Embedded health service development and research: why and how to do it (a ten-stage guide)

In a world of changing disease burdens, poor quality care and constrained health budgets, finding effective approaches to developing and implementing evidence-based health services is crucial. Much has been published on developing service tools and protocols, operational research and getting policy into practice but these are often undertaken in isolation from one another. This paper, based on 25 years of experience in a range of low and middle income contexts as well as wider literature, presents a systematic approach to connecting these activities in an embedded development and research approach. This approach can circumvent common problems such as lack of local ownership of new programmes, unrealistic resource requirements and poor implementation. We lay out a ten-step process, which is based on long-term partnerships and working within local systems and constraints and may be tailored to the context and needs. Service development and operational research is best prioritised, designed, conducted and replicated when it is embedded within ministry of health and national programmes. Care packages should from the outset be designed for scale-up, which is why the piloting stage is so crucial. In this way, the resulting package of care will be feasible within the context and will address local priorities. Researchers must be entrepreneurial and responsive to windows of funding for scale-up, working in real-world contexts where funding and decisions do not wait for evidence, so evidence generation has to be pragmatic to meet and ensure best use of the policy and financing cycles. The research should generate tested and easily usable tools, training materials and processes for use in scale-up. Development of the package should work within and strengthen the health system and other service delivery strategies to ensure that unintended negative consequences are minimised and that the strengthened systems support quality care and effective scale up of the package. While embedded development and research is promoted in theory, it is not yet practiced at scale by many initiatives, leading to wasted resources and un-sustained programmes. This guide presents a systematic and practical guide to support more effective engagements in future, both in developing interventions and supporting evidence-based scale-up

Relationship between obesity and coronary heart disease among urban Bangladeshi men and women.

The aim of the study was to examine the association of different measures of obesity (body mass index or BMI, waist circumference or WC, waist to hip ratio or WHR and waist height ratio or WHtR) with coronary heart disease (CHD) in a Bangladeshi population. The study included 189 hospitalized CHD cases (133 men and 52 women) and 201 controls (137 men and 68 women). Logistic regression was done to assess the associations between obesity and CHD. The mean age was 53.1 ± 8.3 for men and 51.9 ± 8.4 for women. After adjustment for confounders the odds ratio (OR) of CHD for men was 1.69 (95% CI, 1.24-2.32), 1.94 (95% CI 1.40-2.70), and 1.32 (95% CI, 1.01-2.16) per 1 standard deviation (SD) increase in BMI, WC, and WHtR respectively. The OR for women was 2.64 (CI, 1.61-4.34), 1.82 (95% CI 1.12-2.95), 2.32 (95% CI, 1.36-3.96), and 1.94 (95% CI, 1.23-3.07) per 1 SD increase in BMI, WC, WHtR and WHR respectively. Since both total obesity and abdominal adiposity were associated with development of CHD and since measurement of WC and BMI are inexpensive, both should be included in the clinical setting for CHD risk assessment for this group of population

The risk and burden of smoking related heart disease mortality among young people in the United States.

PurposeAlthough cigarette smoking remains the most common risk factor for heart disease among the young, few studies have explored the relationship of smoking with heart disease mortality risk among young people. This prospective study assesses the risk and burden of all heart disease (HD) and coronary heart disease (CHD) mortality associated with smoking among younger adults from a nationally representative sample of the United States.MethodNational Health Interview Survey respondents' data from 1997-2004 were linked to their death records through 2006. The analyses were restricted to individuals 18 to 44 years of age during follow up (n = 121,284). Cox proportional hazard ratios (HR) were estimated with adjustment for sample weights and design effects. Attributable fractions (AF) of smoking were calculated.ResultsAfter controlling for age, race, body mass index, history of hypertension and diabetes, and leisure time physical activity, current smoking related CHD mortality HR was 14.6 [95 % confidence interval or CI, 3.3-64.9] for females and 3.6 [95 % CI, 1.2-10.4] for males. The HR for all HD mortality was 3.1 [95 % CI, 1.3-7.6] for females and 2.4 [95 % CI, 1.2-4.7] for males. The AF of smoking for CHD deaths for female and male were 0.58 and 0.54 respectively. The AF of all HD mortality was 0.31 for male and 0.32 for female. The mean estimates of all HD deaths attributable to smoking during 1997-2006 among this age group were 52,214, of which 45,147 were CHD deaths.ConclusionEven after adjustment for multiple risk factors and without addressing passive smoking, our result showed a strong relationship between smoking and HD and CHD mortality among young adults that is likely causal

The risk and burden of smoking related heart disease mortality among young people in the United States.

PurposeAlthough cigarette smoking remains the most common risk factor for heart disease among the young, few studies have explored the relationship of smoking with heart disease mortality risk among young people. This prospective study assesses the risk and burden of all heart disease (HD) and coronary heart disease (CHD) mortality associated with smoking among younger adults from a nationally representative sample of the United States.MethodNational Health Interview Survey respondents' data from 1997-2004 were linked to their death records through 2006. The analyses were restricted to individuals 18 to 44 years of age during follow up (n = 121,284). Cox proportional hazard ratios (HR) were estimated with adjustment for sample weights and design effects. Attributable fractions (AF) of smoking were calculated.ResultsAfter controlling for age, race, body mass index, history of hypertension and diabetes, and leisure time physical activity, current smoking related CHD mortality HR was 14.6 [95 % confidence interval or CI, 3.3-64.9] for females and 3.6 [95 % CI, 1.2-10.4] for males. The HR for all HD mortality was 3.1 [95 % CI, 1.3-7.6] for females and 2.4 [95 % CI, 1.2-4.7] for males. The AF of smoking for CHD deaths for female and male were 0.58 and 0.54 respectively. The AF of all HD mortality was 0.31 for male and 0.32 for female. The mean estimates of all HD deaths attributable to smoking during 1997-2006 among this age group were 52,214, of which 45,147 were CHD deaths.ConclusionEven after adjustment for multiple risk factors and without addressing passive smoking, our result showed a strong relationship between smoking and HD and CHD mortality among young adults that is likely causal

Relationship between obesity and coronary heart disease among urban Bangladeshi men and women.

The aim of the study was to examine the association of different measures of obesity (body mass index or BMI, waist circumference or WC, waist to hip ratio or WHR and waist height ratio or WHtR) with coronary heart disease (CHD) in a Bangladeshi population. The study included 189 hospitalized CHD cases (133 men and 52 women) and 201 controls (137 men and 68 women). Logistic regression was done to assess the associations between obesity and CHD. The mean age was 53.1 ± 8.3 for men and 51.9 ± 8.4 for women. After adjustment for confounders the odds ratio (OR) of CHD for men was 1.69 (95% CI, 1.24-2.32), 1.94 (95% CI 1.40-2.70), and 1.32 (95% CI, 1.01-2.16) per 1 standard deviation (SD) increase in BMI, WC, and WHtR respectively. The OR for women was 2.64 (CI, 1.61-4.34), 1.82 (95% CI 1.12-2.95), 2.32 (95% CI, 1.36-3.96), and 1.94 (95% CI, 1.23-3.07) per 1 SD increase in BMI, WC, WHtR and WHR respectively. Since both total obesity and abdominal adiposity were associated with development of CHD and since measurement of WC and BMI are inexpensive, both should be included in the clinical setting for CHD risk assessment for this group of population