Social networks are believed to affect health-related behaviors and health. Data to examine the links between social relationships and health in low- and middle-income country settings are limited. We provide guidance for introducing an instrument to collect social network data as part of epidemiological surveys, drawing on experience in urban India. We describe development and fielding of an instrument to collect social network information relevant to health behaviors among adults participating in a large, population-based study of non-communicable diseases in Delhi, India. We discuss basic characteristics of social networks relevant to health including network size, health behaviors of network partners (i.e., network exposures), network homogeneity, network diversity, strength of ties, and multiplexity. Data on these characteristics can be collected using a short instrument of 11 items asked about up to 5 network members and 3 items about the network generally, administered in approximately 20 minutes. We found high willingness to respond to questions about social networks (97% response). Respondents identified an average of 3.8 network members, most often relatives (80% of network ties), particularly blood relationships. Ninety-one percent of respondents reported that their primary contacts for discussing health concerns were relatives. Among all listed ties, 91% of most frequent snack partners and 64% of exercise partners in the last two weeks were relatives. These results demonstrate that family relationships are the crux of social networks in some settings, including among adults in urban India. Collecting basic information about social networks can be feasibly and effectively done within ongoing epidemiological studies.
IMPORTANCE Diabetes prevention is imperative to slowworldwide growth of diabetes-related morbidity and mortality. Yet the long-term efficacy of prevention strategies remains unknown. OBJECTIVE To estimate aggregate long-term effects of different diabetes prevention strategies on diabetes incidence. DATA SOURCES Systematic searches of MEDLINE, EMBASE, Cochrane Library, andWeb of Science databases. The initial search was conducted on January 14, 2014, and was updated on February 20, 2015. Search terms included prediabetes, primary prevention, and risk reduction. STUDY SELECTION Eligible randomized clinical trials evaluated lifestyle modification (LSM) and medication interventions (>6 months) for diabetes prevention in adults (age ≥18 years) at risk for diabetes, reporting between-group differences in diabetes incidence, published between January 1, 1990, and January 1, 2015. Studies testing alternative therapies and bariatric surgery, as well as those involving participants with gestational diabetes, type 1 or 2 diabetes, and metabolic syndrome, were excluded. DATA EXTRACTION AND SYNTHESIS Reviewers extracted the number of diabetes cases at the end of active intervention in treatment and control groups. Random-effects meta-analyses were used to obtain pooled relative risks (RRs), and reported incidence rates were used to compute pooled risk differences (RDs). MAIN OUTCOMES AND MEASURES The main outcomewas aggregate RRs of diabetes in treatment vs control participants. Treatment subtypes (ie, LSM components, medication classes) were stratified. To estimate sustainability, post-washout and follow-up RRs for medications and LSM interventions, respectively, were examined. RESULTS Forty-three studies were included and pooled in meta-analysis (49 029 participants; mean [SD] age, 57.3 [8.7] years; 48.0% [n = 23 549] men): 19 tested medications; 19 evaluated LSM, and 5 tested combined medications and LSM. At the end of the active intervention (range, 0.5-6.3 years), LSM was associated with an RR reduction of 39% (RR, 0.61; 95% CI, 0.54-0.68), and medications were associated with an RR reduction of 36% (RR, 0.64; 95% CI, 0.54-0.76). The observed RD for LSM and medication studies was 4.0 (95% CI, 1.8-6.3) cases per 100 person-years or a number-needed-to-treat of 25. At the end of the washout or follow-up periods, LSM studies (mean follow-up, 7.2 years; range, 5.7-9.4 years) achieved an RR reduction of 28% (RR, 0.72; 95% CI, 0.60-0.86); medication studies (mean follow-up, 17 weeks; range, 2-52 weeks) showed no sustained RR reduction (RR, 0.95; 95% CI, 0.79-1.14). CONCLUSIONS AND RELEVANCE In adults at risk for diabetes, LSM and medications (weight loss and insulin-sensitizing agents) successfully reduced diabetes incidence. Medication effects were short lived. The LSM interventions were sustained for several years; however, their effects declined with time, suggesting that interventions to preserve effects are needed.
Introduction: There was a low level of pandemic preparedness in South Asia, but the region has done well in mounting an appropriate response to the coronavirus disease 2019 (COVID-19) pandemic. The rate and proportion of deaths attributed to COVID-19 are lower despite case surges similar to the rest of the world. Results: The COVID-19 pandemic has revealed the glaring vulnerabilities of the health system. In addition, the high burden of non-communicable diseases in South Asia multiplies the complexities in combating present and future health crises. The advantage offered by the younger population demographics in South Asia may not be sustained with the rising burden of non-communicable diseases and lack of priority setting for improving health systems. Conclusion: The COVID-19 pandemic has provided a window for introspection, scaling up preparedness for future pandemics, and improving the health of the population overall.
Objective: To assess primary care physicians’ (PCPs) knowledge of type 2 diabetes screening guidelines (American Diabetes Association (ADA) and 2008 US Preventive Services Task Force (USPSTF)), the alignment between their self-reported adherence and actual practice, and how often PCPs recommended diabetes prevention and self-management education programs (DPP/DSME).
Research design and methods: An online survey of PCPs to understand knowledge and adherence toward use of USPSTF/ADA guidelines and recommendation of DPP/ DSME. Patient data from electronic medical records (EMRs) for each PCP were used to identify rates of screening in eligible patients as per guidelines and the two sources were compared to assess concordance.
Results: Of 305 surveyed physicians, 38% reported use of both guidelines (33% use ADA only, 25% USPSTF only). Approximately one-third of physicians who reported use of USPSTF/ADA guidelines had non-concordant EMR data. Similarly, while most PCPs reported they are ‘very likely’ to screen patients with risk factors listed in guidelines, for each criterion at least one-fourth (24%) of PCPs survey responses were non-concordant with EMRs. PCPs reported they provide referral to DPP and DSME on average to 45% and 67% of their newly diagnosed patients with pre-diabetes and diabetes, respectively.
Conclusion: Findings show disconnect between PCPs’ perceptions of adherence to screening guidelines and actual practice, and highlight limited referrals to DPP/ DSME programs. More research is needed to understand barriers to guideline consistent screening and uptake of DPP/DSME, particularly in light of recent policy changes such as the linking USPSTF criteria to reimbursement and expected Medicare DPP reimbursement in 2018.
by
Kavita Singh;
Dimple Kondal;
Roopa Shivashankar;
Mohammed Ali;
Rajendra Pradeepa;
Vamadevan S. Ajay;
Viswanathan Mohan;
Muhammad M. Kadir;
Mark Daniel Sullivan;
Nikhil Tandon;
Kabayam Venkat Narayan;
Dorairaj Prabhakaran
Objectives Health-related quality of life (HRQOL) is a key indicator of health. However, HRQOL data from representative populations in South Asia are lacking. This study aims to describe HRQOL overall, by age, gender and socioeconomic status, and examine the associations between selected chronic conditions and HRQOL in adults from three urban cities in South Asia. Methods We used data from 16 287 adults aged ≥20 years from the baseline survey of the Centre for Cardiometabolic Risk Reduction in South Asia cohort (2010-2011). HRQOL was measured using the European Quality of Life Five Dimension - Visual Analogue Scale (EQ5D-VAS), which measures health status on a scale of 0 (worst health status) to 100 (best possible health status). Results 16 284 participants completed the EQ5D-VAS. Mean age was 42.4 (±13.3) years and 52.4% were women. 14% of the respondents reported problems in mobility and pain/discomfort domains. Mean VAS score was 74 (95% CI 73.7 to 74.2). Significantly lower health status was found in elderly (64.1), women (71.6), unemployed (68.4), less educated (71.2) and low-income group (73.4). Individuals with chronic conditions reported worse health status than those without (67.4 vs 76.2): prevalence ratio, 1.8 (95% CI 1.61 to 2.04). Conclusions Our data demonstrate significantly lower HRQOL in key demographic groups and those with chronic conditions, which is consistent with previous studies. These data provide insights on inequalities in population health status, and potentially reveal unmet needs in the community to guide health policies.
This systematic review synthesizes data published between 1988 and 2009 on mean BMI and prevalence of overweight, obesity, and type 2 diabetes among Asian subgroups in the U.S. We conducted systematic searches in Pub- Med for peer-reviewed, English-language citations that reported mean BMI and percent overweight, obesity, and diabetes among South Asians/Asian Indians, Chinese, Filipinos, Koreans, and Vietnamese. We identified 647 database citations and 23 additional citations from hand-searching. After screening titles, abstracts, and full-text publications, 97 citations remained. None were published between 1988 and 1992, 28 between 1993 and 2003, and 69 between 2004 and 2009. Publications were identified for the following Asian subgroups: South Asian (n=8), Asian Indian (n=20), Chinese (n=44), Filipino (n=22), Korean (n= 8), and Vietnamese (n=3). The observed sample sizes ranged from 32 to 4245 subjects with mean ages from 24 to 78 years. Among samples of men and women, the lowest reported mean BMI was in South Asians (22.1 kg/m2), and the highest was in Filipinos (26.8 kg/m2). Estimates for overweight (12.8 - 46.7%) and obesity (2.1 - 59.0%) were variable. Among men and women, the highest rate of diabetes was reported in Asian Indians with BMI ≥ 30 kg/m2 (32.9%, age and sex standardized). This review suggests heterogeneity among U.S. Asian populations in cardiometabolic risk factors, yet comparisons are limited due to variability in study populations, methods, and definitions used in published reports. Future efforts should adopt standardized methods to understand overweight, obesity and diabetes in this growing U.S. ethnic population.
Mohammed Ali and Venkat Narayan describe the challenge of implementing evidence-based interventions for prevention for the large number of people at increased risk of type 2 diabetes.
Background:
Diabetes is an important contributor to global morbidity and mortality. The contributions of population aging and macroeconomic changes to the growth in diabetes prevalence over the past 20 years are unclear.
Methods: We used cross-sectional data on age- and sex-specific counts of people with diabetes by country, national population estimates, and country-specific macroeconomic variables for the years 1990, 2000, and 2008. Decomposition analysis was performed to quantify the contribution of population aging to the change in global diabetes prevalence between 1990 and 2008. Next, age-standardization was used to estimate the contribution of age composition to differences in diabetes prevalence between high-income (HIC) and low-to-middle-income countries (LMICs). Finally, we used non-parametric correlation and multivariate first-difference regression estimates to examine the relationship between macroeconomic changes and the change in diabetes prevalence between 1990 and 2008.
Results: Globally, diabetes prevalence grew by two percentage points between 1990 (7.4 %) and 2008 (9.4 %). Population aging was responsible for 19 % of the growth, with 81 % attributable to increases in the age-specific prevalences. In both LMICs and HICs, about half the growth in age-specific prevalences was from increasing levels of diabetes between ages 45-65 (51 % in HICs and 46 % in LMICs). After age-standardization, the difference in the prevalence of diabetes between LMICs and HICs was larger (1.9 % point difference in 1990; 1.5 % point difference in 2008). We found no evidence that macroeconomic changes were associated with the growth in diabetes prevalence.
Conclusions: Population aging explains a minority of the recent growth in global diabetes prevalence. The increase in global diabetes between 1990 and 2008 was primarily due to an increase in the prevalence of diabetes at ages 45-65. We do not find evidence that basic indicators of economic growth, development, globalization, or urbanization were related to rising levels of diabetes between 1990 and 2008.