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Public health
Original research
Social determinants of diabesity and its association with multimorbidity among older adults in India: a population-based cross-sectional study
  1. Abhinav Sinha1,
  2. Parul Puri2,
  3. Sanghamitra Pati1
  1. 1ICMR-Regional Medical Research Centre Bhubaneswar, Bhubaneswar, Odisha, India
  2. 2Department of Survey Research and Data Analytics, International Institute for Population Sciences, Mumbai, Maharashtra, India
  1. Correspondence to Dr Sanghamitra Pati; drsanghamitra12{at}gmail.com

Abstract

Objectives India, a major contributor to the global diabetes burden, is witnessing a simultaneous epidemic of obesity leading to rise in diabesity. Diabesity, the coexistence of diabetes and obesity in an individual, has emerged as a risk factor for other morbidities and hence, multimorbidity. Although diabesity has long been recognised, still, there is a poor understanding of its social determinants in India, necessary for its control. We aimed to estimate and compare the prevalence and identify social determinants of obesity, diabetes and diabesity; explore their association with other selected non-communicable diseases (NCDs) multimorbidity; and assess its outcomes among adults aged ≥45 years.

Design and setting We undertook an observational analyses employing nationally representative data from Longitudinal Ageing Study in India, 2017–2018.

Participants This study was conducted among n=59 073 respondents.

Outcome measures Descriptive analysis was conducted among n=59 073 respondents to determine prevalence of diabetes, obesity and diabesity. The social determinants were assessed using multinomial regression, reported as adjusted relative risk ratio (RRR). A multiple correspondence analysis generated life satisfaction.

Results The prevalence of obesity, diabetes, and diabesity was 21.2%, 6%, and 5.8%, respectively. We identified women (RRR: 2.16 (1.72 to 2.71)), urban residents (RRR: 3.73 (3.07 to 4.54)) and affluent groups (RRR: 3.60 (2.46 to 4.58)) had a higher likelihood of having diabesity. The association with various NCDs showed multimorbidity to be akin to the diabesity group. We observed inferior life satisfaction and significantly higher healthcare utilisation among participants with diabesity as compared with other two groups.

Conclusion Increased obesity prevalence has led to the rise in diabesity in India. Additionally, the level of multimorbidity in this group cannot be overlooked. Rather than focusing on individual conditions, an in toto approach to multimorbidity is warranted.

  • general diabetes

Data availability statement

Data are available upon reasonable request. The data have been archived in the public repository of the Longitudinal Ageing Study in India, 2017-2018. Access to the dataset requires registration and is granted only for legitimate research purposes. A guide for how to apply for dataset access is available at https://iipsindia.ac.in/sites/default/files/LASI_DataRequestForm_0.pdf. The data can be accessed using the link: https://www.iipsindia.ac.in/content/lasi-wave-i.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2022-061154

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    STRENGTHS AND LIMITATIONS OF THIS STUDY

    • Findings are based on a nationally representative data from Longitudinal Ageing Study in India, wave 1.

    • It is limited by self-reported conditions being taken into consideration.

    • Being a cross-sectional survey, it does not establish causality.

    Introduction

    The WHO, through its global action plan to prevent and control non-communicable diseases (NCDs), focuses on four major chronic diseases: cardiovascular diseases, cancer, diabetes and respiratory diseases.1 India, a low/middle-income country, contributes to a significant part of the global diabetes burden.2 3 According to the Global Burden of Disease (GBD) 2016, the age-standardised diabetes prevalence rose by 29.7% (95% uncertainty interval: 26.5–32.6) in India from 1990 to 2016.4 NCDs can be attributed to behavioural, dietary, environmental and metabolic risk factors which have attracted substantial global attention.5 Additionally, socioeconomic disparities, including childhood inequalities, also lead to NCDs.6

    Obesity, an individual NCD, was identified as the most crucial risk factor for diabetes in India.7 8 A study conducted by GBD collaborators stated that for every 100 overweight adults (≥20 years and above), there were 38 adults (95% uncertainty interval: 34 to 42) with diabetes in India as compared with the global average of 19 (95% uncertainty interval: 17 to 21).4 This evidence shows an alarming rise in diabesity, the coexistence of diabetes, and obesity in an individual.9 Obesity and diabetes are interconnected as excessive weight gain leads to fat accumulation that in turn increases insulin resistance, a known pathway for diabesity.10 Diabesity has emerged as a significant public health challenge warranting greater attention. Further, it acts as a risk factor for other NCDs such as cardiovascular diseases leading to multimorbidity.11 12 Multimorbidity encompasses two or more coexistent chronic conditions in an individual without defining an index disease.13 Additionally, in contrast to many high-income countries, NCDs usually present a decade earlier (around ≥45 years of age) in India, escalating the risk of early-onset multimorbidity.14

    The Sustainable Development Goal 3 aims to reduce the premature deaths caused due to NCDs (target 3.4) by one-third by 2030, which remains a daunting target.15 A wide gap exists between the available and required resources to combat diabesity. Still, continuing with its commitment to achieve Universal Health Coverage (UHC), recently, India rolled out the Ayushman Bharat Programme and is establishing Health and Wellness Centres (HWCs) throughout the country that intend to provide accessible, egalitarian primary care services. Furthermore, HWCs envisage to provide a basket of comprehensive preventive and curative services that can further strengthen existing programmes such as the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular diseases, and Stroke (NPCDCS). However, these programmes and policies need evidence-based guidance for setting priorities and resource allocation. Although the burden of diabesity has steeped, India still lacks comprehensive data which poses a challenge in the formulation and implementation of actions to reduce NCD inequalities. Hence, this study was conceived to address the research gap with an aim to estimate the prevalence and identify social determinants of diabesity among adults aged ≥45 years and further compare it with the individuals having only obesity and diabetes. Additionally, it aimed to explore and compare the association between obesity, diabetes and diabesity with other selected NCDs (multimorbidity); and assess its outcomes such as life satisfaction and healthcare utilisation among older adults using nationally representative data.

    Methods

    Study design and participants

    We undertook an observational analyses employing data from the first wave of the Longitudinal Ageing Study in India (LASI) conducted in 2017–2018. LASI is a longitudinal study of 72 250 older adults (aged 45 years and above along with their spouses irrespective of age) at baseline, recruited in 2017. LASI is a multiparty undertaking by the International Institute for Population Sciences, Harvard T H Chan School of Public Health and the University of Southern California, launched by the Government of India.16

    LASI uses a multistaged stratified area probability cluster sampling design to assemble nationally representative data for all states and union territories (UTs) of the country (except Sikkim).16 LASI adopted a three-stage sampling design in rural areas and a four-stage sampling design in urban areas to reach the eventual sampling unit. Subdistricts formed the primary sampling unit in each state/UT, which were selected randomly from the sampling frame based on the 2011 census. In the second stage, villages from selected subdistricts were chosen randomly, while in urban areas, urban wards followed by census enumeration block (CEB) were selected. After that, households from selected village/CEB were chosen to reach the target individuals making LASI a nationally representative data. Individual response rate to LASI wave 1 was 87.3%. For the present analysis, we merged information from individual (n=72 250) and biomarker (n=65 900) datasets. Further, we excluded all observations pertaining to individuals below the age of 45 years (n=6136), after which information on 59 764 individuals was obtained. As missing data accounted for 1.5% of the data, we employed a complete case analysis for 59 073 older adults.

    Procedures

    LASI included the question: ‘Has any health professional ever diagnosed you with the following chronic conditions or diseases?’ Based on this question, we employed self-reported diagnoses of diabetes in the present study. LASI collected information on anthropometric measures—height (in centimetres) and weight (in kilograms) provided in the biomarker dataset. Height and weight were measured using a stadiometer and a Seca 803 digital weighing scale, respectively. Body mass index (BMI) was calculated as weight (in kg) divided by height (in m2). We used the WHO BMI classification for Asian adults, where a BMI greater than or equal to 25 kg/m2 is considered obese.17

    The WHO’s Commission of Social Determinants of Health proposes a conceptual framework for action on the social determinants of health.18 The framework defines health as a complex phenomenon where social determinants hold a pivotal position. Further, we included information on age (in years) grouped in an interval of 5 years; sex (male and female) and residence (rural/urban). Religion was based on the question: ‘what is your religion’ with responses grouped as Hindu, Muslim, Christian and Others (including Sikh, Buddhist/neo-Buddhist, Jain, Jewish, Parsi/Zorastrian and others). The social group was based on two separate questions asked, that is, ‘What is your caste or tribe?’ with options as (1) caste, specify; (2) tribe, specify; (3) no caste/tribe; and ‘Do you belong to a scheduled caste, a scheduled tribe, other backward class or none of these?’ Participants’ responses to the second question were directly used to form ‘scheduled caste’, ‘scheduled tribes’ and ‘other backward class’ groups, while ‘others’ comprised of participants who responded ‘no caste/tribe’ in the first question along with those who said ‘none of these’ in the second question. Years of schooling were based on the question: ‘How many years of schooling have you had?’, which was categorised as ‘no education’ for 0 years, less than 5 years, 5–9 years, and 10 years or more. The wealth quintile was classified as poorest, poorer, middle, richer and richest based on the monthly per capita expenditure.

    Outcomes

    The primary outcome of interest was a categorical variable ‘disease group’ with four categories, (1) none (no diabetes and no obesity), (2) diabetes (only diabetes and no obesity), (3) obesity (only obesity and no diabetes) and (4) diabesity (both diabetes and obesity simultaneously).

    We examined the influence of categorised disease groups obesity, diabetes and diabesity with selected NCDs (morbidities). We included these conditions based on an extensive literature search. The included NCDs were self-reported based on the question: ‘Has any health professional ever diagnosed you with the following chronic conditions or diseases?’ We included cancer, chronic heart disease, chronic obstructive pulmonary disorder, chronic renal failure, gastrointestinal disorders, high cholesterol, hypertension, stroke and thyroid disorders. These NCDs were selected as they are the most prevalent conditions.19 The study further explored the association of the ‘disease group’ with three outcomes: (1) life satisfaction, (2) the number of inpatient visits and (3) the number of outpatient visits.

    Life satisfaction was computed from the question: ‘Please say how much do you strongly agree, slightly agree, neither agree nor disagree, slightly disagree, somewhat disagree or strongly disagree with the following statements’:

    1. In most ways, my life is close to ideal.

    2. The conditions in my life are excellent.

    3. I am satisfied with my life.

    4. So far, I have got the important things I want in life.

    5. If I could live my life again, I would change almost nothing.

    Multiple correspondence analysis was used to generate life satisfaction scores based on the responses to the questions above. Further, these scores were recategorised to form a life satisfaction index classified as low, medium and high.

    The number of inpatient and outpatient hospital admissions was computed from the questions: ‘Over the last 12 months, how many times were you admitted as a patient to a hospital/long-term care facility for at least one night?’ and ‘In the past 12 months, how many times did you receive healthcare or consultation from a healthcare provider (including home visits)?’, respectively.

    Statistical analysis

    Results were presented as frequencies and weighted percentages to describe the study population. The burden of obesity, diabetes and diabesity was assessed using prevalence (per 100 individuals) calculated as:

    Embedded Image

    In addition, the burden of obesity, diabetes and diabesity was also presented as prevalence across various background characteristics. These values were supplemented with Χ2 p values to study bivariate associations that disease group has with the sociodemographic characteristics. A multinomial regression analysis was conducted, and adjusted relative risk ratios (RRRs) were reported to identify the predictors of obesity, diabetes and diabesity. The multivariable model was adjusted for all common morbidities. These include cancer, chronic heart disease, chronic obstructive pulmonary disorder, chronic renal failure, gastrointestinal disorders, high cholesterol, hypertension, stroke and thyroid disorders.

    Further, to examine the degree of association between selected morbidities and disease groups (obesity, diabetes and diabesity) that formed multimorbidity, defined as the count of two or more chronic conditions, a multivariable binary logistic regression model was executed. The model was adjusted for the background characteristics of the individual. A stacked forest plot was made from the computed adjusted ORs. Finally, a Χ2 test was used to investigate bivariate associations between the disease groups and selected health outcomes, including life satisfaction and number of inpatient and outpatient visits.

    Statistical analysis was performed with STATA V.17.0 (STATA Corp, Texas, USA). Data visualisation was executed using RStudio (R Studio). A p value of <0.05 was considered statistically significant for all the calculations. All the estimates are reported after the appropriate application of sampling weights provided by LASI, 2017–2018.16 The study followed the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline (online supplemental file 1).

    Patient and public involvement

    It was not appropriate or possible to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research.

    Results

    Description of the study population

    This study employed 59 073 participants aged ≥45 years, among whom 54% were female (table 1). Around 70% of the respondents belonged to rural areas, and almost half (50.69%) had no formal education. The majority of the participants followed Hinduism. About 21% of the respondents were from the most deprived wealth quintile.

    View this table:
    Table 1

    Descriptive statistics and patterns of obesity, diabetes and diabesity by background characteristics among older adults (aged 45 years or older), Longitudinal Ageing Study in India, wave 1, 2017–2018

    Prevalence and distribution of obesity, diabetes and diabesity

    The overall prevalence of obesity was 21.21%, diabetes was 6.05% and diabesity was 5.87% among our study participants (table 1). Obesity was commonly observed (28.66%) among participants aged 50–54 years which reduced to 10.41% among respondents aged 70–79 years. Further, obesity was found to be akin among women (25.02%) and urban residents (33.20%). The findings suggest that 33.35% of the participants with 10 or more years of schooling were obese. Additionally, the most affluent group had the highest (29.38%) of people with obesity. Diabetes was observed among 9.38% of participants aged 75–79 years and was more common among men (7.26%). Similar to obesity, diabetes was also more prevalent (8.50%) among the urban population and among the most educated class (8.85%). Diabetes was frequently (7.48%) observed among the most affluent individuals.

    Diabesity, the co-occurrence of diabetes and obesity, was commonly observed among participants aged 55–79 years, with the prevalence ranging from 6.92% to 8.11%. The findings suggest diabesity to be common among women (6.88%) than men. We observed that 12.15% of the urban residents had diabesity, which was also more prevalent (11.32%) among the most educated group. The diabesity burden was observed to be highest (10.52%) among the most affluent class.

    Social determinants of obesity, diabetes and diabesity

    The findings from multinomial analysis suggested age, sex, residence, education, and wealth index were significantly associated with obesity and diabetes (table 2). Participants in the age group of 55–59 years and 60–64 years had a higher likelihood (RRR=1.68; 95% CI: 1.20 to 2.35; p<0.001 and RRR=1.61; 95% CI: 1.18 to 2.19; p<0.001, respectively) of having diabesity. We observed women had a higher chance (RRR=2.16; 95% CI: 1.72 to 2.71; p<0.001) of having diabesity as compared with their male counterparts. Individuals from the urban areas were more likely (RRR=3.73; 95% CI: 3.07 to 4.54; p<0.001) to report diabesity than participants in rural areas. The likelihood of having diabesity increased with an increase in years of schooling (from RRR=1.97; 95% CI: 1.56 to 2.48; p<0.001 among participants who attained less than 5 years of schooling to RRR=3.26; 95% CI: 2.30 to 4.61; p<0.001 among individuals with 10 or more years of education). Among the wealth quintiles, the highest risk of having diabesity was observed among the most affluent quintile (RRR=3.60; 95% CI: 2.46 to 4.58; p<0.001) as compared with the most deprived group. Additionally, the chances of having diabesity increased with the rise in wealth.

    View this table:
    Table 2

    Adjusted relative risk ratio (95% CI) estimates for obesity, diabetes and diabesity among older adults (aged 45 years or older), Longitudinal Ageing Study in India, wave 1, 2017–2018

    Association of obesity, diabetes and diabesity with selected NCDs

    Obesity, diabetes and diabesity were found to be associated with the selected NCDs, which formed multimorbidity. Hypertension, hypercholesterolaemia and thyroid disease were strongly associated with obesity, diabetes and diabesity, while chronic heart disease was found to be associated with diabetes and diabesity (figure 1). This association showed multimorbidity to be akin among this group.

    Figure 1
    Figure 1

    Association of obesity, diabetes and diabesity with selected non-communicable diseases among older adults (aged 45 years or older), Longitudinal Ageing Study in India, wave 1, 2017–2018.

    Life satisfaction and healthcare utilisation among study participants

    Figure 2 illustrates the life satisfaction among participants having obesity, diabetes and diabesity. Life satisfaction was observed to be minimal among the diabesity group, with almost 44 per 100 individuals rating a low life satisfaction as compared with the participants having obesity and diabetes. Overall, participants with only diabetes were most satisfied in life as compared with the obesity and diabesity groups.

    Figure 2
    Figure 2

    Distribution of life satisfaction by obesity, diabetes and diabesity (p<0.001) among older adults (aged 45 years or older), Longitudinal Ageing Study in India, wave 1, 2017–2018.

    Table 3 depicts respondents with diabesity require significantly higher healthcare services than those with obesity and diabetes. While only 50.62% of the obese individuals used outpatient services, a high proportion (64.63% and 66.78%) of participants with diabetes and diabesity required it, respectively. The findings suggest that 9.27% of the individuals with diabesity used inpatient services compared with 4.98% and 8.83% of the participants with obesity and diabetes, respectively.

    View this table:
    Table 3

    Association of selected healthcare utilisation with selected disease groups among older adults, Longitudinal Ageing Study in India, wave 1, 2017–2018

    Discussion

    This study reports comprehensive findings on diabesity and its social determinants in India. We found a high prevalence of obesity as compared with diabetes, which emerged as a major contributor to diabesity. The findings suggest age, sex, residence, education and wealth index were significantly associated with obesity and diabetes. Participants aged 55–64 years, women, urban residents, most educated and the most affluent social group were at a higher risk of having diabesity. Further, we observed multimorbidity to be commonly associated with all three groups. Individuals with diabesity were found to be least satisfied as compared with the obesity and diabetes group. Additionally, participants having diabesity had the highest healthcare utilisation as compared with the other two groups (diabetes and obesity).

    The prevalence of obesity in our study complements the findings of ICMR-INDIAB Study conducted in 2015, which estimated the prevalence of obesity ranged from 11.8% to 31.3% in the country.20 Rapid urbanisation has led to an increased sedentary lifestyle and changes in dietary patterns, a probable cause for the increase in obesity.21 Further, in contrast to the 6% prevalence reported for diabetes in this study, a recent meta-analysis estimated the pooled prevalence of diabetes to be relatively higher, that is, 15%–19%, in rural and urban areas of India, respectively.22 We used self-reported diabetes, which can be a reason for encountering low prevalence in this study. Here, it is worth noting that this also reflects a gap between the known and hidden status of diabetes in the community, which further warrants diagnosis.

    We observed a 6% diabesity prevalence, similar to our previous study’s findings, which estimated almost 10% prevalence of diabesity among participants aged 18 years and above.23 Although diabesity has been long recognised, there is still a paucity of literature that made the comparisons challenging. The various social determinants identified by our study align with the findings of other studies reporting correlates of obesity and diabetes.24 25

    The micro-simulation model jointly developed by the Organization for Economic Co-operation and Development and WHO states that the distal risk factors such as low-fibre, high-fat diet and insufficient physical activity, which seem to be several steps away in the causal pathway, may lead to obesity (intermediary risk factor) which in turn can cause diabetes (proximate exposure) connected directly with disease events leading to conditions such as ischaemic heart disease.26 This shows a link between obesity and diabetes with other NCDs leading to multimorbidity. Our study, in alignment with this, found an association between hypertension, hypercholesterolaemia, thyroid and chronic heart disease with diabesity. Furthermore, not only individual conditions but also multimorbidity can be regarded as a probable cause for most participants to perceive low satisfaction in life.

    Prevention of risk factors and early detection of NCDs can avert advanced disease progression and complications, but limited access to primary care is a significant reason for delayed NCD care and treatment.27 Additionally, adherence and compliance to routine investigations, polypharmacy and lifestyle influence its occurrence.28 This becomes complex with the existing socioeconomic inequalities, as stated by the blue marble health effect, where the poorest people in the wealthiest economies face health disparities.29 Furthermore, this can significantly impact the nation’s ability to attain UHC. Hence, social determinants of diabesity must be addressed to reduce disparities in NCD care. The recent initiatives such as HWCs under the Ayushman Bharat scheme provide a window of opportunity for egalitarian and accessible preventive and curative services.

    Implications for policy and practice

    Managing glycaemic index among patients with diabesity is one of the priorities. Here, it is worth noting that many anti-diabetic agents, including insulin, pose a risk for weight gain. Clinicians should consider this while prescribing anti-diabetics for patients with diabesity. Nonetheless, liraglutide, a glucagon-like peptide-1 receptor agonist (GLP1-RA) used for treating type 2 diabetes mellitus, can also be used for weight control when given at a higher dose.30 New anti-diabetic drugs such as GLP1-RA, sodium-glucose co-transporter 2 (SGLT2) inhibitors and tirzepatide are also effective for patients with diabesity.31 Moreover, several studies have not only shown an effect of SGLT2 inhibitors and GLP1-RA in reducing weight but also in reducing risk of cardiovascular events in patients with type 2 diabetes with high cardiovascular risk and progression of kidney disease.32 Hence, utilisation of these drugs in patients with diabetes, obesity and other NCDs (such as chronic kidney disease, heart failure, ischaemic heart disease) can have an impact on the history of the disease.

    Additionally, the first and foremost line of management should be lifestyle interventions, including changes in diet, adequate sleep, physical exercise and controlling deleterious habits such as tobacco use.33 34 The existing literature shows that even a modest reduction in body weight can improve blood glucose; hence, patient-centred interventions based on age, sex and multimorbidity status should be designed.35 To achieve desired goals, appropriate modifications in the line of treatment should be made after periodical reviews. Sociocultural as well as economic backgrounds of people must also be considered to increase the acceptability and adherence to these changes.

    Additionally, there is an urgent need to design guidelines on diabesity management and training the healthcare workforce in the domain. Existing programmes such as NPCDCS should be aligned to focus on diabesity apart from diabetes as a single condition. HWCs should also be used as an opportunity to provide services pertaining to diabesity management.

    Strengths and limitations

    These findings are based on nationally representative data from LASI wave 1, which is a strength of this study. The study compared the prevalence and correlates of obesity, diabetes and diabesity and their association with other selected NCDs. However, it is limited by self-reported conditions and being a cross-sectional survey, it does not establish causality. Further studies are warranted to investigate the trends in diabesity using panel data.

    Conclusion

    The findings suggest an increase in obesity prevalence has led to diabesity rise in India. Additionally, the association of diabesity with other NCDs has increased multimorbidity, which cannot be overlooked. Preventive services, including lifestyle changes at an early age, are warranted. HWCs should be explored as an opportunity to address NCD care. Rather than focusing on individual conditions, an in toto approach to multimorbidity is required. Further studies are warranted to investigate the trends in diabesity using panel data.

    Data availability statement

    Data are available upon reasonable request. The data have been archived in the public repository of the Longitudinal Ageing Study in India, 2017-2018. Access to the dataset requires registration and is granted only for legitimate research purposes. A guide for how to apply for dataset access is available at https://iipsindia.ac.in/sites/default/files/LASI_DataRequestForm_0.pdf. The data can be accessed using the link: https://www.iipsindia.ac.in/content/lasi-wave-i.

    Ethics statements

    Patient consent for publication

    Ethics approval

    This study is based on secondary data obtained from LASI. The original LASI study took ethical clearance from the Indian Council of Medical Research (ICMR), New Delhi and the International Institute for Population Sciences (IIPS), Mumbai. LASI had obtained written informed consent from all the study participants.

    Acknowledgments

    The authors are grateful to the Longitudinal Ageing Study in India (LASI) for assembling and publishing accurate, nationally representative data on a range of health, biomarkers and healthcare utilisation indicators for the population aged 45 years and older. The authors are also grateful to LASI’s project partners, the International Institute for Population Sciences (IIPS), Harvard T H Chan School of Public Health and the University of Southern California (USC).

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    Footnotes

    • AS and PP contributed equally.

    • Contributors Concept and design—SP. Acquisition, analysis or interpretation of data—PP and AS. Drafting of the manuscript—AS and PP. Critical revision of the manuscript for important intellectual content—SP, PP and AS. Statistical analysis—PP. Administrative and technical support—SP, AS and PP. Supervision—SP. Guarantor responsible for the overall content (full responsibility for the work and/or the conduct of the study, had access to the data, and controlled the decision to publish)—SP

    • Competing interests None declared.

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