Non-invasive imaging biomarkers for liver steatosis in non-alcoholic fatty liver disease: present and future

Non-alcoholic fatty liver disease is currently the most common chronic liver disease, affecting up to 25% of the global population. Simple fatty liver, in which fat is deposited in the liver without fibrosis, has been regarded as a benign disease in the past, but it is now known to be prognostic. In the future, more emphasis should be placed on the quantification of liver fat. Traditionally, fatty liver has been assessed by histological evaluation, which requires an invasive examination; however, technological innovations have made it possible to evaluate fatty liver by non-invasive imaging methods, such as ultrasonography, computed tomography, and magnetic resonance imaging. In addition, quantitative as well as qualitative measurements for the detection of fatty liver have become available. In this review, we summarize the currently used qualitative evaluations of fatty liver and discuss quantitative evaluations that are expected to further develop in the future.

Seasonality of mortality under a changing climate: a time-series analysis of mortality in Japan between 1972 and 2015

BACKGROUND@#Ambient temperature may contribute to seasonality of mortality; in particular, a warming climate is likely to influence the seasonality of mortality. However, few studies have investigated seasonality of mortality under a warming climate.@*METHODS@#Daily mean temperature, daily counts for all-cause, circulatory, and respiratory mortality, and annual data on prefecture-specific characteristics were collected for 47 prefectures in Japan between 1972 and 2015. A quasi-Poisson regression model was used to assess the seasonal variation of mortality with a focus on its amplitude, which was quantified as the ratio of mortality estimates between the peak and trough days (peak-to-trough ratio (PTR)). We quantified the contribution of temperature to seasonality by comparing PTR before and after temperature adjustment. Associations between annual mean temperature and annual estimates of the temperature-unadjusted PTR were examined using multilevel multivariate meta-regression models controlling for prefecture-specific characteristics.@*RESULTS@#The temperature-unadjusted PTRs for all-cause, circulatory, and respiratory mortality were 1.28 (95% confidence interval (CI): 1.27-1.30), 1.53 (95% CI: 1.50-1.55), and 1.46 (95% CI: 1.44-1.48), respectively; adjusting for temperature reduced these PTRs to 1.08 (95% CI: 1.08-1.10), 1.10 (95% CI: 1.08-1.11), and 1.35 (95% CI: 1.32-1.39), respectively. During the period of rising temperature (1.3 °C on average), decreases in the temperature-unadjusted PTRs were observed for all mortality causes except circulatory mortality. For each 1 °C increase in annual mean temperature, the temperature-unadjusted PTR for all-cause, circulatory, and respiratory mortality decreased by 0.98% (95% CI: 0.54-1.42), 1.39% (95% CI: 0.82-1.97), and 0.13% (95% CI: - 1.24 to 1.48), respectively.@*CONCLUSION@#Seasonality of mortality is driven partly by temperature, and its amplitude may be decreasing under a warming climate.

Time trend of malaria in relation to climate variability in Papua New Guinea

OBJECTIVES: This study was conducted to describe the regional malaria incidence in relation to the geographic and climatic conditions and describe the effect of altitude on the expansion of malaria over the last decade in Papua New Guinea. METHODS: Malaria incidence was estimated in five provinces from 1996 to 2008 using national health surveillance data. Time trend of malaria incidence was compared with rainfall and minimum/maximum temperature. In the Eastern Highland Province, time trend of malaria incidence over the study period was stratified by altitude. Spatio-temporal pattern of malaria was analyzed. RESULTS: Nationwide, malaria incidence was stationary. Regionally, the incidence increased markedly in the highland region (292.0/100000/yr, p =0.021), and remained stationary in the other regions. Seasonality of the malaria incidence was related with rainfall. Decreasing incidence of malaria was associated with decreasing rainfall in the southern coastal region, whereas it was not evident in the northern coastal region. In the Eastern Highland Province, malaria incidence increased in areas below 1700 m, with the rate of increase being steeper at higher altitudes. CONCLUSIONS: Increasing trend of malaria incidence was prominent in the highland region of Papua New Guinea, while long-term trend was dependent upon baseline level of rainfall in coastal regions.

Assessment of Climate-sensitive Infectious Diseases in the Federated States of Micronesia

Background: The health impacts of climate change are an issue of growing concern in the Pacific region. Prior to 2010, no formal, structured, evidence-based approach had been used to identify the most significant health risks posed by climate change in Pacific island countries. During 2010 and 2011, the World Health Organization supported the Federated States of Micronesia (FSM) in performing a climate change and health vulnerability and adaptation assessment. This paper summarizes the priority climate-sensitive health risks in FSM, with a focus on diarrheal disease, its link with climatic variables and the implications of climate change. Methods: The vulnerability and adaptation assessment process included a review of the literature, extensive stakeholder consultations, ranking of climate-sensitive health risks, and analysis of the available long-term data on climate and climate-sensitive infectious diseases in FSM, which involved examination of health information data from the four state hospitals in FSM between 2000 and 2010; along with each state’s rainfall, temperature and El Niño-Southern Oscillation data. Generalized linear Poisson regression models were used to demonstrate associations between monthly climate variables and cases of climate-sensitive diseases at differing temporal lags. Results: Infectious diseases were among the highest priority climate-sensitive health risks identified in FSM, particularly diarrheal diseases, vector-borne diseases and leptospirosis. Correlation with climate data demonstrated significant associations between monthly maximum temperature and monthly outpatient cases of diarrheal disease in Pohnpei and Kosrae at a lag of one month and 0 to 3 months, respectively; no such associations were observed in Chuuk or Yap. Significant correlations between disease incidence and El Niño-Southern Oscillation cycles were demonstrated in Kosrae state. Conclusions: Analysis of the available data demonstrated significant associations between climate variables and climate-sensitive infectious diseases. This information should prove useful in implementing health system and community adaptation strategies to avoid the most serious impacts of climate change on health in FSM.

Assessment of climate-sensitive infectious diseases in the Federated States of Micronesia

Background: The health impacts of climate change are an issue of growing concern in the Pacific region.  Prior to 2010, no formal, structured, evidence-based approach had been used to identify the most significant health risks posed by climate change in Pacific island countries.  During 2010 and 2011, the World Health Organization supported the Federated States of Micronesia (FSM) in performing a climate change and health vulnerability and adaptation assessment.  This paper summarizes the priority climate-sensitive health risks in FSM, with a focus on diarrhoeal disease, its link with climatic variables and the implications of climate change. Methods:  The vulnerability and adaptation assessment process included a review of the literature, extensive stakeholder consultations, ranking of climate-sensitive health risks, and analysis of the available long-term data on climate and climate-sensitive infectious diseases in FSM, which involved examination of health information data from the four state hospitals in FSM between 2000 and 2010; along with each state’s rainfall, temperature and El Niño-Southern Oscillation data.  Generalized linear Poisson regression models were used to demonstrate associations between monthly climate variables and cases of climate-sensitive diseases at differing temporal lags. Results: Infectious diseases were among the highest priority climate-sensitive health risks identified in FSM, particularly diarrheal diseases, vector-borne diseases and leptospirosis.  Correlation with climate data demonstrated significant associations between monthly maximum temperature and monthly outpatient cases of diarrheal disease in Pohnpei and Kosrae at a lag of one month and 0 to 3 months, respectively; no such associations were observed in Chuuk or Yap.  Significant correlations between disease incidence and El Niño-Southern Oscillation cycles were demonstrated in Kosrae state. Conclusions:  Analysis of the available data demonstrated some significant associations between climate variables and climate-sensitive infectious diseases.   This information should prove useful in implementing health system and community adaptation strategies to avoid the most serious impacts of climate change on health in FSM.

Determination of optimum daily maximum temperature using climate data

<p><b>OBJECTIVE</b>The relation between daily maximum temperature and mortality rate has a V-shaped pattern; the mortality rate is lowest at a certain temperature, that is, optimum temperature (OT), and the mortality rate increases when the temperature becomes higher or lower than OT. OT is associated with climate, but the relation between OT and long-term average temperature, which is a frequently used index of climate, had an outlier (Okinawa) even in Japan alone. Our objective is to determine the best climate index for OT estimation.</p><p><b>METHODS</b>We obtained death certificate data, meteorological data and population data for Japan from relevant government ministries. All the data obtained were from 1972 to 1995 except for Okinawa's mortality data (1973 to 1995). Using smoothing spline with the degree of freedom fixed to 6, we computed the OTs for 47 prefectures in Japan. These OTs were exhaustively compared with percentiles of daily maximum, average, and minimum temperatures, along with the long-term average temperature.</p><p><b>RESULTS</b>Among the candidates of the best climate index, 80 and 85 percentiles of daily maximum temperatures (Tmax80 and Tmax85) showed the highest correlation coefficient with OT (R>0.9, much higher than the R for the long-term average temperature, i.e., 0.63), and the regression models using Tmax80 and Tmax85 best regressed the OT, that is, the difference between the observed OT and the expected OT was smallest when Tmax80 or Tmax85 was used. Unlike previously used average of daily mean temperature, Tmax80 and Tmax85 made Okinawa a nonoutlier. This characteristic is desirable because Okinawa's being an outlier is due to its maritime climate and the capacity to accommodate a different type of climate may expand the applicability of OT estimation method to wider regions in the world. A direct comparison of OT with Tmax75 to Tmax90 revealed that the difference is smallest for the percentile between Tmax80 and Tmax85.</p><p><b>CONCLUSION</b>We considered that a daily maximum temperature between Tmax80 and Tmax85 is the best climate index for estimating OT in Japan.</p>

Determination of Optimum Daily Maximum Temperature Using Climate Data

Objective: The relation between daily maximum temperature and mortality rate has a V-shaped pattern; the mortality rate is lowest at a certain temperature, that is, optimum temperature (OT), and the mortality rate increases when the temperature becomes higher or lower than OT. OT is associated with climate, but the relation between OT and long-term average temperature, which is a frequently used index of climate, had an outlier (Okinawa) even in Japan alone. Our objective is to determine the best climate index for OT estimation. Methods: We obtained death certificate data, meteorological data and population data for Japan from relevant government ministries. All the data obtained were from 1972 to 1995 except for Okinawa’s mortality data (1973 to 1995). Using smoothing spline with the degree of freedom fixed to 6, we computed the OTs for 47 prefectures in Japan. These OTs were exhaustively compared with percentiles of daily maximum, average, and minimum temperatures, along with the long-term average temperature. Results: Among the candidates of the best climate index, 80 and 85 percentiles of daily maximum temperatures (Tmax80 and Tmax85) showed the highest correlation coefficient with OT (R>0.9, much higher than the R for the long-term average temperature, i.e., 0.63), and the regression models using Tmax80 and Tmax85 best regressed the OT, that is, the difference between the observed OT and the expected OT was smallest when Tmax80 or Tmax85 was used. Unlike previously used average of daily mean temperature, Tmax80 and Tmax85 made Okinawa a nonoutlier. This characteristic is desirable because Okinawa’s being an outlier is due to its maritime climate and the capacity to accommodate a different type of climate may expand the applicability of OT estimation method to wider regions in the world. A direct comparison of OT with Tmax75 to Tmax90 revealed that the difference is smallest for the percentile between Tmax80 and Tmax85. Conclusion: We considered that a daily maximum temperature between Tmax80 and Tmax85 is the best climate index for estimating OT in Japan.