Development of a Rapid Screening Instrument for Mild Cognitive Impairment and Undiagnosed Dementia

Mild cognitive impairment (MCI) often presages development of Alzheimer’s disease (AD). We recently completed a cross-sectional study to test the hypothesis that a combination of a brief cognitive screening instrument (Mini-Cog) with a functional scale (Functional Activities Questionnaire; FAQ) would accurately identify individuals with MCI and undiagnosed dementia. The Mini-Cog consists of a clock drawing task and 3-item recall, and takes less than 5 minutes to administer. The FAQ is a 30-item questionnaire completed by an informant. In addition to the Mini-Cog and FAQ, a traditional cognitive test battery was administered, and two neurologists and a neuropsychologist determined a consensus diagnosis of Normal, MCI, or Dementia. A classification tree algorithm was used to pick optimal cutpoints, and, using these cutpoints, the combined Mini-Cog and FAQ (MC-FAQ) predicted the consensus diagnosis with an accuracy of 83% and a weighted kappa of 0.81. When the population was divided into Normal and Abnormal, the sensitivity, specificity and positive predictive value were 89%, 90%, and 95%, respectively. The MC-FAQ discriminates individuals with MCI from cognitively normal individuals and those with dementia, and its ease of administration makes it an attractive screening instrument to aid detection of cognitive impairment in the elderly

Distribution of Major Health Risks: Findings from the Global Burden of Disease Study

BACKGROUND: Most analyses of risks to health focus on the total burden of their aggregate effects. The distribution of risk-factor-attributable disease burden, for example by age or exposure level, can inform the selection and targeting of specific interventions and programs, and increase cost-effectiveness. METHODS AND FINDINGS: For 26 selected risk factors, expert working groups conducted comprehensive reviews of data on risk-factor exposure and hazard for 14 epidemiological subregions of the world, by age and sex. Age-sex-subregion-population attributable fractions were estimated and applied to the mortality and burden of disease estimates from the World Health Organization Global Burden of Disease database. Where possible, exposure levels were assessed as continuous measures, or as multiple categories. The proportion of risk-factor-attributable burden in different population subgroups, defined by age, sex, and exposure level, was estimated. For major cardiovascular risk factors (blood pressure, cholesterol, tobacco use, fruit and vegetable intake, body mass index, and physical inactivity) 43%–61% of attributable disease burden occurred between the ages of 15 and 59 y, and 87% of alcohol-attributable burden occurred in this age group. Most of the disease burden for continuous risks occurred in those with only moderately raised levels, not among those with levels above commonly used cut-points, such as those with hypertension or obesity. Of all disease burden attributable to being underweight during childhood, 55% occurred among children 1–3 standard deviations below the reference population median, and the remainder occurred among severely malnourished children, who were three or more standard deviations below median. CONCLUSIONS: Many major global risks are widely spread in a population, rather than restricted to a minority. Population-based strategies that seek to shift the whole distribution of risk factors often have the potential to produce substantial reductions in disease burden

Developing an Advanced PM2.5 Exposure Model in Lima, Peru

It is well recognized that exposure to fine particulate matter (PM2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM2.5 measurements. Lima’s topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM2.5 concentrations at a 1 km2 spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R2 (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m3). Mean PM2.5 for ground measurements was 24.7 μg/m3 while mean estimated PM2.5 was 24.9 μg/m3 in the cross-validation dataset. The mean difference between ground and predicted measurements was −0.09 μg/m3 (Std.Dev. = 5.97 μg/m3), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM2.5 measurements at 1 km2 spatial resolution to support future epidemiological studies

Development of a rapid screening instrument for mild cognitive impairment and undiagnosed dementia.

Mild cognitive impairment (MCI) often presages development of Alzheimer's disease (AD). We recently completed a cross-sectional study to test the hypothesis that a combination of a brief cognitive screening instrument (Mini-Cog) with a functional scale (Functional Activities Questionnaire; FAQ) would accurately identify individuals with MCI and undiagnosed dementia. The Mini-Cog consists of a clock drawing task and 3-item recall, and takes less than 5 minutes to administer. The FAQ is a 30-item questionnaire completed by an informant. In addition to the Mini-Cog and FAQ, a traditional cognitive test battery was administered, and two neurologists and a neuropsychologist determined a consensus diagnosis of Normal, MCI, or Dementia. A classification tree algorithm was used to pick optimal cutpoints, and, using these cutpoints, the combined Mini-Cog and FAQ (MC-FAQ) predicted the consensus diagnosis with an accuracy of 83% and a weighted kappa of 0.81. When the population was divided into Normal and Abnormal, the sensitivity, specificity and positive predictive value were 89%, 90%, and 95%, respectively. The MC-FAQ discriminates individuals with MCI from cognitively normal individuals and those with dementia, and its ease of administration makes it an attractive screening instrument to aid detection of cognitive impairment in the elderly

An overview of methods for calculating the burden of disease due to specific risk factors.

There are a number of measures that quantify the public health burden due to specific risk factors for specific diseases. Although these measures are of importance for policymakers, epidemiologists do not often calculate them or may be unfamiliar with some of the issues involved when they do. The primary measure of interest is the attributable fraction (AF), representing the fraction of cases or deaths from a specific disease that would not have occurred in the absence of exposure to a specific risk factor either in the exposed population or the population as a whole. AFs can be multiplied by the total number of cases of a given disease to obtain a "body count"--the absolute number of preventable cases due to a specific risk factor. Two other measures of public health burden, used in conjunction with AFs, are attributable years-of-life-lost and attributable disability-adjusted life-years. We provide an overview of the AF and related measures and discuss some of the specific issues involved in calculating AFs. These issues include calculating the variance of AFs (such as Monte Carlo sensitivity methods), biases arising from some formulas for the AF, sources of data for calculating AFs, dependence of AFs on basic decisions about what exposure-disease associations are causal, and extrapolation from the source population to the target population

Nitrogen dioxide exposures from biomass cookstoves in the Peruvian Andes

Household air pollution from biomass cookstoves is a major contributor to global morbidity and mortality, yet little is known about exposures to nitrogen dioxide (NO ). To characterize NO kitchen area concentrations and personal exposures among women with biomass cookstoves in the Peruvian Andes. We measured kitchen area NO concentrations at high-temporal resolution in 100 homes in the Peruvian Andes. We assessed personal exposure to NO in a subsample of 22 women using passive samplers. Among 97 participants, the geometric mean (GM) highest hourly average NO concentration was 723 ppb (geometric standard deviation (GSD) 2.6) and the GM 24-hour average concentration was 96 ppb (GSD 2.6), 4.4 and 2.9 times greater than WHO indoor hourly (163 ppb) and annual (33 ppb) guidelines, respectively. Compared to the direct-reading instruments, we found similar kitchen area concentrations with 48-hour passive sampler measurements (GM 108 ppb, GSD 3.8). Twenty-seven percent of women had 48-hour mean personal exposures above WHO annual guidelines (GM 18 ppb, GSD 2.3). In univariate analyses, we found that roof, wall, and floor type, as well as higher SES, was associated with lower 24-hour kitchen area NO concentrations. Kitchen area concentrations and personal exposures to NO from biomass cookstoves in the Peruvian Andes far exceed WHO guidelines. More research is warranted to understand the role of this understudied household air pollutant on morbidity and mortality and to inform cleaner-cooking interventions for public health

Household air pollution exposure and associations with household characteristics among biomass cookstove users in Puno, Peru

Household air pollution (HAP) from combustion of biomass fuel, such as wood and animal dung, is among the leading environmental risk factors for preventable disease. Close to half of the world's population relies on biomass cookstoves for their daily cooking needs. Understanding factors that affect HAP can inform measures to maximize the effectiveness of cookstove interventions in a cost-effective manner. However, the impact of kitchen and household characteristics, as well as the presence of secondary stoves, on HAP concentrations is poorly understood in Puno, Peru. To explore how household characteristics explain variability of kitchen area concentrations and personal exposures to CO, PM2.5 and BC from biomass cookstoves among women in rural Peru. Household characteristics (including kitchen materials and layout, wealth, and cooking behaviors) and HAP measurements were collected from 180 households in Puno, Peru, from baseline measurements of a randomized trial. Kitchen area concentrations and personal exposures to carbon monoxide (CO), fine particulate matter (PM2.5) and black carbon (BC) were sampled for 48 h. We implemented simple and multivariable linear regression models to determine the associations between household characteristics and both kitchen area concentration and personal exposure to each pollutant. Mean daily kitchen area concentrations and personal exposures to HAP were, on average, 48 times above World Health Organization indoor guidelines for PM2.5. We found that roof type explained the most variability in HAP and was strongly associated with both kitchen area concentrations and personal exposures for all pollutants after adjusting for other household variables. Personal exposures were 27%–36% lower for PM2.5, CO and BC, in households with corrugated metal roofs, compared to roofs made of natural materials (straw, totora or reed) after adjusting for other factors. Higher kitchen area concentrations were also associated with less wealth, owning more animals, or sampling during the dry season in multivariable models. Having a liquefied petroleum gas (LPG) stove and having a chimney were associated with lower personal exposures, but were not associated with kitchen area concentrations. Personal exposures were lower by 21% for PM2.5 and 28% for CO and BC concentrations among participants who had both LPG and biomass stoves compared to those with only biomass cookstoves adjusting for other household factors. Characterizing HAP within different settings can help identify effective and culturally-relevant solutions to reduce HAP exposures. We found that housing roof type is strongly related to kitchen area concentrations and personal exposures to HAP, perhaps because of greater ventilation in kitchens with metal roofs compared to those with thatch roofs. Although HAP concentrations remained above guidelines for all households, promoting use of metal roof materials and LPG stoves may be actionable interventions that can help reduce exposures to HAP in high-altitude rural Peru and similar settings. •Kitchen concentrations in Puno were, on average, 48 times the PM2.5 recommended levels.•Kitchens with metal roofs had 50% lower PM2.5 compared to straw and reed roofs.•Women who owned LPG stoves had 21% lower personal exposures to PM2.5.•Personal exposures to PM2.5 were 35% lower in kitchens with a chimney.•LPG stoves and ventilation provided by metal roofs reduce personal exposures to HAP