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The burden of disease attributable to obesity is rapidly increasing and becoming a public health challenge globally. Using a nationally representative sample in Australia, this study aims to examine the association of obesity with healthcare service use and work productivity across outcome distributions. We used Household, Income and Labour Dynamics Australia (HILDA) Wave 17 (2017–2018), including 11,211 participants aged between 20 and 65 years. Two-part models using multivariable logistic regressions and quantile regressions were employed to understand variations in the association between obesity levels and the outcomes. The prevalence of overweight and obesity was 35.0% and 27.6%, respectively. After adjusting for socio-demographic factors, low socioeconomic status was associated with a higher probability of overweight and obesity (Obese III: OR = 3.79; 95% CI 2.53–5.68) while high education group was associated with a lower likelihood of being high level of obesity (Obese III OR = 0.42, 95% CI 0.29–0.59). Higher levels of obesity were associated with higher probability of health service use (GP visit Obese, III: OR = 1.42 95% CI 1.04–1.93,) and work productivity loss (number of paid sick leave days, Obese III: OR = 2.40 95% CI 1.94–2.96), compared with normal weight. The impacts of obesity on health service use and work productivity were larger for those with higher percentiles compared to lower percentiles. Overweight and obesity are associated with greater healthcare utilisation, and loss in work productivity in Australia. Australia’s healthcare system should prioritise interventions to prevent overweight and obesity to reduce the cost on individuals and improve labour market outcomes.

The purpose of this study was to examine the determinants of health service utilization in a population at high risk of developing type 2 diabetes mellitus in India. Using Andersen’s behavioral model of healthcare utilization, multivariate logistic regression analysis was performed on baseline data of the Kerala Diabetes Prevention Program. We examined the association between predisposing, enabling, and need factors with outpatient health service use in the past four weeks and inpatient health service use in the past 12 months. More than a quarter (27.9%) and 12.9% of 1007 participants used outpatient services and inpatient services, respectively. Men were less likely to use outpatient services (odds ratio [OR] = 0.56). Outpatient service utilization was positively associated with low social support (OR = 1.69), low general health status (OR = 5.71), and time off from work due to illness (OR = 8.01). Higher educational status (OR = 0.63), low general health status (OR = 3.59), and time off from work due to illness (OR = 1.21) were associated with increased utilization of inpatient services. Although gender, educational status, and social support had important roles, health service utilization in this study population was largely dependent on general health status and presence of illness.

Introduction Despite the public health system’s critical role in non-communicable disease (NCD) surveillance in Vietnam, limited evidence exists on the implementation of NCD surveillance activities within these systems and the need for capacity building across different system levels to meet expected NCD surveillance goals. This study aimed to evaluate the status of and describe factors affecting the implementation of NCD surveillance activities and to identify the NCD surveillance capacity building needs of the public health system in Vietnam. Methods We used a mixed-methods approach in four provinces, conducting self-completed surveys of staff from six Preventive Medicine Institutes (PHI), 53 Centres for Disease Control (CDC) and 148 commune health stations (CHS), as well as 14 in-depth interviews and 22 focus group discussions at four PHI, four CDC, and eight district health centres and CHS. Results Study findings highlighted that although Vietnam has a well-functioning NCD surveillance system, a number of quality issues related to NCD surveillance data were salient. Multifactorial reasons were identified for incomplete, unconfirmed, and inaccurate mortality data and current disease surveillance data. Data on NCD management and treatment were reported to be of better quality than data for screening, targeted treatment, and counselling communication. Main factors affected the effective implementation of NCD surveillance, namely lack of complete and specific guidelines for NCD surveillance, limitations in human resource capacity within NCD departments, and shortage of funding for NCD surveillance activities. Conclusion Study findings provide practical strategies for strengthening health system capacity for NCD surveillance through developing policies, guidelines, and standardised tools to guide NCD surveillance and a road map for integrated NCD surveillance, developing training packages and manuals for all levels of the health system, and conducting utilisation-focused surveillance training programs.

Sub-Saharan Africa (SSA) continues to face adverse maternal and child health (MCH) outcomes compared to other regions of the world. Previous research showed that SSA countries did not reach Millennium Development Goals (MDG)-4 and MDG-5. To further our understanding of levels and correlates of MCH outcomes, numerous studies have focused on socioeconomic factors, both at individual, household, and community levels. This chapter adopted a different approach and emphasized the role of leadership at regional, national, and local levels to improve MCH outcomes in SSA countries. Overall, the chapter demonstrated that without an enlightened leadership, SSA countries will be lagging behind SDG-3 targets. Additionally, evidence to guide policymaking in most countries is lacking mainly due to lack of sound data to specifically meet the needs of policymakers. There is an urgent need to focus on Research and Development (R&D) and Innovation. To achieve this goal, a crucial shift in leadership is compulsory.

Worldwide, approximately 800,000 people die by suicide every year, and non-fatal suicidal thoughts and behaviours are common. Stigma is likely a major barrier to preventing suicide. The purpose of our review is to scope the development, psychometric properties and use of measures that explicitly seek to measure the construct of suicide stigma.We conducted a scoping review. We searched PubMed, PsycINFO, Embase and CINAHL using search terms related to suicide, stigma and measures/scales with no date limits. We included any measure the authors defined as measuring suicide-related stigma. Only peer-reviewed articles published in English were included.We included 106 papers discussing 23 measures of suicide stigma; 82 provided data on psychometric properties. Measures assessed personal or public stigma; and stigma toward a range of suicidal phenomena (e.g., suicidal thoughts, those bereaved by suicide). 'Stigma' definitions varied and were not always provided. The Grief Experience Questionnaire, Suicide Opinion Questionnaire and Stigma of Suicide Scale were the most commonly cited. Measures varied in the strength of their psychometric properties.We only included papers in English. Because we included any measures authors defined as measuring suicide stigma, we may have included measures not commonly considered as measures of suicide stigma, and conversely we might have excluded relevant measures because they did not use the term 'stigma'.This review aimed to assist in better understanding available suicide stigma measures, their strengths and weaknesses and current uses, and will inform the development of future suicide stigma measures.

Background: The COVID-19 pandemic has added to the pre-existing challenges of diabetes management in many countries. It has accelerated the wider use of digital health solutions which have tremendous potential to improve health outcomes for people with diabetes. However, little is known about the attributes and the implementation of these solutions. Objective: To identify and describe digital health solutions for community-based diabetes management and to highlight their key implementation outcomes. Methods: We searched Ovid Medline, CINAHL, Embase, PsycINFO, and Web of Science for relevant articles. A purposive search was also used to identify grey literature. Articles that described digital health solutions that aimed to improve community-based diabetes management were included in this review. We applied a thematic synthesis of evidence to describe the characteristics of digital health solutions, and to summarize their key implementation outcomes. Results: We included 15 articles that reported digital health solutions that primarily focused on community-based diabetes management. Nine of the 15 innovations involved were mobile applications and/or web-based platforms, and five were based on social media platforms. The majority of the digital health solutions were used for diabetes education and support. High engagement, utilization, and satisfaction rates with digital health solutions were observed. The use of digital health solutions was also associated with improvement in self-management, taking medication, and reduction in glycated hemoglobin (HbA1c) levels. Conclusion: COVID-19 triggered digital health solutions have tremendous potential to improve health outcomes for people with diabetes. Further studies are needed to evaluate the sustainability and scale-up of these solutions.

The impact of lifestyle interventions on diabetes and mental health conditions have been documented among people with diabetes. However, the mental health benefits of lifestyle interventions designed for diabetes pre-vention have not been systematically investigated among people at high risk of diabetes, particularly in low-and middle-income countries. We examined the effects of a 12-month peer support lifestyle intervention designed for diabetes prevention on depression and anxiety symptomology in the sample population of the Kerala Diabetes Prevention Program. Mixed-effects linear regression models were used to examine the effect of the intervention on depression and anxiety scores at 12 and 24 months in the total sample of 1007 adults at risk for diabetes and among those with mild-severe depressive or anxiety symptoms at baseline (n = 326 for depression; n = 203 for anxiety). Among all participants, the intervention group had a significantly higher reduction of depressive symptoms as compared to the control group at 12 months (mean diff score =-0.51; 95% CI:-0.95,-0.07; P = 0.02). This effect was not sustained at 24 months. There were no significant intervention effects for anxiety. Among those with mild-severe symptoms at baseline, the intervention group had a significantly higher reduction of depressive symptoms (mean diff score =-1.55; 95% CI-2.50,-0.6; P = 0.001) and anxiety symptoms (mean diff score =-1.64; 95% CI-2.76,-0.52; P = 0.004) at 12 months. The effect was sustained at 24 months for depression, but not anxiety. Lifestyle interventions designed for prevention of diabetes might improve depressive and anxiety symptoms in the short-term, particularly among those with mild-severe symptoms.

Additional file 1. Literature, statistic and document review.

Policy Maker interview guide.

Additional file 2. Facility Assessment Questionnaire.

Patient Questionnaire.

Policy marker interview guide for key informant interviews. Description of data: Sample of interview guide for key informant interviews with policy makers. (DOCX 15Â kb)