Objective: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire. Methods: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score. Results: Compared with the seven conventional machine learning algorithms, the DNN showed higher stability and achieved the best accuracy with 0.88, which also showed good results for identifying normal (F1-score = 0.88), mild cognitive impairment (MCI) (F1-score = 0.87), very mild dementia (VMD) (F1-score = 0.77) and Severe dementia (F1-score = 0.94). Conclusion: The deep neural network (DNN) classification model can effectively help doctors accurately screen patients who have normal cognitive function, mild cognitive impairment (MCI), very mild dementia (VMD), mild dementia (Mild), moderate dementia (Moderate), and severe dementia (Severe). We present a deep neural network (DNN) classification model that can help clinicians accurately screen patients who have normal cognitive function, mild cognitive impairment (MCI), very mild dementia (VMD), mild dementia (Mild), moderate dementia (Moderate), and severe dementia (Severe). I.Dementia characterized by cognitive and intellectual impairment is a kind of neurodegenerative diseases et al.et al.In recent years, deep neural network (DNN) has garnered clinical interests in cognitive diagnostic applications due to its advantages in efficient classification. Moreover, many existing methods have been proposed, where some of them Hence, we propose a DNN multi-class classification model to assist the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), mild dementia (Mild), moderate dementia (Moderate), and severe dementia (Severe) using informant-based questionnaire. In this paper, 6,701 individuals are enrolled, which allow us to have a larger size of samples to train our model.II.There are two major steps in the proposed framework: (1) feature selection: using feature selection algorithms to optimize or even reduce the number of neuropsychological tests; (2) classification: training a deep neural network to classify the participants into normal cognitive function, MCI, VMD, Mild, Moderate, and Severe categories.A.In this work, the study used data collected from the three centers of the Show Chwan Healthcare System. The data selected from the register-based database of the Show Chwan Health System were analyzed anonymously with the informed consent from all participants, and the study was designed retrospectively in accordance with relevant guidelines and regulations. The project was reviewed by the Medical Research Ethics Committee of Show Chwan Memorial Hospital, and the study was approved by the Data Inspectorate.The data for the study consisted of samples of clinical and neuropsychological assessment obtained from 6701 patients. For detailed neuropsychological tests, we assessed the history of cognitive status and objective assessments including the Clinical Dementia Ratings (CDR), Mini Mental Status Examination (MMSE), Cognitive Abilities Screening Instrument (CASI) and Montreal Cognitive Assessment (MoCA) performed to evaluate memory, executive function, orientation, visual-spatial ability, and language function we randomly split the data with the ratio of 9:1, of which 90% are training data sets (6030 participants) and 10% are test data sets (671 participants) B.Neuropsychologists selected 50 items from neuropsychological tests to form an optimal questionnaire for screening patients with varying severities of dementia. While the proposed algorithm still works with the entirety of those features, utilizing feature selection lowers the computational requirements and improves interpretability as we are able to see more clearly which items are directly correlated with a patient’s mental condition Information gain is an information theory method widely used in data mining. The information gain measures how much information the feature can provide the classification model. If a feature has a larger information gain value for a class, the feature contains more classification information for that class. We used the information gain algorithm provided by Weka, which is an open source machine learning and data mining software based on the Java environment.C.We proposed a DNN classification model based on the Keras framework. In order to study the performance of the DNN model for discriminating normal, MCI, VMD, Mild, Moderate, and Severe, we compared its results with others well-known classification models . First, we tested the performance of the model respectively using 50 features selected by the Neuropsychologists and the top 44 features selected by information gain score. Then, for testing the stability of the classification model, we tested 10 training-test runs separately. Finally, we evaluated the accuracy of model classification by measuring the average accuracy of the 10 training-test runs and evaluated the classification performance of each sub-category by using the 10 training-test runs with the lowest F1-score. TP is the number of positive samples predicted by the classifier in the number of true positive samples, FP is the number of positive samples predicted by the classifier in the number of true negative samples, TN is the number of negative samples predicted by the classifier in the number of true negative samples, FN is the number of true positive samples predicted by the classifier as negative samples. The accuracy is the evaluation of the correct rate of the classifier as a whole. It is defined as Accuracy = (TP+TN)/(TP+FN+FP+TN). Generally speaking, the higher the accuracy, the better the classifier. F1-Score is a kind of statistic, which is also called F-measure. F1-Score is the weighted harmonic average of Precision and Recall(Sensitivity). It is defined as F1-Score = 2TP/(2TP+FP+FN) and carries a range of 0 to 1, with higher scores indicating a more robust classification model. It is a commonly used evaluation criterion in the field of IR . It is often used to evaluate the quality of classification models. Precision, also called Positive Predictive Value in clinical settings, refers to how many of the samples that the model is positive are true positive samples, which is defined as Precision = TP/(TP+FP). Recall, also called Sensitivity in clinical settings, refers to how many positive samples are classified as positive by the model, which is defined as Recall = TP/(TP+FN).1)DNN is a multi-hidden layer feedforward neural network, which has a total of L+1 layers, the 0th layer is the input layer, the 1st to L−1 layers are hidden layers, the Lth layer is the output layer. The nodes of adjacent layers are connected by links and the weights of all links form a feedforward weight matrix. As shown in As a feedforward neural network, given an input vector, DNN can get an output vector immediately, that is to say, the output of DNN only depends on the current input, so DNN is suitable for pattern classification problem. This paper adjusts the network parameters of the whole network through supervised training. After repeated extensive training, we got relatively optimal hyperparameters in DNN. The constructed DNN model consists three hidden layers, the first layer uses the relu (rectified linear unit) activation function, the second layer uses the tanh 2)We investigated other commonly used classification models  in Python toolbox An MLP can be seen as a directed graph, consisting of multiple node layers, each layer connected to the next layer. In addition to input nodes, each node is a neuron (or processing unit) with a non-linear activation function0. Unlike the DNN model, the output activation function is not used here. GCForest is a model of a deep forest, which is mainly divided into two parts, multi-grained scanning, and cascade forest structure. GCForest performs well in small sample data. Random forest is an algorithm that integrates multiple trees by the idea of ensemble learning. Its basic unit is a decision tree, which is a subclass of ensemble learning. It depends on the voting choice of a decision tree to determine the final classification results. In the basic Adaboost algorithm, each weak classifier has the right to weight, and the weighted sum of the weak classifier prediction results forms the final prediction result. In training, training samples have also weight, which dynamically adjusts during the training process. The samples that are misclassified by the previous weak classifier will increase the weight, so the algorithm will focus on the difficult samples. The Logitboost algorithm is a discriminant classification algorithm based on machine learning. LogitBoost belongs to the AdaBoost system. The LogitBoost structure is similar in general, but its loss function uses the maximum logarithmic likelihood function. The basic method of Naïve Bayes is to calculate the probability that the current feature samples belong to a certain classification based on the statistical data and the conditional probability formula, and select the maximum probability classification0. Support Vector Machine (SVM) has achieved the best performance in many classification problems. The kernel function subtly transforms the linear indivisible problem into a linear separable problem, and has very good generalization performance.III.A.We find that the classification accuracy decreases with the decrease of the number of features. When the number of features decreases to 44 features, the classification accuracy dropped down. So we discarded the corresponding features by setting the threshold of the information gain score to 0.16.B.C.IV.In this study, we proposed a deep neural network classification model based on the Keras framework. In order to evaluate the advantages of our proposed method, we compared two indicators, accuracy and F1-score. In addition, we compared our method with other well-known machine learning methods. The results showed our DNN method had a stable classification performance, higher classification accuracy and performed well in dealing with class imbalance problems. It has great potential for clinical application. We will discuss these in detail below.From the perspective of model classification stability and accuracy, when using 50 features by the Neuropsychologists, the DNN model shows higher stability and the classification accuracy is the highest compared with the other seven algorithms , basically stable at around 0.88. When it comes to the classification accuracy of each category, our results show that the DNN model improved the overall performance of the classification accuracy of each category.We further studied the classification performance of the DNN model after reducing features by information gain feature selection, which is to simplify the procedure of diagnosis and enhance the practicality in clinic. The overall classification performance of the model has decreased after the reduction. In order to ensure the classification accuracy of the model, we set the threshold of information gain fraction to 0.16, thus discarding some redundant features. Compared with the six classification models, the DNN performed the best accuracy with 0.88, which also showed good results for identifying normal (F1-score = 0.88), MCI (F1-score = 0.87), VMD (F1-score = 0.77) and Severe (F1-score = 0.94).V.We proposed a new approach to diagnosing normal, MCI, VMD, Mild, Moderate, and Severe using a deep learning approach, more specifically, a deep neural network classification model based on the Keras framework. By using the real-world dataset, i.e., the register-based database in the Show Chwan Health System, we tested and validated our method. Overall, the results of this project show that the proposed DNN model provides a tool with accurate and stable performance for clinicians to diagnose the early stages of dementia. Our future work will be carried out from neuroimaging to further improve our diagnostic model.


Faculty members face demands such as research, outreach programs, and management activities. Such demands may expose faculty to burnout. Burnout affects the physical, psychological and social health of faculty members, but it is still unclear how it affects their quality of life. We aimed to assess the impact of burnout on the quality of life (QoL) of faculty members from different fields of knowledge.Oldenburg Burnout Inventory–OLBI and World Health Organization Quality of Life-Abbreviated version–WHOQOL-Bref) in a sample of 366 faculty members from a public university. Scores were analyzed using Student’s t-test, analysis of variance (ANOVA), binary logistic regression, and structural equation modeling (SEM).Cross-sectional study using validated tools for measuring burnout and QoL  suffered from burnout. Men had higher scores of quality of life than women in the physical health , psychological  and social relationships  domains. Women were more exhausted than men . Faculty members’ perception of quality of life and burnout did not differ according to their field of knowledge (p>0.05). Participants who felt tired before arriving at work were less likely to report good quality of life . Faculty members who stated they needed more time to relax after work were less likely to be satisfied with their health . Burnout showed a negative association with quality of life .Burnout negatively affects faculty members’ quality of life, regardless of their field of knowledge. Our results suggest the implementation of programs and actions to prevent burnout to faculty members, especially to women, as their quality of life may affect the quality of the education provided. Working at university as a faculty member may provide satisfactory experiences. However, university teaching can also be stressful and may deteriorate workers’ quality of life , 2. FacuMany studies –10 argueStudies on quality of life of university faculty members are scarce , 13–15. Our hypothesis is that faculty members’ field of knowledge and burnout may affect their quality of life. We aimed to verify (i) the differences in quality of life and burnout according to faculty members’ gender and field of knowledge and (ii) the association between burnout and faculty members’ quality of life. Identifying groups that have higher risks of presenting worse quality of life may help educational managers to direct strategies towards faculty members’ health and retention.This is a cross-sectional study approved by the institutional research ethics committee. This study was held at a public university, with 90 undergraduate programs, 20 PhD programs, 44 academic master’s degree programs, and several other post-graduation programs. Participating faculty members were selected from different academic units and fields of knowledge: applied human/social sciences, life/health sciences, and exact/ technological sciences. As an inclusion criterion, participants should have been working for at least one year in the institution.2 = 0.15; p<0.05), with sampling error set at 5% and statistic power at 80%, the sample size comprised 298 participants [When the study was carried out, 1,324 faculty members met the inclusion criterion. In order to determine the differences with a medium effect size , the Oldenburg Burnout Inventory (OLBI), and the World Health Organization Quality of Life-Abbreviated version (WHOQOL-Bref).OLBI assessed the burnout syndrome in the study population . Its BraThe Brazilian validated version of WHOQOL-Bref, a self-administered general questionnaire on quality of life, assessed participants’ quality of life . It compDescriptive statistics  were used to describe the sample, quality of life, and the burnout syndrome. The assumptions of structural equation modeling (SEM) were verified–sample size, normality, missing data, outliers, multicollinearity, singularity, linearity, homoscedasticity .t-test and analysis of variance (ANOVA). The magnitude of statistically significant differences (effect size) was assessed by determining Cohen’s d [d values were considered small, medium and large effect sizes, respectively: 0.2, 0.5, and 0.8 [The reliability of each scale in this study was assured through internal consistency  , with thohen’s d , 33. The and 0.8 . Medium  and 0.8 .We also carried out a binary logistic regression analysis to assess which items of the OLBI scale are more probable to affect the general perception of quality of life and satisfaction with health. In this case, OLBI and WHOQOL-Bref answers were dichotomized .2/gl), the Root Mean Square Residual (RMR), the Goodness-of-Fit Index (GFI), the Adjusted Goodness-of-Fit Index (AGFI), the Comparative Fit Index (CFI), and the Root-Mean-Square Error of Approximation (RMSEA) as indices of SEM [We performed structural equation modeling (SEM) to confirm the theoretical framework proposed in this study: the impact of the burnout syndrome in faculty members’ quality of life. In the model, OLBI dimensions (disengagement and exhaustion) behaved as independent variables; and the four domains of WHOQOL-Bref  behaved as dependent variables. We used the maximum likelihood method and the traditional chi-square, the chi-square/degrees of freedom ratio  and AMOS  were used for data analysis.The final sample comprised 366 participants. Most of them (59.3%) were men, aged between 28 and 69 years , and married (74%). They have been working in the institution for 12.3 years (mean) (SD = 11.12). Regarding their field of knowledge, 101 (27.6%) were from exact/technological sciences, 124 (33.9%) from applied human/social sciences, and 141 (38.5%) from health/life sciences. Most participants (85%) had a PhD degree and worked with exclusive dedication to the university (40 hours/week) (86.1%) . Accordid≤0.45). Women were more exhausted   . No sign = 0.47) .Concerning the general perception of quality of life, participants who agreed with the items “There are days when I feel tired before I arrive at work” and “After work, I tend to need more time than in the past in order to relax and feel better” were less likely to report good quality of life . Concerning the general perception of health, faculty members who agreed with the item “After work, I tend to need more time than in the past in order to relax and feel better” were also less likely to report satisfaction with their health  .X2 = 30.962; X2/df = 3.84; RMR = 0.012; GFI = 0.972; AGFI = 0.926; CFI = 0.982; RMSEA = 0.091.We confirmed the theoretical framework proposed in this study using the maximum likelihood method. Burnout showed a significant negative association with quality of life  . The modThis study confirms that work issues affect faculty members’ quality of life. This is a pioneer study in approaching the effect of the burnout syndrome on the quality of life of faculty members from different fields of knowledge.Most participants were satisfied with their quality of life and health. Satisfaction refers to a positive general and emotional state. Faculty members’ affective commitment is closely related to job satisfaction and involvement , 37, whiIn our study, female participants had a lower perception of their quality of life in the physical health, psychological, and social relationships domains in comparison to their male peers. They were also more exhausted than male faculty members were. Studies have shown that men and women show different risk behaviors, and men seem to give less importance to their physical and psychological symptoms when compared to women . Based oNo significant difference was found among faculty members from different fields of knowledge. Perhaps, the institutional work environment and faculty members’ relation with work have a direct effect on their quality of life, regardless of the field of knowledge or particularities of the work process. The high levels of burnout among faculty members may be another explanation for these results. More than a third of participants suffered from burnout. This result was already expected, since studies carried out in Portugal, Germany, Spain, Mexico, Colombia, United States, and Canada have shown that the prevalence of the burnout syndrome among faculty members is high , 48–53, The structural equation modeling confirms that burnout may negatively affect faculty members’ quality of life. General feelings of work overload, a strong need for rest, and a state of physical exhaustion are considered important risk factors for the general perception of quality of life and satisfaction with health. Faculty members who feel tired and lack energy are the most affected. Those members have more chances of not enjoying life outside work, not seeing a meaning in their lives, and needing constant medical care. In addition, they are unsatisfied with themselves, with their sleep, their physical appearance, their concentration, their ability to carry out daily activities, and their ability to work. They also have more chances of having negative feelings, such as bad humor, despair, anxiety, and depression. Our results corroborate previous research stating a negative effect of burnout on healthcare faculty members’ quality of life . StudiesQuality of life and burnout among faculty members are not solely determined by the intrinsic character of the job. They are also influenced by how their work is organized, how the educational institution deals with their faculty, and how faculty members see their relationship with their institution . Based oOur research has some methodological limitations. It is a cross-sectional study with a convenience sample from only one public higher education institution, which restricts generalizations of our results to similar institutions. The use of a generic quality of life questionnaire may have limited conclusions about specific aspects related to work. However, this research used valid and reliable tools to investigate the quality of life and burnout among faculty members, which allows replication and comparisons with further studies. In addition, our study confirms the theoretical model on the effect of burnout on faculty members’ quality of life, with practical and important implications for managers. Knowing how burnout relates to quality of life may support and guide more effective health promotion strategies in the work environment of these professionals.For future research, other methodological designs can be used , by including universities of different legal natures (public and private), inserting other variables to explain quality of life, e.g. personal  and organizational variables . The development of future qualitative studies focused on the causes of faculty members’ distress may also contribute to their work, considering individual and social repercussions in their own health and in the quality of the education provided.


Oral functions are known to decline with aging. However, there is limited evidence that supports the relationship between oral health and frailty. This study aimed to clarify the relationship between oral hygiene conditions, measured by remaining teeth and mucosa, and frailty among elderly people dwelling in a Japanese rural community.We surveyed self-reliant elderly individuals aged ≥65 years who were dwelling in the Sasayama-Tamba area of Hyogo, Japan. Frailty was evaluated according to the total score of the Kihon Checklist (KCL). Based on the KCL score, elderly participants were divided into three groups: robust, pre-frail, and frail. The items measured to evaluate oral environment included the number of remaining teeth, denture usage condition, oral hygiene status, dry mouth condition, and salivary bacterial count. For statistical analysis, Fisher’s exact test, one-way analysis of variance, and multiple comparison technique were used.Of 308 elderly participants, 203 (65.9%), 85 (27.6%), and 20 (6.5%) belonged to the robust, pre-frail, and frail groups, respectively. The proportion of participants who were judged to have poor hygiene was significantly higher in the frail group than in the other two groups. The bacterial count was significantly smaller in the frail group than in the robust group, and the frail group had fewer number of remaining teeth than the other two groups, suggesting that the number of remaining teeth may be associated with bacterial count.In elderly adults, physical frailty may affect the oral hygiene status and condition of the remaining teeth. In aged societies such as Japan, there is an urgent need for protocols that outline the appropriate management of age-related diseases and long-term care associated with disability. The provision of appropriate interventions and care for elderly people at the onset of frailty has been shown to have an important role in preventing the progression of disability , 2. SimiTherefore, this study aimed to identify a relationship between oral hygiene conditions, as measured by status of remaining teeth and mucosa, and frailty among elderly people dwelling in a Japanese rural community and assess the association between individuals with potential frailty and their oral health status.This cross-sectional study was approved by the institutional review board of Hyogo College of Medicine  and is part of the Frail Elderly in the Sasayama-Tamba Area (FESTA) study. The purpose of the FESTA study was to clarify associations between lifestyle habits and frailty in elderly people. The Hyogo College of Medicine Sasayama Medical Center is a branch hospital of the Hyogo College of Medicine, located in the Tanba-Sasayama area of Hyogo, Japan. Therefore, this study was conducted in the Sasayama area.Community-dwelling individuals aged ≥65 years were recruited between June 2016 and December 2016 from the Sasayama-Tamba area, a rural area in Hyogo, Japan. The area is a mountainous region where many people work as farmers. It has a population of 41,490, and an aging rate of 32.6%, compared to the 2015 aging rate in urban Japan of 26.7%.We recruited participants for this study by distributing newspaper inserts and poster advertisements at Hyogo College of Medicine Sasayama Medical Center. Individuals participated in this research voluntarily, and all participants provided written informed consent. All participants in this study were independent elderly individuals who required less than level 1 care based on the long-term care insurance system in Japan . IndividA self-administered questionnaire survey was conducted using the Kihon Checklist (KCL) , which cMedical history data were collected on the following 18 items, using closed-ended questions: hypertension, diabetes, hyperlipemia, hyperlipidemia, liver disease, kidney disease, heart disease, asthma, tuberculosis, pneumonia, gastric ulcer, osteoporosis, rheumatism, thyroid, collagen disease, blood disease, stroke, cancer, and other. All participants provided written informed consent prior to administration of the questionnaire. Participants were informed of their right to withdraw from the study at any point without negative consequences or a loss of privileges.Participants who used a removable denture were instructed to insert the denture at the time of the assessment. The oral function of participants was assessed from 10:00 to 14:00. All oral examinations were performed in a dedicated room in the survey venue; a partition was placed between participants to protect their privacy during the intraoral examination. Any equipment used in this study and that came into contact with patients was disposable and changed for each patient.Participants’ number of remaining teeth  and the denture usage condition were assessed while patients were in a reclining chair ; LED headlights  were used by the examiner. We used the Clinical Oral Assessment Chart , Level 2 , Level 3 , Level 4 , Level 5 , Level 6 , and Level 7 (bacteria count ≥108).We examined dry mouth conditions by measuring twice the oral moisture of the dorsum of the tongue and the buccal mucosa using an oral moisture meter   . Scores2 test, Fisher’s exact test, Mann–Whitney U test, one-way ANOVA, and Bonferroni’s modification. A p-value of < 0.05 was considered significant. Statistical analyses were performed with IBM SPSS 22.0 J .All statistical analyses were performed using theχP = 0.045, Mann–Whitney U test).A total of 308 elderly adults participated in this study; of these, 107 were male and 201 were female, with a mean age of 72.7 ± 7.1 years. Frail and pre-frail participants accounted for 7 and 28%, respectively .The proportion of participants who responded as having no problem with “teeth and dentures” was significantly higher in the robust group than in the other two groups Table . A few pThere are currently no standardized items and established criteria for evaluating frailty. The most common method uses the Cardiovascular Health Study (CHS) criteria published by Fried et al. . The prep < 0.001), suggesting that teeth number is positively correlated with bacterial count.In this study, the frail group tended to have a lower number of teeth. Tohara et al.  have repElderly individuals with more remaining teeth are reported to be less frail and have better quality of life than edentulous individuals . Poor orThere are some limitations of this study. First, due to its cross-sectional design, a causal relationship between oral hygiene and frailty could not be confirmed. A longitudinal study for this purpose is ongoing. Second, we did not include data on current medication of the participants in the analyses of this study, because the information we received from participants was not reliable . As several medications used internally can cause thirst, this should be considered in future studies.Oral hygiene status and the condition of the remaining teeth might be affected by the degree of frailty of an individual. The bacterial count of the frail group was significantly smaller than that of the robust group, suggesting that the number of remaining teeth is associated with the salivary bacterial count. Therefore, health care providers should carefully consider management of remaining teeth of older adults.


To document the underlying science of how the Pan American Health Organization (PAHO) adapted the Hanlon method, which prioritizes disease control programs, to its wider range of program areas and used it to implement the PAHO Strategic Plan 2014 – 2019.In 2014, PAHO established a Strategic Plan Advisory Group (SPAG) with representatives from 12 Member States to work closely with the PAHO Technical Team to adapt the Hanlon method to disease and non-disease control programs. Three meetings were held in 2015 – 2016 during which SPAG reviewed existing priority-setting methods, assessed the original Hanlon method and subsequent revisions, and developed the adapted method. This project was initiated by Member States, facilitated by PAHO, and conducted jointly in transparent and horizontal technical cooperation.From the original Hanlon equation, the PAHO-adapted method maintains components A (size of problem), B (seriousness of problem), and C (effectiveness of intervention), drops component D , and adds component E (inequity) and F . The PEARL score was dropped because it serves a purpose for pre-screening process, but not in the priority-setting process for PAHO.The PAHO-adapted Hanlon method provides a refined approach for prioritizing public health programs that include disease and non-disease control areas. The method may be useful for the World Health Organization and country governments with similar needs. Priority-setting is an important component of strategic planning . Given tThis article summarizes the findings of a research project conducted by PAHO and 12 Member States to adapt the Hanlon method for use in developing and implementing the PAHO Strategic Plan 2014 – 2019 . The purIn 2013, during the development of the Strategic Plan , a ProgrIn 2014, a Strategic Plan Advisory Group (SPAG) was established to refine the Framework. Its purpose was to: (a) review other published priority-setting methods; (b) assess the strengths and weaknesses of the original Hanlon method and subsequent revisions; and (c) develop a PAHO-adapted Hanlon method. Given the limitations of the original Hanlon method, the objective of this study was to refine the methodology to suit the breadth of PAHO programs.This research project was conducted during the development of the PAHO Strategic Plan 2014 – 2019. More than 15 priority-setting methods were reviewed in search of a reasonably objective and potentially useful method to prioritize PAHO programs . SeveralThis study combined expert consultation by an advisory group with methodology development by a technical group. Because the final, adapted method would affect all countries and territories of the Americas, the experts needed to represent the entire Region. The expert team, the SPAG, included representatives from 12 Member States hailing from all four sub-regions of the Americas ; namely, the Bahamas, Brazil, Canada, Chile, Costa Rica, Ecuador, El Salvador, Jamaica, Mexico, Paraguay, Peru, and the United States of America.In order to facilitate the process, the SPAG established a technical core group of experts, led by Canada, that included several technically-oriented members of the SPAG and the PAHO Technical Team. The aim of this technical group was to review published priority-setting methods and to develop an adapted Hanlon method. This group also provided technical inputs to address issues and challenges identified by SPAG. This collaborative approach proved to be an effective way to achieve the project objectives. SPAG worked with the PAHO Technical Team via virtual and face-to-face meetings.The technical core group presented findings to three SPAG face-to-face meetings for consideration and approval. The first meeting was held in May 2015 in Washington, D.C. It established an action plan to address Member States’ request to improve the Framework, and produced a revised PAHO-adapted Hanlon equation with refined definitions of several variables. The second meeting was held in August 2015 in Mexico City. It pilot-tested an improved version of the PAHO-adapted Hanlon method and developed a process and timeline for application of the new method by Member States. Pilot testing was conducted during the meeting by two separate groups: the 12 country representatives of the SPAG and 20 senior managers of the Mexican Secretariat of Health. The third meeting was held in April 2016 in Washington, D.C. It validated the final version of PAHO-adapted Hanlon method through a final pilot test by SPAG members.There are a wide variety of methods for priority-setting . Simple The original Hanlon equation was published in 1984 , calculated as the sum of four factors, each worth 0 – 10 points ; C is the effectiveness of the intervention (0 – 10 points); D is its feasibility (0 or 1), determined by five factors (each 0 or 1) that are referred to as “PEARL” . Because the obtainable product of the four components  has a range of 0 – 300 points, dividing by 3 gives the BPR a range of 0 – 100.Vilnius and Dandoy  made sevwhere B ÷ C is benefit-cost ratio (0 – 10 points). With the five components, dividing by 6 gives the BPR a range of 0 – 100.Neiger and colleagues  made a sEarlier versions of the BPR model suggested that analysis of the PEARL criterion should occur after all data had been collected and translated to BPR scores. Since the function of PEARL is to determine whether stakeholders should proceed with or eliminate a health problem, this criterion should be analyzed prior to data collection and analysis .In other words, they consider PEARL to be a pre-screening process to identify the health problems for ranking and not part of the priority-setting process:The PAHO-adapted Hanlon method was designed to rank the breadth of PAHO programs, both disease-oriented and non-disease-oriented . The oriThe PAHO context also needs two new components: E (inequity) and F :where A is the size of the problem (0 – 10 points), meaning prevalence or incidence of the disease (for disease-oriented programs) or extent of system or program deficiencies (for non-disease-oriented programs); B is the seriousness of problem (0 – 20 points), derived from the sum of four factors: B1 (urgency), B2 (severity of consequences), B3 (economic loss), and B4 (negative impact on others), each with 0 – 5 possible points. For non-disease-oriented programs, this also considers seriousness associated with deficiencies of the system or program and consequences of inaction; C is the effectiveness of intervention (0 – 10 points) based on the availability of cost-effective interventions to address the problem or deficiencies in systems or programs. It is the product of 2 factors: C1 (efficacy) and C2 (reach or coverage), each with a range of 0% – 100%. For non-disease-oriented programs, this is a qualitative assessment of effectiveness. E is inequity (0 – 5 points), measured by the degree of differential occurrence of disease or access to health programs between socially-determined population sub-groups  deemed to be unjust . F is the institutional positioning (a factor from 0.67 – 1.50), and refers to the extent to which an institution, such as PAHO, is uniquely positioned to assist a Member State address a program need from its own perspective. Initially, the trial range of F was 0.50  to 2.00 (a doubling effect), but during pilot testing these values were found to be too overwhelming. The maximum value was reduced from 2.00 to 1.50 and found to be satisfactory during further testing. As F is a multiplier, if the maximum is 1.50, the minimum is its reciprocal, or 0.67. A score of 1.00 means a Member State believes PAHO could maintain its technical cooperation at the current level. A score greater than 1.00 signifies that PAHO should increase its technical cooperation, while a score less than 1.00 means PAHO should decrease support because the Member State can deal with the problem or has another strategic partner assisting. Dividing by 5.25 gives the BPR a range of 0 – 100.For component A (size of problem), various measures are used, e.g., the percent of the population exposed to the problem. For diseases, size of the problem refers to the prevalence/incidence. Points: 0 – 3 = low prevalence/incidence; 4 – 6 = medium; and 7 – 10 = high. For non-diseases , size of problem refers to the extent of the problem . It can be measured by percent of the population affected by the problem , or degree of limitations in response capacity. Points: 0 – 3 = low percent of population adversely affected; 4 – 6 = medium; and 7 – 10 = high.Component B (seriousness) has four factors. Urgency (B1) means whether a problem is worsening, stabilizing, or improving, or whether progress is made towards achieving the target, based on previous 5-year trend data. Points: 0 – 1 = problem improving or good progress; 2 – 3 = problem remains the same; and 4 – 5 = worsening. Severity of consequences (B2) is measured by the extent of premature mortality and disability, loss of quality of life, or burden to health services caused by the problem. Points: 0 – 1 = low; 2 – 3 = medium; and 4 – 5 = high. Economic loss (B3) is the social costs, both direct and indirect, associated with the problem. While factor B4, originally defined as “involvement of other people” . In an eEffectiveness (C) is the degree of success of an intervention in producing a desired outcome under usual circumstances. Following the original definition , effectiComponent D (PEARL) is deleted from the equation, consistent with the justifications provide by Neiger and colleagues  and the Component E (inequity) is a new feature in the PAHO-adapted equation. Health inequities are defined as “observable differences in health between two or more socially-determined groups that are judged to be unjust; that is, arbitrary, unnecessary, and avoidable” . InequitComponent F  is another new feature of the PAHO-adapted method. This component was based on the concept first suggested by Musgrove  that higThis PAHO project shows the advantages and effectiveness of the expert consensus approach. Three face-to-face meetings over a total of 8 days with an average of 20 experts (or 160 person-days), plus virtual meetings and individual time invested by members of the SPAG and the PAHO Technical Team, provided the basis for the development of the PAHO-adapted Hanlon method. The final product arising from this expert consensus approach would have been impossible for one expert to complete despite working for 160 days. A single expert would not have been able to identify and resolve the issues from the various sub-regions and multi-factor issues related to the wide range of PAHO programs. The expert group approach also fostered a team environment, as experts from different areas of the Region tackled a common problem together. At the conclusion of this project, the SPAG members had developed strong links that would enhance future country collaboration on PAHO endeavors. Such collaboration has been considered instrumental to developing new strategic frameworks for the Region, as shown by the involvement of more Member States on the new SPAG for the PAHO Strategic Plan 2020 – 2025. Expert consensus is a methodology that has been used successfully in other PAHO prioritization exercises .The PAHO-adapted Hanlon method was approved by the PAHO Directing Council for implementation across the Region. To implement, PAHO first makes an official request to the national health authority of each Member State/Territory for one template (a fillable spreadsheet) with the results for that country. Each country template is based on national and, where appropriate, Regional considerations. PAHO supports a group approach for data collection, following the recommendations of Vilnius and Dandoy . In eachThere were several limitations to this study. First, the participatory process assumed that the only actors with the capacity to contribute to developing the adapted method were among the representatives of the 12 Member States of the SPAG. This might have led to bias as academic institutions were excluded. However, PAHO invited academic professors to provide subject matter expertise at SPAG face-to-face meetings. In addition, several of the SPAG members were also university professors. Second, the range of points for the new components (inequity [E] and positioning [F]) was based on expert consensus and may need to be further adjusted and fine-tuned in the field. Third, there were issues with the operational definitions of some terms, e.g., effectiveness, coverage, and equity. Fourth, limited information for several programs and the subjective nature of criteria assessment may have led to inconsistent application of the methodology across countries in the Region. Fifth, lack of public health experts familiar with the national health situation and the work of PAHO may have been an issue.The PAHO-adapted Hanlon method has many advantages. First, the proposed new Hanlon method applies to both a "disease-oriented (pathogenic) vision” and a "health-oriented  vision" . Second, it has a positive impact in the Region by focusing available PAHO resources on high priority programs. Third, the priority-setting process is transparent, objective, and applied in a systematic manner. Fourth, it is a participatory process that strengthens collaboration between PAHO and its Member States. Fifth, the PAHO-adapted Hanlon method has been validated against the ranking results from a more time consuming Delphi exercise among a group of senior health managers in Mexico, conducted by the Mexican Secretariat of Health independent of the SPAG process. Sixth, the proposed new method allows inclusion of important societal values, such as equity. Seventh, adjustment of the final priority score is enabled by an institutional positioning factor based on political consideration. This prevents the priority-setting exercise from becoming a purely mechanical process, and thereby increases its practical value in a real-world setting.There are other methods for health-related decision making that involve scores, including the EVIDEM framework based on seven modules , and a MThe PAHO-adapted Hanlon method has made a significant contribution to the science and practice of strategic planning. It has expanded the Hanlon method to encompass and align with new regional and global contexts by covering both disease- and non-disease-oriented programs and by considering equity and politics in priority-setting. The adapted method is more relevant and useful to the wider scope of health and public health. It will inform priority-setting as specified in the WHO 2016 Guide for Country Cooperation Strategy . The metBCKC, RAM, and OJM developed and implemented the project methodology. OJM and RAM with the PAHO Technical Team and PAHO SPAG (including BCKC) developed the initial PAHO adaptation of the Hanlon method and applied it with PAHO Member States. BCKC and RAM collected and responded to issues raised by the SPAG. OJM developed data collection and analysis tools for pilot testing. The PAHO Technical Team (including RAM and OJM) provided technical data required by the SPAG, and analytical support on pilot testing. SPAG members (including BCKC) collected issues from Member States and guided progress of the project. All authors contributed to the interpretation of results. BCKC wrote the first draft and RAM and OJM co-wrote the second draft of the manuscript. All authors contributed to subsequent revisions of various drafts, and final approval of the manuscript.Members of the PAHO Strategic Plan Advisory Group who contributed as authors: Martha Leticia Caballero Abraham and Laura E. Gloria Hernández (Chairpersons), the Secretariat of Health, Mexico City, Mexico; Cristina Luna Ribadeneira and Peter N. Skerrett Guanoluisa (Vice Chairpersons), Ministry of Public Health, Quito, Ecuador; Keva S. Thompson, Ministry of Health, Nassau, Bahamas; Thaisa Santos Lima and Juliana V. Borges Vallini, Ministry of Health, Brasilia, Brazil; Bernard C.K. Choi, Public Health Agency of Canada, Ottawa, Ontario, Canada; Odette A. Urrutia Pumeyrau and Raquel Child Goldenberg, Ministry of Health, Santiago, Chile; María Rosibel Vargas Gamboa, Ministry of Health, San José, Costa Rica; Matías Villatoro, Nadia P.R. Villalta, and Maria Elena Marroquin, Ministry of Health, San Salvador, El Salvador; Michele Roofe and Karen Webster-Kerr, Ministry of Health, Kingston, Jamaica; Patricia A. Giménez León and Juan Carlos Coronel Zarate, Ministry of Public Health and Social Welfare, Asunción, Paraguay; Victor Raul Cuba Oré and Martin Yagui, Ministry of Health, Lima, Peru; and Jay McAuliffe, United States Centers for Disease Control and Prevention, Atlanta, Georgia, United States.Members of the PAHO Technical Team who contributed as authors: Federico Gerardo de Cosio, Blanca Cousiño, José A. Escamilla-Cejudo, Travis High, Rony A. Maza, Guillermo Mendoza, Andrea Morales, Oscar J. Mujica, Donna-Lisa Peña, Antonio Sanhueza, Maria Teresa Villen, and Daniel J. Walter, Pan American Health Organization, Regional Office of the World Health Organization for the Americas, Washington, DC, United States.The authors would like to express deep appreciation to PAHO for providing the webinar platform for the virtual meetings and travel funds for many members of the Strategic Plan Advisory Group attending face-to-face meetings. We gratefully acknowledge PAHO and the Secretariat of Health of Mexico for providing the meeting facility in Washington and Mexico City. Our gratitude to PAHO and Member States for their commitment and support of the project, and thanks to the external reviewers for their thoughtful and constructive suggestions on the manuscript.Funded by the Pan American Health Organization, Washington, DC, United States. The funders had no role in the study design, data collection or analysis, decision to publish, or preparation of the manuscript.RPSP/PAJPH and/or PAHO.Authors hold sole responsibility for the views expressed in the manuscript, which may not necessarily reflect the opinion or policy of the The views expressed in this paper are those of the authors and do not necessarily reflect those of the Public Health Agency of Canada (PHAC) and the Government of Canada.The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention (CDC)/the Agency for Toxic Substances and Disease Registry.


Previous neuroimaging studies of autism spectrum disorder (ASD) have focused on subjects with IQ > 70 or ASD without considering IQ levels. It remains unclear whether differences in brain anatomy in this population are associated with variations in clinical phenotype. In this study, 19 children with low functioning autism (LFA) and 19 children with high functioning autism (HFA) were compared with 27 healthy controls (HC). We found increased gray matter volume (GMV) in the left inferior temporal gyrus in subjects with both HFA and LFA and increased GMV of left middle temporal gyrus BA21 was found only in the LFA group. A significant negative correlation was found between the left inferior temporal gyrus (LITG) and the score of repetitive behavior in the HFA group. Autism spectrum disorder (ASD) is a neurodevelopmental disorder that presents with social communication deficits and restricted, repetitive patterns of behavior, interests, and activities . ASD is According to the 10th revision of the International Statistical Classification of Diseases (ICD-10) by the World Health Organization (WHO) in 2016, there is clinical heterogeneity within this disorder, including cognitive functioning, behaviors, and sensory-motor functioning. Subjects with an IQ < 70 are typically defined in the literature as having low functioning autism (LFA) and those with an IQ ≥ 70 are defined as having high functioning autism (HFA). Those who have normal or even superior intellectual abilities and have no history of developmental language delay are classified as having Asperger syndrome , 9. It hSeveral Voxel-based morphometry (VBM) studies  have demonstrated abnormalities in the right fusiform gyrus, right temporal-occipital lobe, left parietal lobe, left middle temporal gyrus, and superior temporal gyrus in patients with ASD, but the conclusions have been inconsistent –15. FurtPreviously, we found four VBM studies which took the individuals' cognitive function into account by separating the whole sample into LFA and HFA. Toal et al.  found thIn summary, there are relatively few VBM studies of children with ASD distinguishing HFA from LFA. It remains unknown whether there are differences in brain anatomy associated with the variable clinical phenotypes, and if so, how those variations in brain structure are related to the symptoms of ASD. To address these questions, it will be necessary to recruit relatively large samples of subjects, acquire and analyze the data in a similar manner, and compare the brain anatomy of these subjects across the full spectrum of autism, to reveal any correlations between the brain structure and autistic symptoms that may exist.One of the aims of our study was to compare the GMV among HFA, LFA, and HC by using the method of VBM-DARTEL; the second aim was to explore the relationship between the clinical phenotype of ASD and autistic symptoms. We hypothesized that LFA has more regions of abnormal brain anatomy than HFA and we assumed that there may be some correlations between specific abnormal brain regions and the severity of autistic symptoms.All subjects were recruited during a 4-year period (2013–2017) from community and clinical sources, including patients from West China Hospital of Sichuan University, special schools, regular schools, and from clinic social skills training groups in Chengdu. Typical developing subjects were recruited from regular schools in Chengdu. After completion of the description of the study purposes to subjects and their parents, written informed consent was obtained. The study was approved by the Ethics Committee of the West China Hospital of Sichuan University. All subjects were ascertained and assessed by a child psychiatrist trained in diagnosis of autism at the Mental Health Center, West China Hospital of Sichuan University. MRI scans, image processing, analysis, and quality control were completed by a professional image technician at the West China Hospital of Sichuan University.After excluding 16 subjects  65 right-handed subjects participated in this study. Among them, there were 19 ASD with HFA , 19 LFA , and 27 HC . HFA, LFA, and HC were group matched on gender, age and handedness, and HFA and HC were also matched on IQ.First, the child was diagnosed by an experienced child psychiatrist based on the Fourth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-4). Second, an interview with the child's parents and an assessment of the child was performed by using the Autism Diagnostic Interview-Revised (ADI-R)  and AutiThe ADI-R is an interview about the individual's current social and early childhood and stereotyped, repetitive behaviors and interests and communication development. The ADOS-G includes activities for young children and activities and an interview for older, verbal children, and it is a semi-structured interactive observation session designed to document signs of autism if they are present. Subjects were tested with one of four different modules depending on their age and verbal ability. Because the caretakers of 2 LFA could not come to our hospital and 14 children could not complete the ADOS-G, we failed to collect the results of ADI-R of 2 LFA children and ADOS-G of 14 children (6 LFA and 8 HFA). All patients were medication-naive and met the criteria of DSM-5. ASD subjects were excluded if they had a history of head injury, seizures, birth asphyxia, and metabolic or genetic disorders such as Fragile-X Syndrome.HC had no history of developmental, learning, neurological, cognitive, or neuropsychiatric problems and they had no history of psychotropic medication use. To confirm that they were typically developing, all of them had extensive testing, including the ADI-R, IQ, and psychiatric testing.IQ was measured by different versions of intelligence tests for ASD and HC. The Wechsler Preschool Intelligence Scale (WPPSI) was used for the 5- to 6-year-old subjects. The Wechsler Intelligence Scale for Children (WISC-III) was used for 6- to 16-year-old subjects . We used* 256; field of view = 24 * 24 cm2; voxel size = 1 * 1 * 1 mm3; thickness = 1 mm; number of slices = 188.Magnetic resonance images were acquired using a standard quadrature head coil, 8-channel, receive-only on a 3.0 T Verio MRI system . Foam padding and earplugs were used to diminish the head movement and scanner noise. A number of pulse sequences  and image contrasts were collected for clinical review. High-resolution images were obtained with a T1-weighted three-dimensional (3D) spoiled gradient (SPGR) sequence. The parameters were as follows: TR = 8.37 ms; TE = 3.88 ms; flip angle = 7°; in-plane matrix resolution = 256 The 15 ASD and 6 HC who could not cooperate with the scanning were sedated using chloral hydrate with parental consent before the MRI was performed. The dosage of chloral hydrate was based on the child's weight (1 ml/kg). The maximum dosage was usually 20 ml. No complications occurred in the subjects who were sedated.http://lcni.uoregon.edu/downloads/mri_convert/mriconvert/view) to transform the DICOM format data collected from magnetic resonance scanning. VBM-DARTEL was conducted with SPM8 (http://www.fil.ion.ucl.ac.uk/spm) software on a Matlab (2012Ra) platform. The pre-processing steps were as follows: (1) Examining the images for evidence of anatomical abnormalities in each subject; (2) Setting the image origin to the AC-PC line; (3) Segmentation in the DARTEL procedure ; (4) Affine transform of segmented brain maps into the MNI space; (5) Modulating the segmented images with the Jacobian determinants derived from the spatial normalization; (6) Using standard smoothing by an 8-mm-full width-half maximum Gaussian kernel. This pre-processing yielded the smoothed modulated normalized data (in the MNI space) used for the statistical analysisWe used MRI Convert software . Two sample Group differences were evaluated for GMV and white matter volume (WMV) absolute volumes and total cerebral volume (TCV) which were obtained in the brain segmentation step of the VBM-DARTEL pre-processing. TCV was calculated as the sum of the volume of GM, WM and cerebrospinal fluid (CSF).t-test contained in SPM8 was applied to analyze the differences of regional GMV between the ASD group and the HC group. Intelligence quotient, age, sex, and GMV were treated as covariates. VBM analysis was employed using a p < 0.001 significance threshold, False Discovery Rate (FDR) uncorrected with an extent threshold of 50 voxels. An absolute threshold mask of 0.2 was used on the GMV. Each individual cluster that showed significant differences between groups was defined as a region of interest (ROI). The GMVs of individual ROIs were extracted from each subject and compared between groups. Pearson's correlation analysis was used to discuss the relationship between the values of GMV and the score of ADI-R and IQ.First, a two sample post hoc analysis was carried out between these three groups. To assess the functional significance of GMV alterations in patients, the values of GMVs in each ROI were correlated with the score of ADI-R, with IQ, age, sex, and GMV being treated as covariates.Second, the ASD group was divided into a HFA group and a LFA group and ANOVA analysis was used to analyze the differences of regional GMV between the three groups. Age, sex, IQ, and GMV were treated as covariates. Then, p = 0.111), gender (p = 0.224); IQ in the ASD group was significantly lower than that in the HC group (p < 0.001)  and gender (p = 0.239) except that the IQ of the LFA group was significantly lower than the HC group (p < 0.05) and the HFA group (p < 0.05). To assess the potential effect of IQ on GM group differences, GM volumes were correlated with IQ within each group. Besides, the score of subscales of ADI-R including communication, verbal interaction, non-verbal interaction, and total score of ADI-R in the LFA group were significantly higher than the HF group  , GMV (p = 0.316), and WMV (p = 0.185) , GMV (p = 0.402), and WMV (p = 0.401)  with regard to TCV p = 0.83, GMV  and bilateral middle temporal gyrus  as compared to HC. The ASD subjects also had three small areas of decreased GMV including the right precuneus , right cerebellum anterior lobe  and the right angular gyrus   which included the left inferior temporal gyrus ; left middle temporal gyrus BA21 ; left parahippocampal gyrus BA35 ; right inferior parietal lobule ; right parahippocampal gyrus ; and right precuneus  ; left middle temporal gyrus BA21   . Yet no significant correlation was found in the LFA or ASD groups  in both the LFA group and the HFA group. This result remains the same while excluding the influence of IQ on brain structure with IQ as covariates. This suggests that the left ITG is implicated in the pathophysiology of ASD across the autistic spectrum at different IQ levels. In agreement with our study, some studies also found an increase of left temporal lobe  in children with ASD –38. But In our study, increased GMV of the left middle temporal gyrus (MTG) BA21 was only found in the LFA group, and not in the HFA group. This suggests that, neuroanatomically, the LFA sample may represents a more heterogeneous population than HFA. MTG BA21 is a site involved in auditory processing, language and social perception implicated in ASD-associated behaviors . It has Finally, in the correlation analysis, we reported significant negative correlations between the GMV of the left ITG and the score of repetitive behavior only in the HFA group. The result remains the same with IQ as covariates. A review summarized the brain regions that related to stereotypical repetitive behaviors reported in previous literature were include basal ganglia, caudate, frontal and temporal cortices. Among these brain areas, basal ganglia was the most commonly reported one and Alterations to frontal and temporal cortices are the most varied and difficult to interpret in relation to RRB, as these regions are also implicated in social and/or communication deficits . 53) fo fo53) foThe strengths of this study include the enrollment of three groups of young children  and the use of rigorous diagnostic tools, strict and validated imaging methodologies, and exact and original analytic methods. More importantly, so far as we know, this was the first investigation of abnormal brain anatomy in HFA and LFA using VBM, and the first to assess correlations between autistic symptoms, making the results more representative of ASD population. But this study also has limitations. First, all of the subjects were children, so we do not know whether our findings can be generalized to adults. Second, our ASD patients were not followed over time, and so it is difficult to know whether the abnormalities would be altered or extinguished as they grow older. Third, the sample size was relatively small (because we found that it is too difficult for many LFA children to cooperate with MRI scanning). Fourth, we did not include a control group of children with other intellectual disorders for comparison, because the purpose of this research was to find different brain regions in different clinical phenotypes of ASD and HC on various levels of IQ in the same sample. Given the higher proportion of LFA in the overall ASD population, ASD phenotype should include different levels of intelligence to make the results more representative. In attempting to “control” for a domain- non-specific construct such as IQ, variability truly associated with autism could be discarded as “non-specific” . In factOur findings supported the hypothesis that the neurological findings LFA and HFA reflect abnormal function in the same brain regions, but LFA had involvement of more abnormal regions of the brain than HFA. Increased GMV in the left ITG was found in both HFA and LFA, but increased GMV of the left MTG BA21 was found only in the LFA group. Furthermore, there was a significant negative correlation between GMV of the LITG and the score of repetitive behavior in the HFA group. These findings seem to provide a theoretical basis for exploration for biological markers and further targeted interventions in ASD. More importantly, our findings indicate that different levels of IQ should be taken into consideration when discussing the biological markers and targeted intervention of ASD in the future research.JC wrote the manuscript of this work. XH, KG, PY, and MS helped recruit and assess the subjects. YH modified the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The school context is exposed to several demanding factors relating to student and family needs and external evaluative processes of students’ learning and process outcomes, such as abilities in planning training courses and a learning environment. However, there is a need to develop tools that adequately support schools in making self-assessment evaluations of the internal organizational climate and teacher morale (TM). The present study proposes an Italian version of the School Organizational Health Questionnaire (SOHQ), developed by  In recent years, schools have been required to adapt and meet new demands coming from society, as well as from students and their parents. Coherently, teachers’ activities and roles become more differentiated, frequently updated, and “sophisticated,” especially in the management of their interaction with students.Therefore, research on teachers’ wellbeing has largely focused on the conditions of stress and burnout related to organizational conditions as well as to the relationships with colleagues and also those with students and their families . HoweverResearch has also shown how teacher wellbeing could be an important determinant for student learning outcomes and wellbeing, highlighting the importance of establishing not only the determinants of teachers’ negative outcomes, but also those of the positive ones associated with their work . MoreoveAmong the factors that could be identified as achieving positive occupational wellbeing outcomes, such as job satisfaction, engagement, or commitment, the concept of morale is one of the least studied. This concept refers to the presence of energy, persistence, cohesion, and cooperation, that reflects a positive psychological state of mind  and can In order to understand factors that could affect TM, the School Organizational Health Questionnaire (SOHQ) was developed by The SOHQ  has thenAppraisal & Recognition , Curriculum Coordination , Effective Discipline Policy (agreement on discipline and rules between teachers), Excessive Work Demands (workload perception), Goal Congruence , Participative Decision Making (perception of being included in school policies), Professional Growth , Professional Interaction , Role Clarity (having clear role expectations), Student Orientation (positive climate toward students), and Support Leadership .Therefore, the SOHQ  has beenThe present study aims to propose an adaptation and validation of the SOHQ  for the Data were collected through a survey, using a cross-sectional and non-randomized design. Teachers from nine public primary schools in a region of Northern Italy were involved in this survey. The study database originally included 407 participants (22% of the entire population), but 82 of them avoided responding to some items. The final dataset used in the analysis consisted of 325 valid cases. According to the target population specificity, most participants were female (93.8%), with a mean age of 44.7 (SD = 9.7) and an average length of experience of 20 years (SD = 11.2). Moreover, 70.6% of the sample was composed of married persons with children, and 17.8% had at least one adult relative to care for. Generally, participants declared that they were quite satisfied with their work and appreciated their life in general: the mean judgments were equal to 7.9 and 8.2, respectively, on a scale of ten points.Data were gathered using self-report questionnaires distributed to all employed teachers during working hours. To ensure anonymity, teachers were instructed to enclose the completed questionnaire in an envelope and leave it in a box placed by the researchers in each school. The participants volunteered for the research without receiving any reward and agreed to anonymously complete the questionnaire, signing the informed consent forms, which were placed in a box before completing the questionnaire. The research conforms to the Declaration of Helsinki of 1995  as a whole?”] Each item was rated on a 10-point scale that ranged between 1 (extremely dissatisfied) and 10 (extremely satisfied).On the basis of previous literature , job satThe SOHQ items were preliminarily analyzed with descriptive statistics; Cronbach’s alpha coefficient was calculated in order to assess scale reliability whereas the contribution of each item to internal consistency was examined by the item–total correlations.a priori hypothesis regarding dimensionality , which is recommended over exploratory factor analysis (EFA) because it allows testing of whether the data fit a structure when there is an ionality ; the SOHThe model fit was considered acceptable if the following criteria were satisfied: root mean square error of approximation (RMSEA) <0.08; comparative fit index (CFI) >0.90; and standardized root mean square residual (SRMR) <0.08 , 1999.The analyses were performed using the IBM SPSS Statistics 25.0 and Mplus7 software programs.r = 0.40, except for item 45 that reported a value of 0.29. All skewness and kurtosis values are included in the range from –1.0 to +1.0.Descriptive statistics for the items are shown in The subscales homogeneity was adequate: Cronbach’s alpha (α) coefficients ranged from 0.7 to 0.91, with the exception of Role Clarity (RC). The items in this sub-dimension resulted in being less coherent because of item 48, which refers to others’ expectations and, in fact, after its deletion, the α coefficient was 0.70, and it became acceptable. In the RC subscale, this was the only item that referred to others, and so its content was quite different and specific .Given its conservative nature, at first this item was included in the CFA, but its performance was quite scarce: a loading of 0.36 and an R-squared of 0.13. Consequently, item 48 was omitted in order to improve the measurement of the “role clarity” factor.A model based on the restricted set of 56 items resulted adequate, with all fit indexes being at least satisfactory , supporting the adequacy of the 12-factor structure for the SOHQ in the Italian sample. All parameters estimated in the confirmatory analyses were high (loadings from 0.40 to 0.89) and significant at the 0.001 level .r = 0.09, p > 0.68) and RC . Excessive Work Demands was significantly correlated with the other nine factors (p < 0.05), but the correlations, ranging between 0.12 and 0.37, were relatively small compared to other correlations between sub-dimensions. The correlations among the other 11 factors were in fact all significant at the 0.001 level and ranged between 0.28 and 0.84 (r = 0.81), Goal Congruence (r = 0.78), Participative Decision Making (r = 0.76), and Professional Interaction (r = 0.77), and, although statistically significant, the correlation was weaker with Professional Growth (r = 0.28) and RC (r = 0.28).The estimated correlations showed that there was no significant relationship between Excessive Work Demands and Curriculum Coordination , whereas Curriculum Coordination, Effective Discipline Policy, Student Orientation, and Excessive Work Demands correlated at the 0.05 level with general life satisfaction; the remaining sub-dimensions reported non-significant associations.Finally, all the SOHQ sub-dimensions significantly correlated with job satisfaction at the 0.001 level, with The aim of the present study was to adapt the SOHQ to an Italian school context. CFA provided evidence for the adequacy of psychometric properties, highlighting that the SOHQ represents a valid tool for monitoring staff perceptions of the school climate and their morale. Therefore, these findings confirmed those of previous healthy promoting school (HPS) surveys, which have adopted the SOHQ with efficacy .Nowadays, investing in positive school climates is a key issue in schools, for the reciprocity of teacher and student wellbeing. The HPS “ethos”  highlighIn the Italian context, where the present study has been conducted, schools are subject to different standardized processes of external evaluation [such as the PISA  and the systematic assessment performed by the INVALSI ] that are intended to assess, on one hand, students’ learning outcomes, and, on the other one, the quality of process outcomes, such as ability in planning training courses and a learning environment with inclusion and differentiation criteria of students’ needs. Despite these aims, the evaluation is only focused on students’ learning outcomes, but it could be helpful to analyze, along with these, the systemic conditions that represent the general frame where those outcomes are developed, like organizational and managing practices, leading processes, strategic orientation and the organization of the school in the identification of the objectives and their monitoring, human resource development and valorization , and theAlthough this study also gives an important insight into the usefulness of this measurement tool for the Italian context, the sample is not representative of the entire Italian teaching population. Future studies could overcome this limit by using this tool on a larger sample that is representative of the larger Italian school system, widening its scope, for example, to the other two teaching levels – middle and high school – and comparing public and private school teacher perceptions of organizational climate.Moreover, the SOHQ should be adapted to other non-English speaking countries in order to evaluate its psychometric characteristics and to improve cross-cultural comparisons.info@insightsrc.com.au).The datasets generated for this study are available on request to the corresponding author. The School Organizational Health Questionnaire is subject to copyright and cannot be used without the permission of Insight SRC Pty Ltd (DC, BL, GG, GM, and SV contributed to research conception and design, and acquired the data. DC, MC, and BL involved in critical revision of the article content. BL, GM, and SV performed quant data analysis. All authors interpreted the data and drafted the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling Editor declared a past collaboration with one of the authors MC.


Tobacco is responsible for infaust oral conditions and many oral diseases. Dental caries is one of the most prevalent oral diseases. The association between tobacco smoking and dental caries has become an important recent topic of research. A systematic review and meta-analysis was conducted to evaluate the association between tobacco smoking and dental caries.A literature search was conducted in the databases PubMed, EMBASE, Medline and Cochrane, up to December 2018. Original observational articles that estimated relevance between tobacco smoking and dental caries in adults were included. Caries were determined by measurements of decayed, missing or filled teeth (DMFT), or decayed, missing or filled surface (DMFS), or caries-related microflora levels. Trials did not include a non-smoking group, exposure to smokeless tobacco products, or participants under 16 years old. Also, literature reviews, comments, case reports and letters to the editor were not considered. Both methods of systematic review and meta-analysis were adopted. Newcastle-Ottawa Scale (NOS) was used to assess the methodological quality of all the included studies.2=93%, chi-squared p<0.00001; I2=70%, chi-squared p=0.07, respectively) and statistically significant . The quality scores of all varied from 7 to 9.Ten out of eleven of the included studies indicated a positive association between tobacco smoking and dental caries. Two meta-analyses were performed: one included five studies using DMFT as an outcome; the other included two studies of DMFS. A random effects model was used. Both were highly heterogeneous (IThere is a correlation between tobacco smoking and an increased risk of dental caries. However, the overall representativeness of the studies is not good. More prospective and extensive research on this topic is needed to get validation. Even so, it is imperative that people quit tobacco smoking. The World Health Organization (WHO) estimates that tobacco is responsible for more than 7 million deaths and hundreds of billions of dollars loss, worldwide each year2. More than 60 toxic chemicals in tobacco such as nicotine can invade the body’s multiple systems3. They lead to cardiovascular diseases, cancers and other systemic diseases4. In addition, tobacco is a harmful product responsible for adverse oral conditions and some oral diseases. Any form of tobacco consumption is responsible for oral diseases like adult periodontal diseases, oral cancer, cleft lip, cleft palate and other congenital defects in children whose mothers smoked tobacco during pregnancy5.Tobacco is detrimental to human health. The latest Surgeon General’s Report (SGR), ‘The Health Consequences of Smoking–50 Years of Progress’, updated the evidence of the infaust effects of smoking on health6, which is only secondary to the flu7. It remains a major global health problem that not only affects adults and school-age children but also pre-school children8. The severity of dental caries and odontogenic infections varies from mild buccal space infection to severe multi-space infection9. If not treated on time, dental caries can cause progressive destruction of tooth hard tissue, perforate into pulp, lead to pulpitis and periapical inflammation, and finally lead to teeth loss10. Many factors such as food, environment and microorganisms are associated with caries.Dental caries is a primary focus of dental health prevention as it is one of the most prevalent chronic diseases11 in Sweden concluded that there was an association between smoking and oral health. Later, more trials emerged, but the conclusions have not been consistent, hence further studies are needed. In the last five years, no systematic review on this topic has been conducted.An increasing number of studies have focused on the association between tobacco smoking and dental caries. Early in 1998, a cross-sectional epidemiological studyA systematic review and meta-analysis has been designed to evaluate the association between tobacco smoking and dental caries. The hypothesis is that tobacco smoking is a risk factor for dental caries.12.This review was based on the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelinesThe inclusion criteria for studies were: 1) original observational articles that estimated relevance between tobacco smoking and dental caries in adults with a non-smoking group for comparison; 2) determining caries by measuring decayed, missing or filled teeth (DMFT), or decayed, missing or filled surface (DMFS), or caries related microflora levels; and 3) studies that were published in English.in vitro studies, or letters to the editor.The exclusion criteria were: 1) without a non-smoking group; 2) exposure to smokeless tobacco products; 3) participants younger than 16 years old; and 4) studies characterized as literature reviews, comments, case reports, A thorough electronic search was conducted in the databases PubMed, EMBASE, Medline and Cochrane to identify relevant research. Studies published up to December 2018 were included. The search string was: (Smoking OR Tobacco OR Tobacco smoking OR Tobacco products OR Cigarette smoking OR Cigarette OR Cigar) AND . No data and language restrictions were applied in searching.In the beginning, duplicate articles were excluded. Then titles and abstracts of studies were independently assessed by two authors based on the eligibility criteria. Finally, the full text of articles that were initially included was evaluated according to the inclusion and exclusion criteria. Along the process, the two authors reached consensus through discussion, if their opinions were different. Cohen’s kappa was adopted to assess the inter-reviewer reliability.The following elements were extracted from each article: the surname of first author, year of publication, study type, search site, sample size, gender contribution, mean age and age range, exposure assessment, caries evaluation, result, and judgment of irrelevant variable. These data were independently extracted by two authors. Any disagreement between them was discussed and agreement was reached in the end.13. Three categories, including selection, comparability and exposure (case-control study) or outcomes (cohort study), were the judging criteria of the NOS. Next, it was divided into 9 items, which included the following groups; S1: Definition of cases; S2: Representativeness of the cases; S3: Selection of controls; S4: Adequate control definition; C1: Comparability of cases; C2: Study controls for the basis of the analysis; E1: Ascertainment of the exposure; E2: Ascertainment of the same method used for cases and controls; E3: Non-response rate. Each item could achieve one score if the study met the criteria. The score of a study below 6 means low quality, 6 and 7 represent moderate quality, while 8 and 9 signify good quality.Newcastle-Ottawa Scale (NOS) was used to assess the methodological quality of all the included studiesThe two authors assessed the quality of the included studies, separately. Any disagreement between them was discussed and agreement was reached in the end.2) were applied to evaluate the heterogeneity of articles. Heterogeneity was regarded as: none (I2<25%), low (25%≤ I2 <50%), moderate (25%≤ I2 <75%), or high (I2>75%). In the case of heterogeneity (chi-squared p<0.05 or I2>50%), the random effects model was preferred14. Impact of study population on the overall findings was detected by subgroup analysis. One-study removed method was used to determine the sensitivity of the meta-analysis.All analyses were conducted by the software RevMan . Mean difference (MD) was used to report results, with a 95% CI for continuous variables. Forest plot, chi-squared homogeneity test and Higgins index  and Lactobacillus (LB).All the participants were adults. Six studies sampled both males and females, while five only included males. One studyOnly studies that used mean value and standard deviation (SD) as data representations of DMFT/DMFS were included in the meta-analysis.2=93%, chi-squared p<0.00001).Five statistical results using DMFT as outcome format were included in a meta-analysis . The est2=70%, chi-squared p=0.07).Data of DMFS extracted from two studies were also analyzed . The estDMFTIn subgroup analysis of study samples, only the randomly selected population showed significant differences, while the specific ones did not. Sensitivity analysis of DMFT through the one-study removed method did not find any study that affected the heterogeneity . There wDMFSSince DMFS data reported by only two articles were available, a sensitivity analysis was not conducted.The scores of all studies ranged from 7 to 9 . Many of24, most were cross-sectional and thus do not allow inferences to be made on causal relations; affirmation of the etiology of tobacco smoking was not possible. Therefore, there was insufficient evidence to confirm the hypothesis that tobacco, as a risk factor, is involved in the dental caries process. More longitudinal studies are needed to come to any conclusion.Findings from this systematic review and meta-analysis indicate the existence of a relation between tobacco smoking and dental caries. For most of the studies reviewed, the results were consistent with a positive association. Except for one longitudinal study by Bernabe et al.20 assessed the amount of SM and LB in the saliva of non-smokers and smokers. They concluded that there was no essential correlation between the number of SM and LB and the number of cigarettes smoked per day, the duration of smoking and the smoking status. However, some experiments have studied the effect of nicotine on SM. A study conducted by Chanea et al.25 found that SM adherence was significantly enhanced in the presence of nicotine. SM makes use of sucrose for metabolism, and its byproducts are mainly responsible for adherence and caries generation. In addition, Ashkanane et al.26 and Mohammed et al.27 have examined the effect of nicotine and cigarette extracts on oral bacteria. In a recent review28 on the effects of nicotine on oral microorganisms and human tissues there is indirect evidence of a link between smoking and caries. Taken together, more research is needed to confirm the association between smoking and cariogenic bacteria growth.One study conducted by Nakonieczna-Rudnicka et al.Several limitations may influence the results of the systematic review and meta-analysis. First, most of the articles included were cross-sectional studies that could only judge whether there was a connection between smoking and dental caries, but could not determine the causal relationship between them.Second, is the subjective bias associated with questionnaire surveys. Participants were aware of the purpose of the investigation in all the studies. However, as it is generally accepted that smoking is harmful to health, the participants may have given socially acceptable responses, especially in front of the medical staff. Only one study included both saliva cotinine test and the questionnaire survey. Cotinine is a nicotine breakdown product used to determine whether people smoke. Therefore, the actual number of smokers and the severity of smoking may be higher than the survey results, leading to biased results on the relationship between tobacco smoking and dental caries.15, Croatian army recruits16, and people in the Italian military academy22, all of whom were unrepresentative. Moreover, five statistical results using DMFT as outcome format were included in a meta-analysis, but the study group was all male. In summary, the overall representativeness of studies is not good so there are some deviations in the conclusions.Third, is the specific population and gender covered in trials. The prevalence of dental caries is impacted upon by different lifestyle habits, regional development level, special occupation, education, expenditure for dental care, age, gender etc. Most of the research only provided classified statistics, without making adjustment when they analyzed the association between tobacco smoking and caries. In addition, the study populations included professional Mexican truck drivers29.Forth, is the completeness of the studies. As the search strategy part stated, the literature search is limited and there may be some omissions, as well as not taking into account Masters and PhD theses etc. Beyond that, there were only five studies of DMFT and two of DMFS included in our analysis, so we have not conducted funnel plots because it is advised to analyse at least ten studiesIn light of the above, there is a correlation between tobacco smoking and an increased risk of dental caries. However, the overall representativeness of the selected studies is not good. More prospective and extensive studies on this topic are needed in the future to get validation, and they will require to adopt both a questionnaire survey and detection of cotinine in saliva, define the specific severity of smoking, distinguish between smoking types, adjust the extraneous variables, select representative groups etc. Nevertheless, it is imperative for people to cease tobacco smoking.


Coffee is a blend of compounds related to gastrointestinal physiology. Given its popularity and the epidemiology of colorectal cancer, the impact of this beverage on public health could be considerable. Our aim was to provide an updated synthesis of the relationship between coffee consumption and the risk of colorectal cancer. We conducted a systematic review and meta-analysis of 26 prospective studies. Regarding colorectal cancer, no significant relationship was detected. Stratifying for ethnicity, a protective effect emerged in US subjects. Concerning colon cancer, coffee proved to exert a protective effect in men and women combined and in men alone. Stratifying for ethnicity, a significant protective effect was noted in European men only and in Asian women only. Concerning rectal cancer, no association was found. Decaffeinated coffee exhibited a protective effect against colorectal cancer in men and women combined. Studies were appraised for their quality by means of the Newcastle-Ottawa Quality Assessment Scale for Cohort studies. Only one study proved to be of low quality. Ethnicity could explain the heterogeneity of the studies. However, little is known about the relationship between the genetic make-up and the risk of colorectal cancer associated with coffee. Further research is warranted. Coffee is a complex blend of bioactive compounds. These are related to gastrointestinal physiology in various ways and may exert contrasting effects. On the one hand, coffee contains anti-oxidants and anti-mutagens, which could, as such, reduce exposure of the epithelial cells of the bowel to carcinogenic chemicals and compounds, and prevent and counteract the effect of potential promoters of intestinal carcinogenesis. Indeed, coffee seems to enhance bowel motility and functioning, reducing fecal transit times and increasing stool output. Furthermore, coffee contains lipidic compounds, such as cafestol and kahweol, which, by finely tuning cholesterol metabolism, reduce the synthesis and release of bile acids . They acMoreover, the quantity of these bioactive compounds varies according to the type of coffee. For example, unfiltered coffees, such as French press coffee or Turkish/Greek coffee, contain significant amounts of these compounds, unlike filtered blends, such as dip-brewed coffee. Moreover, filtered coffee also contains fewer phenols and polyphenols ,9.Being cheap and easy to prepare, coffee is a widespread beverage, its worldwide consumption of two billion cups per day being second only to that of water . Given tThe Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines  were useInclusion criteria were: (1) articles with relevant quantitative details and information on the relationship between coffee consumption and the risk of developing colorectal cancer; (2) prospective studies. Exclusion criteria were: (1) items not directly pertinent to the query string; (2) articles not containing sufficient information on the relationship between coffee consumption and the risk of colorectal cancer; (3) articles not meeting the PICOS criteria ; all such articles were consequently discarded. No time filter or language filter was applied. For further details of the search strategy, see Two of the authors independently screened the literature. Any case of disagreement was solved by discussion until consensus was reached. After the full text review, the papers included were retained for data extraction.Data for the meta-analysis were extracted from the studies included by means of a standardized documentation form. The parameters extracted were: the surname of the first author, the year and country of publication, sample size, percentages of females and males, incidence of colorectal cancer , and the amount and type of coffee consumed. RR of developing colorectal cancer on the basis of coffee consumed  were calculated as effect size estimates, with their 95% confidence intervals (CIs). Additional analyses were performed after stratification by type of coffee, study region, publication period and gender. Study quality was appraised by two researchers, working independently, with respect to the appropriateness of the research questions tested and of the methods employed. For this purpose, the Newcastle-Ottawa Quality Assessment Scale for Cohort studies (NOS) was used; scores of ≥7 indicated high-quality and <7 indicated low-quality studies. Any disagreement was solved by consensus. 2 statistics and chi-square test, heterogeneity being deemed significant if the p value (χ2) was <0.1. In detail, it was determined that the values of 25%, 50% and 75% in the I2 test corresponded to low, moderate and high levels of heterogeneity, respectively. In the event of significant (moderate or high) heterogeneity among the studies, a random-effects model was used for the meta-analysis. The RR of the meta-analyses were deemed significant when the confidence intervals did not contain the value “1”; indeed, if the confidence interval contains the value “1”, we cannot exclude the absence of an association between exposure and disease. A narrower CI than that of the individual studies indicates less imprecision. Statistical heterogeneity was also assessed in our meta-analysis by means of IR  software. To identify sources of variation, further stratification was performed with respect to study quality. In addition, in the sensitivity analyses, the stability of the pooled estimate with respect to each study was investigated by excluding individual studies from the analysis. Possible publication bias was visually inspected by means of a funnel plot. If asymmetry was detected by visual assessment, exploratory analyses using trim and/or fill analysis were performed in order to investigate and adjust the effect-size estimate. In addition, the probability of publication bias was tested by means of Egger’s linear regression, a value of p < 0.05 being indicative of publication bias.Meta-analyses were carried out by means of the STATA SE14Concerning the systematic review, our initial query resulted in 390 hits ; after removal of duplicate items, the resulting list comprised of 270 non-redundant articles. Only 33 studies were retained in the qualitative synthesis, and 26 were finally considered in our systematic review and meta-analysis (186 articles were discarded as not being directly pertinent to the topic under investigation and 51 as not meeting the inclusion criteria). For further details, see The full list of studies included ,35,36,37The studies examined included 3,308,028 subjects. Eleven studies were performed in European countries, seven in Asian countries and seven in the USA. Nineteen studies were on colorectal cancer, 19 on colon cancer and 18 on rectal cancer.With regard to colorectal cancer, from the pooled RR, no significant relationship between coffee consumption and the risk of developing cancer was detected . Stratifying for ethnicity, a significant protective effect emerged among US subjects (men and women), with a RR of 0.83 (95% CI 0.72–0.95). While no statistical significance emerged for caffeinated coffee, decaffeinated coffee exhibited a protective effect on men and women combined (RR 0.88 (95% CI 0.78–0.97)).Concerning colon cancer , coffee Stratifying for ethnicity, a statistically significant protective effect was noted in European men only (RR 0.85 (95% CI 0.72–0.99)), and in Asian women only (RR 0.73 (95% CI 0.58–0.88)) A,B.Focusing on distal colon cancer , coffee Concerning rectal cancer , no signNo significant publication bias was detected. Finally, concerning the assessment of the risk of bias, no significant biases emerged. Only one study was deemed to be of low quality (Wu: NOS score = 5). Retaining or removing this study from the meta-analyses did not change the results.Performing the meta-analysis of prospective studies, we found a significantly high degree of heterogeneity. With specific regard to rectal cancer, no evidence of an association between coffee consumption and the development of the disease could be found. For colorectal cancer, we found evidence of a protective effect only in US men and women together. Stratification by type of coffee—caffeinated or decaffeinated—did not reveal any differences linked to the presence of caffeine, since the types of coffee appeared to be protective in some studies and non-significant in others. Moreover, the quantity of caffeine in coffee also depends on the type of mixture used , a paramRegarding colon cancer, evidence of a protective effect of coffee consumption was found, both considering men and women together and considering men alone. Stratifying for ethnicity, we found that the pooled RR was significant for European men and for Asian women. We can speculate that this might be due to the particular type of coffee consumed; for example, in Asia, coffee is rarely consumed boiled or decaffeinated . AnotherAs potential mechanisms that may explain the inverse association between coffee consumption and the development of colon cancer, a review by Higdon  hypothesA major limitation of the present meta-analysis lies in the fact that most studies did not specify whether the coffee blend was caffeinated or decaffeinated, filtered or unfiltered, thus hindering the possibility of precisely stratifying the pooled RR according to the type of coffee consumed.A major difficulty in interpreting epidemiological data is that studies often do not clearly indicate the quantity of coffee consumed ; estimates are therefore “rough”. Moreover, the considerable variability in the composition of the beverage makes it difficult to accurately determine the potential quantity of bioactive substances involved in the process.The development of colorectal carcinoma is a complex, multi-step process characterized by a series of both genetic and epigenetic changes, which involve different cellular cascades and pathways, including DNA repair, proliferation, apoptosis, intra- and extracellular signaling, and adhesion, among others . EthniciIn conclusion, the available studies are not sufficient to define a protective role of coffee against colorectal cancer.