Unravelling the Relationship between Obesity, Density, Accessibility to Public Transport and Green Spaces in Babol City

doi:10.21203/rs.3.rs-3611485/v1

Due to the high rates of obesity and the lack of research on how the built environment contributes to obesity in Iran, this study aimed to investigate the relationship between density, accessibility to public transport and green spaces, obesity, and related diseases in Babol. Logistic regression was used to analyze data on health indicators such as body mass index (BMI), blood pressure, and diabetes, as well as environmental indicators including population density, residential density, accessibility to bus and taxi stations, the number of stations, the proximity to green spaces, and their proportion. The study found that living in areas with a density of over 120 people per square kilometer reduces the likelihood of being overweight or obese compared to areas with a density of 100 to 120 people. Access to green spaces within a 200-meter radius did not show a significant association with diseases. There was a weak negative relationship between the number of public transportation stations at different distances and body mass index, indicating lower levels of overweight and obesity. Changing from neighborhoods with lower residential densities to higher residential densities reduced the likelihood of developing heart disease and heart disease by about 74% and 44.4%, respectively. The presence of green spaces in neighborhoods had a weak correlation with physical activity and the reduction of obesity and related diseases. Increasing the number of taxi and bus stations from one to three within a 400-meter radius significantly reduced the likelihood of developing high blood pressure by approximately 70% and diabetes by 56.5% within a 200-meter radius. Living within a distance of less than 150 meters from the nearest public transportation station or nodal points was associated with a 51.3% lower probability of developing high blood pressure compared to living within a distance of 300 to 500 meters from public transportation stations.

Built environment

BMI

Diabetes

High blood pressure

Healthy cities

The issue of obesity is a significant public health problem in the current era (Simmonds et al., 2015), resulting in higher mortality rates and the emergence of ailments like diabetes, chronic heart conditions, and cancer (Jenabi & Poorolajal, 2015; Poorolajal & Jenabi, 2016; Simmonds et al., 2015). The global prevalence of obesity is rapidly expanding and is anticipated to rise more than ever before (Low et al., 2009). This has attracted the attention of urban planners, medical researchers, and city designers who aim to comprehend the correlation between the "urban environment" and obesity. Nevertheless, there are obstacles to implementing policy alterations and urban planning due to uncertainty and inconsistencies regarding the effectiveness of physical aspects on well-being (Giles-Corti et al., 2016).

The need for research in this field can be examined from both a theoretical and practical perspective. From a practical standpoint, Iran has a high prevalence of obesity among adults, especially among women. The rates of obesity in Iran are higher than in other areas monitored by the World Health Organization (Khazaei et al., 2020). Additionally, obesity has significant economic consequences in Iran, with healthcare costs being 10% higher for obese individuals compared to those with a normal weight (Yusefzadeh et al., 2019). Furthermore, studies have indicated that over 4 million Iranian adults have diabetes, with a significant portion of it being attributed to the increase in obesity (Esteghamati et al., 2014). A study in 2015 estimated that there were 3.78 million cases of diabetes in Iran in 2009, which is expected to rise to 9.24 million by 2030 (Javanbakht et al., 2015). Consequently, obesity is not only a medical issue but also an economic and social problem that can lead to inequalities among different communities and socioeconomic groups.

Furthermore, despite the increasing interest in understanding the connection between the urban environment and well-being, there is a scarcity of research specifically investigating the link between the physical environment and obesity in Iran. Previous studies have explored various aspects of the urban environment and their impact on health, such as exposure to noise from traffic and its association with diabetes, the walkability of neighborhoods and its influence on quality of life and health, physical activity levels, the role of public spaces in promoting health, the relationship between public spaces and obesity, as well as their effects on cardiovascular diseases, diabetes, and respiratory health (Dzhambov, 2017; Lachowycz & Jones, 2011; Maas et al., 2009; Pereira et al., 2012; Sakhvidi et al., 2018; Villeneuve et al., 2017; Zhao & Chung, 2017). However, there is a lack of similar research in the field of urban studies conducted in Iran. Therefore, it is essential to conduct research that can provide strong evidence on the relationship between different elements of the physical environment and individuals' health. This research will help inform practical policies aimed at addressing the issue of obesity in Iran.

Previous studies have examined the relationship between residential density and overweight/obesity, but the findings have been contradictory (Doyle et al., 2006; Ewing et al., 2003; Lopez-Zetina et al., 2006). Some studies have found a negative relationship between residential density and overweight (Garden & Jalaludin, 2009; Rutt & Coleman, 2005), while others have not found any significant association (Kelly-Schwartz et al., 2004; Pendola & Gen, 2007). A study conducted in China by Wang et al. (2019) found that individuals living in neighborhoods with low and medium residential density were at a higher risk for overweight compared to those living in neighborhoods with higher residential density. Taking gender into accounts, this association was only significant for men. In contrast, another study in China found that adolescents living in neighborhoods with higher and medium residential density were more likely to be overweight compared to those living in neighborhoods with lower residential density (Xu et al., 2010). Furthermore, a study conducted in metropolitan areas of Vancouver and Portland showed that traffic and neighborhood cohesion had an inverse effect on adult physical activity in neighborhoods with higher residential density (Chaudhury et al., 2012). It has been suggested that higher residential density can lead to increased traffic congestion up to a certain threshold, beyond which walking and cycling become more comfortable (Forsyth et al., 2007). Overall, the relationship between residential density and overweight/obesity is complex and can vary depending on factors such as gender, age group, and the specific context of the study. Further research is needed to better understand the mechanisms behind this relationship and to account for potential confounding factors.

Several studies have investigated the relationship between public transportation and obesity, and have consistently found a strong association. Brown et al. (2017), Flint et al. (2016), MacDonald et al. (2010), Martin et al. (2015), and Webb et al. (2016) all examined the changes in body mass index (BMI) associated with public transportation. These studies found that using public transportation was linked to a decrease in BMI. These studies can be divided into two groups based on their focus. The first group examines the level of using public transportation and its association with obesity. The second group explores the accessibility to public transportation and its relationship with physical activity and obesity (Richard Patterson et al., 2019). According to Brown et al. (2019), investing in improving access to public transportation can reduce mortality associated with transportation and the burden of diseases related to physical inactivity and obesity. Some studies have examined the relationship between distance from a participant's home to the nearest bus route and obesity, with mixed findings. Sun et al. (2017) found a negative association between the mentioned distance and body mass index in China, while Hirsch et al. (2014) found no association in the United States. With regard to the second group, Laverty et al. (2018) found that both initiating and increasing use of public transportation are associated with increased physical activity and lower obesity among women. R Patterson et al. (2019) indicated that one-third of public transportation users engage in more than 30 minutes of physical activity, with rail users averaging 28.1 minutes and bus users averaging 16 minutes.

In recent years, there has been a growing body of research exploring the relationship between green space and various health outcomes, including physical activity, obesity, and cardiovascular diseases. These studies have investigated different aspects of green space, such as the ratio of green space in neighborhoods (Dempsey et al., 2018; Pietilä et al., 2015), greenness measured using the normalized difference vegetation index (Dadvand et al., 2016; O'Callaghan-Gordo et al., 2020), distance to the nearest green space from residential areas (Ghimire et al., 2017; Pietilä et al., 2015; Tamosiunas et al., 2014), and green space density or per capita (Carlson et al., 2012; Galvez et al., 2013; Ghimire et al., 2017; Young et al., 2014). Some studies have found positive effects of green space on reducing obesity rates. For example, research has shown that an increased number of parks, green belts, and open spaces is associated with higher levels of weekly walking and reduced risk of overweight and obesity among women (Meurs & Haaijer, 2001; O'Callaghan-Gordo et al., 2020). Access to green space has also been linked to increased levels of moderate to vigorous physical activity, reduced sedentary behavior, and a lower risk of overweight or obesity for women (Astell-Burt et al., 2014). Additionally, proximity to green spaces has been associated with higher levels of physical activity in older adults (Dadvand et al., 2016), as well as improved cognitive health in this population (Broekhuizen et al., 2013). Forest coverage has also been found to be related to lower overweight rates and lower body mass index at the county level (Boncinelli et al., 2015; Ghimire et al., 2017). However, the findings regarding the relationship between green space and health outcomes are not always consistent. Some studies have found no association between park improvements in neighborhoods and physical activity (Cohen et al., 2009). Similarly, no direct relationship has been observed between residence in greener areas and increased overweight and obesity (Cummins & Fagg, 2012). Other studies have also failed to find a significant association between green space and body mass index among older adults (Broekhuizen et al., 2013; Sander, Ghosh, & Hodson, 2017), or between natural amenities and the prevalence of obesity and physical activity in non-metropolitan areas in the United States (Michimi & Wimberly, 2012). Overall, these differences can be partially explained by various factors such as different indicators, the mismatch between perceived and objective measures, self-selection, different scales, gender changes, and age groups.

3.1. Variables

3.1.1. Dependent variable of health

Body Mass Index (BMI): A statistical measure for comparing the weight and height of an individual. In fact, this index does not measure the level of obesity, but it is a suitable tool for estimating the health of an individual's weight relative to their height. This index is obtained by dividing the person's weight in kilograms by the square of their height in meters as follows:

$$BMI=\frac{mass\left(kg\right)}{{\left(height\right(m\left)\right)}^{2}}$$

Underlying diseases: The study examined several underlying diseases as dependent variables, including blood pressure, diabetes, and cardiovascular diseases. Individuals with these diseases were coded as 1, while those without were coded as 0.

3.1.2. Modifier variables

Medical studies define different body mass index for different age groups. In this regard, this study used these age groups to classify this variable on an ordinal scale. Gender was found to be a variable that may modify the impact of environmental characteristics on health outcomes. Marital status can also have an impact on general health. Furthermore, improving educational level can be effective in reducing obesity by creating awareness in lifestyle. Therefore, individuals' educational status was classified into five groups on an ordinal scale. Moreover, occupational groups can have an impact on body mass index and the prevalence of related diseases based on the duration of sedentary behavior and level of activity during work. Occupational groups were classified according to the categorization of the SIB website on a nominal scale (Table 1).

3.1.3. Independent variables of neighborhood environment

Density: Residential density is a commonly used measure of the built environment that is associated with physical activity. Residential density refers to the number of people living in a specific geographical unit within a neighborhood.

Proximity to public transportation: Many studies have explored the relationship between public transportation use and body mass index. These studies can be divided into two categories: those that examine the level of using public transportation and those that investigate the accessibility to public transportation in relation to physical activity and obesity. This study falls into the second category, focusing on indicators such as the number of bus and taxi stations, average distance to main streets, and distance to the nearest bus and taxi stop. Proximity to main streets is considered an indicator of accessibility based on the city's transportation infrastructure.

Green space: Different indicators have been used in various studies to measure green space. Some studies have utilized greenness measured by the normalized difference vegetation index, while others have examined the distance between residential areas and the nearest green space. Another approach has been to measure green space density or per capita. In this particular study, the distance to the nearest park and the proportion of green space within 200, 400, and 800 meters of residential areas or buffers are used as indicators.

Table 1

Variables
Variables	Description
Health indicators
BMI	(1) Severely underweight < 16, (2) Underweight 16.5–18.5, (3) Healthy weight 18.5–25, (4) Overweight 25–30, (5) Obesity 30<
Diabetes
High blood pressure
Sociodemographic factors
Gender	(1) Male, (2) Female
Age groups	(1) 19–24 ages, (2) 24–34 ages, (3) 35–44 ages, (4) 45–54 ages, (5) 55–64 ages, (6) 65 and older
Occupations	(1) Housewives, (2) Students, (3) Staffs, (51) Labors, (6) Unemployed people with income
Education levels	(1) Primary school, (2) High school, (3) Bachelor’s degree, (4) Master’s degree, (5) PhD
Measures of walkability
Density	Population density, Residential density
public transport	The number of bus and taxi stations, Average distance to main streets, and distance to the nearest bus and taxi stop
green spaces	The distance to the nearest park and the proportion of green space

3.2. Data collection

The primary method of collecting health data in this study involved documenting information such as body mass index, blood pressure, diabetes, and cardiovascular diseases, which were extracted from the integrated database for hygiene in Iran called SIB. Caregivers or family physicians entered these data into the SIB for each individual, ensuring that the data collected was not subject to self-reporting bias. Additionally, data on the number of cardiac patients hospitalized in each neighborhood was obtained from hospital patient records. Then, based on individuals' national identification code and its compatibility with the SIB website, spatial data of individuals were integrated. Modifier variables such as age, gender, marital status, educational level, and occupational status were extracted from the SIB system.

To assess the relationship between environmental components and physical activity and obesity, data on individuals' physical activity levels, published by the SIB website, were collected through self-reported questionnaires. A random sample of individuals was selected and their data were analyzed and monitored. These questionnaires were developed based on the International Physical Activity Questionnaire to ensure validity. The questionnaire included information on walking time, intensity of walking and physical activity, number of walking days per week, duration of physical activity during work, duration and intensity of sports-recreational activities, heart problems or high blood pressure, and discomfort during daily activities or physical activity. In the end, all individuals within the study age range and their registered residential areas were recorded for selected neighborhoods.

Data on environmental components were obtained from the latest comprehensive plan for Babol city in 2018. The study area was determined based on the street network boundary, which is a better criterion for indicating residents' accessibility compered to radial buffers or administrative units. Based on this, four areas were used as analysis units, considering access to data. The environmental components extracted from the SIB website for the analyzed individuals are within the 100, 200, and 400-meter radius of their residential areas. The data related to hospitalized individuals due to cardiac diseases in hospitals were evaluated based on the street network boundary and identifiable neighborhoods.

3.3. Statistical analysis

In terms of statistical analysis, the dependent and modifier variables in the research were measured using different scales. Variables such as body mass index, age groups, and education level followed an ordinal scale, while variables like blood pressure, diabetes, and heart disease outputs, as well as gender, occupation groups, and marital status, followed a nominal scale. Therefore, two different methods were used to measure the relationship between environmental variables, neighborhood cohesion, and health. Binary logistic regression was used to measure the impact of environmental variables on the likelihood of heart disease, blood pressure, and diabetes, while ordinal logistic regression was used to measure the effect of each independent and modifier variable on body mass index. The Kendall rank correlation coefficient was also used to measure and control the relationship between environmental variables and physical health. The impact of entropy, the commercial-residential ratio, and the share of non-residential uses on obesity indices were analyzed both based on a three-ordinal scale and a five-ordinal scale, with the first group having a lower rank and the third and fifth groups having a higher rank.

4. BMI

4.1.1. Density

According to the regression model, one unit increase in population density within a 200-meter radius at the neighborhood level is statistically significant with a small error level of less than 0.05. An increase of one unit in population density in the third category decreases the probability of individuals being overweight or obese by 0.505, assuming all other variables remain constant. Moreover, increasing the density of residential units, from the second level (56–134) to the third level (135–262), results in an increase in body mass index by 0.584 units. Marital status is the only variable that significantly affects body mass index (Table 2).

To ensure the validity of the results, Kendall's tau correlation was conducted between the mediating variables, population density, and body mass index. The results show a significant negative correlation between population density and body mass index, indicating that neighborhoods with higher population density are less likely to have overweight and obese individuals. Additionally, there is a positive and significant correlation between age, marital status, and obesity (Table 3).

Table 2

the association between residential density within a buffer of 200-meter, population density and BMI
		Estimate		Std. Error	Wald	df	Sig.	95% Confidence Interval
		Estimate		Std. Error	Wald	df	Sig.	Lower Bound	Upper Bound
Threshold	[BMI5 = 1]		-4.850	.871	30.972	1	.000	-6.557	-3.142
	[BMI5 = 2]		− .743	.826	.810	1	.368	-2.362	.876
	[BMI5 = 3]		1.172	.826	2.011	1	.156	− .448	2.791
	[BMI5 = 4]		2.605	.833	9.790	1	.002	.973	4.237
Location	[Age = 1]		− .433	.484	.798	1	.372	-1.382	.517
	[Age = 2]		− .058	.220	.069	1	.793	− .489	.373
	[Age = 3]		− .061	.192	.101	1	.751	− .437	.316
	[Age = 4]		.018	.208	.008	1	.930	− .389	.426
	[Age = 5]		0^a	.	.	0	.	.	.
	[Gender = 1]		− .109	.176	.387	1	.534	− .454	.235
	[Gender = 2]		0^a	.	.	0	.	.	.
	[Marital = 1]		− .964	.387	6.194	1	.013	-1.723	− .205
	[Marital = 2]		− .064	.297	.046	1	.830	− .645	.517
	[Marital = 3]		0^a	.	.	0	.	.	.
	[Education = 1]		.318	.484	.431	1	.511	− .631	1.267
	[Education = 2]		.163	.467	.121	1	.728	− .753	1.078
	[Education = 3]		− .046	.460	.010	1	.920	− .947	.854
	[Education = 4]		− .017	.487	.001	1	.972	− .972	.938
	[Education = 5]		0^a	.	.	0	.	.	.
	[Job = 1]		− .636	.691	.849	1	.357	-1.990	.717
	[Job = 2]		.142	.647	.049	1	.826	-1.125	1.410
	[Job = 3]		− .551	.780	.499	1	.480	-2.080	.978
	[Job = 4]		− .209	.650	.104	1	.748	-1.483	1.065
	[Job = 5]		− .716	.764	.880	1	.348	-2.213	.780
	[Job = 6]		.164	.654	.063	1	.802	-1.119	1.447
	[Job = 8]		0^a	.	.	0	.	.	.
	[Res-Density = 1]		.341	.222	2.351	1	.125	− .095	.776
	[Res-Density = 2]		.584	.210	7.697	1	.006	.171	.996
	[Res-Density = 3]		.262	.195	1.815	1	.178	− .119	.644
	[Res-Density = 4]		.250	.190	1.736	1	.188	− .122	.622
	[Res-Density = 5]		0^a	.	.	0	.	.	.
	[Pop-Density = 1]		− .094	.156	.364	1	.546	− .401	.212
	[Pop-Density = 2]		− .360	.242	2.203	1	.138	− .835	.115
	[Pop-Density = 3]		− .505	.221	5.203	1	.023	− .939	− .071
	[Pop-Density = 4]		0^a	.	.	0	.	.	.
Link function: Logit.
a. This parameter is set to zero because it is redundant.

4.1.2. Green spaces

The regression model shows that the proportion of green space in the second category is statistically significant, with a small error level of less than 0.05. The estimated coefficient for the share of green space is -0.229, indicating that an increase of one unit in the share of green space in the second category decreases the probability of individuals being overweight or obese by 0.229, assuming all other variables remain constant. This suggests that residents living in neighborhoods with higher levels of green space are less likely to be in higher categories of BMI compared to those living in neighborhoods with lower levels of green space. The results also highlight the significance of variables such as marital status and age in affecting BMI (Table 4).

Table 3

The Spearman correlation between population density and BMI
			Age	Gender	Marital	Edu	Job	BMI	BMI4	BMI5	Density3
	Age	CC	1.000	.103^**	.475^**	− .104^**	.157^**	.173^**	.170^**	.176^**	− .007
		Sig. (2-tailed)	.	.000	.000	.000	.000	.000	.000	.000	.799
		N	1020	1020	1020	1020	1020	1020	1020	1020	1020
	Gender	CC	.103^**	1.000	− .037	.068^*	.487^**	− .007	− .010	− .009	− .005
		Sig. (2-tailed)	.000	.	.227	.019	.000	.807	.728	.768	.877
		N	1020	1020	1020	1020	1020	1020	1020	1020	1020
	Marital	CC	.475^**	− .037	1.000	.012	.117^**	.226^**	.224^**	.232^**	− .117^**
		Sig. (2-tailed)	.000	.227	.	.678	.000	.000	.000	.000	.000
		N	1020	1020	1020	1020	1020	1020	1020	1020	1020
	Edu	CC	− .104^**	.068^*	.012	1.000	.386^**	− .026	− .025	− .024	.088^**
		Sig. (2-tailed)	.000	.019	.678	.	.000	.339	.362	.379	.001
		N	1020	1020	1020	1020	1020	1020	1020	1020	1020
	Job	CC	.157^**	.487^**	.117^**	.386^**	1.000	.029	.031	.032	.048
		Sig. (2-tailed)	.000	.000	.000	.000	.	.273	.243	.219	.073
		N	1020	1020	1020	1020	1020	1020	1020	1020	1020
	BMI	CC	.173^**	− .007	.226^**	− .026	.029	1.000	.985^**	.978^**	− .061^*
		Sig. (2-tailed)	.000	.807	.000	.339	.273	.	.000	.000	.030
		N	1020	1020	1020	1020	1020	1020	1020	1020	1020
	BMI4	CC	.170^**	− .010	.224^**	− .025	.031	.985^**	1.000	.993^**	− .063^*
		Sig. (2-tailed)	.000	.728	.000	.362	.243	.000	.	.000	.022
		N	1020	1020	1020	1020	1020	1020	1020	1020	1020
	BMI5	CC	.176^**	− .009	.232^**	− .024	.032	.978^**	.993^**	1.000	− .064^*
		Sig. (2-tailed)	.000	.768	.000	.379	.219	.000	.000	.	.020
		N	1020	1020	1020	1020	1020	1020	1020	1020	1020
	Density	CC	− .007	− .005	− .117^**	.088^**	.048	− .061^*	− .063^*	− .064^*	1.000
		Sig. (2-tailed)	.799	.877	.000	.001	.073	.030	.022	.020	.
		N	1020	1020	1020	1020	1020	1020	1020	1020	1020
*. Correlation is significant at the 0.01 level (2-tailed). . Correlation is significant at the 0.05 level (2-tailed).

Table 4

the association between green spaces and BMI
		Estimate	Std. Error	Wald	df	Sig.	95% Confidence Interval
		Estimate	Std. Error	Wald	df	Sig.	Lower Bound	Upper Bound
Threshold	[BMI = 1]	− .621	.324	3.663	1	.056	-1.256	.015
	[BMI = 2]	.746	.326	5.219	1	.022	.106	1.386
	[BMI = 3]	2.110	.340	38.555	1	.000	1.444	2.776
	[BMI = 4]	3.463	.388	79.762	1	.000	2.703	4.223
Location	[Age = 1]	− .482	.228	4.464	1	.035	− .929	− .035
	[Age = 2]	− .199	.131	2.294	1	.130	− .456	.058
	[Age = 3]	− .124	.114	1.175	1	.278	− .347	.100
	[Age = 4]	.098	.119	.676	1	.411	− .136	.332
	[Age = 5]	0^a	.	.	0	.	.	.
	[Gender = 1]	.049	.078	.396	1	.529	− .103	.201
	[Gender = 2]	0^a	.	.	0	.	.	.
	[marital = 1]	− .818	.255	10.294	1	.001	-1.318	− .318
	[marital = 2]	.055	.188	.087	1	.767	− .312	.423
	[marital = 3]	0^a	.	.	0	.	.	.
	[Education = 1]	− .261	.265	.965	1	.326	− .781	.259
	[Education = 2]	− .226	.259	.761	1	.383	− .733	.282
	[Education = 3]	− .320	.263	1.486	1	.223	− .836	.195
	[Education = 4]	− .339	.289	1.371	1	.242	− .905	.228
	[Education = 5]	0^a	.	.	0	.	.	.
	[GreenSpace = 1]	− .229	.099	5.332	1	.021	− .423	− .035
	[GreenSpace = 2]	− .244	.158	2.388	1	.122	− .553	.065
	[GreenSpace = 3]	− .247	.154	2.568	1	.109	− .549	.055
	[GreenSpace = 4]	0^a	.	.	0	.	.	.
Link function: Negative Log-log.
a. This parameter is set to zero because it is redundant.

4.1.3. Public transport

The findings indicate that there is no significant association between the accessibility to bus and taxi stops and BMI (Table 5). However, Kendall's rank correlation was used to ensure the reliability of the results. The analysis revealed a negative correlation between the number of public transportation stations (including taxis and buses) within different radii (100, 200, 400, and 800 meters) and BMI. Among these radii, the 400-meter buffer zone appears to have the greatest potential for reducing overweight and obesity. This suggests that having a higher number of public transportation stations within a 400-meter radius can contribute to a lower BMI (Table 6).

Table 5

the association between accessibility to public transport and BMI
		Estimate	Std. Error	Wald	df	Sig.	95% Confidence Interval
		Estimate	Std. Error	Wald	df	Sig.	Lower Bound	Upper Bound
Threshold	[BMI3 = 1]	− .478	.318	2.256	1	.133	-1.102	.146
Threshold	[BMI3 = 2]	.886	.321	7.631	1	.006	.257	1.515
Location	[Age = 1]	− .481	.228	4.438	1	.035	− .929	− .034
	[Age = 2]	− .197	.132	2.248	1	.134	− .455	.061
	[Age = 3]	− .130	.114	1.308	1	.253	− .354	.093
	[Age = 4]	.100	.119	.703	1	.402	− .134	.334
	[Age = 5]	0^a	.	.	0	.	.	.
	[Gender = 1]	.046	.078	.357	1	.550	− .106	.198
	[Gender = 2]	0^a	.	.	0	.	.	.
	[marital = 1]	− .796	.256	9.693	1	.002	-1.297	− .295
	[marital = 2]	.077	.188	.166	1	.684	− .293	.446
	[marital = 3]	0^a	.	.	0	.	.	.
	[Education = 1]	− .295	.264	1.247	1	.264	− .813	.223
	[Education = 2]	− .237	.258	.842	1	.359	− .742	.269
	[Education = 3]	− .342	.262	1.702	1	.192	− .856	.172
	[Education = 4]	− .364	.288	1.605	1	.205	− .928	.199
	[Education = 5]	0^a	.	.	0	.	.	.
	[PublicTransit = 1]	− .019	.091	.042	1	.837	− .198	.160
	[PublicTransit = 2]	− .129	.093	1.926	1	.165	− .311	.053
	[PublicTransit = 3]	0^a	.	.	0	.	.	.
Link function: Negative Log-log.
a. This parameter is set to zero because it is redundant.

Table 6

The Spearman correlation between accessibility to public transport and BMI
			Age	Gender	marital	Edu	M100	M200	M400	M800	BMI3	BMI
Kendall's tau_b	Age	CC	1.000	.104^**	.449^**	− .108^**	− .040	− .013	− .098^**	− .070^**	.177^**	.173^**
		Sig.	.	.000	.000	.000	.129	.610	.000	.005	.000	.000
		N	1166	1166	1166	1166	1166	1166	1166	1166	1166	1166
	Gender	CC	.104^**	1.000	− .028	.064^*	.001	.008	− .033	− .018	− .016	− .019
		Sig.	.000	.	.340	.019	.968	.773	.244	.522	.567	.492
		N	1166	1166	1166	1166	1166	1166	1166	1166	1166	1166
	marital	CC	.449^**	− .028	1.000	.013	− .113^**	− .106^**	− .147^**	− .107^**	.225^**	.223^**
		Sig.	.000	.340	.	.625	.000	.000	.000	.000	.000	.000
		N	1166	1166	1166	1166	1166	1166	1166	1166	1166	1166
	Edu	CC	− .108^**	.064^*	.013	1.000	.024	− .025	.033	.020	− .039	− .039
		Sig.	.000	.019	.625	.	.380	.331	.198	.431	.125	.128
		N	1166	1166	1166	1166	1166	1166	1166	1166	1166	1166
	M100	CC	− .040	.001	− .113^**	.024	1.000	.565^**	.438^**	.412^**	− .055^*	− .051
		Sig.	.129	.968	.000	.380	.	.000	.000	.000	.047	.059
		N	1166	1166	1166	1166	1166	1166	1166	1166	1166	1166
	M200	CC	− .013	.008	− .106^**	− .025	.565^**	1.000	.544^**	.420^**	− .069^**	− .063^*
		Sig.	.610	.773	.000	.331	.000	.	.000	.000	.010	.016
		N	1166	1166	1166	1166	1166	1166	1166	1166	1166	1166
	M400	CC	− .098^**	− .033	− .147^**	.033	.438^**	.544^**	1.000	.621^**	− .088^**	− .086^**
		Sig.	.000	.244	.000	.198	.000	.000	.	.000	.001	.001
		N	1166	1166	1166	1166	1166	1166	1166	1166	1166	1166
	M800	CC	− .070^**	− .018	− .107^**	.020	.412^**	.420^**	.621^**	1.000	− .075^**	− .076^**
		Sig.	.005	.522	.000	.431	.000	.000	.000	.	.004	.003
		N	1166	1166	1166	1166	1166	1166	1166	1166	1166	1166
	BMI3	CC	.177^**	− .016	.225^**	− .039	− .055^*	− .069^**	− .088^**	− .075^**	1.000	.982^**
		Sig.	.000	.567	.000	.125	.047	.010	.001	.004	.	.000
		N	1166	1166	1166	1166	1166	1166	1166	1166	1166	1166
	BMI	CC	.173^**	− .019	.223^**	− .039	− .051	− .063^*	− .086^**	− .076^**	.982^**	1.000
		Sig.	.000	.492	.000	.128	.059	.016	.001	.003	.000	.
		N	1166	1166	1166	1166	1166	1166	1166	1166	1166	1166
*. Correlation is significant at the 0.01 level (2-tailed). . Correlation is significant at the 0.05 level (2-tailed).

4.2. Underlying diseases

4.2.1. Diabetes

According to Table 7, the model fit was found to be acceptable, with an error level of less than 0.05. Regarding the impact of residential density on diabetes, as Table 8 shows, the pseudo-R-squared coefficients are relatively low, with values of 0.124 and 0.222. This suggests that the three independent variables included in the study have limited explanatory power (12.4%-22.2%) when it comes to understanding the variance and changes in the presence or absence of diabetes. This indicates that there are likely other factors or variables not included in the analysis that have a significant influence on the likelihood of developing diabetes.

Table 7

the result of Omnibus test
		Chi-square	df	Sig.
Step 1	Step	154.782	15	.000
	Block	154.782	15	.000
	Model	154.782	15	.000

Table 8

the result of the pseudo-R-squared coefficients
Step	-2 Log likelihood	Cox & Snell R Square	Nagelkerke R Square
Diabetes
Step 1: res_density	799.559^a	.124	.222
Step 1: publictransit	791.692^a	.130	.233
High blood pressure
Step 1: publictransit	994.556^a	.084	.137
Step 1: station	937.047^a	.128	.210
Heart disease
Step 1: res_density	647.101^a	.102	.211
a. Estimation terminated at iteration number 8 because parameter estimates changed by less than .001.

As Table 9 shows, age, marital status, and residential density within a 200-meter radius from individuals' place of residence significantly affect the likelihood of having diabetes. However, this significance is not consistent across different education levels. The table also indicates that decreasing residential density from higher to average can increase the likelihood of having diabetes by 0.556 (equivalent to a 4.44% increase).

With regard to the impact of accessibility to public transport on diabetes, the coefficients are relatively low at 0.130 and 0.223, meaning that the three independent variables were only able to account for between 13% and 22.3% of the variations observed in the samples regarding diabetes (Table 8).

The results indicate that having three transportation stations within close proximity to their home decreases the likelihood of developing diabetes. Conversely, reducing the number of transportation stations can increase the likelihood of developing diabetes by 0.435 (equivalent to 56.5%). However, this relationship was not observed for any of the other buffer distances examined (100, 400, and 800 meters). This difference may be attributed to the pedestrian scale in Babol city, suggesting that access to public transportation within a specific range has a unique impact on the likelihood of developing diabetes in this particular urban context (Table 10).

Table 9

the association between residential density and diabetes
		B	S.E.	Wald	df	Sig.	Exp(B)
Step 1^a	Age			73.225	4	.000
	Age(1)	-3.038	1.101	7.610	1	.006	.048
	Age(2)	-2.350	.361	42.483	1	.000	.095
	Age(3)	-1.769	.251	49.836	1	.000	.171
	Age(4)	− .719	.226	10.089	1	.001	.487
	Gender(1)	.212	.188	1.277	1	.258	1.236
	marital			4.952	2	.084
	marital(1)	-1.823	.866	4.433	1	.035	.162
	marital(2)	− .091	.385	.056	1	.813	.913
	Education			7.715	4	.103
	Education(1)	1.658	1.061	2.441	1	.118	5.248
	Education(2)	1.172	1.057	1.229	1	.268	3.228
	Education(3)	1.573	1.063	2.192	1	.139	4.823
	Education(4)	1.175	1.116	1.108	1	.293	3.238
	Residential_Density			5.438	4	.245
	Residential_Density(1)	− .195	.285	.465	1	.495	.823
	Residential_Density(2)	− .587	.299	3.846	1	.050	.556
	Residential_Density(3)	− .346	.277	1.562	1	.211	.707
	Residential_Density(4)	− .561	.311	3.256	1	.071	.571
	Constant	-1.600	1.115	2.059	1	.151	.202

Table 10

the association between accessibility to public transport and diabetes
		B	S.E.	Wald	df	Sig.	Exp(B)
Step 1^a	Age			68.045	4	.000
	Age(1)	-3.074	1.099	7.828	1	.005	.046
	Age(2)	-2.300	.363	40.268	1	.000	.100
	Age(3)	-1.716	.257	44.753	1	.000	.180
	Age(4)	− .704	.234	9.047	1	.003	.495
	Gender(1)	.239	.190	1.580	1	.209	1.270
	marital			4.609	2	.100
	marital(1)	-1.740	.865	4.043	1	.044	.175
	marital(2)	− .074	.391	.036	1	.850	.929
	Education			6.995	4	.136
	Education(1)	1.648	1.070	2.371	1	.124	5.195
	Education(2)	1.192	1.066	1.250	1	.264	3.293
	Education(3)	1.579	1.072	2.168	1	.141	4.848
	Education(4)	1.249	1.127	1.229	1	.268	3.488
	M100			1.569	2	.456
	M100(1)	.419	.664	.398	1	.528	1.520
	M100(2)	.125	.631	.039	1	.843	1.133
	M200			4.321	2	.115
	M200(1)	− .831	.409	4.135	1	.042	.435
	M200(2)	− .332	.301	1.215	1	.270	.718
	M400			3.590	3	.309
	M400(1)	− .105	.803	.017	1	.896	.900
	M400(2)	.327	.676	.234	1	.629	1.386
	M400(3)	− .108	.622	.030	1	.862	.897
	M800			.894	3	.827
	M800(1)	− .322	.927	.121	1	.728	.725
	M800(2)	.250	.369	.460	1	.498	1.284
	M800(3)	.156	.273	.327	1	.568	1.169
	Constant	-2.105	1.323	2.532	1	.112	.122

4.2.2. High blood pressure

The model fit was found to be acceptable, with an error level of less than 0.01. However, the pseudo-R-squared coefficients, which indicate the explanatory power of the independent variables in relation to the dependent variable (high blood pressure), are relatively low, with about 8%-13% for accessibility to public transport and 12%-21% for the number of bus and taxi stations (Table 8). This suggests that the three independent variables included in the study have limited ability to explain the variance and changes in the likelihood of developing high blood pressure.

As Table 11 shows, an increase in access to public transport stations, from level 2 to higher levels, can decrease the likelihood of developing high blood pressure by 0.487. Gender and age are intermediate variables that have a negative impact on the likelihood of developing high blood pressure. Table 12 also shows among different radii, only the number of stations within a 400-meter radius has a negative effect on the likelihood of developing high blood pressure, with an odds ratio of 0.293. This means that decreasing the number of stations within a 400-meter radius increases the likelihood of developing high blood pressure by 0.293 (or 70.7%). Age and gender are other variables that have a negative impact on the likelihood of developing high blood pressure.

Table 11

the association between accessibility to public transport and high blood pressure
		B	S.E.	Wald	df	Sig.	Exp(B)
Step 1^a	Age			42.357	4	.000
	Age(1)	-2.836	.698	16.509	1	.000	.059
	Age(2)	-1.550	.306	25.650	1	.000	.212
	Age(3)	− .869	.223	15.259	1	.000	.419
	Age(4)	− .234	.221	1.119	1	.290	.791
	Gender(1)	− .389	.163	5.705	1	.017	.678
	marital			1.450	2	.484
	marital(1)	− .522	.517	1.021	1	.312	.593
	marital(2)	− .414	.357	1.346	1	.246	.661
	Education			2.665	4	.615
	Education(1)	.674	.656	1.056	1	.304	1.962
	Education(2)	.496	.648	.586	1	.444	1.642
	Education(3)	.516	.655	.620	1	.431	1.676
	Education(4)	.844	.692	1.484	1	.223	2.325
	Transit			9.272	4	.055
	Transit(1)	− .424	.248	2.936	1	.087	.654
	Transit(2)	− .718	.260	7.665	1	.006	.487
	Transit(3)	− .368	.245	2.264	1	.132	.692
	Transit(4)	− .124	.238	.272	1	.602	.883
	Constant	− .420	.738	.324	1	.569	.657

Table 12

the association between the number of public transport stations and high blood pressure
		B	S.E.	Wald	df	Sig.	Exp(B)
Step 1^a	Age			48.492	4	.000
	Age(1)	-3.037	.704	18.603	1	.000	.048
	Age(2)	-1.702	.318	28.685	1	.000	.182
	Age(3)	-1.019	.237	18.431	1	.000	.361
	Age(4)	− .239	.235	1.040	1	.308	.787
	Gender(1)	− .492	.170	8.432	1	.004	.611
	marital			1.935	2	.380
	marital(1)	− .724	.526	1.894	1	.169	.485
	marital(2)	− .387	.364	1.132	1	.287	.679
	Education			4.544	4	.337
	Education(1)	1.120	.680	2.707	1	.100	3.064
	Education(2)	.900	.672	1.792	1	.181	2.459
	Education(3)	.865	.680	1.619	1	.203	2.376
	Education(4)	1.242	.720	2.978	1	.084	3.464
	M100			1.513	2	.469
	M100(1)	.652	.604	1.164	1	.281	1.919
	M100(2)	.699	.572	1.492	1	.222	2.012
	M200			4.049	2	.132
	M200(1)	− .270	.377	.514	1	.474	.763
	M200(2)	.280	.236	1.405	1	.236	1.323
	M400			18.720	3	.000
	M400(1)	− .234	.643	.133	1	.716	.791
	M400(2)	-1.227	.498	6.061	1	.014	.293
	M400(3)	− .316	.441	.514	1	.473	.729
	M800			7.148	3	.067
	M800(1)	− .912	.775	1.384	1	.239	.402
	M800(2)	− .958	.368	6.783	1	.009	.383
	M800(3)	− .376	.209	3.253	1	.071	.686
	Constant	− .776	.947	.672	1	.412	.460

4.2.3. Heart disease

The model fit was found to be acceptable, with an error level of less than 0.01. However, the pseudo-R-squared coefficients, which indicate the explanatory power of the independent variables in relation to the dependent variable (heart disease), are very low, with about 10%-21% (Table 8).

According to results, the only variable that influences the likelihood of developing heart disease is residential density. Increasing residential density from average to high can decrease the chances of developing heart disease by 26.1%. This suggests that higher population densities, which lead to increased social interactions and physical activity levels, may result in lower rates of obesity and heart disease (Table 13).

Table 13

the association between residential density and heart disease
		B	S.E.	Wald	df	Sig.	Exp(B)
Step 1^a	Age			52.138	4	.000
	Age(1)	-19.719	3452.281	.000	1	.995	.000
	Age(2)	-2.744	.497	30.471	1	.000	.064
	Age(3)	-1.626	.276	34.736	1	.000	.197
	Age(4)	− .733	.250	8.581	1	.003	.480
	Gender(1)	− .098	.211	.217	1	.642	.907
	marital			.463	2	.793
	marital(1)	− .386	.747	.268	1	.605	.680
	marital(2)	.041	.434	.009	1	.925	1.041
	Education			2.581	4	.630
	Education(1)	− .184	.678	.073	1	.787	.832
	Education(2)	− .448	.669	.449	1	.503	.639
	Education(3)	− .209	.680	.095	1	.758	.811
	Education(4)	− .831	.831	1.001	1	.317	.435
	Residential_Density2			10.675	4	.030
	Residential_Density2(1)	.100	.325	.094	1	.759	1.105
	Residential_Density2(2)	.206	.357	.335	1	.563	1.229
	Residential_Density2(3)	-1.345	.667	4.070	1	.044	.261
	Residential_Density2(4)	− .876	.523	2.806	1	.094	.417
	Constant	− .573	.804	.508	1	.476	.564

5.1. Population and residential density

According to Griffin et al. (2013), research has shown that higher residential densities are associated with a decrease in obesity and cardiovascular diseases. This is because higher population densities lead to increased social interactions, which encourage more physical activity. Kendall's correlation findings also support this, showing a significant inverse relationship between neighborhood population density and body mass index. This study found that individuals living in areas with a density of over 120 people per square kilometer are less likely to be overweight or obese compared to those living in areas with a density of 100 to 120 people. However, no significant association was found between residential densities within a 200-meter buffer and obesity. This could be due to the presence of behavior settings within a larger radius than 200 meters, as well as the mismatch between perceived and objective density. Roda found that there is a stronger alignment between objective and perceived density when there are higher recreational and welfare facilities available in the environment, providing a spatial environment for increased social interactions. Examining residential environments in areas with high population densities, it has been observed that those living in close proximity and with convenient access to gathering points and centers, especially parks, have experienced the greatest impact in reducing obesity. On the other hand, population density does not have much effect on the body mass index for residents of isolated residential complexes without public spaces, such as Omid residential complex, that lack opportunities for interaction.

In terms of diseases, logistic regression results show that changing from neighborhoods with average residential densities (between 130 to 262 housing units per hectare) to neighborhoods with very high residential densities (between 674 to 1501 housing units per hectare) reduces the likelihood of developing heart disease by about 74%. These findings partially support Griffin et al. (2013) findings that women living in more densely populated areas are less likely to develop any form of cardiovascular disease. Additionally, they agree that among all density indicators, residential density is the most prominent independent predictor. This effect is also observed in relation to diabetes. Approximately 44.4% of the risk of developing diabetes in the samples is reduced by changing from relatively low-density neighborhoods to neighborhoods with very high densities. This finding is consistent with a systematic review of 103 studies that examined the relationship between environmental factors, physical activity, and diabetes risk, suggesting that residential density has the strongest link with physical activity and possibly a reduced risk of diabetes (Amuda & Berkowitz, 2019). Other studies have also explored the combination of residential density with factors such as land use mix and intersection density, a composite index of walkability, and found that there is a link between density and type 2 diabetes (Booth et al., 2019; Hajna et al., 2016). However, it should be noted that economic deprivation, dietary habits, genetic characteristics, and other factors can also influence these effects, which were not specifically addressed in this study and should be considered as research limitations.

5.2. Green spaces

Results show that individuals living in neighborhoods with larger green spaces are less likely to be overweight or obese compared to those living in neighborhoods with fewer green spaces, which is in consistence with the study that was conducted by O'Callaghan-Gordo et al. (2020) in Spain. However, caustic factors for this association are still unclear because this study did not find a significant association between access to green spaces within a 200-meter radius and diseases. This could be due to factors such as the quantity and quality of green spaces, which need to be considered as daily or weekly gathering and interaction places. Additionally, the study did not consider mediating variables such as individuals' physical activity or social and economic deprivation, which could impact the results. It is also worth noting that a significant number of participants in the study did not have any green spaces within a 200-meter buffer of their residence, suggesting that larger radii may provide a better understanding of the relationship between green spaces and health outcomes.

In the context of Babel, the study also found that many users of these spaces come from outside the area for which these parks and gardens were designed. Parks with better functionality, including convenient locations for both pedestrians and drivers, had a higher ability to attract individuals. Therefore, there is a weak correlation between the presence of green spaces in neighborhoods and physical activity, as well as the reduction of obesity and related diseases. Factors such as safety, versatility, provision of facilities for different age and gender groups, and integration of pedestrian and vehicular access contribute to the effectiveness and efficiency of local green spaces in attracting populations for interaction and activity in Babel.

5.3. Public transport

According to Howell and Booth (2022), access to public transportation can contribute to increased physical activity in individuals' daily routines, even without a structured exercise plan. The availability of transportation stations and shorter distances to the nearest public transportation nodes can encourage individuals to use public transportation and engage in active trips. Kendall's correlation test results support this idea, showing a weak negative relationship between the number of public transportation stations within various radii (100, 200, 400, and 800 meters) and body mass index, indicating lower levels of overweight and obesity. Plus, when considering factors such as age, gender, education level, and marital status, ordinal logistic regression analysis did not find any significant effect between the number of stations and the likelihood of reducing overweight and obesity.

The results indicated that there is a relationship between access to public transportation and diabetes and high blood pressure. Specifically, the findings showed that increasing the number of public transportation stations from one to three within a 400-meter radius was associated with a significant reduction in the likelihood of developing high blood pressure by approximately 70%. This trend was also observed when considering the number of stations within an 800-meter radius, with individuals who had access to three or more stations being 61.7% less likely to develop high blood pressure compared to those with access to only one station. These findings suggest that individuals with limited access to transportation stations may rely more on personal vehicles, which can contribute to a sedentary lifestyle and increased risk of chronic diseases. The study also referenced previous research conducted in America, which found that public transportation users walk an average of 19 minutes for their daily trips. Additionally, a meta-analysis conducted in 2008 revealed that walking or cycling for transportation is associated with an 11% reduction in the risk of cardiovascular diseases globally (Hamer & Chida, 2008).

It is important to note that the mode of transportation used in this study differed from previous research. Instead of using metro, light rail, and buses as the main modes of public transportation, taxis were utilized due to the limited bus network serving only one main artery of the city. This choice may have implications for the distance individuals need to travel to reach transportation stations, which can vary economically and socially across different parts of the city. However, despite these differences, the overall results align with previous findings that highlight the benefits of access to public transportation in promoting physical activity and reducing the risk of chronic diseases (Millett et al., 2013; Richard Patterson et al., 2019).

This study also found that individuals living in certain areas of the city, such as Shahid Modarres, Shariati, and Valiasr Boulevard, were more likely to walk daily for using public transportation compared to those with better access to the western streets of the city, such as Madar and Janbazan Boulevard. This reveals that taxi stations that serve as hubs and connectors for the city's main arteries can increase the effectiveness and efficiency of public transportation in attracting pedestrian trips in cities with limited coverage of public transportation.

Additionally, increasing the number of taxi and bus stations from one to three or more within a 200-meter radius of individuals' residences was found to reduce the probability of developing diabetes by approximately 56.5% with a significant level of 95%. Furthermore, living within a distance of less than 150 meters from the nearest public transportation station or nodal points was associated with a 51.3% lower probability of developing high blood pressure compared to living within a distance of 300 to 500 meters from public transportation stations. In contrast, the study by Barr et al. (2016) conducted in Australia did not find an association between access to public transportation and obesity, metabolic syndrome, and diabetes. However, this study did find a link between areas with moderate to high accessibility to public transportation and an average walking time of 90 minutes per week. The discrepancy between these findings may be due to the neglect of physical activity as a mediating variable in the Australian study. It is possible that participants in that study engaged in moderate to vigorous physical activity during the week, which could influence the impact of walking for accessing public transportation on obesity and diseases.

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Unravelling the Relationship between Obesity, Density, Accessibility to Public Transport and Green Spaces in Babol City

Status:

Version 1

Abstract

1. Introduction

2. Literature Review

3. Research Methods