Feasibility of Using a Factory-Calibrated Continuous Glucose Monitoring System to Diagnose Type 2 Diabetes.

Context: Plasma glucose or A1C criteria can be used to establish the diagnosis of type 2 diabetes (T2D). Objective: We examined whether continuous glucose monitoring (CGM) data from a single 10-day wear period could form the basis of an alternative diagnostic test for T2D. Design: We developed a binary classification diagnostic CGM (dCGM) algorithm using a dataset of 716 individual CGM sensor sessions from 563 participants with associated A1C measurements from seven clinical trials. Data from 470 participants were used for training and 93 participants for testing (49 normoglycemic [A1C <5.7%], 27 prediabetes, and 17 T2D [A1C ≥6.5%] not using pharmacotherapy). dCGM performance was evaluated against the accompanying A1C measurement, which was assumed to provide the correct diagnosis. Results: The dCGM algorithm's overall sensitivity, specificity, positive predictive value, and negative predictive value were 71%, 93%, 71%, and 93%, respectively. At other clinically relevant A1C thresholds, dCGM specificity among normoglycemic participants was 98% (48/49 correctly classified), and for participants with suboptimally controlled diabetes (A1C ≥7%, above the American Diabetes Association recommended A1C goal) the sensitivity was 100% (8/8 participants correctly diagnosed with T2D). Conclusions: Classifications based on the dCGM algorithm were in good agreement with traditional methods based on A1C. The dCGM algorithm may provide an alternative method for screening and diagnosing T2D, and warrants further investigation.

Air pollution exposure disparities across US population and income groups.

Air pollution contributes to the global burden of disease, with ambient exposure to fine particulate matter of diameters smaller than 2.5 µm (PM2.5) being identified as the fifth-ranking risk factor for mortality globally1. Racial/ethnic minorities and lower-income groups in the USA are at a higher risk of death from exposure to PM2.5 than are other population/income groups2-5. Moreover, disparities in exposure to air pollution among population and income groups are known to exist6-17. Here we develop a data platform that links demographic data (from the US Census Bureau and American Community Survey) and PM2.5 data18 across the USA. We analyse the data at the tabulation area level of US zip codes (N is approximately 32,000) between 2000 and 2016. We show that areas with higher-than-average white and Native American populations have been consistently exposed to average PM2.5 levels that are lower than areas with higher-than-average Black, Asian and Hispanic or Latino populations. Moreover, areas with low-income populations have been consistently exposed to higher average PM2.5 levels than areas with high-income groups for the years 2004-2016. Furthermore, disparities in exposure relative to safety standards set by the US Environmental Protection Agency19 and the World Health Organization20 have been increasing over time. Our findings suggest that more-targeted PM2.5 reductions are necessary to provide all people with a similar degree of protection from environmental hazards. Our study is observational and cannot provide insight into the drivers of the identified disparities.

Examining the density in out-of-pocket spending share in the estimation of catastrophic health expenditures.

Universal health coverage (UHC) aims to provide access to health services for all without financial hardship. Moving toward UHC while ensuring financial risk protection (FRP) from out-of-pocket (OOP) health expenditures is a critical objective of the Sustainable Development Goal for Health. In tracking country progress toward UHC, analysts and policymakers usually report on two summary indicators of lack of FRP: the prevalence of catastrophic health expenditures (CHE) and the prevalence of impoverishing health expenditures. In this paper, we build on the CHE indicator: we examine the distribution (density) of health OOP budget share as a way to capture both the magnitude and dispersion in the ratio of households' OOP health expenditures relative to consumption or income at the population level. We illustrate our approach with country-specific examples using data from the World Health Organization's World Health Surveys.

Financial risk of road traffic trauma care in public and private hospitals in Addis Ababa, Ethiopia: A cross-sectional observational study.

BACKGROUND: Road traffic injuries are among the most important causes of morbidity and mortality and cause substantial economic loss to households in Ethiopia. This study estimates the financial risks of seeking trauma care due to road traffic injuries in Addis Ababa, Ethiopia. METHODS: This is a cross-sectional survey on out-of-pocket (OOP) expenditures related to trauma care in three public and one private hospital in Addis Ababa from December 2018 to February 2019. Direct medical and non-medical costs (2018 USD) were collected from 452 trauma cases. Catastrophic health expenditures were defined as OOP health expenditures of 10% or more of total household expenditures. Additionally, we investigated the impoverishment effect of OOP expenditures using the international poverty line of $1.90 per day per person (adjusted for purchasing power parity). RESULTS: Trauma care seeking after road traffic injuries generate catastrophic health expenditures for 67% of households and push 24% of households below the international poverty line. On average, the medical OOP expenditures per patient seeking care were $256 for outpatient visits and $690 for inpatient visits per road traffic injury. Patients paid more for trauma care in private hospitals, and OOP expenditures were six times higher in private than in public hospitals. Transport to facilities and caregiver costs were the two major cost drivers, amounting to $96 and $68 per patient, respectively. CONCLUSION: Seeking trauma care after a road traffic injury poses a substantial financial threat to Ethiopian households due to lack of strong financial risk protection mechanisms. Ethiopia's government should enact multisectoral interventions for increasing the prevention of road traffic injuries and implement universal public finance of trauma care.

Incorporating equity in infectious disease modeling: Case study of a distributional impact framework for measles transmission.

INTRODUCTION: Deterministic compartmental models of infectious diseases like measles typically reflect biological heterogeneities in the risk of infection and severity to characterize transmission dynamics. Given the known association of socioeconomic status and increased vulnerability to infection and mortality, it is also critical that such models further incorporate social heterogeneities. METHODS: Here, we aimed to explore the influence of integrating income-associated differences in parameters of traditional dynamic transmission models. We developed a measles SIR model, in which the Susceptible, Infected and Recovered classes were stratified by income quintile, with income-specific transmission rates, disease-induced mortality rates, and vaccination coverage levels. We further provided a stylized illustration with secondary data from Ethiopia, where we examined various scenarios demonstrating differences in transmission patterns by income and in distributional vaccination coverage, and quantified impacts on disparities in measles mortality. RESULTS: The income-stratified SIR model exhibited similar dynamics to that of the traditional SIR model, with amplified outbreak peaks and measles mortality among the poorest income group. All vaccination coverage strategies were found to substantially curb the overall number of measles deaths, yet most considerably for the poorest, with select strategies yielding clear reductions in measles mortality disparities. DISCUSSION: The incorporation of income-specific differences can reveal distinct outbreak patterns across income groups and important differences in the subsequent effects of preventative interventions like vaccination. Our case study highlights the need to extend traditional modeling frameworks (e.g. SIR models) to be stratified by socioeconomic factors like income and to consider ensuing income-associated differences in disease-related morbidity and mortality. In so doing, we build on existing tools and characterize ongoing challenges in achieving health equity.

Modeling the acute effects of exercise on glucose dynamics in healthy nondiabetic subjects.

To shed light on how acute exercise affects blood glucose (BG) concentrations in nondiabetic subjects, we develop a physiological pharmacokinetic/pharmacodynamic model of postprandial glucose dynamics during exercise. We unify several concepts of exercise physiology to derive a multiscale model that includes three important effects of exercise on glucose dynamics: increased endogenous glucose production (EGP), increased glucose uptake in skeletal muscle (SM), and increased glucose delivery to SM by capillary recruitment (i.e. an increase in surface area and blood flow in capillary beds). We compare simulations to experimental observations taken in two cohorts of healthy nondiabetic subjects (resting subjects (n = 12) and exercising subjects (n = 12)) who were each given a mixed-meal tolerance test. Metabolic tracers were used to quantify the glucose flux. Simulations reasonably agree with postprandial measurements of BG concentration and EGP during exercise. Exercise-induced capillary recruitment is predicted to increase glucose transport to SM by 100%, causing hypoglycemia. When recruitment is blunted, as in those with capillary dysfunction, the opposite occurs and higher than expected BG levels are predicted. Model simulations show how three important exercise-induced phenomena interact, impacting BG concentrations. This model describes nondiabetic subjects, but it is a first step to a model that describes glucose dynamics during exercise in those with type 1 diabetes (T1D). Clinicians and engineers can use the insights gained from the model simulations to better understand the connection between exercise and glucose dynamics and ultimately help patients with T1D make more informed insulin dosing decisions around exercise.

Is cycle network expansion cost-effective? A health economic evaluation of cycling in Oslo.

BACKGROUND: Expansion of designated cycling networks increases cycling for transport that, in turn, increases physical activity, contributing to improvement in public health. This paper aims to determine whether cycle-network construction in a large city is cost-effective when compared to the status-quo. We developed a cycle-network investment model (CIM) for Oslo and explored its impact on overall health and wellbeing resulting from the increased physical activity. METHODS: First, we applied a regression technique on cycling data from 123 major European cities to model the effect of additional cycle-networks on the share of cyclists. Second, we used a Markov model to capture health benefits from increased cycling for people starting to ride cycle at the age of 30 over the next 25 years. All health gains were measured in quality-adjusted life years (QALYs). Costs were estimated in US dollars. Other data to populate the model were derived from a comprehensive literature search of epidemiological and economic evaluation studies. Uncertainty was assessed using deterministic and probabilistic sensitivity analyses. RESULTS: Our regression analysis reveals that a 100 km new cycle network construction in Oslo city would increase cycling share by 3%. Under the base-case assumptions, where the benefits of the cycle-network investment relating to increased physical activity are sustained over 25 years, the predicted average increases in costs and QALYs per person are $416 and 0.019, respectively. Thus, the incremental costs are $22,350 per QALY gained. This is considered highly cost-effective in a Norwegian setting. CONCLUSIONS: The results support the use of CIM as part of a public health program to improve physical activity and consequently avert morbidity and mortality. CIM is affordable and has a long-term effect on physical activity that in turn has a positive impact on health improvement.

The burden of household out-of-pocket health expenditures in Ethiopia: estimates from a nationally representative survey (2015-16).

In Ethiopia, little is known about the extent of out-of-pocket health expenditures and the associated financial hardships at national and regional levels. We estimated the incidence of both catastrophic and impoverishing health expenditures using data from the 2015/16 Ethiopian household consumption and expenditure and welfare monitoring surveys. We computed incidence of catastrophic health expenditures (CHE) at 10% and 25% thresholds of total household consumption and 40% threshold of household capacity to pay, and impoverishing health expenditures (IHE) using Ethiopia's national poverty line (ETB 7184 per adult per year). Around 2.1% (SE: 0.2, P < 0.001) of households would face CHE with a 10% threshold of total consumption, and 0.9% (SE: 0.1, P < 0.001) of households would encounter IHE, annually in Ethiopia. CHE rates were high in the regions of Afar (5.8%, SE: 1.0, P < 0.001) and Benshangul-Gumuz (4.0%, SE: 0.8, P < 0.001). Oromia (n = 902 000), Amhara (n = 275 000) and Southern Nations Nationalities and Peoples (SNNP) (n = 268 000) regions would have the largest numbers of affected households, due to large population size. The IHE rates would also show similar patterns: high rates in Afar (5.0%, SE: 0.96, P < 0.001), Oromia (1.1%, SE: 0.22, P < 0.001) and Benshangul-Gumuz (0.9%, SE: 0.4, P = 0.02); a large number of households would be impoverished in Oromia (n = 356 000) and Amhara (n = 202 000) regions. In summary, a large number of households is facing financial hardship in Ethiopia, particularly in Afar, Benshangul-Gumuz, Oromia, Amhara and SNNP regions and this number would likely increase with greater health services utilization. We recommend regional-level analyses on services coverage to be conducted as some of the estimated low CHE/IHE regional values might be due to low services coverage. Periodic analyses on the financial hardship status of households could also be monitored to infer progress towards universal health coverage.

Toward health system strengthening in low- and middle-income countries: insights from mathematical modeling of drug supply chains.

BACKGROUND: Global health priority setting increasingly focuses on understanding the functioning of health systems and on how they can be strengthened. Beyond vertical programs, health systems research should examine system-wide delivery platforms (e.g. health facilities) and operational elements (e.g. supply chains) as primary units of study and evaluation. METHODS: We use dynamical system methods to develop a simple analytical model for the supply chain of a low-income country's health system. In doing so, we emphasize the dynamic links that integrate the supply chain within other elements of the health system; and we examine how the evolution over time of such connections would affect drug delivery, following the implementation of selected interventions (e.g. enhancing road networks, expanding workforce). We also test feedback loops and forecasts to study the potential impact of setting up a digital system for tracking drug delivery to prevent drug stockout and expiration. RESULTS: Numerical simulations that capture a range of supply chain scenarios demonstrate the impact of different health system strengthening interventions on drug stock levels within health facilities. Our mathematical modeling also points to how implementing a digital drug tracking system could help anticipate and prevent drug stockout and expiration. CONCLUSION: Our mathematical model of drug supply chain delivery represents an important component toward the development of comprehensive quantitative frameworks that aim at describing health systems as complex dynamical systems. Such models can help predict how investments in system-wide interventions, like strengthening drug supply chains in low-income settings, may improve population health outcomes.

Financial burden of HIV and TB among patients in Ethiopia: a cross-sectional survey.

OBJECTIVES: HIV and tuberculosis (TB) are major global health threats and can result in household financial hardships. Here, we aim to estimate the household economic burden and the incidence of catastrophic health expenditures (CHE) incurred by HIV and TB care across income quintiles in Ethiopia. DESIGN: A cross-sectional survey. SETTING: 27 health facilities in Afar and Oromia regions for TB, and nationwide household survey for HIV. PARTICIPANTS: A total of 1006 and 787 individuals seeking HIV and TB care were enrolled, respectively. OUTCOME MEASURES: The economic burden (ie, direct and indirect cost) of HIV and TB care was estimated. In addition, the CHE incidence and intensity were determined using direct costs exceeding 10% of the household income threshold. RESULTS: The mean (SD) age of HIV and TB patient was 40 (10), and 30 (14) years, respectively. The mean (SD) patient cost of HIV was $78 ($170) per year and $115 ($118) per TB episode. Out of the total cost, the direct cost of HIV and TB constituted 69% and 46%, respectively. The mean (SD) indirect cost was $24 ($66) per year for HIV and $63 ($83) per TB episode. The incidence of CHE for HIV was 20%; ranges from 43% in the poorest to 4% in the richest income quintile (p<0.001). Similarly, for TB, the CHE incidence was 40% and ranged between 58% and 20% among the poorest and richest income quintiles, respectively (p<0.001). This figure was higher for drug-resistant TB (62%). CONCLUSIONS: HIV and TB are causes of substantial economic burden and CHE, inequitably, affecting those in the poorest income quintile. Broadening the health policies to encompass interventions that reduce the high cost of HIV and TB care, particularly for the poor, is urgently needed.

Health gains and financial risk protection afforded by public financing of selected malaria interventions in Ethiopia: an extended cost-effectiveness analysis.

BACKGROUND: Malaria is a public health burden and a major cause for morbidity and mortality in Ethiopia. Malaria also places a substantial financial burden on families and Ethiopia's national economy. Economic evaluations, with evidence on equity and financial risk protection (FRP), are therefore essential to support decision-making for policymakers to identify best buys amongst possible malaria interventions. The aim of this study is to estimate the expected health and FRP benefits of universal public financing of key malaria interventions in Ethiopia. METHODS: Using extended cost-effectiveness analysis (ECEA), the potential health and FRP benefits were estimated, and their distributions across socio-economic groups, of publicly financing a 10% coverage increase in artemisinin-based combination therapy (ACT), long-lasting insecticide-treated bed nets (LLIN), indoor residual spraying (IRS), and malaria vaccine (hypothetical). RESULTS: ACT, LLIN, IRS, and vaccine would avert 358, 188, 107 and 38 deaths, respectively, each year at a net government cost of $5.7, 16.5, 32.6, and 5.1 million, respectively. The annual cost of implementing IRS would be two times higher than that of the LLIN interventions, and would be the main driver of the total costs. The averted deaths would be mainly concentrated in the poorest two income quintiles. The four interventions would eliminate about $4,627,800 of private health expenditures, and the poorest income quintiles would see the greatest FRP benefits. ACT and LLINs would have the largest impact on malaria-related deaths averted and FRP benefits. CONCLUSIONS: ACT, LLIN, IRS, and vaccine interventions would bring large health and financial benefits to the poorest households in Ethiopia.

Health system modelling research: towards a whole-health-system perspective for identifying good value for money investments in health system strengthening.

Global health research has typically focused on single diseases, and most economic evaluation research to date has analysed technical health interventions to identify 'best buys'. New approaches in the conduct of economic evaluations are needed to help policymakers in choosing what may be good value (ie, greater health, distribution of health, or financial risk protection) for money (ie, per budget expenditure) investments for health system strengthening (HSS) that tend to be programmatic. We posit that these economic evaluations of HSS interventions will require developing new analytic models of health systems which recognise the dynamic connections between the different components of the health system, characterise the type and interlinks of the system's delivery platforms; and acknowledge the multiple constraints both within and outside the health sector which limit the system's capacity to efficiently attain its objectives. We describe priority health system modelling research areas to conduct economic evaluation of HSS interventions and ultimately identify good value for money investments in HSS.

Modeling the acute effects of exercise on insulin kinetics in type 1 diabetes.

Our objective is to develop a physiology-based model of insulin kinetics to understand how exercise alters insulin concentrations in those with type 1 diabetes (T1D). We reveal the relationship between the insulin absorption rate ([Formula: see text]) from subcutaneous tissue, the insulin delivery rate ([Formula: see text]) to skeletal muscle, and two physiological parameters that characterize the tissue: the perfusion rate (Q) and the capillary permeability surface area (PS), both of which increase during exercise because of capillary recruitment. We compare model predictions to experimental observations from two pump-wearing T1D cohorts [resting subjects ([Formula: see text]) and exercising subjects ([Formula: see text])] who were each given a mixed-meal tolerance test and a bolus of insulin. Using independently measured values of Q and PS from literature, the model predicts that during exercise insulin concentration increases by 30% in plasma and by 60% in skeletal muscle. Predictions reasonably agree with experimental observations from the two cohorts, without the need for parameter estimation by curve fitting. The insulin kinetics model suggests that the increase in surface area associated with exercise-induced capillary recruitment significantly increases [Formula: see text] and [Formula: see text], which explains why insulin concentrations in plasma and skeletal muscle increase during exercise, ultimately enhancing insulin-dependent glucose uptake. Preventing hypoglycemia is of paramount importance in determining the proper insulin dose during exercise. The presented model provides mechanistic insight into how exercise affects insulin kinetics, which could be useful in guiding the design of decision support systems and artificial pancreas control algorithms.