Introduction
Social network means the structure of the network and the quality and understanding of the elderly and defines relationships with others [1]. Factors such as modernization [2], retirement [3], leaving home by children, and the death of spouses and friends [4] can lead to changes in the social network of the elderly. 
Considering the importance of the social network of the elderly, it seems that in addition to individual factors such as age [5], gender [6], marriage [7], education level, and economic status [8], regional factors also play a role in the social network of the elderly. The regional level is a region’s characteristic, not a person’s. These factors include the culture of organizations, the economic status of communities, access to volunteer groups, social services, and transportation services [9, 10, 11]. This study aimed to determine the contribution of individual and regional factors to the social network of the elderly with a multilevel analysis approach on Urban HEART-2 data.
Methods
In this study, two data sources were practiced. The first data was Health equity and response tool round II (urban HEART-2), including 22 municipal districts of Tehran. This source was used to measure the social network of the elderly and individual and regional determinants. The second source for regional data variables was the Tehran Municipality statistical yearbook [12]. In this study, 5670 elderly people were studied. The sampling method is given in articles [13, 14]. The tool included the social network of the elderly in the form of two general questions about the distance or proximity of the elderly to others and the frequency of visits daily, weekly, monthly, annually, or never at the levels of family members and relatives, friends, and neighbors. Individual variables included age, gender, education, marital status, retirement, self-reported health, mental health (GHQ-28 questionnaire), household economic situation, and the household dimensions. The household’s economic status was measured by the household asset estimation method and the PCA method, and per capita variables based on residence, occupation of residence, bathroom, kitchen, water close, sewage, car, cellphone, freezer, dishwasher, microwave, computer. Regional level variables include per capita green space, per capita health care centers, per capita recreational cultural centers, number of elderly centers, sense of security, social identity, sense of obligation, the rule of law, accountability and corruption control, waiting time for the bus to arrive in minutes, percentage The population was below the poverty line, the old age index and the Gini coefficient of the region.
To analyze the data, multilevel linear regression was used, and an Intra-class Correlation Coefficient (ICC) index was used to select between the ordinary regression model and multilevel regression model, which according to ICC texts, is acceptable between 0.05 and 0.20 [15]. AIC, BIC, and LL indices were used to fit the model. This study was registered at the University of Welfare and Rehabilitation Sciences (Code: IR.USWR.REC.1397.175).
Results
The social network of the elderly 60 to 85 years old and elderly women and married elderly over 85 years old, elderly men, and elderly without a spouse. With the increase in the educational level of the elderly, the social network of the elderly increased. Also, elderly people without pensions had a higher social network. Finally, the elderly who reported better physical health and the elderly who had mental health reported a more elevated social network. Elderly people of district 10 of Tehran municipality had the lowest social network (1.07±0.32), and elderly people of district 6 of Tehran municipality had the highest social network (2.70±0.82).
Several steps were taken to present the multilevel model of the social network of the elderly. At first, for the single-level null, the outcome variable, namely the social network of the elderly, was entered into the model, and other predictor variables were avoided at the individual and district level. Next, the 2-level null model was implemented by entering the outcome variable of the social network of the elderly and the level of other variables - the district level. Since the ICC value was equal to 0.108 and was in the range of 0.05 to 0.2, the multilevel model was considered the appropriate model.
 The values ​​of AIC, BIC, and LL in the normal regression model were 7879, 7912, and -3947, respectively, and in the multilevel regression model were 7104, 7163, and -3586, respectively. AIC and BIC values ​​in the multilevel regression model decreased compared to the ordinary regression model, and LL also increased. Therefore, the multilevel model had a better fit than ordinary regression. Individual level 0.05 and regional level 0.006 explain the total variance of 0.056. By adding individual level predictor variables, 20.60% ([0.056-1.21]/0.056) of the variance of the elderly social network and regional level predictor variables explained 19% ([0.006-0.12]/0.006) of the elderly social network variance. 
Next, individual and district predictor variables were entered into the model. According to Table 1, the regression coefficients showed that the elderly in the age group of 60 to 84 years had a higher relationship with the social network compared to the elderly over 85 years old, the illiterate elderly, and the elderly with self-reported good physical health.


At the same time, the elderly with poor self-reported physical health was associated with a lower social network. At the district level, a feeling of regional security and high corruption control was associated with a higher social network, high accountability, and a longer waiting time for the bus to arrive with a lower social network.
Discussion
Considering that the subjects studied in the field of aging are biological and psycho-social phenomena and are doubly intertwined during old age, it is necessary to better understand the issues of the elderly at different individual and regional levels. According to the ICC obtained in this study, about 11% of the variance of the social network of the elderly can be explained by the variables of the region, and 79% of the variance of the social network can be presented at the individual level. Therefore, considering the importance of the social network in the health, quality of life, and well-being of the elderly, in addition to individual-level variables, district-level variables should be considered. Individual variables such as age, education, physical and mental health, and regional level variables such as sense of security, corruption control, and responsibility along with urban life facilities (waiting time for the arrival of the bus) explained the social network of the elderly.

Ethical Considerations
Compliance with ethical guidelines
This study was registered with the code of ethics IR.USWR.REC.1397.175 at the University of Welfare and Rehabilitation Sciences.

Funding
This article is the result of the doctoral thesis of Mr. Seyed Hamid Nabavi in the Faculty of Educational Sciences and Social Welfare, University of Welfare and Rehabilitation Sciences.

Authors' contributions
All authors contributed equally in preparing all parts of the research.

Conflicts of interest
The authors declared no conflict of interest.

Acknowledgements
The authors appreciate the effective contribution of Manouchehr Timuri.


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