Methodshttps://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(18)30135-X/fulltext#%2030135-X/fulltext#%20)
We studied 15 428 adults aged 45–64 years, in four US communities...The primary outcome was all-cause mortality. We investigated the association between the percentage of energy from carbohydrate intake and all-cause mortality, accounting for possible non-linear relationships in this cohort.
Findings
[T]here was a U-shaped association between the percentage of energy consumed from carbohydrate (mean 48·9%, SD 9·4) and mortality: a percentage of 50–55% energy from carbohydrate was associated with the lowest risk of mortality. In the meta-analysis of all cohorts (432 179 participants), both low carbohydrate consumption (<40%) and high carbohydrate consumption (>70%) conferred greater mortality risk than did moderate intake, which was consistent with a U-shaped association (pooled hazard ratio 1·20, 95% CI 1·09–1·32 for low carbohydrate consumption; 1·23, 1·11–1·36 for high carbohydrate consumptio**n). However, results varied by the source of macronutrients: mortality increased when carbohydrates were exchanged for animal-derived fat or protein (1·18, 1·08–1·29) and mortality decreased when the substitutions were plant-based (0·82, 0·78–0·**87).
Interpretation
Both high and low percentages of carbohydrate diets were associated with increased mortality, with minimal risk observed at 50–55% carbohydrate intake. Low carbohydrate dietary patterns favouring animal-derived protein and fat sources, from sources such as lamb, beef, pork, and chicken, were associated with higher mortality, whereas those that favoured plant-derived protein and fat intake, from sources such as vegetables, nuts, peanut butter, and whole-grain breads, were associated with lower mortality, suggesting that the source of food notably modifies the association between carbohydrate intake and mortality.
submitted by LupinePublishers to u/LupinePublishers [link] [comments] Lupine Publishers- Archives of Diabetes & Obesity (ADO)- Associated Risk Factors in Pre-diabetes and Type 2 Diabetes in Saudi Community AbstractBackground and Objective: The prevalence and incidence of type 2 diabetes mellitus (T2DM) are increasing worldwide. Pre diabetes is a high-risk state for the development of diabetes and its associated complications. This study aims to determine the associated risk factors among T2DM and pre diabetes patients among adult Saudi population.Methods: For the present study, we analyzed participants who are older than 20 years old and had undergone a blood test to assess HbA1c. A total of 1095 were selected to be enrolled for the present study. All patients were from the population of the Primary health and Diabetic Centres at King Fahad Armed Forces Hospital. Participants were defined as having T2DM according to self-report, clinical reports, use of anti diabetic agents and HbA1c (≥6.5). Non T2DM participants were divided into normoglycemic or pre diabetic group as follows: HbA1c < 5.7, (normoglycemic) or HbA1c 5.7-6.4 (pre diabetes). Laboratory assessments included HbA1c, lipids, creatinine and urinary micro albumin. Main results: Of the 1095 participants analyzed, 796 were women (72.7%). Age was 45.1±11.1 and BMI was 30.7±5.7. Hypertension had been diagnosed in 415 (38.2%) participants. Blood measurements revealed the following values: creatinine 68.2±22.0umol/L , Urine micro albumin (g/min) 55.4±200.3, total cholesterol levels 4.9±1.0mmol/L, high density lipoprotein 1.3±0.3mmol/L, triglyceride levels 1.5±0.7 and low density lipoprotein 3.0±0.9mmol/L. Of the overall 1095 analyzed participants, pre diabetes was present in 362(33.1%), 368(33.6%) were classified as T2DM and 365 (33.3%) as normoglycemic. When comparing pre diabetic with normoglycemic and T2DM population, pre diabetic subjects were more likely to have hypertension and higher triglyceride than normoglycemic but less than T2DM subjects. In addition, pre diabetic patients compared with T2DM ones had higher levels of low density lipoprotein and high density lipoprotein. Logistic regression analysis showed no significant association of any of the co variables with normoglycemic subjects in front of the pre diabetic reference group, whereas the odds of being in the diabetic group gets multiplied by 7.56 for each unitary increase in male gender (p< 0.0001, OR: 7.56, 95% CI 3.16-18.23). Also, individuals with hypertension had higher odds of being in the DM group than in the prediabetic (p<0 .0001, OR: 6.06, 95% CI 3.25- 11.28). Age of subjects had lower odds of being in the DM group than in the pre diabetic (p<0 .0001, OR: 0.85, 95% CI (0.82-0.89). Conclusion: This study found the major clinical differences between pre diabetic and T2DM Patients were the higher hypertension and hypertriglyceridenia in the T2DM patients. Clearly, despite the small sample size, this study has posed important public health issues that require immediate attention from the health authority. Unless immediate steps are taken to contain the increasing prevalence of obesity, diabetes, pre diabetes, the health care costs for chronic diseases will pose an enormous financial burden to the country Keywords: Type 2 Diabetes; Pre diabetes; Risk factors Abbreviations: T2DM: Type 2 Diabetes Mellitus; IFG: Impaired Fasting Glucose; BMI: Body Mass Index; HTN: Hypertension; AER: Albumin Excretion Rate; DN: Diabetic Nephropathy; OR: Odds Ratio; CI: Confidence Interval; I-IFG: Isolated Impaired Fasting Glucose IntroductionDiabetes mellitus is a major cause of excess mortality and morbidity. The prevalence and incidence of type 2 diabetes mellitus (T2DM) are increasing worldwide [1]. T2DM patients have a higher risk of developing microvascular and macrovascular disease than the general population. The occurrence of these complications depends largely on the degree of glycemic control as well as on the adequate control of cardiovascular risk factors [2-5]. In Saudi Arabia, primary epidemiological diabetes features are not different. The diabetes mellitus prevalence among adult Saudi population has reached 23.7%, a percentage being the highest across the globe [6,7]. Statistics regarding the increasing trend of diabetes and pre diabetes in the world have also been observed in Saudi Arabia. As per the WHO country profile 2016, 14.4% of Saudi population has diabetes, while prevalence in males is 14.7% [8]. In 2015, the prevalence of pre diabetics was found to be 9.0% in Jeddah with 9.4% in men, while for diabetes, it was 12.1% with 12.9% adult male population suffering from it [9]. Another study conducted in Saudi population revealed that the diabetes prevalence in their study was found to be 25.4%, while impaired fasting glucose (IFG) was 25.5%. The strongest risk factors were age > 45 years, high triglycerides levels, and hypertension [10].Pre diabetes is a high-risk state for the development of diabetes and its associated complications [11-13]. Recent data have shown that in developed countries, such as the Unites States and the United Kingdom, more than one-third of adults have pre diabetes, but most of these individuals are unaware they have the condition [14-16]. Once detected, pre diabetes needs to be acknowledged with a treatment plan to prevent or slow the transition to diabetic [17,18]. Treatment of pre diabetes is associated with delay of the onset of diabetes [19]. Detection and treatment of pre diabetes is therefore a fundamental strategy in diabetes prevention [11]. Current recommendations for pre diabetes screening by the American Diabetes Association focus nearly exclusively on adults who are overweight or obese as defined by body mass index (BMI) until the patient meets the age-oriented screening at 45 years [11]. Further, the recently released recommendation from the US Preventive Services Task Force regarding screening for abnormal glucose levels and T2DM limits screening to individuals who are overweight or obese [20]. This focus on obese or overweight individuals, although obesity and pre diabetes have shown trends of increasing prevalence. United States Preventive Services Task Force has recommended screening of diabetes in adults devoid of precise symptoms and in individuals with BP higher than 135/80mmHg [21]. This study aims to determine the associated risk factors among T2DM and pre diabetes patients among adult Saudi population. MethodsFor the present study, we analyzed participants who are older than 20 years old and had undergone a blood test to assess HbA1c. A total of 1095 were selected to be enrolled for the present study. All patients were from the population of the Primary health and Diabetic Centers at King Fahad Armed Forces Hospital. Participants were defined as having T2DM according to self-report, clinical reports, use of anti diabetic agents and HbA1c (≥6.5) [11]. Non T2DM participants were divided into normoglycemic or pre diabetic group as follows: HbA1c<5.7, (normoglycemic) or HbA1c 5.7-6.4 (pre diabetes) \[11\]. 362 subjects were found to be pre diabetic. Almost similar number of normoglyceic and T2DM subjects was selected to be analyzed for comparison. All data were collected by personal interview and on the basis of a review of electronic medical data. Weight (kg) and height (cm) were measured by physician and nurse interviewers and recorded. Overweight and obesity were defined as BMI 25-29.9 and ≥30.0kg/m2 respectively \[22\]. Blood Pressure readings were within a gap of 15 minutes using a mercury sphygmomanometer by palpation and auscultation method in right arm in sitting position. Two readings were taken 15 min apart and the average of both the readings was taken for analysis. Hypertension (HTN) was also diagnosed based on anti HTN medications or having a prescription of antihypertensive drugs and were classified as Hypertensive irrespective of their current blood pressure reading or if the blood pressure was greater than 140/90 mmHg i.e. systolic BP more than 140 and diastolic BP more than 90 mm of Hg – Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines \[23\]. Laboratory assessments included HbA1c, lipids, creatinine and urinary micro albumin. HbA1c was expressed as percentage. High performance liquid chromatography was used. Fasting serum lipids were measured on a sample of blood after fasting for 14 hours. We used the enzymatic method for determining the cholesterol and trigylcerides levels. Diabetic nephropathy (DN) was assessed by measurement of mean albumin excretion rate (AER) on timed, overnight urine collections. We use a polyclonal radioimmunoassay for albumin measurement. DN is defined as an albumin excretion rate of >20g/min in a timed or a 24hr urine collection which is an equivalent to >30 mg/g creatinine in a random spot sample.Statistical AnalysisUnivariate analysis of demographic and clinical laboratory was accomplished using one-way analysis of variance (ANOVA) with posy hoc analysis between variables, to estimate the significance of different between groups where appropriate. Chi square (X2) test were used for categorical data comparison. The adjusted odds ratio (OR) with a 95% confidence interval (CI) was calculated. In order to evaluate the adjusted association of aforementioned factors on being normoglycemic or diabetic in relation to the pre diabetes group, a multinomial logistic regression model was fit, in which the categorical dependent variable was normoglycemia, pre diabetes or T2DM(with pre diabetes as the reference category), and significant variables in bivariate analyses were included as explanatory variables. Despite of the ordinal nature of the dependent variable, ordered logistic regression was not adjusted because the aim of the study was not the association of factors with a latent degree of diabetes but the differential profile of pre diabetes in front of normoglicemia and diabetes. As all the participants were the same age, adjusting for age was not applied. All statistical analyses were performed using SPSS Version 22.0. The difference between groups was considered significant when P<0.05. ResultsOf the 1095 participants analyzed, 796 were women (72.7%). Age was 45.1±11.1 and BMI was 30.7±5.7. Hypertension had been diagnosed in 415 (38.2%) participants. Blood measurements revealed the following values: creatinine 68.2±22.0umol/L, Urine microalbumin (g/min) 55.4±200.3, total cholesterol levels 4.9±1.0mmol/L, high density lipoprotein 1.3±0.3mmol/L, triglyceride levels 1.5±0.7 and low density lipoprotein 3.0 ±0.9mmol/L. Of the overall 1095 analyzed participants, pre diabetes was present in 362(33.1%), 368(33.6%) were classified as T2DM and 365 (33.3%) as normoglycemic. Table 1 shows the clinical characteristics and laboratory data of the three groups according to the predefined glycemic status. When comparing pre diabetic with normoglycemic and T2DM population, pre diabetic subjects were more likely to have hypertension and higher triglyceride than normoglycemic but less than T2DM subjects. In addition, prediabetic patients compared with T2DM ones had higher levels of low density lipoprotein and high density lipoprotein. In Table 2, logistic regression analysis showed no significant association of any of the covariables with normoglycemic subjects in front of the pre diabetic reference group, whereas the odds of being in the diabetic group gets multiplied by 7.56 for each unitary increase in male gender (p<0.0001, OR: 7.56, 95% CI 3.16-18.23). Also, individuals with hypertension had higher odds of being in the DM group than in the pre diabetic (p<0 .0001, OR: 6.06, 95% CI 3.25-11.28). Age of subjects had lower odds of being in the DM group than in the pre diabetic (p<0 .0001, OR: 0.85, 95% CI (0.82-0.89).DiscussionThis study showed that multiple risk factors are related to T2DM, but not to the pre diabetes group, including age, female gender and HTN. Generalization to all population could not be due to regionalized characteristics. In addition, it does not evaluate the healthcare services offered in our city. The size of our sample and the cross section type of the study should be of consideration.T2DM is a major health concern worldwide and is increasing in parallel with the obesity epidemic [24]. Prevalence of T2DM has increased dramatically with 1 million people reported to have been diagnosed with T2DM in 1994, increasing to 382 million by 2013, and with prediction of 592 million by 2035 [25]. Given that both genetic and environmental factors contribute to T2DM progression, it has been proposed that amongst increasing globalization, Asian ethnicities including Saudi Arabia have been unable to adapt to food and lifestyle related aspects of westernized culture [26]. Hence when matched for the same gender, age, and body weight, those with Asian ethnicity appear to have a greater risk of poor metabolic health than Caucasian counterparts including Europeans people [27]. This increased risk for T2DM has been reported in both Asians and Saudi Arabia [6-10,28]. Currently, the population with pre-diabetes has reached approximately 318 million around the world, accounting for 6.7% of the total number of adults. About 69.2% of the prediabetes population lives in low or middle-income countries [29]. Understanding pre diabetes may be crucial to reducing the global T2DM epidemic and is defined either by the presence of isolated impaired fasting glucose (I-IFG); or isolated impaired glucose tolerance (I-IGT); or both IFG and IGT. To maintain glucose homeostasis greater secretion of insulin is required from the pancreatic cells, and hence hyperinsulinemia develops. Prolonged hyperinsulinemia and/or fatty pancreas may in turn lead to the dysfunction of pancreatic cells, resulting in impaired insulin secretion [30]. Decreased insulin secretion and concomitant increased blood glucose levels consequently also lead to the reduced uptake of glucose by skeletal muscle, thereby enhancing muscle insulin resistance [31]. IFG, determined from fasting plasma glucose, occurs as a result of poor glucose regulation, resulting in raised blood glucose even after an overnight fast, while IGT is due to an individual being unable to respond to glucose consumed as part of a meal, resulting in increased postprandial blood glucose [11]. More recently, prediabetes has also been identified by mildly elevated HbA1c [32,33]. The younger age of T2DM in our cohort is consistent with that seen among other groups such as the Australians, the American Indian and Alaska natives [34-36]. Age of subjects had lower odds of being in the DM group than in the pre diabetic (p<0 .0001, OR: 0.85, 95% CI (0.82-0.89) in concordance with earlier reports [37,38]. Odds of being in the diabetic group gets multiplied by 7.56 for each unitary increase in male gender (p< 0.0001, OR: 7.56, 95% CI 3.16- 18.23). As seen in this study, majority of the female participants were either overweight (59.6%) or obese (78.6%). The reason for such an observation has not been completely elucidated but is proposed to be associated with obesity which is highly prevalent in the populations worldwide. Since obesity is closely linked to increased insulin resistance and decreased insulin sensitivity and higher risk of diabetes, arresting the obesity pandemic among our population should be a priority [39-41]. Special, culturally oriented community-based intervention programs need to be implemented. The frequency of pre diabetes in 27.2% of the female cases out of the total cohort in this study was six times higher than other, estimated to be 4.2% in 2006 [42,43]. Due to our small sample size, this is inconclusive and needs to be verified by extending our study to more of our communities. Nevertheless, our findings warrant special attention from the health authorities since although HbA1c is not as sensitive as IGT test, it has consistently been shown to be a good predictor of increased risk for cardiovascular diseases and T2DM in many populations around the world [44,45]. Previous cross-sectional studies have reported that multiple risk factors are related to pre-diabetes, Such as increased age, overweight, obesity, blood pressure, and dyslipidemia [37,46,47]. More importantly, impaired glucose tolerance was found to be an independent risk factor for cardiovascular disease, the hazard ratio of death was 2.22 (95% CI = 1.08–4.58), and arterial stiffness and pathological changes in the arterial intima occurred in the stage of IGT [48]. The participants in our study with pre-diabetes had higher BMI, more frequent HTN, higher triglyceride, frequent renal failure and DN than those without pre-diabetes but lower than participants with T2DM. logistic regression analysis showed no significant association of any of the covariables with normoglycemic subjects in front of the pre diabetic reference group, whereas the odds of being in the diabetic group gets multiplied by 7.56 for each unitary increase in male gender. Also, individuals with hypertension had higher odds of being in the DM group than in the pre diabetic. Age of subjects had lower odds of being in the DM group than in the pre diabetic which was consistent with earlier studies [37,38]. Previous studies have reported that overweight and obesity were the mainly factors contributing to insulin resistance, and insulin resistance was the basis of diabetes and other chronic diseases [49,50]. In the present study, BMI was significantly higher in the pre diabetes than the normal groups, p=0.03. When BMI was classified into three types. The total numbers of overweight and obese people in the pre-diabetes and normal groups were 293 and 291, respectively (the total number were 362 and 365, respectively), and there were statistically non significant differences in being overweight or obese between the pre-diabetes and normal groups (OR = 1.02, 95% CI = 0.86–1.21, p=0.8). Increasing evidence suggests that the excess body fat in overweight/obese people might lead to increased degradation of fat, which resulted in the production of large amounts of free fatty acids (FFAs). When the level of FFAs was higher in blood, the capacity of liver tissue for insulin-mediated glucose uptake and utilization was lower, so the blood glucose level was high in circulation [51]. In other words, high FFAs in the blood were one of the important pathogenic factors of obesity caused by insulin resistance [52]. The fact that BMI categories was not a significant factor in our study is the cohort mean BMI was in the obesity range, p=0.3. However, the mean BMI was significantly different between the studied groups, p=0.03. A high level of triglycerides was not significantly associated as a risk factor for developing pre-diabetes and T2DM (OR = 1.09, 95% CI = (0.60-2.00), P=0.8, 1.44(0.86-2.40),P=0.2) respectively. High level of triglycerides could increase the fat deposition in muscle, liver, and pancreas, and it could damage the function of mitochondria and induce oxidative stress which, in turn, could cause insulin resistance, but also lead to impaired islet B cell function [53]. Some studies suggested an interrelation between hyper triglyceridemia and insulin resistance and that they promote each other’s development [54,55]. In concordance with our result, in some epidemiological studies, for instance, the Framingham Heart Study, hyper triglyceridemia was more prevalent in type 2 diabetes mellitus patients than in the normal population, suggesting that hyper triglyceridemia is a causal factor of type 2 diabetes mellitus [56]. However, this paper was a cross-sectional study, thus it was impossible to determine the causal relationship between hyper triglyceridemia and pre-diabetes and T2DM. Hypertension was found to be a risk factor for T2DM but not for the pre diabetes group in our study (OR = 6.06, 95% CI =3.25- 11.28, p<0.0001, OR = 0.95, 95% CI = 0.50-1.82, p=0.9) respectively. A possible mechanism is that the activity of angiotensin II is increased in the circulatory system of patient with hypertension. Angiotensin II activates renin-angiotensin-aldosterone system and affects the function of the pancreatic islets, resulting in islet fibrosis and reduced synthesis of insulin, and ultimately leading to insulin resistance [57,58]. Insulin resistance can also aggravate the condition of hypertension. Directly or indirectly through the activity of renin-angiotensin-aldosterone system, insulin promotes renal tubular to reabsorb Na+ and water, leading to the increased blood volume and cardiac output; this is considered as one of reasons for the development of hypertension [59]. Interactions between abnormal glucose tolerance, hypertension, and dyslipidemia could impair endothelial cell and result in atherosclerosis or other cardiovascular complications. Therefore, the management of daily diet of people with pre-diabetes and the monitoring of body weight, blood lipids, and blood pressure is very important. Results of our investigation must be interpreted in light of some limitations such as the cross-sectional design, which does not let to establish any causal relation with respect to prediabetic state and only provides mere associations. Moreover, the classification of glycemic state was based on HbA1c, instead of its combination with a glucose tolerance test. Then, it is expected that the lack of glucose tolerance test data leads to a suboptimal estimation of glycemic state because normoglycemic group may include some individuals with impaired glucose tolerance that should have been included in pre diabetic group. Considering the goal population, a larger cohort would have probably provided a greater power of the statistical analyses. ConclusionThis study found the major clinical differences between pre diabetic and T2DM patients were the higher hypertension and hyper triglyceridenia in the T2DM patients. Clearly, despite the small sample size, this study has posed important public health issues that require immediate attention from the health authority. Unless immediate steps are taken to contain the increasing prevalence of obesity, diabetes, pre diabetes, the health care costs for chronic diseases will pose an enormous financial burden to the country.ConclusionUse a plant based protein blend diet pea - lowers levels of hunger hormone, ghrelin. Quinoa -chock full of anti-inflammatory compounds called flavonoids. Hemp - contains 20 amino acids, healthy omega fats and fiber (including 9 the body cannot make on its own). Coconut - packed full of healthy saturated fats that go straight to the liver for a quick energy boost. Monk fruit - contains powerful antioxidants called mogrosides. Cinnamon - clinically proven to support healthy blood sugar levels AND healthy triglyceride levels. Vanilla Bean - loaded with minerals like magnesium, potassium, and calcium. Vanilla also has mood-boosting and energy enhancing effects on body. Zero alcohol use.AcknowledgmentWe are grateful to the staffs from the diabetic centre at King Fahad Armed Forces Hospital for their valuable contributions in data collection. The authors have no conflict of interest to disclose.For more Lupine Publishers Open Access Journals Please visit our website: https://lupinepublishersgroup.com/ For more articles open access Diabetes and Obesity Journals Please Click Here: https://lupinepublishers.com/diabetes-obesity-journal/ To Know More About Open Access Publishers Please Click on Lupine Publishers |
Lupine Publishers| Journal of Diabetes and Obesity submitted by LupinePublishers to u/LupinePublishers [link] [comments] AbstractBackground and Objective: The prevalence and incidence of type 2 diabetes mellitus (T2DM) are increasing worldwide. Pre diabetes is a high-risk state for the development of diabetes and its associated complications. This study aims to determine the associated risk factors among T2DM and pre diabetes patients among adult Saudi population.Methods: For the present study, we analyzed participants who are older than 20 years old and had undergone a blood test to assess HbA1c. A total of 1095 were selected to be enrolled for the present study. All patients were from the population of the Primary health and Diabetic Centres at King Fahad Armed Forces Hospital. Participants were defined as having T2DM according to self-report, clinical reports, use of anti diabetic agents and HbA1c (≥6.5). Non T2DM participants were divided into normoglycemic or pre diabetic group as follows: HbA1c < 5.7, (normoglycemic) or HbA1c 5.7-6.4 (pre diabetes). Laboratory assessments included HbA1c, lipids, creatinine and urinary micro albumin. Main results: Of the 1095 participants analyzed, 796 were women (72.7%). Age was 45.1±11.1 and BMI was 30.7±5.7. Hypertension had been diagnosed in 415 (38.2%) participants. Blood measurements revealed the following values: creatinine 68.2±22.0umol/L , Urine micro albumin (g/min) 55.4±200.3, total cholesterol levels 4.9±1.0mmol/L, high density lipoprotein 1.3±0.3mmol/L, triglyceride levels 1.5±0.7 and low density lipoprotein 3.0±0.9mmol/L. Of the overall 1095 analyzed participants, pre diabetes was present in 362(33.1%), 368(33.6%) were classified as T2DM and 365 (33.3%) as normoglycemic. When comparing pre diabetic with normoglycemic and T2DM population, pre diabetic subjects were more likely to have hypertension and higher triglyceride than normoglycemic but less than T2DM subjects. In addition, pre diabetic patients compared with T2DM ones had higher levels of low density lipoprotein and high density lipoprotein. Logistic regression analysis showed no significant association of any of the co variables with normoglycemic subjects in front of the pre diabetic reference group, whereas the odds of being in the diabetic group gets multiplied by 7.56 for each unitary increase in male gender (p< 0.0001, OR: 7.56, 95% CI 3.16-18.23). Also, individuals with hypertension had higher odds of being in the DM group than in the prediabetic (p<0 .0001, OR: 6.06, 95% CI 3.25- 11.28). Age of subjects had lower odds of being in the DM group than in the pre diabetic (p<0 .0001, OR: 0.85, 95% CI (0.82-0.89). Conclusion: This study found the major clinical differences between pre diabetic and T2DM Patients were the higher hypertension and hypertriglyceridenia in the T2DM patients. Clearly, despite the small sample size, this study has posed important public health issues that require immediate attention from the health authority. Unless immediate steps are taken to contain the increasing prevalence of obesity, diabetes, pre diabetes, the health care costs for chronic diseases will pose an enormous financial burden to the country Keywords: Type 2 Diabetes; Pre diabetes; Risk factors Abbreviations: T2DM: Type 2 Diabetes Mellitus; IFG: Impaired Fasting Glucose; BMI: Body Mass Index; HTN: Hypertension; AER: Albumin Excretion Rate; DN: Diabetic Nephropathy; OR: Odds Ratio; CI: Confidence Interval; I-IFG: Isolated Impaired Fasting Glucose IntroductionDiabetes mellitus is a major cause of excess mortality and morbidity. The prevalence and incidence of type 2 diabetes mellitus (T2DM) are increasing worldwide [1]. T2DM patients have a higher risk of developing microvascular and macrovascular disease than the general population. The occurrence of these complications depends largely on the degree of glycemic control as well as on the adequate control of cardiovascular risk factors [2-5]. In Saudi Arabia, primary epidemiological diabetes features are not different. The diabetes mellitus prevalence among adult Saudi population has reached 23.7%, a percentage being the highest across the globe [6,7]. Statistics regarding the increasing trend of diabetes and pre diabetes in the world have also been observed in Saudi Arabia. As per the WHO country profile 2016, 14.4% of Saudi population has diabetes, while prevalence in males is 14.7% [8]. In 2015, the prevalence of pre diabetics was found to be 9.0% in Jeddah with 9.4% in men, while for diabetes, it was 12.1% with 12.9% adult male population suffering from it [9]. Another study conducted in Saudi population revealed that the diabetes prevalence in their study was found to be 25.4%, while impaired fasting glucose (IFG) was 25.5%. The strongest risk factors were age > 45 years, high triglycerides levels, and hypertension [10].Pre diabetes is a high-risk state for the development of diabetes and its associated complications [11-13]. Recent data have shown that in developed countries, such as the Unites States and the United Kingdom, more than one-third of adults have pre diabetes, but most of these individuals are unaware they have the condition [14-16]. Once detected, pre diabetes needs to be acknowledged with a treatment plan to prevent or slow the transition to diabetic [17,18]. Treatment of pre diabetes is associated with delay of the onset of diabetes [19]. Detection and treatment of pre diabetes is therefore a fundamental strategy in diabetes prevention [11]. Current recommendations for pre diabetes screening by the American Diabetes Association focus nearly exclusively on adults who are overweight or obese as defined by body mass index (BMI) until the patient meets the age-oriented screening at 45 years [11]. Further, the recently released recommendation from the US Preventive Services Task Force regarding screening for abnormal glucose levels and T2DM limits screening to individuals who are overweight or obese [20]. This focus on obese or overweight individuals, although obesity and pre diabetes have shown trends of increasing prevalence. United States Preventive Services Task Force has recommended screening of diabetes in adults devoid of precise symptoms and in individuals with BP higher than 135/80mmHg [21]. This study aims to determine the associated risk factors among T2DM and pre diabetes patients among adult Saudi population. MethodsFor the present study, we analyzed participants who are older than 20 years old and had undergone a blood test to assess HbA1c. A total of 1095 were selected to be enrolled for the present study. All patients were from the population of the Primary health and Diabetic Centers at King Fahad Armed Forces Hospital. Participants were defined as having T2DM according to self-report, clinical reports, use of anti diabetic agents and HbA1c (≥6.5) [11]. Non T2DM participants were divided into normoglycemic or pre diabetic group as follows: HbA1c<5.7, (normoglycemic) or HbA1c 5.7-6.4 (pre diabetes) \[11\]. 362 subjects were found to be pre diabetic. Almost similar number of normoglyceic and T2DM subjects was selected to be analyzed for comparison. All data were collected by personal interview and on the basis of a review of electronic medical data. Weight (kg) and height (cm) were measured by physician and nurse interviewers and recorded. Overweight and obesity were defined as BMI 25-29.9 and ≥30.0kg/m2 respectively \[22\]. Blood Pressure readings were within a gap of 15 minutes using a mercury sphygmomanometer by palpation and auscultation method in right arm in sitting position. Two readings were taken 15 min apart and the average of both the readings was taken for analysis. Hypertension (HTN) was also diagnosed based on anti HTN medications or having a prescription of antihypertensive drugs and were classified as Hypertensive irrespective of their current blood pressure reading or if the blood pressure was greater than 140/90 mmHg i.e. systolic BP more than 140 and diastolic BP more than 90 mm of Hg – Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines \[23\]. Laboratory assessments included HbA1c, lipids, creatinine and urinary micro albumin. HbA1c was expressed as percentage. High performance liquid chromatography was used. Fasting serum lipids were measured on a sample of blood after fasting for 14 hours. We used the enzymatic method for determining the cholesterol and trigylcerides levels. Diabetic nephropathy (DN) was assessed by measurement of mean albumin excretion rate (AER) on timed, overnight urine collections. We use a polyclonal radioimmunoassay for albumin measurement. DN is defined as an albumin excretion rate of >20g/min in a timed or a 24hr urine collection which is an equivalent to >30 mg/g creatinine in a random spot sample.Statistical AnalysisUnivariate analysis of demographic and clinical laboratory was accomplished using one-way analysis of variance (ANOVA) with posy hoc analysis between variables, to estimate the significance of different between groups where appropriate. Chi square (X2) test were used for categorical data comparison. The adjusted odds ratio (OR) with a 95% confidence interval (CI) was calculated. In order to evaluate the adjusted association of aforementioned factors on being normoglycemic or diabetic in relation to the pre diabetes group, a multinomial logistic regression model was fit, in which the categorical dependent variable was normoglycemia, pre diabetes or T2DM(with pre diabetes as the reference category), and significant variables in bivariate analyses were included as explanatory variables. Despite of the ordinal nature of the dependent variable, ordered logistic regression was not adjusted because the aim of the study was not the association of factors with a latent degree of diabetes but the differential profile of pre diabetes in front of normoglicemia and diabetes. As all the participants were the same age, adjusting for age was not applied. All statistical analyses were performed using SPSS Version 22.0. The difference between groups was considered significant when P<0.05. ResultsOf the 1095 participants analyzed, 796 were women (72.7%). Age was 45.1±11.1 and BMI was 30.7±5.7. Hypertension had been diagnosed in 415 (38.2%) participants. Blood measurements revealed the following values: creatinine 68.2±22.0umol/L, Urine microalbumin (g/min) 55.4±200.3, total cholesterol levels 4.9±1.0mmol/L, high density lipoprotein 1.3±0.3mmol/L, triglyceride levels 1.5±0.7 and low density lipoprotein 3.0 ±0.9mmol/L. Of the overall 1095 analyzed participants, pre diabetes was present in 362(33.1%), 368(33.6%) were classified as T2DM and 365 (33.3%) as normoglycemic. Table 1 shows the clinical characteristics and laboratory data of the three groups according to the predefined glycemic status. When comparing pre diabetic with normoglycemic and T2DM population, pre diabetic subjects were more likely to have hypertension and higher triglyceride than normoglycemic but less than T2DM subjects. In addition, prediabetic patients compared with T2DM ones had higher levels of low density lipoprotein and high density lipoprotein. In Table 2, logistic regression analysis showed no significant association of any of the covariables with normoglycemic subjects in front of the pre diabetic reference group, whereas the odds of being in the diabetic group gets multiplied by 7.56 for each unitary increase in male gender (p<0.0001, OR: 7.56, 95% CI 3.16-18.23). Also, individuals with hypertension had higher odds of being in the DM group than in the pre diabetic (p<0 .0001, OR: 6.06, 95% CI 3.25-11.28). Age of subjects had lower odds of being in the DM group than in the pre diabetic (p<0 .0001, OR: 0.85, 95% CI (0.82-0.89).Discussion This study showed that multiple risk factors are related to T2DM, but not to the pre diabetes group, including age, female gender and HTN. Generalization to all population could not be due to regionalized characteristics. In addition, it does not evaluate the healthcare services offered in our city. The size of our sample and the cross section type of the study should be of consideration. T2DM is a major health concern worldwide and is increasing in parallel with the obesity epidemic [24]. Prevalence of T2DM has increased dramatically with 1 million people reported to have been diagnosed with T2DM in 1994, increasing to 382 million by 2013, and with prediction of 592 million by 2035 [25]. Given that both genetic and environmental factors contribute to T2DM progression, it has been proposed that amongst increasing globalization, Asian ethnicities including Saudi Arabia have been unable to adapt to food and lifestyle related aspects of westernized culture [26]. Hence when matched for the same gender, age, and body weight, those with Asian ethnicity appear to have a greater risk of poor metabolic health than Caucasian counterparts including Europeans people [27]. This increased risk for T2DM has been reported in both Asians and Saudi Arabia [6-10,28]. Currently, the population with pre-diabetes has reached approximately 318 million around the world, accounting for 6.7% of the total number of adults. About 69.2% of the prediabetes population lives in low or middle-income countries [29]. Understanding pre diabetes may be crucial to reducing the global T2DM epidemic and is defined either by the presence of isolated impaired fasting glucose (I-IFG); or isolated impaired glucose tolerance (I-IGT); or both IFG and IGT. To maintain glucose homeostasis greater secretion of insulin is required from the pancreatic cells, and hence hyperinsulinemia develops. Prolonged hyperinsulinemia and/or fatty pancreas may in turn lead to the dysfunction of pancreatic cells, resulting in impaired insulin secretion [30]. Decreased insulin secretion and concomitant increased blood glucose levels consequently also lead to the reduced uptake of glucose by skeletal muscle, thereby enhancing muscle insulin resistance [31]. IFG, determined from fasting plasma glucose, occurs as a result of poor glucose regulation, resulting in raised blood glucose even after an overnight fast, while IGT is due to an individual being unable to respond to glucose consumed as part of a meal, resulting in increased postprandial blood glucose [11]. More recently, prediabetes has also been identified by mildly elevated HbA1c [32,33]. The younger age of T2DM in our cohort is consistent with that seen among other groups such as the Australians, the American Indian and Alaska natives [34-36]. Age of subjects had lower odds of being in the DM group than in the pre diabetic (p<0 .0001, OR: 0.85, 95% CI (0.82-0.89) in concordance with earlier reports [37,38]. Odds of being in the diabetic group gets multiplied by 7.56 for each unitary increase in male gender (p< 0.0001, OR: 7.56, 95% CI 3.16- 18.23). As seen in this study, majority of the female participants were either overweight (59.6%) or obese (78.6%). The reason for such an observation has not been completely elucidated but is proposed to be associated with obesity which is highly prevalent in the populations worldwide. Since obesity is closely linked to increased insulin resistance and decreased insulin sensitivity and higher risk of diabetes, arresting the obesity pandemic among our population should be a priority [39-41]. Special, culturally oriented community-based intervention programs need to be implemented. The frequency of pre diabetes in 27.2% of the female cases out of the total cohort in this study was six times higher than other, estimated to be 4.2% in 2006 [42,43]. Due to our small sample size, this is inconclusive and needs to be verified by extending our study to more of our communities. Nevertheless, our findings warrant special attention from the health authorities since although HbA1c is not as sensitive as IGT test, it has consistently been shown to be a good predictor of increased risk for cardiovascular diseases and T2DM in many populations around the world [44,45]. Previous cross-sectional studies have reported that multiple risk factors are related to pre-diabetes, Such as increased age, overweight, obesity, blood pressure, and dyslipidemia [37,46,47]. More importantly, impaired glucose tolerance was found to be an independent risk factor for cardiovascular disease, the hazard ratio of death was 2.22 (95% CI = 1.08–4.58), and arterial stiffness and pathological changes in the arterial intima occurred in the stage of IGT [48]. The participants in our study with pre-diabetes had higher BMI, more frequent HTN, higher triglyceride, frequent renal failure and DN than those without pre-diabetes but lower than participants with T2DM. logistic regression analysis showed no significant association of any of the covariables with normoglycemic subjects in front of the pre diabetic reference group, whereas the odds of being in the diabetic group gets multiplied by 7.56 for each unitary increase in male gender. Also, individuals with hypertension had higher odds of being in the DM group than in the pre diabetic. Age of subjects had lower odds of being in the DM group than in the pre diabetic which was consistent with earlier studies [37,38]. Previous studies have reported that overweight and obesity were the mainly factors contributing to insulin resistance, and insulin resistance was the basis of diabetes and other chronic diseases [49,50]. In the present study, BMI was significantly higher in the pre diabetes than the normal groups, p=0.03. When BMI was classified into three types. The total numbers of overweight and obese people in the pre-diabetes and normal groups were 293 and 291, respectively (the total number were 362 and 365, respectively), and there were statistically non significant differences in being overweight or obese between the pre-diabetes and normal groups (OR = 1.02, 95% CI = 0.86–1.21, p=0.8). Increasing evidence suggests that the excess body fat in overweight/obese people might lead to increased degradation of fat, which resulted in the production of large amounts of free fatty acids (FFAs). When the level of FFAs was higher in blood, the capacity of liver tissue for insulin-mediated glucose uptake and utilization was lower, so the blood glucose level was high in circulation [51]. In other words, high FFAs in the blood were one of the important pathogenic factors of obesity caused by insulin resistance [52]. The fact that BMI categories was not a significant factor in our study is the cohort mean BMI was in the obesity range, p=0.3. However, the mean BMI was significantly different between the studied groups, p=0.03. A high level of triglycerides was not significantly associated as a risk factor for developing pre-diabetes and T2DM (OR = 1.09, 95% CI = (0.60-2.00), P=0.8, 1.44(0.86-2.40),P=0.2) respectively. High level of triglycerides could increase the fat deposition in muscle, liver, and pancreas, and it could damage the function of mitochondria and induce oxidative stress which, in turn, could cause insulin resistance, but also lead to impaired islet B cell function [53]. Some studies suggested an interrelation between hyper triglyceridemia and insulin resistance and that they promote each other’s development [54,55]. In concordance with our result, in some epidemiological studies, for instance, the Framingham Heart Study, hyper triglyceridemia was more prevalent in type 2 diabetes mellitus patients than in the normal population, suggesting that hyper triglyceridemia is a causal factor of type 2 diabetes mellitus [56]. However, this paper was a cross-sectional study, thus it was impossible to determine the causal relationship between hyper triglyceridemia and pre-diabetes and T2DM. Hypertension was found to be a risk factor for T2DM but not for the pre diabetes group in our study (OR = 6.06, 95% CI =3.25- 11.28, p<0.0001, OR = 0.95, 95% CI = 0.50-1.82, p=0.9) respectively. A possible mechanism is that the activity of angiotensin II is increased in the circulatory system of patient with hypertension. Angiotensin II activates renin-angiotensin-aldosterone system and affects the function of the pancreatic islets, resulting in islet fibrosis and reduced synthesis of insulin, and ultimately leading to insulin resistance [57,58]. Insulin resistance can also aggravate the condition of hypertension. Directly or indirectly through the activity of renin-angiotensin-aldosterone system, insulin promotes renal tubular to reabsorb Na+ and water, leading to the increased blood volume and cardiac output; this is considered as one of reasons for the development of hypertension [59]. Interactions between abnormal glucose tolerance, hypertension, and dyslipidemia could impair endothelial cell and result in atherosclerosis or other cardiovascular complications. Therefore, the management of daily diet of people with pre-diabetes and the monitoring of body weight, blood lipids, and blood pressure is very important. Results of our investigation must be interpreted in light of some limitations such as the cross-sectional design, which does not let to establish any causal relation with respect to prediabetic state and only provides mere associations. Moreover, the classification of glycemic state was based on HbA1c, instead of its combination with a glucose tolerance test. Then, it is expected that the lack of glucose tolerance test data leads to a suboptimal estimation of glycemic state because normoglycemic group may include some individuals with impaired glucose tolerance that should have been included in pre diabetic group. Considering the goal population, a larger cohort would have probably provided a greater power of the statistical analyses. ConclusionThis study found the major clinical differences between pre diabetic and T2DM patients were the higher hypertension and hyper triglyceridenia in the T2DM patients. Clearly, despite the small sample size, this study has posed important public health issues that require immediate attention from the health authority. Unless immediate steps are taken to contain the increasing prevalence of obesity, diabetes, pre diabetes, the health care costs for chronic diseases will pose an enormous financial burden to the country.ConclusionUse a plant based protein blend diet pea - lowers levels of hunger hormone, ghrelin. Quinoa -chock full of anti-inflammatory compounds called flavonoids. Hemp - contains 20 amino acids, healthy omega fats and fiber (including 9 the body cannot make on its own). Coconut - packed full of healthy saturated fats that go straight to the liver for a quick energy boost. Monk fruit - contains powerful antioxidants called mogrosides. Cinnamon - clinically proven to support healthy blood sugar levels AND healthy triglyceride levels. Vanilla Bean - loaded with minerals like magnesium, potassium, and calcium. Vanilla also has mood-boosting and energy enhancing effects on body. Zero alcohol use.For more Lupine Publishers Open Access Journals Please visit our website: https://lupinepublishersgroup.com/ For more Open Access Journal of Diabetes and Obesity articles Please Click Here: https://lupinepublishers.com/diabetes-obesity-journal/ To Know More About Open Access Publishers Please Click on Lupine Publishers |
Following an in-depth state aid investigation launched in June 2014, the European Commission has concluded that two tax rulings issued by Ireland to Apple have substantially and artificially lowered the tax paid by Apple in Ireland since 1991. The rulings endorsed a way to establish the taxable profits for two Irish incorporated companies of the Apple group (Apple Sales International and Apple Operations Europe), which did not correspond to economic reality: almost all sales profits recorded by the two companies were internally attributed to a "head office". The Commission's assessment showed that these "head offices" existed only on paper and could not have generated such profits. These profits allocated to the "head offices" were not subject to tax in any country under specific provisions of the Irish tax law, which are no longer in force. As a result of the allocation method endorsed in the tax rulings, Apple only paid an effective corporate tax rate that declined from 1% in 2003 to 0.005% in 2014 on the profits of Apple Sales International.As Tim Cook noted in a blistering letter posted on Apple's website, in 1980 Apple set up a factory in Cork, Ireland, and the company has, in Cook's words, "Operated continuously in Cork ever since." It wasn't all smooth sailing though. Specifically, by 1990 Apple had expanded its Cork operations to cover most of its European business in addition to its Cork factory, and the company asked for a meeting with the government to discuss its tax situation; the Financial Times has the notes:
This selective tax treatment of Apple in Ireland is illegal under EU state aid rules, because it gives Apple a significant advantage over other businesses that are subject to the same national taxation rules. The Commission can order recovery of illegal state aid for a ten-year period preceding the Commission's first request for information in 2013. Ireland must now recover the unpaid taxes in Ireland from Apple for the years 2003 to 2014 of up to €13 billion, plus interest.
In fact, the tax treatment in Ireland enabled Apple to avoid taxation on almost all profits generated by sales of Apple products in the entire EU Single Market. This is due to Apple's decision to record all sales in Ireland rather than in the countries where the products were sold. This structure is however outside the remit of EU state aid control. If other countries were to require Apple to pay more tax on profits of the two companies over the same period under their national taxation rules, this would reduce the amount to be recovered by Ireland.
[The tax adviser’s employee representing Apple] mentioned by way of background information that Apple was now the largest employer in the Cork area with 1,000 direct employees and 500 persons engaged on a subcontract basis. It was stated that the company is at present reviewing its worldwide operations and wishes to establish a profit margin on its Irish operations. [The tax adviser’s employee representing Apple] produced the accounts prepared for the Irish branch for the accounting period ended […] 1989 which showed a net profit of $270m on a turnover of $751m. It was submitted that no quoted Irish company produced a similar net profit ratio. In [the tax adviser’s employee representing Apple]’s view the profit is derived from three sources – technology, marketing and manufacturing. Only the manufacturing element relates to the Irish branch.It's hard to view that "background information" as anything other than an implied threat that Apple was willing to leave Cork and those 1,500 employees unless it got a better tax deal, and the Irish government was happy to come to an agreement: it would only collect taxes on the parts of Apple's business related to manufacturing in Cork. Over the following decades Apple would come to use the non-manufacturing (and thus non-taxed) portion of its Cork subsidiary as a conduit for all of its non-U.S. revenue; that revenue stream has obviously grown humongously, while Cork's manufacturing revenue has not (although, and probably not coincidentally, it remains the only factory Apple owns), resulting in the European Commission's complaint that Apple only paid a tax rate of 0.005% on revenue from its Ireland-based Apple Sales International subsidiary.
We conducted an international double-blind, controlled trial in 674 centers in 33 countries, in which 13,199 patients with a nonsevere ischemic stroke or high-risk transient ischemic attack who had not received intravenous or intraarterial thrombolysis and were not considered to have had a cardioembolic stroke were randomly assigned within 24 hours after symptom onset, in a 1:1 ratio, to receive either ticagrelor (180 mg loading dose on day 1 followed by 90 mg twice daily for days 2 through 90) or aspirin (300 mg on day 1 followed by 100 mg daily for days 2 through 90). The primary end point was the time to the occurrence of stroke, myocardial infarction, or death within 90 daysDouble-blind means that the study participants (the patients) and the study investigators (the authors) are unaware of whether the patient belongs to the ticagrelor group or the aspirin group. Their primary end point here (which is the result of the intervention) is whether or not patients had a stroke, a myocardial infarction (a heart attack) or died within 90 days. This is what is known as a composite endpoint – and they kind of suck. You have to dig deeper in the study itself to make sure that the intervention was statistically significant for all endpoints and not just 1 or 2 instead of all three. This can be a problem when study investigators lump in endpoints that aren't really the same (like getting hospitalized versus dying) although strokes, heart attacks and dying all suck pretty hard (with 1 being especially shitty). What does statistically significant mean?
During the 90 days of treatment, a primary end-point event occurred in 442 of the 6589 patients (6.7%) treated with ticagrelor, versus 497 of the 6610 patients (7.5%) treated with aspirin (hazard ratio, 0.89; 95% confidence interval [CI], 0.78 to 1.01; P=0.07). Ischemic stroke occurred in 385 patients (5.8%) treated with ticagrelor and in 441 patients (6.7%) treated with aspirin (hazard ratio, 0.87; 95% CI, 0.76 to 1.00). Major bleeding occurred in 0.5% of patients treated with ticagrelor and in 0.6% of patients treated with aspirin, intracranial hemorrhage in 0.2% and 0.3%, respectively, and fatal bleeding in 0.1% and 0.1%A hazard ratio a fancy statistic that study investigators compile from their data (% of endpoint happening in group A compared to % of endpoint happening in group B). The 95% confidence interval is another way to interpret the data, although it is a bit more expansive and gives a better picture of what really happened in the trial. For this trial, they report it is between 0.78 to 1.01. If you notice – the hazard ratio reported is smack in the middle of this – so imagine that this hazard ratio is zenith of a bell curve and the Confidence Interval is the rest of the curve. Visually - here (ridiculously labeled bell curve on purpose because it's hilarious).
In our trial involving patients with acute ischemic stroke or transient ischemic attack, ticagrelor was not found to be superior to aspirin in reducing the rate of stroke, myocardial infarction, or death at 90 days.Not statistically significant. The authors cannot say with 95% or more confidence that this drug actually helps.
Two of the three elements of the ACA’s “premium stabilization program,” reinsurance and risk corridors, are set to expire in 2017, leaving risk adjustment alone to protect plans against risk of high-cost cases. This paper considers potential modifications of the HHS risk adjustment methodology to maintain plan protection against risk from high-cost cases within the current regulatory framework. We show analytically that modifications of the transfer formula and of the risk adjustment model itself are mathematically equivalent to a conventional actuarially fair reinsurance policy. Furthermore, closely related modifications of the transfer formula or the risk adjustment model can improve on conventional reinsurance by figuring transfers or estimating risk adjustment model weights recognizing the presence of a reinsurance function. In the empirical section, we estimate risk adjustment models with an updated and selected version of the data used to calibrate the federal payment models, and show, using simulation methods, that proposed modifications improve fit at the person level and protect small insurers against high-cost risk better than conventional reinsurance. We simulate various “attachment points” for the reinsurance equivalent policies and quantify the tradeoffs of higher and lower attachment points.
The home-market effect, first hypothesized by Linder (1961) and later formalized by Krugman (1980), is the idea that countries with larger demand for some products at home tend to have larger sales of the same products abroad. In this paper, we develop a simple test of the home-market effect using detailed drug sales data from the global pharmaceutical industry. The core of our empirical strategy is the observation that a country’s exogenous demographic composition can be used as a predictor of the diseases that its inhabitants are most likely to die from and, in turn, the drugs that they are most likely to demand. We find that the correlation between predicted home demand and sales abroad is positive and greater than the correlation between predicted home demand and purchases from abroad. In short, countries tend to be net sellers of the drugs that they demand the most, as predicted by Linder (1961) and Krugman (1980).
I use a field experiment in rural Kenya to study how temporary incentives to save impact long-run economic outcomes. Study participants randomly selected to receive large temporary interest rates on an individual bank account had significantly more income and assets 2.5 years after the interest rates expired. These changes are much larger than the short-run impacts on experimental bank account use and almost entirely driven by growth in entrepreneurship. Temporary interest rates directed to joint bank accounts had no detectable long-run impacts on entrepreneurship or income, but increased investment in household public goods and spousal consensus over finances.Unintended Consequences of Rewards for Student Attendance: Results from a Field Experiment in Indian Classrooms: Sujata Visaria, Rajeev Dehejia, Melody M. Chao, Anirban Mukhopadhyay
In an experiment in non-formal schools in Indian slums, a reward scheme for attending a target number of school days increased average attendance when the scheme was in place, but had heterogeneous effects after it was removed. Among students with high baseline attendance, the incentive had no effect on attendance after it was discontinued, and test scores were unaffected. Among students with low baseline attendance, the incentive lowered post-incentive attendance, and test scores decreased. For these students, the incentive was also associated with lower interest in school material and lower optimism and confidence about their ability. This suggests incentives might have unintended long-term consequences for the very students they are designed to help the most.Can Natural Gas Save Lives? Evidence from the Deployment of a Fuel Delivery System in a Developing Country: Resul Cesur, Erdal Tekin, Aydogan Ulker
There has been a widespread displacement of coal by natural gas as space heating and cooking technology in Turkey in the last two decades, triggered by the deployment of natural gas networks. In this paper, we examine the impact of this development on mortality among adults and the elderly. Our research design exploits the variation in the timing of the deployment and the intensity of expansion of natural gas networks at the provincial level using data from 2001 to 2014. The results indicate that the expansion of natural gas services has caused significant reductions in both the adult and the elderly mortality rates. According to our point estimates, a one-percentage point increase in the rate of subscriptions to natural gas services would lower the overall mortality rate by 1.4 percent, the adult mortality rate by 1.9 percent, and the elderly mortality rate by 1.2 percent. These findings are supported by our auxiliary analysis, which demonstrates that the expansion of natural gas networks has indeed led to a significant improvement in air quality. Furthermore, we show that the mortality gains for both the adult and the elderly populations are primarily driven by reductions in cardio-respiratory deaths, which are more likely to be due to conditions caused or exacerbated by air pollution. Finally, our analysis does not reveal any important gender differences in the estimated relationship between the deployment of natural gas networks and mortality.
This paper presents a novel methodology for estimating impacts on domestic supply of oil and natural gas arising from changes in the tax treatment of oil and gas production. It corrects a downward bias when the ratio of aggregate tax expenditures to domestic production is used to measure the subsidy value of tax preferences. That latter approach underestimates the value of the tax preferences to firms by ignoring the time value of money.Estimating Path Dependence in Energy Transitions: Kyle C. Meng
The paper introduces the concept of the equivalent price impact, the change in price that has the same impact on aggregate drilling decisions as a change in the tax provisions for oil and gas drilling and production. Using this approach I find that removing the three largest tax preferences for the oil and gas industry would likely have very modest impacts on global oil production, consumption or prices. Domestic oil and gas production is estimated to decline by 4 to 5 percent over the long run. Global oil prices would rise by less than one percent. Domestic natural gas prices are estimated to rise by 7 to 10 percent. Changes to these tax provisions would have modest to negligible impacts on greenhouse gas emissions or energy security.
Addressing climate change requires transitioning away from coal-based energy. Recent structural change models demonstrate that temporary interventions could induce permanent fuel switching when transitional dynamics exhibit strong path dependence. Exploiting changes in local coal supply driven by subsurface coal accessibility, I find that transitory shocks have strengthening effects on the fuel composition of two subsequent generations of U.S. electricity capital. To facilitate a structural interpretation, I develop a model which informs: tests that find scale effects as the relevant mechanism; recovery of the elasticity of substitution between coal and non-coal electricity; and simulations of future carbon emissions following temporary interventions.Trophy Hunting vs. Manufacturing Energy: The Price-Responsiveness of Shale Gas: Richard G. Newell, Brian C. Prest, Ashley Vissing
We analyze the relative price elasticity of unconventional versus conventional natural gas extraction. We separately analyze three key stages of gas production: drilling wells, completing wells, and producing natural gas from the completed wells. We find that the important margin is drilling investment, and neither production from existing wells nor completion times respond strongly to prices. We estimate a long-run drilling elasticity of 0.7 for both conventional and unconventional sources. Nonetheless, because unconventional wells produce on average 2.7 times more gas per well than conventional ones, the long-run price responsiveness of supply is almost 3 times larger for unconventional compared to conventional gas.Price of Long-Run Temperature Shifts in Capital Markets: Ravi Bansal, Dana Kiku, Marcelo Ochoa
We use the forward-looking information from the US and global capital markets to estimate the economic impact of global warming, specifically, long-run temperature shifts. We find that global warming carries a positive risk premium that increases with the level of temperature and that has almost doubled over the last 80 years. Consistent with our model, virtually all US equity portfolios have negative exposure (beta) to long-run temperature fluctuations. The elasticity of equity prices to temperature risks across global markets is significantly negative and has been increasing in magnitude over time along with the rise in temperature. We use our empirical evidence to calibrate a long-run risks model with temperature-induced disasters in distant output growth to quantify the social cost of carbon emissions. The model simultaneously matches the projected temperature path, the observed consumption growth dynamics, discount rates provided by the risk-free rate and equity market returns, and the estimated temperature elasticity of equity prices. We find that the long-run impact of temperature on growth implies a significant social cost of carbon emissions.What Would it Take to Reduce US Greenhouse Gas Emissions 80% by 2050?: Geoffrey Heal
I investigate the cost and feasibility of reducing US GHG emissions by 80% from 2005 levels by 2050. The US has stated in its Paris COP 21 submission that this is its aspiration, and Hillary Clinton has chosen this as one of the goals of her climate policy. I suggest that this goal can be reached at a cost in the range of $42 to $176 bn/year, but that it is challenging. I assume that the goal is to be reached by extensive use of solar PV and wind energy (66% of generating capacity), in which case the cost of energy storage plays a key role in the overall cost. I conclude tentatively that more limited use of renewables (less than 50%) together with increased use of nuclear power might be less costly.Collective Intertemporal Choice: the Possibility of Time Consistency: Antony Millner, Geoffrey Heal
Recent work on collective intertemporal choice suggests that non-dictatorial social preferences are generically time inconsistent. We argue that this claim conflates time consistency with two distinct properties of preferences: stationarity and time invariance. While the conjunction of time invariance and stationarity implies time consistency, the converse does not hold. Although social preferences cannot be stationary, they may be time consistent if time invariance is abandoned. If individuals are discounted utilitarians, revealed preference provides no guidance on whether social preferences should be time consistent or time invariant. Nevertheless, we argue that time invariant social preferences are often normatively and descriptively problematic.
We use new timestamp data from the two Securities Information Processors (SIPs) to examine SIP reporting latencies for quote and trade reports. Reporting latencies average 1.13 milliseconds for quotes and 22.84 milliseconds for trades. Despite these latencies, liquidity-taking orders gain on average $0.0002 per share when priced at the SIP-reported national best bid or offer (NBBO) rather than the NBBO calculated using exchanges’ direct data feeds. Trading surrounding SIP-priced trades shows little evidence that fast traders initiate these liquidity-taking orders to pick-off stale quotes. These findings contradict claims that fast traders systematically exploit traders who transact at the SIP NBBO.Measuring Institutional Investors' Skill from Their Investments in Private Equity: Daniel R. Cavagnaro, Berk A. Sensoy, Yingdi Wang, Michael S. Weisbach
Using a large sample of institutional investors’ private equity investments in venture and buyout funds, we estimate the extent to which investors’ skill affects returns from private equity investments. We first consider whether investors have differential skill by comparing the distribution of investors’ returns relative to the bootstrapped distribution that would occur if funds were randomly distributed across investors. We find that the variance of actual performance is higher than the bootstrapped distribution, suggesting that higher and lower skilled investors consistently outperform and underperform. We then use a Bayesian approach developed by Korteweg and Sorensen (2015) to estimate the incremental effect of skill on performance. The results imply that a one standard deviation increase in skill leads to about a three percentage point increase in returns, suggesting that variation in institutional investors’ skill is an important driver of their returns.Geographic Diversification and Banks' Funding Costs: Ross Levine, Chen Lin, Wensi Xie
We assess the impact of the geographic expansion of bank assets on the cost of banks’ interest-bearing liabilities. Existing research suggests that expansion can both intensify agency problems that increase funding costs and facilitate risk diversification that decreases funding costs. Using a newly developed identification strategy, we discover that the geographic expansion of banks across U.S. states lowered their funding costs, especially when banks are headquartered in states with lower macroeconomic covariance with the overall U.S. economy. The results are consistent with the view that geographic expansion offers large risk diversification opportunities that reduce funding costs.The I Theory of Money: Markus K. Brunnermeier, Yuliy Sannikov
A theory of money needs a proper place for financial intermediaries. Intermediaries diversify risks and create inside money. In downturns, micro-prudent intermediaries shrink their lending activity, fire-sell assets and supply less inside money, exactly when money demand rises. The resulting Fisher disinflation hurts intermediaries and other borrowers. Shocks are amplified, volatility spikes and risk premia rise. Monetary policy is redistributive. Accommodative monetary policy that boosts assets held by balance sheet-impaired sectors, recapitalizes them and mitigates the adverse liquidity and disinflationary spirals. Since monetary policy cannot provide insurance and control risk-taking separately, adding macroprudential policy that limits leverage attains higher welfare.Risk Preferences and The Macro Announcement Premium: Hengjie Ai, Ravi Bansal
The paper develops a theory for equity premium around macroeconomic announcements. Stock returns realized around pre-scheduled macroeconomic announcements, such as the employment report and the FOMC statements, account for 55% of the market equity premium during the 1961-2014 period, and virtually 100% of it during the later period of 1997-2014, where more announcement data are available. We provide a characterization theorem for the set of intertemporal preferences that generate a positive announcement premium. Our theory establishes that the announcement premium identifies a significant deviation from expected utility and constitutes an asset market based evidence for a large class of non-expected models that features aversion to ”Knightian uncertainty”, for example, Gilboa and Schmeidler [30]. We also present a dynamic model to account for the evolution of equity premium around macroeconomic announcements.Cash Flow Duration and the Term Structure of Equity Returns: Michael Weber
The term structure of equity returns is downward-sloping: stocks with high cash flow duration earn 1.10% per month lower returns than short-duration stocks in the cross section. I create a measure of cash flow duration at the firm level using balance sheet data to show this novel fact. Factor models can explain only 50% of the return differential, and the difference in returns is three times larger after periods of high investor sentiment. I use institutional ownership as a proxy for short-sale constraints, and find the negative cross-sectional relationship between cash flow duration and returns is only contained within short-sale constrained stocks.Assessing Point Forecast Accuracy by Stochastic Error Distance: Francis X. Diebold, Minchul Shin
We propose point forecast accuracy measures based directly on distance of the forecast-error c.d.f. from the unit step function at 0 ("stochastic error distance," or SED). We provide a precise characterization of the relationship between SED and standard predictive loss functions, and we show that all such loss functions can be written as weighted SED's. The leading case is absolute-error loss. Among other things, this suggests shifting attention away from conditional-mean forecasts and toward conditional-median forecasts.
There is continuing controversy about the extent to which publicly insured children are treated differently than privately insured children, and whether differences in treatment matter. We show that on average, hospitals are less likely to admit publicly insured children than privately insured children who present at the ER and the gap grows during high flu weeks, when hospital beds are in high demand. This pattern is present even after controlling for detailed diagnostic categories and hospital fixed effects, but does not appear to have any effect on measurable health outcomes such as repeat ER visits and future hospitalizations. Hence, our results raise the possibility that instead of too few publicly insured children being admitted during high flu weeks, there are too many publicly and privately insured children being admitted most of the time.Early Effects of the 2010 Affordable Care Act Medicaid Expansions on Federal Disability Program Participation: Pinka Chatterji, Yue Li
We test whether early Affordable Care Act (ACA) Medicaid expansions in Connecticut (CT), Minnesota (MN), California (CA), and the District of Columbia (DC) affected SSI applications, SSI and DI awards, and the number of SSI and DI beneficiaries. We use a difference-in-difference (DD) approach, comparing SSI/DI outcomes pre and post each early Medicaid expansion (“Early Expanders”) to SSI/DI outcomes in states that expanded Medicaid in January 2014 (“Later Expanders”). We also use a synthetic control approach, in which we examine SSI/DI outcomes before and after the Medicaid expansion in each Early Expander state, utilizing a weighted combination of Later Expanders as a comparison group. In CT, the Medicaid expansion is associated a statistically significant, 7 percent reduction in SSI beneficiaries; this finding is consistent across the DD and synthetic control methods. For DC, MN and CA, we do not find consistent evidence that the Medicaid expansions affected disability-related outcomes.The Pros and Cons of Sick Pay Schemes: Testing for Contagious Presenteeism and Noncontagious Absenteeism Behavior: Stefan Pichler, Nicolas R. Ziebarth
This paper provides an analytical framework and uses data from the US and Germany to test for the existence of contagious presenteeism and negative externalities in sickness insurance schemes. The first part exploits high-frequency Google Flu data and the staggered implementation of U.S. sick leave reforms to show in a reduced-from framework that population-level influenza-like disease rates decrease after employees gain access to paid sick leave. Next, a simple theoretical framework provides evidence on the underlying behavioral mechanisms. The model theoretically decomposes overall behavioral labor supply adjustments ('moral hazard') into contagious presenteeism and noncontagious absenteeism behavior and derives testable conditions. The last part illustrates how to implement the model exploiting German sick pay reforms and administrative industry-level data on certified sick leave by diagnoses. It finds that the labor supply elasticity for contagious diseases is significantly smaller than for noncontagious diseases. Under the identifying assumptions of the model, in addition to the evidence from the U.S., this finding provides indirect evidence for the existence of contagious presenteeism.Immunization and Moral Hazard: The HPV Vaccine and Uptake of Cancer Screening: Ali Moghtaderi, Avi Dor
Immunization can cause moral hazard by reducing the cost of risky behaviors. In this study, we examine the effect of HPV vaccination for cervical cancer on participation in the Pap test, which is a diagnostic screening test to detect potentially precancerous and cancerous process. It is strongly recommended for women between 21-65 years old even after taking the HPV vaccine. A reduction in willingness to have a Pap test as a result of HPV vaccination would signal the need for public health intervention. The HPV vaccination is recommended for women age eleven to twelve for regular vaccination or for women up to age 26 not vaccinated previously. We present evidence that probability of vaccination changes around this threshold. We identify the effect of vaccination using a fuzzy regression discontinuity design, centered on the recommended vaccination threshold age. The results show no evidence of ex ante moral hazard in the short-run. Sensitivity analyses using alternative specifications and subsamples are in general agreement. The estimates show that women who have been vaccinated are actually more likely to have a Pap test in the short-run, possibly due to increased awareness of its benefits.The Rise in Life Expectancy, Health Trends among the Elderly, and the Demand for Care - A Selected Literature Review: Bjorn Lindgren
The objective is to review the evidence on (a) ageing and health and (b) the demand for health- and social services among the elderly. Issues are: does health status of the elderly improve over time, and how do the trends in health status of the elderly affect the demand for health- and elderly care? It is not a complete review, but it covers most of recent empirical studies.
The reviewed literature provides strong evidence that the prevalence of chronic disease among the elderly has increased over time. There is also fairly strong evidence that the consequences of disease have become less problematic due to medical progress: decreased mortality risk, milder and slower development over time, making the time with disease (and health-care treatment) longer but less troublesome than before. Evidence also suggests the postponement of functional limitations and disability. Some of the reduction in disability can be attributed to improvements in treatments of chronic diseases, but it is also due to the increased use of assistive technology, accessibility of buildings, etc. The results indicate that the ageing individual is expected to need health care for a longer period of time than previous generations but elderly care for a shorter.
This article examines patterns of entry and exit in a relatively homogeneous product market to investigate the impact of entry on incumbent firms and market structure. In particular, we are interested in whether the organizational form of entrants matters for the competitive decisions of incumbents. We assess the impact of chain stores on independent retailers in the Melbourne coffee market using annual data on the location and entry status of 4,768 coffee retailers between 1991 and 2010. The long panel enables us to include market fixed effects to address the endogeneity of store locations. Logit regressions indicate that chain stores have no discernible effect on the exit or entry decisions of independent stores. However, each additional chain store increases the probability of another chain store exiting by 2.5 percentage points, and each additional independent cafe increases the probability of another independent cafe exiting by 0.5 percent. These findings imply that neighboring independents and chains operate almost as though they are in separate markets. We offer additional analysis suggesting consumer information as a cause of this differentiation.
We study wealth inequality in childhood using Danish wealth records from three decades. While teenagers have some earnings, we estimate that transfers account for at least 50 percent of wealth at age 18, and much more so for the rich children. Inheritance from grandparents does not appear quantitatively important, but we do find evidence that children receive inter vivos transfers. While wealth holdings are small in childhood, they have strong predictive power for future wealth in adulthood. Asset holdings at age 18 are more informative than parental wealth in predicting wealth of children many years later when they are in their 40s. Hence, childhood wealth reveals significant heterogeneity in the intergenerational transmission of wealth, which is not simply captured by parental wealth alone. We investigate why this is the case and rule out that childhood wealth in itself can accumulate enough to explain later wealth inequality. Our evidence indicates that childhood wealth is a proxy for a broad set of circumstances related to intergenerational transmission and future wealth accumulation, including savings/investment behavior and additional transfers.Human Capital Investments and Expectations about Career and Family: Matthew Wiswall, Basit Zafar
This paper studies how individuals "believe" human capital investments will affect their future career and family life. We conducted a survey of high-ability currently enrolled college students and elicited beliefs about how their choice of college major, and whether to complete their degree at all, would affect a wide array of future events, including future earnings, employment, marriage prospects, potential spousal characteristics, and fertility. We find that students perceive large "returns" to human capital not only in their own future earnings, but also in a number of other dimensions (such as future labor supply and potential spouse's earnings). In a recent follow-up survey conducted six years after the initial data collection, we find a close connection between the expectations and current realizations. Finally, we show that both the career and family expectations help explain human capital choices.Long-Term Orientation and Educational Performance: David Figlio, Paola Giuliano, Umut Özek, Paola Sapienza
We use remarkable population-level administrative education and birth records from Florida to study the role of Long-Term Orientation on the educational attainment of immigrant students living in the US. Controlling for the quality of schools and individual characteristics, students from countries with long term oriented attitudes perform better than students from cultures that do not emphasize the importance of delayed gratification. These students perform better in third grade reading and math tests, have larger test score gains over time, have fewer absences and disciplinary incidents, are less likely to repeat grades, and are more likely to graduate from high school in four years. Also, they are more likely to enroll in advanced high school courses, especially in scientific subjects. Parents from long term oriented cultures are more likely to secure better educational opportunities for their children. A larger fraction of immigrants speaking the same language in the school amplifies the effect of Long-Term Orientation on educational performance. We validate these results using a sample of immigrant students living in 37 different countries.Employment Effects of the ACA Medicaid Expansions: Pauline Leung, Alexandre Mas
We examine whether the recent expansions in Medicaid from the Affordable Care Act reduced “employment lock” among childless adults who were previously ineligible for public coverage. We compare employment in states that chose to expand Medicaid versus those that chose not to expand, before and after implementation. We find that although the expansion increased Medicaid coverage by 3.0 percentage points among childless adults, there was no significant impact on employment.Family Descent as a Signal of Managerial Quality: Evidence from Mutual Funds: Oleg Chuprinin, Denis Sosyura
We study the relation between mutual fund managers’ family backgrounds and their professional performance. Using hand-collected data from individual Census records on the wealth and income of managers’ parents, we find that managers from poor families deliver higher alphas than managers from rich families. This result is robust to alternative measures of fund performance, such as benchmark-adjusted return and value extracted from capital markets. We argue that managers born poor face higher entry barriers into asset management, and only the most skilled succeed. Consistent with this view, managers born rich are more likely to be promoted, while those born poor are promoted only if they outperform. Overall, we establish the first link between family descent of investment professionals and their ability to create value.
How does the partisan composition of an electorate impact the policies adopted by an elected representative? We take advantage of variation in the partisan composition of Congressional districts stemming from Census-initiated redistricting in the 1990’s, 2000’s, and 2010’s. Using this variation, we examine how an increase in Democrat share within a district impacts the district representative’s roll call voting. We find that an increase in Democrat share within a district causes more leftist roll call voting. This occurs because a Democrat is more likely to hold the seat, but also because – in contrast to existing empirical work – partisan composition has a direct effect on the roll call voting of individual representatives. This is true of both Democrats and Republicans. It is also true regardless of the nature of the redistricting (e.g., whether the redistricting was generated by a partisan or non-partisan process).The Marginal Propensity to Consume Over the Business Cycle: Tal Gross, Matthew J. Notowidigdo, Jialan Wang
This paper estimates how the marginal propensity to consume (MPC) varies over the business cycle by exploiting exogenous variation in credit card borrowing limits. Ten years after an individual declares Chapter 7 bankruptcy, the record of the bankruptcy is removed from her credit report, generating an immediate and persistent increase in credit score. We study the effects of “bankruptcy flag” removal using a sample of over 160,000 bankruptcy filers whose flags were removed between 2004 and 2011. We document that in the year following flag removal, credit card limits increase by $780 and credit card balances increase by roughly $290, implying an “MPC out of liquidity” of 0.37. We find a significantly higher MPC during the Great Recession, with an average MPC roughly 20–30 percent larger between 2007 and 2009 compared to surrounding years. We find no evidence that the counter-cyclical variation in the average MPC is accounted for by compositional changes or by changes over time in the supply of credit following bankruptcy flag removal. These results are consistent with models where liquidity constraints bind more frequently during recessions.
We investigate the strength of the Penn effect in the most recent version of the Penn World Tables (PWTs). We find that the earlier findings of a Penn effect are confirmed, but that there is some evidence for nonlinearity. Developed and developing countries display different types of nonlinear behaviors. The nonlinear behaviors are likely attributable to differences across countries and do not change when additional control variables are added. We confirm earlier findings of large RMB misalignment in the mid-2000’s, but find that by 2011, the RMB seems near equilibrium. While the Penn effect is quite robust across datasets, estimated misalignment can noticeably change from a linear to a nonlinear specification, and from dataset to dataset.From Chronic Inflation to Chronic Deflation: Focusing on Expectations and Liquidity Disarray Since WWII: Guillermo A. Calvo
The paper discusses policy relevant models, going from (1) chronic inflation in the 20th century after WWII, to (2) credit sudden stop episodes that got exacerbated in Developed Market economies after the 2008 Lehman crisis, and appear to be associated with chronic deflation. The discussion highlights the importance of expectations and liquidity, and warns about the risks of relegating liquidity to a secondary role, as has been the practice in mainstream macro models prior to the Great Recession.
This paper shows that accounting for variation in mistakes can be crucial for welfare analysis. Focusing on consumer underreaction to not-fully-salient sales taxes, we show theoretically that the efficiency costs of taxation are amplified by 1) individual differences in underreaction and 2) the degree to which attention is increasing with the size of the tax rate. To empirically assess the importance of these issues, we implement an online shopping experiment in which 2,998 consumers--matching the U.S. adult population on key demographics--purchase common household products, facing tax rates that vary in size and salience. We find that: 1) there are significant individual differences in underreaction to taxes. Accounting for this heterogeneity increases the efficiency cost of taxation estimates by at least 200%, as compared to estimates generated from a representative agent model. 2) Tripling existing sales tax rates roughly doubles consumers' attention to taxes. Our results provide new insights into the mechanisms and determinants of boundedly rational processing of not-fully-salient incentives, and our general approach provides a framework for robust behavioral welfare analysis.
This paper provides direct empirical evidence on the relationship between technology and firms’ global sourcing strategies. Using new data on U.S. firms’ decisions to contract for manufacturing services from domestic or foreign suppliers, I show that changes in firm use of communication technology between 2002 to 2007 can explain almost one quarter of the increase in fragmentation over the period. The effect of firm technology also differs significantly across industries; in 2007, it is 20 percent higher, relative to the mean, in industries with production specifications that are easier to codify in an electronic format. These patterns suggest that technology lowers coordination costs, though its effect is disproportionately higher for domestic rather than foreign sourcing. The larger impact on domestic fragmentation highlights its importance as an alternative to offshoring, and can be explained by complementarities between technology and worker skill. High technology firms and industries are more likely to source from high human capital countries, and the differential impact of technology across industries is strongly increasing in country human capital.Heterogeneous Frictional Costs Across Industries in Cross-border Mergers and Acquisitions: Bruce A. Blonigen, Donghyun Lee
While there has been significant research to explore the determinants (and frictions) of foreign direct investment (FDI), past literature primarily focuses on country-wide FDI patterns with little examination of sectoral heterogeneity in FDI. Anecdotally, there is substantial sectoral heterogeneity in FDI patterns. For example, a substantial share of FDI (around 40-50%) is in the manufacturing sector, yet manufacturing accounts for a relatively small share of production activity in the developed economies responsible for most cross-border M&A. In this paper, we extend the Head and Ries (2008) model of cross-border M&A to account for sectoral heterogeneity and estimate the varying effects of FDI frictions across sectors using cross-border M&A data spanning 1985 through 2013. We find that non-manufacturing sectors generally have greater sensitivity to cross-border M&A frictions than is true for manufacturing, including such frictions as physical distance, cultural distance, and common language. Tradeability is positively associated with greater cross-border M&A, and is an additional friction for the many non-manufacturing sectors because they consist of mainly non-tradeable goods.
For Cox models where you want to express a hazard ratio for some particular percentage change in a continuous predictor, it can be useful to make an appropriate change of base of the logarithm before you perform the regression. For example, $\log_22$=1, so a doubling of cost would represent a 1-unit increase in the $\log_2$ scale. The Hazard ratio (HR) is one of the measures that in clinical research are most often difficult to interpret for students and researchers. In this post we will try to explain this measure in terms of its practical use. You should know what the Hazard Ratio is, but we will repeat it again. Let’s take […] Hazard Ratio Calculator. Use this hazard ratio calculator to easily calculate the relative hazard, confidence intervals and p-values for the hazard ratio (HR) between an exposed/treatment and control group. One and two-sided confidence intervals are reported, as well as Z-scores based on the log-rank test. Hazard ratio is not always valid …. Nelson-Aalen cumulative hazard estimates, by group analysis time 0 10 20 30 40 0.00 1.00 2.00 3.00 4.00 group 0 group 1 Hazard Ratio = .71 Kaplan-Meier survival estimates, by group analysis time 0 10 20 30 40 0.00 0.25 0.50 0.75 1.00 group 0 group 1 Hazard ratio is reported most commonly in time-to-event analysis or survival analysis (i.e. when we are interested in knowing how long it takes for a particular event/outcome to occur). Hazard ratio can be obtained and calculated from the Cox regression - or Cox proportional hazard regression model. The hazard ratio, sometimes called a relative hazard, is typically used to compare time to event data between two treatment groups. The hazard ratio of death for the intervention group compared with the control group was 0.46 (0.22 to 0.95). As time progresses, percentage survival decreases in both groups. Plotting curves on the graphs allows statistical analysis to be performed to calculate the hazard (absolute risk over time) for each group. Dividing the hazard in the treatment group by the hazard in the control group produces the hazard ratio. Hazard Ratio Age group Cause-speci c HR P-value 95% CI 18-59 1.00 - - 60-84 0.96 0.073 0.92 to 1.01 85+ 2.11 <0.001 1.93 to 2.32 Table:Cause-speci c hazard ratios for breast cancer. Sally R. Hinchli e University of Leicester, 2012 14 / 34 Each one faces an annual risk of death, whose technical name is their ‘hazard’. Therefore a hazard ratio of 1.13 means that, for two people like Mike and Sam who are similar apart from the extra meat, the one with the risk factor – Mike - has a 13% increased annual risk of death over the follow-up period (around 20 years). The numerical value can be a fraction of 1.0 or it can be greater than 1.0. For example, a hazard ratio of 0.70 means that the study drug provides 30% risk reduction compared to the control treatment (25). A hazard ratio of exactly 1.0 means that the study drug provides zero risk reduction, compared to the control treatment.
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A brief conceptual introduction to hazard ratios and survival curves (also known as Kaplan Meier plots). Hopefully this gives you the information you need to... Ref: Student4bestevidence.net This is a short presentation on hazard ratio, its uses, interpretation, and a talk about some relevant concepts. IN this video we are describing you tips and tricks of percentage for data interpretation. . Most of the exams including SBI PO, IBPS,CLERK, RAILWAYS,SSC CG... This short video describes how to interpret a survival plot. Please post any comments or questions below, or at our Statistics for Citizen Scientists group: ... The Kaplan Meier (Kaplan-Meier) curve is frequently used to perform time-to-event analysis in the medical literature. The Kaplan Meier curve, also known as ... Kaplan Meier curve and hazard ratio tutorial (Kaplan Meier curve and hazard ratio made simple!) - Duration: 52:54. Eric McCoy 16,925 views How to calculate the hazard ratio of two groups' survival times.Thanks for watching!! ️♫ Eric Skiff - Chibi Ninjahttp://freemusicarchive.org/music/Eric_Skif... In survival analysis, the hazard ratio (HR) is the ratio of the hazard rates corresponding to the conditions described by two levels of an explanatory variab...
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