Browsing by Subject "EXPENDITURE"

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  • Dieleman, Joseph; Campbell, Madeline; Chapin, Abigail; Eldrenkamp, Erika; Fan, Victoria Y.; Haakenstad, Annie; Kates, Jennifer; Liu, Yingying; Matyasz, Taylor; Micah, Angela; Reynolds, Alex; Sadat, Nafis; Schneider, Matthew T.; Sorensen, Reed; Evans, Tim; Evans, David; Kurowski, Christoph; Tandon, Ajay; Abbas, Kaja M.; Abera, Semaw Ferede; Kiadaliri, Aliasghar Ahmad; Ahmed, Kedir Yimam; Ahmed, Muktar Beshir; Alam, Khurshid; Alizadeh-Navaei, Reza; Alkerwi, Ala'a; Amini, Erfan; Ammar, Walid; Amrock, Stephen Marc; Antonio, Carl Abelardo T.; Atey, Tesfay Mehari; Avila-Burgos, Leticia; Awasthi, Ashish; Barac, Aleksandra; Alberto Bernal, Oscar; Beyene, Addisu Shunu; Beyene, Tariku Jibat; Birungi, Charles; Bizuayehu, Habtamu Mellie; Breitborde, Nicholas J. K.; Cahuana-Hurtado, Lucero; Estanislao Castro, Ruben; Catalia-Lopez, Ferran; Dalal, Koustuv; Dandona, Lalit; Dandona, Rakhi; de Jager, Pieter; Dharmaratne, Samath D.; Dubey, Manisha; Meretoja, Atte; Global Burden Dis Hlth Financing (2017)
    Background An adequate amount of prepaid resources for health is important to ensure access to health services and for the pursuit of universal health coverage. Previous studies on global health financing have described the relationship between economic development and health financing. In this study, we further explore global health financing trends and examine how the sources of funds used, types of services purchased, and development assistance for health disbursed change with economic development. We also identify countries that deviate from the trends. Methods We estimated national health spending by type of care and by source, including development assistance for health, based on a diverse set of data including programme reports, budget data, national estimates, and 964 National Health Accounts. These data represent health spending for 184 countries from 1995 through 2014. We converted these data into a common inflation-adjusted and purchasing power-adjusted currency, and used non-linear regression methods to model the relationship between health financing, time, and economic development. Findings Between 1995 and 2014, economic development was positively associated with total health spending and a shift away from a reliance on development assistance and out-of-pocket (OOP) towards government spending. The largest absolute increase in spending was in high-income countries, which increased to purchasing power-adjusted $5221 per capita based on an annual growth rate of 3.0%. The largest health spending growth rates were in upper-middle-income (5.9) and lower-middle-income groups (5.0), which both increased spending at more than 5% per year, and spent $914 and $267 per capita in 2014, respectively. Spending in low-income countries grew nearly as fast, at 4.6%, and health spending increased from $51 to $120 per capita. In 2014, 59.2% of all health spending was financed by the government, although in low-income and lower-middle-income countries, 29.1% and 58.0% of spending was OOP spending and 35.7% and 3.0% of spending was development assistance. Recent growth in development assistance for health has been tepid; between 2010 and 2016, it grew annually at 1.8%, and reached US$37.6 billion in 2016. Nonetheless, there is a great deal of variation revolving around these averages. 29 countries spend at least 50% more than expected per capita, based on their level of economic development alone, whereas 11 countries spend less than 50% their expected amount. Interpretation Health spending remains disparate, with low-income and lower-middle-income countries increasing spending in absolute terms the least, and relying heavily on OOP spending and development assistance. Moreover, tremendous variation shows that neither time nor economic development guarantee adequate prepaid health resources, which are vital for the pursuit of universal health coverage.
  • Global Burden Dis Hlth Financing; Micah, Angela E.; Su, Yanfang; Bachmeier, Steven D.; Meretoja, Tuomo J.; Meretoja, Atte (2020)
    Background Sustainable Development Goal (SDG) 3 aims to "ensure healthy lives and promote well-being for all at all ages". While a substantial effort has been made to quantify progress towards SDG3, less research has focused on tracking spending towards this goal. We used spending estimates to measure progress in financing the priority areas of SDG3, examine the association between outcomes and financing, and identify where resource gains are most needed to achieve the SDG3 indicators for which data are available. Methods We estimated domestic health spending, disaggregated by source (government, out-of-pocket, and prepaid private) from 1995 to 2017 for 195 countries and territories. For disease-specific health spending, we estimated spending for HIV/AIDS and tuberculosis for 135 low-income and middle-income countries, and malaria in 106 malaria-endemic countries, from 2000 to 2017. We also estimated development assistance for health (DAH) from 1990 to 2019, by source, disbursing development agency, recipient, and health focus area, including DAH for pandemic preparedness. Finally, we estimated future health spending for 195 countries and territories from 2018 until 2030. We report all spending estimates in inflation-adjusted 2019 US$, unless otherwise stated. Findings Since the development and implementation of the SDGs in 2015, global health spending has increased, reaching $7.9 trillion (95% uncertainty interval 7.8-8.0) in 2017 and is expected to increase to $11.0 trillion (10.7-11.2) by 2030. In 2017, in low-income and middle-income countries spending on HIV/AIDS was $20.2 billion (17.0-25.0) and on tuberculosis it was $10.9 billion (10.3-11.8), and in malaria-endemic countries spending on malaria was $5.1 billion (4.9-5.4). Development assistance for health was $40.6 billion in 2019 and HIV/AIDS has been the health focus area to receive the highest contribution since 2004. In 2019, $374 million of DAH was provided for pandemic preparedness, less than 1% of DAH. Although spending has increased across HIV/AIDS, tuberculosis, and malaria since 2015, spending has not increased in all countries, and outcomes in terms of prevalence, incidence, and per-capita spending have been mixed. The proportion of health spending from pooled sources is expected to increase from 81.6% (81.6-81.7) in 2015 to 83.1% (82.8-83.3) in 2030. Interpretation Health spending on SDG3 priority areas has increased, but not in all countries, and progress towards meeting the SDG3 targets has been mixed and has varied by country and by target. The evidence on the scale-up of spending and improvements in health outcomes suggest a nuanced relationship, such that increases in spending do not always results in improvements in outcomes. Although countries will probably need more resources to achieve SDG3, other constraints in the broader health system such as inefficient allocation of resources across interventions and populations, weak governance systems, human resource shortages, and drug shortages, will also need to be addressed. Copyright (C) 2020 The Author(s). Published by Elsevier Ltd.
  • Drummen, Mathijs; Tischmann, Lea; Gatta-Cherifi, Blandine; Fogelholm, Mikael; Raben, Anne; Adam, Tanja C.; Westerterp-Plantenga, Margriet S. (2020)
    Background: Weight loss has been associated with adaptations in energy expenditure. Identifying factors that counteract these adaptations are important for long-term weight loss and weight maintenance. Objective: The aim of this study was to investigate whether increased protein/carbohydrate ratio would reduce adaptive thermogenesis (AT) and the expected positive energy balance (EB) during weight maintenance after weight loss in participants with prediabetes in the postobese state. Methods: In 38 participants, the effects of 2 diets differing in protein/carbohydrate ratio on energy expenditure and respiratory quotient (RQ) were assessed during 48-h respiration chamber measurements similar to 34 mo after weight loss. Participants consumed a high-protein (HP) diet In = 20; 13 women/7 men; age: 64.0 +/- 6.2 y; BMI: 28.9 +/- 4.0 kg/m(2)) with 25:45:30% or a moderate-protein (MP) diet (n = 18; 9 women/9 men; age: 65.1 +/- 5.8 y; BMI: 29.0 +/- 3.8 kg/m(2)) with 15:55:30% of energy from protein:carbohydrate:fat. Predicted resting energy expenditure (REEp) was calculated based on fat-free mass and fat mass. AT was assessed by subtracting measured resting energy expenditure (REE) from REEp. The main outcomes included differences in components of energy expenditure, substrate oxidation, and AT between groups. Results: EB (MP = 0.2 +/- 0.9 MJ/d; HP = -0.5 +/- 0.9 MJ/d) and RO (MP = 0.84 +/- 0.02; HP = 0.82 +/- 0.02) were reduced and REE (MP: 73 +/- 0.2 MJ/d compared with HP: 78 +/- 0.2 MJ/d) was increased in the HP group compared with the MP group (P <0.05). REE was not different from REEp in the HP group, whereas REE was lower than REEp in the MP group (P <0.05). Furthermore, EB was positively related to AT (r(s) = 0.74; P <0.001) and RQ (r(s) = 0.47; P <0.01) in the whole group of participants. Conclusions: In conclusion, an HP diet compared with an MP diet led to a negative EB and counteracted AT similar to 34 mo after weight loss, in participants with prediabetes in the postobese state. These results indicate the relevance of compliance to an increased protein/carbohydrate ratio for long-term weight maintenance after weight loss.
  • Kettunen, Oona; Heikkilä, Maria; Linnamo, Vesa; Ihalainen, Johanna K. (2021)
    The aim of this study was to provide information on energy availability (EA), macronutrient intake, nutritional periodization practices, and nutrition knowledge in young female cross-country skiers. A total of 19 skiers filled in weighted food and training logs before and during a training camp. Nutrition knowledge was assessed via a validated questionnaire. EA was optimal in 11% of athletes at home (mean 33.7 +/- 9.6 kcal center dot kgFFM(-1)center dot d(-1)) and in 42% at camp (mean 40.3 +/- 17.3 kcal center dot kgFFM(-1)center dot d(-1)). Most athletes (74%) failed to meet recommendations for carbohydrate intake at home (mean 5.0 +/- 1.2 g center dot kg(-1)center dot d(-1)) and 63% failed to do so at camp (mean 7.1 +/- 1.6 g center dot kg(-1)center dot d(-1)). The lower threshold of the pre-exercise carbohydrate recommendations was met by 58% and 89% of athletes while percentages were 26% and 89% within 1 h after exercise, at home and at camp, respectively. None of the athletes met the recommendations within 4 h after exercise. Nutrition knowledge was associated with EA at home (r = 0.52, p = 0.023), and with daily carbohydrate intake at home (r = 0.62, p = 0.005) and at camp (r = 0.52, p = 0.023). Carbohydrate intake within 1 and 4 h post-exercise at home was associated with better nutrition knowledge (r = 0.65, p = 0.003; r = 0.53, p = 0.019, respectively). In conclusion, young female cross-county skiers had difficulties meeting recommendations for optimal EA and carbohydrate intake. Better nutrition knowledge may help young athletes to meet these recommendations.
  • IDEFICS and I.Family consortia; Masip-Manuel, Guiomar; Foraita, Ronja; Silventoinen, Karri; Keski-Rahkonen, Anna; Bogl, Leonie-Helen; Kaprio, Jaakko (2021)
    Background: Many genes and molecular pathways are associated with obesity, but the mechanisms from genes to obesity are less well known. Eating behaviors represent a plausible pathway, but because the relationships of eating behaviors and obesity may be bi-directional, it remains challenging to resolve the underlying pathways. A longitudinal approach is needed to assess the contribution of genetic risk during the development of obesity in childhood. In this study we aim to examine the relationships between the polygenic risk score for body mass index (PRS-BMI), parental concern of overeating and obesity indices during childhood. Methods: The IDEFICS/I.Family study is a school-based multicenter pan-European cohort of children observed for 6 years (mean +/- SD follow-up 5.8 +/- 0.4). Children examined in 2007/2008 (wave 1) (mean +/- SD age: 4.4 +/- 1.1, range: 2-9 years), in 2009/2010 (wave 2) and in 2013/2014 (wave 3) were included. A total of 5112 children (49% girls) participated at waves 1, 2 and 3. For 2656 children with genome-wide data we constructed a PRS based on 2.1 million single nucleotide polymorphisms. Z-score BMI and z-score waist circumference (WC) were assessed and eating behaviors and relevant confounders were reported by parents via questionnaires. Parental concern of overeating was derived from principal component analyses from an eating behavior questionnaire. Results: In cross-lagged models, the prospective associations between z-score obesity indices and parental concern of overeating were bi-directional. In mediation models, the association between the PRS-BMI and parental concern of overeating at wave 3 was mediated by baseline z-BMI (beta = 0.16, 95% CI: 0.10, 0.21) and baseline z-WC (beta = 0.17, 95% CI: 0.11, 0.23). To a lesser extent, baseline parental concern of overeating also mediated the association between the PRS-BMI and z-BMI at wave 3 (beta = 0.10, 95% CI: 0.07, 0.13) and z-WC at wave 3 (beta = 0.09, 95% CI: 0.07, 0.12). Conclusions: The findings suggest that the prospective associations between obesity indices and parental concern of overeating are likely bi-directional, but obesity indices have a stronger association with future parental concern of overeating than vice versa. The findings suggest parental concern of overeating as a possible mediator in the genetic susceptibility to obesity and further highlight that other pathways are also involved. A better understanding of the genetic pathways that lead to childhood obesity can help to prevent weight gain.
  • Ganelli, Giovanni; Tervala, Juha (2020)
    We analyze the welfare multipliers of public spending-the consumption equivalent change in welfare for a one dollar change in public spending-in a DSGE model. The welfare multiplier of public investment depends crucially not only on the productivity (output elasticity) of public capital, as shown by earlier studies, but also on the depreciation rate of public capital and the efficiency of public investment defined as a fraction of public investment spending that translates into the public capital stock. When the key parameter values are set based on the empirical estimates for advanced economies and the output multipliers are consistent with the empirical estimates, the welfare multiplier is positive and sizable. The welfare multiplier is roughly zero when the key parameter values are set to match the features of developing economies. A public infrastructure push in advanced economies makes sense, but developing economies should enhance the efficiency and productivity of public investment.