This review examines the effectiveness of diet and physical activity interventions to reduce cardiometabolic risk among Chinese immigrants and their descendants living in high income countries. The objective of this review is to provide information to help build future interventions aimed at improving diet and increasing physical activity levels among Chinese immigrants.Outcomes included BMI, weight, waist circumference (WC), waist-hip ratio (WHR), cholesterol , systolic and diastolic blood pressure , hemoglobin A1c (HgbA1c), fasting blood glucose (FBG), and HOMA-IR. Six databases were systematically searched from database inception to date of search (February 2020). Meta-analyses used random effect models to estimate pooled effects of outcomes with 95% confidence intervals. The outcomes assessed were changes in mean outcomes (post-intervention versus baseline) among the intervention group versus control groups.2; , I2 = 31%). There were also significant effects of intervention among adults in terms of changes in SBP and DBP, as the pooled effect across three studies was − 6.08 mmHg , I2 = 0% and − 3.81 mmHg , I2 = 0%, respectively. Among adults there were no other significant effects among the meta-analyses conducted .Twenty-one articles were included for synthesis, and eight of these were included in the meta-analysis. Among children/adolescents, there were no significant effects of intervention for any of the outcomes having sufficient data for meta-analysis . Among adults, the pooled effect including three studies showed significant changes in BMI , and among children, there was no evidence of effect for any cardiometabolic outcomes. Given our mixed findings, more work is needed to support the design of successful interventions, particularly those targeting children and their families.CRD42018117842).The systematic review protocol was registered in PROSPERO on December 17, 2018, the international prospective register of systematic reviews (registration number:  People of Chinese origin make up one of the fastest expanding groups in high-income countries such as the United States, Australia and Canada . The carThere is good evidence of differences in physical activity and dietary practices between Chinese migrant groups and the rest of the population in a number of countries with the largest Chinese-origin populations. There was a higher prevalence of inactivity among Chinese Australians than non-Chinese Australians , CanadiaDietary differences are harder to characterize. Those of Chinese origin ate greater amounts of fruit and vegetables than the general population in the UK and fat intake was relatively low , while sConsiderations for developing interventions for Chinese migrants and/or their descendants include: 1) language ; 2) health literacy; 3) traditional Chinese diet; 4) migration and acculturation; and 5) traditional Chinese medicine . SuccessIn the context of some concerns about diet and physical activity in those of Chinese origin living in high-income countries, and evidence that this group may benefit from tailored interventions, this review examines the effectiveness of interventions designed to modify dietary and physical activity behaviors to reduce cardiometabolic risk in this group. The objective of this review is to provide information to build future interventions aimed at improving the diet and increasing physical activity levels among Chinese immigrants.CRD42018117842).The review was conducted following the PRISMA Protocol for Systematic Reviews (PRISMA)  and the In February 2020, co-author (TR), an experienced Medical Librarian, searched PubMed Central, Ovid Medline, Ovid Embase, CABI, Food Science Technology Cinahl and the Cochrane Central Register of Controlled Trials. The Ovid Medline Search is included as supplementary material  they quantitatively described the effect of an intervention designed to modify dietary and/or physical activity behaviors on cardiometabolic risk factors , waist-hip ratio (WHR), LDL and/or HDL cholesterol, systolic and diastolic blood pressure (SBP and DBP), hemoglobin A1c (HgbA1c), fasting blood glucose (FBG), and HOMA-IR), and 2) the recipients of the intervention were of Chinese origin and living in a high-income economy, as defined by the World Bank . ExclusiTitles and abstracts were screened by four independent reviewers , with each citation receiving two votes. The full-texts of studies with relevant abstracts were assessed for eligibility by two screeners independently . Any conflicts were discussed and resolved through consensus of all four reviewers.Data from studies eligible for inclusion were extracted using a data extraction form adapted from published sources such as the Cochrane review , 22. If A narrative synthesis was used as it allows the compilation of data despite potential differences in research questions, design, or context in order to find a common underlying pattern. If at least two studies included the same outcome variable and pre- and post-intervention values were reported for both the intervention and control group, a meta-analysis was conducted. In cases where multiple post-intervention measurements were available, we extracted the measure that corresponded most closely to the endpoint of the intervention. We stratified analyses by age group (children/adolescents and adults).2 statistic. Results are to be interpreted with caution where there is significant heterogeneity (I2 > 50%). Meta-analyses were performed in R software using the ‘meta’ package.Where meta-analysis was possible (e.g. pre-post measures were available for intervention and control groups), the analyses involved two steps. The first step was to assess mean differences (MD) in outcomes for both the intervention and control group by comparing changes in the mean as the difference between post-intervention and baseline measures. For calculating MD, available adjusted or unadjusted means as reported in the included studies were used. The corresponding changes in standard deviation (SD) were not directly reported in most studies, and therefore was estimated using the formula suggested by the Cochrane handbook for systematic reviews of interventions . A correAfter duplicates were removed, 4443 articles were identified Fig. . The iniAmong children/adolescents, the first study was published in 2008  and the Among studies conducted in children/adolescents Fig.  and b, oAmong studies conducted in adults Fig. a and b, Among children/adolescents, four studies were randomized controlled trials, three studies were pre-post single-arm interventions, and one study included a historical control group Table . The mosAmong adults, three studies were randomized controlled trials, nine studies were pre-post single-arm interventions, and two studies were two-group repeated measures quasi-experimental design Table . Interve2;   Fig. a. For WHFor the three single group design studies, Chen 2008 only reported changes in BMI stratified by overweight status , while t2; 95%CI − 2.06, − 0.21) , and just three of the adult interventions were conducted outside the United States . Eight were conducted in children/adolescents; of these, seven were conducted by the same research group in San Francisco.As of February 2020, there were 21 published studies describing behavioral diet and physical interventions in Chinese migrants living in high-income countries. The majority were conducted in adults  how interventions are culturally adapted; (2) the types of behavior change techniques and theories that are used to underpin interventions; (3) loss to follow-up by study arm; (4) variability within the Chinese-origin population, particularly with respect to generational differences that may be important for the design of interventions; and (5) contextual factors, such as whether the setting is rural or urban. These recommendations would enable reviewers to assess how behavior change techniques and theories moderate effectiveness, to assess the equity impacts of interventions, and to examine explanations for heterogeneity between interventions.Given our mixed findings, more work is needed to support the design of successful interventions, particularly those targeting children and their families. The development of effective interventions may well require a great deal of qualitative and quantitative research on knowledge, attitudes, behaviors, and perceptions. More research is needed into the differential effects of lifestyle interventions for Chinese immigrants compared with other ethnicities.Additional file 1: Supplemental Table 1 Ovid Medline Database Search Strategy.


This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by bio-signal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals such as respiration (RSP) and heart rate variability (HRV) were acquired in an experiment from 53 participants when six basic emotion states were induced. Two RSP parameters were acquired from a chest-band respiration sensor, and five HRV parameters were acquired from a finger-clip blood volume pulse (BVP) sensor. A newly designed deep-learning model based on a convolutional neural network (CNN) was adopted for detecting the identification accuracy of individual emotions. Additionally, the signal combination of the acquired parameters was proposed to obtain high classification accuracy. Furthermore, a dominant factor influencing the accuracy was found by comparing the relativeness of the parameters, providing a basis for supporting the results of emotion classification. The users of this proposed model will soon be able to improve the emotion recognition model further based on CNN using multimodal physiological signals and their sensors. The negative stimuli that people experience in their daily lives vary widely, and the accumulation of these negative stimuli causes stress and mental illnesses such as depression. However, owing to the lack of awareness of the dangers of mental illness and the negative social perception, treatment behaviors are often passive. Additionally, efforts to alleviate negative stimuli are ongoing, but it is difficult to grasp the degree of stress of an individual, and, in many cases, emotional differences make assessment difficult. Therefore, differences in personal emotions are very important in relieving stress and treating mental illnesses.Representative techniques for recognizing emotions include image-based recognition, voice-based recognition, and physiological-signal-based recognition . All thrK-nearest neighbor (KNN) [Emotion classification through defined emotions and measured signals is performed in various ways, ranging from traditional statistical methods and machine learning  to the latest technique, deep learning. In particular, the support vector machine (SVM) is a widely used method for emotion classification ,10,11. Tor (KNN) ,15, and or (KNN)  have beeor (KNN) ,18, Deepor (KNN) , and Spaor (KNN) , or modeor (KNN) ,22. In aor (KNN) ,24,25,26As mentioned above, previous studies classified emotions by acquiring physiological signals and then classifying these signals in various ways. In other words, the research focus was on the processing of data and classification of signals using classification models. In contrast, the present study proposes an optimum emotion classification model beyond simple emotion classification. For this purpose, we compared the classification accuracy of single-signal and multi-signal approaches, experimented with various combinations between input signals for classify emotions into various stages, and verified the dominance of parameters using principal component analysis. Furthermore, the emotion recognition methods using various deep-learning techniques proposed in this study are expected to be referred to classify emotions efficiently. The overall flow of this study is shown in In many studies related to existing emotions, human emotions are divided into arousal and valence  by usingEmotions defined in this way can be measured using physiological signals. Physiological signals can be easily measured using non-invasive sensors and wearable devices, and various signals such as electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), galvanic skin response (GSR), skin temperature (SKT) and respiration (RSP) are closely related to human emotions . DiffereTo analyze emotions through respiration, the subject’s respiration should be measured in real time. There are many ways to measure respiration, including the direct measurement of air flow during inhalation and exhalation, the use of a photoplethysmogram (PPG) sensor, and a method of measuring changes in the width of the thoracic cavity by using a respiratory band. The direct measurement of air flow allows the precise measurement of breathing volume, but it involves specific testing equipment and requires subjects to wear inconvenient airflow transducers. As an alternative to this problem, PPG sensors, which use peripheral circulation changes through respiration, may be used ,37. HoweEmotions can also be recognized using heartbeat signals in addition to respiratory signals. Heartbeat signals can be measured using various methods. Of these, a method of acquiring an electrocardiogram (ECG) directly through an attached electrode and a measurement method of using a PPG/blood volume pulse (BVP) sensor on a finger or an earlobe are the most commonly used. The direct method of acquiring an ECG utilizes the principle that the skin senses electrical impulses that occur each time the heart beats. The measurement using a PPG/BVP sensor measures the blood flow and extracts the heartbeat signal from optical electronics by absorbing the infrared light of the cell and blood vessels. The method of measuring and applying bio-signals in this manner has the advantages of simplicity and ease of operation even with the newest smartphones .The heart rate (HR) is the rate of heartbeats, and it is measured as the number of beats of the heart per minute. In other words, HR is the number of peak-to-peak periods in the ECG per minute. One cycle of an ECG consists of QRS waves. The highest peak in one cycle is referred to as the R peak, and the peak-to-peak time interval of each electrocardiogram is called the RR or NN interval or inter-beat interval . A contiThe analysis of HRV can be divided into analysis in the time domain and analysis in the frequency domain. The simplest method of analyzing HRV is time-domain analysis, in which the time interval of the QRS wave or the instantaneous heart rate at a specific time is analyzed. Analysis in the frequency domain is used to analyze the power distribution of the function to frequency and to quantify the balance of the autonomic nervous system ,42. The In this study, a finger-clip BVP sensor was used to obtain HRV parameters in the time domain and frequency domain. The different variables that can be obtained from the time-domain analysis are the mean RR interval (RRI), standard deviation of normal to normal interval (SDNN), root-mean-square of successive differences (RMSSD) and pNN50(proportion of NN50), but we used the most basic HR and HRV amplitudes. In addition, different variables such as very low frequency (VLF), low frequency (LF), high frequency (HF), VLF/HF ratio, and LF/HF ratio can be extracted through analysis in the frequency domain. The present study used LF, which indicates the activity of the sympathetic nervous system, and HF, which indicates the activity of the parasympathetic nervous system, as parameters, and they were obtained through analysis in the frequency domain. In addition, the LF/HF ratio , which cThere have been many studies using various parameters of bio signals using sensors. Fujiwara et al.  proposedResearch on classifying human emotions using physiological signals has been performed using various methods and systems. Emotion classification starts with the definition of the emotions to be recognized, the measurement of signals, and the selection of classifiers. In the definition of emotions, the emotions to recognize are selected based on the discrete emotion model or by using a two- or three-dimensional emotion model. Verma et al.  and Wen In the present study, we classified emotions using CNN. Many other studies have classified emotions in other ways. As mentioned in the Introduction, a method of classifying emotions uses conventional statistical analysis techniques and deep learning. Many studies used both—statistical analysis and deep learning—methods or combined them to increase the accuracy of the model. Yin et al.  classifiHowever, unlike previous studies, the present study proposes an emotion classification into various stages using CNN model. In As mentioned in the Introduction, experiments were conducted to classify six emotions and compare the classifiers. It was an open label and single arm experiment performed from November to December 2017 at Seoul Metropolitan Government—Seoul National University (SMG-SNU) Borame Medical Center, Seoul, Korea. Ethical approval was provided by the Institutional Review Board (IRB) at site, and the study adhered to the principles outlined in the Declaration of Helsinki and good clinical practice guidelines. The trial is registered in the Boramae IRB . The experiment was conducted in a controlled environment where a monitor capable of displaying video clips with six distinct emotions and a sensor capable of acquiring physiological signals were installed. To stimulate the participants, video clips expressing happiness, fear, surprise, anger, sadness, and disgust were played for one minute in that order. In order of emotion, the video clips used in the experiment were part of About Time (2013), The Shining (1980), Capricorn One (1977), The Attorney (2013), The Champ (1979), and Pink Flamingos (1972). Snapshots of video clips are not provided in this paper in order not to be restricted by copyright issues.Before the start of the experiment, participants were sufficiently briefed about the experiment and its side effects. When seated in front of the monitor, the participant put a BVP sensor on the finger and a respiration band on the abdomen. When the experiment began and it was determined that physiological stability was ensured, we measured the physiological signal in the neutral state for 1 min to be used as a different emotion for Control group. Then, participants relaxed for 2 min. After the measurement of the physiological signal of the neutral state and relax, the participant watched the happiness video for 1 min and maintained a relaxed state for 2 min thereafter. When the second relaxed state ended, the participant watched the fear video and the remaining videos in the order indicated in The software and hardware used in the experiment were products of Mind Media B.V. . The BVP signal was from the BVP finger-clip sensor of NeXus-10 MKII, and the sampling rate of the sensor is 32 samples/s. The measurement unit of the BVP sensor is Beats per minute (BPM), and parameters such as Relative Blood Flow and Heart Rate (HR) can be extracted. The measuring range is 40–240 BPM. The wavelength of the BVP sensor IR light is ±940 nm . The resThe participants wore the BVP sensor as a finger clip. The BVP finger-clip sensor has built-in optical electronics that measures blood flow through the absorption of infrared light in tissue and blood vessels. Every heartbeat causes blood to flow into the arteries and veins. The waveform amplitude of the BVP signal is related to blood flow as well as to the vasodilation and vasoconstriction of blood vessels. The participants wore the respiration band on the chest or abdomen, and the magnetic-field sensor of the band measured the stretching of the band when the participant breathed. The measured band-expansion value is related to the respiration signal. Because this non-invasive and simple measurement method was used to extract physiological signals by using the above-mentioned equipment, the burden on the participants was minimized. In the actual experiment, the sensor mentioned above and shown in In the BVP signal, various parameters can be obtained to confirm the response of the autonomic nervous system. In this experiment, HRV parameters such as heart rate, HRV amplitude, LF, HF, and LF/HF were extracted. From the RSP signal, the RSP value and RSP rate, which are the RSP parameters, can be extracted. The HRV and RSP parameters in the raw signal were automatically calculated, processed, and stored using Biotrace + software, which did not require manual intervention.All signals obtained from the experiment can be recorded and confirmed through Biotrace+ software. Based on this software, we selected the data suitable for the study and split them based on the experiment time. From the 53 participants’ signals, 49 signals  were used for the study. The remaining four signals were discarded due to signal instability or other reasons. The signal to be analyzed was selected from among the HRV parameters  and RSP parameters , which are stored as time-series data. Subsequently, we split the data according to the markers, as recorded in the experiment, and extracted the data corresponding to the six emotions for 1 min. Data processing was completed by labeling each of the six extracted signals with their corresponding emotion. The overall procedure of data processing is represented in The number and type of data obtained through data preprocessing procedures are summarized in Although the number of all data is specified here, the data used in the actual analysis may vary slightly depending on the individual characteristics of the experiment participants. The data used for analysis depend on the combination of features. The combination of features can also be found in The pre-processed data obtained from the data-processing step were applied to the classification of emotions. Although emotions can be classified in various ways, in this study, we compared results of single-signal and multi-signal classification and classified emotions into various stages. For this study, we constructed a CNN model and applied the pre-processed data. The overall procedure of comparing single-signal and multi-signal from data acquisition from each sensor is shown in The obtained HRV-  and RSP-parameter  data were applied to the CNN as parameters to classify the emotions. Each CNN model used emotion-  and neutral-labeled data as input data to classify a given emotion.The phased combination of each parameter was proceeded to find the optimal classification model. The combination of all physiological signal parameters obtained through experiments is depicted in Combination steps (1) and (2) correspond to single-signal classification (RSP parameter or HRV parameter). Combination steps (3) and (4) correspond to multi-signal classification (combination of RSP and each domain of HRV parameters). Combination step (5) corresponds to multi-signal classification (RSP and HRV parameters).Particularly, Combination step (3) was applied to the classification model through the fusion of the RSP parameters and the frequency-domain HRV parameters. Similarly, Combination step (4) was applied through the fusion of the RSP parameters and the time-domain HRV parameters. These step-by-step combinations of parameters were intended to verify the optimal emotional classification model.CNN is generally used for the classification and prediction of images. However, by modifying the structure of CNN, we used a method of row-by-row reading of input data to classify physiological signals. CNN used Python’s frameworks, Keras, and Tensorflow. CNN utilized 70% of all data as a training set and the other 30% as a test set. However, when the Combination steps (1)–(5) were classified using the CNN, the input data varied according to the number of input parameters. That is, when classifying the RSP: Combination step (1), the model used the input data shape (2 1 1), while Combination step (2) with HRV used (5 1 1). Thus, for RSP and HRV (Combination step (5)), (7 1 1) was used. Overall, the model was constructed to have similar tendencies in large frameworks. The input data shapes are listed in n, n-1, n-2, n-3, etc.). Therefore, in Model 1, the input shape was (2 1 1) because two input parameters were applied, and the shape decreased as learning progressed. Models 2–4 were constructed to have the same tendency. Furthermore, in Models 2–4, we reduced the number of convolution filters from 50 to 25 and then increased it to 50. In all models, batch normalization and activation functions were applied between two convolution layers. Finally, maxpooling proceeded. We also initialized weights with the “He kernel initializer” function in all convolution layers. Finally, a He kernel initializer was used in the ReLu and Softmax functions along with Dense, and the dropout rate was set to 0.5.The tendency of the overall CNN model constructed in this study was designed by reducing the size of the shape by one , false positive (FP), true negative (TN), and false negative (FN). In these four cases, the formula for accuracy is shown in Equation (1).We classified the parameters into six emotions based on the CNN models described in the preceding subsection and evaluated the single-signal and multi-signal classification performance. The classification results are summarized in The classification accuracy was 63.18% and 77.42% when emotions were classified using the single-signal with CNN Models 1 and 2, respectively. This result suggests that both classifiers can classify emotions with proper performance when using the HRV parameters and show better results compared to classification using the RSP parameters alone. The results of the classification were identified, but the results contained some meanings that were insufficient to be used as a model for general emotion classification. Therefore, instead of classifying emotions using a single-signal, we verified the classification results of emotions using multi-signal. The classification accuracy was 78.31% and 76.02% when parameters which combined RSP and some HRV were classified using the multi-signal with CNN Models 2 and 3, respectively. This result suggests that both classifiers can classify emotions with proper performance when using the parameters which combine RSP and some HRV parameters. Unfortunately, while CNN Models 2 and 3 were used to classify emotions properly, similar to some of the results in These five cases reveal that multi-signal classification is better than single-signal classification. However, not all multi-signal models have outstanding results, and multi-signal consisting of only some domains of HRV did not yield high classification results. Therefore, this study confirms that the accuracy of multi-signal emotion classification is higher than that of single-signal emotion classification. When classifying emotions using multi-signal, all domains of the HRV parameter must be used.Various physiological signals can be detected in the human body, and each physiological signal can describe various states of the body. In addition, since physiological signals cannot be changed at will, it is a good indicator for analyzing a person’s emotional state. Much research has been conducted on emotional analysis ,58,59,60The optimal emotion classification model can reduce unnecessary procedures in analysis and experimentation. Therefore, we compared the accuracy of multi-signal and single-signal classification, performed classification using combined physiological signals into various stage, and then judged the accuracy of emotion classification using some parts of cardiac and respiratory signals.A total of 53 subjects participated in the experiment. They watched video clips corresponding to the six basic emotions while wearing a finger-clip BVP sensor and chest-band respiration band. The BVP and RSP signals of the subjects were measured while they watched the video clips. Of the raw signals, 49 signals were used in the study, removing inappropriate data. The signal selecting and splitting proceeded according to the research purpose. Subsequently, labeling and data pre-processing were performed.The labeled data obtained in the pre-processing step were applied to the classifier. RSP  and HRV  were applied to CNN to identify single-signal classification performance. For this purpose, two CNN models (CNN Models 1 and 2) were created for each parameter. Through the results, when single-signal was used, it was confirmed that proper emotion classification performance can be obtained. However, it cannot be used for an independent emotion classification model because of the low performance.To solve this problem, we decided to classify emotions through the combination of different domains of HRV or all of HRV and RSP. Combining different types of signals can improve classification performance. The signals were combined in three ways: the combination between the RSP signal and frequency-domain HRV signal; the combination between the RSP signal and time-domain HRV signal; and the combination between the RSP signal and HRV signal. These were named Combination steps (3)–(5), respectively.The combined signal was classified with CNN in the same manner as in the previous cases. As a result of classification using Combination steps (3) and (4) signals with CNN, the accuracy was confirmed to be similar for the two classifiers. In addition, Combination step (5) results show an accuracy greater than 94%; thus, it was confirmed that Combination step (5) yielded higher performance with all classifiers. Multi-signal classification showed the better performance than single-signal classification. Therefore, it can be said that the use of many types of input signals can enhance the emotion classification performance. However, when using multi-signal, all domains of HRV must be applied as input data.However, in the combination of RSP and some of HRV signal, the results of emotion classification are similar. In other words, the accuracies of Combination steps (3) and (4) are similar. We identified the dominance of the entire HRV parameters to verify why both results are similar. Therefore, we performed principal component analysis (PCA) for all the HRV parameters. Based on the results shown in As shown in Based on the above studies, we can determine that the optimal model for classifying emotions using multi-signal is CNN Model 4, and it is difficult to classify general emotions with single-signal or some combination of data. Therefore, when classifying emotions, both RSP and HRV parameters should be used.By modifying the structure of the CNN model, we developed a general signal classifier. The data were acquired through six emotion-based video viewing experiments and preprocessed. Four different CNN models were developed for preprocessed data. Based on this, the classification accuracy of single- and multi-signal was compared. In addition, emotions were classified at various stages by signal combinations, and the dominance of HRV parameters was verified to confirm that it is difficult to classify emotions with multi-signal combined with several domains. Finally, an optimal model for classifying emotions with high accuracy is presented.The response of the human autonomic nervous system is a good indicator of emotions because it cannot be manipulated at will. In this study, we studied the optimal emotion classification models. Unlike previous studies using many physiological signals for emotion classification, the present study attempted to obtain an optimal emotion classification model. Respiration and cardiac signals corresponding to six basic emotions were extracted through experiments; the signals were combined variously to find the optimal classification model; and several CNN models were built to classify emotions. PCA was also used to determine the cause of similar emotional classification results.Although multi-signal emotion classification shows very good results, it takes a relatively long time because of the many parameters and data used, and the calculation requires high computing power. Therefore, further research should be conducted to improve the performance of emotion classification with fewer parameters and advanced CNN models.Finally, signals were measured for one minute in the experiment. If the constructed model were commercialized, this may be a rather long time. Since classification results may vary depending on the measurement time of the signal, further research will be required to shorten the time of signal extraction and improve the model accuracy. Although there are limitations in the present research, it is possible to develop a more efficient emotion estimation technology from the results of this study.


To describe the human resources for health and analyze the inequality in its distribution in Mexico. Cross-sectional study based on the National Occupation and Employment Survey (ENOE in Spanish) for the fourth quarter of 2018 in Mexico. Graduated physicians and nurses, and auxiliary/technician nurses with completed studies were considered as human resources for health. States were grouped by degree of marginalization. Densities of human resources for health per 1,000 inhabitants, Index of Dissimilarity (DI) and Concentration Indices (CI) were estimated as measures of unequal distribution. The density of human resources for health was 4.6 per 1,000 inhabitants. We found heterogeneity among states with densities from 2.3 to 10.5 per 1,000 inhabitants. Inequality was higher in the states with a very low degree of marginalization (CI = 0.4) than those with high marginalization (CI = 0.1), and the inequality in the distribution of physicians (CI = 0.5) was greater than in graduated nurses (CI = 0.3) among states. In addition, 17 states showed a density above the threshold of 4.5 per 1,000 inhabitants proposed in the Global Strategy on Human Resources for Health. That implies a deficit of nearly 60,000 human resources for health among the 15 states below the threshold. For all states, to reach a density equal to the national density of 4.6, about 12.6% of human health resources would have to be distributed among states that were below national density. In Mexico, there is inequality in the distribution of human resources for health, with state differences. Government mechanisms could support the balance in the labor market of physicians and nurses through a human resources policy. Anand and Bärnighausen criticize the conceptual framework of health systems proposed by the World Health Organization (WHO), which considers human resources for health (HRH) as one of the building blocks of the system1. They propose a conceptual framework in which human resources are the core of health systems3. The fulfillment of goals and patient satisfaction depend on the HRH, since they provide preventive and curative care, as well as information on diagnosis, treatment and monitoring, and decide which technology and/or drug to use. Therefore, their size, composition and distribution are very relevant to ensure the population’s access to health services3.Human resources working in health institutions are considered one of the fundamental components for the proper performance of health systems4. In 2004, the Joint Learning Initiative, [JLI] recommended a density of 2.5 per 1,000 inhabitants to achieve 80.0% measles immunization coverage and deliveries performed by health personnel5. Under the Millennium Development Goals (MDG), the 2006 WHO Global Report suggested a minimum threshold of 2.3 physicians, nurses and midwives per thousand inhabitants to reach 80.0% of deliveries performed by qualified personnel; 57 countries did not reach such an indicator4.The availability of HRH, measured by density per number of inhabitants, has been used as a health coverage indicator. A positive relationship has been found with some interventions, such as vaccination or births performed by qualified personnel6, the WHO established a density of 4.5 physicians, nurses and midwives per 1,000 inhabitants as the minimum threshold required for the implementation of the Global Strategy on Human Resources for Health (GSHRH) under Universal Health Coverage (UHC)7. Unlike the 2006 indicator, this threshold includes 12 indicators related to UHC and SDG targets. With this indicator, the WHO predicted that 18 million more health workers would be needed in low-middle-income countries to achieve SDG8, although it became an emphasis that it should not be a target for all countries given their differences in health needs7.In 2016, under the Sustainable Development Goals (SDG)Seguro Popular de Salud  in 2003, to provide financial protection to the population excluded from social security9. At the end of 2017, 53.5 million Mexicans were affiliated with PHI10. In 2005–2016, public spending on health per capita increased by 31.0%, and the density of physicians and nurses per thousand inhabitants increased by 34.0 and 32.0%, respectively. Despite these advances, the public health system faces problems of accessibility and quality in care, which can be explained by the lack of an explicit policy on human resources for health11 and by the imbalance between the health needs of the population and human and financial resources12.In Mexico, the federal government launched the public health insurance known as Secretaría del Trabajo y Previsión Social  makes periodic estimates of the number of graduated physicians and nurses who are employed13; however, these estimates do not show whether employed professionals practice the medical profession nor specify the length of the working day. Other sources of information such as the Organization for Economic Cooperation and Development (OECD) indicate for Mexico a density of 2.4 and 2.9 physicians and nurses, respectively, in 2017. However, these data include intern physicians (undergraduate) and residents , use different sources of information and recognize the possibility of duplication14. In both cases, the HRH density indicator may be overestimated, and it is not possible to estimate the professional deficit that is necessary to reach the 4.5 threshold recommended in GSHRH. Therefore, this study aimed to describe the HRH and analyze it’s inequal distribution in Mexico.At national level, the 15. Databases are publicly available and do not contain identifiable information about individuals.Cross-sectional study based on secondary data analysis to describe characteristics of the physicians and nurses. The National Occupation and Employment Survey , conducted by the National Institute of Statistics and Geography  was used. ENOE has a probabilistic, two-phased, stratified and clustered design. It is carried out quarterly, has national and state geographic coverage, and it aims to provide information on the occupational characteristics of the population aged 15 and olderENOE-based inequality indicators were constructed for the fourth quarter of 2018 (quarter IV-2018). All professionals with medical or nursing studies who reported completing their undergraduate studies at the time of the survey were considered Human Resources for Health (HRH) or health workers. We also included those who studied nursing at the technical level (auxiliary nursing).Employed (Emp): physicians and nurses who worked 20 hours or more a week performing functions according to their proffesional training in the health area, or performing administrative functions in the health sector.Unemployed (Une): physicians and nurses who looked for a job because they did not have an economic activity.Quantitative underemployment (QuantiUn): physicians and nurses employed less than 20 hours a week and with functions according to their profession, or who exercised the health profession as a secondary job.Qualitative underemployment : physicians and nurses employed but with functions or activities outside their professional training, regardless of working hours per week.Economically Active Population (EAP): physicians and nurses employed, underemployed and unemployed.Densities of Human Resources for Health per 1,000 inhabitants:a.Density of employed human resources (DHRH): b.Density of Human Resources EAP: In both cases, the HRH deficit was obtained from the threshold recommended in GSHRH according to the following formulas:c.Employed deficit: d.EAP Deficit: Socio-economic factors  in both professions were analyzed, and state densities of human resources for health of 1) employed health personnel per 1,000 inhabitants (DHRH), and 2) economically active population (EAP) per 1,000 inhabitants were estimated. In both cases, densities were compared with the density of 4.5 per 1,000 inhabitants recommended in GSHRH, and the number of health workers needed to meet this threshold was estimated.19. It is estimated using the formula sjh is the proportion of HRH in state j with respect to the national total of HRH, and sjp is the proportion of the population in state j with respect to the national population. In this study, the ID was interpreted as the proportion (or percentage) of workers who would have to be redistributed among states so that everyone had the same DHRH.The Index of dissimilarity (ID) is one indicator used in the analysis of health inequalities19. In this study, curves were elaborated considering states as an analysis unit. The proportion of HRH in state j with respect to the national total of HRH (sjh) was estimated, and the proportion of Disability-Adjusted Life Year (DALY) in state j was estimated from the national total of DALYs (sjd). The concentration curve plots the cumulative proportion of DALY by the states  with the accumulated proportions of HRH in the Y-axis. If workers are equally distributed among states according to their percentage of the national disease burden, the concentration curve matches the diagonal joining the points (0.0) and (1.1), which is called the equality line. The area between the CC and the equality line is the Concentration Index (CI) and the higher the absolute value of the larger IC, higher the inequality. A scatter plot was built between the DHRH per 1,000 inhabitants and the DALY per 100 thousand inhabitants in each state18. In addition, another dispersion plot was built between the DHRH per 1,000 inhabitants and the Gross Domestic Product (GDP) per capita, and the Spearman’s correlation was estimated.Another index used in the analysis of health inequalities is the index from the Concentration Curve (CC)20. The classification and grouping of states according to degree of marginalization comes from the classification proposed by CONAPO21. Data on the Gross Domestic Product (GDP) were obtained from the INEGI website22. DALY information was obtained from global disease burden estimates conducted by the Institute for Health Metrics and Evaluation (IHME)23.The three inequality indicators in the distribution of human resources at the national level and by states grouped by degree of marginalization were used: the DHRH per 1,000 inhabitants, the ID and the CI. Population size information comes from the population projections of the National Population Council  available on the website of the Directorate-General for Health Information  of the Ministry of HealthThe analyses were conducted using the survey weights, and its complex design was considered using the SVY module of the STATA MP 13.0 package. Pearson Chi-square tests assess the differences in distribution between the type of profession (medicine or nursing) and its socioeconomic factors considering p < 0.05 for statistical significance and confidence intervals at 95% (95%CI).In 2018, there were 413,000 physicians and 714,000 nurses in Mexico, of whom 62.9% and 44.4%, respectively, were employed 20 hours or more in the health sector , which iThe density of health professionals (DHRH) per 1,000 inhabitants was 4.6 , exceeding the threshold of 4.5 recommended in GSHRH . Howeverper capita . This coincided with what was described above, according to which the groups of entities of very low and very high marginalization had higher and lower DHRH, respectively  with the smallest proportions of DALY concentrated less than 25.0% of the entire workforce, while five of them  concentrated more than 40.0% (CI = 0.4). This pattern was mainly caused by the inequality in the distribution of medical personnel (CI = 0.5) and technical nursing (CI = 0.5), and to a lesser extent to the distribution of professional nursing (CI = 0.3) . In addiectively .Seventeen states reached a density of 4.5  and 3b. 24. While, according to GSHRH criteria, the density of health workers could be considered acceptable, inequality within the country is reflected in the variability among states, particularly those with high marginalization that do not reach the threshold. Considering that OECD data include interns and residents, and there is the possibility of double counting of physicians and nurses working simultaneously in the public and private sector, the availability of HRH for lower-density areas is likely to be below current demand and with a larger deficit for future years. In this sense, requirement projections by medical specialists in Mexico have estimated that health needs arising from population aging will require more specialists in internal medicine and surgery than in pediatrics, which will imply a growing challenge for the provision of services25.Our study shows a disadvantage of Mexico in the availability of HRH compared with the average of OECD countries26. Our results could reflect other weak areas of the health system itself, such as insufficient equipment or required inputs, poor quality of care, as well as conditions typical of the demographic and epidemiological transition. The consequences of these multiple factors are chronic-degenerative health needs that lead to disability for a greater proportion of the population27.A relevant finding was the lack of consistency in the relation between the density of health workers and DALY rates. As has been documented in other studies, a negative correlation would be expected, which would support evidence of having more health professionals to reduce the burden of disease28, due to their high economic development, which provide better job opportunities and better income expectations29; or 2) the distribution of the number of students enrolled at universities across the country where six states, of very low or low marginalization, concentrate to 50.0% and 35.0% of the enrollments of medical and nursing students in the 2017–2018 school year, respectively; in contrast, three very high-marginalization entities account for 15.7% of registered nursing students and 6.5% of medical students30. These imbalances in the distribution of human resources pose a challenge to the health system as the population living in areas of high degree of marginalization is less likely to have access to health services.By contrast, the positive relationship between DHRH and state GDP could be explained by the following reasons: 1) qualified medical personnel can switch residency to Mexican regions with attractive cities for medical mobility31, but are not considered in the estimation of the 4.5 per 1,000 Inhabitants threshold; c) the estimation of employment rates does not include physicians and nurses in health teaching and research activities. Therefore, they may have included as underemployed, and 5) ENOE is a national and state representative survey of the entire population over 15 years; medical and nursing staff account for less than 2.0% of the population, which could be construed as a weakness in outcomes at the state level. However, given the random design of ENOE and the sample sizes are large for both professions, representativeness can be assumed for this subgroup.Limitations of this analysis are the difficulties in identifying labor mobility, which may affect the estimation of state densities over time, and limitations from ENOE: a) it is not possible to distinguish between levels of care or between insurance schemes, which prevents more precise inequalities among population groups or health needs; b) midwives were not included, since it is not possible to identify them in the survey, nor were other categories of health personnel such as dentists, pharmacists, laboratory technicians or community promoters, who are also considered a health workforceIn brief, the results of this study indicate inequality in the distribution of HRH across states, which may potentially be linked to the number of enrollment offered in educational institutions, the preference of health personnel to be placed into areas with better living conditions, and greater availability of sources of work in states with greater health infrastructure. The establishment of a new human resources policy is a priority that, based on the health needs of the population, articulates the training of physicians, nurses, and other health professionals for incorporation into health institutions, considering the areas of greatest demand. In addition, it is necessary to regulate professional practice, which encourages balance in the public and private labor market. 2. Anand y Bärnighausen hacen una crítica al marco conceptual de los sistemas de salud propuesto por la Organización Mundial de la Salud (OMS) que considera a los recursos humanos en salud (RHS) como uno de los bloques del sistema1. Proponen un marco conceptual en el que los recursos humanos son el centro de los sistemas de salud3. El cumplimiento de las metas y la satisfacción de los pacientes dependen de los RHS, dado que son ellos quienes realizan las actividades de provisión de servicios preventivos y curativos, entregan información sobre el diagnóstico, tratamiento y seguimiento, y deciden qué tecnología y/o medicamento utilizar, por lo cual su tamaño, composición y distribución son muy relevantes para asegurar el acceso de la población a los servicios de salud3.Los recursos humanos que laboran en las instituciones de salud son considerados uno de los pilares para el adecuado desempeño de los sistemas de salud4. En 2004, la Iniciativa Conjunta de Aprendizaje , recomendó una densidad de 2,5 por 1.000 habitantes para alcanzar una cobertura de 80,0% de inmunización contra el sarampión y partos atendidos por personal de salud5. En el marco de los Objetivos de Desarrollo del Milenio (ODM), el informe mundial de la OMS de 2006 sugirió un umbral mínimo de 2,3 médicos, enfermeras y parteras por cada mil habitantes para alcanzar un 80,0% de partos atendidos por personal calificado; 57 países no alcanzaban tal indicador4.La disponibilidad de RHS, medida a través de la densidad por número de habitantes, ha sido relacionada con indicadores de cobertura. Se ha encontrado una relación positiva con algunas intervenciones como cobertura de vacunación o cobertura de partos atendidos por personal calificado6, la OMS estableció una densidad de 4,5 médicos, enfermeras y parteras por 1.000 habitantes como umbral mínimo requerido para la implementación de la Estrategia Global sobre Recursos Humanos para la Salud (EGRHS) en el marco de la cobertura universal de salud (CUS)7. A diferencia del indicador de 2006, este umbral contempla 12 indicadores relacionados con metas de la CUS y los ODS. Con este indicador, la OMS pronosticó que se necesitarían 18 millones más de trabajadores de la salud en los países de ingreso medio-bajo para alcanzar los ODS8, aunque se hizo enfasis en que este no debe representar una meta para todos los paises dadas sus diferencias en las necesidades de salud7.En 2016, en el marco de los Objetivos de Desarrollo Sostenible (ODS)9, con un logro de afiliación de 53,5 millones de mexicanos hacia finales de 201710. Adicionalmente, en el período 2005–2016, el gasto público en salud per cápita aumentó 31,0% y la densidad de médicos y enfermeras por cada mil habitantes se incrementó en 34,0 y 32,0%, respectivamente. A pesar de estos avances, el sistema público de salud enfrenta problemas de accesibilidad y calidad de atención, que pueden ser explicados por la carencia de una política explícita de recursos humanos en salud11, y por el desequilibrio entre las necesidades de salud de la población y los recursos humanos y financieros12.En México, el gobierno federal lanzó en 2003 un seguro público de salud conocido como Seguro Popular de Salud (SPS) para brindar protección financiera a la población excluida de la seguridad social13; no obstante, estas estimaciones no muestran si los profesionales ejercen la profesión médica ni se especifica la duración de la jornada laboral. Otras fuentes de información como la Organización para la Cooperación y el Desarrollo Economicos (OCDE) reportaron para México una densidad de 2,4 y 2,9 médicos y enfermeras, respectivamente, en 2017. Sin embargo, estos datos incluyen a médicos internos (pre-grado) y residentes , utilizan datos provenientes de distintas fuentes de información y en ellas se reconoce la posibilidad de duplicación14. En ambos casos, el indicador de densidad de RHS puede estar sobreestimado y no es posible calcular el déficit de profesionales que son necesarios para alcanzar el umbral de 4,5 recomendado en la EGRHS. Por lo anterior, el presente estudio tuvo como objetivo describir los RHS y analizar la desigualdad en su distribución en México.A nivel nacional, la Secretaría del Trabajo y Previsión Social (STPS) realiza estimaciones periódicas del número de médicos y enfermeras con licenciatura que están empleados15. Las bases de datos están disponibles públicamente y no contienen información identificable de los individuos.Estudio transversal realizado a partir de análisis secundario de datos para describir características del personal médico y de enfermería. Se utilizó la Encuesta Nacional de Ocupación y Empleo (ENOE), realizada por el Instituto Nacional de Estadística y Geografía (INEGI). La ENOE tiene un diseño probabilístico, biétapico, estratificado y por conglomerados, se levanta trimestralmente, tiene cobertura geográfica nacional y estatal, y su objetivo es proporcionar información sobre las características ocupacionales de la población de 15 años y másSe construyeron indicadores de desigualdad con base en la ENOE del cuarto trimestre de 2018 (trimestre IV-2018). Todas las personas con estudios en medicina o enfermería que refirieron haber terminado sus estudios de licenciatura al momento de la encuesta fueron consideradas Recursos Humanos en Salud (RHS) o personal sanitario. También se incluyeron a quienes estudiaron enfermería a nivel técnico (enfermería auxiliar).Personal empleado/ocupado (Emp): personal sanitario que trabajaba 20 horas o más a la semana realizando funciones de acuerdo a su formación acádemica en el área de la salud, o realizaba funciones administrativas en el sector salud.Personal desempleado (Des): personal sanitario que en la semana de referencia de la encuesta buscó trabajo porque no estaba vinculado a una actividad económica.Personal con subempleo cuantitativo (SubCt): personal sanitario empleado/ocupado menos de 20 horas a la semana y con funciones de acuerdo a su profesión, o bien, que ejercía la profesión sanitaria como un empleo secundario.Personal con subempleo cualitativo (SubCl): personal sanitario empleado/ocupado pero con funciones o actividades ajenas a su formación acádemica, independientemente de las horas laborales por semana.Población Económicamente Activa (PEA): personal sanitario empleado, subempleado y desempleado.Densidades de Recursos Humanos en Salud por 1.000 habitantes:a.Densidad de recursos humanos empleados (DRHS): b.Densidad de recursos humanos PEA: En ambos casos, se obtuvo el déficit de RHS respecto al umbral recomendado en la EGRHS de acuerdo con las siguientes fórmulas:c.Déficit de empleados: d.Déficit de PEA: Se analizaron los factores socioecónomicos  en ambas profesiones, y se calcularon las densidades de recursos humanos en salud por entidad federativa de 1) personal sanitario empleado por 1.000 habitantes (DRHS), y 2) población económicamente activa (PEA) por 1.000 habitantes. En ambos casos, se compararon las densidades con el umbral de 4,5 por 1.000 recomendado en la EGRHS y se estimó el número de trabajadores de la salud necesarios para alcanzar dicho umbral.19. Su cálculo proviene de la fórmula sjh es la proporción del personal sanitario en el estado j respecto al total nacional, y sjp es la proporción de la población en el estado j respecto a la población nacional. En este estudio, el ID se interpretó como la proporción (o porcentaje) de trabajadores que tendrían que redistribuirse entre los estados para que todos tuvieran la misma DRHS.El índice de disimilitud (ID) es uno de los indicadores usados en el análisis de las desigualdades en salud19. En este estudio, se construyeron las curvas considerando a los estados como unidad de análisis. Se calculó la proporción de RHS en el estado j respecto al total nacional (sjh), y se calculó la proporción de los Años de Vida Saludable perdidos por Discapacidad (AVISA) en el estado j respecto al total nacional (sjd). La curva de concentración grafica la proporción acumulada de AVISA por los estados  con las proporciones acumuladas de RHS. Si los trabajadores están igualmente distribuidos entre los estados, de acuerdo con su porcentaje de la carga de la enfermedad nacional, la curva de concentración coincide con la diagonal que une los puntos  y , llamada línea de igualdad. El área entre la CC y la línea de igualdad es el Índice de Concentración (IC) y entre mayor sea el valor absoluto del IC más grande es la desigualdad. Se construyó un gráfico de dispersión entre la DRHS por 1.000 habitantes y los AVISA por 100 mil habitantes en cada estado18. Adicionalmente, se construyó otro gráfico de dispersión entre la DRHS por 1.000 habitantes y el Producto Interno Bruto (PIB) per cápita, y se estimó la correlación de Spearman.Otro índice usado en el análisis de las desigualdades en salud es el índice que proviene de la Curva Concentración (CC)20. La clasificación y agrupación de los estados según grado de marginación proviene de la clasificación propuesta por CONAPO21. Los datos del Producto Interno Bruto (PIB) se obtuvieron de la página electrónica del Instituto Nacional de Estadística y Geografía (INEGI)22. La información de los AVISA se obtuvo de las estimaciones de la Carga Global de la Enfermedad realizadas por el Instituto de Métricas y Evaluación de la Salud de 2017 23.Se utilizaron los tres indicadores de desigualdad en la distribución de recursos humanos a nivel nacional y por estados agrupados por grado de marginación: la DRHS por 1.000 habitantes, el ID y el IC. La información del tamaño de la población proviene de las proyecciones de población del Consejo Nacional de Población (CONAPO) disponibles en la página electrónica de la Dirección General de Información en Salud (DGIS) de la Secretaría de SaludLos análisis se realizaron utilizando los factores de expansión de la encuesta y se consideró su diseño complejo utilizando el módulo SVY del paquete STATA MP 13.0. Pruebas Chi-cuadrada de Pearson evalúan las diferencias en la distribución entre el tipo de profesión (medicina o enfermería) y sus factores socioeconómicos considerando p < 0.05 para la significancia estadística e intervalos de confianza al 95% (IC95%).En 2018, había 413 mil médicos y 714 mil enfermeras en México, de los cuales 62,9% y 44,4%, respectivamente, estaban empleados 20 horas o más en el sector salud , equivalLa densidad de profesionales de la salud (DRHS) por 1.000 habitantes fue de 4,6 , superando el umbral de 4,5 recomendado en la EGRHS . Sin embper cápita . Esto coincidió con lo descrito anteriomente, según lo cual los grupos de entidades de muy baja y muy alta marginación tenían mayores y menores DRHS, respectivamente  con las menores proporciones de AVISA concentraron menos de 25,0% de toda la fuerza laboral, mientras que cinco de ellos  concentraron más de 40,0% . Este patrón fue originado principalmente por la desigualdad en la distribución del personal médico  y de enfermería técnica , y en menor medida a la distribución de la enfermería profesional  . Adicionivamente .Diecisiete entidades alcanzaron una densidad de 4,5 , 3a y 3b24. Si bien, de acuerdo a los criterios de la EGRHS, la densidad de trabajadores de la salud podría considerarse aceptable, la desigualdad al interior del país se refleja en la variabilidad entre estados, en particular aquellos con alta marginación que no alcanzan el umbral. Si se reconoce que en los datos de la OCDE se incluye a los médicos internos y residentes, y existe la posibilidad de doble conteo de médicos y enfermeras que trabajan simultáneamente en el sector público y privado, la disponibilidad de RHS para las áreas con menor densidad probablemente quedará por debajo de la demanda actual y con mayor déficit para años futuros. En este sentido, proyecciones de requerimiento de médicos especialistas en México han estimado que las necesidades de salud derivadas del envejecimiento poblacional requerirán mayor número de especialistas en medicina interna y cirugía que en pediatría, lo que implicará un reto creciente para la provisión de servicios25.Nuestro estudio muestra una desventaja de México en la disponibilidad de RHS respecto al promedio de los países pertenecientes a la OCDE26. Dicho resultado pudiera reflejar otras áreas débiles del propio sistema de salud, como insuficiencia en equipamiento o insumos requeridos, deficiencia en la calidad de la atención, así como condiciones propias de la transición demográfica y epidemiológica. Eso resulta en necesidades de salud crónico-degenerativas que derivan en discapacidad para una mayor proporción de la población27.Un hallazgo relevante fue la falta de consistencia en la relación entre la densidad de trabajadores de la salud y las tasas de AVISA. Se esperaría una correlación negativa, como ha sido documentado en otros estudios, que apoyaría la evidencia de contar con una mayor cantidad de profesionales de la salud para disminuir la carga de enfermedad28, que tienen mayor desarrollo económico y brindan mayores oportunidades laborales y expectativas de mejor ingreso29; o bien, 2) la distribución del número de matrículas registradas en las universidades a lo largo del país donde seis estados, de marginación muy baja o baja, concentran al 50 y 35,0% de las matrículas de estudiantes de medicina y enfermería en el ciclo escolar 2017–2018, respectivamente, y tres entidades de muy alta marginación concentran al 15,7% de los estudiantes de enfermería matriculados y 6,5% de los estudiantes de medicina matriculados30. Estos desequilibrios en la distribución de los recursos humanos representan un reto para el sistema de salud, ya que la población que vive en las zonas de alto grado de marginación presenta menor posibilidad en el acceso a los servicios de salud.En contraste, la relación positiva entre la DRHS y el PIB estatal podría ser explicada por las siguientes razones: 1) el personal médico calificado puede cambiar su residencia a las regiones de México con ciudades identificadas como focos de atracción en la movilidad médica31, pero que no se consideran en la construcción del umbral de 4,5 por 1.000 habitantes; c) la construcción de la tasas de empleo no contempla la posibilidad de que médicos y enfermeras pudieran dedicarse a actividades de docencia e investigación en salud, por lo que es muy posible que existan profesionales que son profesores e investigadores que estén incluidos en la categoría de personal subempleado, y 5) la ENOE es una encuesta representativa a nivel nacional y estatal de toda la población mayor de 15 años; el personal médico y de enfermería representa menos de 2,0% de la población, lo que podría interpretarse como una debilidad de los resultados a nivel estatal. Sin embargo, dado el diseño aleatorio de la ENOE y que los tamaños de muestra son grandes para ambas profesiones, se puede asumir representatividad para este subgrupo.Son limitaciones de este análisis la dificultad para identificar la movilidad laboral, que puede afectar la estimación de las densidades estatales en el tiempo, y limitaciones provenientes de la ENOE: a) no es posible distinguir entre niveles de atención ni entre esquemas de aseguramiento, lo que impide establecer de manera más precisa desigualdades entre grupos poblacionales o necesidades de salud; b) no se incluyó a las parteras debido a que no es posible identificarlas en la encuesta, y tampoco se incluyeron otras categorías de personal de salud como dentistas, farmacéuticos, técnicos de laboratorio o promotores comunitarios que también son considerados como fuerza laboral en saludLos resultados indican desigualdad en la distribución de los RHS a través de los estados, la cual puede estar potencialmente ligada al número de matrículas que se ofertan en las instituciones educativas, la preferencia del personal de salud para insertarse laboralmente en áreas con mejores condiciones de vida, y mayor disponibilidad de fuentes de trabajo en estados con mayor infraestructura en salud. Es prioritario el establecimiento de una nueva política de recursos humanos que, a partir de las necesidades de salud de la población, articule la formación de médicos, enfermeras y otros profesionales de la salud para su incorporación en las instituciones de salud considerando las áreas de mayor demanda. Además, es necesaria la regulación de la práctica profesional, que incentive el equilibrio en el mercado de trabajo público y privado.