In the transition from a planned economy to a market-oriented economy, China’s state funding for health care declined and traditional coverage plans collapsed, leaving China’s poor exposed to potentially ruinous health care costs. In reforming health care for the 21st century, equity in health care financing has become a major policy goal. To assess progress towards this goal, this paper examines the equity characteristics of health care financing in a province of northwestern China, comparing the equity performance between urban and rural areas at two different points in time.Analysis of whether health care financing contributions were progressive according to income were made using the Kakwani index for each of the four health care financing channels of general taxes, public and private health insurance, and out-of-pocket payments. Two rounds of surveys were conducted, the first in 2003  and the second in 2008 . Household socio-economic, health care payment, and utilization information were recorded in household interviews.Low-income households have undertaken a larger share of the health care financing burden in recent years, reflected by negative Kakwani indices, which indicate a regressive system. We found that the indices for general taxation were −0.0024 (urban) and −0.0281  in 2002, and −0.0177 (urban) and −0.0097  in 2007. Public health insurance presented different financing distributions in urban and rural areas . Out-of-pocket payments were progressive but not equitable. Public health insurance coverage has expanded but financing equity has decreased.Health care financing policies in China need ongoing reform. Given the inequity of general consumption taxes, elimination of these would improve financing equity considerably. Optimizing benefit packages in public health insurance is as important as expanding coverage, both for health care financing and for utilization management as well. Although they are progressive, out-of-pocket payments are not equitable in China and have the effect of excluding the poor from health care as they cannot afford to pay for medical care and so withdraw from treatment. Questions always follow health care reform. Is the reform designed to improve equity? If so, does it work?To make health care reform sustainable through financial support and acceptable to a majority of the public, policy-makers and researchers prefer that a health care system should be renovated at the starting point. Consequently, financing equity has been set up as one major policy goal in health care reform across a number of countries -3. In a Influenced by economic reforms since the 1980s, China’s health care system experienced a huge transformation from a planned economic pattern to a market-oriented model. Along with decentralization of financial responsibility for managing health care, state funding for health care declined rapidly. As a result, the proportion of public financing in the health care system decreased while the share of private financing increased ,15. In tConfronted with such a situation, growing attention has been given to equity, especially after the recommendation of the World Health Report 2000 which implied that China has a most inequitable health care system. The achievement of financing equity has become the health policy thrust in health care innovation. Since 2003, China has been launching health sector reform gradually and implementing a specific policy package. In particular, this package incorporates innovation in health care financing, such as: an increase in government health input, the introduction of new types of health insurance , and an expansion in health insurance coverage for the uninsured. These policies aim to reduce the share of OOP health payments through increasing the proportion of government health spending and health insurance funding .Since then, China has established a multi-level health care financing system step by step for the urban and rural populations, in an attempt to make significant progress in the equitable distribution of health care funding. In Table Taxation is a stable resource among health care financing channels, especially in developing and underdeveloped countries. In China, the government budget for health is an important part of the financial resources at all levels of government spending, being mainly financed through a variety of tax revenues. No earmarked health taxes exist in China. The revenue mainly comes from direct taxes and indirect taxes. Direct taxes include personal income taxes, corporate taxes, property taxes, and agricultural taxes in China, while indirect taxes include the value-added tax (VAT), excise taxes, and sales taxes. The taxes above are allocated to all forms of qualified individuals, regardless of whether they are the rich or the poor. Tax revenue is turned over as part of the state financial income, a portion of which is dedicated to health care financing system as the government budget on health. Tax financing usually refers to a pay-as-you-go arrangement, where current revenues are used to finance current expenditures. Generally, the rich pay more taxes than the poor in absolute terms. However, it doesn’t mean that tax funding is always the progressive channel that is preferred by the poor. For instance, the payroll tax, sales tax and cigarette tax are regressive in that the tax rate decreases as the amount subject to taxation increases. In other words, a regressive tax imposes a greater burden on the poor than on the rich. On the contrary, a progressive tax implies that the rich bear more burden than the poor, such as with individual income taxes. In China, the amount financed by taxes is rising steadily, from 15.69% in 2002 to 22.31% in 2007.Public health insurance plays a significant role in the health care financing system. Owing to the dual structure of urban–rural areas in China, two national health insurance schemes have been formed separately. These are the urban health insurance called Basic Medical Insurance (BMI) in cities and counties , and theThe Urban Workers Basic Medical Insurance (UWBMI), the initiation of BMI, was officially launched at the end of 1998 and is run by the local government that organizes universal health insurance for urban formal-sector workers. The premium is funded jointly by employers and employees, while the funding amount depends on the individual’s age. Generally, employers provide 6-8% of employees’ salaries for urban workers under the age of 45 and provide 8-10% of the salaries for those aged 45 or above. The employees themselves contribute around 2% of their salaries . Two meaThe New Rural Cooperative Medical Scheme (NCMS) is a 2003 initiative to rebuild rural health insurance after the collapse of CMS in the 1990s and to overhaul the health care system in villages. NCMS is a voluntary program that covers only those who join; it has maintained a high level of coverage of the population, increasing from 75.20% in 2004 to 86.20% in 2007. NCMS is funded by equal contributions from every enrollee and by increasingly generous subsidies from central and local governments. NCMS operates at the county rather than the commune level and hence has a much larger risk pool. Finally, NCMS focuses in most localities largely on the costs of inpatient care rather than those of basic medical services which include personal and communal preventive interventions. The premium and reimbursement rate grow steadily every year. Generally, the coinsurance has lowered and the medical services covered by NCMS have increased year by year.In China, private health insurance is at the initial stage without a clear direction. Access to commercial health insurance is highly correlated to economic status and personal awareness of the insurance. The insured also need to consider how much to purchase according to their income. In addition, the premiums paid by lower income groups are only slightly lower than those charged to higher income groups. Private health insurance plays a minor role in health care payments, around 3% of the total health care financing amount.OOP payments are those expenditures that individuals must pay directly to the health care provider without any compensation to the patients. As the complementary mechanism to the other financing channels, the OOP payment has been robust since the 1980s in China, to improve the efficiency and quality of the health care system. However, it has become the main source of the Chinese health care financing system  .Although there are many different methods to calculate the equity of health care financing, the approach in our study was to employ progressivity to measure the extent of equity. The progressivity of a health care financing system refers to the extent to which health care payments rise as a proportion of a household’s income when the latter rises. Specifically, we use the Kakwani index of progressivity to calculate the extent of the equity of the health care financing system -36.inc) represents the relationship between the cumulative percentage of income and the cumulative percentage of the households in population , while the concentration curve for health care payments (Lpay) displays the cumulative healthcare share of payments against the cumulative percentage of the households in population. The Kakwani index of progressivity of health care payments on gross income, is defined by twice the area between the Lorenz curve for gross income (Linc) and the concentration curve for health care payments (Lpay). Thus we haveFigure k) is calculated by the difference between the concentration index and the Gini coefficient, reflecting the degree of progressivity of health care financing [k > 0), the health care financing system is progressive, so that the Lorenz curve of income (Linc) lies above the concentration curve of payments (Lpay). When the Kakwani index is negative (πk < 0), the system is regressive, so that the concentration curve of payments (Lpay) lies above the Lorenz curve of income (Linc). When the Kakwani index equals zero (πk = 0), it indicates that the system is proportional, and therefore there is a coincidence of the Lorenz and concentration payments curves. Furthermore, not only can the Kakwani index quantify the degree of financing equity in one certain area in a specific period, but it also enables a comparison of the levels of progressivity at different times and areas without consideration of their different social-economic context for the reason that economic status and health financing factors are included in the model [where C is the concentration index (CI) of health care payments, defined as the distribution of health care financing contributions across the population ranked by gross income and G refers to the Gini coefficient of gross income. The range of the concentration index is defined by the range . A positive (negative) value of concentration index indicates that the rich (poor) contribute a larger proportion than the poor (rich). The value of the index equals zero if everyone pays the same. However, the value of the concentration index does not denote whether a health care financing system is equitable or not, for it does not take the household income (ATP) into consideration. Only if the proportion of health care payments paid by the rich are not lower than the share of income they received, or the proportion of the payment paid by the poor is not higher than the share of income they received, is the health care system considered to have financing equity. The Kakwani index  the Lorenz curve of household expenditure. For the dominance tests, standard errors of the ordinates of curves and of differences in ordinates are computed, allowing for dependence between curves where appropriate ,40. A muTable In both 2002 and 2007, the values of concentration indices were all positive, suggesting that the rich contributed a greater proportion of the health care payment than the poor. In 2002, in both urban and rural regions, almost all the values were higher than 0.4 except for the rural public health insurance (0.3926). Compared to the year 2002, all values in 2007 were nearly lower than 0.4 except for rural private health insurance (0.5936), especially the rural public health insurance (0.1966) that kept a much lower level. Three findings were discovered on the basis of the results of concentration indices. First, it was the better-off who financed a greater proportion of health care resources in China’s health care system. Second, the values of the concentration indices in rural public health insurance in both 2002 and 2007 were typically lower than the values of other health care financing channels at the same period. Third, the average level of the concentration indices in 2002 was higher than that in 2007, indicating that more financing burdens have been allocated to the low- and middle- income individuals in the more recent year, although the Gini coefficient has also decreased, which implies that the rich-poor gap is narrowing.However, the concentration index is not a standard to judge financing equity but just a measurement to assess which population accounts for a higher share of health care resources in absolute terms. In order to evaluate the financing distribution, the Kakwani index is employed to estimate the degree of equity in health care financing system.In 2002, in both cities and villages, tax finance was regressive, while the finances of private health insurance and OOP payments were progressive. Unlike the progressivity consistency in urban and rural areas, public health insurance presented opposite results in cities and villages. In health care financing, insurance was progressive in urban areas but regressive in rural areas. The results indicate that, in both cities and villages, the health care financing channels of private health insurance and OOP payments were equitable, whereas the financing channel of taxes was not. In addition, fairness can be reflected by the urban public health insurance, namely the BMI. Nevertheless, it was not equitable in the rural public health insurance, CMS. Generally, the overall Kakwani index was 0.0431 in urban and 0.0148 in rural areas in 2002, while, in 2007, it was 0.0351 in urban and −0.0226 in rural areas. The classification of health care progressivity stayed the same in 2007. These results are observed directly in Figure Based on the results of the dominance test, the concentration curve of taxes dominated the Lorenz curve in each year and area, indicating that the financing was not equal, for the poor contributed a larger share of financing than the better off through taxes, compared with the living standard. With regard to public health insurance, compared to household expenditure, the concentration curve of the BMI crossed with the Lorenz curve in 2002, indicating that the middle class contributed a greater share of financing than the other income groups. However, the concentration curve of CMS was indistinguishable from the Lorenz curve in 2002. In 2007, the concentration curve of BMI showed a reduction in inequality, whereas the concentration curve of NCMS represented an increase in inequality. It is interesting to note that the concentration curves of OOP payments were all indistinguishable, implying that the OOP payments were not inequality-reducing and were highly correlated with the living standard.Although the progressivity kept parallel levels between 2002 and 2007, improvement and setback could be found in the health care financing system. The differences of values of the Kakwani indices by regions and times are shown in Table Did China’s health care reform work in terms of the equity of health care financing? The short answer is that this is still under way. The health care financing by taxes, where the value of the Kakwani indices are close to zero but negative, was slightly regressive in both urban and rural areas in 2002 and 2007. This suggests that the tax burden for health care funding was slightly concentrated on the poor. Generally, it is universally acknowledged that tax finance is a progressive channel to fund health care, in both high and middle-income countries . Compareearns less, pays equal. This resulted in an inequitable financing outcome for the NCMS. By contrast, persons who are covered in the BMI are required to pay a certain proportion of their earnings as premiums. In other words, earns more, pays more.The progressivity of public health insurance differed in urban and rural areas. In cities, BMI was progressive, while CMS (or NCMS) was regressive in villages. Besides, the values of the Kakwani indices declined in both urban and rural areas from 2002 to 2007. Evidence has shown that public health insurance can be progressive or regressive ,47, and who pays, who gets. In some developing countries, OOP payment is not a challenge for the high-income group in their consumption of medical goods or services, even for higher quality care at higher prices, whereas the low- and middle- income groups cannot afford to seek medical service, even the most basic medical treatment [The OOP health care payment deserves more attention, not only because it accounts for the highest proportion of total health care financing in China, but also because it is a post-paid payment so that the identification of its financing equity is unique compared to other pre-paid payments. In some high-income countries or nations with prominent social health care financing systems, the poor households provide a larger share of financing resources with OOP payment. By contrast, in many low and middle economies, OOP payments are progressive to ability-to-pay, indicating that the better-off finance more of the funds . In the reatment -51. In oFinancing equity is just one side of health care equity and does not reflect the side of utilization. The analysis of health care utilization considers whether the better-off not only pay more but also receive more health care. With pre-paid payment, we can infer whether the poor enjoy more services or not since they are more inclined to make a greater contribution relative to their ability to pay. Different from the financing equity that is assessed by the Kakwani index, the equity of utilization is judged by the concentration index directly since an individual’s need for health care utilization has nothing to do with income . The disIn 2002, almost all the values of concentration indices for hospital care were positive, indicating that the rich received a larger share of health care resources. Similarly in 2007, the better-off absorbed a higher proportion of care than the poor. It was suggested that for each category of hospital care, there was basically a pro-rich bias, which is consistent with our finding that OOP payment was heavily relied upon by the high-income group in Gansu province. In other word, the rich paid more and receive more while the poor received less because they simply could not afford medical expenses and so sought no treatment. From 2002 to 2007, for the increase of concentration index for each category of urban hospital care, we tentatively put forward that OOP payment played a dominant role in health care payment and that other prepayments such as public health insurance could not perform as well in improving health care utilization. On the contrary, the concentration index for each category of rural hospital care has fallen in recent years, which is attributed to the introduction of NCMS, and more importantly, lower coinsurance and more covered services that lead to the villagers having more access to health care, especially to outpatient care at lower prices. We believe that a health insurance (e.g. BMI), even if it is financing equitable, would not improve the extent of use of health care, especially for the low-income group, if the policy’s goal exclusively focused on the coverage rather than the benefit package, such as lower copayment and deductibles, more insured health care services, and so on.CNY 850 billion Plan for the health sector. Benefited by the additional government spending, the average premiums of NCMS and URBMI have increased from 58.9 RMB in 2007 to 230 RMB in 2011, and 100 RMB in 2007 to 300 RMB in 2011 [However, our study primarily used data primarily prior to the year 2007. China’s health care reform after 2007 has been affected by the  in 2011 , respectSome limitations of our data must however be acknowledged, because they may affect the interpretation of our results and call for caution in their generalization as a basis for informing the policy debate about healthcare reforms in China.One major limitation in our study is that it examined one single province of China. It is not likely to fully represent national health care financing characteristics. Notwithstanding this limitation, our study employed percentages and indices to evaluate the implementation of national policies and programs in a whole population. Because of this feature, it is less linked with the provincial economic level and its geographic location. To some extent, a sub-national financing equity can reflect the national financing distribution. We look forward to cross-province comparisons of financing equity to be conducted in the future. Although regional economic development has been taken into consideration in our research through using the Kakwani index, we cannot say with certainty that the renovations implemented in the health care financing system, for example, the expansions of NCMS and BMI, have led to the observed changes in progressivity. Other factors that are difficult to control in our study, such as changes of geographic access to health care, patient satisfaction, and awareness of prevention, would influence financing progressivity. This may be avoided in future studies, should another similar socioeconomic province without similar health policy interventions be collected as the control group, using a difference-in-differences approach.A limitation in data collection is that we used the financing distribution of indirect taxes to represent general tax distribution, owing to the inaccessibility of data collection for direct taxes in the household health interview surveys. Although indirect taxes constitute the major part of general taxes in China, it should be noted that progressivity of tax financing might be underestimated in our study, considering the progressive effect of direct taxes. In addition, a proxy for living standards was household expenditures , which is likely to understate the living standards of rural residents, for household production cannot be neglected in these areas.Last, our study focused on the period between 2002 and 2007. However, as discussed above, policy implemented after 2007 has affected health care financing already. A new round of household survey and analysis would explore the latest financing pattern in health care reform in further studies.st century is the struggle for health care reform. Based on our empirical study, we found that more low- and middle-income households have begun to undertake a higher proportion of the health care financing burden compared to the past. On reason is that the economic gap between the rich and the poor has narrowed. Another reason is the outcomes of the measures launched by renovations in the health sector. In health care financing reform, general taxes are slightly pro-rich since the indirect taxes account for the majority and are preferred by the better-off. Based on a successful experience in rural areas, some small-scale tax exemption, especially for those taxes that concentrate on vulnerable groups, will play a large part in equity improvement.The theme of China’s health sector in the first decade of 21Both urban and rural health insurance have two sides: the Basic Medical Insurance (BMI) does well in financing equity but not for utilization; the New Rural Cooperative Medical Scheme (NCMS) is not equitable in financing, but the poor have more access to health services. Results indicate that, by financing standards, BMI in cities is evenly distributed while NCMS is not. However, the equity performance is partially associated with the coverage. Key for health care reform is not only increasing the number of the insured population but also optimizing the benefit packages’ to broaden access to medical care for more persons, especially for poor individuals. This also explains why the rural areas had better utilization equity than urban areas. Despite their flaws, BMI and NCMS did improve financing equity and health utilization, respectively. However, OOP payment still dominated as the major part of total financing in the year 2007 Table , which lATP: Ability-to-pay; GWIS: Government Welfare Insurance Scheme; LIS: Labor Insurance Scheme; OOP: Out-of-pocket; TEH: Total expenditure on health; VAT: Value-added tax; BMI: Basic Medical Insurance; UWBMI: Urban Workers Basic Medical Insurance; URBMI: Urban Resident Basic Medical Insurance scheme; CMS: Cooperative Medical System; NCMS: New Rural Cooperative Medical Scheme.The authors declare that they have no competing interests.Prof Yuxin Zhao led the study. She designed the study, led the data collection, analysis, and interpretation. Mingsheng Chen contributed to the study design, provided input into the data analysis, and wrote the first draft of the manuscript. Prof Wen Chen contributed to the study design, reviewed the manuscript and helped the writing of the final draft manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/12/466/prepub


Hymenoptera, the insect order that includes sawflies, bees, wasps, and ants, exhibits an incredible diversity of phenotypes, with over 145,000 species described in a corpus of textual knowledge since Carolus Linnaeus. In the absence of specialized training, often spanning decades, however, these articles can be challenging to decipher. Much of the vocabulary is domain-specific , historically without a comprehensive glossary, and contains much homonymous and synonymous terminology. The Hymenoptera Anatomy Ontology was developed to surmount this challenge and to aid future communication related to hymenopteran anatomy, as well as provide support for domain experts so they may actively benefit from the anatomy ontology development. As part of HAO development, an active learning, dictionary-based, natural language recognition tool was implemented to facilitate Hymenoptera anatomy term discovery in literature. We present this tool, referred to as the ‘Proofer’, as part of an iterative approach to growing phenotype-relevant ontologies, regardless of domain. The process of ontology development results in a critical mass of terms that is applied as a filter to the source collection of articles in order to reveal term occurrence and biases in natural language species descriptions. Our results indicate that taxonomists use domain-specific terminology that follows taxonomic specialization, particularly at superfamily and family level groupings and that the developed Proofer tool is effective for term discovery, facilitating ontology construction. The vast majority of our biological knowledge exists only in printed, prosaic natural language, or ‘analog’ texts Journal of Hymenoptera Research (JHR) in the BHL. We extracted OCR text for JHR  from the BHL and manually partitioned the 353 articles for upload into the mx database In the biological sciences one of the important and growing online resources is the Biodiversity Heritage Library (BHL) Once the articles were in the database, a simple dictionary-based, entity recognition tool was developed in mx to match terms captured for the HAO within blocks of text. The tool, or ‘Proofer’, uses string matching, allowing for commonly found exceptions and special cases, thus reducing the impact of malformed OCR commonly found in the BHL-delivered JHR text. The Proofer displays for the user a list of matches on terms in the ontology  that word is added to the final stop words list and no longer presented in subsequent articles, thus reducing the total number of words presented to the user for evaluation. Links to the source code for mx (including the Proofer) are available in the CharParser Other string matching software exists for examining BHL-generated OCR text, primarily focused on taxon name discovery. TaxonFinder For each of the 353 articles a small amount of metadata (as ‘tags’) was captured in the database to facilitate creating lists of terms specific for analysis. First, the articles were reviewed and placed into one of two categories: ‘description of new taxon’ or ‘non-description’. Articles were deemed descriptive based on the use of the words ‘description of’ in the article title or if taxonomic treatments were contained within the body of the article. Additionally for each article, the name of the taxon being described was captured in the database at the family level. Finally, terms representing morphological  concepts and those representing qualitative concepts were differentiated.The resulting data were then used to produce text files useful in R Synonyms and plural terms were summed in the analysis and terms were analyzed as they were recorded in the database. The characters were scored in a binary matrix as presence (1) or absence (0) of the term occurrence within the text of a given article. Four permutations of the matrix were created based on the occurrence of a term. Analyses were preformed that included terms that occurred 2, 10, 50 and 100 times in at least one article. Choosing articles for analysis based solely on occurrence of a term in all articles did not retrieve discrete articles sets at higher numbers, as common terms  are ubiqIn order to assess the occurrence of terms contained within articles, matrices were investigated using agglomerative hierarchical clustering methods performed in the R The grouping of terminals on the basis of binary characters was extensively investigated by agglomerative hierarchical clustering using the linkage methods  From the 353 articles we collected 1189 new morphological terms used by Hymenoptera taxonomists. These were added to the mx database, augmenting the development of the Hymenoptera Anatomy Ontology. The Proofer tool developed to assist analysis of these articles improved the efficiency of term extraction from legacy literature by reducing the number of terms presented to the user for review. Comparison of the number of terms presented to the user with and without the Proofer stop words list for 25 randomly selected articles demonstrated that the Proofer stop word list reduced the number of terms displayed to the user by 1/3 of the total actual word count of the article, which was an 80% reduction in the number of combinations of words displayed to a user by the Proofer tool.180 of the 353 articles were identified to contain descriptions of new taxa, wholly or in part. The most frequently found anatomical terms in those 180 articles are listed in The shortest tree was returned from the Sorensen Average cluster analysis, including characters that were coded for 2 or more terminals, and pruned to superfamily level. This tree results in 63 distinct groupings when the tree was pruned, with observable large clusters of Ichneumonoidea, Chalcidoidea, Symphyta, and Aculeata .The Proofer application and workflow presented here allows for reviewing descriptive text relatively quickly for new terms to supplement the construction of anatomy ontologies. The workflow required the input of domain experts, and open access publications, resulting in the collection of 1189 new terms for the HAO. Although the Proofer tool accumulated numerous terms for inclusion in the HAO, mapping terms to existing classes or creating ontology compliant definitions for those concepts requires further expertise and citation. At present only 144 of the collected terms are tied to HAO concepts. To define concepts, HAO curators initially selected literature that was generally inclusive, taxonomically, of Hymenoptera (including glossary and online resources). This process was done, in part, prior to looking at the BHL JHR articles. Most of the very common terms were already included in the database prior to the term discovery exercise, leaving predominantly highly granular, and superfamily-specific terms used in taxonomic descriptions to be discovered using the Proofer, accounting for the low number of these terms presently fully incorporated in the HAO. These terms will be utilized as the HAO continues to grow, and curators focus on publications from domain experts working exclusively within superfamilies, as this is where the term granularity is demonstrated.The importance of domain expertise in the process of incorporating these terms cannot be overstated. Jenesen and Bork, 2010 JHR between 1993–2007. Also, there is a strong bias in the number of papers concerning Ichneumonoidea and Chalcidoidea represented. This is due, in part, to the large number of taxonomists interested in these diverse superfamilies. Despite these idiosyncrasies in the data obvious groupings for Ichneumonoidea, Chalcidoidea, “Symphyta” and Aculeata were retrieved. On a more detailed level, many family level groupings were recovered, demonstrating that we can group articles, and those taxa described in the articles, simply by the terms used to describe those organisms. In the comparison of cluster analysis, the number of clusters recovered decreased with an increase of characters (terms) used in the analysis. The terms found more commonly are generally used across Hymenoptera. Terms like head, wing, and carina are almost universally used, and thus provide very little signal to group articles. In order to capture the variation in the terminology of the authors, to manifest any observable signal in the analysis, much less frequently used terms needed to be included.Cluster analysis lends evidence for the observation that the Hymenoptera community tends to use granular, domain-specific  terminology. Datasets were analyzed extensively using different permutations of clustering methods and datasets delimited by term occurrence. As expected, a high amount of variation in the number of groups recovered was observed in the analysis results. Not all hymenopteran families were in analysis, because taxonomic descriptions of some groups were not published in The corpus of biological literature will continue to grow and with it the need for more automated methods to utilize and discover the information contained within the articles. Natural language processing methods for biological data discovery is only possible through open access publications, and efforts such as the Biodiversity Heritage Library to make legacy literature freely available. This exercise to observe trends in the terminology illustrates how the accessibility to literature facilitates anatomy ontology construction, and an underlying community trend toward domain specificity and, thus, disparate term usage, one of the primary justifications for unifying Hymenoptera terminology through the HAO.S1 Supplementary Materialhttp://purl.oclc.org/NET/mx-database). The specific version of mx used during analysis is archived on SourceForge and in this combined file.The most recent version of mx code, including the Proofer tool, is available through SourceForge ((ZIP)Click here for additional data file.S2 Supplementary MaterialThe specific JHR BHL article list, list of terms present in the mx database, and R-scripts used in analysis are supplied in this combined file. These files, and all resulting trees, are additionally archived in the Dryad data repository (doi:10.5061/dryad.3g57k).(ZIP)Click here for additional data file.


This study aims to create a convenient reference for both clinicians and researchers so that vis-à-vis comparisons between brain disorders can be made quickly and accurately. We report here the incidence and prevalence of the major adult-onset brain disorders in the United States using a meta-analysis approach.Epidemiological figures were collected from the most recent, reliable data available in the research literature. Population statistics were based on the most recent census from the US Census Bureau. Extrapolations were made only when necessary. The most current epidemiological studies for each disorder were chosen. All effort was made to use studies based on national cohorts. Studies reviewed were conducted between 1950 and 2009. The data of the leading studies for several neurological studies was compiled in order to obtain the most accurate extrapolations. Results were compared to commonly accepted values in order to evaluate validity.It was found that 6.75% of the American adult population is afflicted with brain disorders. This number was eclipsed by the 8.02% of Floridians with brain disorders, which is due to the large aged population residing in the state.There was a noticeable lack of epidemiological data concerning adult-onset brain disorders. Since approximately 1 out of every 7 households is affected by brain disorders, increased research into this arena is warranted. Aging, especially within the brain, has been a topic of major importance to the field of public health. Age-related brain disorders will assuredly increase dramatically in the United States over the coming decades due to the aging baby boomer generation . The most common age-related brain disorders include amyotrophic lateral sclerosis (ALS), Alzheimer’s disease, brain tumor, epilepsy, HIV dementia, Huntington’s disease, multiple sclerosis, Parkinson’s disease, stroke, and traumatic brain injury (TBI). Among the more common epidemiological statistics often quoted are incidence and prevalence. Incidence is defined as the annual number of individuals that have been diagnosed with a particular disorder, while prevalence describes the number of individuals that currently have a particular disorder. A review of the scientific literature over the past ten years indicates that the incidence and prevalence of adult-onset brain disorders have not been explicitly stated together as a group. Rather, the incidence and prevalence are often mentioned in the introduction section of many research articles dedicated for each specific brain disorder, thereby making vis-à-vis comparisons between diseases overly cumbersome. Thus, a reference guide summarizing the most current incidence and prevalence of the most common aging-related brain disorders is lacking. This study provides the most current, well-supported data regarding the incidence and prevalence of the major adult-onset brain disorders in the United States using a meta-analysis approach -27 reveaAn analysis of epidemiological studies was performed to obtain the most recent and reliable incidence and prevalence rates for the most common adult-onset brain disorders. This study adhered to the meta-analysis of observational studies in epidemiology (MOOSE) guidelines   was also obtained using the 2005 US Census estimates The estimated number of adults with brain disorders was calculated by summing the prevalence of each of the major adult-onset brain disorders. The percentage of the United States adult population afflicted with brain disorders was obtained by dividing the estimated number of adults with brain disorders by the 2009 US population estimate. The following equation clearly demonstrates this calculation:In order to obtain state-by-state data, the national data figures were simply scaled down to state levels based on population ratios. It should be noted, however, that state data do exist for the prevalence of Alzheimer’s disease. For the sake of accuracy, these prevalence figures for Alzheimer’s disease were used directly rather than extrapolated.The incidences of disorders in the state of Florida were obtained by multiplying the United States’ incidence data by Florida’s percentage of the entire United States population. The following equation accurately demonstrates this relationship:While this study focused on the state of Florida, the same procedures can be used to estimate the prevalence, incidence, and percentage of those affected by brain disorders for any state in America. The following equations describe how to obtain figures of interest:The incidences of the most common adult-onset brain disorders in the United States were obtained from sources that have been published during the past decade with the exception of ALS, Huntington’s disease, and TBI . All effThe prevalence figures were obtained from studies conducted within the past ten years with the exception of Huntington’s disease. The prevalence of Huntington’s disease was obtained from sources published between 1955 and 1994. The prevalence of the most common brain disorders is given in By using recent US Census data and current data for prevalence in the United States, an estimate of the number of adults and households affected by brain disorders in the United States was obtained. The results of these estimations are summarized in The incidence and prevalence of brain disorders in the state of Florida were obtained using US Census data for both the nation and the state. The data for incidence and prevalence of the most common adult-onset brain disorders in Florida is summarized in The number of Floridians and households afflicted with brain disorders was calculated using the most recent Florida population statistics. The percentage of households and adults affected by brain disorders in the state of Florida is described in During the compilation of the prevalence and incidence data for brain disorders in the United States -27, it sWhile the lack of data was alarming, the scarcity was not consistent amongst the different brain disorders. Low frequency diseases such as ALS and Huntington’s disease tended to have the weaker data, while high frequency diseases such as stroke, Parkinson’s disease, and Alzheimer’s disease had relatively large data sets. For example, although studies regarding the prevalence of Alzheimer’s disease have been published every five years, it was necessary to go back as far as the 1950s to obtain accurate data for the prevalence of Huntington’s disease. This discrepancy only further emphasizes the need for national disease registries, especially for the rarer conditions.While the data were limited, enlightening statistics could be deduced regarding adult-onset brain disorders. In a review of historical data, current figures show an increase in brain disorders over the past fifty years, which is entirely expected considering the increasing number of aged individuals in the United States. Furthermore, the data generally agree with many of the most commonly quoted figures of incidence and prevalence. While these quoted statistics are at times completely unsupported by epidemiological studies, it was reassuring to find that the data were not in complete disagreement with the data that are most often divulged to the public. Finally, the incidence and prevalence data matched up fairly well. For example, the incidence of Huntington’s disease was found to be 1,053 individuals, while the prevalence was found to be 15,611. The calculated mean duration from these two figures is 14.8 years, which is entirely consistent with the 15.6 average duration of the disease that is mentioned in the literature .The sheer number of Americans with brain disorders was extremely alarming. An estimated 6.75% of the US adult population and 8.02% of the Florida adult population are affected by brain disorders. This discrepancy is not surprising due to the relatively more aged population in the state of Florida. Since nearly 15.5 million individuals are currently affected by brain disorders in the United States, continued research into the causes and treatments of these disorders is of the utmost importance. Furthermore, the fact that nearly 1 out of every 7 households in the United States is affected by brain disorders emphasizes the need for continued education on how to care for those afflicted with brain disorders, especially since these percentages are expected to rise due to the aging baby boomer generation.Our present estimates of neurological disorders were based on the U.S. Census Bureau. National surveys, such as the National Health Interview Survey (NHIS) also uses the same data collection agency in generating health statistics, such as comparing male Veterans versus nonVeterans. Moreover, when NHIS addresses the issue of multiple chronic conditions, the focus is on hypertension, diabetes, heart disease, and cancer with neurological disorders largely relegated to stroke. Another survey, called the National Health and Nutrition Examination Survey (NHANES), focuses on assessment of US health and nutritional status. These NHANES statistics generally summarize caloric intake from food and diet in adult and young Americans, and when diseases are examined, the focus is on obesity and hypertension, which obviously are of important health- and nutrition-related diseases. Finally, national insurance databases are also a good source for general health statistics, but found that many of these health insurance coverage surveys utilize the NHIS data, which as we noted above were based on U.S. Census Bureau data that we used in the present survey. Unfortunately, just like the NHIS and the NHANES, these insurance databases  do not report prevalence of neurological disorder estimates. Accordingly, our present survey reports timely and accurate prevalence rates of neurological disorders, which are not covered by NHIS, NHANES or national insurance statistic surveys. We acknowledge the following limitations of this study. The current formula used for calculating the percentage of US households affected by brain disorders did not take into account the numbers of households with more than one case of brain disorder, which will require modifications ^[average size of household])]). We also caution that our focus is adult neurological disorders, requiring new calculations for children neurological disorders. Moreover, the actual prevalence of certain brain disorders, like Huntington’s disease, may be higher than that reported in the literature. This is the overarching objective of our study, in that there seems to be a disconnect between the reported literature and the actual prevalence numbers. Case in point is that many of the numbers reported are based on literature published at least one year earlier and in most cases many years earlier therefore not capturing the current numbers. Our proposed formula employs the prevailing household census thus providing more updated numbers approximating the current national health system numbers. Finally, our epidemiological study did not examine mental disorders, which will be subsequently analyzed.Because government funding and other national research foundations, in part, based their research grant funding appropriations on the prevalence of specific brain disorders, the reporting of actual prevalence numbers is pivotal to guiding the future research direction that will eventually lead to clinical applications, thereby directly affecting the health care status. The availability of recently updated and accurate epidemiological data is likely to serve as key guidance figures for basic science and clinical research, as well as health care. In this regard, at least in the US, stroke, epilepsy, Alzheimer’s disease, and a surging increase in the number of cases of traumatic brain injury represent disease indications that would urgently merit from reporting of precise epidemiological data. The successful solicitation of public awareness and governmental support will benefit from a collective discussion of brain disorders supported by systematic and objective numbers of prevalence as being reported here. The precedent success garnered by the cancer field via a unified approach in getting the attention of both public and government sectors should be considered.The aggregation of the most current and well-supported data regarding brain disorders will be a powerful tool for both clinicians and researchers. As presented, this study allows for a convenient comparison of the most common adult-onset brain disorders. This study fills a glaring void in the available literature, and this clear, logical format will only become more useful as the epidemiological data improve with the concurrent aging of the American population.Checklist S1MOOSE Checklist.(DOCX)Click here for additional data file.


Caring for patients with chronic medical and psychiatric disorders is associated with significant burden. However little is known about the burden experience by caregivers of patients with epilepsy in Nigeria. The objective of this study, therefore, was to assess the level and correlates of burden among caregivers of patients with epilepsy.It was a cross-sectional study carried out among 231 eligible caregivers of patients with epilepsy attending the psychiatric clinic of government psychiatric hospital in Kaduna, Northern Nigeria. Sociodemographic/clinical characteristics of patients and socio-demographic characteristics of caregivers were recorded, and the Zarit Burden Interview administered to caregivers to assess their experience of burden.The mean age of the caregivers was 43.6 ±9.5 years, 52.4% lived outside Kaduna and the mean seizure-free period for the patients was 26.4 ±36.5 weeks. One hundred and twenty (51.9%) caregivers had high burden. High burden was significantly associated with patients aged less than20 years, patient’s unemployment, long duration of epilepsy, short seizure-free period, family history of epilepsy and living outside Kaduna .Caregivers of patients with epilepsy experience significant burden while caring for their relatives and this is mainly associated with patient’s factors and location of residence. Therefore efforts should be made control seizure and make health care available and affordable to all citizens irrespective of where they live. Burden of care is a multi-factorial construct which includes emotional, psychological, physical and economic impact as well as related distressing feelings such as shame, embarrassment, anger, feeling of guilt and self-blame . It is cFamily caregivers have been described as forgotten patients and it was suggested that caregiver’s symptoms such as mood swing, fatigue, headaches, joint and muscle pains, marital and family conflicts, and financial problems may be a reflection of caregiver stress in looking after a sick relative .Studies have shown that caregivers of patients with epilepsy have high levels of strains, fears that the illness may cause injury or death as well as concern about what will happen to patients in future when the caregiver will not be available to cater for patients , 5. In aThis cross-sectional study was carried out among primary caregivers of patients with epilepsy attending the outpatient clinic of the Federal Neuropsychiatric Hospital, Kaduna in the North-western part of Nigeria. Kaduna, the capital of Kaduna State is about 200km from the Federal Capital Territory, Abuja. The hospital receives referral from all states in the Northern Nigeria. Some patients and relatives travel for about 300-350 km to access our health care facility.All consecutive primary caregivers who have been caring for the patients with epilepsy, for at least one year, seen in the outpatient clinic between March, 2008 and September, 2008 were recruited for the study excluding relatives whose patients had any of the following; comorbid chronic psychotic or affective disorders, other chronic physical disorders apart from epilepsy, cognitive disorder such as mental retardation or dementia.The socio-demographic/clinical characteristics of the patients and socio-demographic variables of the caregivers were recorded. The clinical information such as duration of illness, seizure-free period , past medical and psychiatric history as well as family history of epilepsy were obtained from the case files. In addition we conducted mental state assessment and physical examination on the patients. We administered the Zarit Burden Interview (ZBI) instrument  to all tThe test-retest reliability and face validity of ZBI has been established in Nigeria  and the The Research and Ethics committee of the Federal Neuropsychiatric Hospital, Kaduna gave approval for the study and all the participants gave informed consent.th edition of the Statistical Package for Social Sciences we calculated the frequencies and percentages for the variables. Chi-square(X2) test was used to determine the association between burden and categorical variables while logistic regression analysis was performed to determine the predictors of high burden. The level of significance was set at P value less than 0.05.Using the 15The median ZBI score was 25. One hundred and twenty (51.9%) caregivers scored 25 and above and were rated as having “high burden”.The mean and standard deviation of each item of the ZBI score for all the caregivers are shown in Caring for patients below the age of 20 years , patient’s unemployment , long duration of illness , short seizure-free period , family history of epilepsy  and living outside Kaduna  were all significantly associated with caregiver high burden . The othAfter a logistic regression analysis the following factors were predictive of high burden in caregivers: seizure duration , seizure-free period  and location of residence The results of this study show that about 52% of our subjects experienced high level of burden while caring for their relatives and this was significantly associated with caring for younger patients (below 20 years of age) who were unemployed, had longer duration of illness, lived outside Kaduna, had family history of epilepsy and short seizure-free periods.Majority of the caregivers are female and close to 40% are mothers. This is similar to a recent report among caregivers of patients with schizophrenia in Nigeria . The culMore than half of the caregiver had high burden with their greatest concern being what will happen to the relatives in future when they (the caregivers) may not likely be available to look after them. However the low mean burden scores on items 16 and 18  suggest The association of caring for patients below the age of 20 and high burden was inconsistent with Martyns-Yellowe report  among caIndeed, in this study unemployment was also found to be associated with high burden. In patients who have had the illness for a long time, the burden experienced by caregivers may be the cumulative effects of the disease over the years. A short seizure-free period may be an indication of severe illness. Repeated seizure attacks may challenge the coping ability of the caregiver. This may also involve frequent hospital visits, using high doses of anticonvulsant drugs (and hence increase cost), close monitoring of patients at home (with less time for other responsibilities), fear of receiving visitors because of stigma and fear of what future holds for the patient. Any of these (if present) may explain the burden experienced by the caregivers. Where there is family history of epilepsy, the burden experienced by caregivers may be the total burden of caring for two or more “sick relatives”, the burden associated with stigma, inability to meet financial requirement or a combination of all the above.Caregivers living outside Kaduna had higher burden in this study. This is similar to previous reports among caregivers of patients with schizophrenia in Nigeria who lived in rural areas -11. IndeIt is a cross-sectional study carried out in only one centre in the country. We also excluded caregivers of patients with co-morbid medical or psychiatric disorders. However this was done to be certain that the burden experienced by caregivers actually resulted from caring for relatives with epilepsy and not other disorders. In addition, variables like duration of weekly or daily contact with the patients were not assessed.Caring for patients with epilepsy is really challenging and it is associated with enormous burden. Factors that predict high burden are long duration of epilepsy, poor seizure control and living far away from treatment centre. We therefore recommend that all efforts should be made to control seizure in epileptics and to make health care readily available to the entire populace at affordable price. Assistance could also come through the proper implementation of the National Health Insurance Scheme (NHIS).We declare that there is no competing interest.FT Nuhu developed the idea and initiated the design of the study, took part in the data collection, data analysis and writing of the paper, and he read and approved the final copy.  AJ Yusuf took part in the design, data analysis and writing of the paper. He read and approved the final copy. Dr A Akinbiyi contributed to the design, data analysis and writing of the paper. He also read and approved the final copy. JO Fawole contributed to the design, data collection and writing. He read and approved the final copy. OJ Babalola, ZT Sulaiman and OO Ayilara contributed to the design, data collection, writing and approval of the final copy.


In recent years there has been a large scientific and public debate on climate change and its direct as well as indirect effects on human health. In particular, a large amount of research on the effects of climate changes on human health has addressed two fundamental questions. First, can historical data be of some help in revealing how short-run or long-run climate variations affect the occurrence of infectious diseases? Second, is it possible to build more accurate quantitative models which are capable of predicting the future effects of different climate conditions on the transmissibility of particularly dangerous infectious diseases? The primary goal of this paper is to review the most relevant contributions which have directly tackled those questions, both with respect to the effects of climate changes on the diffusion of non-infectious and infectious diseases, with malaria as a case study. Specific attention will be drawn on the methodological aspects of each study, which will be classified according to the type of quantitative model considered, namely time series models, panel data and spatial models, and non-statistical approaches. Since many different disciplines and approaches are involved, a broader view is necessary in order to provide a better understanding of the interactions between climate and health. In this respect, our paper also presents a critical summary of the recent literature related to more general aspects of the impacts of climate changes on human health, such as: the economics of climate change; how to manage the health effects of climate change; the establishment of Early Warning Systems for infectious diseases.  JEL Classification: C2; C3; I1; Q54In recent years there has been a large scientific and public debate on climate change and its direct as well as indirect effects on human health. According to , some 2.In particular, lead exposure has been estimated to account for 2% of the ischaemic heart disease burden and 3% of the cerebrovascular disease burden . In DenmAnopheles gambiae, the soil moisture predicts up to 45% and 56% of the variability of human biting rate and entomological inoculation rate, respectively. The link between malaria and extreme climatic events has long been the subject of study on the Indian subcontinent as well as in various other countries. Early in the twentieth century, the Punjab region experienced periodic epidemics of malaria. Excessive monsoon rainfall and the resultant high humidity were clearly identified as major factors in the occurrence of malaria epidemics. More recently, time-series analyses have shown that the risk of a malaria epidemic increased approximately five-fold during the year following an El Niño event in the Indian region  co co38] coIn  its authAlthough the results of  go on thAs seen above, ,44 find (i) the current and predicted risk distribution of the risk factor; (ii) the exposure-response relationship of the associated disease; (iii) the total burden of diseases  lost to the various diseases associated with the risk factor. The proportion of the total burden of a disease that is attributable to e specific risk factor is called Impact Fraction and is defined as:The comparative risk assessment (CRA) approach has been developed in the late 90s by the WHO with aim of estimating the contribution of that different public health factors make to the global burden of diseases. The CRA is based on the following data for each risk factor:  is the proportion of population in the exposure category,  is an alternative proportion and  is the relative risk exposure at category i compared to the reference level.where Using CRA, which integrate climate models and the evaluation of the health effects of rising temperatures,  estimateIn  projectiThe authors of  considerper capita GDP, taking into account different IPCC climate scenarios. The results show large regional differences in the impacts.Finally,  relate wThe increases in temperatures may cause changes in the environment with increased risk of malaria transmission. The correlation between the disease and the temperature increase is however complex and multifaceted as it requires a comprehension of how local vectors can spread and transmit. The study of  represenVarious time series studies explore the relationship between average temperatures, mid-night temperatures, temperatures in conjunction with rainfall rates, as well as November and December temperatures on malaria. In particular, ,56,57,58Plasmodium falciparum and Plasmodium vivax, whose range is limited at high altitudes by low temperatures. They investigate whether global warming could drive the geographical spread of the disease and produce an increase in incidence at higher-altitude sites. They use data for four high-altitude sites in East Africa from 1950 to 2006. A nonparametric analysis that decomposes the variability in the data into different components is performed and reveals that the dominant signal in three of the sites and the subdominant signal in the fourth one correspond to a warming trend. To assess the biological significance of this trend, the authors drive a dynamical model for the population dynamics of the mosquito vector with the temperature time series and the corresponding detrended versions. This approach suggests that the observed temperature changes would be significantly amplified by the mosquito population dynamics with a difference in the biological response at least one order of magnitude larger than that in the environmental variable. By using parametric models they also find the existence of significant (linear) trends.It has been hypothesized that increasing temperatures could be part of the reason why malaria can now survive at higher altitudes. Many other confounding factors, however, could be causing the increase in malaria in these areas . The dynIn  whether The spatial variation of malaria is analyzed by , who exaIn a subsequent article , the autAnother interesting study in this context is the one of , who proThe work described in  consider0) as the vector capacity multiplied by the duration of the infectious period in humans. The factors that are involved in the calculation of (R0) include: the mosquitoes/people ratio, the number of mosquito bites per person per day, the probability that an infected mosquito infects a human, the chances that a mosquito becomes infected during a blood meal, the incubation period, and the daily survival probability of the mosquito. Indirect factors include: the availability of breeding sites which is related to precipitation, human population density, human population migration, the feeding habits of the mosquitoes, the presence of other animals on which the mosquitoes feed, human exposure (which can be affected by the use of bed nets or other interventions), temperature the immunological and nutritional status of the population, the effectiveness of medical treatment, natural enemies of the mosquitoes, and control efforts. This model is further complicated by algorithms that predict changing genetic adaptations in the parasite and vector that lead to resistance. Based on this approach, evidence is found that the number of people in developing countries likely to be at risk of malaria infection will increase by 5–15% because of climate change, depending on which the Global Circulation Model (GCM) and climate change scenario is used. The areas that are expected to have the most increase in malaria transmission are ones at the fringes of transmission. Unless they are able to use effective control strategies, these regions have low levels of immunity and are likely to experience epidemics [The authors of  propose pidemics .In general, there is considerable uncertainty about the magnitude of the overall impact of malaria. While some models project a net increase in the population exposed to malaria (and in the incidence rate) due to climate change , others The previous section has concentrated on recent quantitative contributions on the relationship between climate and health. However, since this issue involves different disciplines and approaches, a broader view becomes necessary, in order to provide a better understanding of the interactions between climate and health. In this section we present a critical summary of the recent literature related to the following more general aspects of the impacts of climate changes on human health: the economics of climate change; how to manage the health effects of climate change; Early Warning Systems and infectious diseases. Reference  is a keyThe measurement of costs of climate  is a challenging task. The main reasons being that this kind of analyses involves the use of variables and projections that are highly uncertain . Moreover, the effects can be seen only over several decades and with a long-time delay. Based on a review of the studies of the costs of climate warming, the author concludes that the Integrated Assessment Models (IAM) constitute a valid methodological foundation; however first-round IAM studies consider the effects of climate at temperatures that are now likely to be exceeded. The mixed evidence found by different authors crucially relies on what increase in temperature is considered. Indeed, there is a common evidence that the warming above 3–4 °C would reduce global welfare, and that and temperatures increases of 5–6 °C are estimated to result in a 5%–10% reduction in global GDP relative to the “no-climate-change” scenario.per capita reduction of income of 5% at a minimum. This proportion could increase to 11% by considering the direct effects on environment and health . In cas impacts , prioritGlobal warming is expected to increasingly impact food security, water availability and quality, and exact a toll on public health, spurring chronic disease, malaria prevalence, and cardiovascular and respiratory diseases.Current weather conditions heavily impact the health of poor people in developing nations, and climate change has a multiplying effect. A changing climate further affects the essential ingredients of maintaining good health: clean air and water, sufficient food and adequate shelter. A warmer and more variable climate leads to higher levels of some air pollutants and increases transmission of diseases through unclean water and contaminated food. It compromises agricultural production in some of the least developed countries, and it increases the hazards of weather-related disasters.Therefore global warming, together with the changes in food and water supplies it causes, can indirectly spurs increases in such diseases as malnutrition, diarrhea, cardiovascular and respiratory diseases, and water borne and insect-transmitted diseases. This is especially worrisome because a massive number of people are already impacted by these diseases. Also, there is an inter-relationship among these health outcomes. For example malnutrition is linked with malaria and diarrhea which can cause significant weight loss in affected children when accompanied with food scarcity. Malaria and diarrhea can be both cause and effect of malnutrition.“Managing the Health Effects of Climate Change” is a wide multidisciplinary overview of the major threats - both direct and indirect - to global health from climate change, carried out by . EffectsThe starting point of the analysis is that during this century, the Earth’s average surface temperature rises are likely to exceed the safe threshold of 2 °C above preindustrial average temperatures. Rises will be greater at higher latitudes, with medium-risk scenarios predicting 2–3 °C rises on average by 2090 and 4–5 °C rises in northern Canada, Greenland, and Siberia.Health effects of the predicted climate change will cause vector-borne diseases to expand their reach and death tolls, especially among elderly people. Moreover, the indirect effects of climate change on water, food security, and extreme climatic events are likely to have even bigger effects on global health.An integrated and multidisciplinary approach to reduce the adverse health effects of climate change requires at least three levels of action. First, policies must be adopted to reduce carbon emissions and to increase carbon biosequestration, and thereby slow down global warming and eventually stabilize temperatures. Second, further research is needed to understand clearly the links between climate change and disease occurrence. Third, appropriate public health systems should be put into place to deal with adverse outcomes in terms of efficient and cost-effective adaptation measures at local, and national levels.Reference  considerOur capacity to respond to the negative health effects of climate change relies on the generation of reliable, relevant, and up-to-date information. Strengthening informational, technological, and scientific capacity within developing countries is crucial for the success of a new public health movement. This capacity building will help to keep vulnerability to a minimum and build resilience in local, regional, and national infrastructures.Few comprehensive assessments on the effect of climate change on health have been completed in low-income and middle-income countries, and none in Africa. The report endorses the  recommenA key challenge is to improve surveillance and primary health information systems in the poorest countries, and to share the knowledge and adaptation strategies of local communities on a wide scale. Essential data need to include region-specific projections of changes in health-related exposures, projections of health outcomes under different future emissions and adaptation scenarios, crop yields, food prices, measures of household food security, local hydrological and climate data, estimates of the vulnerability of human settlements , risk factors, and response options for extreme climatic events, vulnerability to migration as a result of sea-level changes or storms, and key health, nutrition, and demographic indicators by country and locality.In the view of the commission the key factors to management of health effects of climate change will be: reduction of poverty and inequity in health; incentives for the development of new technologies and application of existing technologies in developing countries; change in lifestyle; improved coordination and accountability of global governance; increase advocacy to reduce climate change trough public health awareness.Considerable research is currently being conducted to elucidate linkages between climate and epidemics. Of the 14 diseases meeting the defined criteria for potential for climate-informed Early Warning Systems EWS, few  are not associated with some sort of EWS research or development activity. For the West Nile virus, an operational and effective warning system has been developed which relies solely on detection of viral activity and it remains unclear whether the addition of climatic predictors would improve the predictive accuracy or lead-time. For the remaining diseases , research projects have demonstrated a temporal link between climatic factors and variations in disease rates. In some of these cases the power of climatic predictors to predict epidemics has been tested. The research reviewed in this report demonstrates that climate information can be used to improve epidemic prediction, and therefore has the potential to improve disease control. In order to make full use of this resource, however, it is necessary to carry out further operational development. The true value of climate-based early warning systems will come when they are fully integrated as one component in well-supported systems for infectious disease surveillance and response. This paper has focused on the critical evaluation of recent quantitative assessments of health risks associated with climate change. The main contribution of our paper is to offer an integrated vision of the main scientific conclusions on the effects of climate change on human health, which are supported by the use of formal qualitative analyses.In this respect, the journal articles surveyed in this paper have been classified according to the quantitative models adopted, which have been identified in the broad classes of time-series models, cross-section, panel and spatial models, and non-statistical approaches, such as computable general equilibrium models and comparative risk assessments. Moreover, our paper has presented a critical summary of the recent literature related to more general aspects of the impacts of climate changes on human health, such as: the economics of climate change; how to manage the health effects of climate change; Early Warning Systems and infectious diseases; evaluating the risks to human health which are related to climate change; the implications of modifiable environmental risk factors. Climate change is already affecting human health, livelihoods, safety, and society and the expectation is that these effects will become greater. The climate impact is still difficult to assess with accuracy because it results from a complex interplay of factors. It is challenging to isolate the human impact of climate change definitively from other factors such as natural variability, population growth, land use and governance. In several areas, the base of scientific evidence is still not sufficient to make definitive estimates with great precision on the human impacts of climate change. However, data and models do exist which form a robust starting point for making estimates and projections that can inform public debate, policy-making and future research. The pressure for increased precision in estimates presents a rallying cry for investment in research on the social implications of climate change. Three areas which require additional research in the near future are:– The attribution of weather-related disasters to climate change, as no consensus estimate of the global attribution has yet been made;– Estimate of economic losses today, as the current models are forward looking;– Regional analysis, as the understanding of the human impact at regional level is often very limited but also crucial to guide effective adaptation interventions.