Socio economic crisis and mortality. Epidemiological testimony of the financial collapse of Argentina

BACKGROUND: Natural disasters, war, and terrorist attacks, have been linked to cardiac mortality. We sought to investigate whether a major financial crisis may impact on the medical management and outcomes of acute coronary syndromes. METHODS: We analyzed the Argentine cohort of the international multicenter Global Registry of Acute Coronary Events (GRACE). The primary objective was to estimate if there was an association between the financial crisis period (April 1999 to December 2002) and in- hospital cardiovascular mortality, with the post-crisis period (January 2003 to September 2004) as the referent. Each period was defined according to the evolution of the Gross Domestic Product. We investigated the demographic characteristics, diagnostic and therapeutic procedures, morbidity and mortality. RESULTS: We analyzed data from 3220 patients, 2246 (69.8%) patients in the crisis period and 974 (30.2%) in the post-crisis frame. The distribution of demographic and clinical baseline characteristics were not significantly different between both periods. During the crisis period the incidence of in-hospital myocardial infarction was higher (6.9% Vs 2.9%; p value \u3c 0.0001), as well as congestive heart failure (16% Vs 11%; p value \u3c 0.0001). Time to intervention with angioplasty was longer during the crisis, especially among public sites (median 190 min Vs 27 min). The incidence proportion of mortality during hospitalization was 6.2% Vs 5.1% after crisis. The crude OR for mortality was 1.2 (95% C.I. 0.87, 1.7). The odds for mortality were higher among private institutions {1.9 (95% C.I. 0.9, 3.8)} than for public centers {1.2 (95% C.I. 0.83, 1.79)}. We did not observe a significant interaction between type of hospital and crisis. CONCLUSION: Our findings suggest that the financial crisis may have had a negative impact on cardiovascular mortality during hospitalization, and higher incidence of medical complications

Knowledge, attitude and practice of Lebanese community pharmacists with regard to self-management of low back pain

Purpose: To determine the knowledge, attitude and reported practice of Lebanese community pharmacists who advise persons who present with low back pain.Methods: This was a multi-center cross-sectional study conducted in over 300 community pharmacies across Lebanon from December 2017 to May 2018. Pharmacists working at a community pharmacy were considered eligible, and those who volunteered to participate completed the questionnaire. The questionnaire was designed for self-completion by the pharmacist and included demographic questions about the respondent, questions that assessed knowledge and attitude toward low back pain, and questions about treatment to reflect and characterize the nature of practice. The primary outcome was to determine the knowledge, attitude and reported practice of the Lebanese pharmacists advising people who presented with low back pain. The secondary outcome was to assess factors that affect knowledge, attitude, and practice.Results: The response of 320 community pharmacists was analysed. The proportion of pharmacists with good knowledge about low back pain (51. 7 %) was slightly higher than those with poor knowledge (48. 3 %). Oral therapy was the most prescribed dosage form for back pain compared to local patch and cream. Among oral dosage forms, non-steroidal anti-inflammatory drugs were the most prescribed medications (42 %). Of the patients’ referral to the physician if necessary, 73.1 % of the referrals were by pharmacists.Conclusion: Community pharmacists in Lebanon demonstrate an acceptable level of knowledge of back pain, yet major gaps still exist, particularly in terms of the quality of advice. Hence, more education is needed to provide better quality of advice. Keywords: Attitude, Knowledge, Low back pain, Reported practice, Quality of advic

Six-month mortality rates are lower in patients with an acute coronary syndrome treated with the combination of clopidogrel and a statin than in patients treated with either therapy alone: An analysis from the global registry of acute coronary events

La remunta la podem data de l'any 1928, aproximadament.Primer pla d'un edifici d'habitages, inicialment de planta baixa i pis, en què es remunten tres plantes en la part de la parcel·la que forma xamfrà a tres carrers. La remunta forma un volum molt potent que revalora estèticament l'edifici

Intervention in acute coronary syndromes:do patients undergo intervention on the basis of their risk characteristics? The Global Registry of Acute Coronary Events (GRACE)

OBJECTIVE: To determine whether revascularisation is more likely to be performed in higher-risk patients and whether the findings are influenced by hospitals adopting more or less aggressive revascularisation strategies. METHODS: GRACE (Global Registry of Acute Coronary Events) is a multinational, observational cohort study. This study involved 24,189 patients enrolled at 73 hospitals with on-site angiographic facilities. RESULTS: Overall, 32.5% of patients with a non-ST elevation acute coronary syndrome (ACS) underwent percutaneous coronary intervention (PCI; 53.7% in ST segment elevation myocardial infarction (STEMI)) and 7.2% underwent coronary artery bypass grafting (CABG; 4.0% in STEMI). The cumulative rate of in-hospital death rose correspondingly with the GRACE risk score (variables: age, Killip class, systolic blood pressure, ST segment deviation, cardiac arrest at admission, serum creatinine, raised cardiac markers, heart rate), from 1.2% in low-risk to 3.3% in medium-risk and 13.0% in high-risk patients (c statistic = 0.83). PCI procedures were more likely to be performed in low- (40% non-STEMI, 60% STEMI) than medium- (35%, 54%) or high-risk patients (25%, 41%). No such gradient was apparent for patients undergoing CABG. These findings were seen in STEMI and non-ST elevation ACS, in all geographical regions and irrespective of whether hospitals adopted low (4.2-33.7%, n = 7210 observations), medium (35.7-51.4%, n = 7913 observations) or high rates (52.6-77.0%, n = 8942 observations) of intervention. CONCLUSIONS: A risk-averse strategy to angiography appears to be widely adopted. Proceeding to PCI relates to referral practice and angiographic findings rather than the patient\u27s risk status. Systematic and accurate risk stratification may allow higher-risk patients to be selected for revascularisation procedures, in contrast to current international practice

Circulating Secretory Phospholipase A2 Activity Predicts Recurrent Events in Patients With Severe Acute Coronary Syndromes

ObjectivesThe purpose of this study was to determine the prognostic value of circulating secretory phospholipase A2 (sPLA2) activity in patients with acute coronary syndromes (ACS).BackgroundThe plasma level of type IIA sPLA2 is a risk factor for coronary artery disease (CAD) and is associated with adverse outcomes in patients with stable CAD. The prognostic impact of sPLA2 in patients with ACS is unknown.MethodsSecretory phospholipase A2 antigen levels and activity were measured in plasma samples of 446 patients with ACS, obtained at the time of enrollment.ResultsBaseline sPLA2 activity was associated with the risk of death and myocardial infarction (MI). The unadjusted rate of death and MI increased in a stepwise fashion with increasing tertiles of sPLA2 activity (p < 0.0001). The association remained significant in the subgroup of patients who had MI with ST-segment elevation (p = 0.014) and the subgroup of patients who had unstable angina or non–ST-segment elevation MI (p < 0.002). After adjustment for clinical and biological variables, the hazard ratios for the combined end point of death or MI in the third tertile of sPLA2 compared with the first and second tertiles was 3.08 (95% confidence interval, 1.37 to 6.91, p = 0.006).ConclusionsA single measurement of plasma sPLA2 activity at the time of enrollment provides strong independent information to predict recurrent events in patients with ACS

Air quality and urban sustainable development: the application of machine learning tools

[EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. International Journal of Environmental Science and Technology. 18(4):1-18. https://doi.org/10.1007/s13762-020-02896-6S118184Al-Dabbous A, Kumar P, Khan A (2017) Prediction of airborne nanoparticles at roadside location using a feed–forward artificial neural network. Atmos Pollut Res 8:446–454. https://doi.org/10.1016/j.apr.2016.11.004Antanasijević D, Pocajt V, Povrenović D, Ristić M, Perić-Grujić A (2013) PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci Total Environ 443:511–519. https://doi.org/10.1016/j.scitotenv.2012.10.110Brink H, Richards JW, Fetherolf M (2016) Real-world machine learning. Richards JW, Fetherolf M (eds) Manning Publications Co. Berkeley, CA. https://www.manning.com/books/real-world-machine-learning. Accessed 26 Apr 2020Cervone G, Franzese P, Ezber Y, Boybeyi Z (2008) Risk assessment of atmospheric emissions using machine learning. Nat Hazard Earth Syst 8:991–1000. https://doi.org/10.5194/nhess-8-991-2008Chen S, Kan G, Li J, Liang K, Hong Y (2018) Investigating China’s urban air quality using big data, information theory, and machine learning. Pol J Environ Stud 27:565–578. https://doi.org/10.15244/pjoes/75159Corani (2005) Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning. Ecol Model 185:513–529. https://doi.org/10.1016/j.ecolmodel.2005.01.008Cruz C, Gómez A, Ramírez L, Villalva A, Monge O, Varela J, Quiroz J, Duarte H (2017) Calidad del aire respecto de metales (Pb, Cd, Ni, Cu, Cr) y relación con salud respiratoria: caso Sonora, México. Rev Int Contam Ambient 33:23–34. https://doi.org/10.20937/RICA.2017.33.esp02.02de Hoogh K, Héritier H, Stafoggia M, Künzli N, Kloog I (2018) Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland. Environ Pollut 233:1147–1154. https://doi.org/10.1016/j.envpol.2017.10.025Franceschi F, Cobo M, Figueredo M (2018) Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using Artificial Neural Networks, Principal Component Analysis, and k-means clustering. Atmos Pollut Res 9:912–922. https://doi.org/10.1016/j.apr.2018.02.006García N, Combarro E, del Coz J, Montañes E (2013) A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): a case study. Appl Math Comput 219:8923–8937. https://doi.org/10.1016/j.amc.2013.03.018Gibert K, Sànchez-Màrre M, Sevilla B (2012) Tools for environmental data mining and intelligent decision support. In iEMSs. Leipzig, Germany. http://www.iemss.org/society/index.php/iemss-2012-proceedings. Accessed 26 Nov 2018Gibert K, Sànchez-Marrè M, Izquierdo J (2016) A survey on pre-processing techniques: relevant issues in the context of environmental data mining. Ai Commun 29:627–663. https://doi.org/10.3233/AIC-160710Gounaridis D, Chorianopoulos I, Koukoulas S (2018) Exploring prospective urban growth trends under different economic outlooks and land-use planning scenarios: the case of Athens. Appl Geogr 90:134–144. https://doi.org/10.1016/j.apgeog.2017.12.001Holloway J, Mengersen K (2018) Statistical machine learning methods and remote sensing for sustainable development goals: a review. Remote Sens 10:1–21. https://doi.org/10.3390/rs10091365Ifaei P, Karbassi A, Lee S, Yoo Ch (2017) A renewable energies-assisted sustainable development plan for Iran using techno-econo-socio-environmental multivariate analysis and big data. Energy Convers Manag 153:257–277. https://doi.org/10.1016/j.enconman.2017.10.014Kadiyala A, Kumar A (2017a) Applications of R to evaluate environmental data science problems. Environ Prog Sustain 36:1358–1364. https://doi.org/10.1002/ep.12676Kadiyala A, Kumar A (2017b) Vector time series-based radial basis function neural network modeling of air quality inside a public transportation bus using available software. Environ Prog Sustain 36:4–10. https://doi.org/10.1002/ep.12523Karimian H, Li Q, Wu Ch, Qi Y, Mo Y, Chen G, Zhang X, Sachdeva S (2019) Evaluation of different machine learning approaches to forecasting PM2.5 mass concentrations. Aerosol Air Qual Res 19:1400–1410. https://doi.org/10.4209/aaqr.2018.12.0450Krzyzanowski M, Apte J, Bonjour S, Brauer M, Cohen A, Prüss-Ustun A (2014) Air pollution in the mega-cities. Curr Environ Health Rep 1:185–191. https://doi.org/10.1007/s40572-014-0019-7Lässig K, Morik (2016) Computat sustainability. Springer, Berlin. https://doi.org/10.1007/978-3-319-31858-5Li Y, Wu Y-X, Zeng Z-X, Guo L (2006) Research on forecast model for sustainable development of economy-environment system based on PCA and SVM. In: Proceedings of the 2006 international conference on machine learning and cybernetics, vol 2006. IEEE, Dalian, China, pp 3590–3593. https://doi.org/10.1109/ICMLC.2006.258576Liu B-Ch, Binaykia A, Chang P-Ch, Tiwari M, Tsao Ch-Ch (2017) Urban air quality forecasting based on multi- dimensional collaborative support vector regression (SVR): a case study of Beijing-Tianjin-Shijiazhuang. PLoS ONE 12:1–17. https://doi.org/10.1371/journal.pone.0179763Lubell M, Feiock R, Handy S (2009) City adoption of environmentally sustainable policies in California’s Central Valley. J Am Plan Assoc 75:293–308. https://doi.org/10.1080/01944360902952295Ma D, Zhang Z (2016) Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere. J Hazard Mater 311:237–245. https://doi.org/10.1016/j.jhazmat.2016.03.022Madu C, Kuei N, Lee P (2017) Urban sustainability management: a deep learning perspective. Sustain Cities Soc 30:1–17. https://doi.org/10.1016/j.scs.2016.12.012Mellos K (1988) Theory of eco-development. In: Perspectives on ecology. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-19598-5_4Ni XY, Huang H, Du WP (2017) Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data. Atmos Environ 150:146–161. https://doi.org/10.1016/j.atmosenv.2016.11.054Oprea M, Dragomir E, Popescu M, Mihalache S (2016) Particulate matter air pollutants forecasting using inductive learning approach. Rev Chim 67:2075–2081Paas B, Stienen J, Vorländer M, Schneider Ch (2017) Modelling of urban near-road atmospheric PM concentrations using an artificial neural network approach with acoustic data input. Environments 4:1–25. https://doi.org/10.3390/environments4020026Pandey G, Zhang B, Jian L (2013) Predicting submicron air pollution indicators: a machine learning approach. Environ Sci Proc Impacts 15:996–1005. https://doi.org/10.1039/c3em30890aPeng H, Lima A, Teakles A, Jin J, Cannon A, Hsieh W (2017) Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods. Air Qual Atmos Health 10:195–211. https://doi.org/10.1007/s11869-016-0414-3Pérez-Ortíz M, de La Paz-Marín M, Gutiérrez PA, Hervás-Martínez C (2014) Classification of EU countries’ progress towards sustainable development based on ordinal regression techniques. Knowl Based Syst 66:178–189. https://doi.org/10.1016/j.knosys.2014.04.041Phillis Y, Kouikoglou V, Verdugo C (2017) Urban sustainability assessment and ranking of cities. Comput Environ Urban 64:254–265. https://doi.org/10.1016/j.compenvurbsys.2017.03.002Saeed S, Hussain L, Awan I, Idris A (2017) Comparative analysis of different statistical methods for prediction of PM2.5 and PM10 concentrations in advance for several hours. Int J Comput Sci Netw Secur 17:45–52Sayegh A, Munir S, Habeebullah T (2014) Comparing the performance of statistical models for predicting PM10 concentrations. Aerosol Air Qual Res 14:653–665. https://doi.org/10.4209/aaqr.2013.07.0259Shaban K, Kadri A, Rezk E (2016) Urban air pollution monitoring system with forecasting models. IEEE Sens J 16:2598–2606. https://doi.org/10.1109/JSEN.2016.2514378Sierra B (2006) Aprendizaje automático conceptos básicos y avanzados Aspectos prácticos utilizando el software Weka. Madrid Pearson Prentice Hall, MadridSingh K, Gupta S, Rai P (2013) Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmos Environ 80:426–437. https://doi.org/10.1016/j.atmosenv.2013.08.023Song L, Pang S, Longley I, Olivares G, Sarrafzadeh A (2014) Spatio-temporal PM2.5 prediction by spatial data aided incremental support vector regression. In: International joint conference on neural networks. IEEE, Beijing, pp 623–630. https://doi.org/10.1109/IJCNN.2014.6889521Souza R, Coelho G, da Silva A, Pozza S (2015) Using ensembles of artificial neural networks to improve PM10 forecasts. Chem Eng Trans 43:2161–2166. https://doi.org/10.3303/CET1543361Suárez A, García PJ, Riesgo P, del Coz JJ, Iglesias-Rodríguez FJ (2011) Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain). Math Comput Model 54:453–1466. https://doi.org/10.1016/j.mcm.2011.04.017Tamas W, Notton G, Paoli C, Nivet M, Voyant C (2016) Hybridization of air quality forecasting models using machine learning and clustering: an original approach to detect pollutant peaks. Aerosol Air Qual Res 16:405–416. https://doi.org/10.4209/aaqr.2015.03.0193Toumi O, Le Gallo J, Ben Rejeb J (2017) Assessment of Latin American sustainability. Renew Sustain Energy Rev 78:878–885. https://doi.org/10.1016/j.rser.2017.05.013Tzima F, Mitkas P, Voukantsis D, Karatzas K (2011) Sparse episode identification in environmental datasets: the case of air quality assessment. Expert Syst Appl 38:5019–5027. https://doi.org/10.1016/j.eswa.2010.09.148United Nations, Department of Economic and Social Affairs (2019) World urbanization prospects The 2018 Revision. New York. https://doi.org/10.18356/b9e995fe-enWang B (2019) Applying machine-learning methods based on causality analysis to determine air quality in China. Pol J Environ Stud 28:3877–3885. https://doi.org/10.15244/pjoes/99639Wang X, Xiao Z (2017) Regional eco-efficiency prediction with support vector spatial dynamic MIDAS. J Clean Prod 161:165–177. https://doi.org/10.1016/j.jclepro.2017.05.077Wang W, Men C, Lu W (2008) Online prediction model based on support vector machine. Neurocomputing 71:550–558. https://doi.org/10.1016/j.neucom.2007.07.020WCED (1987) Report of the world commission on environment and development: our common future: report of the world commission on environment and development. WCED, Oslo. https://doi.org/10.1080/07488008808408783Weizhen H, Zhengqiang L, Yuhuan Z, Hua X, Ying Z, Kaitao L, Donghui L, Peng W, Yan M (2014) Using support vector regression to predict PM10 and PM2.5. In: IOP conference series: earth and environmental science, vol 17. IOP. https://doi.org/10.1088/1755-1315/17/1/012268WHO (2016) OMS | La OMS publica estimaciones nacionales sobre la exposición a la contaminación del aire y sus repercusiones para la salud. WHO. http://www.who.int/mediacentre/news/releases/2016/air-pollution-estimates/es/. Accesed 26 Nov 2018Yeganeh N, Shafie MP, Rashidi Y, Kamalan H (2012) Prediction of CO concentrations based on a hybrid partial least square and support vector machine model. Atmos Environ 55:357–365. https://doi.org/10.1016/j.atmosenv.2012.02.092Zalakeviciute R, Bastidas M, Buenaño A, Rybarczyk Y (2020) A traffic-based method to predict and map urban air quality. Appl Sci. https://doi.org/10.3390/app10062035Zeng L, Guo J, Wang B, Lv J, Wang Q (2019) Analyzing sustainability of Chinese coal cities using a decision tree modeling approach. Resour Policy 64:101501. https://doi.org/10.1016/j.resourpol.2019.101501Zhan Y, Luo Y, Deng X, Grieneisen M, Zhang M, Di B (2018) Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment. Environ Pollut 233:464–473. https://doi.org/10.1016/j.envpol.2017.10.029Zhang Y, Huan Q (2006) Research on the evaluation of sustainable development in Cangzhou city based on neural-network-AHP. In: Proceedings of the fifth international conference on machine learning and cybernetics, vol 2006. pp 3144–3147. https://doi.org/10.1109/ICMLC.2006.258407Zhang Y, Shang W, Wu Y (2009) Research on sustainable development based on neural network. In: 2009 Chinese control and decision conference. IEEE, pp 3273–3276. https://doi.org/10.1109/CCDC.2009.5192476Zhou Y, Chang F-J, Chang L-Ch, Kao I-F, Wang YS (2019) Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J Clean Prod 209:134–145. https://doi.org/10.1016/j.jclepro.2018.10.24

Local and distant recurrences in rectal cancer patients are predicted by the nonspecific immune response; specific immune response has only a systemic effect - a histopathological and immunohistochemical study

BACKGROUND: Invasion and metastasis is a complex process governed by the interaction of genetically altered tumor cells and the immunological and inflammatory host reponse. Specific T-cells directed against tumor cells and the nonspecific inflammatory reaction due to tissue damage, cooperate against invasive tumor cells in order to prevent recurrences. Data concerning involvement of individual cell types are readily available but little is known about the coordinate interactions between both forms of immune response. PATIENTS AND METHODS: The presence of inflammatory infiltrate and eosinophils was determined in 1530 patients with rectal adenocarcinoma from a multicenter trial. We selected 160 patients to analyze this inflammatory infiltrate in more detail using immunohistochemistry. The association with the development of local and distant relapses was determined using univariate and multivariate log rank testing. RESULTS: Patients with an extensive inflammatory infiltrate around the tumor had lower recurrence rates (3.4% versus 6.9%, p = 0.03), showing the importance of host response against tumor cells. In particular, peritumoral mast cells prevent local and distant recurrence (44% versus 15%, p = 0.007 and 86% versus 21%, p < 0.0001, respectively), with improved survival as a consequence. The presence of intratumoral T-cells had independent prognostic value for the occurrence of distant metastases (32% versus 76%, p < 0.0001). CONCLUSIONS: We showed that next to properties of tumor cells, the amount and type of inflammation is also relevant in the control of rectal cancer. Knowledge of the factors involved may lead to new approaches in the management of rectal cancer

The phocein homologue SmMOB3 is essential for vegetative cell fusion and sexual development in the filamentous ascomycete Sordaria macrospora

Members of the striatin family and their highly conserved interacting protein phocein/Mob3 are key components in the regulation of cell differentiation in multicellular eukaryotes. The striatin homologue PRO11 of the filamentous ascomycete Sordaria macrospora has a crucial role in fruiting body development. Here, we functionally characterized the phocein/Mob3 orthologue SmMOB3 of S. macrospora. We isolated the gene and showed that both, pro11 and Smmob3 are expressed during early and late developmental stages. Deletion of Smmob3 resulted in a sexually sterile strain, similar to the previously characterized pro11 mutant. Fusion assays revealed that ∆Smmob3 was unable to undergo self-fusion and fusion with the pro11 strain. The essential function of the SmMOB3 N-terminus containing the conserved mob domain was demonstrated by complementation analysis of the sterile S. macrospora ∆Smmob3 strain. Downregulation of either pro11 in ∆Smmob3, or Smmob3 in pro11 mutants by means of RNA interference (RNAi) resulted in synthetic sexual defects, demonstrating for the first time the importance of a putative PRO11/SmMOB3 complex in fruiting body development

Prevalence and risk factors for diabetic neuropathy and painful diabetic neuropathy in primary and secondary health care in Qatar.

AIMS/INTRODUCTION:This study determined the prevalence and risk factors for DPN and pDPN in patients with type 2 diabetes (T2D) in primary health care (PHC) and secondary health care (SHC) in Qatar. MATERIALS AND METHODS:This is a cross-sectional multi-center study. Adults with T2D were randomly enrolled from four PHC centres and two Diabetes Centres in SHC in Qatar. Subjects underwent assessment of clinical and metabolic parameters, DPN and pDPN. RESULTS:1,386 subjects with T2D (297 from PHC and 1,089 from SHC) were recruited. The prevalence of DPN (14.8% vs 23.9%, P=0.001) and pDPN (18.1% vs 37.5%, P<0.0001) was significantly lower in PHC compared to SHC, whilst those with DPN at high risk for DFU (31.8% vs 40.0%, P=0.3) was comparable. The prevalence of undiagnosed DPN (79.5% vs 82.3%, P=0.66) was comparably high but undiagnosed pDPN (24.1% vs 71.5%, P<0.0001) was lower in PHC compared to SHC. The odds of DPN and pDPN increased with age and diabetes duration and DPN increased with poor glycemic control, hyperlipidemia and hypertension, whilst pDPN increased with obesity and reduced physical activity. CONCLUSIONS:The prevalence of DPN and pDPN in T2D is lower in PHC compared to SHC and is attributed to overall better control of risk factors and referral bias due to patients with poorly managed complications being referred to SHC. However, ~80% of patients had not been previously diagnosed with DPN in PHC and SHC. Further, we identify a number of modifiable risk factors for PDN and pDPN