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1. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants

Author: Zhou, B, Lu, Y, Hajifathalian, K, Bentham, J, Di Cesare, M, Danaei, G, Bixby, H, Cowan, MJ, Ali, MK, Taddei, C, Lo, WC, Reis-Santos, B, Stevens, GA, Riley, LM, Miranda, JJ, Bjerregaard, P, Rivera, JA, Fouad, HM, Ma, G, Mbanya, JC, McGarvey, ST, Mohan, V, Onat, A, Pilav, A, Ramachandran, A, Romdhane, HB, Paciorek, CJ, Bennett, JE, Ezzati, M, Abdeen, ZA, Abdul Kadir, K, Abu-Rmeileh, NM, Acosta-Cazares, B, Adams, R, Aekplakorn, W, Aguilar-Salinas, CA, Agyemang, C, Ahmadvand, A, Al-Othman, AR, Alkerwi, A, Amouyel, P, Amuzu, A, Andersen, LB, Anderssen, SA, Anjana, RM, Aounallah-Skhiri, H, Aris, T, Arlappa, N, Arveiler, D, Assah, FK, Avdicová, M, Azizi, F, Balakrishna, N, Bandosz, P, Barbagallo, CM, Barceló, A, Batieha, AM, Baur, LA, Benet, M, Bernabe-Ortiz, A, Bharadwaj, S, Bhargava, SK, Bi, Y, Bjertness, E, Bjertness, MB, Björkelund, C, Blokstra, A, Bo, S, Boehm, BO, Boissonnet, CP, Bovet, P, Brajkovich, I, Breckenkamp, J, Brenner, H, Brewster, LM, Brian, GR, Bruno, G, Bugge, A, Cabrera de León, A, Can, G, Cândido, AP, Capuano, V, Carlsson, AC, Carvalho, MJ, Casanueva, FF, Casas, JP, Caserta, CA, Castetbon, K, Chamukuttan, S, Chaturvedi, N, Chen, CJ, Chen, F, Chen, S, Cheng, CY, Chetrit, A, Chiou, ST, Cho, Y, Chudek, J, Cifkova, R, Claessens, F, Concin, H, Cooper, C, Cooper, R, Costanzo, S, Cottel, D, Cowell, C, Crujeiras, AB, D'Arrigo, G, Dallongeville, J, Dankner, R, Dauchet, L, de Gaetano, G, De Henauw, S, Deepa, M, Dehghan, A, Deschamps, V, Dhana, K, Di Castelnuovo, AF, Djalalinia, S, Doua, K, Drygas, W, Du, Y, Dzerve, V, Egbagbe, EE, Eggertsen, R, El Ati, J, Elosua, R, Erasmus, RT, Erem, C, Ergor, G, Eriksen, L, Escobedo-de la Peña, J, Fall, CH, Farzadfar, F, Felix-Redondo, FJ, Ferguson, TS, Fernández-Bergés, D, Ferrari, M, Ferreccio, C, Feskens, EJ, Finn, JD, Föger, B, Foo, LH, Forslund, AS, Francis, DK, Franco Mdo, C, Franco, OH, Frontera, G, Furusawa, T, Gaciong, Z, Garnett, SP, Gaspoz, JM, Gasull, M, Gates, L, Geleijnse, JM, Ghasemian, A, Ghimire, A, Giampaoli, S, Gianfagna, F, Giovannelli, J, Giwercman, A, Gross, MG, González Rivas, JP, Gorbea, MB, Gottrand, F, Grafnetter, D, Grodzicki, T, Grøntved, A, Gruden, G, Gu, D, Guan, OP, Guerrero, R, Guessous, I, Guimaraes, AL, Gutierrez, L, Hambleton, IR, Hardy, R, Hari Kumar, R, Hata, J, He, J, Heidemann, C, Herrala, S, Hihtaniemi, IT, Ho, SY, Ho, SC, Hofman, A, Hormiga, CM, Horta, BL, Houti, L, Howitt, C, Htay, TT, Htet, AS, Htike, MM, Hu, Y, Hussieni, AS, Huybrechts, I, Hwalla, N, Iacoviello, L, Iannone, AG, Ibrahim, MM, Ikeda, N, Ikram, MA, Irazola, VE, Islam, M, Iwasaki, M, Jacobs, JM, Jafar, T, Jamil, KM, Jasienska, G, Jiang, CQ, Jonas, JB, Joshi, P, Kafatos, A, Kalter-Leibovici, O, Kasaeian, A, Katz, J, Kaur, P, Kavousi, M, Keinänen-Kiukaanniemi, S, Kelishadi, R, Kengne, AP, Kersting, M, Khader, YS, Khalili, D, Khang, YH, Kiechl, S, Kim, J, Kolsteren, P, Korrovits, P, Kratzer, W, Kromhout, D, Kujala, UM, Kula, K, Kyobutungi, C, Laatikainen, T, Lachat, C, Laid, Y, Lam, TH, Landrove, O, Lanska, V, Lappas, G, Laxmaiah, A, Leclercq, C, Lee, J, Lehtimäki, T, Lekhraj, R, León-Muñoz, LM, Li, Y, Lim, WY, Lima-Costa, MF, Lin, HH, Lin, X, Lissner, L, Lorbeer, R, Lozano, JE, Luksiene, D, Lundqvist, A, Lytsy, P, Machado-Coelho, GL, Machi, S, Maggi, S, Magliano, DJ, Makdisse, M, Mallikharjuna Rao, K, Manios, Y, Manzato, E, Margozzini, P, Marques-Vidal, P, Martorell, R, Masoodi, SR, Mathiesen, EB, Matsha, TE, McFarlane, SR, McLachlan, S, McNulty, BA, Mediene-Benchekor, S, Meirhaeghe, A, Menezes, AM, Merat, S, Meshram, II, Mi, J, Miquel, JF, Mohamed, MK, Mohammad, K, Mohammadifard, N, Mohd Yusoff, MF, Møller, NC, Molnár, D, Mondo, CK, Morejon, A, Moreno, LA, Morgan, K, Moschonis, G, Mossakowska, M, Mostafa, A, Mota, J, Motta, J, Mu, TT, Muiesan, ML, Müller-Nurasyid, M, Mursu, J, Nagel, G, Námešná, J, Nang, EE, NangThetia, VB, Navarrete-Muñoz, EM, Ndiaye, NC, Nenko, I, Nervi, F, Nguyen, ND, Nguyen, QN, Nieto-Martínez, RE, Ning, G, Ninomiya, T, Noale, M, Noto, D, Nsour, MA, Ochoa-Avilés, AM, Oh, K, Ordunez, P, Osmond, C, Otero, JA, Owusu-Dabo, E, Pahomova, E, Palmieri, L, Panda-Jonas, S, Panza, F, Parsaeian, M, Peixoto, SV, Pelletier, C, Peltonen, M, Peters, A, Peykari, N, Pham, ST, Pitakaka, F, Piwonska, A, Piwonski, J, Plans-Rubió, P, Porta, M, Portegies, ML, Poustchi, H, Pradeepa, R, Price, JF, Punab, M, Qasrawi, RF, Qorbani, M, Radisauskas, R, Rahman, M, Raitakari, O, Rao, SR, Ramke, J, Ramos, R, Rampal, S, Rathmann, W, Redon, J, Reganit, PF, Rigo, F, Robinson, SM, Robitaille, C, Rodríguez-Artalejo, F, Rodriguez-Perez Mdel, C, Rodríguez-Villamizar, LA, Rojas-Martinez, R, Ronkainen, K, Rosengren, A, Rubinstein, A, Rui, O, Ruiz-Betancourt, BS, Russo Horimoto, RV, Rutkowski, M, Sabanayagam, C, Sachdev, HS, Saidi, O, Sakarya, S, Salanave, B, Salonen, JT, Salvetti, M, Sánchez-Abanto, J, Santos, D, dos Santos, RN, Santos, R, Saramies, JL, Sardinha, LB, Sarrafzadegan, N, Saum, KU, Scazufca, M, Schargrodsky, H, Scheidt-Nave, C, Sein, AA, Sharma, SK, Shaw, JE, Shibuya, K, Shin, Y, Shiri, R, Siantar, R, Sibai, AM, Simon, M, Simons, J, Simons, LA, Sjostrom, M, Slowikowska-Hilczer, J, Slusarczyk, P, Smeeth, L, Snijder, MB, So, HK, Sobngwi, E, Söderberg, S, Solfrizzi, V, Sonestedt, E, Soumare, A, Staessen, JA, Stathopoulou, MG, Steene-Johannessen, J, Stehle, P, Stein, AD, Stessman, J, Stöckl, D, Stokwiszewski, J, Stronks, K, Strufaldi, MW, Sun, CA, Sundström, J, Sung, YT, Suriyawongpaisal, P, Sy, RG, Tai, ES, Tamosiunas, A, Tang, L, Tarawneh, M, Tarqui-Mamani, CB, Taylor, A, Theobald, H, Thijs, L, Thuesen, BH, Tolonen, HK, Tolstrup, JS, Topbas, M, Torrent, M, Traissac, P, Trinh, OT, Tulloch-Reid, MK, Tuomainen, TP, Turley, ML, Tzourio, C, Ueda, P, Ukoli, FA, Ulmer, H, Uusitalo, HM, Valdivia, G, Valvi, D, van Rossem, L, van Valkengoed, IG, Vanderschueren, D, Vanuzzo, D, Vega, T, Velasquez-Melendez, G, Veronesi, G, Verschuren, WM, Verstraeten, R, Viet, L, Vioque, J, Virtanen, JK, Visvikis-Siest, S, Viswanathan, B, Vollenweider, P, Voutilainen, S, Vrijheid, M, Wade, AN, Wagner, A, Walton, J, Wan Mohamud, WN, Wang, F, Wang, MD, Wang, Q, Wang, YX, Wannamethee, SG, Weerasekera, D, Whincup, PH, Widhalm, K, Wiecek, A, Wijga, AH, Wilks, RJ, Willeit, J, Wilsgaard, T, Wojtyniak, B, Wong, TY, Woo, J, Woodward, M, Wu, FC, Wu, SL, Xu, H, Yan, W, Yang, X, Ye, X, Yoshihara, A, Younger-Coleman, NO, Zambon, S, Zargar, AH, Zdrojewski, T, Zhao, W, Zheng, Y, and Zuñiga Cisneros, J
Published: 2016
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2. Effects of diabetes definition on global surveillance of diabetes prevalence and diagnosis: a pooled analysis of 96 population-based studies with 331 288 participants

Author: Danaei, G, Fahimi, S, Lu, Y, Zhou, B, Hajifathalian, K, Di Cesare, M, Lo, WC, Reis-Santos, B, Cowan, MJ, Shaw, JE, Bentham, J, Lin, JK, Bixby, H, Magliano, D, Bovet, P, Miranda, JJ, Khang, YH, Stevens, GA, Riley, LM, Ali, MK, Ezzati, M, Abdeen, ZA, Kadir, KA, Abu-Rmeileh, M, Acosta-Cazares, B, Aekplakorn, W, Aguilar-Salinas, CA, Ahmadvand, A, Al Nsour, M, Alkerwi, A, Amouyel, P, Andersen, LB, Anderssen, SA, Andrade, DS, Anjana, RM, Aounallah-Skhiri, H, Aris, T, Arlappa, N, Arveiler, D, Assah, FK, Avdicová, M, Balakrishna, N, Bandosz, P, Barbagallo, CM, Barceló, A, Batieha, AM, Baur, LA, Ben Romdhane, H, Bernabe-Ortiz, A, Bhargava, SK, Bi, Y, Bjerregaard, P, Björkelund, C, Blake, M, Blokstra, A, Bo, S, Boehm, BO, Boissonnet, CP, Brajkovich, I, Breckenkamp, J, Brewster, LM, Brian, GR, Bruno, G, Bugge, A, Cabrera de León, A, Can, G, Cândido, AP, Capuano, V, Carvalho, MJ, Casanueva, FF, Caserta, CA, Castetbon, K, Chamukuttan, S, Chaturvedi, N, Chen, CJ, Chen, F, Chen, S, Cheng, CY, Chetrit, A, Chiou, ST, Cho, Y, Chudek, J, Cifkova, R, Claessens, F, Concin, H, Cooper, C, Cooper, R, Costanzo, S, Cottel, D, Cowell, C, Crujeiras, AB, D'Arrigo, G, Dallongeville, J, Dankner, R, Dauchet, L, de Gaetano, G, De Henauw, S, Deepa, M, Dehghan, A, Dhana, K, Di Castelnuovo, AF, Djalalinia, S, Doua, K, Drygas, W, Du, Y, Egbagbe, EE, Eggertsen, R, El Ati, J, Elosua, R, Erasmus, RT, Erem, C, Ergor, G, Eriksen, L, Escobedo-de la Peña, J, Fall, CH, Farzadfar, F, Felix-Redondo, FJ, Ferguson, TS, Fernández-Bergés, D, Ferrari, M, Ferreccio, C, Finn, JD, Föger, B, Foo, LH, Fouad, HM, Francis, DK, Franco Mdo, C, Frontera, G, Furusawa, T, Gaciong, Z, Galbarczyk, A, Garnett, SP, Gaspoz, JM, Gasull, M, Gates, L, Geleijnse, JM, Ghasemain, A, Giampaoli, S, Gianfagna, F, Giovannelli, J, Gonzalez Gross, M, González Rivas, JP, Gorbea, MB, Gottrand, F, Grant, JF, Grodzicki, T, Grøntved, A, Gruden, G, Gu, D, Guan, OP, Guerrero, R, Guessous, I, Guimaraes, AL, Gutierrez, L, Hardy, R, Hari Kumar, R, Heidemann, C, Hihtaniemi, IT, Ho, SY, Ho, SC, Hofman, A, Horimoto, AR, Hormiga, CM, Horta, BL, Houti, L, Hussieni, AS, Huybrechts, I, Hwalla, N, Iacoviello, L, Iannone, AG, Ibrahim, MM, Ikeda, N, Ikram, MA, Irazola, VE, Islam, M, Iwasaki, M, Jacobs, JM, Jafar, T, Jasienska, G, Jiang, CQ, Jonas, JB, Joshi, P, Kafatos, A, Kalter-Leibovici, O, Kasaeian, A, Katz, J, Kaur, P, Kavousi, M, Kelishadi, R, Kengne, AP, Kersting, M, Khader, YS, Kiechl, S, Kim, J, Kiyohara, Y, Kolsteren, P, Korrovits, P, Koskinen, S, Kratzer, W, Kromhout, D, Kula, K, Kurjata, P, Kyobutungi, C, Lachat, C, Laid, Y, Lam, TH, Lanska, V, Lappas, G, Laxmaiah, A, Leclercq, C, Lee, J, Lehtimäki, T, Lekhraj, R, León-Muñoz, LM, Li, Y, Lim, WY, Lima-Costa, MF, Lin, HH, Lin, X, Lissner, L, Lorbeer, R, Lozano, JE, Lundqvist, A, Lytsy, P, Ma, G, Machado-Coelho, GL, Machi, S, Maggi, S, Makdisse, M, Mallikharjuna v, K, Manios, Y, Manzato, E, Margozzini, P, Marques-Vidal, P, Martorell, R, Masoodi, SR, Matsha, TE, Mbanya, JC, McFarlane, SR, McGarvey, ST, McLachlan, S, McNulty, BA, Mediene-Benchekor, S, Meirhaeghe, A, Menezes, AM, Merat, S, Meshram, II, Mi, J, Miquel, JF, Mohamed, MK, Mohammad, K, Mohan, V, Mohd Yusoff, MF, Møller, NC, Molnar, D, Mondo, CK, Moreno, LA, Morgan, K, Moschonis, G, Mossakowska, M, Mostafa, A, Mota, J, Muiesan, ML, Müller-Nurasyid, M, Mursu, J, Nagel, G, Námešná, J, Nang, EE, Nangia, VB, Navarrete-Muñoz, EM, Ndiaye, NC, Nervi, F, Nguyen, ND, Nieto-Martínez, RE, Alvarado, L, Ning, G, Ninomiya, T, Noale, M, Noto, D, Ochoa-Avilés, M, Oh, K, Onat, A, Osmond, C, Otero, JA, Palmieri, L, Panda-Jonas, S, Panza, F, Parsaeian, M, Peixoto, SV, Pereira, AC, Peters, A, Peykari, N, Pilav, A, Pitakaka, F, Piwonska, A, Piwonski, J, Plans-Rubió, P, Porta, M, Portegies, ML, Poustchi, H, Pradeepa, R, Price, JF, Punab, M, Qasrawi, RF, Qorbani, M, Raitakari, O, Ramachandra Rao, S, Ramachandran, A, Ramos, R, Rampal, S, Rathmann, W, Redon, J, Reganit, PF, Rigo, F, Robinson, SM, Robitaille, C, Rodríguez, LA, Rodríguez-Artalejo, F, del Cristo Rodriguez-Perez, M, Rojas-Martinez, R, Romaguera, D, Rosengren, A, Rubinstein, A, Rui, O, Ruiz-Betancourt, BS, Rutkowski, M, Sabanayagam, C, Sachdev, HS, Saidi, O, Sakarya, S, Salanave, B, Salonen, JT, Salvetti, M, Sánchez-Abanto, J, Santos, RN, Santos, R, Sardinha, LB, Scazufca, M, Schargrodsky, H, Scheidt-Nave, C, Shibuya, K, Shin, Y, Shiri, R, Siantar, R, Sibai, AM, Simon, M, Simons, J, Simons, LA, Sjostrom, M, Slowikowska-Hilczer, J, Slusarczyk, P, Smeeth, L, Snijder, MB, Solfrizzi, V, Sonestedt, E, Soumare, A, Staessen, JA, Steene-Johannessen, J, Stehle, P, Stein, AD, Stessman, J, Stöckl, D, Stokwiszewski, J, Strufaldi, MW, Sun, CA, Sundström, J, Suriyawongpaisal, P, Sy, RG, Tai, ES, Tarawneh, M, Tarqui-Mamani, CB, Thijs, L, Tolstrup, JS, Topbas, M, Torrent, M, Traissac, P, Trinh, OT, Tulloch-Reid, MK, Tuomainen, TP, Turley, ML, Tzourio, C, Ueda, P, Ukoli, FM, Ulmer, H, Valdivia, G, van Valkengoed, IG, Vanderschueren, D, Vanuzzo, D, Vega, T, Velasquez-Melendez, G, Veronesi, G, Verschuren, M, Vioque, J, Virtanen, J, Visvikis-Siest, S, Viswanathan, B, Vollenweider, P, Voutilainen, S, Wade, AN, Wagner, A, Walton, J, Mohamud, WN, Wang, MD, Wang, YX, Wannamethee, SG, Weerasekera, D, Whincup, PH, Widhalm, K, Wiecek, A, Wilks, RJ, Willeit, J, Wojtyniak, B, Wong, TY, Woo, J, Woodward, M, Wu, AG, Wu, FC, Wu, SL, Xu, H, Yang, X, Ye, X, Yoshihara, A, Younger-Coleman, NO, Zambon, S, Zargar, AH, Zdrojewski, T, Zhao, W, and Zheng, Y
Published: 2015
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3. Effect of the Bolsa Familia Programme on the outcome of tuberculosis treatment : a prospective cohort study

Author: Oliosi, J. G. N., Reis-Santos, B., Locatelli, R. L., Sales, C. M. M., Filho, W. G. D., da Silva, K. C., Sanchez, M. N., Andrade, K. V. F., Shete, P. B., Pereira, S. M., Riley, L. W., Lienhardt, Christian, Maciel, E. L. N., and De Araojo, G. S.
Abstract: Background Social protection interventions might improve tuberculosis outcomes and could help to control the epidemic in Brazil. The aim of this study was to evaluate the independent effect of the Bolsa Familia Programme (BFP) on tuberculosis treatment outcomes in Brazil. Methods We prospectively recruited and followed up individuals (aged >= 18 years) who initiated tuberculosis treatment at 42 health-care centres across seven cities in Brazil, between March 1, 2014, and April 30, 2017. Patients were interviewed at health-care centres and information about individual characteristics, socioeconomic status, living conditions, lifestyle, and comorbidities was recorded. Patients were separated into two groups according to BFP beneficiary status: BFP (exposed) or non-BFP (not exposed). Treatment outcome (cure, dropout, death, or development of drug-resistant tuberculosis or treatment failure) was recorded after 6 months of therapy. Pearson's chi(2) test and ANOVA were used to compare tuberculosis treatment outcomes between the two groups, and we estimated the propensity score of being a beneficiary of the BFP using a logit model. We used multinomial regression models to evaluate the effect of the BFP on tuberculosis treatment outcomes. Findings 1239 individuals were included in the study, of whom 196 (16%) were beneficiaries of the BFP and 1043 (84%) were not. After 6 months of treatment, 912 (87%) of 1043 patients in the non-BFP group and 173 (88%) of 196 patients in the BFP group were cured of tuberculosis, 103 (10%) patients in the non-BFP group and 17 (9%) patients in the BFP group had dropped out, and 25 (3%) patients in the non-BFP group and six (3%) patients in the BFP group had died. Three (< 1%) of 1043 patients in the non-BFP group developed drug-resistant tuberculosis. Being a BFP beneficiary had a positive effect for cure (average effect 0.076 [95% CI 0.037 to 0.11]) and a negative effect for dropout (-0.070 [-0.105 to 0.036]) and death (-0.002 [-0.021 to 0.017]). Interpretation BFP alone had a direct effect on tuberculosis treatment outcome and could greatly contribute to the goals of the WHO End TB Strategy.
Published: 2019

4. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants

Author: Zhou, B., Lu, Y., Hajifathalian, K., Bentham, J., Di Cesare, M., Danaei, G., Bixby, H., Cowan, M.J., Ali, M.K., Teddei, C., Lo, W.C., Reis-Santos, B., Stevens, G.A., Reily, L.M., Miranda, J.J., Bjerregaard, P., Rivera, J.A., Fouad, H.M., Ma, G., Mbanya, J.C., McGarvey, S.T., Mohan, V., Onat, A., Pilav, A., Ramachandran, A., Romdhane, H.B., Paciorek, C.J., Bennett, J.E., Ezzati, M., Abdeen, Z.A., Abdul Kadir, K., Abu-Rmeileh, N.M., Acosta-Cazares, B., Adams, R., Aekplakorn, W., Aguilar-Salinas, C.A., Agyemang, C., Ahmadvand, A., Al-Othman, A.R., Alkerwi, A., Amouyel, P., Amuzu, A., Andersen, L.B., Anderssen, S.A., Anjana, R.M., Aounallah-Skhiri, H., Cooper, C., Fall, C.H., Osmond, C., and Robinson, S
Abstract: Background: One of the global targets for non-communicable diseases is to halt, by 2025, the rise in the age-standardised adult prevalence of diabetes at its 2010 levels. We aimed to estimate worldwide trends in diabetes, how likely it is for countries to achieve the global target, and how changes in prevalence, together with population growth and ageing, are affecting the number of adults with diabetes.Methods: We pooled data from population-based studies that had collected data on diabetes through measurement of its biomarkers. We used a Bayesian hierarchical model to estimate trends in diabetes prevalence—defined as fasting plasma glucose of 7·0 mmol/L or higher, or history of diagnosis with diabetes, or use of insulin or oral hypoglycaemic drugs—in 200 countries and territories in 21 regions, by sex and from 1980 to 2014. We also calculated the posterior probability of meeting the global diabetes target if post-2000 trends continue.Findings: We used data from 751 studies including 4?372?000 adults from 146 of the 200 countries we make estimates for. Global age-standardised diabetes prevalence increased from 4·3% (95% credible interval 2·4–7·0) in 1980 to 9·0% (7·2–11·1) in 2014 in men, and from 5·0% (2·9–7·9) to 7·9% (6·4–9·7) in women. The number of adults with diabetes in the world increased from 108 million in 1980 to 422 million in 2014 (28·5% due to the rise in prevalence, 39·7% due to population growth and ageing, and 31·8% due to interaction of these two factors). Age-standardised adult diabetes prevalence in 2014 was lowest in northwestern Europe, and highest in Polynesia and Micronesia, at nearly 25%, followed by Melanesia and the Middle East and north Africa. Between 1980 and 2014 there was little change in age-standardised diabetes prevalence in adult women in continental western Europe, although crude prevalence rose because of ageing of the population. By contrast, age-standardised adult prevalence rose by 15 percentage points in men and women in Polynesia and Micronesia. In 2014, American Samoa had the highest national prevalence of diabetes (>30% in both sexes), with age-standardised adult prevalence also higher than 25% in some other islands in Polynesia and Micronesia. If post-2000 trends continue, the probability of meeting the global target of halting the rise in the prevalence of diabetes by 2025 at the 2010 level worldwide is lower than 1% for men and is 1% for women. Only nine countries for men and 29 countries for women, mostly in western Europe, have a 50% or higher probability of meeting the global target.Interpretation: Since 1980, age-standardised diabetes prevalence in adults has increased, or at best remained unchanged, in every country. Together with population growth and ageing, this rise has led to a near quadrupling of the number of adults with diabetes worldwide. The burden of diabetes, both in terms of prevalence and number of adults affected, has increased faster in low-income and middle-income countries than in high-income countries.
Published: 2016

5. Effects of diabetes definition on global surveillance of diabetes prevalence and diagnosis : a pooled analysis of 96 population-based studies with 331288 participants

Author: Danaei, G., Fahimi, S., Lu, Y., Zhou, B., Hajifathalian, K., Di Cesare, M., Lo, W. C., Reis-Santos, B., Cowan, M. J., Shaw, J. E., Bentham, J., Lin, J. K., Bixby, H., Magliano, D., Bovet, P., Miranda, J. J., Khang, Y. H., Stevens, G. A., Riley, L. M., Ali, M. K., Ezzati, M., Abdeen, Z. A., Kadir, K. A., and Traissac, Pierre (collab.)
Subjects: endocrine system diseases, nutritional and metabolic diseases
Abstract: Background Diabetes has been defined on the basis of different biomarkers, including fasting plasma glucose (FPG), 2-h plasma glucose in an oral glucose tolerance test (2hOGTT), and HbA(1c). We assessed the effect of different diagnostic definitions on both the population prevalence of diabetes and the classification of previously undiagnosed individuals as having diabetes versus not having diabetes in a pooled analysis of data from population-based health examination surveys in different regions. Methods We used data from 96 population-based health examination surveys that had measured at least two of the biomarkers used for defining diabetes. Diabetes was defined using HbA(1c) (HbA(1c) >= 6 . 5% or history of diabetes diagnosis or using insulin or oral hypoglycaemic drugs) compared with either FPG only or FPG-or-2hOGTT definitions (FPG >= 7 . 0 mmol/L or 2hOGTT >= 11 . 1 mmol/L or history of diabetes or using insulin or oral hypoglycaemic drugs). We calculated diabetes prevalence, taking into account complex survey design and survey sample weights. We compared the prevalences of diabetes using different definitions graphically and by regression analyses. We calculated sensitivity and specificity of diabetes diagnosis based on HbA1c compared with diagnosis based on glucose among previously undiagnosed individuals (ie, excluding those with history of diabetes or using insulin or oral hypoglycaemic drugs). We calculated sensitivity and specificity in each survey, and then pooled results using a random-effects model. We assessed the sources of heterogeneity of sensitivity by meta-regressions for study characteristics selected a priori. Findings Population prevalence of diabetes based on FPG- or-2hOGTT was correlated with prevalence based on FPG alone (r= 0 . 98), but was higher by 2-6 percentage points at different prevalence levels. Prevalence based on HbA(1c) was lower than prevalence based on FPG in 42 . 8% of age-sex-survey groups and higher in another 41 . 6%; in the other 15 . 6%, the two definitions provided similar prevalence estimates. The variation across studies in the relation between glucose-based and HbA(1c)-based prevalences was partly related to participants' age, followed by natural logarithm of per person gross domestic product, the year of survey, mean BMI, and whether the survey population was national, subnational, or from specific communities. Diabetes defined as HbA(1c) 6 . 5% or more had a pooled sensitivity of 52 . 8% (95% CI 51 . 3-54 . 3%) and a pooled specificity of 99 . 74% (99 . 71-99 . 78%) compared with FPG 7 . 0 mmol/L or more for diagnosing previously undiagnosed participants; sensitivity compared with diabetes defined based on FPG-or-2hOGTT was 30 . 5% (28 . 7-32 . 3%). None of the preselected study-level characteristics explained the heterogeneity in the sensitivity of HbA(1c) versus FPG. Interpretation Different biomarkers and definitions for diabetes can provide different estimates of population prevalence of diabetes, and differentially identify people without previous diagnosis as having diabetes. Using an HbA(1c)-based definition alone in health surveys will not identify a substantial proportion of previously undiagnosed people who would be considered as having diabetes using a glucose-based test.
Published: 2015

6. Determinants of tuberculosis in Brazil: from conceptual framework to practical application

Author: Ethel Leonor Maciel and Reis-Santos, B.
Subjects: lcsh:Arctic medicine. Tropical medicine, Patient Dropouts, lcsh:RC955-962, Social Determinants of Health, Antitubercular Agents, lcsh:Medicine, Comorbidity, vigilancia sanitaria, Cause of Death, Tuberculosis, Multidrug-Resistant, Humans, Tuberculosis, Tuberculosis, epidemiología, Treatment Failure, Retrospective Studies, análisis multivariante, determinantes sociales de la salud, Brasil, lcsh:Public aspects of medicine, lcsh:R, lcsh:RA1-1270, Models, Theoretical, Cross-Sectional Studies, Treatment Outcome, Population Surveillance, Disease Susceptibility, análisis de datos, Brazil
Abstract: OBJECTIVE: To leverage a conceptual analytical model for TB determination to identify factors that influence emergence of new cases of tuberculosis (TB) and poor TB treatment outcomes in Brazil. METHODS: This was a cross-sectional study based on data from Brazil's Notifiable Disease Surveillance System database (SINAN). It included all confirmed, incident TB cases reported in Brazil in 2007 - 2011: a total of 432 958 TB cases, of which 318 465 cases with complete data on treatment outcomes were included. Analysis to explain the causal network that influences TB treatment outcomes was based on a theoretical model for determining TB. Adjusted analyses were used to assess the model fit. Hierarchical logistic regression was used to model the dichotomous TB outcome; hierarchical polytomous regression was used for multinomial TB outcome. RESULTS: Of the 318 465 TB cases included, 222 186 (69.8%) were classified as "cured" and 96 279 (30.2%) as "treatment failure." Among the latter, 37 604 (11.8%) abandoned treatment; 13 193 (4.1%) died due to TB; 15 440 (4.8%) died due to causes other than TB; 28 848 (9.1%) were transferred to another municipality; and 1 194 (0.4%) developed multidrug-resistant TB. The dichotomous models were more likely to show spurious associations when compared with the polytomous model. In the polytomous model, individuals assigned to Directly Observed Treatment Short-course were more likely to be cured than others. CONCLUSIONS: Theoretical models are dynamic structures that need ongoing re-evaluation according to new findings; therefore, this is not a definitive proposal for a TB determination model or analysis plan, but rather a proposal that, at present, is adequate in Brazil and has the potential to be extrapolated or adapted to other areas.
Published: 2014

7. Worldwide trends in diabetes since 1980: A pooled analysis of 751 population-based studies with 4.4 million participants

Author: Zhou, B. (Bin), Lu, Y. (Yuan), Hajifathalian, K. (Kaveh), Bentham, J. (James), Di Cesare, M. (Mariachiara), Danaei, G. (Goodarz), Bixby, H. (Honor), Cowan, M.J. (Melanie J.), Ali, M.K. (Mohammed), Taddei, C. (Cristina), Lo, W.-C. (Wei-Cheng), Reis-Santos, B. (Barbara), Stevens, G.A. (Gretchen A.), Riley, L.M. (Leanne M.), Miranda, J.J. (J. Jaime), Bjerregaard, P. (Peter), Rivera, J.A. (Juan A.), Fouad, H.M. (Heba M.), Ma, G. (Guansheng), Mbanya, J.C.N. (Jean Claude N.), McGarvey, S.T. (Stephen T.), Mohan, V. (Viswanathan), Onat, A. (Altan), Pilav, A. (Aida), Ramachandran, A. (Ambady), Ben Romdhane, H. (Habiba), Paciorek, C.J. (Christopher J.), Bennett, J.E. (James E.), Ezzati, M. (Majid), Abdeen, Z.A. (Ziad A.), Kadir, K.A. (Khalid Abdul), Abu-Rmeileh, N.M. (Niveen M.), Acosta-Cazares, B. (Benjamin), Adams, R. (Robert), Aekplakorn, W. (Wichai), Aguilar-Salinas, C.A. (Carlos), Agyemang, C.O. (Charles), Ahmadvand, A. (Alireza), Al-Othman, A.R. (Amani Rashed), Alkerwi, A. (Ala'a), Amouyel, P. (Philippe), Amuzu, A. (Antoinette), Bo Andersen, L. (Lars), Anderssen, S.A. (Sigmund A.), Anjana, R.M. (Ranjit Mohan), Aounallah-Skhiri, H. (Hajer), Aris, T. (Tahir), Arlappa, N. (Nimmathota), Arveiler, D. (Dominique), Assah, F.K. (Felix K.), Avdicová, M. (Mária), Azizi, J. (Joshan), Balakrishna, N. (Nagalla), Bandosz, P. (Piotr), Barbagallo, C.M. (Carlo M.), Barceló, A. (Alberto), Batieha, A.M. (Anwar M.), Baur, L.A. (Louise A.), Benet, M. (Mikhail), Bernabe-Ortiz, A. (Antonio), Bharadwaj, S. (Sumit), Bhargava, S.K. (Santosh K.), Bi, Y. (Yufang), Bjertness, E. (Espen), Bjertness, M.B. (Marius B.), Björkelund, C. (Cecilia), Blokstra, A. (Anneke), Bo, S. (Simona), Boehm, B.O. (Bernhard), Boissonnet, C.P. (Carlos P.), Bovet, P. (Pascal), Brajkovich, I. (Imperia), Breckenkamp, J. (Juergen), Brenner, H. (Hermann), Brewster, L.M. (Lizzy), Brian, G.R. (Garry R.), Bruno, G. (Graziella), Bugge, A. (Anna), De León, A.C. (Antonio Cabrera), Can, G. (Günay), Cåndido, A.P.C. (Ana Paula C.), Capuano, V. (Vincenzo), Carlsson, A.C. (Axel C.), Carvalho, M.J. (Maria J.), Casanueva, F.F. (Felipe F.), Casas, J.P. (Juan Pablo), Caserta, C.A. (Carmelo A.), Castetbon, K., Chamukuttan, S. (Snehalatha), Chaturvedi, N. (Nishi), Chen, C.-J. (Chien-Jen), Chen, F. (Fangfang), Chen, S. (Shuohua), Cheng, C.-Y. (Ching-Yu), Chetrit, A. (Angela), Chiou, S.-T. (Shu-Ti), Cho, Y. (Yumi), Chudek, J. (Jerzy), Cifkova, R. (Renata), Claessens, F., Concin, H. (Hans), Cooper, C. (Charles), Cooper, R. (Rachel), Costanzo, S. (Simona), Cottel, D. (Dominique), Cowell, C. (Chris), Crujeiras, A.B. (Ana B.), D'Arrigo, G. (Graziella), Dallongeville, J., Dankner, R. (Rachel), Dauchet, L. (Luc), De Gaetano, G. (Giovanni), De Henauw, S. (Stefaan), Deepa, M. (Mohan), Dehghan, A. (Abbas), Deschamps, V. (Valerie), Dhana, K. (Klodian), Di Castelnuovo, A.F. (Augusto F.), Djalalinia, S. (Shirin), Doua, K. (Kouamelan), Drygas, W. (Wojciech), Du, Y. (Yong), Dzerve, V. (Vilnis), Egbagbe, E.E. (Eruke E.), Eggertsen, R. (Robert), El Ati, J. (Jalila), Elosua, R. (Roberto), Erasmus, R.T. (Rajiv T.), Erem, C. (Cihangir), Ergor, G. (Gul), Eriksen, L. (Louise), Escobedo-De La Peña, J. (Jorge), Fall, C.H. (Caroline H.), Farzadfar, F. (Farshad), Felix-Redondo, F.J. (Francisco J.), Ferguson, T.S. (Trevor S.), Fernández-Bergés, D. (Daniel), Ferrari, M. (Marika), Ferreccio, C. (Catterina), Feskens, E.J.M. (Edith), Finn, J.D. (Joseph D.), Föger, B. (Bernhard), Foo, L.H. (Leng Huat), Forslund, A.-S. (Ann-Sofie), Francis, D.K. (Damian K.), Do Carmo Franco, M. (Maria), Franco, O.H. (Oscar), Frontera, G. (Guillermo), Furusawa, T. (Takuro), Gaciong, Z. (Zbigniew), Garnett, S.P. (Sarah P.), Gaspoz, J.-M. (Jean-Michel), Gasull, M. (Magda), Gates, L. (Louise), Geleijnse, J.M. (Marianne), Ghasemian, A. (Anoosheh), Ghimire, A. (Anup), Giampaoli, S. (Simona), Gianfagna, F. (Francesco), Giovannelli, J. (Jonathan), Giwercman, A. (Aleksander), Gross, M., Rivas, J.P.G. (Juan P. González), Gorbea, M.B. (Mariano Bonet), Gottrand, F. (Frederic), Grafnetter, D. (Dušan), Grodzicki, T. (Tomasz), Grøntved, A. (Anders), Gruden, G. (Grabriella), Gu, D. (Dongfeng), Guan, O.P. (Ong Peng), Guerrero, R. (Ramiro), Guessous, I. (Idris), Guimaraes, A.L. (Andre L.), Gutierrez, L. (Laura), Hambleton, I.R. (Ian R.), Hardy, R., Kumar, R.H. (Rachakulla Hari), Hata, J. (Jun), He, J. (Jiang), Heidemann, C. (Christin), Herrala, S. (Sauli), Hihtaniemi, I.T. (Ilpo Tapani), Ho, S.Y. (Sai Yin), Ho, S.C. (Suzanne C.), Hofman, A. (Albert), Hormiga, C.M. (Claudia M.), Horta, B.L. (Bernardo L.), Houti, L. (Leila), Howitt, C. (Christina), Htay, T.T. (Thein Thein), Htet, A.S. (Aung Soe), Htike, M.M.T. (Maung Maung Than), Hu, Y. (Yang), Hussieni, A.S. (Abdullatif S.), Huybrechts, I. (Inge), Hwalla, N. (Nahla), Iacoviello, L. (Licia), Iannone, A.G. (Anna G.), Ibrahim, M.M. (M. Mohsen), Ikeda, N. (Nayu), Ikram, M.A. (M. Arfan), Irazola, V. (Vilma), Islam, M. (Muhammad), Iwasaki, M. (Masanori), Jacobs, J.M. (Jeremy M.), Jafar, T.H. (Tazeen), Jamil, K.M. (Kazi M.), Jasienska, G. (Grazyna), Jiang, C.Q. (Chao Qiang), Jonas, J.B., Joshi, P. (Pradeep), Kafatos, A. (Anthony), Kalter-Leibovici, O. (Ofra), Kasaeian, A. (Amir), Katz, J. (Joanne), Kaur, P. (Prabhdeep), Kavousi, M. (Maryam), Keinanen-Kiukaanniemi, S. (Sirkka), Kelishadi, R. (Roya), Kengne, A.P. (Andre Pascal), Kersting, M. (Mathilde), Khader, Y.S. (Yousef Saleh), Khalili, D. (Davood), Khang, Y.-H. (Young-Ho), Kiechl, S. (Stefan), Kim, J. (Jeongseon), Kolsteren, P. (Patrick), Korrovits, P. (Paul), Kratzer, W. (Wolfgang), Kromhout, D. (Daan), Kujala, U.M. (Urho), Kula, K. (Krzysztof), Kyobutungi, C. (Catherine), Laatikainen, T. (Tiina), Lachat, C. (Carl), Laid, Y. (Youcef), Lam, T.H. (Tai Hing), Landrove, O. (Orlando), Lanska, V. (Vera), Lappas, G. (Georg), Laxmaiah, A. (Avula), Leclercq, C. (Catherine), Lee, J. (Jeannette), Lee, J. (Jeonghee), Lehtimäki, T. (Terho), Lekhraj, R. (Rampal), León-Muñoz, L.M. (Luz M.), Li, Y. (Yanping), Lim, W.-Y. (Wei-Yen), Lima-Costa, M.F. (M. Fernanda), Lin, H.-H. (Hsien-Ho), Lin, X. (Xu), Lissner, L. (Lauren), Lorbeer, R. (Roberto), Lozano, J.E. (José Eugenio), Luksiene, D. (Dalia), Lundqvist, A. (Annamari), Lytsy, P. (Per), Machado-Coelho, G.L.L. (George L.L.), Machi, S. (Suka), Maggi, S. (Stefania), Magliano, D.J., Makdisse, M. (Marcia), Rao, K.M. (Kodavanti Mallikharjuna), Manios, Y., Manzato, E. (Enzo), Margozzini, P. (Paula), Marques-Vidal, P. (Pedro), Martorell, R. (Reynaldo), Masoodi, S.R. (Shariq R.), Mathiesen, E.B. (Ellisiv), Matsha, T.E. (Tandi E.), McFarlane, S.R. (Shelly R.), McLachlan, S. (Stela), McNulty, B.A. (Breige A.), Mediene-Benchekor, S. (Sounnia), Meirhaeghe, A. (Aline), Menezes, A.M.B. (Ana Maria B.), Merat, S. (Shahin), Meshram, I.I. (Indrapal I.), Mi, J. (Jie), Miquel, J.F. (Juan Francisco), Mohamed, M.K. (Mostafa K.), Mohammad, K. (Kazem), Mohammadifard, N. (Noushin), Mohd Yusoff, M.F. (Muhammad Fadhli), Møller, N.C. (Niels C.), Molnár, D. (Dénes), Mondo, C.K. (Charles K.), Morejon, A. (Alain), Moreno, L.A. (Luis A.), Morgan, K. (Karen), Moschonis, G., Mossakowska, M. (Malgorzata), Mostafa, A. (Aya), Mota, J. (Jorge), Motta, J. (Jorge), Mu, T.T. (Thet Thet), Muiesan, M.L. (Maria Lorenza), Müller-Nurasyid, M. (Martina), Mursu, J. (Jaakko), Nagel, G. (Gabriele), Námešná, J. (Jana), Nang, E.E.K. (Ei Ei K.), Nangia, M. (Monika), Navarrete-Muñoz, E.M., Ndiaye, N.C. (Ndeye Coumba), Nenko, I. (Ilona), Nervi, F. (Flavio), Nguyen, N.D. (Nguyen D.), Nguyen, Q.N. (Quang Ngoc), Nieto-Martínez, R.E. (Ramfis E.), Ning, G. (Guang), Ninomiya, T. (Toshiharu), Noale, M. (Marianna), Noto, D. (Davide), Al Nsour, M. (Mohannad), Ochoa-Avilés, A.M. (Angélica M.), Oh, K. (Kyungwon), Ordunez, P. (Pedro), Osmond, C. (Clive), Otero, J.A. (Johanna A.), Owusu-Dabo, E. (Ellis), Pahomova, E. (Elena), Palmieri, L. (Luigi), Panda-Jonas, S. (Songhomitra), Panza, F. (Francesco), Parsaeian, M. (Mahboubeh), Peixoto, S.V. (Sergio Viana), Peltonen, M. (Markku), Peters, A., Peykari, N. (Niloofar), Pham, S.T. (Son Thai), Pitakaka, F. (Freda), Piwonska, A. (Aleksandra), Piwonski, J. (Jerzy), Plans-Rubió, P. (Pedro), Porta, M., Portegies, M.L.P. (Marileen), Poustchi, H. (Hossein), Pradeepa, R. (Rajendra), Price, J.F. (Jackie F.), Punab, M. (Margus), Qasrawi, R.F. (Radwan F.), Qorbani, M. (Mostafa), Radisauskas, R. (Ricardas), Rahman, M. (Mahmudur), Raitakari, O. (Olli), Rao, S.R. (Sudha Ramachandra), Ramke, J. (Jacqueline), Ramos, R. (Rafel), Rampal, S. (Sanjay), Rathmann, W. (Wolfgang), Redón, J. (Josep), Reganit, P.F.M. (Paul Ferdinand M.), Rigo, F. (Fernando), Robinson, S.M. (Siân), Robitaille, C. (Cynthia), Rodríguez Artalejo, F. (Fernando), Del CristoRodriguez-Perez, M. (María), Rodríguez-Villamizar, L.A. (Laura A.), Rojas-Martinez, R. (Rosalba), Ronkainen, K. (Kimmo), Rosengren, A. (Annika), Rubinstein, A. (Adolfo), Rui, O. (Ornelas), Ruiz-Betancourt, B.S. (Blanca Sandra), Horimoto, A.R.V.R. (Andrea R.V. Russo), Rutkowski, M. (Marcin), Sabanayagam, C. (Charumathi), Sachdev, H.S. (Harshpal S.), Saidi, O. (Olfa), Sakarya, S. (Sibel), Salanave, B. (Benoit), Salonen, J.T. (Jukka T.), Salvetti, M. (Massimo), Sánchez-Abanto, J. (Jose), Santos, D. (Diana), Dos Santos, R.N. (Renata Nunes), Santos, R. (Rute), Saramies, J. (Jouko), Sardinha, L.B. (Luis B.), Sarrafzadegan, N. (Nizal), Saum, K.-U. (Kai-Uwe), Scazufca, M. (Marcia), Schargrodsky, H. (Herman), Scheidt-Nave, C. (C.), Sein, A.A. (Aye Aye), Sharma, S.K. (Sanjb K.), Shaw, J.E., Shibuya, K. (Kenji), Shin, Y. (Youchan), Shiri, R. (Rahman), Siantar, R. (Rosalynn), Sibai, A.M. (Abla M.), Simon, M. (Mary), Simons, J. (Judith), Simons, L.A. (Leon A.), Sjostrom, M. (Michael), Slowikowska-Hilczer, J. (Jolanta), Slusarczyk, P. (Przemyslaw), Smeeth, L. (Liam), Snijder, M.B. (Marieke), So, H.-K. (Hung-Kwan), Sobngwi, E. (Eugène), Söderberg, S. (Stefan), Solfrizzi, V. (Vincenzo), Sonestedt, E. (Emily), Soumare, A. (Aicha), Staessen, J.A. (Jan A.), Stathopoulou, M.G. (Maria G.), Steene-Johannessen, J. (Jostein), Stehle, P. (Peter), Stein, A.D. (Aryeh D.), Stessman, J. (Jochanan), Stöckl, D. (Doris), Stokwiszewski, J. (Jakub), Stronks, K. (Karien), Strufaldi, M.W. (Maria Wany), Sun, C.-A. (Chien-An), Sundstrom, J. (Johan), Sung, Y.-T. (Yn-Tz), Suriyawongpaisal, P. (Paibul), Sy, R.G. (Rody G.), Tai, E.S. (E. Shyong), Tamosiunas, A. (Abdonas), Tang, L. (Line), Tarawneh, M. (Mohammed), Tarqui-Mamani, C.B. (Carolina B), Taylor, A. (Anne), Theobald, H. (Holger), Thijs, L. (Lutgarde), Thuesen, L. (Leif), Tolonen, H.K. (Hanna K.), Tolstrup, J.S. (Janne S.), Topbas, M. (Murat), Torrent, M. (Maties), Traissac, P. (Pierre), Trinh, O.T.H. (Oanh T.H.), Tulloch-Reid, M.K. (Marshall K.), Tuomainen, T.-P. (Tomi-Pekka), Turley, M.L. (Maria L.), Tzourio, C. (Christophe), Ueda, P. (Peter), Ukoli, F.A.M. (Flora A.M.), Ulmer, H. (Hanno), Uusitalo, H.M.T. (Hannu M.T.), Valdivia, G. (Gonzalo), Valvi, D. (Damaskini), Rossem, L. (Lenie) van, Valkengoed, I. (Irene) van, Vanderschueren, D. (Dirk), Vanuzzo, D. (Diego), Vega, T. (Tomas), Velasquez-Melendez, G. (Gustavo), Veronesi, G. (Giovanni), Verschuren, W.M.M. (W.M. Monique), Verstraeten, R. (Roosmarijn), Viet, L. (Lucie), Vioque, J. (Jesus), Virtanen, J.K. (Jyrki K.), Visvikis-Siest, S. (Sophie), Viswanathan, B. (Bharathi), Vollenweider, P. (Peter), Voutilainen, S. (Sari), Vrijheid, M. (Martine), Wade, A.N. (Alisha N.), Wagner, A. (Aline), Walton, J. (Janette), Wan Mohamud, W.N. (Wan Nazaimoon), Wang, F. (Feng), Wang, M.-D. (Ming-Dong), Wang, Q. (Qian), Wang, Y. (Ying), Wannamethee, S.G. (Goya), Weerasekera, D. (Deepa), Whincup, P.H. (Peter), Widhalm, K. (Kurt), Wiecek, A. (Andrzej), Wijga, A.H. (Alet), Wilks, R.J. (Rainford J.), Willeit, J. (Johann), Wilsgaard, T. (Tom), Wojtyniak, B. (Bogdan), Wong, T.Y. (Tien Yin), Woo, J. (Jean), Woodward, M. (Mark), Wu, F.C. (Frederick C.), Wu, S.L. (Shou Ling), Xu, H. (Haiquan), Yan, W. (Weili), Yang, X. (Xiaoguang), Ye, X. (Xingwang), Yoshihara, A. (Akihiro), Younger-Coleman, N.O. (Novie O.), Zambon, S. (Sabina), Zargar, A.H. (Abdul Hamid), Zdrojewski, T. (T.), Zhao, W. (Wenhua), Zheng, Y. (Yingfeng), Cisneros, J.Z. (Julio Zuñiga), Zhou, B. (Bin), Lu, Y. (Yuan), Hajifathalian, K. (Kaveh), Bentham, J. (James), Di Cesare, M. (Mariachiara), Danaei, G. (Goodarz), Bixby, H. (Honor), Cowan, M.J. (Melanie J.), Ali, M.K. (Mohammed), Taddei, C. (Cristina), Lo, W.-C. (Wei-Cheng), Reis-Santos, B. (Barbara), Stevens, G.A. (Gretchen A.), Riley, L.M. (Leanne M.), Miranda, J.J. (J. Jaime), Bjerregaard, P. (Peter), Rivera, J.A. (Juan A.), Fouad, H.M. (Heba M.), Ma, G. (Guansheng), Mbanya, J.C.N. (Jean Claude N.), McGarvey, S.T. (Stephen T.), Mohan, V. (Viswanathan), Onat, A. (Altan), Pilav, A. (Aida), Ramachandran, A. (Ambady), Ben Romdhane, H. (Habiba), Paciorek, C.J. (Christopher J.), Bennett, J.E. (James E.), Ezzati, M. (Majid), Abdeen, Z.A. (Ziad A.), Kadir, K.A. (Khalid Abdul), Abu-Rmeileh, N.M. (Niveen M.), Acosta-Cazares, B. (Benjamin), Adams, R. (Robert), Aekplakorn, W. (Wichai), Aguilar-Salinas, C.A. (Carlos), Agyemang, C.O. (Charles), Ahmadvand, A. (Alireza), Al-Othman, A.R. (Amani Rashed), Alkerwi, A. (Ala'a), Amouyel, P. (Philippe), Amuzu, A. (Antoinette), Bo Andersen, L. (Lars), Anderssen, S.A. (Sigmund A.), Anjana, R.M. (Ranjit Mohan), Aounallah-Skhiri, H. (Hajer), Aris, T. (Tahir), Arlappa, N. (Nimmathota), Arveiler, D. (Dominique), Assah, F.K. (Felix K.), Avdicová, M. (Mária), Azizi, J. (Joshan), Balakrishna, N. (Nagalla), Bandosz, P. (Piotr), Barbagallo, C.M. (Carlo M.), Barceló, A. (Alberto), Batieha, A.M. (Anwar M.), Baur, L.A. (Louise A.), Benet, M. (Mikhail), Bernabe-Ortiz, A. (Antonio), Bharadwaj, S. (Sumit), Bhargava, S.K. (Santosh K.), Bi, Y. (Yufang), Bjertness, E. (Espen), Bjertness, M.B. (Marius B.), Björkelund, C. (Cecilia), Blokstra, A. (Anneke), Bo, S. (Simona), Boehm, B.O. (Bernhard), Boissonnet, C.P. (Carlos P.), Bovet, P. (Pascal), Brajkovich, I. (Imperia), Breckenkamp, J. (Juergen), Brenner, H. (Hermann), Brewster, L.M. (Lizzy), Brian, G.R. (Garry R.), Bruno, G. (Graziella), Bugge, A. (Anna), De León, A.C. (Antonio Cabrera), Can, G. (Günay), Cåndido, A.P.C. (Ana Paula C.), Capuano, V. (Vincenzo), Carlsson, A.C. (Axel C.), Carvalho, M.J. (Maria J.), Casanueva, F.F. (Felipe F.), Casas, J.P. (Juan Pablo), Caserta, C.A. (Carmelo A.), Castetbon, K., Chamukuttan, S. (Snehalatha), Chaturvedi, N. (Nishi), Chen, C.-J. (Chien-Jen), Chen, F. (Fangfang), Chen, S. (Shuohua), Cheng, C.-Y. (Ching-Yu), Chetrit, A. (Angela), Chiou, S.-T. (Shu-Ti), Cho, Y. (Yumi), Chudek, J. (Jerzy), Cifkova, R. (Renata), Claessens, F., Concin, H. (Hans), Cooper, C. (Charles), Cooper, R. (Rachel), Costanzo, S. (Simona), Cottel, D. (Dominique), Cowell, C. (Chris), Crujeiras, A.B. (Ana B.), D'Arrigo, G. (Graziella), Dallongeville, J., Dankner, R. (Rachel), Dauchet, L. (Luc), De Gaetano, G. (Giovanni), De Henauw, S. (Stefaan), Deepa, M. (Mohan), Dehghan, A. (Abbas), Deschamps, V. (Valerie), Dhana, K. (Klodian), Di Castelnuovo, A.F. (Augusto F.), Djalalinia, S. (Shirin), Doua, K. (Kouamelan), Drygas, W. (Wojciech), Du, Y. (Yong), Dzerve, V. (Vilnis), Egbagbe, E.E. (Eruke E.), Eggertsen, R. (Robert), El Ati, J. (Jalila), Elosua, R. (Roberto), Erasmus, R.T. (Rajiv T.), Erem, C. (Cihangir), Ergor, G. (Gul), Eriksen, L. (Louise), Escobedo-De La Peña, J. (Jorge), Fall, C.H. (Caroline H.), Farzadfar, F. (Farshad), Felix-Redondo, F.J. (Francisco J.), Ferguson, T.S. (Trevor S.), Fernández-Bergés, D. (Daniel), Ferrari, M. (Marika), Ferreccio, C. (Catterina), Feskens, E.J.M. (Edith), Finn, J.D. (Joseph D.), Föger, B. (Bernhard), Foo, L.H. (Leng Huat), Forslund, A.-S. (Ann-Sofie), Francis, D.K. (Damian K.), Do Carmo Franco, M. (Maria), Franco, O.H. (Oscar), Frontera, G. (Guillermo), Furusawa, T. (Takuro), Gaciong, Z. (Zbigniew), Garnett, S.P. (Sarah P.), Gaspoz, J.-M. (Jean-Michel), Gasull, M. (Magda), Gates, L. (Louise), Geleijnse, J.M. (Marianne), Ghasemian, A. (Anoosheh), Ghimire, A. (Anup), Giampaoli, S. (Simona), Gianfagna, F. (Francesco), Giovannelli, J. (Jonathan), Giwercman, A. (Aleksander), Gross, M., Rivas, J.P.G. (Juan P. González), Gorbea, M.B. (Mariano Bonet), Gottrand, F. (Frederic), Grafnetter, D. (Dušan), Grodzicki, T. (Tomasz), Grøntved, A. (Anders), Gruden, G. (Grabriella), Gu, D. (Dongfeng), Guan, O.P. (Ong Peng), Guerrero, R. (Ramiro), Guessous, I. (Idris), Guimaraes, A.L. (Andre L.), Gutierrez, L. (Laura), Hambleton, I.R. (Ian R.), Hardy, R., Kumar, R.H. (Rachakulla Hari), Hata, J. (Jun), He, J. (Jiang), Heidemann, C. (Christin), Herrala, S. (Sauli), Hihtaniemi, I.T. (Ilpo Tapani), Ho, S.Y. (Sai Yin), Ho, S.C. (Suzanne C.), Hofman, A. (Albert), Hormiga, C.M. (Claudia M.), Horta, B.L. (Bernardo L.), Houti, L. (Leila), Howitt, C. (Christina), Htay, T.T. (Thein Thein), Htet, A.S. (Aung Soe), Htike, M.M.T. (Maung Maung Than), Hu, Y. (Yang), Hussieni, A.S. (Abdullatif S.), Huybrechts, I. (Inge), Hwalla, N. (Nahla), Iacoviello, L. (Licia), Iannone, A.G. (Anna G.), Ibrahim, M.M. (M. Mohsen), Ikeda, N. (Nayu), Ikram, M.A. (M. Arfan), Irazola, V. (Vilma), Islam, M. (Muhammad), Iwasaki, M. (Masanori), Jacobs, J.M. (Jeremy M.), Jafar, T.H. (Tazeen), Jamil, K.M. (Kazi M.), Jasienska, G. (Grazyna), Jiang, C.Q. (Chao Qiang), Jonas, J.B., Joshi, P. (Pradeep), Kafatos, A. (Anthony), Kalter-Leibovici, O. (Ofra), Kasaeian, A. (Amir), Katz, J. (Joanne), Kaur, P. (Prabhdeep), Kavousi, M. (Maryam), Keinanen-Kiukaanniemi, S. (Sirkka), Kelishadi, R. (Roya), Kengne, A.P. (Andre Pascal), Kersting, M. (Mathilde), Khader, Y.S. (Yousef Saleh), Khalili, D. (Davood), Khang, Y.-H. (Young-Ho), Kiechl, S. (Stefan), Kim, J. (Jeongseon), Kolsteren, P. (Patrick), Korrovits, P. (Paul), Kratzer, W. (Wolfgang), Kromhout, D. (Daan), Kujala, U.M. (Urho), Kula, K. (Krzysztof), Kyobutungi, C. (Catherine), Laatikainen, T. (Tiina), Lachat, C. (Carl), Laid, Y. (Youcef), Lam, T.H. (Tai Hing), Landrove, O. (Orlando), Lanska, V. (Vera), Lappas, G. (Georg), Laxmaiah, A. (Avula), Leclercq, C. (Catherine), Lee, J. (Jeannette), Lee, J. (Jeonghee), Lehtimäki, T. (Terho), Lekhraj, R. (Rampal), León-Muñoz, L.M. (Luz M.), Li, Y. (Yanping), Lim, W.-Y. (Wei-Yen), Lima-Costa, M.F. (M. Fernanda), Lin, H.-H. (Hsien-Ho), Lin, X. (Xu), Lissner, L. (Lauren), Lorbeer, R. (Roberto), Lozano, J.E. (José Eugenio), Luksiene, D. (Dalia), Lundqvist, A. (Annamari), Lytsy, P. (Per), Machado-Coelho, G.L.L. (George L.L.), Machi, S. (Suka), Maggi, S. (Stefania), Magliano, D.J., Makdisse, M. (Marcia), Rao, K.M. (Kodavanti Mallikharjuna), Manios, Y., Manzato, E. (Enzo), Margozzini, P. (Paula), Marques-Vidal, P. (Pedro), Martorell, R. (Reynaldo), Masoodi, S.R. (Shariq R.), Mathiesen, E.B. (Ellisiv), Matsha, T.E. (Tandi E.), McFarlane, S.R. (Shelly R.), McLachlan, S. (Stela), McNulty, B.A. (Breige A.), Mediene-Benchekor, S. (Sounnia), Meirhaeghe, A. (Aline), Menezes, A.M.B. (Ana Maria B.), Merat, S. (Shahin), Meshram, I.I. (Indrapal I.), Mi, J. (Jie), Miquel, J.F. (Juan Francisco), Mohamed, M.K. (Mostafa K.), Mohammad, K. (Kazem), Mohammadifard, N. (Noushin), Mohd Yusoff, M.F. (Muhammad Fadhli), Møller, N.C. (Niels C.), Molnár, D. (Dénes), Mondo, C.K. (Charles K.), Morejon, A. (Alain), Moreno, L.A. (Luis A.), Morgan, K. (Karen), Moschonis, G., Mossakowska, M. (Malgorzata), Mostafa, A. (Aya), Mota, J. (Jorge), Motta, J. (Jorge), Mu, T.T. (Thet Thet), Muiesan, M.L. (Maria Lorenza), Müller-Nurasyid, M. (Martina), Mursu, J. (Jaakko), Nagel, G. (Gabriele), Námešná, J. (Jana), Nang, E.E.K. (Ei Ei K.), Nangia, M. (Monika), Navarrete-Muñoz, E.M., Ndiaye, N.C. (Ndeye Coumba), Nenko, I. (Ilona), Nervi, F. (Flavio), Nguyen, N.D. (Nguyen D.), Nguyen, Q.N. (Quang Ngoc), Nieto-Martínez, R.E. (Ramfis E.), Ning, G. (Guang), Ninomiya, T. (Toshiharu), Noale, M. (Marianna), Noto, D. (Davide), Al Nsour, M. (Mohannad), Ochoa-Avilés, A.M. (Angélica M.), Oh, K. (Kyungwon), Ordunez, P. (Pedro), Osmond, C. (Clive), Otero, J.A. (Johanna A.), Owusu-Dabo, E. (Ellis), Pahomova, E. (Elena), Palmieri, L. (Luigi), Panda-Jonas, S. (Songhomitra), Panza, F. (Francesco), Parsaeian, M. (Mahboubeh), Peixoto, S.V. (Sergio Viana), Peltonen, M. (Markku), Peters, A., Peykari, N. (Niloofar), Pham, S.T. (Son Thai), Pitakaka, F. (Freda), Piwonska, A. (Aleksandra), Piwonski, J. (Jerzy), Plans-Rubió, P. (Pedro), Porta, M., Portegies, M.L.P. (Marileen), Poustchi, H. (Hossein), Pradeepa, R. (Rajendra), Price, J.F. (Jackie F.), Punab, M. (Margus), Qasrawi, R.F. (Radwan F.), Qorbani, M. (Mostafa), Radisauskas, R. (Ricardas), Rahman, M. (Mahmudur), Raitakari, O. (Olli), Rao, S.R. (Sudha Ramachandra), Ramke, J. (Jacqueline), Ramos, R. (Rafel), Rampal, S. (Sanjay), Rathmann, W. (Wolfgang), Redón, J. (Josep), Reganit, P.F.M. (Paul Ferdinand M.), Rigo, F. (Fernando), Robinson, S.M. (Siân), Robitaille, C. (Cynthia), Rodríguez Artalejo, F. (Fernando), Del CristoRodriguez-Perez, M. (María), Rodríguez-Villamizar, L.A. (Laura A.), Rojas-Martinez, R. (Rosalba), Ronkainen, K. (Kimmo), Rosengren, A. (Annika), Rubinstein, A. (Adolfo), Rui, O. (Ornelas), Ruiz-Betancourt, B.S. (Blanca Sandra), Horimoto, A.R.V.R. (Andrea R.V. Russo), Rutkowski, M. (Marcin), Sabanayagam, C. (Charumathi), Sachdev, H.S. (Harshpal S.), Saidi, O. (Olfa), Sakarya, S. (Sibel), Salanave, B. (Benoit), Salonen, J.T. (Jukka T.), Salvetti, M. (Massimo), Sánchez-Abanto, J. (Jose), Santos, D. (Diana), Dos Santos, R.N. (Renata Nunes), Santos, R. (Rute), Saramies, J. (Jouko), Sardinha, L.B. (Luis B.), Sarrafzadegan, N. (Nizal), Saum, K.-U. (Kai-Uwe), Scazufca, M. (Marcia), Schargrodsky, H. (Herman), Scheidt-Nave, C. (C.), Sein, A.A. (Aye Aye), Sharma, S.K. (Sanjb K.), Shaw, J.E., Shibuya, K. (Kenji), Shin, Y. (Youchan), Shiri, R. (Rahman), Siantar, R. (Rosalynn), Sibai, A.M. (Abla M.), Simon, M. (Mary), Simons, J. (Judith), Simons, L.A. (Leon A.), Sjostrom, M. (Michael), Slowikowska-Hilczer, J. (Jolanta), Slusarczyk, P. (Przemyslaw), Smeeth, L. (Liam), Snijder, M.B. (Marieke), So, H.-K. (Hung-Kwan), Sobngwi, E. (Eugène), Söderberg, S. (Stefan), Solfrizzi, V. (Vincenzo), Sonestedt, E. (Emily), Soumare, A. (Aicha), Staessen, J.A. (Jan A.), Stathopoulou, M.G. (Maria G.), Steene-Johannessen, J. (Jostein), Stehle, P. (Peter), Stein, A.D. (Aryeh D.), Stessman, J. (Jochanan), Stöckl, D. (Doris), Stokwiszewski, J. (Jakub), Stronks, K. (Karien), Strufaldi, M.W. (Maria Wany), Sun, C.-A. (Chien-An), Sundstrom, J. (Johan), Sung, Y.-T. (Yn-Tz), Suriyawongpaisal, P. (Paibul), Sy, R.G. (Rody G.), Tai, E.S. (E. Shyong), Tamosiunas, A. (Abdonas), Tang, L. (Line), Tarawneh, M. (Mohammed), Tarqui-Mamani, C.B. (Carolina B), Taylor, A. (Anne), Theobald, H. (Holger), Thijs, L. (Lutgarde), Thuesen, L. (Leif), Tolonen, H.K. (Hanna K.), Tolstrup, J.S. (Janne S.), Topbas, M. (Murat), Torrent, M. 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(Peter), Widhalm, K. (Kurt), Wiecek, A. (Andrzej), Wijga, A.H. (Alet), Wilks, R.J. (Rainford J.), Willeit, J. (Johann), Wilsgaard, T. (Tom), Wojtyniak, B. (Bogdan), Wong, T.Y. (Tien Yin), Woo, J. (Jean), Woodward, M. (Mark), Wu, F.C. (Frederick C.), Wu, S.L. (Shou Ling), Xu, H. (Haiquan), Yan, W. (Weili), Yang, X. (Xiaoguang), Ye, X. (Xingwang), Yoshihara, A. (Akihiro), Younger-Coleman, N.O. (Novie O.), Zambon, S. (Sabina), Zargar, A.H. (Abdul Hamid), Zdrojewski, T. (T.), Zhao, W. (Wenhua), Zheng, Y. (Yingfeng), and Cisneros, J.Z. (Julio Zuñiga)
Abstract: Background: One of the global targets for non-communicable diseases is to halt, by 2025, the rise in the age standardised adult prevalence of diabetes at its 2010 levels. We aimed to estimate worldwide trends in diabetes, how likely it is for countries to achieve the global target, and how changes in prevalence, together with population growth and ageing, are aff ecting the number of adults with diabetes. Methods: We pooled data from population-based studies that had collected data on diabetes through measurement of its biomarkers. We used a Bayesian hierarchical model to estimate trends in diabetes prevalence-defined as fasting plasma glucose of 7.0 mmol/L or higher, or history of diagnosis with diabetes, or use of insulin or oral hypoglycaemic drugs-in 200 countries and territories in 21 regions, by sex and from 1980 to 2014. We also calculated the posterior probability of meeting the global diabetes target if post-2000 trends continue. Findings: We used data from 751 studies including 4372000 adults from 146 of the 200 countries we make estimates for. Global age-standardised diabetes prevalence increased from 4.3% (95% credible interval 2.4-17.0) in 1980 to 9.0% (7.2-11.1) in 2014 in
Published: 2016
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8. Effects of diabetes definition on global surveillance of diabetes prevalence and diagnosis: a pooled analysis of 96 population-based studies with 331288 participants

Author: Danaei, G, Fahimi, S, Lu, Y, Zhou, B, Hajifathalian, K, Di Cesare, M, Lo, W-C, Reis-Santos, B, Cowan, MJ, Shaw, JE, Bentham, J, Lin, JK, Bixby, H, Magliano, D, Bovet, P, Miranda, JJ, Khang, Y-H, Stevens, GA, Riley, LM, Ali, MK, Ezzati, M, Abdeen, ZA, Kadir, KA, Abu-Rmeileh, NM, Acosta-Cazares, B, Aekplakorn, W, Aguilar-Salinas, CA, Ahmadvand, A, Al Nsour, M, Alkerwi, A, Amouyel, P, Andersen, LB, Anderssen, SA, Andrade, DS, Anjana, RM, Aounallah-Skhiri, H, Aris, T, Arlappa, N, Arveiler, D, Assah, FK, Avdicova, M, Balakrishna, N, Bandosz, P, Barbagallo, CM, Barcelo, A, Batieha, AM, Baur, LA, Ben Romdhane, H, Bernabe-Ortiz, A, Bhargava, SK, Bi, Y, Bjerregaard, P, Bjorkelund, C, Blake, M, Blokstra, A, Bo, S, Boehm, BO, Boissonnet, CP, Brajkovich, I, Breckenkamp, J, Brewster, LM, Brian, GR, Bruno, G, Bugge, A, de Leon, AC, Can, G, Candido, APC, Capuano, V, Carvalho, MJ, Casanueva, FF, Caserta, CA, Castetbon, K, Chamukuttan, S, Chaturvedi, N, Chen, C-J, Chen, F, Chen, S, Cheng, C-Y, Chetrit, A, Chiou, S-T, Cho, Y, Chudek, J, Cifkova, R, Claessens, F, Concin, H, Cooper, C, Cooper, R, Costanzo, S, Cottel, D, Cowell, C, Crujeiras, AB, D'Arrigo, G, Dallongeville, J, Dankner, R, Dauchet, L, de Gaetano, G, De Henauw, S, Deepa, M, Dehghan, A, Dhana, K, Di Castelnuovo, AF, Djalalinia, S, Doua, K, Drygas, W, Du, Y, Egbagbe, EE, Eggertsen, R, El Ati, J, Elosua, R, Erasmus, RT, Erem, C, Ergor, G, Eriksen, L, la Penaa, JE-D, Fall, CH, Farzadfar, F, Felix-Redondo, FJ, Ferguson, TS, Fernandez-Berges, D, Ferrari, M, Ferreccio, C, Finn, JD, Foger, B, Foo, LH, Fouad, HM, Francis, DK, Franco, MDC, Franco, OH, Frontera, G, Furusawa, T, Gaciong, Z, Galbarczyk, A, Garnett, SP, Gaspoz, J-M, Gasull, M, Gates, L, Geleijnse, JM, Ghasemain, A, Giampaoli, S, Gianfagna, F, Giovannelli, J, Gross, MG, Rivas, JPG, Gorbea, MB, Gottrand, F, Grant, JF, Grodzicki, T, Grontved, A, Gruden, G, Gu, D, Guan, OP, Guerrero, R, Guessous, I, Guimaraes, AL, Gutierrez, L, Hardy, R, Kumar, RH, He, J, Heidemann, C, Hihtaniemi, IT, Ho, SY, Ho, SC, Hofman, A, Russo, ARV, Hormiga, CM, Horta, BL, Houti, L, Hussieni, AS, Huybrechts, I, Hwalla, N, Iacoviello, L, Iannone, AG, Ibrahim, MM, Ikeda, N, Ikram, MA, Irazola, VE, Islam, M, Iwasaki, M, Jacobs, JM, Jafar, T, Jasienska, G, Jiang, CQ, Jonas, JB, Joshi, P, Kafatos, A, Kalter-Leibovici, O, Kasaeian, A, Katz, J, Kaur, P, Kavousi, M, Kelishadi, R, Kengne, AP, Kersting, M, Khader, YS, Kiechl, S, Kim, J, Kiyohara, Y, Kolsteren, P, Korrovits, P, Koskinen, S, Kratzer, W, Kromhout, D, Kula, K, Kurjata, P, Kyobutungi, C, Lachat, C, Laid, Y, Lam, TH, Landrove, O, Lanska, V, Lappas, G, Laxmaiah, A, Leclercq, C, Lee, J, Lehtimaki, T, Lekhraj, R, Leon-Munoz, LM, Li, Y, Lim, W-Y, Lima-Costa, MF, Lin, H-H, Lin, X, Lissner, L, Lorbeer, R, Lozano, JE, Lundqvist, A, Lytsy, P, Ma, G, Machado-Coelho, GLL, Machi, S, Maggi, S, Makdisse, M, Rao, KM, Manios, Y, Manzato, E, Margozzini, P, Marques-Vidal, P, Martorell, R, Masoodi, SR, Matsha, TE, Mbanya, JCN, McFarlane, SR, McGarvey, ST, McLachlan, S, McNulty, BA, Mediene-Benchekor, S, Meirhaeghe, A, Menezes, AMB, Merat, S, Meshram, II, Mi, J, Miquel, JF, Mohamed, MK, Mohammad, K, Mohan, V, Yusoff, MFM, Moller, NC, Molnar, D, Mondo, CK, Moreno, LA, Morgan, K, Moschonis, G, Mossakowska, M, Mostafa, A, Mota, J, Muiesan, ML, Muller-Nurasyid, M, Mursu, J, Nagel, G, Namesna, J, Nang, EEK, Nangia, VB, Navarrete-Munoz, EM, Ndiaye, NC, Nervi, F, Nguyen, ND, Nieto-Martinez, RSE, Ning, G, Ninomiya, T, Noale, M, Noto, D, Ochoa-Aviles, AM, Oh, K, Onat, A, Osmond, C, Otero, JA, Palmieri, L, Panda-Jonas, S, Panza, F, Parsaeian, M, Peixoto, SV, Pereira, AC, Peters, A, Peykari, N, Pilav, A, Pitakaka, F, Piwonska, A, Piwonski, J, Plans-Rubio, P, Porta, M, Portegies, MLP, Poustchi, H, Pradeepa, R, Price, JF, Punab, M, Qasrawi, RF, Qorbani, M, Raitakari, O, Rao, SR, Ramachandran, A, Ramos, R, Rampal, S, Rathmann, W, Redon, J, Reganit, PFM, Rigo, F, Robinson, SM, Robitaille, C, Rodriguez, LA, Rodriguez-Artalejo, F, Rodriguez-Perez, MDC, Rojas-Martinez, R, Romaguera, D, Rosengren, A, Rubinstein, A, Rui, O, Ruiz-Betancourt, BS, Rutkowski, M, Sabanayagam, C, Sachdev, HS, Saidi, O, Sakarya, S, Salanave, B, Salonen, JT, Salvetti, M, Sanchez-Abanto, J, Nunes, R, Santos, R, Sardinha, LB, Scazufca, M, Schargrodsky, H, Scheidt-Nave, C, Shibuya, K, Shin, Y, Shiri, R, Siantar, R, Sibai, AM, Simon, M, Simons, J, Simons, LA, Sjostrom, M, Slowikowska-Hilczer, J, Slusarczyk, P, Smeeth, L, Snijder, MB, Solfrizzi, V, Sonestedt, E, Soumare, A, Staessen, JA, Steene-Johannessen, J, Stehle, P, Stein, AD, Stessman, J, Stockl, D, Stokwiszewski, J, Strufaldi, MW, Sun, C-A, Sundstrom, J, Suriyawongpaisal, P, Sy, RG, Tai, ES, Tarawneh, M, Tarqui-Mamani, CB, Thijs, L, Tolstrup, JS, Topbas, M, Torrent, M, Traissac, P, Trinh, OTH, Tulloch-Reid, MK, Tuomainen, T-P, Turley, ML, Tzourio, C, Ueda, P, Ukoli, FM, Ulmer, H, Valdivia, G, Van Valkengoed, IGM, Vanderschueren, D, Vanuzzo, D, Vega, T, Velasquez-Melendez, G, Veronesi, G, Verschuren, M, Vioque, J, Virtanen, J, Visvikis-Siest, S, Viswanathan, B, Vollenweider, P, Voutilainen, S, Wade, AN, Wagner, A, Walton, J, Mohamud, WNW, Wang, M-D, Wang, YX, Wannamethee, SG, Weerasekera, D, Whincup, PH, Widhalm, K, Wiecek, A, Wilks, RJ, Willeit, J, Wojtyniak, B, Wong, TY, Woo, J, Woodward, M, Wu, AG, Wu, FC, Wu, SL, Xu, H, Yang, X, Ye, X, Yoshihara, A, Younger-Coleman, NO, Zambon, S, Zargar, AH, Zdrojewski, T, Zhao, W, Zheng, Y, Danaei, G, Fahimi, S, Lu, Y, Zhou, B, Hajifathalian, K, Di Cesare, M, Lo, W-C, Reis-Santos, B, Cowan, MJ, Shaw, JE, Bentham, J, Lin, JK, Bixby, H, Magliano, D, Bovet, P, Miranda, JJ, Khang, Y-H, Stevens, GA, Riley, LM, Ali, MK, Ezzati, M, Abdeen, ZA, Kadir, KA, Abu-Rmeileh, NM, Acosta-Cazares, B, Aekplakorn, W, Aguilar-Salinas, CA, Ahmadvand, A, Al Nsour, M, Alkerwi, A, Amouyel, P, Andersen, LB, Anderssen, SA, Andrade, DS, Anjana, RM, Aounallah-Skhiri, H, Aris, T, Arlappa, N, Arveiler, D, Assah, FK, Avdicova, 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Ochoa-Aviles, AM, Oh, K, Onat, A, Osmond, C, Otero, JA, Palmieri, L, Panda-Jonas, S, Panza, F, Parsaeian, M, Peixoto, SV, Pereira, AC, Peters, A, Peykari, N, Pilav, A, Pitakaka, F, Piwonska, A, Piwonski, J, Plans-Rubio, P, Porta, M, Portegies, MLP, Poustchi, H, Pradeepa, R, Price, JF, Punab, M, Qasrawi, RF, Qorbani, M, Raitakari, O, Rao, SR, Ramachandran, A, Ramos, R, Rampal, S, Rathmann, W, Redon, J, Reganit, PFM, Rigo, F, Robinson, SM, Robitaille, C, Rodriguez, LA, Rodriguez-Artalejo, F, Rodriguez-Perez, MDC, Rojas-Martinez, R, Romaguera, D, Rosengren, A, Rubinstein, A, Rui, O, Ruiz-Betancourt, BS, Rutkowski, M, Sabanayagam, C, Sachdev, HS, Saidi, O, Sakarya, S, Salanave, B, Salonen, JT, Salvetti, M, Sanchez-Abanto, J, Nunes, R, Santos, R, Sardinha, LB, Scazufca, M, Schargrodsky, H, Scheidt-Nave, C, Shibuya, K, Shin, Y, Shiri, R, Siantar, R, Sibai, AM, Simon, M, Simons, J, Simons, LA, Sjostrom, M, Slowikowska-Hilczer, J, Slusarczyk, P, Smeeth, L, Snijder, MB, Solfrizzi, V, Sonestedt, E, Soumare, A, Staessen, JA, Steene-Johannessen, J, Stehle, P, Stein, AD, Stessman, J, Stockl, D, Stokwiszewski, J, Strufaldi, MW, Sun, C-A, Sundstrom, J, Suriyawongpaisal, P, Sy, RG, Tai, ES, Tarawneh, M, Tarqui-Mamani, CB, Thijs, L, Tolstrup, JS, Topbas, M, Torrent, M, Traissac, P, Trinh, OTH, Tulloch-Reid, MK, Tuomainen, T-P, Turley, ML, Tzourio, C, Ueda, P, Ukoli, FM, Ulmer, H, Valdivia, G, Van Valkengoed, IGM, Vanderschueren, D, Vanuzzo, D, Vega, T, Velasquez-Melendez, G, Veronesi, G, Verschuren, M, Vioque, J, Virtanen, J, Visvikis-Siest, S, Viswanathan, B, Vollenweider, P, Voutilainen, S, Wade, AN, Wagner, A, Walton, J, Mohamud, WNW, Wang, M-D, Wang, YX, Wannamethee, SG, Weerasekera, D, Whincup, PH, Widhalm, K, Wiecek, A, Wilks, RJ, Willeit, J, Wojtyniak, B, Wong, TY, Woo, J, Woodward, M, Wu, AG, Wu, FC, Wu, SL, Xu, H, Yang, X, Ye, X, Yoshihara, A, Younger-Coleman, NO, Zambon, S, Zargar, AH, Zdrojewski, T, Zhao, W, and Zheng, Y
Abstract: BACKGROUND: Diabetes has been defined on the basis of different biomarkers, including fasting plasma glucose (FPG), 2-h plasma glucose in an oral glucose tolerance test (2hOGTT), and HbA1c. We assessed the effect of different diagnostic definitions on both the population prevalence of diabetes and the classification of previously undiagnosed individuals as having diabetes versus not having diabetes in a pooled analysis of data from population-based health examination surveys in different regions. METHODS: We used data from 96 population-based health examination surveys that had measured at least two of the biomarkers used for defining diabetes. Diabetes was defined using HbA1c (HbA1c ≥6·5% or history of diabetes diagnosis or using insulin or oral hypoglycaemic drugs) compared with either FPG only or FPG-or-2hOGTT definitions (FPG ≥7·0 mmol/L or 2hOGTT ≥11·1 mmol/L or history of diabetes or using insulin or oral hypoglycaemic drugs). We calculated diabetes prevalence, taking into account complex survey design and survey sample weights. We compared the prevalences of diabetes using different definitions graphically and by regression analyses. We calculated sensitivity and specificity of diabetes diagnosis based on HbA1c compared with diagnosis based on glucose among previously undiagnosed individuals (ie, excluding those with history of diabetes or using insulin or oral hypoglycaemic drugs). We calculated sensitivity and specificity in each survey, and then pooled results using a random-effects model. We assessed the sources of heterogeneity of sensitivity by meta-regressions for study characteristics selected a priori. FINDINGS: Population prevalence of diabetes based on FPG-or-2hOGTT was correlated with prevalence based on FPG alone (r=0·98), but was higher by 2-6 percentage points at different prevalence levels. Prevalence based on HbA1c was lower than prevalence based on FPG in 42·8% of age-sex-survey groups and higher in another 41·6%; in the other 15·6%, the two
Published: 2015

9. Directly observed therapy of tuberculosis in Brazil: associated determinants and impact on treatment outcome

Author: Reis-Santos, B., primary, Pellacani-Posses, I., additional, Macedo, L. R., additional, Golub, J. E., additional, Riley, L. W., additional, and Maciel, E. L., additional
Published: 2015
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10. Treatment outcomes of tuberculosis patients in Brazilian prisons: a polytomous regression analysis

Author: Ribeiro Macedo, L., primary, Reis-Santos, B., additional, Riley, L. W., additional, and Maciel, E. L., additional
Published: 2013
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11. Nursing: Care Challenge of People with Low Education

Author: Reis-Santos, B., primary and Maciel, E. L.N., additional
Published: 2012
Full Text: View/download PDF

12. Tuberculosis Prevalence in Kidney Transplant Recipients: Systematic Review and Meta-Analysis

Author: Reis-Santos, B., primary and Maciel, E. L.N., additional
Published: 2012
Full Text: View/download PDF

13. The Kidney Transplantation Team: A Gap for All Nurse Abilities

Author: Reis-Santos, B., primary and Maciel, E. L.N., additional
Published: 2012
Full Text: View/download PDF

14. Delay for Kidney Transplantation

Author: Reis-Santos, B., primary and Maciel, E. L.N., additional
Published: 2012
Full Text: View/download PDF

15. Tuberculosis prevalence in renal transplant recipients: systematic review and meta-analysis

Author: Reis-Santos, B., Gomes, T., Horta, B. L., and Ethel Leonor Maciel

16. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants

Author: NCD Risk Factor Collaboration (NCD-RisC), Zhou, B., Lu, Y., Hajifathalian, K., Bentham, J., Di Cesare, M., Danaei, G., Bixby, H., Cowan, M.J., Ali, M.K., Taddei, C., Lo, W.C., Reis-Santos, B., Stevens, G.A., Riley, L.M., Miranda, J.J., Bjerregaard, P., Rivera, J.A., Fouad, H.M., Ma, G., Mbanya, J.C., McGarvey, S.T., Mohan, V., Onat, A., Pilav, A., Ramachandran, A., Romdhane, H.B., Paciorek, C.J., Bennett, J.E., Ezzati, M., Abdeen, Z.A., Abdul Kadir, K., Abu-Rmeileh, N.M., Acosta-Cazares, B., Adams, R., Aekplakorn, W., Aguilar-Salinas, C.A., Agyemang, C., Ahmadvand, A., Al-Othman, A.R., Alkerwi, A., Amouyel, P., Amuzu, A., Andersen, L.B., Anderssen, S.A., Anjana, R.M., Aounallah-Skhiri, H., Aris, T., Arlappa, N., Arveiler, D., Assah, F.K., Avdicová, M., Azizi, F., Balakrishna, N., Bandosz, P., Barbagallo, C.M., Barceló, A., Batieha, A.M., Baur, L.A., Benet, M., Bernabe-Ortiz, A., Bharadwaj, S., Bhargava, S.K., Bi, Y., Bjertness, E., Bjertness, M.B., Björkelund, C., Blokstra, A., Bo, S., Boehm, B.O., Boissonnet, C.P., Bovet, P., Brajkovich, I., Breckenkamp, J., Brenner, H., Brewster, L.M., Brian, G.R., Bruno, G., Bugge, A., Cabrera de León, A., Can, G., Cândido, A.P., Capuano, V., Carlsson, A.C., Carvalho, M.J., Casanueva, F.F., Casas, J.P., Caserta, C.A., Castetbon, K., Chamukuttan, S., Chaturvedi, N., Chen, C.J., Chen, F., Chen, S., Cheng, C.Y., Chetrit, A., Chiou, S.T., Cho, Y., Chudek, J., Cifkova, R., Claessens, F., Concin, H., Cooper, C., Cooper, R., Costanzo, S., Cottel, D., Cowell, C., Crujeiras, A.B., D'Arrigo, G., Dallongeville, J., Dankner, R., Dauchet, L., de Gaetano, G., De Henauw, S., Deepa, M., Dehghan, A., Deschamps, V., Dhana, K., Di Castelnuovo, A.F., Djalalinia, S., Doua, K., Drygas, W., Du, Y., Dzerve, V., Egbagbe, E.E., Eggertsen, R., El Ati, J., Elosua, R., Erasmus, R.T., Erem, C., Ergor, G., Eriksen, L., Escobedo-de la Peña, J., Fall, C.H., Farzadfar, F., Felix-Redondo, F.J., Ferguson, T.S., Fernández-Bergés, D., Ferrari, M., Ferreccio, C., Feskens, E.J., Finn, J.D., Föger, B., Foo, L.H., Forslund, A.S., Francis, D.K., Franco, M.do C., Franco, O.H., Frontera, G., Furusawa, T., Gaciong, Z., Garnett, S.P., Gaspoz, J.M., Gasull, M., Gates, L., Geleijnse, J.M., Ghasemian, A., Ghimire, A., Giampaoli, S., Gianfagna, F., Giovannelli, J., Giwercman, A., Gross, M.G., González Rivas, J.P., Gorbea, M.B., Gottrand, F., Grafnetter, D., Grodzicki, T., Grøntved, A., Gruden, G., Gu, D., Guan, O.P., Guerrero, R., Guessous, I., Guimaraes, A.L., Gutierrez, L., Hambleton, I.R., Hardy, R., Hari Kumar, R., Hata, J., He, J., Heidemann, C., Herrala, S., Hihtaniemi, I.T., Ho, S.Y., Ho, S.C., Hofman, A., Hormiga, C.M., Horta, B.L., Houti, L., Howitt, C., Htay, T.T., Htet, A.S., Htike, M.M., Hu, Y., Hussieni, A.S., Huybrechts, I., Hwalla, N., Iacoviello, L., Iannone, A.G., Ibrahim, M.M., Ikeda, N., Ikram, M.A., Irazola, V.E., Islam, M., Iwasaki, M., Jacobs, J.M., Jafar, T., Jamil, K.M., Jasienska, G., Jiang, C.Q., Jonas, J.B., Joshi, P., Kafatos, A., Kalter-Leibovici, O., Kasaeian, A., Katz, J., Kaur, P., Kavousi, M., Keinänen-Kiukaanniemi, S., Kelishadi, R., Kengne, A.P., Kersting, M., Khader, Y.S., Khalili, D., Khang, Y.H., Kiechl, S., Kim, J., Kolsteren, P., Korrovits, P., Kratzer, W., Kromhout, D., Kujala, U.M., Kula, K., Kyobutungi, C., Laatikainen, T., Lachat, C., Laid, Y., Lam, T.H., Landrove, O., Lanska, V., Lappas, G., Laxmaiah, A., Leclercq, C., Lee, J., Lehtimäki, T., Lekhraj, R., León-Muñoz, L.M., Li, Y., Lim, W.Y., Lima-Costa, M.F., Lin, H.H., Lin, X., Lissner, L., Lorbeer, R., Lozano, J.E., Luksiene, D., Lundqvist, A., Lytsy, P., Machado-Coelho, G.L., Machi, S., Maggi, S., Magliano, D.J., Makdisse, M., Mallikharjuna Rao, K., Manios, Y., Manzato, E., Margozzini, P., Marques-Vidal, P., Martorell, R., Masoodi, S.R., Mathiesen, E.B., Matsha, T.E., McFarlane, S.R., McLachlan, S., McNulty, B.A., Mediene-Benchekor, S., Meirhaeghe, A., Menezes, A.M., Merat, S., Meshram, I.I., Mi, J., Miquel, J.F., Mohamed, M.K., Mohammad, K., Mohammadifard, N., Mohd Yusoff, M.F., Møller, N.C., Molnár, D., Mondo, C.K., Morejon, A., Moreno, L.A., Morgan, K., Moschonis, G., Mossakowska, M., Mostafa, A., Mota, J., Motta, J., Mu, T.T., Muiesan, M.L., Müller-Nurasyid, M., Mursu, J., Nagel, G., Námešná, J., Nang, E.E., NangThetia, V.B., Navarrete-Muñoz, E.M., Ndiaye, N.C., Nenko, I., Nervi, F., Nguyen, N.D., Nguyen, Q.N., Nieto-Martínez, R.E., Ning, G., Ninomiya, T., Noale, M., Noto, D., Nsour, M.A., Ochoa-Avilés, A.M., Oh, K., Ordunez, P., Osmond, C., Otero, J.A., Owusu-Dabo, E., Pahomova, E., Palmieri, L., Panda-Jonas, S., Panza, F., Parsaeian, M., Peixoto, S.V., Peltonen, M., Peters, A., Peykari, N., Pham, S.T., Pitakaka, F., Piwonska, A., Piwonski, J., Plans-Rubió, P., Porta, M., Portegies, M.L., Poustchi, H., Pradeepa, R., Price, J.F., Punab, M., Qasrawi, R.F., Qorbani, M., Radisauskas, R., Rahman, M., Raitakari, O., Rao, S.R., Ramke, J., Ramos, R., Rampal, S., Rathmann, W., Redon, J., Reganit, P.F., Rigo, F., Robinson, S.M., Robitaille, C., Rodríguez-Artalejo, F., Rodriguez-Perez, M.del C., Rodríguez-Villamizar, L.A., Rojas-Martinez, R., Ronkainen, K., Rosengren, A., Rubinstein, A., Rui, O., Ruiz-Betancourt, B.S., Russo Horimoto, R.V., Rutkowski, M., Sabanayagam, C., Sachdev, H.S., Saidi, O., Sakarya, S., Salanave, B., Salonen, J.T., Salvetti, M., Sánchez-Abanto, J., Santos, D., dos Santos, R.N., Santos, R., Saramies, J.L., Sardinha, L.B., Sarrafzadegan, N., Saum, K.U., Scazufca, M., Schargrodsky, H., Scheidt-Nave, C., Sein, A.A., Sharma, S.K., Shaw, J.E., Shibuya, K., Shin, Y., Shiri, R., Siantar, R., Sibai, A.M., Simon, M., Simons, J., Simons, L.A., Sjostrom, M., Slowikowska-Hilczer, J., Slusarczyk, P., Smeeth, L., Snijder, M.B., So, H.K., Sobngwi, E., Söderberg, S., Solfrizzi, V., Sonestedt, E., Soumare, A., Staessen, J.A., Stathopoulou, M.G., Steene-Johannessen, J., Stehle, P., Stein, A.D., Stessman, J., Stöckl, D., Stokwiszewski, J., Stronks, K., Strufaldi, M.W., Sun, C.A., Sundström, J., Sung, Y.T., Suriyawongpaisal, P., Sy, R.G., Tai, E.S., Tamosiunas, A., Tang, L., Tarawneh, M., Tarqui-Mamani, C.B., Taylor, A., Theobald, H., Thijs, L., Thuesen, B.H., Tolonen, H.K., Tolstrup, J.S., Topbas, M., Torrent, M., Traissac, P., Trinh, O.T., Tulloch-Reid, M.K., Tuomainen, T.P., Turley, M.L., Tzourio, C., Ueda, P., Ukoli, F.A., Ulmer, H., Uusitalo, H.M., Valdivia, G., Valvi, D., van Rossem, L., van Valkengoed, I.G., Vanderschueren, D., Vanuzzo, D., Vega, T., Velasquez-Melendez, G., Veronesi, G., Verschuren, W.M., Verstraeten, R., Viet, L., Vioque, J., Virtanen, J.K., Visvikis-Siest, S., Viswanathan, B., Vollenweider, P., Voutilainen, S., Vrijheid, M., Wade, A.N., Wagner, A., Walton, J., Wan Mohamud, W.N., Wang, F., Wang, M.D., Wang, Q., Wang, Y.X., Wannamethee, S.G., Weerasekera, D., Whincup, P.H., Widhalm, K., Wiecek, A., Wijga, A.H., Wilks, R.J., Willeit, J., Wilsgaard, T., Wojtyniak, B., Wong, T.Y., Woo, J., Woodward, M., Wu, F.C., Wu, S.L., Xu, H., Yan, W., Yang, X., Ye, X., Yoshihara, A., Younger-Coleman, N.O., Zambon, S., Zargar, A.H., Zdrojewski, T., Zhao, W., Zheng, Y., and Zuñiga Cisneros, J.
Subjects: Adult, Age Distribution, Age of Onset, Bayes Theorem, Blood Glucose/metabolism, Cost of Illness, Developing Countries/statistics & numerical data, Diabetes Mellitus/blood, Diabetes Mellitus/drug therapy, Diabetes Mellitus/epidemiology, Female, Global Health, Health Surveys, Humans, Hypoglycemic Agents/therapeutic use, Incidence, Insulin/therapeutic use, Male, Prevalence, Research Report/standards, Sex Distribution
Abstract: One of the global targets for non-communicable diseases is to halt, by 2025, the rise in the age-standardised adult prevalence of diabetes at its 2010 levels. We aimed to estimate worldwide trends in diabetes, how likely it is for countries to achieve the global target, and how changes in prevalence, together with population growth and ageing, are affecting the number of adults with diabetes. We pooled data from population-based studies that had collected data on diabetes through measurement of its biomarkers. We used a Bayesian hierarchical model to estimate trends in diabetes prevalence-defined as fasting plasma glucose of 7.0 mmol/L or higher, or history of diagnosis with diabetes, or use of insulin or oral hypoglycaemic drugs-in 200 countries and territories in 21 regions, by sex and from 1980 to 2014. We also calculated the posterior probability of meeting the global diabetes target if post-2000 trends continue. We used data from 751 studies including 4,372,000 adults from 146 of the 200 countries we make estimates for. Global age-standardised diabetes prevalence increased from 4.3% (95% credible interval 2.4-7.0) in 1980 to 9.0% (7.2-11.1) in 2014 in men, and from 5.0% (2.9-7.9) to 7.9% (6.4-9.7) in women. The number of adults with diabetes in the world increased from 108 million in 1980 to 422 million in 2014 (28.5% due to the rise in prevalence, 39.7% due to population growth and ageing, and 31.8% due to interaction of these two factors). Age-standardised adult diabetes prevalence in 2014 was lowest in northwestern Europe, and highest in Polynesia and Micronesia, at nearly 25%, followed by Melanesia and the Middle East and north Africa. Between 1980 and 2014 there was little change in age-standardised diabetes prevalence in adult women in continental western Europe, although crude prevalence rose because of ageing of the population. By contrast, age-standardised adult prevalence rose by 15 percentage points in men and women in Polynesia and Micronesia. In 2014, American Samoa had the highest national prevalence of diabetes (>30% in both sexes), with age-standardised adult prevalence also higher than 25% in some other islands in Polynesia and Micronesia. If post-2000 trends continue, the probability of meeting the global target of halting the rise in the prevalence of diabetes by 2025 at the 2010 level worldwide is lower than 1% for men and is 1% for women. Only nine countries for men and 29 countries for women, mostly in western Europe, have a 50% or higher probability of meeting the global target. Since 1980, age-standardised diabetes prevalence in adults has increased, or at best remained unchanged, in every country. Together with population growth and ageing, this rise has led to a near quadrupling of the number of adults with diabetes worldwide. The burden of diabetes, both in terms of prevalence and number of adults affected, has increased faster in low-income and middle-income countries than in high-income countries. Wellcome Trust.
Published: 2016

17. Time from treatment initiation to HIV viral suppression in public care facilities in Brazil: A nationwide linked databases cohort.

Author: Nemes MIB, Sayuri Sato AP, Reis-Santos B, Maroso Alves A, Parra do Nascimento F, and Agins B
Subjects: Humans, Brazil epidemiology, Male, Female, Adult, Middle Aged, Cohort Studies, Adolescent, Young Adult, Databases, Factual, CD4 Lymphocyte Count, Time-to-Treatment, Time Factors, HIV Infections drug therapy, HIV Infections virology, HIV Infections epidemiology, Viral Load, Anti-HIV Agents therapeutic use
Abstract: Objectives: To analyze the time between antiretroviral therapy (ART) initiation and the first HIV viral load (VL) test <40 copies-time to suppression (TS)-in a cohort of persons aged ≥15 years, between 2015-2018 in outpatient HIV care facilities of the Brazilian Unified Health System, as well as to analyze whether individual and facility characteristics accelerate or delay TS., Methods: This was a cohort study with data from a linkage of national HIV databases, following a previously published procedure. Two types of variables were examined: individual-level (sex, age group, race/skin color, education, baseline CD4 cell count and VL, initial ART regimen, adherence, ART regimen change and number of VL tests until suppression) and facility-level (national and metropolitan region, caseload). Multilevel parametric accelerated failure time survival models were used. Fixed and random effects were analyzed through null, sociodemographic, combined sociodemographic and clinical, and facility-related variables, adjusted for the number of VL tests until suppression. Likelihood, interquartile range, and proportion of change in variance were used for comparisons., Results: Of 132,540 participants, 89.4% (114,696) achieved viral suppression: 20.8% within three months, and 56.4% within six months. Median TS was 161 days, varying from 31 to 1,426 days, depending on the time interval between initiation and VL testing. Among those who had VL testing within 66 days, median TS was 55 days. All individual and facility-related variables were associated with TS, explaining the 16.2% and 13.2% variability, respectively., Conclusions: This was the first Brazilian nationwide cohort to analyze TS. It is also one of the largest operational cohorts globally to assess healthcare facility characteristics. The findings indicated that both individual and facility-related characteristics contribute to TS. Strengthening VL monitoring should be included as part of a coordinated effort to improve the quality of care provided for people living with HIV/AIDS in Brazil., Competing Interests: The authors have declared that no competing interests exist, (Copyright: © 2024 Nemes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Published: 2024
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18. A critical analysis of the decreasing trends in tuberculosis cure indicators in Brazil, 2001-2022.

Author: Pavinati G, Lima LV, Bernardo PHP, Dias JR, Reis-Santos B, and Magnabosco GT
Subjects: Humans, Brazil epidemiology, Time Factors, Retreatment statistics & numerical data, Tuberculosis, Pulmonary epidemiology, Tuberculosis, Pulmonary drug therapy, Coinfection, HIV Infections epidemiology
Abstract: Objective: To analyze the temporal trend of tuberculosis cure indicators in Brazil., Methods: An ecological time-series study using administrative data of reported cases of the disease nationwide between 2001 and 2022. We estimated cure indicators for each federative unit (FU) considering individuals with pulmonary tuberculosis, tuberculosis-HIV coinfection, and those in tuberculosis retreatment. We used regression models using joinpoint regression for trend analysis, reporting the annual percentage change and the average annual percentage change., Results: For the three groups analyzed, we observed heterogeneity in the annual percentage change in the Brazilian FUs, with a predominance of significantly decreasing trends in the cure indicator in most FUs, especially at the end of the time series. When considering national indicators, an average annual percentage change of -0.97% (95% CI: -1.23 to -0.74) was identified for the cure of people with pulmonary tuberculosis, of -1.11% (95% CI: -1.42 to -0.85) for the cure of people with tuberculosis-HIV coinfection, and of -1.44% (95% CI: -1.62 to -1.31) for the cure of people in tuberculosis retreatment., Conclusions: The decreasing trends of cure indicators in Brazil are concerning and underscore a warning to public authorities, as it points to the possible occurrence of other treatment outcomes, such as treatment discontinuity and death. This finding contradicts current public health care policies and requires urgent strategies aiming to promote follow-up of patients during tuberculosis treatment in Brazil.
Published: 2024
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19. Immunization Agenda 2030 and Brazil's challenges.

Author: Braga C and Reis-Santos B
Subjects: Humans, Brazil, Vaccination, Immunization
Published: 2023
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20. Health Information Systems: how much progress are we making?

Author: Reis-Santos B
Subjects: Humans, Brazil, Health Information Systems
Published: 2023
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21. A Matter of Inclusion: A Cluster-Randomized Trial to Access the Effect of Food Vouchers Versus Traditional Treatment on Tuberculosis Outcomes in Brazil.

Author: Reis-Santos B, Locatelli R, Oliosi J, Sales CM, do Prado TN, Shete PB, Riley LW, and Maciel EL
Subjects: Adult, Humans, Brazil epidemiology, Food, Food Supply, Tuberculosis drug therapy, Tuberculosis, Pulmonary drug therapy, Tuberculosis, Pulmonary diagnosis
Abstract: We assessed the effectiveness of food vouchers as a social protection strategy to enhance the adherence to tuberculosis treatment in health-care facilities in Brazil between 2014 and 2017. A cluster-randomized controlled trial was performed in four Brazilian capital cities. A total of 774 adults with newly diagnosed pulmonary tuberculosis were included in this study. Eligible participants initiated standard tuberculosis treatment per National Tuberculosis Program guidelines. Health clinics were assigned randomly to intervention groups (food voucher or standard treatment). The food voucher was provided by researchers, which could be used by subjects only for buying food. Most people with tuberculosis were poor, did receive benefits of the Bolsa Familia Program, and were unemployed. By Poisson regression analysis, with the total number of subjects included in the study, we found that individuals with tuberculosis who received food vouchers had a 1.13 greater risk of cure (95% CI, 1.03-1.21) compared with those who did not receive food vouchers. The provision of food vouchers improved outcomes of tuberculosis treatment and it should be enhanced even further as social protection for people with tuberculosis.
Published: 2022
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22. Open Science, equity and the Brazilian context.

Author: Reis-Santos B and Braga C
Subjects: Brazil, Humans, Health Equity
Published: 2022
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23. Epidemiology and Health Services (Epidemiologia e Serviços de Saúde) journal, its role and contributions in the pandemic context.

Author: Braga C and Reis-Santos B
Published: 2021
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24. Factors associated with COVID-19 hospital deaths in Espírito Santo, Brazil, 2020.

Author: Maciel EL, Jabor P, Goncalves Júnior E, Tristão-Sá R, Lima RCD, Reis-Santos B, Lira P, Bussinguer ECA, and Zandonade E
Subjects: Adult, Age Distribution, Aged, Brazil epidemiology, COVID-19, Cause of Death, Comorbidity, Coronavirus Infections epidemiology, Cross-Sectional Studies, Female, Hospitalization statistics & numerical data, Humans, Logistic Models, Male, Middle Aged, Odds Ratio, Pandemics, Patient Discharge statistics & numerical data, Pneumonia, Viral epidemiology, SARS-CoV-2, Sex Distribution, Young Adult, Betacoronavirus, Coronavirus Infections mortality, Hospital Mortality, Pneumonia, Viral mortality
Abstract: Objective: To analyze factors associated with the deaths of individuals hospitalized with COVID-19 in the state of Espírito Santo, Brazil., Methods: This was a cross-sectional study using secondary data. Logistic regression models were used to estimate crude and adjusted odds ratios (OR)., Results: As at May 14, 2020, 200 individuals had been discharged and 220 had died. Of the total number of people studied, 57.1% were male, 46.4% were >60 years old, 57.9% were cases notified by a private institution, and 61.7% had >1 comorbidity. In the adjusted analysis, hospital mortality was higher for the 51-60 age group (OR=4.33 - 95%CI 1.50;12.46), the over 60 age group (OR=11.84 - 95%CI 4.31;32.54), cases notified by public institutions (OR=8.23 - 95%CI 4.84;13.99) and cases with a greater number of comorbidities (two [OR=2.74 - 95%CI 1.40;5.34] and three [OR=2.90 - 95%CI 1.07;7.81])., Conclusion: Higher mortality was found in older individuals, those with comorbidities and users of public hospitals.
Published: 2020
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25. Tuberculosis: an instrument of early mortality in life course.

Author: Reis-Santos B and Rocha MS
Subjects: Antitubercular Agents, Brazil, Cause of Death, Humans, Longitudinal Studies, Tuberculosis
Published: 2020
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26. Effect of the Bolsa Familia Programme on tuberculosis treatment outcomes - Authors' reply.

Author: Reis-Santos B, Oliosi JGN, and Maciel ELN
Subjects: Humans, Prospective Studies, Socioeconomic Factors, Treatment Outcome, Public Assistance, Tuberculosis
Published: 2019
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27. Tuberculosis in Brazil and cash transfer programs: A longitudinal database study of the effect of cash transfer on cure rates.

Author: Reis-Santos B, Shete P, Bertolde A, Sales CM, Sanchez MN, Arakaki-Sanchez D, Andrade KB, Gomes MGM, Boccia D, Lienhardt C, and Maciel EL
Subjects: Adult, Aged, Brazil epidemiology, Female, Humans, Incidence, Longitudinal Studies, Male, Middle Aged, Socioeconomic Factors, Databases, Factual, Government Programs economics, Poverty, Tuberculosis, Pulmonary economics, Tuberculosis, Pulmonary epidemiology, Tuberculosis, Pulmonary therapy
Abstract: Introduction: Tuberculosis incidence is disproportionately high among people in poverty. Cash transfer programs have become an important strategy in Brazil fight inequalities as part of comprehensive poverty alleviation policies. This study was aimed at assessing the effect of being a beneficiary of a governmental cash transfer program on tuberculosis (TB) treatment cure rates., Methods: We conducted a longitudinal database study including people ≥18 years old with confirmed incident TB in Brazil in 2015. We treated missing data with multiple imputation. Poisson regression models with robust variance were carried out to assess the effect of TB determinants on cure rates. The average effect of being beneficiary of cash transfer was estimated by propensity-score matching., Results: In 2015, 25,084 women and men diagnosed as new tuberculosis case, of whom 1,714 (6.8%) were beneficiaries of a national cash transfer. Among the total population with pulmonary tuberculosis several determinants were associated with cure rates. However, among the cash transfer group, this association was vanished in males, blacks, region of residence, and people not deprived of their freedom and who smoke tobacco. The average treatment effect of cash transfers on TB cure rates, based on propensity score matching, found that being beneficiary of cash transfer improved TB cure rates by 8% [Coefficient 0.08 (95% confidence interval 0.06-0.11) in subjects with pulmonary TB]., Conclusion: Our study suggests that, in Brazil, the effect of cash transfer on the outcome of TB treatment may be achieved by the indirect effect of other determinants. Also, these results suggest the direct effect of being beneficiary of cash transfer on improving TB cure rates., Competing Interests: The authors have declared that no competing interests exist.
Published: 2019
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28. Effect of the Bolsa Familia Programme on the outcome of tuberculosis treatment: a prospective cohort study.

Author: Oliosi JGN, Reis-Santos B, Locatelli RL, Sales CMM, da Silva Filho WG, da Silva KC, Sanchez MN, de Andrade KVF, de Araújo GS, Shete PB, Pereira SM, Riley LW, Lienhardt C, and Maciel ELN
Subjects: Adult, Brazil, Cohort Studies, Female, Humans, Male, Middle Aged, Prospective Studies, Socioeconomic Factors, Treatment Outcome, Tuberculosis economics, Program Evaluation methods, Public Assistance statistics & numerical data, Public Policy, Tuberculosis therapy
Abstract: Background: Social protection interventions might improve tuberculosis outcomes and could help to control the epidemic in Brazil. The aim of this study was to evaluate the independent effect of the Bolsa Familia Programme (BFP) on tuberculosis treatment outcomes in Brazil., Methods: We prospectively recruited and followed up individuals (aged ≥18 years) who initiated tuberculosis treatment at 42 health-care centres across seven cities in Brazil, between March 1, 2014, and April 30, 2017. Patients were interviewed at health-care centres and information about individual characteristics, socioeconomic status, living conditions, lifestyle, and comorbidities was recorded. Patients were separated into two groups according to BFP beneficiary status: BFP (exposed) or non-BFP (not exposed). Treatment outcome (cure, dropout, death, or development of drug-resistant tuberculosis or treatment failure) was recorded after 6 months of therapy. Pearson's χ 2 test and ANOVA were used to compare tuberculosis treatment outcomes between the two groups, and we estimated the propensity score of being a beneficiary of the BFP using a logit model. We used multinomial regression models to evaluate the effect of the BFP on tuberculosis treatment outcomes., Findings: 1239 individuals were included in the study, of whom 196 (16%) were beneficiaries of the BFP and 1043 (84%) were not. After 6 months of treatment, 912 (87%) of 1043 patients in the non-BFP group and 173 (88%) of 196 patients in the BFP group were cured of tuberculosis, 103 (10%) patients in the non-BFP group and 17 (9%) patients in the BFP group had dropped out, and 25 (3%) patients in the non-BFP group and six (3%) patients in the BFP group had died. Three (<1%) of 1043 patients in the non-BFP group developed drug-resistant tuberculosis. Being a BFP beneficiary had a positive effect for cure (average effect 0·076 [95% CI 0·037 to 0·11]) and a negative effect for dropout (-0·070 [-0·105 to 0·036]) and death (-0·002 [-0·021 to 0·017])., Interpretation: BFP alone had a direct effect on tuberculosis treatment outcome and could greatly contribute to the goals of the WHO End TB Strategy., Funding: Brazilian National Council for Scientific and Technological Development (CNPq) and Brazilian Ministry of Health Department of Science and Technology (DECIT)., (Copyright © 2019 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)
Published: 2019
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29. Can Brazil achieve the new World Health Organization global targets for tuberculosis control?

Author: Maciel ELN, Sales CMM, Bertolde AI, and Reis-Santos B
Subjects: Brazil epidemiology, Humans, Incidence, Tuberculosis epidemiology, Tuberculosis mortality, World Health Organization, Global Health, Tuberculosis prevention & control
Published: 2018
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30. Is there a causal effect of parity on body composition: a birth cohort study.

Author: Reis-Santos B, Barros FC, and Horta BL
Subjects: Adult, Body Mass Index, Brazil, Causality, Cohort Studies, Female, Humans, Male, Pregnancy, Waist Circumference, Body Composition, Parity
Abstract: Background: Non-communicable diseases are the leading cause of death, worldwide. Obesity is one of the factors that is associated with the development of such diseases. The role of reproductive factors on women body composition has been evaluated, but the findings are controversial. This study was aimed at assessing the association of parity with body composition among women., Methods: In 1982, the maternity hospital of Pelotas, a southern Brazilian city, were visited daily and all deliveries were identified. Those livebirths whose family lived in the urban area of the city have been prospectively followed (n = 5914). In 2012-13, we tried to follow the whole cohort, the subjects were interviewed and examined. We evaluated the association of parity with the following body composition variables: body mass index, waist circumference and fat mass %. Estimates were adjusted for family income, skin color, maternal schooling, occupational status, alcohol, smoking, physical activity, and consumption of processed and ultraprocessed foods. All these analyses were replicated among the cohort men as a comparison. We also assessed whether duration of breastfeeding moderated the association., Results: In the 2012-13 visit, 3701 subjects were evaluated (mean age of 30.2 years). In the present analysis, we included 1620 women and 1653 men. 33% of women were nulliparous and 48% of men were without children. Even after controlling for confounding, parous women had a BMI 0.96 kg/m 2 (95% CI: 0.30; 1.62) higher than nulliparous and for men the regression coefficient was 0.79 kg/m 2 (95% CI: 0.29; 1.29). Waist circumference was also higher among parous women. Among men, the association was not linear and the regression coefficients were lower than that observed among women [3.41 cm (95% CI: -0.91; 7.73) among men and 4.83 cm (95% CI: 2.43; 7.24) among women with more than 3 children when compared with those without children], but this difference was not statistically significant (interaction p value = 0.58). Fat mass % was not associated with parity. Breastfeeding did not modify the association between parity and body composition., Conclusions: Parity was positively associated with body mass index and waist circumference among women. However, similar results among men suggest that there is no causal effect of parity.
Published: 2018
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31. Risk factors associated with cluster size of Mycobacterium tuberculosis (Mtb) of different RFLP lineages in Brazil.

Author: Peres RL, Vinhas SA, Ribeiro FKC, Palaci M, do Prado TN, Reis-Santos B, Zandonade E, Suffys PN, Golub JE, Riley LW, and Maciel EL
Subjects: Adult, Brazil epidemiology, Cities, Cross-Sectional Studies, Female, Genotype, Humans, Male, Middle Aged, Mycobacterium tuberculosis isolation & purification, Mycobacterium tuberculosis pathogenicity, Prevalence, Risk Factors, Young Adult, Mycobacterium tuberculosis genetics, Polymorphism, Restriction Fragment Length, Tuberculosis epidemiology, Tuberculosis microbiology
Abstract: Background: Tuberculosis (TB) transmission is influenced by patient-related risk, environment and bacteriological factors. We determined the risk factors associated with cluster size of IS6110 RFLP based genotypes of Mycobacterium tuberculosis (Mtb) isolates from Vitoria, Espirito Santo, Brazil., Methods: Cross-sectional study of new TB cases identified in the metropolitan area of Vitoria, Brazil between 2000 and 2010. Mtb isolates were genotyped by the IS6110 RFLP, spoligotyping and RD Rio . The isolates were classified according to genotype cluster sizes by three genotyping methods and associated patient epidemiologic characteristics. Regression Model was performed to identify factors associated with cluster size., Results: Among 959 Mtb isolates, 461 (48%) cases had an isolate that belonged to an RFLP cluster, and six clusters with ten or more isolates were identified. Of the isolates spoligotyped, 448 (52%) were classified as LAM and 412 (48%) as non-LAM. Our regression model found that 6-9 isolates/RFLP cluster were more likely belong to the LAM family, having the RD Rio genotype and to be smear-positive (adjusted OR = 1.17, 95% CI 1.08-1.26; adjusted OR = 1.25, 95% CI 1.14-1.37; crude OR = 2.68, 95% IC 1.13-6.34; respectively) and living in a Serra city neighborhood decrease the risk of being in the 6-9 isolates/RFLP cluster (adjusted OR = 0.29, 95% CI, 0.10-0.84), than in the others groups. Individuals aged 21 to 30, 31 to 40 and > 50 years were less likely of belonging the 2-5 isolates/RFLP cluster than unique patterns compared to individuals < 20 years of age (adjusted OR = 0.49, 95% CI 0.28-0.85, OR = 0.43 95% CI 0.24-0.77and OR = 0. 49, 95% CI 0.26-0.91), respectively. The extrapulmonary disease was less likely to occur in those infected with strains in the 2-5 isolates/cluster group (adjustment OR = 0.45, 95% CI 0.24-0.85) than unique patterns., Conclusions: We found that a large proportion of new TB infections in Vitoria is caused by prevalent Mtb genotypes belonging to the LAM family and RD Rio genotypes. Such information demonstrates that some genotypes are more likely to cause recent transmission. Targeting interventions such as screening in specific areas and social risk groups, should be a priority for reducing transmission.
Published: 2018
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32. Docking-based virtual screening of Brazilian natural compounds using the OOMT as the pharmacological target database.

Author: Carregal AP, Maciel FV, Carregal JB, Dos Reis Santos B, da Silva AM, and Taranto AG
Subjects: Databases, Factual, Molecular Structure, Drug Design, Molecular Docking Simulation
Abstract: The demand for new therapies has encouraged the development of faster and cheaper methods of drug design. Considering the number of potential biological targets for new drugs, the docking-based virtual screening (DBVS) approach has occupied a prominent role among modern strategies for identifying new bioactive substances. Some tools have been developed to validate docking methodologies and identify false positives, such as the receiver operating characteristic (ROC) curve. In this context, a database with 31 molecular targets called the Our Own Molecular Targets Data Bank (OOMT) was validated using the root-mean-square deviation (RMSD) and the area under the ROC curve (AUC) with two different docking methodologies: AutoDock Vina and DOCK 6. Sixteen molecular targets showed AUC values of >0.8, and those targets were selected for molecular docking studies. The drug-likeness properties were then determined for 473 Brazilian natural compounds that were obtained from the ZINC database. Ninety-six compounds showed similar drug-likeness property values to the marked drugs (positive values). These compounds were submitted to DBVS for 16 molecular targets. Our results showed that AutoDock Vina was more appropriate than DOCK 6 for performing DBVS experiments. Furthermore, this work suggests that three compounds-ZINC13513540, ZINC06041137, and ZINC1342926-are inhibitors of the three molecular targets 1AGW, 2ZOQ, and 3EYG, respectively, which are associated with cancer. Finally, since ZINC and the PDB were solely created to store biomolecule structures, their utilization requires the application of filters to improve the first steps of the drug development process. Graphical Abstract Evaluation of docking methods used for virtual screening.
Published: 2017
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33. Octopus: a platform for the virtual high-throughput screening of a pool of compounds against a set of molecular targets.

Author: Maia EH, Campos VA, Dos Reis Santos B, Costa MS, Lima IG, Greco SJ, Ribeiro RI, Munayer FM, da Silva AM, and Taranto AG
Abstract: Octopus is an automated workflow management tool that is scalable for virtual high-throughput screening (vHTS). It integrates MOPAC2016, MGLTools, PyMOL, and AutoDock Vina. In contrast to other platforms, Octopus can perform docking simulations of an unlimited number of compounds into a set of molecular targets. After generating the ligands in a drawing package in the Protein Data Bank (PDB) format, Octopus can carry out geometry refinement using the semi-empirical method PM7 implemented in MOPAC2016. Docking simulations can be performed using AutoDock Vina and can utilize the Our Own Molecular Targets (OOMT) databank. Finally, the proposed software compiles the best binding energies into a standard table. Here, we describe two successful case studies that were verified by biological assay. In the first case study, the vHTS process was carried out for 22 (phenylamino)urea derivatives. The vHTS process identified a metalloprotease with the PDB code 1GKC as a molecular target for derivative LE&007. In a biological assay, compound LE&007 was found to inhibit 80% of the activity of this enzyme. In the second case study, compound Tx001 was submitted to the Octopus routine, and the results suggested that Plasmodium falciparum ATP6 (PfATP6) as a molecular target for this compound. Following an antimalarial assay, Tx001 was found to have an inhibitory concentration (IC 50 ) of 8.2 μM against PfATP6. These successful examples illustrate the utility of this software for finding appropriate molecular targets for compounds. Hits can then be identified and optimized as new antineoplastic and antimalarial drugs. Finally, Octopus has a friendly Linux-based user interface, and is available at www.drugdiscovery.com.br . Graphical Abstract Octopus: A platform for inverse virtual screening (IVS) to search new molecular targets for drugs.
Published: 2017
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34. Zika outbreak, mega-events, and urban reform.

Author: de Sá TH, Reis-Santos B, and Rodrigues LC
Subjects: Disease Outbreaks, Humans, Zika Virus, Zika Virus Infection epidemiology
Published: 2016
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35. [Human Development Index and secondary prevention of breast and cervical cancer: an ecological study].

Author: Sadovsky AD, Poton WL, Reis-Santos B, Barcelos MR, and Silva IC
Subjects: Adult, Aged, Brazil, Female, Humans, Middle Aged, Socioeconomic Factors, Surveys and Questionnaires, Breast Neoplasms diagnosis, Breast Neoplasms prevention & control, Mammography statistics & numerical data, Mass Screening statistics & numerical data, Papanicolaou Test, Secondary Prevention methods, Uterine Cervical Neoplasms diagnosis, Uterine Cervical Neoplasms prevention & control, Vaginal Smears statistics & numerical data
Abstract: This ecological study assessed mammography and Papanicolaou test (Pap smear) screening rates and their association with the Human Development Index (HDI) in Brazilian state capitals and Federal District in 2011, based on VIGITEL telephone survey data. More than 70% of women had a mammogram some time in life in all the state capitals, while the Pap smear rates any time in life and in the previous three years fell short of the 80% target in most state capitals. There was a strong positive correlation between HDI and the test rates (r = 0.52 and 0.66 for mammogram any time in life and in the previous two years and r = 0.66 and 0.71 for Pap smear any time in life and in the previous three years, respectively). Prevalence ratio (PR) for mammogram in the previous two years was 1.06 (95%CI: 1.01-1.10) and PR for Pap smear in the previous three years was 1.07 (IC95%: 1.04-1.10). The promotion of public health policies that encourage Brazil's urban economic and social development should help increase coverage for these tests.
Published: 2015
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36. Determinants of tuberculosis in Brazil: from conceptual framework to practical application.

Author: Maciel EL and Reis-Santos B
Subjects: Antitubercular Agents therapeutic use, Brazil epidemiology, Cause of Death, Comorbidity, Cross-Sectional Studies, Disease Susceptibility, Humans, Patient Dropouts, Population Surveillance, Retrospective Studies, Treatment Failure, Treatment Outcome, Tuberculosis drug therapy, Tuberculosis, Multidrug-Resistant drug therapy, Tuberculosis, Multidrug-Resistant epidemiology, Models, Theoretical, Social Determinants of Health, Tuberculosis epidemiology
Abstract: Objective: To leverage a conceptual analytical model for TB determination to identify factors that influence emergence of new cases of tuberculosis (TB) and poor TB treatment outcomes in Brazil., Methods: This was a cross-sectional study based on data from Brazil's Notifiable Disease Surveillance System database (SINAN). It included all confirmed, incident TB cases reported in Brazil in 2007 - 2011: a total of 432 958 TB cases, of which 318 465 cases with complete data on treatment outcomes were included. Analysis to explain the causal network that influences TB treatment outcomes was based on a theoretical model for determining TB. Adjusted analyses were used to assess the model fit. Hierarchical logistic regression was used to model the dichotomous TB outcome; hierarchical polytomous regression was used for multinomial TB outcome., Results: Of the 318 465 TB cases included, 222 186 (69.8%) were classified as "cured" and 96 279 (30.2%) as "treatment failure." Among the latter, 37 604 (11.8%) abandoned treatment; 13 193 (4.1%) died due to TB; 15 440 (4.8%) died due to causes other than TB; 28 848 (9.1%) were transferred to another municipality; and 1 194 (0.4%) developed multidrug-resistant TB. The dichotomous models were more likely to show spurious associations when compared with the polytomous model. In the polytomous model, individuals assigned to Directly Observed Treatment Short-course were more likely to be cured than others., Conclusions: Theoretical models are dynamic structures that need ongoing re-evaluation according to new findings; therefore, this is not a definitive proposal for a TB determination model or analysis plan, but rather a proposal that, at present, is adequate in Brazil and has the potential to be extrapolated or adapted to other areas.
Published: 2015

37. Treatment outcomes in tuberculosis patients with diabetes: a polytomous analysis using Brazilian surveillance system.

Author: Reis-Santos B, Gomes T, Locatelli R, de Oliveira ER, Sanchez MN, Horta BL, Riley LW, and Maciel EL
Subjects: Adolescent, Adult, Brazil epidemiology, Child, Child, Preschool, Diabetes Complications drug therapy, Drug Resistance, Multiple, Female, Humans, Infant, Infant, Newborn, Male, Middle Aged, Regression Analysis, Treatment Outcome, Tuberculosis drug therapy, Young Adult, Diabetes Complications epidemiology, Diabetes Complications therapy, Epidemiological Monitoring, Tuberculosis epidemiology, Tuberculosis therapy
Abstract: Background: The impact of non-communicable diseases on tuberculosis incidence has received significant attention. It has been suggested that the risk of tuberculosis is higher among subjects with diabetes and these subjects also has poor TB treatment outcomes.This study was aimed at assessing the socio-demographic and clinical factors that may influence different outcome of TB in patients with DM (TB-DM) identified in the Brazilian national database from 2001 to 2011., Methods: TB-DM cases reported in the Brazilian information system were identified and compared.Covariates associated with the outcomes of interest (cure, default, deaths, and development of TB MDR) were included in a hierarchical regression model., Results: TB-DM cases increased from 380/100,000/year in 2001 to 6,150/100,000/year in 2011. Some of the main associations found are pointed. The odds of default was higher among those in the age group 20-39 years (OR = 2.07, 95%CI 1.32-3.24); alcoholics (OR = 2.17, 95%CI 1.86-2.54), and HIV/AIDS (OR = 2.16, 95%CI 1.70-2.74);positive monitoring smear (OR = 1.94, 95%CI 1.55-2.43); prior default (OR = 5.41, 95%CI 4.47-6.54), and unknown type of treatment (OR = 3.33, 95%CI 1.54-7.22). The odds of death was greater for subjects ≥60 years old (OR = 2.74, 95%CI 1.74-4.29); institutionalized in shelter (OR = 2.69, 95%CI 1.07-6.77); alcoholics (OR = 2.70, 95%CI 2.27-3.22); HIV/AIDS (OR = 2.87, 95%CI 2.13-3.86); pulmonary+extrapulmonary TB (OR = 2.49, 95%CI 1.79-3.46); with unknown type of treatment (OR = 14.12, 95%CI 7.04-28.32).Development of MDR TB was more related to relapse (OR = 9.60, 95%CI 6.07-15.14);previous default (OR = 17.13, 95%CI 9.58-30.63); and transfer of treatment center (OR = 7.87, 95%CI 4.74-13.07)., Conclusions: Older subjects and those with comorbidities and with a previous treatment of TB had poorest outcomes. TB control program in Brazil will need to expand efforts to focus on treatment of TB-DM patients to improve their cure rates in order to achieve the goals of tuberculosis elimination.
Published: 2014
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38. Epidemiology of extrapulmonary tuberculosis in Brazil: a hierarchical model.

Author: Gomes T, Reis-Santos B, Bertolde A, Johnson JL, Riley LW, and Maciel EL
Subjects: Adolescent, Adult, Aged, Brazil epidemiology, Chi-Square Distribution, Child, Child, Preschool, Comorbidity, Cross-Sectional Studies, Female, HIV Infections epidemiology, Humans, Infant, Male, Middle Aged, Young Adult, Tuberculosis epidemiology
Abstract: Background: Although extrapulmonary tuberculosis (EPTB) is less frequent than Pulmonary Tuberculosis (PTB) and is a secondary target for national TB control programs, its significance has increased worldwide during the HIV epidemic. The objective of this study was to examine the epidemiology of EPTB in Brazil between 2007 and 2011., Methods: Cross-sectional study involving all cases of TB reported to the Brazilian Notifiable Diseases Surveillance System (Sistema de Informações de Agravo de Notificação - SINAN) in Brazil between 2007 and 2011. Sociodemographic and clinical characteristics of patients with exclusively PTB and exclusively EPTB were compared. Following analysis with Pearson's chi-square test, variables with p < 0.05 were included in a hierarchical regression model. Variables with p < 0.05 in the corresponding level were kept in the model., Results: A total of 427,548 cases of TB were included. Of these, 356,342 cases (83.35%; 95% confidence interval (CI) 83.23% - 83.45%) were PTB, 57,217 (13.37%; 95% CI 13.28% - 13.48%) were EPTB, 13,989 (3.27%; 95% CI 3.21% - 3.32%) were concurrent pulmonary and extrapulmonary TB. Patients with EPTB were mainly white (16.7%), and most (29.1%) patients had five to eight years of education. Among comorbidities, HIV infection was prominent (OR 2.15; 95% CI 2.09 - 2.21), although the proportion of cases awaiting test results or untested was high (39%). Ethanol use (OR 0.45; 95% CI 0.43 - 0.46), diabetes mellitus (OR 0.54; 95% CI 0.51 - 0.57) and mental illness (OR 0.88; 95% CI 0.82 - 0.95) were associated with PTB., Conclusions: Thirteen percent of patients diagnosed with TB in Brazil have only EPTB. More effective diagnostic strategies and control measures are needed to reduce the number of cases of extrapulmonary TB in Brazil.
Published: 2014
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39. Spatial analysis of distribution of dengue cases in Espírito Santo, Brazil, in 2010: use of Bayesian model.

Author: Honorato T, Lapa PP, Sales CM, Reis-Santos B, Tristão-Sá R, Bertolde AI, and Maciel EL
Subjects: Bayes Theorem, Brazil epidemiology, Humans, Linear Models, Spatial Analysis, Dengue epidemiology
Abstract: Objective: To study the relationship between the risk of dengue and sociodemographic variables through the use of spatial regression models fully Bayesian in the municipalities of Espírito Santo in 2010., Method: This is an ecological study and exploration that used spatial analysis tools in preparing thematic maps with data obtained from SinanNet. An analysis by area, taking as unit the municipalities of the state, was performed. Thematic maps were constructed by the computer program R 2.15.00 and Deviance Information Criterion (DIC), calculated in WinBugs, Absolut and Normalized Mean Error (NMAE) were the criteria used to compare the models., Results: We were able to geocode 21,933 dengue cases (rate of 623.99 cases per 100 thousand habitants) with a higher incidence in the municipalities of Vitória, Serra and Colatina; model with spatial effect with the covariates trash and income showed the best performance at DIC and Nmae criteria., Conclusion: It was possible to identify the relationship of dengue with factors outside the health sector and to identify areas with higher risk of disease.
Published: 2014
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40. Extrapulmonary tuberculosis: Mycobacterium tuberculosis strains and host risk factors in a large urban setting in Brazil.

Author: Gomes T, Vinhas SA, Reis-Santos B, Palaci M, Peres RL, Aguiar PP, Ribeiro FK, Marques HS, Dettoni Vdo V, Johnson JL, Riley LW, and Maciel EL
Subjects: Adolescent, Adult, Aged, Brazil epidemiology, Child, Child, Preschool, Female, Genotype, Humans, Male, Middle Aged, Mycobacterium tuberculosis genetics, Retrospective Studies, Risk Factors, Species Specificity, Tuberculosis, Pulmonary epidemiology, Tuberculosis, Pulmonary microbiology, Young Adult, Cities epidemiology, Mycobacterium tuberculosis physiology, Tuberculosis epidemiology, Tuberculosis microbiology
Abstract: Background: Factors related to the development of extrapulmonary forms of tuberculosis (EPTB) are still poorly understood, particularly in high-endemic countries like Brazil. The objective of the paper is to determine host and Mycobacterium tuberculosis (MTB) strain-related factors associated with the development of EPTB in Espírito Santo state, Brazil., Methods and Findings: We conducted a retrospective laboratory-based surveillance study of new tuberculosis (TB) cases diagnosed in Espírito Santo state, Brazil between 1998 and 2007. We genotyped 612 isolates of MTB from 606 TB patients using spoligotyping and IS6110-restriction fragment length polymorphism (RFLP) typing and compared sociodemographic and clinical characteristics of patients with pulmonary TB (PTB) and EPTB. Among 606 patients, 464 (77%) had PTB, 79 (13%) had EPTB, 51 (8%) had both, and 12 (2%) had miliary TB. The IS6110 RFLP analysis demonstrated that 250 (41%) isolates belonged to clustered RFLP patterns, 27 (11%) of which were from EPTB. We identified 73 clusters including 35 (48%) composed of 2 isolates each. By spoligotyping, 506 (83%) MTB isolates fell into known patterns and 106 (17%) fell into patterns with no family assignment; 297 (48%) isolates belonged to the Latin-American Mediterranean family. Higher school level (4-7 years OR: 0.16 95% CI 0.34-0.73 and > 8 years of education, OR 0.06 95% CI 0.009-0.50) white ethnicity (OR: 2.54 95% CI 1.03-6.25) and HIV infection (OR: 16.83 95% CI 5.23-54.18) were associated with EPTB. No specific strain lineage or percentage of clustering was associated with EPTB., Conclusions: These results demonstrate that risk factors for EPTB are related more to host than to MTB strain lineage characteristics.
Published: 2013
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41. The outcome of tuberculosis treatment in subjects with chronic kidney disease in Brazil: a multinomial analysis.

Author: Reis-Santos B, Gomes T, Horta BL, and Maciel EL
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Brazil, Child, Child, Preschool, Educational Status, Epidemiological Monitoring, Female, Humans, Likelihood Functions, Logistic Models, Male, Middle Aged, Treatment Outcome, Young Adult, Databases, Factual statistics & numerical data, Renal Insufficiency, Chronic complications, Tuberculosis complications, Tuberculosis drug therapy
Abstract: Objective: To analyze the association between clinical/epidemiological characteristics and outcomes of tuberculosis treatment in patients with concomitant tuberculosis and chronic kidney disease (CKD) in Brazil., Methods: We used the Brazilian Ministry of Health National Case Registry Database to identify patients with tuberculosis and CKD, treated between 2007 and 2011. The tuberculosis treatment outcomes were compared with epidemiological and clinical characteristics of the subjects using a hierarchical multinomial logistic regression model, in which cure was the reference outcome., Results: The prevalence of CKD among patients with tuberculosis was 0.4% (95% CI: 0.37-0.42%). The sample comprised 1,077 subjects. The outcomes were cure, in 58%; treatment abandonment, in 7%; death from tuberculosis, in 13%; and death from other causes, in 22%. The characteristics that differentiated the ORs for treatment abandonment or death were age; alcoholism; AIDS; previous noncompliance with treatment; transfer to another facility; suspected tuberculosis on chest X-ray; positive results in the first smear microscopy; and indications for/use of directly observed treatment, short-course strategy., Conclusions: Our data indicate the importance of sociodemographic characteristics for the diagnosis of tuberculosis in patients with CKD and underscore the need for tuberculosis control strategies targeting patients with chronic noncommunicable diseases, such as CKD.
Published: 2013
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42. Prevalence and patterns of multimorbidity among tuberculosis patients in Brazil: a cross-sectional study.

Author: Reis-Santos B, Gomes T, Macedo LR, Horta BL, Riley LW, and Maciel EL
Subjects: Adult, Aged, Brazil epidemiology, Chronic Disease, Comorbidity, Cross-Sectional Studies, Female, Humans, Logistic Models, Male, Middle Aged, Prevalence, Risk Factors, Young Adult, Tuberculosis epidemiology
Abstract: Introduction: The number of subjects with tuberculosis (TB) presenting with co-occurrence of multiple chronic medical conditions, or multimorbidity (MM) is increasing in Brazil. This manuscript aimed to characterize subjects with TB, according to their MM status and to analyse factors associated with TB treatment outcomes., Methods: This is a cross-sectional study that included 39,881 TB subjects reported in Brazil, in 2011. MM were defined as any (two or more) occurrence of chronic medical conditions in a TB patient (TB-MM). Data analysis was performed by hierarchical logistic regression models comparing TBMM with those with only TB., Results: Of the reported TB cases in 2011, 454 (1.14%) had MM. The subjects in the age group 40-59 years (OR: 17.89; 95% CI, 5.71-56.03) and those ≥ 60 years (OR: 44.11; 95% CI, 14.09-138.06) were more likely to develop TB-MM. The TB-MM subjects were less likely to be male (OR: 0.63; 95% CI, 0.52-0.76), institutionalized (OR: 0.59; 95% CI, 0.23-0.80) and live in rural areas (OR: 0.63; 95% CI, 0.42-0.95). Death from causes other than TB was higher among TB-MM subjects (OR: 1.76; 95% CI, 1.36-2.28). Of 454 TB-MM subjects 302 (66.5%) were cured and 152 (33.5%) were not cured. The odds of not being cured was 1.55 (95% CI, 1.04-2.32) among males, 2.85 (95% CI, 1.12-7.28) among institutionalized subjects, and 3.93 (IC 95%, 1.86-8.30) among those who were infected with HIV. TB retreatment after previous abandonment (OR: 7.53; 95% CI, 2.58-21.97) and transfer from a treatment site (OR: 2.76; 95% CI, 1.20-6.38) were higher for subjects not cured compared to those who were cured., Conclusions: While TB is well recognized to be a disease engendered by social inequity, we found that even among TB patients, those who have MM have greater inequity in terms of socioeconomic status and adverse clinical outcomes. Addressing the problem of TB and TB-MM requires a multisectorial approach that includes health and social service organizations.
Published: 2013
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43. Tuberculosis prevalence in renal transplant recipients: systematic review and meta-analysis.

Author: Reis-Santos B, Gomes T, Horta BL, and Maciel EL
Subjects: Humans, Prevalence, Kidney Transplantation adverse effects, Tuberculosis epidemiology, Tuberculosis etiology
Abstract: Introduction: Tuberculosis (TB) prevalence in subjects with kidney transplantation (KTX) is greater than in general population. We aimed to realize a systematic review and meta-analysis of prevalence of TB in KTX (TB-KTX)., Methods: We searched by the texts in electronic databases and references were reviewed. We estimated the pooled prevalence of TB-KTX subjects and we also conducted analysis by meta-regression. TB prevalence in general population (0.18%; 95% IC = 0.16-0.20) was reference to comparison., Results: We screened 253 papers, which 41 studies entered in analysis. The pooled prevalence of TB-KTX was 2.51% (95% CI = 2.17-2.85). In the meta-regression sample size > 2.501 subjects and high prevalence of TB in general population remained associated with TB-KTX., Conclusion: TB prevalence in KTX was 14 times greater than in general population. Thus, we highlighted the necessity that planning of measures for prevention and control of TB for this population should be agenda in discussions of health sector.
Published: 2013
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44. Socio-demographic and clinical differences in subjects with tuberculosis with and without diabetes mellitus in Brazil--a multivariate analysis.

Author: Reis-Santos B, Locatelli R, Horta BL, Faerstein E, Sanchez MN, Riley LW, and Maciel EL
Subjects: Adolescent, Adult, Aged, Brazil epidemiology, Female, Humans, Male, Middle Aged, Multivariate Analysis, Population Surveillance, Socioeconomic Factors, Treatment Outcome, Tuberculosis diagnosis, Tuberculosis therapy, Young Adult, Diabetes Mellitus, Diabetes Mellitus, Type 2 complications, Tuberculosis complications, Tuberculosis epidemiology
Abstract: Background: Several studies have evaluated the relationship between diabetes mellitus (DM) and tuberculosis (TB), but the nature of this relationship is not fully understood. TB incidence may be influenced by immunosuppression from DM, but this association may be confounded by other clinical and socioeconomic factors. We aimed to assess socio-demographic and clinical differences in TB patients with and without DM., Methods: Using the Brazilian national surveillance system (SINAN), we compared 1,797 subjects with TB and DM with 29,275 subjects diagnosed with TB only in 2009. We performed multivariate analysis to identify factors associated with the presence of DM among TB patients., Results: Subjects with TB - DM were older; have initial positive sputum smear test (OR = 1.42, 95% CI 1.26-1.60), and were more likely to die from TB (OR = 1.44, 95% CI 1.03-2.01). They were less likely to have been institutionalized [in prison, shelter, orphanage, psychiatric hospital (OR = 0.74, 95% CI 0.60-0.93)]; developed extra pulmonary TB (OR = 0.62, 95% CI 0.51-0.75) and to return to TB treatment after abandonment (OR = 0.66, 95% CI 0.51-0.86)., Conclusions: Prevalence of NCD continues to rise in developing countries, especially with the rise of elderly population, the prevention and treatment of infectious diseases will be urgent. DM and TB represent a critical intersection between communicable and non-communicable diseases in these countries and the effect of DM on TB incidence and outcomes provide numerous opportunities for collaboration and management of these complex diseases in the national public health programs.
Published: 2013
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