increasing household size was associated with a decreased likelihood (OR: 0.69 (CI: 0.59-0.81). Widowed/divorced/separated participants were 1.73 times more likely to receive home-delivered meals. Those who did not complete the 9th grade were 2 times more likely to report home-delivered meals, and very low food secure participants were 5.09 times more likely.

Figure 2.

Coefficient plot – plotting odds ratios from a multivariate logistic regression model predicting the likelihood of receiving home-delivered meals.

Based on the aforementioned regression model, we plotted predicted probabilities of reporting home-delivered meals depending on household size in Supplemental Figure 2. No significant differences were found between sexes (panel A). Panel B shows significant differences between widowed/divorced/separated participants and married/with partner participants (contrast: +0.013, P = .027). Panel C shows considerable contrasts between the various educational levels, particularly between college graduates versus those without a degree (contrast: −0.040, P = .001). Panel D depicts differences between those below and above the poverty level (contrast: +0.014, P = .058). As for the food security levels, the most critical contrasts were found for very low versus whole food secure participants (contrast: +0.068, P < .001). All panels suggest that the larger a household, the smaller the differences in predicted probabilities between the various levels of the examined sociodemographic predictors. The highest predicted probabilities were found in very low food secure participants living alone (predicted probability: 0.12) and participants who lived alone and did not complete the 9th grade (predicted probability: 0.07).

In Figure 3, we observed a different trend when plotting predicted probabilities of reporting home-delivered meals depending on age. The higher the participant’s age, the larger the differences in predicted probabilities between the various levels of the examined sociodemographic predictors. This applied in particular to the different educational levels (panel C) and food security levels (panel E). The highest predicted probability was found in very low food-secure participants at 80 years or older (predicted probability: 0.16).

Figure 3.

Margins plot – plotting predicted probabilities of reporting home-delivered meals (HDMs) depending on participants’ age.

Finally, we performed a sub-analysis in participants who had available data from the NHANES disability module included in the NHANES from 2013 to 2018. This analysis was based on a subsample of n = 4744 participants, shown in Figure 4. All panels (A-E) indicate a comparable trend: the higher the participants’ age, the larger the differences in predicted probabilities between those without and with physical disabilities. Considerable contrasts were found between those participants without and with vision difficulties (contrast: +0.032, P = .001), for those without and with walking difficulties (contrast: +0.032, P < .001), and for those without and with difficulties doing errands alone (contrast: +0.045, P < .001). The highest predicted probabilities were found in participants aged 80 years or older who were experiencing problems seeing and had difficulties doing errands alone. Supplemental Table 1 displays the OR for all disabilities.

Figure 4.

Margins plot – plotting predicted probabilities of reporting home-delivered meals (HDMs) depending on disability status and participants’ age.

Discussion

The present study sought to explore secular trends in and sociodemographic factors associated with home-delivered meals from community programs, “Meals on Wheels,” and other government programs in the US-based National Health and Nutrition Examination Surveys (NHANES) in adults aged 60 years or older. Up to 4% of the older US population reported home-delivered meals from community programs in the 2021-2023 NHANES cycle – the highest weighted proportion in all examined continuous NHANES cycles. In a multivariate logistic regression model to estimate the predicted probabilities of reporting home-delivered meals, increasing age and decreasing household size were significant predictors. In line with the available literature on this topic,³ our data suggest that home-delivered meal recipients were more likely to live alone (being widowed/divorced/separated) and in poverty. Very low food security increased the odds for receiving home-delivered meals by 5.09. Vision difficulties and difficulties doing errands alone emerged as the leading causes of disability associated with an increased likelihood of home-delivered meals. While data on the pandemic years of 2019 and 2020 were omitted in our analysis, our results suggest an increase in home-delivered meals over the past few years.

Home-delivered meals may profoundly impact food security and the risk of institutionalization.³ As reviewed by Sahyoun and Vaudin,³ up to 90% of home-delivered meal participants reported that their meals helped them live independently in their homes.^22,23 Inadequate nutrition remains a key risk factor contributing to the development and worsening of chronic health conditions in the aging population.²⁴ An adequate nutrient intake may support older adults in maintaining muscle mass and cognitive performance, thereby preventing falls and other adverse events.²⁴ A recently published study, including n = 1090 community-dwelling older adults who received home-delivered meal services between June 2020 and December 2021, investigated whether home-delivered meals reduced frailty.²⁵ Results suggested that frailty status assessed with the home care frailty scale (HCFS) significantly declined throughout a 6-month follow-up period (Δ HCFS-score = −1.9; 95% CI: [−2.7, −1.1]; P < .001) in those at high nutritional risk receiving home-delivered meals.²⁵ Frailty is a significant risk factor for institutionalization, and numerous studies have outlined that the risk of adverse health outcomes increases with frailty in a higher number of domains, with a subhazard ratio of 3.48 (95%-CI: 1.83-6.62, P < .001) for institutionalization.^26,27 Additionally, the social component of regular (often daily) contact with older, frequently isolated adults may not be neglected.²⁸ Psychological frailty, for example, the combination of physical frailty with depressive symptoms, is a strong predictor for mortality.²⁷

In times when aging societies call for restructuring processes in healthcare and delivery, said findings may be taken into account when it comes to critical decisions regarding resource allocation.¹ This is paramount in light of the ongoing shift in focus from lifespan extension toward enhancing the quality of life of older adults.¹ Aging in place is preferred by the vast majority of adults in the US, but without an adequate support system providing direct assistance (such as with transportation, housekeeping, organizing medication, sometimes even personal care), aging in place can quickly become fraught with challenges.^29,30 The herein presented data emphasize a continuous demand for home-delivered meals and reiterate that certain population strata (home-bound, food insecure, and widowed/never married individuals) are at a particular risk. To initiate and expand meal services, meal-delivering agencies could partner with local institutions working with older adults (e.g., community centers) and seek the support of older adult volunteers who are often well placed within their respective communities and have a wealth of local knowledge and networks.^31,32

The herein presented data could play an important role in the ongoing debate about budgetary cuts related to the US Department of Health and Human Services.^9,33 The US Department of Health and Human Services houses the Administration for Community Living (ACL), which is responsible for the administration of programs and services authorized by the Older American Act.⁹ Plans to dissolve the ACL could diminish current programs and services, which provide home-delivered meals to approximately 1.3 million Americans.⁹ In this context, it is essential to emphasize that home-delivered meal programs were rated “effective“ in a US government performance report/assessment.³ With “effective“ being the highest rating a program can achieve, it is clear that efforts should be undertaken to meet current demands.

Our analysis is not without limitations. In particular, we did not include the incomplete 2019/2020 NHANES cycle, which did not permit nationally representative estimations. Several potentially essential predictor variables (e.g., alcohol intake data and geographical data [rural vs urban]) were not included for sample size considerations or data availability restrictions. In this context, the lack of geographic identifiers is worth highlighting, as funding and eligibility for home-delivered meal programs vary by location (state and county). Regrettably, we had no access to (restricted-use) NHANES geocode variables. Moreover, individuals 80 years and over are top-coded at 80 in the NHANES for confidentiality reasons. Predicted probability estimations for participants in this age group might thus be biased. Regrettably, NHANES did not include any additional variables related to home-delivered meals. Data on the delivery frequency (e.g., daily, weekly, etc.) and meal type (e.g., frozen meals, ready-to-eat meals, etc.) was not available and we clearly acknowledge this limitation.

Nevertheless, we believe in the nationally representative data analyzed and presented here. It is the first NHANES-based analysis on secular trends and epidemiology in home-delivered meals. The analysis procedure followed the STROBE guidelines (Supplemental Table 2). The data presented here may be necessary for public health strategies and policymakers involved in resource allocation and healthcare delivery when tailoring home-delivered meal programs and strategies. Nevertheless, additional studies are needed to implement such programs in a sparse resource setting.

Conclusions

The data presented here suggest an increasing demand for home-delivered meals among US adults aged 60 years or older, particularly among widowed/unmarried/divorced individuals living in precarious (food insecure) situations. Physical disabilities, such as vision problems, significantly increase the odds of reporting home-delivered meals. The data presented here may be of relevance for resource allocation and new strategies when healthcare systems and delivery must be restructured due to an imbalance between demand for and availability of resources.

Supplemental Material

sj-docx-3-jpc-10.1177_21501319251370989 – Supplemental material for A Trend Analysis of Home-Delivered Meal Programs in the US

Supplemental material, sj-docx-3-jpc-10.1177_21501319251370989 for A Trend Analysis of Home-Delivered Meal Programs in the US by Maximilian Andreas Storz and Ruopeng An in Journal of Primary Care & Community Health

Supplemental Material

sj-docx-4-jpc-10.1177_21501319251370989 – Supplemental material for A Trend Analysis of Home-Delivered Meal Programs in the US

Supplemental material, sj-docx-4-jpc-10.1177_21501319251370989 for A Trend Analysis of Home-Delivered Meal Programs in the US by Maximilian Andreas Storz and Ruopeng An in Journal of Primary Care & Community Health

Supplemental Material

sj-jpg-1-jpc-10.1177_21501319251370989 – Supplemental material for A Trend Analysis of Home-Delivered Meal Programs in the US

Supplemental material, sj-jpg-1-jpc-10.1177_21501319251370989 for A Trend Analysis of Home-Delivered Meal Programs in the US by Maximilian Andreas Storz and Ruopeng An in Journal of Primary Care & Community Health

Supplemental Material

sj-jpg-2-jpc-10.1177_21501319251370989 – Supplemental material for A Trend Analysis of Home-Delivered Meal Programs in the US

Supplemental material, sj-jpg-2-jpc-10.1177_21501319251370989 for A Trend Analysis of Home-Delivered Meal Programs in the US by Maximilian Andreas Storz and Ruopeng An in Journal of Primary Care & Community Health

Footnotes

Acknowledgements

n/a

List of abbreviations

CI Confidence interval

HCFS Home Care Frailty Scale

NCHS National Center for Health Statistics

NHANES National Health and Nutrition Examination Surveys

OR Odds ratio

STROBE Strengthening the Reporting of Observational Studies in Epidemiology

US United States

ORCID iDs

Maximilian Andreas Storz

Ruopeng An

Ethical Considerations

The study was performed in accordance with the Declaration of Helsinki and approved by the NCHS Research Ethics Review Board (). NHANES was approved by the National Center for Health Statistics research ethics review board.

Consent to Participate

Informed consent was obtained from all participants.

Consent for Publication

Informed consent was obtained from all NHANES participants.

Author Contributions

Conceptualization: MS; Data curation: MS; Formal Analysis: MS, RA; Funding acquisition: MS; Investigation: MS, RA; Methodology: MS; Project administration: MS, RA; Resources: MS; Software: MS; Supervision: RA; Validation: MS, RA; Visualization: MS; Writing – original draft: MS; Writing – review & editing: MS, RA

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We acknowledge support by the Open Access Publication Fund of the University of Freiburg.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data is publicly available online (). The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental Material

Supplemental material for this article is available online.

Declaration of Generative AI in Scientific Writing

No generative AI was used by the authors.

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