Estimating Indoor PM2.5 and CO Concentrations in Households in Southern Nepal: The Nepal Cookstove Intervention Trials

High concentrations of household air pollution (HAP) due to biomass fuel usage with unvented, insufficient combustion devices are thought to be an important health risk factor in South Asia population. To better characterize the indoor concentrations of particulate matter (PM2.5) and carbon monoxide (CO), and to understand their impact on health in rural southern Nepal, this study analyzed daily monitoring data collected with DataRAM pDR-1000 and LASCAR CO data logger in 2980 households using traditional biomass cookstove indoor through the Nepal Cookstove Intervention Trial–Phase I between March 2010 and October 2011. Daily average PM2.5 and CO concentrations collected in area near stove were 1,376 (95% CI, 1,331–1,423) μg/m3 and 10.9 (10.5–11.3) parts per million (ppm) among households with traditional cookstoves. The 95th percentile, hours above 100μg/m3 for PM2.5 or 6ppm for CO, and hours above 1000μg/m3 for PM2.5 or 9ppm for CO were also reported. An algorithm was developed to differentiate stove-influenced (SI) periods from non-stove-influenced (non-SI) periods in monitoring data. Average stove-influenced concentrations were 3,469 (3,350–3,588) μg/m3 for PM2.5 and 21.8 (21.1–22.6) ppm for CO. Dry season significantly increased PM2.5concentration in all metrics; wood was the cleanest fuel for PM2.5 and CO, while adding dung into the fuel increased concentrations of both pollutants. For studies in rural southern Nepal, CO concentration is not a viable surrogate for PM2.5 concentrations based on the low correlation between these measures. In sum, this study filled a gap in knowledge on HAP in rural Nepal using traditional cookstoves and revealed very high concentrations in these households

Characterizing and Quantifying Human Movement Patterns Using GPS Data Loggers in an Area Approaching Malaria Elimination in Rural Southern Zambia

In areas approaching malaria elimination, human mobility patterns are important in determining the proportion of malaria cases that are imported or the result of low-level, endemic transmission. A convenience sample of participants enrolled in a longitudinal cohort study in the catchment area of Macha Hospital in Choma District, Southern Province, Zambia, was selected to carry a GPS data logger for one month from October 2013 to August 2014. Density maps and activity space plots were created to evaluate seasonal movement patterns. Time spent outside the household compound during anopheline biting times, and time spent in malaria high- and lowrisk areas, were calculated. There was evidence of seasonal movement patterns, with increased long-distance movement during the dry season. A median of 10.6% (interquartile range (IQR): 5.8-23.8) of time was spent away from the household, which decreased during anopheline biting times to 5.6% (IQR:1.7-14.9). The per cent of time spent in malaria high-risk areas for participants residing in high-risk areas ranged from 83.2% to 100%, but ranged from only 0.0% to 36.7% for participants residing in low-risk areas. Interventions targeted at the household may be more effective because of restricted movement during the rainy season, with limited movement between high- and low-risk areas

Spatial and Temporal Changes in Household Structure Locations Using High-Resolution Satellite Imagery for Population Assessment: An Analysis in Southern Zambia, 2006-2011

Satellite imagery is increasingly available at high spatial resolution and can be used for various purposes in public health research and programme implementation. Comparing a census generated from two satellite images of the same region in rural southern Zambia obtained four and a half years apart identified patterns of household locations and change over time. The length of time that a satellite image-based census is accurate determines its utility. Households were enumerated manually from satellite images obtained in 2006 and 2011 of the same area. Spatial statistics were used to describe clustering, cluster detection, and spatial variation in the location of households. A total of 3821 household locations were enumerated in 2006 and 4256 in 2011, a net change of 435 houses (11.4% increase). Comparison of the images indicated that 971 (25.4%) structures were added and 536 (14.0%) removed. Further analysis suggested similar household clustering in the two images and no substantial difference in concentration of households across the study area. Cluster detection analysis identified a small area where significantly more household structures were removed than expected; however, the amount of change was of limited practical significance. These findings suggest that random sampling of households for study participation would not induce geographic bias if based on a 4.5-year-old image in this region. Application of spatial statistical methods provides insights into the population distribution changes between two time periods and can be helpful in assessing the accuracy of satellite imagery

Help-Seeking Behavior during Elevated Temperature in Chinese Population

The negative impact of extreme temperatures on health is well-established. Individual help-seeking behavior, however, may mitigate the extent of morbidity and mortality during elevated temperatures. This study examines individual help-seeking behavior during periods of elevated temperatures among a Chinese population. Help-seeking patterns and factors that influence behavior will be identified so that vulnerable subgroups may be targeted for health protection during heat crises. A retrospective time-series Poisson generalized additive model analysis, using meteorological data of Hong Kong Observatory and routine emergency help call data from The Hong Kong Senior Citizen Home Safety Association during warm seasons (June–September) 1998–2007, was conducted. A “U”-shaped association was found between daily emergency calls and daily temperature. About 49% of calls were for explicit health-related reasons including dizziness, shortness of breath, and general pain. The associate with maximum temperature was statistically significant (p = 0.034) with the threshold temperature at which the frequency of health-related calls started to increase being around 30–32°C. Mean daily relative humidity (RH) also had a significant U-shaped association with daily emergency health-related calls with call frequency beginning to increase with RH greater than 70–74% (10–25% of the RH distribution). Call frequency among females appeared to be more sensitive to high temperatures, with a threshold between 28.5°C and 30.5°C while calls among males were more sensitive to cold temperatures (threshold 31.5–33.5°C). Results indicate differences in community help-seeking behavior at elevated temperatures. Potential programs or community outreach services might be developed to protect vulnerable subgroups from the adverse impact of elevated temperatures

Comparing composite likelihood methods based on pairs for spatial Gaussian random fields

In the last years there has been a growing interest in proposing methods for estimating covariance functions for geostatistical data. Among these, maximum likelihood estimators have nice features when we deal with a Gaussian model. However maximum likelihood becomes impractical when the number of observations is very large. In this work we review some solutions and we contrast them in terms of loss of statistical efficiency and computational burden. Specifically we focus on three types of weighted composite likelihood functions based on pairs and we compare them with the method of covariance tapering. Asymptotic properties of the three estimation methods are derived. We illustrate the effectiveness of the methods through theoretical examples, simulation experiments and by analyzing a data set on yearly total precipitation anomalies at weather stations in the United States.In the last years there has been a growing interest in proposing methods for estimating covariance functions for geostatistical data. Among these, maximum likelihood estimators have nice features when we deal with a Gaussian model. However maximum likelihood becomes impractical when the number of observations is very large. In this work we review some solutions and we contrast them in terms of loss of statistical efficiency and computational burden. Specifically we focus on three types of weighted composite likelihood functions based on pairs and we compare them with the method of covariance tapering. Asymptotic properties of the three estimation methods are derived. We illustrate the effectiveness of the methods through theoretical examples, simulation experiments and by analyzing a data set on yearly total precipitation anomalies at weather stations in the United States

The association between farming activities, precipitation, and the risk of acute gastrointestinal illness in rural municipalities of Quebec, Canada: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Increasing livestock density and animal manure spreading, along with climate factors such as heavy rainfall, may increase the risk of acute gastrointestinal illness (AGI). In this study we evaluated the association between farming activities, precipitation and AGI.</p> <p>Methods</p> <p>A cross-sectional telephone survey of randomly selected residents (n = 7006) of 54 rural municipalities in Quebec, Canada, was conducted between April 2007 and April 2008. AGI symptoms and several risk factors were investigated using a phone questionnaire. We calculated the monthly prevalence of AGI, and used multivariate logistic regression, adjusting for several demographic and risk factors, to evaluate the associations between AGI and both intensive farming activities and cumulative weekly precipitation. Cumulative precipitation over each week, from the first to sixth week prior to the onset of AGI, was analyzed to account for both the delayed effect of precipitation on AGI, and the incubation period of causal pathogens. Cumulative precipitation was treated as a four-category variable: high (≥90^thpercentile), moderate (50^thto <90^thpercentile), low (10^thto <50^thpercentile), and very low (<10^thpercentile) precipitation.</p> <p>Results</p> <p>The overall monthly prevalence of AGI was 5.6% (95% CI 5.0%-6.1%), peaking in winter and spring, and in children 0-4 years old. Living in a territory with intensive farming was negatively associated with AGI: adjusted odds ratio (OR) = 0.70 (95% CI 0.51-0.96). Compared to low precipitation periods, high precipitation periods in the fall (September, October, November) increased the risk of AGI three weeks later (OR = 2.20; 95% CI 1.09-4.44) while very low precipitation periods in the summer (June, July, August) increased the risk of AGI four weeks later (OR = 2.19; 95% CI 1.02-4.71). Further analysis supports the role of water source on the risk of AGI.</p> <p>Conclusions</p> <p>AGI poses a significant burden in Quebec rural municipalities with a peak in winter. Intensive farming activities were found to be negatively associated with AGI. However, high and very low precipitation levels were positively associated with the occurrence of AGI, especially during summer and fall. Thus, preventive public health actions during such climate events may be warranted.</p

Effects of apparent temperature on daily mortality in Lisbon and Oporto, Portugal

<p>Abstract</p> <p>Background</p> <p>Evidence that elevated temperatures can lead to increased mortality is well documented, with population vulnerability being location specific. However, very few studies have been conducted that assess the effects of temperature on daily mortality in urban areas in Portugal.</p> <p>Methods</p> <p>In this paper time-series analysis was used to model the relationship between mean apparent temperature and daily mortality during the warm season (April to September) in the two largest urban areas in Portugal: Lisbon and Oporto. We used generalized additive Poisson regression models, adjusted for day of week and season.</p> <p>Results</p> <p>Our results show that in Lisbon, a 1°C increase in mean apparent temperature is associated with a 2.1% (95%CI: 1.6, 2.5), 2.4% (95%CI: 1.7, 3.1) and 1.7% (95%CI: 0.1, 3.4) increase in all-causes, cardiovascular, and respiratory mortality, respectively. In Oporto the increase was 1.5% (95%CI: 1.0, 1.9), 2.1% (95%CI: 1.3, 2.9) and 2.7% (95%CI: 1.2, 4.3) respectively. In both cities, this increase was greater for the group >65 years.</p> <p>Conclusion</p> <p>Even without extremes in apparent temperature, we observed an association between temperature and daily mortality in Portugal. Additional research is needed to allow for better assessment of vulnerability within populations in Portugal in order to develop more effective heat-related morbidity and mortality public health programs.</p

Geographic Variations in Retention in Care among HIV-Infected Adults in the United States

ObjectiveTo understand geographic variations in clinical retention, a central component of the HIV care continuum and key to improving individual- and population-level HIV outcomes.DesignWe evaluated retention by US region in a retrospective observational study.MethodsAdults receiving care from 2000–2010 in 12 clinical cohorts of the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) contributed data. Individuals were assigned to Centers for Disease Control and Prevention (CDC)-defined regions by residential data (10 cohorts) and clinic location as proxy (2 cohorts). Retention was ≥2 primary HIV outpatient visits within a calendar year, >90 days apart. Trends and regional differences were analyzed using modified Poisson regression with clustering, adjusting for time in care, age, sex, race/ethnicity, and HIV risk, and stratified by baseline CD4+ count.ResultsAmong 78,993 adults with 444,212 person-years of follow-up, median time in care was 7 years (Interquartile Range: 4–9). Retention increased from 2000 to 2010: from 73% (5,000/6,875) to 85% (7,189/8,462) in the Northeast, 75% (1,778/2,356) to 87% (1,630/1,880) in the Midwest, 68% (8,451/12,417) to 80% (9,892/12,304) in the South, and 68% (5,147/7,520) to 72% (6,401/8,895) in the West. In adjusted analyses, retention improved over time in all regions (p<0.01, trend), although the average percent retained lagged in the West and South vs. the Northeast (p<0.01).ConclusionsIn our population, retention improved, though regional differences persisted even after adjusting for demographic and HIV risk factors. These data demonstrate regional differences in the US which may affect patient care, despite national care recommendations

Excess cardiovascular mortality associated with cold spells in the Czech Republic

<p>Abstract</p> <p>Background</p> <p>The association between cardiovascular mortality and winter cold spells was evaluated in the population of the Czech Republic over 21-yr period 1986–2006. No comprehensive study on cold-related mortality in central Europe has been carried out despite the fact that cold air invasions are more frequent and severe in this region than in western and southern Europe.</p> <p>Methods</p> <p>Cold spells were defined as periods of days on which air temperature does not exceed -3.5°C. Days on which mortality was affected by epidemics of influenza/acute respiratory infections were identified and omitted from the analysis. Excess cardiovascular mortality was determined after the long-term changes and the seasonal cycle in mortality had been removed. Excess mortality during and after cold spells was examined in individual age groups and genders.</p> <p>Results</p> <p>Cold spells were associated with positive mean excess cardiovascular mortality in all age groups (25–59, 60–69, 70–79 and 80+ years) and in both men and women. The relative mortality effects were most pronounced and most direct in middle-aged men (25–59 years), which contrasts with majority of studies on cold-related mortality in other regions. The estimated excess mortality during the severe cold spells in January 1987 (+274 cardiovascular deaths) is comparable to that attributed to the most severe heat wave in this region in 1994.</p> <p>Conclusion</p> <p>The results show that cold stress has a considerable impact on mortality in central Europe, representing a public health threat of an importance similar to heat waves. The elevated mortality risks in men aged 25–59 years may be related to occupational exposure of large numbers of men working outdoors in winter. Early warnings and preventive measures based on weather forecast and targeted on the susceptible parts of the population may help mitigate the effects of cold spells and save lives.</p

Comparing estimates of influenza-associated hospitalization and death among adults with congestive heart failure based on how influenza season is defined

<p>Abstract</p> <p>Background</p> <p>There is little consensus about how the influenza season should be defined in studies that assess influenza-attributable risk. The objective of this study was to compare estimates of influenza-associated risk in a defined clinical population using four different methods of defining the influenza season.</p> <p>Methods</p> <p>Using the Studies of Left Ventricular Dysfunction (SOLVD) clinical database and national influenza surveillance data from 1986–87 to 1990–91, four definitions were used to assess influenza-associated risk: (a) three-week moving average of positive influenza isolates is at least 5%, (b) three-week moving average of positive influenza isolates is at least 10%, (c) first and last positive influenza isolate are identified, and (d) 5% of total number of positive isolates for the season are obtained. The clinical data were from adults aged 21 to 80 with physician-diagnosed congestive heart failure. All-cause hospitalization and all-cause mortality during the influenza seasons and non-influenza seasons were compared using four definitions of the influenza season. Incidence analyses and Cox regression were used to assess the effect of exposure to influenza season on all-cause hospitalization and death using all four definitions.</p> <p>Results</p> <p>There was a higher risk of hospitalization associated with the influenza season, regardless of how the start and stop of the influenza season was defined. The adjusted risk of hospitalization was 8 to 10 percent higher during the influenza season compared to the non-influenza season when the different definitions were used. However, exposure to influenza was not consistently associated with higher risk of death when all definitions were used. When the 5% moving average and first/last positive isolate definitions were used, exposure to influenza was associated with a higher risk of death compared to non-exposure in this clinical population (adjusted hazard ratios [HR], 1.16; 95% confidence interval [CI], 1.04 to 1.29 and adjusted HR, 1.19; 95% CI, 1.06 to 1.33, respectively).</p> <p>Conclusion</p> <p>Estimates of influenza-attributable risk may vary depending on how influenza season is defined and the outcome being assessed.</p