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A community-based, cross-sectional study was carried out in five regions of India by adopting a multistage random sampling procedure. Information was collected from the participants about socio-demographic particulars such as age, sex, occupation, education, etc. Anthropometric measurements such as height, weight and waist and hip circumferences we...
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... present community-based, cross-sectional study was carried out in five geographical regions of India -that is, North, South, East, West and Northeast -by adopting a multistage stratified random sampling method. From each of these regions, two states were randomly selected ( Fig. 1), and from each selected state three districts were selected. From each district, two blocks were selected, and from each selected block five villages were selected. From each village, ten households (HH) were selected randomly by dividing the village into five areas, and from each area two HH were covered. Thus, a total of 600 HH were ...
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Citations
... In addition to SLT use, other determinants of hypertension among women in India were age, BMI, alcohol consumption, education, wealth quintile, and region. In agreement with previous studies, [3,[17][18][19] the prevalence of hypertension was found to increase with an increase in age and BMI. Further, both age and BMI were positively associated with increased odds of hypertension among women in India. ...
Background
Hypertension significantly contributes to avoidable morbidity and mortality. The literature indicates an increased risk of hypertension among tobacco users. This study examines the determinants of undetected hypertension among women in India and infers its relationship with smokeless tobacco (SLT) use.
Materials and Methods
A nationally representative sample of 699,686 women (aged 15–49 years) in the National Family Health Survey-4 (2015–2016) was utilized. Women participants who did not self-report hypertension but their mean blood pressure measured during the interview were above 140/90 mmHg were considered as having undetected hypertension. Multivariate logistic regression was used to examine the association between hypertension and predictor variables including the use of SLT.
Results
The prevalence of undetected hypertension was found to be 9.1% among women in India, and in comparison to non-SLT users, a higher prevalence of hypertension was observed among current SLT users. Age, illiteracy, obesity, use of SLT, alcohol consumption, and residing in Northeast India were found to be significant determinants of hypertension.
Conclusion
Preventing the use of SLT through socioculturally tailored tobacco control interventions, raising awareness for behavior and lifestyle changes, and regular screening for hypertension in communities may have the potential to reduce the increasing burden of hypertension among women in India.
... Research on weight issues across geographical regions found that in areas such as India, the overall frequency of being overweight or obese was determined to be 25%, with abdominal obesity levels calculated at 21%, with both measures affecting women more prevalently than men. This strengthens the idea that sex may interact with other elements to in uence the prevalence of obesity (33) . ...
Introduction: Obesity is a global epidemic affecting millions of people worldwide. Its diagnosis and treatment are crucial for the prevention of associated chronic diseases.
Objectives: To assess the prevalence of obesity according to different diagnostic criteria, analyze the concordance between various diagnostic methods, and identify associated factors.
Methods: Peru's Demographic and Family Health Survey (ENDES) from 2019 to 2022 was utilized. The cut-off points for defining obesity were ≥ 30 for body mass index (BMI), the ATPIII criteria for waist circumference (WC-ATPIII), and 0.56 for the waist-to-height ratio (WHtR). Concordance analyses were performed to compare diagnostic methods and regression analyses were conducted to identify associated factors.
Results: The prevalence of obesity according to BMI, WC-ATPIII, and WHtR was 25.65%, 42.04%, and 46.49%, respectively. The concordance between the three criteria was evaluated through the Kappa index. The concordance between obesity by BMI and WC-ATPIII was 0.5141. The concordance between BMI and WHtR was 0.5099. Finally, the concordance between WC-ATPIII and WHtR was 0.7514. Men showed a lower prevalence of obesity compared to women. The obesity trend increased overall during the study period, with marked differences in prevalence according to the obesity measure used.
Conclusions: The findings reveal differences in obesity prevalence according to the diagnostic method employed and underscore the need to consider multiple approaches to assess obesity. The results significantly affect public health and provide a foundation for future interventions and policies to combat obesity in Peru.
... Factors, such as increasing age, male sex, obesity, and abdominal obesity, have already been characterized as the main risk factors for hypertension and diabetes. [12][13][14][15] Nevertheless, there is a dearth of quality evidence regarding the relative burden of multimorbidity, due to hypertension, type-2 diabetes mellitus, and obesity among males versus females. Comparing the actual burden of multimorbidity among males and females will result in better risk stratification. ...
... This finding corresponds with the current evidence and can be explained by the increased risk of cardiometabolic disorders with a rising age irrespective of sex. 12,14,17 This study also assessed the likelihood of multimorbidity caused by diabetes mellitus, hypertension, and obesity among females as compared to males by applying multiple multinomial regression. The study identified that the females were more likely to be obese but less likely to be hypertensive as compared to males. ...
Background
Literature has reported differences in the epidemiology or natural history of non-communicable diseases among both the male and female sexes. Stratification of multimorbidity burden based on sex is crucial to identify and implement targeted prevention and control interventions for chronic diseases.
Objectives
To determine the burden of hypertension, type-2 diabetes mellitus, and obesity; and to compare the related multimorbidity among male and female patients.
Methods
The study was a retrospective analysis of 375 802 medical records from primary care centers. Data was extracted from March 2022 to March 2023. A multivariate probit estimation methodology was employed using a 3-equations multivariate multiple probit model to jointly estimate the association of a person’s sex with the diagnosis of the 3 chronic conditions: obesity, diabetes, and hypertension. A multinomial logistic regression analysis was conducted to allow each unique combination of these 3 chronic diseases.
Results
Females had a relatively higher proportion of obesity (58.1% vs 41.2%), obesity and diabetes only (58.9% vs 41.1%), obesity and hypertension (63.6% vs 36.4%), and joint diagnosis with 3 conditions (65.7% vs 34.3%). Females’ participants consistently had a significantly higher likelihood of diagnosis compared with males except for diabetes (OR = 0.59, 95% CI: 0.56-0.62) and the combination of only diabetes and hypertension (OR = 0.67, 95% CI: 0.61-0.74). The likelihood of other combinations ranged from 1.04 (95% CI: 0.98-1.10) for only hypertension to 2.30 (95% CI: 2.10-2.53) for the joint diagnosis of all 3 conditions. An increased likelihood of a single or combined occurrence of 3 chronic conditions was observed with increased age.
Conclusion
The multimorbidity distribution for diabetes mellitus, hypertension, and obesity differs significantly among male and female patients. The overall burden of morbidity, and mortality, however, tends to rise after 46 years of age, with the highest burden among individuals above 60 years of age.
... The independent variables used were sex (female and male), age group (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35), 60-69, and 70 years to more), marital status (with a partner and without a partner), region (Metropolitan Lima, rest of the coast, highlands, and jungle), education (no level/primary and secondary/higher), wealth index (poorest, poor, middle, rich, and richest), area of residence (urban vs. rural), physical disability (no vs. yes), health insurance (no vs yes), consumption of fruits and vegetables greater than ve portions per day (yes and no), presence of Arterial Hypertension (HTN) (yes and no), presence of type 2 diabetes mellitus (T2DM) (yes and no), Altitude (0 to 499, 500 to 1499, 1500 to 2999, 3000 or more), Smoker Status and Alcohol consumption (18) . ...
... Research on weight issues across geographical regions found that in areas such as India, the overall frequency of being overweight or obese was determined to be 25%, with abdominal obesity levels calculated at 21%, with both measures affecting women more prevalently than men. This strengthens the idea that sex may interact with other elements to in uence the prevalence of obesity (33) . ...
Introduction: Obesity is a global epidemic affecting millions of people worldwide. Its diagnosis and treatment are crucial for the prevention of associated chronic diseases.
Objectives: To assess the prevalence of obesity according to different diagnostic criteria, analyze the concordance between various diagnostic methods, and identify associated factors.
Methods: Peru's Demographic and Family Health Survey (ENDES) from 2014 to 2022 was utilized. The cut-off points for defining obesity were ≥ 30 for body mass index (BMI), the ATPIII criteria for waist circumference (WC-ATPIII), and 0.56 for the waist-to-height ratio (WHtR). Concordance analyses were performed to compare diagnostic methods and regression analyses were conducted to identify associated factors.
Results: The prevalence of obesity according to BMI, WC-ATPIII, and WHtR was 25.65%, 42.04%, and 46.49%, respectively. The concordance between the three criteria was evaluated through the Kappa index. The concordance between obesity by BMI and WC-ATPIII was 0.5141. The concordance between BMI and WHtR was 0.5099. Finally, the concordance between WC-ATPIII and WHtR was 0.7514. Men showed a lower prevalence of obesity compared to women. The obesity trend increased overall during the study period, with marked differences in prevalence according to the obesity measure used.
Conclusions: The findings reveal differences in obesity prevalence according to the diagnostic method employed and underscore the need to consider multiple approaches to assess obesity. The results significantly affect public health and provide a foundation for future interventions and policies to combat obesity in Peru.
... For this reason, they recommended developing urgent primary and secondary prevention initiatives to reduce further growth in areas with high frequency. In India's adult rural population, there is regional variation in the prevalence of overweight/obesity, hypertension, and diabetes as well as their correlates, according to Meshram et al. [6]. In contrast to diabetes, which was more prevalent in the Southern and Western regions, overweight/obesity and hypertension were more common in the Southern region. ...
To investigate the prevalence, risk factors, and healthcare-seeking patterns of hypertension and diabetes in Karnataka, India, and to offer knowledge that might guide public health initiatives intended to lessen the burden of these illnesses.
In order to examine the prevalence, risk factors, and healthcare-seeking behaviour of hypertension and diabetes in Karnataka, India, a cross-sectional study is carried out using the information gathered from 26,574 households on 30,455 women and 4516 men (who were in their reproductive period) from the National Family Health Survey (2019–20). The information was summarised using descriptive statistics, which included frequencies and percentages. The association between different risk variables and the likelihood of getting diabetes and hypertension was examined using the chi-squared test and a logistic regression model. Data were analysed using STATA software version 16.
The study found that age, gender, education level, religion, and BMI are all significantly associated with hypertension and diabetes (p < 0.001). Tobacco use and alcohol consumption were not significantly associated with hypertension, but tobacco use was significantly associated with diabetes (p < 0.001). However, alcohol consumption was not found to be significantly associated with diabetes whereas the older age groups, males, underweight, overweight and obese, and tobacco use were all associated with increased odds of diabetes. On the other hand, females, secondary education or higher, and alcohol consumption were associated with decreased odds of diabetes.
In conclusion, the study found a high prevalence of hypertension and diabetes in Karnataka, India, and identified several risk factors associated with these diseases. The study also highlighted the need for improved healthcare-seeking behaviour among people with hypertension and diabetes. The findings can inform public health interventions aimed at reducing the burden of these diseases in Karnataka and similar settings.
... For example, Kerala is the unique southern state where the highest prevalence of diabetes has been found followed by the eastern state of West Bengal. A growing number of studies suggested that larger regional and unequal distribution of socioeconomic diversity among the people is the main reason for higher diabetes prevalence in India [13][14][15]. Diabetes prevalence was highest among the older adult age group particularly in the age above 60 years because of the increasing number of the aging population, extreme growth of urbanization, health, and lifestyle behavior with western dietary patterns, and without physical activities. The earlier evidence mentioned that the rising prevalence of overweight/obesity is one of the important leading causes of diabetes in the country [16]. ...
Background
The complication of Diabetes is one of the important health issues among the older adult population in any region. The higher risks of diabetes prevalence among older adult people in the countries was due to social-cultural changes such as increasing urbanization, dietary changes, without physical activity, and unhealthy lifestyle behavior. The present study examines the prevalence and associated risk factors of diabetes among older adults in the state of West Bengal.
Methods
The first wave of the Longitudinal Ageing Study in India 2017-18 was used to achieve the study objectives. Descriptive statistics with multinomial logistic regression models were used to carry out crude and adjusted odds ratios with 95% confidence intervals and examine the associated risk factors of diabetes prevalence among older adults.
Results
The findings of the study indicate that the overall prevalence of diabetes among the study participants was found to be 12.4% which was significantly higher in urban areas (19%) compare to rural areas (6%). The socio-economic and bio-demographic factors like educational status, richest background family, marital status, obesity, and family history of diabetes were significantly associated with higher risks of diabetes prevalence among the older adult population in West Bengal. The risks of diabetes in the richest adult people were significantly higher than in the poorest adult people (OR = 2.78; 95% CI: 1.974–3.917). The higher risks of diabetes mellitus among the richest wealthy people are because of lifestyle behavior, smoking, and tobacco consumption respectively.
Conclusion
The study needs to policy and awareness program to reduce economic inequality and prevention of diabetes care and treatment-seeking behavior, especially for the older adult population in West Bengal.
... 21 Among them, 33.7% were overweight/obese after being diagnosed with type 2 DM, and the prevalence was significantly higher in women. 21 In Turkey, the prevalence of obesity among DM individuals in 1999 was 35.6%, which increased with age. 22 In the US, the prevalence of obesity among adults with diagnosed DM in 2004 was 54.8%, 23 while among young aged less than 20 years old with type 2 DM, most of them were obese (79.4%). ...
... 34 A previous study in a rural population in India 35 also revealed that the prevalence of overweight and obesity was higher in women than in men. This study 35 also concluded that abdominal and central obesity was more prevalent in women with diabetes subjects. A study in Thailand 36 observed diabetes subjects with chronic kidney disease (CKD), and disclosed that the prevalence of obesity in this entire study population was 51.5% (68.2% in women and 31.8% in men, p=0.01). ...
Background: Obesity and diabetes mellitus (DM), both individually or simultaneously, increase the risk of morbidity and mortality. The present study aimed to determine the prevalence and determinants of obesity among diabetic individuals in Indonesia.
Methods: Data were extracted based on 2018 Indonesian Basic Health Survey (Riset Kesehatan Dasar=RISKESDAS). This study involved all individuals with DM and categorized obesity based on body mass index. After data clearing, this study analyzed 3911 DM subjects of the 33,905 subjects acquired from the 2018 RISKESDAS. The study also observed demographic data, diabetes control parameters, history of hypertension, lipid profiles, and food consumption patterns. These variables were involved in a Chi-square test, and related variables were then involved in the Binary logistic regression to define the independent determinants of obesity among DM subjects.
Results: Of the 3911DM subjects included, the study found an obesity prevalence of 32.9%. This study found that female (prevalence odds ratio [POR]=2.15; 95% CI: 1.76-2.62), age 15-44 years (POR=2.46; 95% CI: 1.83-3.33), urban residence (POR=1.49; 95% CI: 1.25-1.77), history of hypertension (POR=1.25; 95% CI: 1.04-1.51), high diastolic blood pressure (POR=1.90; 95% CI: 1.58-2.29), high LDL (POR=1.44; 95% CI: 1.13-1.84), and high triglycerides (POR=1.27; 95% CI: 1.07-1.50) were the risk factor of obesity among DM subjects; while high HDL (POR=0.60; 95% CI: 0.46-0.78 higher education (POR=0.64; 95% CI: 0.53-0.78) and unmarried (POR=0.73; 95% CI: 0.59-0.90) were protective factors of obesity among DM subjects.
Conclusions:. The study concluded that almost one-third of DM subjects in Indonesia were obese. Female, age, urban residence, education level, history of hypertension, diastolic blood pressure, and lipid profiles were all associated with obesity among DM subjects in Indonesia. These findings suggest that monitoring and controlling of related determinants is needed to prevent complications caused by the doubled burden of diabetes and obesity.
... 34 A previous study in a rural population in India 35 also revealed that the prevalence of overweight and obesity was higher in women than in men. This study 35 also concluded that abdominal and central obesity was more prevalent in women with diabetes subjects. A study in Thailand 36 observed diabetes subjects with chronic kidney disease (CKD), and disclosed that the prevalence of obesity in this entire study population was 51.5% (68.2% in women and 31.8% in men, p = 0.01). ...
Background: Obesity and diabetes mellitus (DM), both individually or simultaneously, increase the risk of morbidity and mortality. The present study aimed to determine the prevalence and determinants of obesity among diabetic individuals in Indonesia.
Methods: Data were extracted based on 2018 Indonesian Basic Health Survey (Riset Kesehatan Dasar=RISKESDAS). This study involved all individuals with DM and categorized obesity based on body mass index. After data clearing, this study analyzed 3911 DM subjects of the 33.905 subjects acquired from the 2018 RISKESDAS. The study also observed demographic data, diabetes control parameters, history of hypertension, lipid profiles, and food consumption patterns. These variables were involved in a Chi-square test, and related variables were then involved in the Binary logistic regression to define the independent determinants of obesity among DM subjects.
Results: Of the 3911DM subjects included, the study found an obesity prevalence of 32.9%. This study found that female (prevalence odds ratio [POR]=2.15; 95% CI: 1.76-2.62), age 15-44 years (POR=2.46; 95% CI: 1.83-3.33), urban residence (POR=1.49; 95% CI: 1.25-1.77), history of hypertension (POR=1.25; 95% CI: 1.04-1.51), high diastolic blood pressure (POR=1.90; 95% CI: 1.58-2.29), high LDL (POR=1.44; 95% CI: 1.13-1.84), and high triglycerides (POR=1.27; 95% CI: 1.07-1.50) were the risk factor of obesity among DM subjects; while high HDL (POR=0.60; 95% CI: 0.46-0.78 higher education (POR=0.64; 95% CI: 0.53-0.78) and married (POR=0.73; 95% CI: 0.59-0.90) were protective factors of obesity among DM subjects.
Conclusions:. The study concluded that almost one-third of DM subjects in Indonesia were obese. Female, age, urban residence, education level, history of hypertension, diastolic blood pressure, and lipid profiles were all associated with obesity among DM subjects in Indonesia. These findings suggest that monitoring and controlling of related determinants is needed to prevent complications caused by the doubled burden of diabetes and obesity.
... 2 Excessive adiposity accumulation, particularly visceral fat deposition, is an established risk factor. 3 The pathogenesis of obesity-induced diabetes is complex and multifactorial. Anthropometric measures can provide a convenient and effective prescreening tool for identifying individuals with obesity because they are noninvasive, cost-effective, valid, and easy to implement. ...
Background:
Traditional anthropometric measures, including body mass index (BMI), are insufficient for evaluating the risk of diabetes. This study aimed to evaluate the performance of new anthropometric measures and a combination of anthropometric measures for identifying diabetes.
Methods:
A total of 46 979 participants in the National Health and Nutrition Examination Survey program were included in this study. Anthropometric measures, including weight, BMI, waist circumference (WC), waist-to-height ratio (WtHR), conicity index (CI), and A Body Shape Index (ABSI), were calculated. Logistic regression analysis and restricted cubic splines were used to evaluate the association between the anthropometric indices and diabetes. The receiver operating characteristic (ROC) curve analysis was performed to compare the discrimination of different anthropometric measures.
Results:
All anthropometric measures were positively and independently associated with the risk of diabetes. After adjusting for covariates, the per SD increment in WC, WtHR, and CI increased the risk of diabetes by 81%, 83%, and 81%, respectively. In the ROC analysis, CI showed superior discriminative ability for diabetes (area under the curve 0.714), and its optimum cutoff value was 1.31. Results of the combined use of BMI and other anthropometric measures showed that among participants with BMI <30 kg/m2 , an elevated level of another metric increased the risk of having diabetes (P < .001). Similarly, at low levels of weight, CI, and ABSI, an elevated BMI increased diabetes risk (P < .001).
Conclusions:
WtHR and CI had the best ability to identify diabetes when applied to the US noninstitutionalized population. Anthropometric measures containing WC information could improve the discrimination ability.
... Few studies have evaluated the prevalence of abdominal obesity in rural populations worldwide, with reported rates of prevalence of 20.6% in India [73] to 59.2% in Sri Lanka [52]. In the region analyzed in this study, overweight status and abdominal obesity were slightly lower than the prevalence found in the South of Brazil [26,58], similar to the Southeast [60] and higher than in the Midwest regions of the country [57]. ...
The objectives of this study were to assess the nutritional status of rural workers from a municipality in Southeastern Brazil and estimate the association of sociodemographic, labor, lifestyle, and dietary pattern factors with obesity and abdominal obesity of men and women of this rural area. This is a cross-sectional, epidemiological study of 740 farmers (51.5%, n = 381 males; 48.5%, n = 359 females). The sociodemographic, labor, lifestyle and dietary patterns determinants were assessed. Food intake data were obtained by applying three 24-hour recalls and dietary patterns were determined by Principal Component Analysis with Varimax orthogonal rotation. Poisson regression with robust variance stratified by sex was applied. The general prevalence of overweight status was 31.5% (95% CI 28.2–34.8%), 19.7% of obesity (95% CI 16.8–22.6%) and 31.5% of abdominal obesity (95% CI 28.2–34.8%), with higher rates in women ( P < 0.001). Men of higher socioeconomic class had a 2.3 times higher prevalence of obesity (95% CI 1.08–4.90). In addition, the shorter travel time to purchase food increased the prevalence of abdominal obesity in males. For women, the older the age group, the greater the general and central obesity. A lower adherence to traditional dietary patterns (approximately PR [prevalence ratio] 1.6 for general obesity and PR 1.3 for abdominal obesity) and a greater number of places to buy food were associated with higher rates of obesity in women. Finally, women farmers with a higher workload had a 20% lower prevalence of central obesity (PR 0.80; 95% CI 0.65–0.97). Such findings demonstrate that obesity must be an issue in the health care of remote and rural populations. There is a need to promote healthier environments that respect traditional food culture through multiple approaches that consider the heterogeneity of rural areas and the differences between sexes.
