A literature review of current technologies on health data integration for patient-centered health management.

Health data integration enables a collaborative utilization of data across different systems. It not only provides a comprehensive view of a patient's health but can also potentially cope with challenges faced by the current healthcare system. In this literature review, we investigated the existing work on heterogeneous health data integration as well as the methods of utilizing the integrated health data. Our search was narrowed down to 32 articles for analysis. The integration approaches in the reviewed articles were classified into three classifications, and the utilization approaches were classified into five classifications. The topic of health data integration is still under debate and problems are far from being resolved. This review suggests the need for a more efficient way to invoke the various services for aggregating health data, as well as a more effective way to integrate the aggregated health data for supporting collaborative utilization. We have found that the combination of Web Application Programming Interface and Semantic Web technologies has the potential to cope with the challenges based on our analysis of the review result.

Meaningful Integration of Data from Heterogeneous Health Services and Home Environment Based on Ontology.

The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable.

Virtual water trade and time scales for loss of water sustainability: a comparative regional analysis.

Assessment and policy design for sustainability in primary resources like arable land and water need to adopt long-term perspective; even small but persistent effects like net export of water may influence sustainability through irreversible losses. With growing consumption, this virtual water trade has become an important element in the water sustainability of a nation. We estimate and contrast the virtual (embedded) water trades of two populous nations, India and China, to present certain quantitative measures and time scales. Estimates show that export of embedded water alone can lead to loss of water sustainability. With the current rate of net export of water (embedded) in the end products, India is poised to lose its entire available water in less than 1000 years; much shorter time scales are implied in terms of water for production. The two cases contrast and exemplify sustainable and non-sustainable virtual water trade in long term perspective.

Quantitative assessment of relative roles of drivers of acute respiratory diseases.

Several thousands of people, including children, suffer from acute respiratory disease (ARD) every year worldwide. Pro-active planning and mitigation for these diseases require identification of the major drivers in a location-specific manner. While the importance of air pollutants in ARD has been extensively studied and emphasized, the role of weather variables has been less explored. With Delhi with its large population and pollution as a test case, we examine the relative roles of air pollution and weather (cold days) in ARD. It is shown that both the number of cold days and air pollution play important roles in ARD load; however, the number of cold days emerges as the major driver. These conclusions are consistent with analyses for several other states in India. The robust association between ARD load and cold days provides basis for estimating and predicting ARD load through dynamical model, as well as impact of climate change.

Towards seasonal forecasting of malaria in India.

BACKGROUND: Malaria presents public health challenge despite extensive intervention campaigns. A 30-year hindcast of the climatic suitability for malaria transmission in India is presented, using meteorological variables from a state of the art seasonal forecast model to drive a process-based, dynamic disease model. METHODS: The spatial distribution and seasonal cycles of temperature and precipitation from the forecast model are compared to three observationally-based meteorological datasets. These time series are then used to drive the disease model, producing a simulated forecast of malaria and three synthetic malaria time series that are qualitatively compared to contemporary and pre-intervention malaria estimates. The area under the Relative Operator Characteristic (ROC) curve is calculated as a quantitative metric of forecast skill, comparing the forecast to the meteorologically-driven synthetic malaria time series. RESULTS AND DISCUSSION: The forecast shows probabilistic skill in predicting the spatial distribution of Plasmodium falciparum incidence when compared to the simulated meteorologically-driven malaria time series, particularly where modelled incidence shows high seasonal and interannual variability such as in Orissa, West Bengal, and Jharkhand (North-east India), and Gujarat, Rajastan, Madhya Pradesh and Maharashtra (North-west India). Focusing on these two regions, the malaria forecast is able to distinguish between years of "high", "above average" and "low" malaria incidence in the peak malaria transmission seasons, with more than 70% sensitivity and a statistically significant area under the ROC curve. These results are encouraging given that the three month forecast lead time used is well in excess of the target for early warning systems adopted by the World Health Organization. This approach could form the basis of an operational system to identify the probability of regional malaria epidemics, allowing advanced and targeted allocation of resources for combatting malaria in India.

Relative roles of weather variables and change in human population in malaria: comparison over different states of India.

BACKGROUND: Pro-active and effective control as well as quantitative assessment of impact of climate change on malaria requires identification of the major drivers of the epidemic. Malaria depends on vector abundance which, in turn, depends on a combination of weather variables. However, there remain several gaps in our understanding and assessment of malaria in a changing climate. Most of the studies have considered weekly or even monthly mean values of weather variables, while the malaria vector is sensitive to daily variations. Secondly, rarely all the relevant meteorological variables have been considered together. An important question is the relative roles of weather variables (vector abundance) and change in host (human) population, in the change in disease load. METHOD: We consider the 28 states of India, characterized by diverse climatic zones and changing population as well as complex variability in malaria, as a natural test bed. An annual vector load for each of the 28 states is defined based on the number of vector genesis days computed using daily values of temperature, rainfall and humidity from NCEP daily Reanalysis; a prediction of potential malaria load is defined by taking into consideration changes in the human population and compared with the reported number of malaria cases. RESULTS: For most states, the number of malaria cases is very well correlated with the vector load calculated with the combined conditions of daily values of temperature, rainfall and humidity; no single weather variable has any significant association with the observed disease prevalence. CONCLUSION: The association between vector-load and daily values of weather variables is robust and holds for different climatic regions (states of India). Thus use of all the three weather variables provides a reliable means of pro-active and efficient vector sanitation and control as well as assessment of impact of climate change on malaria.

Assessing reliability of regional climate projections: the case of Indian monsoon.

Projections of climate change are emerging to play major roles in many applications. However, assessing reliability of climate change projections, especially at regional scales, remains a major challenge. An important question is the degree of progress made since the earlier IPCC simulations (CMIP3) to the latest, recently completed CMIP5. We consider the continental Indian monsoon as an example and apply a hierarchical approach for assessing reliability, using the accuracy in simulating the historical trend as the primary criterion. While the scope has increased in CMIP5, there is essentially no improvement in skill in projections since CMIP3 in terms of reliability (confidence). Thus, it may be necessary to consider acceptable models for specific assessment rather than simple ensemble. Analysis of climate indices shows that in both CMIP5 and CMIP3 certain common processes at large and regional scales as well as slow timescales are associated with successful simulation of trend and mean.

Association of NALP2 polymorphism with arsenic induced skin lesions and other health effects.

Prolonged consumption of arsenic-laden water above the threshold limit of 10µg/L causes a plethora of dermatological and non-dermatological multi-organ health problems, including cancer and death. Among several mechanisms of arsenic-induced toxicity and carcinogenicity studied so far, role of arsenic in impairment of immune system is less understood. Epidemiological data, animal model as well as cell line based studies have indicated that arsenic targets immune system and is associated with characteristic immunosupression, which may further adversely affect respiratory function. However, to the best of our knowledge, there is no study with respect to arsenic susceptibility investigating the role of genetic variation having immunological function. Hence, we have recruited a total of 432 arsenic-exposed individuals, of which 219 individuals with characteristic arsenic-induced skin lesions (cases) and 213 individuals without arsenic-induced skin lesion(controls), from arsenic-exposed districts of West Bengal, India. To find any probable association between arsenicism and the exonic single nucleotide polymorphisms (SNPs) in NALP2 gene, an important component of inflammasome complex, we screened the entire coding region (exon) in all the study participants. Among 9 SNPs found in NALP2 gene, the A1052E polymorphism (at least with one minor allele), was significantly overrepresented in controls and hence implies decreased risk toward the development of skin lesions [OR=0.67, 95% CI: 0.46-0.97]. Since, development of non-dermatological health effects are also important factor to properly look into, we have attempted to correlate the genetic variation of NALP2 with the extent of cytogenetic damage as measured by chromosomal aberration assay and adverse health effects including peripheral neuropathy, eye problem and respiratory diseases in the study population. We observed individuals with the protective genotype had less chromosomal aberration (p<0.05), and were also less susceptible toward arsenic-related respiratory diseases [OR=0.47; 95%CI: 0.23-0.89]. These findings suggest that NALP2 A1052E SNP plays an important role toward development of arsenic-induced skin lesions, chromosomal damage and respiratory diseases.

A model of malaria epidemiology involving weather, exposure and transmission applied to north East India.

BACKGROUND: Quantitative relations between weather variables and malaria vector can enable pro-active control through meteorological monitoring. Such relations are also critical for reliable projections in a changing climate, especially since the vector abundance depends on a combination of weather variables, each in a given range. Further, such models need to be region-specific as vector population and exposure depend on regional characteristics. METHODS: We consider days of genesis based on daily temperature, rainfall and humidity in given ranges. We define a single model parameter based on estimates of exposure and transmission to calibrate the model; the model is applied to 12 districts of Arunachal Pradesh, a region endemic to malaria. The epidemiological data is taken as blood samples that test positive. The meteorological data is adopted from NCEP daily Reanalysis on a global grid; population data is used to estimate exposure and transmission coefficients. RESULTS: The observed annual cycles (2006-2010) and the interannual variability (2002-2010) of epidemiology are well simulated for each of the 12 districts by the model. While no single weather variable like temperature can reproduce the observed epidemiology, a combination of temperature, rainfall and humidity provides an accurate description of the annual cycle as well as the inter annual variability over all the 12 districts. CONCLUSION: Inclusion of the three meteorological variables, along with the expressions for exposure and transmission, can quite accurately represent observed epidemiology over multiple locations and different years. The model is potentially useful for outbreak forecasts at short time scales through high resolution weather monitoring; however, validation with longer and independent epidemiological data is required for more robust estimation of realizable skill. While the model has been examined over a specific region, the basic algorithm is easily applicable to other regions; the model can account for shifting vulnerability due to regional climate change.

Impact of weather variables on mosquitoes infected with Japanese encephalitis virus in Kurnool district, Andhra Pradesh.

OBJECTIVE: To assess the virus infection in mosquitoes during different seasons and correlated with various climatic factors. METHODS: The field collected vectors were screened for Japanese encephalitis (JE) virus after dessication using ELISA method. Most of the positive pools were recorded from Culex tritaeniorhynchus (Cx. tritaeniorhynchus) and Culex. gelidus (Cx. gelidus) during JE transmission season (winter) and some positive pools were also reported during non JE transmission periods (i.e. summer and rainy seasons). RESULTS: The minimum infection rates (MIR) of 1.75 from Cx. tritaeniorhynchus and 0.17 from Cx. gelidus has been reported in the year 2002 at the beginning of the study and the values were found nil at the end of the study (2006) from the study areas of Kurnool district. CONCLUSIONS: From this study it is noted that MIR of Cx. gelidus and Cx. tritaeniorhynchus were modulated by various meteorological parameters. The mosquito vector abundance increases after the monsoon period (winter) and lowest in dry season (summer). Similarly, MIR fluctuated between seasons with higher MIR recorded after monsoon period and lower in the rest of season. Impact of these metrological parameters in JE virus infected mosquitoes is discussed in this paper.