Group-based trajectory models of integrated vaccine delivery and equity in low- and middle-income countries.

BACKGROUND: Integrated vaccine delivery - the linkage of routine vaccination with provision of other essential health services - is a hallmark of robust primary care systems that has been linked to equitable improvements in population health outcomes. METHODS: We gathered longitudinal data relating to routine immunization coverage and vaccination equity in 78 low- and middle-income countries that have ever received support from Gavi, the Vaccine Alliance, using multiple imputation to handle missing values. We then estimated several group-based trajectory models to describe the relationship between integrated vaccine delivery and vaccination equity in these countries. Finally, we used multinomial logistic regression to identify predictors of group membership. RESULTS: We identified five distinct trajectories of geographic vaccination equity across both the imputed and non-imputed datasets, along with two and four trajectories of socioeconomic vaccination equity in the imputed and non-imputed datasets, respectively. Integration was associated with reductions in the slope index of inequality of measles vaccination in the countries analyzed. Integration was also associated with an increase in the percentage of districts reporting high measles vaccination coverage. CONCLUSIONS: Integrated vaccine delivery is most strongly associated with improvements in vaccination equity in settings with high baseline levels of inequity. Continued scholarship is needed to further characterize the relationship between integration and health equity, as well as to improve measurement of vaccination coverage and integration.

Saving lives through road safety risk factor interventions: global and national estimates.

Global road mortality is a leading cause of death in many low-income and middle-income countries. Data to support priority setting under current resource constraints are urgently needed to achieve Sustainable Development Goal (SDG) 3.6. This Series paper estimates the potential number of lives saved if each country implemented interventions to address risk factors for road injuries. We did a systematic review of all available evidence-based, preventive interventions for mortality reduction that targeted the four main risk factors for road injuries (ie, speeding, drink driving, helmet use, and use of seatbelt or child restraint). We used literature review variables and considered three key country-level variables (gross domestic product per capita, population density, and government effectiveness) to generate country-specific estimates on the potential annual attributable number of lives that would be saved by interventions focusing on these four risk factors in 185 countries. Our results suggest that the implementation of evidence-based road safety interventions that target the four main road safety risk factors could prevent between 25% and 40% of all fatal road injuries worldwide. Interventions addressing speed could save about 347 258 lives globally per year, and at least 16 304 lives would be saved through drink driving interventions. The implementation of seatbelt interventions could save about 121 083 lives, and 51 698 lives could be saved by helmet interventions. We identify country-specific estimates of the potential number of lives saved that would be attributable to these interventions. Our results show the potential effectiveness of the implementation and scaling of these interventions. This paper presents key evidence for priority setting on road safety interventions and shows a path for reaching SDG 3.6.

Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs.

Clustering of independent component (IC) topographies of Electroencephalograms (EEG) is an effective way to find brain-generated IC processes associated with a population of interest, particularly for those cases where event-related potential features are not available. This paper proposes a novel algorithm for the clustering of these IC topographies and compares its results with the most currently used clustering algorithms. In this study, 32-electrode EEG signals were recorded at a sampling rate of 500 Hz for 48 participants. EEG signals were pre-processed and IC topographies computed using the AMICA algorithm. The algorithm implements a hybrid approach where genetic algorithms are used to compute more accurate versions of the centroids and the final clusters after a pre-clustering phase based on spectral clustering. The algorithm automatically selects the optimum number of clusters by using a fitness function that involves local-density along with compactness and separation criteria. Specific internal validation metrics adapted to the use of the absolute correlation coefficient as the similarity measure are defined for the benchmarking process. Assessed results across different ICA decompositions and groups of subjects show that the proposed clustering algorithm significantly outperforms the (baseline) clustering algorithms provided by the software EEGLAB, including CORRMAP.

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review.

BACKGROUND: Frailty syndrome (FS) is one of the most common noncommunicable diseases, which is associated with lower physical and mental capacities in older adults. FS diagnosis is mostly focused on biological variables; however, it is likely that this diagnosis could fail owing to the high biological variability in this syndrome. Therefore, artificial intelligence (AI) could be a potential strategy to identify and diagnose this complex and multifactorial geriatric syndrome. OBJECTIVE: The objective of this scoping review was to analyze the existing scientific evidence on the use of AI for the identification and diagnosis of FS in older adults, as well as to identify which model provides enhanced accuracy, sensitivity, specificity, and area under the curve (AUC). METHODS: A search was conducted using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines on various databases: PubMed, Web of Science, Scopus, and Google Scholar. The search strategy followed Population/Problem, Intervention, Comparison, and Outcome (PICO) criteria with the population being older adults; intervention being AI; comparison being compared or not to other diagnostic methods; and outcome being FS with reported sensitivity, specificity, accuracy, or AUC values. The results were synthesized through information extraction and are presented in tables. RESULTS: We identified 26 studies that met the inclusion criteria, 6 of which had a data set over 2000 and 3 with data sets below 100. Machine learning was the most widely used type of AI, employed in 18 studies. Moreover, of the 26 included studies, 9 used clinical data, with clinical histories being the most frequently used data type in this category. The remaining 17 studies used nonclinical data, most frequently involving activity monitoring using an inertial sensor in clinical and nonclinical contexts. Regarding the performance of each AI model, 10 studies achieved a value of precision, sensitivity, specificity, or AUC ≥90. CONCLUSIONS: The findings of this scoping review clarify the overall status of recent studies using AI to identify and diagnose FS. Moreover, the findings show that the combined use of AI using clinical data along with nonclinical information such as the kinematics of inertial sensors that monitor activities in a nonclinical context could be an appropriate tool for the identification and diagnosis of FS. Nevertheless, some possible limitations of the evidence included in the review could be small sample sizes, heterogeneity of study designs, and lack of standardization in the AI models and diagnostic criteria used across studies. Future research is needed to validate AI systems with diverse data sources for diagnosing FS. AI should be used as a decision support tool for identifying FS, with data quality and privacy addressed, and the tool should be regularly monitored for performance after being integrated in clinical practice.

Inter- and Intramolecular Proton Transfer in an Isolated (Cytosine-Guanine)H+ Pair: Direct Evidence from IRMPD Spectroscopy.

The collision-induced dissociation of the protonated cytosine-guanine pair was studied using tandem mass spectrometry (MS3) coupled to infrared multiple photon dissociation spectroscopy with the free electron laser at Orsay (CLIO) to determine the structure of the CH+ and GH+ ionic fragments. The results were rationalized with the help of electronic structure calculations at the density functional theory level with the B3LYP/6-311++G(3df,2p) method. Several tautomers of each fragment were identified for the first time, some of which were previously predicted by other authors. In addition, two unexpected and minor tautomers were also found: cytosine keto-imino [CKI(1,2,3,4)H+] and guanine keto-amino [GKA(1,3,7)H+]. These results highlight the importance of the DNA base tautomerization assisted by inter- and intramolecular proton or hydrogen transfer within the protonated pairs.

A Novel Score for mHealth Apps to Predict and Prevent Mortality: Further Validation and Adaptation to the US Population Using the US National Health and Nutrition Examination Survey Data Set.

BACKGROUND: The C-Score, which is an individual health score, is based on a predictive model validated in the UK and US populations. It was designed to serve as an individualized point-in-time health assessment tool that could be integrated into clinical counseling or consumer-facing digital health tools to encourage lifestyle modifications that reduce the risk of premature death. OBJECTIVE: Our study aimed to conduct an external validation of the C-Score in the US population and expand the original score to improve its predictive capabilities in the US population. The C-Score is intended for mobile health apps on wearable devices. METHODS: We conducted a literature review to identify relevant variables that were missing in the original C-Score. Subsequently, we used data from the 2005 to 2014 US National Health and Nutrition Examination Survey (NHANES; N=21,015) to test the capacity of the model to predict all-cause mortality. We used NHANES III data from 1988 to 1994 (N=1440) to conduct an external validation of the test. Only participants with complete data were included in this study. Discrimination and calibration tests were conducted to assess the operational characteristics of the adapted C-Score from receiver operating curves and a design-based goodness-of-fit test. RESULTS: Higher C-Scores were associated with reduced odds of all-cause mortality (odds ratio 0.96, P<.001). We found a good fit of the C-Score for all-cause mortality with an area under the curve (AUC) of 0.72. Among participants aged between 40 and 69 years, C-Score models had a good fit for all-cause mortality and an AUC >0.72. A sensitivity analysis using NHANES III data (1988-1994) was performed, yielding similar results. The inclusion of sociodemographic and clinical variables in the basic C-Score increased the AUCs from 0.72 (95% CI 0.71-0.73) to 0.87 (95% CI 0.85-0.88). CONCLUSIONS: Our study shows that this digital biomarker, the C-Score, has good capabilities to predict all-cause mortality in the general US population. An expanded health score can predict 87% of the mortality in the US population. This model can be used as an instrument to assess individual mortality risk and as a counseling tool to motivate behavior changes and lifestyle modifications.

Developing a model to evaluate and improve user experience with hand motions in virtual reality environments.

In video games, the evaluation of the user experience (UX) mainly refers to two main groups of aspects, those that refer to the player that is mainly oriented to make the player feel good while playing and those that refer to the video game that is oriented to make the video game easy to understand and play. The aspects considered that are related to the player are engagement, enjoyment, and flow; the aspects related to video game, usability, and dependability. Virtual reality environments today have changed the paradigm in various fields of application, such as health, education, entertainment, among others. Therefore, it is important to observe the effects of handedness with hand movements in virtual reality environments. This work proposes a model to evaluate and improve the user experience considering player and video game aspects, taking into account handedness with hand movements in virtual reality environments. Player and video game aspects can be added to evaluations of the effect of handedness, especially in virtual reality environments, in order to know the user's behavior in terms of skill, performance, and accuracy, among other features by using a particular hand to perform specific tasks. Next, a case study is presented with two groups of users using a virtual reality environment to perform several user tasks considering the dominant and non-dominant hand. By evaluating the user tasks it is possible to know the levels of engagement, enjoyment, motivation, and usability in a virtual reality environment. Finally, an analysis of results is presented in which several improvements of UX are presented.

A cost study for mobile phone health surveys using interactive voice response for assessing risk factors of noncommunicable diseases.

BACKGROUND: This is the first study to examine the costs of conducting a mobile phone survey (MPS) through interactive voice response (IVR) to collect information on risk factors for noncommunicable diseases (NCD) in three low- and middle-income countries (LMIC); Bangladesh, Colombia, and Uganda. METHODS: This is a micro-costing study conducted from the perspective of the payer/funder with a 1-year horizon. The study evaluates the fixed costs and variable costs of implementing one nationally representative MPS for NCD risk factors of the adult population. In this costing study, we estimated the sample size of calls required to achieve a population-representative survey and associated incentives. Cost inputs were obtained from direct economic costs incurred by a central study team, from country-specific collaborators, and from platform developers who participated in the deployment of these MPS during 2017. Costs were reported in US dollars (USD). A sensitivity analysis was conducted assessing different scenarios of pricing and incentive strategies. Also, costs were calculated for a survey deployed targeting only adults younger than 45 years. RESULTS: We estimated the fixed costs ranging between $47,000 USD and $74,000 USD. Variable costs were found to be between $32,000 USD and $129,000 USD per nationally representative survey. The main cost driver was the number of calls required to meet the sample size, and its variability largely depends on the extent of mobile phone coverage and access in the country. Therefore, a larger number of calls were estimated to survey specific harder-to-reach sub-populations. CONCLUSION: Mobile phone surveys have the potential to be a relatively less expensive and timely method of collecting survey information than face-to-face surveys, allowing decision-makers to deploy survey-based monitoring or evaluation programs more frequently than it would be possible having only face-to-face contact. The main driver of variable costs is survey time, and most of the variability across countries is attributable to the sampling differences associated to reaching out to population subgroups with low mobile phone ownership or access.

Infrared Multiple Photon Dissociation Spectroscopy of Protonated Cyameluric Acid.

The present study reports the first structural characterization of protonated cyameluric acid ([CA + H]+) in the gas phase, which paves the way for prospective bottom-up research on the condensed-phase chemistry of CA in the protonated form. A number of [CA + H]+ keto-enol isomers can a priori be produced as a result of protonation at available N and O positions of precursor neutral CA tautomers, yet ab initio computations predict different reduced [CA + H]+ isomer populations dominating the solution and gas phases that are involved in the ion generation process (i.e., electrospray ionization). Infrared multiple photon dissociation spectra were recorded in the 990-1900 and 3300-3650 cm-1 regions and compared with theoretical [B3LYP/6-311++G(d,p)] IR absorption spectra of several [CA + H]+ isomers, providing a satisfactory agreement for the most stable monohydroxy form in the gas phase, [1358a]+, yet the contribution of its nearly isoenergetic OH rotamer, [1358b]+, cannot be neglected. This is indicative of the occurrence of [CA + H]+ isomer interconversion reactions, assisted by protic solvent molecules, during their transfer into the gas phase. The results suggest that available O positions on neutral CA are energetically favored protonation sites in the gas phase.

Examining the cost-effectiveness of personal protective equipment for formal healthcare workers in Kenya during the COVID-19 pandemic.

BACKGROUND: Healthcare workers are at a higher risk of COVID-19 infection during care encounters compared to the general population. Personal Protective Equipment (PPE) have been shown to protect COVID-19 among healthcare workers, however, Kenya has faced PPE shortages that can adequately protect all healthcare workers. We, therefore, examined the health and economic consequences of investing in PPE for healthcare workers in Kenya. METHODS: We conducted a cost-effectiveness and return on investment (ROI) analysis using a decision-analytic model following the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) guidelines. We examined two outcomes: 1) the incremental cost per healthcare worker death averted, and 2) the incremental cost per healthcare worker COVID-19 case averted. We performed a multivariate sensitivity analysis using 10,000 Monte Carlo simulations. RESULTS: Kenya would need to invest $3.12 million (95% CI: 2.65-3.59) to adequately protect healthcare workers against COVID-19. This investment would avert 416 (IQR: 330-517) and 30,041 (IQR: 7243 - 102,480) healthcare worker deaths and COVID-19 cases respectively. Additionally, such an investment would result in a healthcare system ROI of $170.64 million (IQR: 138-209) - equivalent to an 11.04 times return. CONCLUSION: Despite other nationwide COVID-19 prevention measures such as social distancing, over 70% of healthcare workers will still be infected if the availability of PPE remains scarce. As part of the COVID-19 response strategy, the government should consider adequate investment in PPE for all healthcare workers in the country as it provides a large return on investment and it is value for money.

Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis.

Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. Although dyslexia diagnosis has been addressed by these tests in clinical practice, it is difficult to extract information about the brain processes involved in the different tasks and, then, to go deeper into its biological basis. Thus, the use of biomarkers can contribute not only to the diagnosis but also to a better understanding of specific learning disorders such as dyslexia. In this work, we use Electroencephalography (EEG) signals to discover differences among controls and dyslexic subjects using signal processing and artificial intelligence techniques. Specifically, we measure phase synchronization among channels, to reveal the functional brain network activated during auditory processing. On the other hand, to explore synchronicity patterns risen by low-level auditory processing, we used specific stimuli consisting in band-limited white noise, modulated in amplitude at different frequencies. The differential information contained in the functional (i.e., synchronization) network has been processed by an anomaly detection system that addresses the problem of subjects variability by an outlier-detection method based on vector quantization. The results, obtained for 7 years-old children, show that the proposed method constitutes an useful tool for clinical use, with the area under ROC curve (AUC) values up to 0.95 in differential diagnosis tasks.

Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms.

Electroencephalography (EEG) signal classification is a challenging task due to the low signal-to-noise ratio and the usual presence of artifacts from different sources. Different classification techniques, which are usually based on a predefined set of features extracted from the EEG band power distribution profile, have been previously proposed. However, the classification of EEG still remains a challenge, depending on the experimental conditions and the responses to be captured. In this context, the use of deep neural networks offers new opportunities to improve the classification performance without the use of a predefined set of features. Nevertheless, Deep Learning architectures include a vast number of hyperparameters on which the performance of the model relies. In this paper, we propose a method for optimizing Deep Learning models, not only the hyperparameters, but also their structure, which is able to propose solutions that consist of different architectures due to different layer combinations. The experimental results corroborate that deep architectures optimized by our method outperform the baseline approaches and result in computationally efficient models. Moreover, we demonstrate that optimized architectures improve the energy efficiency with respect to the baseline models.

Gun-carrying restrictions and gun-related mortality, Colombia: a difference-in-difference design with fixed effects.

OBJECTIVE: To assess the effect of a permanent gun-carrying restriction on gun-related mortality in Colombia between 2008 and 2014, and determine differences in the effect of the restriction by place of death and sex. METHODS: In 2012, Bogotá and Medellín introduced a permanent gun-carrying restriction. We compared gun-related mortality rates in these cities (intervention cities) with the rates in all other Colombian cities with more than 500 000 inhabitants (control cities). We used data from the Colombian National Department of Statistics to calculate monthly gun-related mortality rates between 2008 and 2014 for intervention and control cities. We used a differences-in-differences method with fixed effects to assess differences in gun-related mortality in intervention and control cities before and after the introduction of the gun-carrying restriction. We stratified effects by place of death (public area or residence) and sex. We made robustness checks to test the assumptions of the models. FINDINGS: Gun-related deaths in the control and intervention cities decreased between 2008 and 2014; however, the decrease was greater in the intervention cities (from 20.29 to 14.93 per 100 000 population; 26.4%) than in the control cities (from 37.88 to 34.56 per 100 000 population; 8.8%). The restriction led to a 22.3% reduction in the monthly gun-related mortality rate in Bogotá and Medellín. The reduction was greater in public areas and for males. Robustness checks supported the assumptions of the models. CONCLUSION: The permanent restriction on carrying guns reduced gun-related deaths. This policy could be used to reduce gun-related injuries in urban centres of other countries with large numbers of gun-related deaths.

On the Interaction between Deprotonated Cytosine [C(-H) ]- and Ba2+ : Infrared Multiphoton Spectroscopy and Dynamics.

Gas-phase interactions between Ba2+ and deprotonated cytosine (C(-H) ) were studied in [C(-H) Ba]+ and [C(-H) BaC]+ complexes by IRMPD spectroscopy coupled to tandem mass-spectrometry in combination with DFT calculations. For the [C(-H) BaC]+ complex only one [C(-H) KAN1O-Ba-Canti ]+ isomer was found, although the presence of another structure cannot be excluded. This isomer features a central tetracoordinated Ba2+ that simultaneously interacts with keto-amino [C(-H) ]- deprotonated on N1 and neutral keto-amino C. Both moieties are in different planes as a consequence of an additional NH…O=C hydrogen bond between C and [C(-H) ]- . A sequential IRMPD dynamics is observed in this complex. For the [C(-H) Ba]+ complex produced by electrospray ionization two isomers ([C(-H) KAN1OBa]+ and [C(-H) KAN3OBa]+ ) were identified, in which Ba2+ interacts simultaneously with the C=O group and the N1 or N3 atom of the keto-amino [C(-H) ]- , respectively. A comparison with the related [C(-H) Pb]+ complex (J. Y. Salpin et al., Chem. Phys. Chem. 2014, 15, 2959-2971) is also presented.

Gaussian Pseudorandom Number Generator Based on Cyclic Rotations of Linear Feedback Shift Registers.

This paper presents a new proposal to generate optimal pseudorandom numbers with Gaussian distribution. The generator is especially designed for low-cost hardware implementation, although the software version is also considered. For this reason, Linear Feedback Shift Registers in conjunction with cyclic rotations are employed. The proposal presents a low implementation cost and overcomes the limitations of the previous Gaussian generators based on linear feedback shift registers by means of a less complex algorithm to find optimal configurations. As a consequence, it turns into a really usable generator. Moreover, a further improvement, based on the simulated annealing algorithm, is applied in order for the random values to be better adjusted to the normal distribution.

Autosomal Dominantly Inherited Alzheimer Disease: Analysis of genetic subgroups by Machine Learning.

Despite subjects with Dominantly-Inherited Alzheimer's Disease (DIAD) represent less than 1% of all Alzheimer's Disease (AD) cases, the Dominantly Inherited Alzheimer Network (DIAN) initiative constitutes a strong impact in the understanding of AD disease course with special emphasis on the presyptomatic disease phase. Until now, the 3 genes involved in DIAD pathogenesis (PSEN1, PSEN2 and APP) have been commonly merged into one group (Mutation Carriers, MC) and studied using conventional statistical analysis. Comparisons between groups using null-hypothesis testing or longitudinal regression procedures, such as the linear-mixed-effects models, have been assessed in the extant literature. Within this context, the work presented here performs a comparison between different groups of subjects by considering the 3 genes, either jointly or separately, and using tools based on Machine Learning (ML). This involves a feature selection step which makes use of ANOVA followed by Principal Component Analysis (PCA) to determine which features would be realiable for further comparison purposes. Then, the selected predictors are classified using a Support-Vector-Machine (SVM) in a nested k-Fold cross-validation resulting in maximum classification rates of 72-74% using PiB PET features, specially when comparing asymptomatic Non-Carriers (NC) subjects with asymptomatic PSEN1 Mutation-Carriers (PSEN1-MC). Results obtained from these experiments led to the idea that PSEN1-MC might be considered as a mixture of two different subgroups including: a first group whose patterns were very close to NC subjects, and a second group much more different in terms of imaging patterns. Thus, using a k-Means clustering algorithm it was determined both subgroups and a new classification scenario was conducted to validate this process. The comparison between each subgroup vs. NC subjects resulted in classification rates around 80% underscoring the importance of considering DIAN as an heterogeneous entity.

UV Photofragmentation of Cold Cytosine-M+ Complexes (M+: Na+, K+, Ag+).

The UV photofragmentation spectra of cold cytosine-M+ complexes (M+: Na+, K+, Ag+) were recorded and analyzed through comparison with geometry optimizations and frequency calculations of the ground and excited states at the SCS-CC2/Def2-SVPD level of theory. While in all complexes, the ground state minimum geometry is planar (Cs symmetry), the ππ* state minimum geometry has the NH2 group slightly twisted and an out-of-plane metal cation. This was confirmed by comparing the simulated ππ* Franck-Condon spectra with the vibrationally resolved photofragmentation spectra of CytNa+ and CytK+. Vertical excitation transitions were also calculated to evaluate the energies of the CT states involving the transfer of an electron from the Cyt moiety to M+. For both CytK+ and CytNa+ complexes, the first CT state corresponds to an electron transfer from the cytosine aromatic π ring to the antibonding σ* orbital centered on the alkali cation. This πσ* state is predicted to lie much higher in energy (>6 eV) than the band origin of the π-π* electronic transition (around 4.3 eV) unlike what is observed for the CytAg+ complex for which the first excited state has a nOσ* electronic configuration. This is the reason for the absence of the Cyt+ + M charge transfer fragmentation channel for CytK+ and CytNa+ complexes.

Distributional health and financial benefits of increased tobacco taxes in Colombia: results from a modelling study.

BACKGROUND: In Colombia, smoking is the second leading modifiable risk factor for premature mortality. In December 2016, Colombia passed a major tax increase on tobacco products in an effort to decrease smoking and improve population health. While tobacco taxes are known to be highly effective in reducing the prevalence of smoking, they are often criticised as being regressive in consumption. This analysis attempts to assess the distributional impact (across socioeconomic groups) of the new tax on selected health and financial outcomes. METHODS: This study builds on extended cost-effectiveness analysis methods to study the new tobacco tax in Colombia, and estimates, over a time period of 20 years and across income quintiles of the current urban population (80% of the country population), the years of life gained with smoking cessation and the increased tax revenues, all associated with a 70% relative price increase of the pack of cigarettes. Where possible, we use parameters that vary by income quintile, including price elasticity of demand for cigarettes (average of -0.44 estimated from household survey data). FINDINGS: Over 20 years, the tax increase would lead to an estimated 191 000 years of life gained among Colombia's current urban population, with the largest gains among the bottom two income quintiles. The additional annual tax revenues raised would amount to about 2%-4% of Colombia's annual government health expenditure, with the poorest quintiles bearing the smallest tax burden increase. CONCLUSIONS: The tobacco tax increase passed by Colombia has substantial implications for the country's population health and financial well-being, with large benefits likely to accrue to the two poorest quintiles of the population.

Consenso multidisciplinario de diagnóstico y tratamiento del dolor neuropático periférico y localizado en México.

Neuropathic pain is an entity that causes patient disability and its diagnosis and treatment is a challenge for physicians. In a significant percentage of patients with neuropathic pain, it is restricted to one dermatome or to a particular region of the body; in this case, it is referred to as localized neuropathic pain. There are no Mexican clinical guidelines proposing recommendations for the diagnosis and treatment of localized neuropathic pain in our population. This article presents the recommendations of a multidisciplinary consensus of specialists from different areas involved in the diagnosis and treatment of this type of patients.

El dolor neuropático es una entidad que provoca discapacidad al paciente y su diagnóstico y tratamiento es un reto para los médicos. En un porcentaje importante de pacientes afectados, el dolor neuropático se presenta circunscrito a un dermatoma o a una región concreta del cuerpo, denominándose en ese caso dolor neuropático localizado. No existen guías clínicas mexicanas que postulen recomendaciones para el diagnóstico y tratamiento del dolor neuropático localizado en nuestra población. En este artículo se exponen las recomendaciones de un consenso multidisciplinario realizado con especialistas de distintas áreas implicadas en el diagnóstico y tratamiento de este tipo de pacientes.