|Year : 2022 | Volume
| Issue : 4 | Page : 340-346
Built environment correlates of diabetes and obesity: Methodology
Garudam R Aarthi1, Rajendra Pradeepa2, Viswanathan Mohan3, Padma Venkatasubramanian4, Ranjit M Anjana3
1 Department of Research Operations, Madras Diabetes Research Foundation, Chennai, Tamil Nadu, India; School of Public Health, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India
2 Department of Research Operations, Madras Diabetes Research Foundation, Chennai, Tamil Nadu, India
3 Department of Diabetology, Madras Diabetes Research Foundation and Dr. Mohan’s Diabetes Speciality Centre, Chennai, Tamil Nadu, India
4 School of Public Health, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India
|Date of Submission||27-Aug-2022|
|Date of Decision||22-Sep-2022|
|Date of Acceptance||28-Sep-2022|
|Date of Web Publication||21-Dec-2022|
Dr. Ranjit M Anjana
Department of Diabetology, Dr. Mohan’s Diabetes Speciality Centre, Madras Diabetes Research Foundation, 4, Conran Smith Road, Gopalapuram, Chennai 600086, Tamil Nadu
Source of Support: None, Conflict of Interest: None
Background: A city’s planning, design, and construction can have a profound influence on health, specifically on non-communicable diseases such as diabetes and obesity, which are often referred to as “diabesity.” This study describes the designs and methods to understand the relationship between food and physical activity environments on diabesity. Materials and Methods: This study was a community-based cross-sectional door-to-door survey conducted as part of a large National Institute of Health and Care Research-funded surveillance project. For this study, two wards in Chennai were selected randomly. In each ward, five community enumeration blocks were selected using systematic random sampling technique. A consecutive sampling approach was used to select the study participants. Two categories of data were collected: (1) health data and (2) built environment (BE) data. Health and lifestyle questionnaires, anthropometric, and biochemical data were collected from all the study participants. For categorizing BE, an online questionnaire was developed using the KoBo toolbox to collect information about food and physical activity environments, as well as geographic locations. Expected Outcome: This study is expected to reveal data on the relationship between food and physical activity environments and diabesity. It will help policy-makers to understand the importance of access to healthy foods and spaces for physical activity in prevention and control of diabesity. It can also enable community-based interventions to improve health outcomes and help urban planners to plan cities that promote active lifestyles for its residents.
Keywords: Built environment, diabesity, food environment, physical activity areas
|How to cite this article:|
Aarthi GR, Pradeepa R, Mohan V, Venkatasubramanian P, Anjana RM. Built environment correlates of diabetes and obesity: Methodology. J Diabetol 2022;13:340-6
| Introduction|| |
Non-communicable diseases (NCDs) are the leading cause of death, accounting for 7 out of 10 deaths globally. Across low- and middle-income countries (LMICs), NCDs are responsible for 86% of premature deaths under the age of 70, indicating a shift in disease burden from infectious diseases to NCDs. Numerous factors contribute to this transition, such as the shift from local traditional foods to highly processed items high in fat, sugar, and sodium, as well as an increase in sedentary lifestyles. Although most NCDs can be attributed to genetic and lifestyle factors, recent studies report that an increasing proportion are being caused by environmental factors. As a result, the World Health Organization (WHO) NCD Global Action Plan in 2017 outlined how environmental factors contribute to NCDs., A review conducted by Prüss-Ustün et al. reported that environment-related risks caused the largest increases in NCD mortality worldwide between 2010 and 2016.
The term “built environment” (BE) encompasses all structures, including buildings, transportation systems, and other physical surroundings constructed by humans where people devote most of their time. In today’s world, city planning is recognized as an integral part of addressing NCDs. The design of cities can significantly affect the incidence and prevalence of NCDs. Over the last decade, 12.6 million deaths have been attributed to the environment, with nearly two-thirds occurring due to NCDs, globally. Healthier environment could prevent 23% of deaths worldwide. By 2050, two-thirds of the world’s population will live in cities, with India already rapidly urbanizing. Tackling NCDs in urban cities must therefore be a national priority.
Diabetes has recently grown to become one of the leading causes of mortality worldwide. LMICs are expected to experience a steady increase in diabetes prevalence, with the largest increase being in the Indian subcontinent. Over 60% of individuals with diabetes worldwide are Asians, with China and India being number 1 and 2 in diabetes prevalence, respectively. The high prevalence of diabetes in India has been partly attributed to the density of its population and also to the interaction among numerous factors such as rapid economic development, unplanned urbanization, and shifts in lifestyles that influence physical activity, diet, and obesity., Obesity is an important risk factor and is very common in type 2 diabetes (T2D), which is illustrated by the term “diabesity.” The National Family Health Survey (NFHS-5) and the Indian Council of Medical Research (ICMR)-India Diabetes (INDIAB) reported that generalized obesity was 24.6% in Tamil Nadu and 41.9% in Chennai, respectively. Abdominal obesity was 26.6% in Tamil Nadu and 59.5% in Chennai. The prevalence of obesity reported in NFHS-5 was only for females. Diabetes prevalence in Tamil Nadu was about 10.4% and in Chennai, it was 27.2% among women and 22.1% among men.,, The risk of developing diabetes and obesity is strongly related to lifestyle and environmental factors.,
Most of the studies that examine the relationship between BE and NCDs have been done in developed nations with very little data for developing nations. The quality and characteristics of the different types of neighborhood environment can remarkably differ among developed and developing nations, which may distinctively impact obesity and T2D patterns among local residents. In the upcoming years, Asia is believed to encounter a remarkable transformation in terms of urban expansion. As a result, urban planners have an opportunity to design smart cities and environments that promote an active lifestyle among residents. The prime target cannot be achieved without a thorough understanding of the relationship between the BE and disease condition. Furthermore, knowledge about the role of the BE on metabolic risk factors from a practical and evidence-based viewpoint may help prevent the onset or progression of these conditions. Thus, this study aims to understand the relationship between different BE factors such as food environment and physical activity environment on diabesity among adults in urban Chennai, and this paper presents the methodological details.
- To estimate the proportion of adults with diabesity residing in Chennai, south India;
- To examine the relationship between specific BE factors such as food environment and physical activity environment on diabesity in Chennai;
- To analyze the relationship between accessibility to physical activity areas and subjectively measured physical activity in this population.
| Materials and Methods|| |
The city of Chennai (formerly Madras) has a population of 4.64 million, making it the fourth largest city in India, according to a 2011 census survey. The Greater Chennai Corporation is divided into 15 zones and 200 wards. It covers a total area of 175 km2 and is a coastal region with the Bay of Bengal to its east. The current study is part of a large National Institute of Health and Care Research (NIHR)-funded surveillance project that investigated patterns and determinants of health in urban Chennai. NIHR-funded study was conducted on the representative population (aged ≥ 18 years) of Chennai, between May 2018 and December 2019.
This study was a cross-sectional, door-to-door survey which adopted a multistage sampling technique. In the first stage, two wards were randomly selected, each having approximately 40 community enumeration blocks (CEBs) [Figure 1]. From each ward, approximately 550 participants took part in this study. A CEB is an administrative unit with approximately 100–125 households with a population of 650–700 persons. In the second stage, 5 CEBs were selected using a simple random sampling technique for each ward. In the final stage, complete enumeration was done for all the selected CEBs and participants were selected using the consecutive sampling technique. A novel approach was used to examine the relationship between exposure to the BE (food and physical activity environments) and diabesity. The proximity of food outlets and the physical environment around their homes were linked to individual health outcome data.
Sample size calculation
According to the ICMR INDIAB study, the prevalence of diabetes and obesity in Tamil Nadu was 10.4% and 24.6%, respectively. Considering two disease conditions diabetes and obesity, assuming an expected prevalence of 50% and an α error of 5%, the sample size was estimated to be 384.
where n is the sample size.
Z is the standard normal deviation. For 95% confidence interval, the Z-value is 1.96 (2 SD).
p is the proportion (prevalence), q= (1-p), and d is the margin of error.
Adults (men and women) aged 18 years and above living at their residence for more than 12 months, within the geographical boundaries of the selected wards, were included in the study. Participants with severe mental or psychiatric illness, pregnant or breastfeeding, terminal illness or other serious illnesses such as stroke and cancer, and subjects who do not give written consent were excluded from the study.
Training field staff
Field investigators and technicians were trained to administer questionnaires and collect blood samples through vacutainers, as well as to measure anthropometric indicators such as height, weight, and waist circumference. They were educated to collect Global Positioning System (GPS) locations of the participant’s household; food environment and physical activity environment using the Kobo tool app; and were trained to calibrate the machines. A non-stretchable measuring tape was used to calibrate the stadiometer, whereas a known weight was used to calibrate the bioimpedance machine.
Fieldwork was started by distributing participant information sheets (PISs) to all households in the selected CEBs, followed by an explanation of the study’s objectives. [Figure 2] represents the methodology for data collection. Two categories of data were collected: (1) data from study participants and (2) data on food and physical activity environments.
Interviewers administered health and lifestyle-structured and pretested questionnaires to all study participants to collect data on demographic, socio-economic, medical and family history of diabetes and heart disease; behavioral characteristics including tobacco use, alcohol use, and consumption of fruits and vegetables. Geolocation of the participants household was captured using the Kobo tool app installed on a tablet. The field staff captured the geolocation by standing in front of the household and clicking the button which automatically captured the GPS location and autofilled the address field. Physical activity was also captured using the WHO STEP Global Physical Activity Questionnaire (GPAQ).
The following anthropometric measurements were obtained:
- i. Using a stadiometer (SECA Model 213, Hamburg, Deutschland, Germany), height was measured. Participants were instructed to stand straight, without shoes, with their backs against the vertical back support, heels together, and eyes directed forward.
- ii. Body mass index (BMI) and weight measurements were taken using a (Omron BF511, Omron Healthcare Group, Kyoto, Japan) bioimpedance machine on a firm flat horizontal surface. Participants were advised to wear light clothing, remove any accessories such as phones or keys, and remove their footwear.
- iii. A non-stretchable measuring tape was used to measure the waist circumference (WC). It was measured at the girth between the last rib and anterior superior iliac crest.
Physical activity: The Physical Activity Questionnaire (www.who.int/chp/step) used in this study was developed by the WHO for physical activity surveillance and was validated in nine populations including Asian Indians and found to be reproducible and reliable. The questionnaire captures information on physical activity participation in four domains which includes work activity, travel to and from the workplace, activities around the house, and recreational activities. Participants were categorized according to their physical activity levels as sedentary, moderate, and vigorous.
Biochemical parameters: Participants were requested to observe at least 8 h of fasting overnight in order to collect the fasting venous blood samples, the next day morning. Trained technicians collected the venous blood samples. The fasting venous blood glucose level was measured using Jana Care AINA mini-Glucose meter, Jana Care, Inc., Boston, MA, USA. The collected blood samples were sent to the laboratory for HbA1c testing. The samples were tested using Variant II Turbo Machine, Bio-Rad Laboratories, Inc., USA.
Obesity: Obesity was defined as either abdominal obesity, i.e., WC ≥ 90 cm in males and ≥ 80 cm in female, or generalized obesity defined as BMI > 25 kg/m2.,
Diabetes: Individuals with diabetes had a fasting venous blood glucose level of ≥ 126 mg/dL or glycated hemoglobin levels (HbA1c) ≥ 6.5%.
Diabesity: Diabesity indicates the coexistence of both diabetes and obesity: FBG ≥ 126 mg/dL and/or HbA1c ≥ 6.5 and BMI ≥ 25 kg/m2 or WC ≥ 90 cm in males and ≥ 80 cm in females.
Metabolic equivalents: MET stands for metabolic equivalent time, which measures the ratio between resting and working metabolic rates. METs were defined as the energy consumed by sitting quietly and were equivalent to 1 kcal/kg/h of food consumption.
Physical activity: MET scores for each domain and subdomain were calculated using existing guidelines to assess physical activity. A person’s overall energy expenditure was calculated using GPAQ data by assigning four METs to moderate activities and eight METs to vigorous activities. The total amount of physical activity during a typical week, the number of days, and the intensity of activities were considered when calculating the categorical indicator. The GPAQ guidelines suggest that individuals are classified as active, if throughout the week (including activities done for work, during transport and for leisure) they engage in 150 min of moderate-intensity physical activity or 75 min of vigorous-intensity physical activity, each achieving at least 600 metabolic equivalent minutes. The levels of physical activity were further classified based on MET-minutes into three categories: low (<600 met-minutes), moderate (600–1200 met-minutes), and high (>1200 met-minutes).
Two wards were mapped in order to define the BE in relation to food and physical activity. Food and physical activity environments were measured with a list of questions, including geographic locations, types of physical activity areas, and types of food outlets and items sold (e.g., fruits, vegetables, confectionery, fast food) adapted from the International Network for Food and Obesity/NCDs Research, Monitoring and Action Support (www.informas.org), and Johns Hopkins University’s Maryland Food Systems Map (https://mdfoodsystemmap.org). An online questionnaire was created using the KoBo Toolbox application (www.kobotoolbox.org) as the study instrument. In order to conduct the quality check, the coordinates were reviewed on the KoBo Toolbox website and Google Maps together. Data were collected on geolocations (latitude and longitude) for food outlets and physical activity areas. By visiting the study sites both in the mornings and afternoons, data were captured about mobile food carts and produce vendors were captured.
Due to the lack of food outlet classification in South Asia, the international approach described in the Retail Food Environment Index (RFEI) was adopted. Four categories were established for food outlets based on the availability of food items:(1) fast-food outlets, such as those where customers can buy sweetened beverages and ready-to-eat meals that are highly processed, calorie-dense, confections, bakery goods, and chat items were deemed unhealthy; (2) supermarkets with self-service aisles that offer fresh produce, including fruits and vegetables that are deemed healthy; (3) corner stores providing packaged goods were categorized as convenience stores; and (4) produce vendors included temporary markets, grocery stores, mobile fruit and vegetable carts, and vendors of rice, eggs, and oil, which are made from natural sources and are therefore seen as healthy. Although supermarkets sell both healthy and unhealthy products, in international classifications, they were considered as healthy due to their greater likelihood of offering healthier options compared with fast-food restaurants or corner stores. Physical activity areas include recreation centers, public parks, health clubs for elderly people, sports clubs, playgrounds, and gyms.
Statistical analysis plan
Proportion of people with diabesity
The SPSS 24 Statistical Package is used to conduct statistical analysis. With descriptive analysis, continuous variables are expressed as means and standard deviations, and categorical variables are expressed as frequencies and percentages. For continuous variables, χ2 tests are used to compare proportions and Student’s t-tests are used to compare means.
Food environment and diabesity
As for the food environment, the density of fast-food outlets, supermarkets, convenience stores, and produce vendors was calculated, all of which were included in the food environment. Data on food environment geolocation and individual-level data were used to analyze the density of different food outlets within 400 m of each participant’s home. The total number of each food outlet was counted, and the density was defined as a percentage of all food outlets within a 400-m buffer. The largest effects were seen in 400 m for both line-based and radial buffers, and this effect decreased with larger buffer sizes. By dividing the number of fast-food restaurants and convenience stores by the number of grocery stores and produce vendors within a radius surrounding an individual’s residence, RFEI was calculated. The RFEI was then divided as quartiles to find association with the individual’s health outcome.
Physical activity environments and diabesity:
The distance between the physical activity areas and participants’ households were calculated. Physical activity environment geolocation data measuring access to physical activity environments were merged with individual-level data for a measurement of the relationship among diabesity, physical activity environment, and physical activity measured by GPAQ. Geospatial analyses were conducted on ArcMap 10.7.1.
Written informed consent was obtained from all participants to ensure their understanding and acceptance of his/her role in the study. Participants with diabetes and obesity were given general advice on healthy lifestyle. All participants were informed about their health status, and a health report was provided the next day.
This study is one of the first community-based cross-sectional studies based on specific BE characteristics of diabesity in India. Once the results of this study are published, it is likely to shed light on the prevalence and risk factors of diabesity in adults of Chennai city. The study is expected to reveal data on the relationship between food and physical activity environments and diabesity. Considering the growing NCD epidemic and its high costs, these data will aid health organizations, policy-makers, and society to shift resources toward improving quality of life and preventing chronic diseases such as T2D and obesity.
This study has few limitations. In spite of the fact that this study examined the relationship between food environment and diabetes, it does not explore the participants’ online food purchasing choices or online food delivery services. Since this study is cross-sectional, the ecological fallacy and generalizability of the results cannot be addressed.
| Conclusion|| |
In the past few decades, NCDs have dominated the global epidemiological landscape, largely caused by raising obesity rates, unhealthy lifestyles, pollution, and other environmental hazards. Hence, for NCDs to be adequately controlled, there is a need for the community to take a proactive role in shaping the environmental drivers of health, taking on new leadership and collaborative activities across all relevant sectors. This study will provide insights into the association between built environment and diabesity. This information will be useful to urban planners and policy-makers to help plan and build “healthier cities” in India.
We acknowledge the support of the National Institute for Health Research (NIHR) in carrying out this project.
Financial support and sponsorship
This research was funded by the National Institute for Health Research-Global Health Research Unit (NIHR-GHRU) (16/136/68) using UK aid from the UK Government to support global health research.
Conflicts of interest
There are no conflicts of interest.
This study was approved by the Madras Diabetes Research Foundation Institutional Ethics Committee (Ref. no.: MDRF/NCT/06-01-2018).
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[Figure 1], [Figure 2]