In India, the prevalence of overweight and obesity has increased rapidly in recent decades. Given the association between overweight and obesity with many non-communicable diseases, forecasts of the ...future prevalence of overweight and obesity can help inform policy in a country where around one sixth of the world's population resides.
We used a system of multi-state life tables to forecast overweight and obesity prevalence among Indians aged 20-69 years by age, sex and urban/rural residence to 2040. We estimated the incidence and initial prevalence of overweight using nationally representative data from the National Family Health Surveys 3 and 4, and the Study on global AGEing and adult health, waves 0 and 1. We forecasted future mortality, using the Lee-Carter model fitted life tables reported by the Sample Registration System, and adjusted the mortality rates for Body Mass Index using relative risks from the literature.
The prevalence of overweight will more than double among Indian adults aged 20-69 years between 2010 and 2040, while the prevalence of obesity will triple. Specifically, the prevalence of overweight and obesity will reach 30.5% (27.4%-34.4%) and 9.5% (5.4%-13.3%) among men, and 27.4% (24.5%-30.6%) and 13.9% (10.1%-16.9%) among women, respectively, by 2040. The largest increases in the prevalence of overweight and obesity between 2010 and 2040 is expected to be in older ages, and we found a larger relative increase in overweight and obesity in rural areas compared to urban areas. The largest relative increase in overweight and obesity prevalence was forecast to occur at older age groups.
The overall prevalence of overweight and obesity is expected to increase considerably in India by 2040, with substantial increases particularly among rural residents and older Indians. Detailed predictions of excess weight are crucial in estimating future non-communicable disease burdens and their economic impact.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We have reviewed the distinctive features of excess weight, its causes, and related prevention and management efforts, as well as data gaps and recommendations for future research in low- and ...middle-income countries (LMICs). Obesity is rising in every region of the world, and no country has been successful at reversing the epidemic once it has begun. In LMICs, overweight is higher in women compared with men, in urban compared with rural settings, and in older compared with younger individuals; however, the urban-rural overweight differential is shrinking in many countries. Overweight occurs alongside persistent burdens of underweight in LMICs, especially in young women. Changes in the global diet and physical activity are among the hypothesized leading contributors to obesity. Emerging risk factors include environmental contaminants, chronic psychosocial stress, neuroendocrine dysregulation, and genetic epigenetic mechanisms. Data on effective strategies to prevent the onset of obesity in LMICs or elsewhere are limited. Expanding the research in this area is a key priority and has important possibilities for reverse innovation that may also inform interventions in high-income countries.
Early COVID-19 pandemic research found changes in health care and diabetes management, as well as increased diabetes distress. This study aims to determine the association between COVID-19 ...pandemic-related healthcare interruptions and diabetes distress among adults with Type 1 and Type 2 diabetes in the US in 2021.
Multinomial logistic regression was used to analyze moderate and high levels of diabetes distress (reference = no diabetes distress) in 228 individuals with Type 1 diabetes and 2534 individuals with Type 2 diabetes interviewed in the National Health Interview Survey in 2021.
Among adults with Type 1 diabetes, 41.2% experienced moderate diabetes distress and 19.1% experienced high diabetes distress, and among adults with Type 2 diabetes, 40.8% experienced moderate diabetes distress and 10.0% experienced high diabetes distress. In adults with Type 1 diabetes, experiencing delayed medical care was associated with an adjusted odds ratio (aOR) of 4.31 (95% CI: 1.91-9.72) for moderate diabetes distress and 3.69 (95% CI: 1.20-11.30) for high diabetes distress. In adults with Type 2 diabetes, experiencing delayed medical care was associated with an aOR of 1.61 (95% CI: 1.25-2.07) for moderate diabetes distress and 2.27 (95% CI: 1.48-3.49) for high diabetes distress. Similar associations were observed between not receiving medical care due to the pandemic and diabetes distress.
Among people with diabetes, experiencing delayed medical care and not receiving care due to the pandemic were associated with higher reports of diabetes distress.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background: Diabetes detection and treatment in married couples presents opportunities for designing dyadic interventions to improve screening and management.
Methods: Using data from 59237 ...heterosexual married couples (women: 18-49y, men: 21-54y) assessed in India’s National Family Health Survey (2015-16) , we estimated the diabetes care cascade as the percent diagnosed, treated, and controlled with measured high blood glucose (BG) by spouse’s diabetes status.
Results: The prevalence of high BG was 8.8% (men) and 14.2% (women) . In adults with high BG, the percent diagnosed, treated, and controlled was 22.6%, 18.7%, and 9.2% (men) and 22.5%, 18.0%, and 8.5% (women) , respectively. Of adults with high BG, the prevalence of diabetes diagnosis was higher in men (prevalence ratio PR: 1.85 1.37, 2.49) and women (PR: 2.03 1.47, 2.8) whose spouse was diagnosed (ref=spouse undiagnosed) . Among the diagnosed, the prevalence of being treated for diabetes did not differ by their spouse’s diagnosis status in men or women. Among the treated, the prevalence of blood glucose control was higher in women (PR: 1.45, 0.93, 2.25) , but not men (PR: 0.96 0.66, 1.40) , whose spouse was diagnosed (ref=spouse undiagnosed) .
Conclusions: Spouse’s diabetes was associated with higher probability of detection of diabetes but not with treatment. Outreach among couples may improve medical management of diabetes in India.
Disclosure
J.Varghese: None. S.A.Patel: Research Support; Johnson & Johnson.
Purpose of Review
The last 2–3 decades have witnessed a decline in age-standardized cardiovascular mortality rates in high-income regions, whereas this has only slightly decreased or even increased ...in most of the low- and middle-income countries. A systematic comparison of global CVD mortality by regions attributable to various modifiable risk factors such as diabetes, obesity, hypertension, poor diet, and physical inactivity is not available.
Recent Findings
We present a summary of time trends and heterogeneity in the distribution of global CVD mortality and the attribution of risk factors between 1990 and 2017 using the Global Burden of Disease (GBD) 2017 study. Globally, an estimated ~ 17.8 million (233.1 per 100,000) people died of CVD in 2017. The rate of CVD death was decreased in high-income countries (1990: 271.8 (95% UI (uncertainty interval), 270.9–273.5); 2017: 128.5 (95% UI, 126.4–130.7) per 100,000)) whereas it remained the same in lower- and middle-income countries (1990: 368.2 (95% UI, 335.6–383.3); 2017: 316.9 (95% UI, 307.0–325.5) per 100,000). Among the various traditional risk factors, high systolic blood pressure, unhealthy diet, high fasting plasma glucose, and high low-density lipoprotein levels were attributed to most of the CVD death and disability-adjusted life year lost. We also observed gender variations in tobacco and increased alcohol consumption. In addition to the traditional risk factors, poor air quality is associated with increased CVD burden in developing countries.
Summary
Surveillance, country-specific guidelines, evidence-based policies, reinforcement of multisectoral health systems, and innovative solutions are urgently needed in resource-challenged settings to curb CVD risk factors and overall burden.
The household is a potentially important but understudied unit of analysis and intervention in chronic disease research. We sought to estimate the association between living with someone with a ...chronic condition and one's own chronic condition status.
We conducted a cross-sectional analysis of population-based household- and individual-level data collected in 4 socioculturally and geographically diverse settings across rural and urban India in 2013 and 2014. Of 10,703 adults ages 18 years and older with coresiding household members surveyed, data from 7,522 adults (mean age 39 years) in 2,574 households with complete covariate information were analyzed. The main outcome measures were diabetes (fasting plasma glucose ≥ 126 mg/dL or taking medication), common mental disorder (General Health Questionnaire score ≥ 12), hypertension (blood pressure ≥ 140/90 mmHg or taking medication), obesity (body mass index ≥ 30 kg/m2), and high cholesterol (total blood cholesterol ≥ 240 mg/dL or taking medication). Logistic regression with generalized estimating equations was used to model associations with adjustment for a participant's age, sex, education, marital status, religion, and study site. Inverse probability weighting was applied to account for missing data. We found that 44% of adults had 1 or more of the chronic conditions examined. Irrespective of familial relationship, adults who resided with another adult with any chronic condition had 29% higher adjusted relative odds of having 1 or more chronic conditions themselves (adjusted odds ratio aOR = 1.29; 95% confidence interval 95% CI 1.10-1.50). We also observed positive statistically significant associations of diabetes, common mental disorder, and hypertension with any chronic condition (aORs ranging from 1.19 to 1.61) in the analysis of all coresiding household members. Associations, however, were stronger for concordance of certain chronic conditions among coresiding household members. Specifically, we observed positive statistically significant associations between living with another adult with diabetes (aOR = 1.60; 95% CI 1.23-2.07), common mental disorder (aOR = 2.69; 95% CI 2.12-3.42), or obesity (aOR = 1.82; 95% CI 1.33-2.50) and having the same condition. Among separate analyses of dyads of parents and their adult children and dyads of spouses, the concordance between the chronic disease status was striking. The associations between common mental disorder, hypertension, obesity, and high cholesterol in parents and those same conditions in their adult children were aOR = 2.20 (95% CI 1.28-3.77), 1.58 (95% CI 1.15-2.16), 4.99 (95% CI 2.71-9.20), and 2.57 (95% CI 1.15-5.73), respectively. The associations between diabetes and common mental disorder in husbands and those same conditions in their wives were aORs = 2.28 (95% CI 1.52-3.42) and 3.01 (95% CI 2.01-4.52), respectively. Relative odds were raised even across different chronic condition phenotypes; specifically, we observed positive statistically significant associations between hypertension and obesity in the total sample of all coresiding adults (aOR = 1.24; 95% CI 1.02-1.52), high cholesterol and diabetes in the adult-parent sample (aOR = 2.02; 95% CI 1.08-3.78), and hypertension and diabetes in the spousal sample (aOR = 1.51; 95% CI 1.05-2.17). Of all associations examined, only the relationship between hypertension and diabetes in the adult-parent dyads was statistically significantly negative (aOR = 0.62; 95% CI 0.40-0.94). Relatively small samples in the dyadic analysis and site-specific analysis call for caution in interpreting qualitative differences between associations among different dyad types and geographical locations. Because of the cross-sectional nature of the analysis, the findings do not provide information on the etiology of incident chronic conditions among household members.
We observed strong concordance of chronic conditions within coresiding adults across diverse settings in India. These data provide early evidence that a household-based approach to chronic disease research may advance public health strategies to prevent and control chronic conditions.
Clinical Trials Registry India CTRI/2013/10/004049; http://ctri.nic.in/Clinicaltrials/login.php.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Seasonal influenza vaccination is an important public health strategy to reduce preventable illness, hospitalization, and death. Because of overlapping risk factors for severe illness from seasonal ...influenza and COVID-19, uptake of the seasonal influenza vaccination has heightened importance during the COVID-19 pandemic. We analyzed receipt of seasonal influenza vaccination among COVID-19 priority groups and further examined socio-demographic and behavioral factors associated with receiving the seasonal influenza vaccine among US adults.
Using the 2018 National Health Interview Survey, we classified 24,772 adults into four COVID-19 priority groups: healthcare workers, medically vulnerable, non-healthcare essential workers, and the general population. We performed multiple logistic regression to compare the relative odds of receiving the influenza vaccine by COVID-19 priority group, socio-demographics, and health-related factors.
Healthcare workers, medically vulnerable adults, essential workers, and the general population comprised 8.9%, 58.4%, 6.6%, and 26.1 % of the US population, respectively. Compared with healthcare workers, the adjusted odds ratio (aOR) of receiving influenza vaccine were significantly lower in medically vulnerable adults (aOR=0.43, 95% CI=0.37, 0.48), essential workers (aOR=0.28, 95% CI=0.23, 0.34), and the general population (aOR=0.32, 95% CI=0.28, 0.37). Being young, male, Black, and having no health insurance were associated with lower relative odds of receiving the flu vaccine.
Patterns of influenza vaccine cause concern for under-coverage of populations at high risk for both seasonal influenza and COVID-19. Achieving optimal protection against vaccine-preventable respiratory illness in US adults will require emphasis on those employed outside of the healthcare sector, younger age groups, and adults with lower socioeconomic resources.
Tailoring diabetes management strategies based on phenotyping presentation may be an approach to address racial disparities in diabetes outcomes among women. We identified diabetes phenotypes and ...their distribution by race/ethnicity among US women. Methods: We analyzed metabolic parameters of n=497 women ages 30-64 with diagnosed diabetes assessed in the National Health and Nutrition Examination Surveys (1988-1999 and 1999-2013). Phenotypes were identified through latent class analysis. The method grouped women into homogeneous clusters ("latent classes") based on observed patterns of HbA1c, BMI, HOMA2-IR, waist circumference (WC), triglycerides (TG), systolic blood pressure (SBP), and duration of diabetes. Results: We identified three major phenotypes of diabetes. On average, Phenotype 3, "least controlled" had the highest levels of glycemia and insulin resistance, as well as anthropometric and metabolic risks. In contrast, Phenotype 1, "better control", had the lowest BMI, WC, HOMA2-IR, TG levels and moderate HbA1c, SBP. Phenotype 2, "moderate control" had moderate BMI, HbA1c, HOMA2-IR, WC, TG and SBP. The prevalence of the least controlled phenotype ranged from 21.8% in Hispanic women to 28.3% in black women. Conclusion: We observed three distinct diabetes control phenotypes among US women. The phenotype with least control was highest in African American women.
Metabolically healthy (MH) and unhealthy (MUH) obesity may have heterogeneous long-term health risks for diabetes and related adverse effects. We investigated trends in the prevalence of obesity ...phenotypes in US adults from 1988-2018. Methods: We used data from adults ages 20-79 years assessed in the National Health and Nutrition Examination Survey 1988-2018. Participants were classified into six mutually exclusive groups based on the combination of weight status and presence of metabolic morbidity. Results: The prevalence of MUH obesity increased from 23.6% in 1988-1994 to 36.7% in 2015-2018 (p<.05), while the prevalence of obesity without metabolic morbidity rose from 2.6% to 7.3% (p<.05) in the same period. The prevalence of other phenotypes declined in the same period. In particular, the prevalence of MUH normal-weight has declined from 21.5% in 1988-1994 to 10.7% in 2015-2018 (p<.05). Discussion: In contrast to the perception that "healthy obesity" is rising, our findings suggest that indeed obesity accompanied by metabolic morbidity has risen dramatically in the past 30 years. This may signal future declines in population health and an increase in healthcare costs.
Recent studies have reported the protective efficacy of both natural
and vaccine-induced
immunity against challenge with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in rhesus ...macaques. However, the importance of humoral and cellular immunity for protection against infection with SARS-CoV-2 remains to be determined. Here we show that the adoptive transfer of purified IgG from convalescent rhesus macaques (Macaca mulatta) protects naive recipient macaques against challenge with SARS-CoV-2 in a dose-dependent fashion. Depletion of CD8
T cells in convalescent macaques partially abrogated the protective efficacy of natural immunity against rechallenge with SARS-CoV-2, which suggests a role for cellular immunity in the context of waning or subprotective antibody titres. These data demonstrate that relatively low antibody titres are sufficient for protection against SARS-CoV-2 in rhesus macaques, and that cellular immune responses may contribute to protection if antibody responses are suboptimal. We also show that higher antibody titres are required for treatment of SARS-CoV-2 infection in macaques. These findings have implications for the development of SARS-CoV-2 vaccines and immune-based therapeutic agents.
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