Does Living Near Public Transport Equate to Food (In)security in the United States? – Evidence from the 2020 National Health Interview Survey (NHIS)

1. Introduction

“Food security” is the basic need of humans critical to creating a healthy and sustainable society. Food security meeting the nutritional needs of urban and rural populations has widespread socioeconomic impacts through the network of food aggregators in a supply-chain context. In this process, transportation plays a vital role by facilitating and upholding food security through the much-needed circulation of food for the masses. However, very little is known about how people relocate close to a transportation system to ensure their food security is met or unmet as they reside close to the transportation facilities. In addition, the success of food security must be gauged by its ability to feed the last consumer in the food supply chain, especially those who belong to low-income communities residing near public transport. This again needs some current understanding of the existing food insecurity among the residents near transit, requiring further policy discussion and research, which this paper investigates. Therefore, the objective of this paper is to develop our understanding of how food security (if at all) is prevalent among people residing near transit, or, in other words, investigating if living near public transport equate to food (in)security or vice-versa. We use the data from the United States of America (USA), as an example, to answer this research question.

Food insecurity has been identified as a public health and environmental issue affecting many people in the United States ([1,2]). Lack of access to adequate food in the US arise because of poor financial and resource availability [1] – since food insecurity is mainly prevalent in families with low-income, are disabled or belong to minority races [3]. In 2021, there were 13.5 million households in the US who were food insecure, determined by many factors, including household circumstances, the economy, and Federal, State, and local policies [4]. The chart in Fig. 1 shows how food insecurity has been prevalent over the years in US households from 2001 to 2021.

Figure 1. Trends in food insecurity in US households, 2001-21 (Source: USDA, Economic Research Service using data from US Department of Commerce, Bureau of the Census, Current Population Survey Food Security Supplements [4]).

Figure 1. Trends in food insecurity in US households, 2001-21 (Source: USDA, Economic Research Service using data from US Department of Commerce, Bureau of the Census, Current Population Survey Food Security Supplements [4]).

The United Nations has defined food security as when “all people, at all times, have physical, social, and economic access to sufficient, safe, and nutritious food that meets their food preferences and dietary needs for an active and healthy life” [5]. The World Food Programme (WFP) of the United Nations, focusing on food security and hunger, points out that in 2022, 879 million people did not have sufficient food consumption across 92 countries worldwide. About half of these are from Asia and the Pacific, with India having the largest population, predicted to have about 244 million people with insufficient food consumption [6].

Various studies show that the integration of agriculture and political will combined with the logistics of food delivery can ensure food security [7]. However, the process cannot be entirely successful unless an effective public policy with strong implementation measures exists on food security. In the USA, Food Stamp Program, now known as the Supplemental Nutrition Assistance Program (SNAP), provides nutritional benefits to supplement the food budget of needy families and enables them to purchase healthy food [8]. The SNAP aims to help those that meet the requirements with income below certain gross and net income limits for given household size. It is expected that the households would spend about 30 percent of their resources on food. Thus, for a household size of 1, the maximum monthly benefits (known as ‘allotment’) for 2021-2022 is fixed at $250. The allotment is calculated by multiplying a household’s net monthly income by 0.3 and subtracting the result from the maximum monthly allotment for your household size. Thus, the country's federal government ensures food security for the household.

Transportation plays a critical role in food security and ensuring that every individual has access to various food production sources and markets. As per the Centers for Disease Control and Prevention (CDC) – “A poor transportation system cuts off access to many food outlets—especially for those who do not own a car or have no access to reliable and affordable public transportation.” A strong nexus among the three, namely the farmers (as food producers), retailers, and consumers facilitated by transport, ensure an appropriate level of food security is guaranteed for all. Often it is a challenge for some to travel to stores to purchase food. Transit or other cheap nonmotorized transport is a natural choice for low-income households to access food stores. Over the last few decades, investments in public transport in the US have been steadily increasing to stimulate ridership and provide an alternate mode of transportation (addressing equity issues) to low-income communities [9]. Successful investment planning for transit has also led to decentralizing poverty in the suburbs [10]. However, transit improvements and development often lead to gentrification and socioeconomic changes over time in low-income neighborhoods that already reside close to transit. This can severely paralyze policies meant to uplift low-income households that earn their daily livelihood by continued use of public transport. A recent study by Liang et al. (2022) showed that a new rail line increased the number of advanced degree holders in the proximity of the rail line in the City of Hong Kong [11]. This indicates that rail transit investment induces high-educated people to move into transit-rich low-income areas, thereby compelling low-income households to move out and away from being able to access transit to fulfill daily commuting needs. In another study, for the city of Rosengård in Malmö in Sweden, gentrification and displacement of low-income neighborhoods were observed with the transit-orient development programs [12]. Similar debates in the media have been on ensuring affordable housing for low-income groups in cities like Delhi [13].

In developed nations like the USA, dense urban areas where food consumption is the highest because of the high population, the cost of living (housing) and commuting for low-income commuters to access food often pose a significant challenge. To minimize housing costs and prevent the movement of low-income households from transit-rich locations, various cities in developed nations like the USA have taken steps to provide affordable housing for low-income families. For example, the densely populated City of Long Beach in California has seen a surge in several affordable housing projects close to the A Line transit of the Los Angeles Metro area in the past five years [14] . The A Line transit serves a large community of low-income households.

The map in Fig. 2a shows the percentage of SNAP participants in the year 2020 across the four analyzed regions of the aggregated States (Data source: USDA, 2023 [28]). Clearly, the subscription of SNAP has been the highest in the Southern states compared to the others. Subsequently, using the data from the American Community Survey (ACS) [15], the latest commuting patterns across the four regions were compared to the percentage of SNAP subscription. The percentage distribution of workers commuting by transit is shown in Fig. 2b, which points out the least transit usage by the Southern states. Therefore, this preliminary analysis showed that, although the transit use is minimum in the Southern States, the reliance on SNAP among the residents living close to transit is high in these states. This motivates further research to be conducted surrounding a specific transit line for any required city or town to understand if commuters residing near the transit use transit, have a significant subscription to SNAP, which could reduce food insecurity addressed in this paper.

Figure 2. Percentage distribution of SNAP participants and transit commuters across regions of the USA.

Figure 2. Percentage distribution of SNAP participants and transit commuters across regions of the USA.

Studies have shown that along with welfare benefits and socioeconomic factors, food security depends on proximity to retail grocery stores and food prices ([16,17]), and public transport is a natural mode for low-income households to access such retail stores. In another study, Baek (2014) concluded that an extra bus-equivalent vehicle serving 10,000 people decreases the probability of food insecurity by 1.6 percent [18].

Personal vehicles serve as the first mode of choice to access food stores in urban areas that are deprived of an efficient and reliable public transportation system [19]. Studies to develop the relationship between the spatial availability of food and the influence of travel mode and travel time necessary to purchase food have also been well-documented [20]. Prior studies have shown that for the City of Baltimore in the US, residents with lack of food stores had a significantly high travel time compared to those living in communities who were close to a supermarket [21]. Findings from other cities of the US, such as San Diego, showed that minimum travel time by car to any store was 4.22 minutes, while it was 15.88 minutes with transit [20]. All of these examples show availability of transit does not guarantee efficiency in access to food in a developed nation. Thus, this topic of research is much of interest for investigating if transit helps attenuate food insecurity in the USA.

However, to our knowledge, no research has been conducted to evaluate the association between living near transit and food security among Americans. This paper investigates if those living near transit in the USA are also the ones who are facing food insecurity- and if yes, it would serve as a worthwhile motivation to identify policies that would minimize food insecurity for a healthy society thriving on transit for daily needs. To the best of our knowledge, this is the first study conducted on this issue of food security among the population residing near transit, which can be replicated for other regions of the world with similar demographics to the USA.

In essence, this paper evaluates the impacts of various factors on food insecurity among the American population residing near public transport. Factors that are key socioeconomic variables such as age, income, education level, and dependency on food stamps in the last twelve months are studied for food insecurity measured across four combined inputs gathered on the following information from the respondents living near transit if they, in the past, had to - i) worry food would run out, ii) food didn't last, iii) couldn't afford to eat balanced meals, and iv) cut the size of meals or skip meals. The source of this information is further discussed in detail in the next section. In the next section, we describe the methodology used in this paper to deduce the above findings on food security for those residing near transit.

2. Materials and Methods

2.1. Regression Analysis

In this research, food insecurity is the modeled output variable. Food insecurity, as the dependent variable, assumes a binary value with a dichotomous output. When its value is equal to 1, it indicates that food insecurity is 1; when it is equal to 0, it indicates food security. With this assumption, a logistic regression fits the best as the method to understand how various socioeconomic explanatory variables impact food insecurity. Research manuscripts reporting large datasets that are deposited in a publicly available database should specify where the data have been deposited and provide the relevant accession numbers. If the accession numbers have not yet been obtained at the time of submission, please state that they will be provided during review. They must be provided prior to publication.

A brief theoretical background of the logistic regression applicable in statistical modeling for dichotomous outputs with X being a vector of explanatory variables is provided below (source: [22]):

$$\log \left(\frac{\pi }{1-\pi }\right)=\beta X_i$$

(1)

where,

π is the probability of an event, and β are the estimated coefficients. The coefficient estimation is determined using the principle of maximum likelihood technique, with n observations:

$$L\left(\beta |X_i\right)={\displaystyle \prod _{i=1}^n{\pi }^{Y_i}{\left(1-\pi \right)}^{1-Y_i}}$$

(2)

Using natural logarithm on Eq. (2) gives a sum for the likelihood function as,

$$\log \left\{L\left(\beta |X_i\right)\right\}={\displaystyle \sum _{i=1}^n\left[Y_i\log \pi +\left(1-Y_i\right)\log \left(1-\pi \right)\right]}$$

(3)

Maximizing the log-likelihood equation in Eq. (3) by finding the first derivative and equating it to zero, will give the estimation of the coefficients, β. Various statistical software packages can be used to carry-out logistic regression for the theoretical information presented above. However, it must be noted that it is necessary to identify the socioeconomic variables as categorical or continuous variables before performing the regression.

Furthermore, we use the odds ratio to quantify the association between food insecurity and each of the individual variables. The odds ratio in the context of this research is defined as the ratio of the odds of an independent variable that causes food insecurity to the odds of the same variable not impacting food insecurity. Mathematically, the odds ratio is calculated based on information presented in the contingency Table 1 and the formula is:

(Odds ratio) = (X₂₂/ X₂₁)/(X₁₂/ X₁₁)

(4)

where,

X11 = number of times in the data the value of the independent variable is 0, with food security

X12 = number of times in the data the value of the independent variable is 0, with food insecurity

X21 = number of times in the data the value of the independent variable is 1, with food security

X22 = number of times in the data the value of the independent variable is 1, with food insecurity

The significance of the odds ratio is that if it is greater than 1, it ‘indicates a positive association between the independent variable and food insecurity. An odds ratio less than 1 indicates a negative association, while a ratio equal to 1 indicates no association between the independent variable and food insecurity.

Table 1. Contingency table.

		Food insecurity
		0	1
Independent Variable	0	X11	X12
	1	X21	X22

Table 1. Contingency table.

		Food insecurity
		0	1
Independent Variable	0	X11	X12
	1	X21	X22

3. Application Example

3.1. Study Region and Data Collection

Data from the 2020 National Health Interview Survey (NHIS) collected by the National Center for Health Statistics (NCHS) of the USA are analyzed in this research. Although the NHIS data primarily focuses on health-related information of the noninstitutionalized population of the US, the survey, for the first time in the year 2020, includes information on those populations that reside near transit while inquiring about their food security status.

Based on the survey documentation, it has been pointed out that, in 2020, due to the COVID-19 pandemic, data collection procedures were disrupted. From April to June, all interviews were conducted by telephone only. From July to December, interviews were attempted by telephone first, with follow-ups to complete interviews by personal visit. In this way, 31,568 sample adult interviews were carried out [23]. The information from these many sample interviews is publicly available for researchers. Our primary focus was to extract only the relevant information on food (in)security, residing status of respondents if located near transit and their socioeconomic characteristics. There were 15,786 respondents who lived within walking distance (or close to a transit stop) at the time of the interview.

The 2020 National Health Interview Survey (NHIS) data on food insecurity and associated explanatory variables were at the spatial scale of counties spanning four regions of aggregated states, namely the Northeast, Midwest, South, and Western. Further, the counties within these aggregated states were classified based on the 2013 NCHS Urban–Rural Classification Scheme [24] . This classification scheme is based on the county population. It has six divisions: four metropolitans (large central metro, large fringe metro, medium metro, and small metro) and two nonmetropolitan (micropolitan and noncore).

A county that is a large central metro is defined as in metropolitan statistical area (MSA) of 1 million or more population that with the entire population of the largest principal city of the MSA. A large fringe metro has a population size between 1 million or more, which does not qualify as large central metro county. A medium metro county in MSAs has a population of 250,000 to 999,999, whereas a small metro county in MSAs has a population of less than 250,000. The nonmetropolitan categories of micropolitan and noncore are the respective counties in micropolitan statistical areas and the nonmetropolitan counties that did not qualify as micropolitan.

However, the survey combines the data from medium and small metro county respondents into one, and the same was done for the micropolitan and noncore nonmetropolitan counties. Thus, instead of six classification counties of the 2013 NCHS Urban-Rural Classification Scheme, the survey data has information on four levels of county classification: large central metro, large fringe metro, medium and small metro, and nonmetropolitan.

The association between the likelihood of facing food insecurity by the survey respondents residing close to transit and the socioeconomic variables of age, income, education level, and dependency on food stamps were estimated for significance using logistic regression. Note that the socioeconomic variables, as well as the food insecurity, were treated as dichotomous variables. Table 2 shows each variable definition used in the logistic regression. It is noted that the age of 38.6 years was identified as a classification level based on the 2020 median age of the US population. The purpose of keeping the median age as a classification level was to understand if there was any significance in food insecurity below this age among the respondents.

Further, the income levels were classified based on the poverty and median household incomes of $21,960 and $67,521, respectively. With an income that is below the poverty benchmark, it would be expected that food insecurity might be a concern among those living near transit. Like median age, median income was a natural choice as a classification level in understanding food insecurity.

Education as the explanatory variable was classified based on the respondents’ qualifications in holding a degree, i.e., if the respondent had a degree, was it an associate degree, a bachelor’s, or a higher educational degree. The purpose was to understand if food security was at all impacted among the respondents with their level of educational attainment.

The last variable tested for impact on food insecurity is the respondents’ utilizing food stamps in the previous twelve months. Availing food stamps or the SNAP by respondents would not mean food insecurity occurs. Since, SNAP is intended to eliminate food insecurity that stems from worrying if the food would not last, run out, or the respondent has to cut the size of meals or skip meals.

Table 2. Variables definition.

Variable	Notation	Coding
Male	Male	1 = Male, 0 otherwise
Age ≤ 38.6 (USA 2020 Median age: 38.6α)	Age_386	1 if Age ≤ 38.6, 0 otherwise
Age in-between 38.6 and 50	Age_38650	1 if 38.6 < Age ≤ 50, 0 otherwise
Age in-between 50-65	Age_5065	1 if 50 < Age ≤ 65, 0 otherwise
Age > 65	Age_65	1 if Age > 65, 0 otherwise
Income ≤ $21,960 (below poverty with a mean family size of 3β)	Inc_p	1 if Income ≤ $21,960, 0 otherwise
Income $21,960 to $67,521 (Median household income in the USA was $67,521 in 2020γ)	Inc_pm	1 if $21,960 < Income ≤ $67,521, 0 otherwise
Income > $67,521	Inc_m	1 if Income > $67,521, 0 otherwise
Education level with no degree	Edu_nd	1 if Education with no degree, 0 otherwise
Education level with associate degree and aboveθ but below bachelor level	Edu_ad	1 if Education with a degree below undergraduate, 0 otherwise
Education level with bachelors and above degree	Edu_bd	1 if Education with undergraduate and above degree, 0 otherwise
Received food stamp in the last 12 months	Food_S	1 if received food stamp in last 12 months, 0 otherwise

^αSource: Statistica.com, 2021 [25]; ^βSource: Statistica.com 2021 [27]; ^γUS Department of Health and Human Services, 2021 [26]; ^θAssociate degree in occupational, technical, vocational program, academic program etc.

Table 2. Variables definition.

Variable	Notation	Coding
Male	Male	1 = Male, 0 otherwise
Age ≤ 38.6 (USA 2020 Median age: 38.6α)	Age_386	1 if Age ≤ 38.6, 0 otherwise
Age in-between 38.6 and 50	Age_38650	1 if 38.6 < Age ≤ 50, 0 otherwise
Age in-between 50-65	Age_5065	1 if 50 < Age ≤ 65, 0 otherwise
Age > 65	Age_65	1 if Age > 65, 0 otherwise
Income ≤ $21,960 (below poverty with a mean family size of 3β)	Inc_p	1 if Income ≤ $21,960, 0 otherwise
Income $21,960 to $67,521 (Median household income in the USA was $67,521 in 2020γ)	Inc_pm	1 if $21,960 < Income ≤ $67,521, 0 otherwise
Income > $67,521	Inc_m	1 if Income > $67,521, 0 otherwise
Education level with no degree	Edu_nd	1 if Education with no degree, 0 otherwise
Education level with associate degree and aboveθ but below bachelor level	Edu_ad	1 if Education with a degree below undergraduate, 0 otherwise
Education level with bachelors and above degree	Edu_bd	1 if Education with undergraduate and above degree, 0 otherwise
Received food stamp in the last 12 months	Food_S	1 if received food stamp in last 12 months, 0 otherwise

^αSource: Statistica.com, 2021 [25]; ^βSource: Statistica.com 2021 [27]; ^γUS Department of Health and Human Services, 2021 [26]; ^θAssociate degree in occupational, technical, vocational program, academic program etc.

A total of sixteen spatially distinct scenarios covering almost the entire US were analyzed. These scenarios were from four of the 2013 NCHS Urban–Rural Classification Scheme counties times the four aggregated states, Northeast, Midwest, South, and West.

Table 3 shows the descriptive statistics for the explanatory variables. The table was prepared for those respondents living close to transit.

A very high percentage of almost 94% respondents living near transit and in the large central metro counties of the Northeast states indicated food insecurity. The percentage of respondents living near transit and in the nonmetropolitan counties of the South states had the lowest (8%) food insecurity. Overall, for the respondents living near a transit and in the nonmetropolitan counties of the Midwest and South states, the percentages of explanatory variables were found to be lower than in the other two regions of the aggregated states. The percentage distributions of all the explanatory variables were the largest for the large central metro counties across all the four regions of the aggregated states.

The maps in Figs. 3-5 show the spatial variations in the percentages of the food insecurity and the thirteen explanatory variables. The map of food insecurity, the gender variables (males and females, age of respondents between 38.5 to 50 years and 50 years to 65 years, income greater than $67.5k and education level bachelors and above appear to have a similar color shade – although differing in their respective percentages.

Table 3. Descriptive statistics of the dependent and explanatory variables (shown as percentages)

Variable	Aggregated States
	Northeast States				Midwest States				Southern States				Western States
	Large central metro	Large fringe metro	Medium and small metro	Nonmetropolitan	Large central metro	Large fringe metro	Medium and small metro	Nonmetropolitan	Large central metro	Large fringe metro	Medium and small metro	Nonmetropolitan	Large central metro	Large fringe metro	Medium and small metro	Nonmetropolitan
Food Insecurity (consists of aggregated affirmative responses to - ‘worry food would run out’, ‘food didn't last’, ‘couldn't afford to eat balanced meals’, and ‘cut the size of meals or skip meals’)	94	73	59	36	83	52	57	24	70	53	41	8	89	78	76	45
Male	38	25	21	11	34	16	23	7	28	19	14	3	41	35	31	16
Female	45	28	22	16	39	19	24	8	32	21	18	4	42	40	34	16
Age ≤ 38.6 (USA 2020 Median age: 38.6)	25	15	11	5	26	10	18	3	23	13	9	2	28	20	20	9
Age in-between 38.6 and 50	14	10	7	5	13	6	8	2	10	7	5	1	16	14	12	6
Age in-between 50-65	21	14	12	9	18	9	11	4	14	10	8	1	20	18	16	8
Age ≥ 65	23	14	13	8	17	8	10	6	14	11	10	2	19	22	17	9
Income ≤ $21,960 (below poverty with a mean family size of 3)	17	5	6	6	9	4	10	4	11	4	7	2	9	6	8	5
Income $21,960 to $67,521 (Median household income in the USA was $67,521 in 2020)	29	17	19	10	28	13	20	7	22	15	14	3	27	24	27	13
Income ≥ $67,521	36	31	19	11	36	18	17	5	27	21	11	1	47	44	30	14
Education level with no degree	36	21	19	14	29	15	23	8	25	17	16	4	34	29	32	16
Education level with associate degree and above but below bachelor level	7	6	5	3	7	5	6	3	6	5	5	1	10	11	9	4
Education level with bachelors and above degree	39	25	19	10	37	15	17	5	29	19	11	2	40	34	23	11
Received food stamp in the last 12 months (2019)	15	9	11	19	10	9	14	14	12	9	15	24	8	7	12	17

Table 3. Descriptive statistics of the dependent and explanatory variables (shown as percentages)

Variable	Aggregated States
	Northeast States				Midwest States				Southern States				Western States
	Large central metro	Large fringe metro	Medium and small metro	Nonmetropolitan	Large central metro	Large fringe metro	Medium and small metro	Nonmetropolitan	Large central metro	Large fringe metro	Medium and small metro	Nonmetropolitan	Large central metro	Large fringe metro	Medium and small metro	Nonmetropolitan
Food Insecurity (consists of aggregated affirmative responses to - ‘worry food would run out’, ‘food didn't last’, ‘couldn't afford to eat balanced meals’, and ‘cut the size of meals or skip meals’)	94	73	59	36	83	52	57	24	70	53	41	8	89	78	76	45
Male	38	25	21	11	34	16	23	7	28	19	14	3	41	35	31	16
Female	45	28	22	16	39	19	24	8	32	21	18	4	42	40	34	16
Age ≤ 38.6 (USA 2020 Median age: 38.6)	25	15	11	5	26	10	18	3	23	13	9	2	28	20	20	9
Age in-between 38.6 and 50	14	10	7	5	13	6	8	2	10	7	5	1	16	14	12	6
Age in-between 50-65	21	14	12	9	18	9	11	4	14	10	8	1	20	18	16	8
Age ≥ 65	23	14	13	8	17	8	10	6	14	11	10	2	19	22	17	9
Income ≤ $21,960 (below poverty with a mean family size of 3)	17	5	6	6	9	4	10	4	11	4	7	2	9	6	8	5
Income $21,960 to $67,521 (Median household income in the USA was $67,521 in 2020)	29	17	19	10	28	13	20	7	22	15	14	3	27	24	27	13
Income ≥ $67,521	36	31	19	11	36	18	17	5	27	21	11	1	47	44	30	14
Education level with no degree	36	21	19	14	29	15	23	8	25	17	16	4	34	29	32	16
Education level with associate degree and above but below bachelor level	7	6	5	3	7	5	6	3	6	5	5	1	10	11	9	4
Education level with bachelors and above degree	39	25	19	10	37	15	17	5	29	19	11	2	40	34	23	11
Received food stamp in the last 12 months (2019)	15	9	11	19	10	9	14	14	12	9	15	24	8	7	12	17

Figure 3. Spatial distribution of percentages of explanatory variables – a. Food insecurity, b. Male, c. Female, d. Age ≤ 38.6, e. Age in-between 38.6 and 50, and f. Age in-between 50-65.

Figure 3. Spatial distribution of percentages of explanatory variables – a. Food insecurity, b. Male, c. Female, d. Age ≤ 38.6, e. Age in-between 38.6 and 50, and f. Age in-between 50-65.

Figure 4. Spatial distribution of percentages of explanatory variables – g. Age ≥ 65, h. Income ≤ $21,960, i. Income $21,960 to $67,521, j. Income ≥ $67,521, k. Education level with no degree, and l. Education level with associate degree and above but below bachelor level.

Figure 4. Spatial distribution of percentages of explanatory variables – g. Age ≥ 65, h. Income ≤ $21,960, i. Income $21,960 to $67,521, j. Income ≥ $67,521, k. Education level with no degree, and l. Education level with associate degree and above but below bachelor level.

Figure 5. Spatial distribution of percentages of explanatory variables – m. Education level with bachelors and above degree and n. Received food stamp in the last 12 months.

Figure 5. Spatial distribution of percentages of explanatory variables – m. Education level with bachelors and above degree and n. Received food stamp in the last 12 months.