; ******************************************************************************* ; * Template file for TB/MDR-TB case generation ; * e-TB Manager System ; * Rio de Janeiro, ago-2010 ; * ; * Change the values after = to generate your own testing database ; * ; ******************************************************************************* ; ******************************************************************************* ; * Se true, apaga todos os casos pré-existentes nos anos selecionados ; * Se false, os casos novos serão incluídos e os pré-existentes não serão modificados ; ******************************************************************************* remExistingCases= true ; ******************************************************************************* ; * Variação em dias do tempo de tratamento ; * Exemplo= 10 - Indica que o tratamento poderá ser de + ou - 10 dias além do tempo previsto no regime ; ******************************************************************************* cohortVarTime = 7 ; ******************************************************************************* ; * Indique o número de casos de TB e TBMR por ano ; ******************************************************************************* cases = [ { year= 2005, numTBCases= 1021, numMDRTBCases= 80 } { year= 2006, numTBCases= 1038, numMDRTBCases= 83 } { year= 2007, numTBCases= 1062, numMDRTBCases= 87 } { year= 2008, numTBCases= 1085, numMDRTBCases= 91 } { year= 2009, numTBCases= 1097, numMDRTBCases= 94 } { year= 2010, numTBCases= 557, numMDRTBCases= 46 } ] ; ******************************************************************************* ; * Número mínimo e máximo de dias que pode variar entre ; * a data de registro e a notificação ; ******************************************************************************* varDaysRegDiag=1..30 ; ******************************************************************************* ; * Peso para diagnósticos de casos de TBMR ; * Exemplo: CONFIRMED=10, SUSPECT=1 ; * Significa que as changes de gerar casos confirmados são 10x maior que para suspeitos ; ******************************************************************************* mdrDiagnosis = [ CONFIRMED = 10 SUSPECT = 2 ] ; ******************************************************************************* ; * Peso para geração de casos do sexo masculino e feminino ; * Exemplo= ; * MALE= 2, FEMALE= 1 ; * As changes de gerar casos do sexo masculino são o ; * dobro de chances de gerar casos do sexo feminino ; ******************************************************************************* genders = [ MALE= 2 FEMALE= 1 ] ; ******************************************************************************* ; * Nacionalidade dos pacientes ; * Exemplo: NATIVE=100, FOREIGN=5 ; * A quantidade de casos nativos serão 100/5 vezes maior que a quantidade de casos estrangeiros ; ******************************************************************************* nationalities = [ NATIVE = 100 FOREIGN = 15 ] ; ******************************************************************************* ; * Peso na geração do tipo de pacientes ; ******************************************************************************* patientTypesTB = [ NEW= 70 RELAPSE= 7 AFTER_DEFAULT= 15 FAILURE_FT= 5 FAILURE_RT= 3 TRANSFER_IN= 2 OTHER= 1 ] patientTypesMDR = [ NEW= 30 RELAPSE= 12 AFTER_DEFAULT= 18 FAILURE_FT= 15 FAILURE_RT= 25 TRANSFER_IN= 4 OTHER= 1 ] ; ******************************************************************************* ; * Pesos para geração do tipo pulmonar ou extrapulmonar ; ******************************************************************************* infectionSites = [ PULMONARY = 80 EXTRAPULMONARY = 15 BOTH = 5 ] ; ******************************************************************************* ; * Pesos para geração de casos pulmonares ; ******************************************************************************* pulmonaryForms = [ 'Unilateral Cavitary'= 30 'Bilateral Cavitary'= 50 'Unilateral Infiltrate'= 15 'Bilateral Infiltrate'= 20 'Destruction'= 13 'Normal'= 3 'Other'= 1 ] ; ******************************************************************************* ; * Pesos para geração de casos extrapulmonares ; ******************************************************************************* extrapulmonaryForms = [ 'Pleura'= 47 'Lymph Nodes'= 30 'Abdomen'= 5 'Genitourinary Tract'= 7 'Skin'= 2 'Joints and Bones'= 4 'Meninges'= 6 'Intrathoracic Lymphadenopathy'= 4 'Tuberculous Pleural Effusion'= 5 'Other'= 2 ] ; ******************************************************************************* ; * Pesos para geração do tipo rural ou urbano ; ******************************************************************************* localityTypes = [ RURAL = 5 URBAN = 100 ] ; ******************************************************************************* ; * Fator para geração de regimes padronizados ou individualizados ; * Exemplo= 10/1 ; * Indica que a chance do caso gerado usar um regime padronizado é 10 vezes ; * maior que de um regime individualizado (só para TBMR) ; ******************************************************************************* facRegimenStdInd = 10/2 ; ******************************************************************************* ; * Idade dos pacientes por faixa etária. Indique o peso de cada faixa ; * para a geração dos casos ; ******************************************************************************* ageRanges = [ { range= '<= 4', weightRange = 3..30 } = 1 { range= '5 - 14', weightRange = 35..60 } = 3 { range= '15 - 24', weightRange = 45..70 } = 5 { range= '25 - 34', weightRange = 55..90 } = 9 { range= '35 - 44', weightRange = 55..90 } = 6 { range= '45 - 54', weightRange = 55..90 } = 4 { range= '55 - 64', weightRange = 55..90 } = 4 { range= '>= 65', weightRange = 55..80 } = 3 ] ; ******************************************************************************* ; * Peso para escolha do regime padronizado para o caso ; * Coloque o nome do regime como aparece no sistema ; * Ou caso queira usar o seu código, use um $ antes do código ; * Exemplo, se o código do regime 'GLC' for 22750, você poderá definir o peso ; * desde regime como ; * GLC = 10 ; * ou ; * $22750 = 10 ; ******************************************************************************* regimensTB = [ 'New Patient Regimen' = 85 'Retreatment Regimen (1st line)' = 20 ] regimensMDR = [ 'MDR Regimen - 1'= 70 'MDR Regimen - 2'= 30 ] ; ******************************************************************************* ; * Peso para a geração de casos por paciente (só para TBMR) ; ******************************************************************************* numTreatments = [ ONE_TREATMENT = 50 ; Pacientes com 1 tratamento TWO_TREATMENTS = 35 ; Pacientes com 2 tratamentos THREE_TREATMENTS= 15 ] ; ******************************************************************************* ; * Peso para a geração dos casos por região (indique todas ou apenas as regiões ; * para que você quer que sejam gerados casos) ; ******************************************************************************* regions = [ 'Region A' = 40 'Region B' = 20 'Region C' = 10 ] ; ******************************************************************************* ; * Percentual de casos que são transferidos (0 = nenhum, 100 = todos) ; ******************************************************************************* percTransfCases = 7 ; ******************************************************************************* ; * Peso no resultado do desfecho de cada caso ; ******************************************************************************* outcomesTB = [ CURED = 57 TREATMENT_COMPLETED = 15 FAILED = 5 DEFAULTED = 10 DIED = 6 TRANSFERRED_OUT = 2 DIAGNOSTIC_CHANGED = 1 OTHER = 1 ] outcomesMDR = [ CURED = 48 TREATMENT_COMPLETED = 11 FAILED = 18 DEFAULTED = 10 DIED = 15 TRANSFERRED_OUT = 2 DIAGNOSTIC_CHANGED = 1 OTHER = 1 ] ; ******************************************************************************* ; * Tempo médio em dias para iniciar o tratamento de TB e TBMR a partir da data de diagnóstico ; * 0..10 significa entre 0 e 10 dias a partir da data de diagnóstico ; ******************************************************************************* startTreatmentTB = 0..7 startTreatmentMDR = 0..15 ; ******************************************************************************* ; * Medicines to be used in susceptibility tests ; ******************************************************************************* substances = [ R, H, Z, E, S, Eto, Lfx, Mfx, Km, Cm, PAS, Cs ] ; ******************************************************************************* ; * Número de casos de TB com resistência a algum medicamento (entre 0 e 100) ; * Exemplo: = 5 ; * Para cada 100 casos de TB, 5 tem resistência a algum medicamento ; ******************************************************************************* resTB = 12 ; ******************************************************************************* ; * Resistance patterns and its weight-factor ; ******************************************************************************* resPatternsTB = [ [ R ] = 15 [ H ] = 15 [ Z ] = 10 [ E ] = 10 ] resPatternsMDR = [ [ R, H ] = 100 [ R, H, Z ] = 10 [ R, H, E ] = 20 [ R, H, Z, E ] = 15 [ R, H, Z, E, S ] = 10 [ R, H, Eto, E ] = 10 [ R, H, Lfx, S] = 8 [ R, H, Lfx, Km ] = 4 ] ; ******************************************************************************* ; * Pesos para resultados de exames de teste de resistência ; ******************************************************************************* dstResultsTB = [ NOTDONE = 80 RESISTANT = 0 SUSCEPTIBLE = 20 CONTAMINATED = 2 ] dstResultsMDR = [ NOTDONE = 0 RESISTANT = 60 SUSCEPTIBLE = 15 CONTAMINATED = 1 ] ; ******************************************************************************* ; * Período em que será feito o primeiro exame em relação a ; * data de diagnóstico ; * exemplo: ; * sputumFirst = 20..5 ; * Indica que o exame será feito entre 20 e 5 dias antes da data de diagnóstico ; ******************************************************************************* microscopyFirst = 14..7 cultureFirst = 60..30 dstFirst = 90..30 hivFirst = 90..30 ; ******************************************************************************* ; * Frequência em dias para realizar novos exames ; * Exemplo: ; * sputumFreq = 90 ; * Indica que a cada 90 dias será feito um novo exame de baciloscopia ; ******************************************************************************* microscopyFreqTB = 30 cultureFreqTB = 30 dstFreqTB = 0 hivFreqTB = 0 microscopyFreqMDR = 30 cultureFreqMDR = 30 dstFreqMDR = 0 hivFreqTB = 0 ; ******************************************************************************* ; * Variação máxima percentual na data de realização de um próximo exame ; * Exemplo: ; * varDateExam = 10 ; * Significa que o próximo exame do caso pode acontecer com uma margem de erro ; * de 10% (para mais ou menos) da data prevista de realização em relação ao ; * exame anterior ; * Se a frequência de realização de cultura for de 30 dias, então no exemplo acima ; * ela poderá variar entre 27 e 33 dias (+ ou - 3 dias) ; ******************************************************************************* varDateExam = 7 ; ******************************************************************************* ; * Pesos para resultados de exames de baciloscopia ; ******************************************************************************* microscopyResults = [ NEGATIVE = 5 POSITIVE = 10 PLUS = 20 PLUS2 = 25 PLUS3 = 20 ] ; ******************************************************************************* ; * Pesos para resultados de exames de cultura ; ******************************************************************************* cultureResults = [ NEGATIVE = 5 POSITIVE = 10 PLUS = 20 PLUS2 = 25 PLUS3 = 20 ] ; ******************************************************************************* ; * Pesos para resultados de exames de HIV ; ******************************************************************************* hivResults = [ NEGATIVE = 80 POSITIVE = 20 ] ; ******************************************************************************* ; * Pesos para apresentação de x-ray ; ******************************************************************************* xrayPresentation = [ Cavitary=60 Infiltrate=20 Normal=1 Other=2 ] ; ******************************************************************************* ; *** Próximo Raio-X após o primeiro (que será realizado na data da coleta da cultura) *** ; ******************************************************************************* xrayNextResult = 110..130 ; ******************************************************************************* ; *** Percentual de casos que tem comorbidades (de 0 a 100%) ; ******************************************************************************* percComorbidities = 32 ; ******************************************************************************* ; *** Peso das comorbidades para casos que possuem comorbidades ; ******************************************************************************* comorbidities = [ 'Diabetes Mellitus'=15 'Renal Dysfunction'=2 'Cancer Disease'=3 'Homeless'=4 'Health Care Worker'=10 'Refugee/Immigrant'=6 'Detained/Imprisoned'=7 'Prolonged cortisone therapy'=2 'Organ transplant'=1 'Alcoholism'=21 'Aids'=16 'Illicit drugs abuse'=11 'Mental disorders'=5 'Other'=9 ] ; ******************************************************************************* ; *** Percentual de casos com efeitos colaterais (0 a 100%) ; ******************************************************************************* percAdverseReactions=41 ; ******************************************************************************* ; *** Peso dos efeitos colaterais para casos (0 a 100% em cada item) ; ******************************************************************************* adverseReactions = [ 'Seizures'=4 'Peripheral Neuropathy'=10 'Hearing loss and vestibular disturbances'=18 'Mental disorder'=13 'Hypothyroidism'=8 'Other'=3 'Gastro-intestinal intolerance'=22 'Headache'=16 'Visual disturbances'=9 'Insomnia'=15 'Joint pain'=19 'Renal insufficiency'=2 'Dizziness'=12 ] ; ******************************************************************************* ; *** Percentual de casos que possuem contatos (entre 0 e 100%) ; ******************************************************************************* percContacts=60 ; ******************************************************************************* ; *** Número médio de contatos para casos que possuem ; ******************************************************************************* contactsRange=1..3 ; ******************************************************************************* ; *** Peso para os tipos de contatos ; ******************************************************************************* contactType= [ 'Household'=25 'Work/School'=6 'Community'=12 'Health Care Worker'=11 'Nosocomial'=8 'Penitentiary'=5 'Institutional (asylum, shelter, orphanage, etc.)'=2 'Other'=1 ] ; ******************************************************************************* ; *** Peso para as condutas dos contatos ; ******************************************************************************* contactConduct= [ 'Start TB treatment'=9 'Start Chemoprophylaxis'=18 'Guidance/clarification'=43 ] ; ******************************************************************************* ; * Nomes de homens usados para compor o nome do paciente ; ******************************************************************************* firstNamesMale = [ Airton Augusto Abu Abdul Albert Antony Anderson Arnaldo Andre Alan Alexandre Alex Aleksey Arin Arnauld Agnaldo Albert Alberto Abaeté Abdão Abdias Abel Ademar Barnabas Barnaby Barton Bernard Bevis Bond Booth Brick Brier Brigham Brinley Baltasar Baren Beto Benedito Bernardo Benjamin Becan Bruno Brandon Biafra Baltazar Bartolomeu Batista Belisario Benjamin Bonifácio Bruce Carter Cecil Cedric Charles Chick Chilton Chip Christian Christopher Cicero Cláudio Clenildo Carlos Caetano Clayton Claude Conrado Constantino Constantin Caio Clarence Clay Clayland Clayton Cleave Clem Clemens Clement Cliff Clifford Clint Clinton Dado Daniel Dilson David Dean Dean Diamond Dixon Duff Ebenezer Edgar Edward Edwin Eliah Eliezer Ellery Ellison Emmanuel Enock Eric Ernes Errol Erv Ethan Eustace Everett Flávio Fábio Fernando Francisco Fernandes Gustavo Glauco Geonory Gabriel Germano Giulio Gardner Garfield Garrick Garrison Garron Garson Garron Garth Gates Gene Gent Geoffrey George Gerald Gibson Gilbert Gomer Gordon Gordy Gore Graham Gram Grayson Gregory Grover Ivo Ivan Ítalo Iuri Yuri Jean Jeremy Joel Jorge José Jair John Joseph João Jefferson Júlio Jonatas Jonathan Jeremias Jacob Karaman Kurt Kyle Karran Konstantin George Gilberto Gabriel Gregory Garcia Louis Luiz Gustavo Lucas Lean Legolas Marcos Martin Mark Moises Manoel Mohamed Nuno Norberto Nilo Nicholas Noel Osmar Oswald Paulo Pedro Peter Paul Phillip Patrick Raymond Ricardo Ronaldo Roger Rubens Ronald Rivaldo Renan Robson Roosevelt Rennan Renato Raul Ramon Rachid Ringo Sergey Samuel Sandro Silvester Steffano Steve Will Xavier Xisto ] ; ******************************************************************************* ; * Nomes de mulheres usados para compor o nome do paciente ; ******************************************************************************* firstNamesFemale = [ Alynn Aaliyah Aaming Aamy Abbey Aaron Abbie Abbigail Abby Abigail Abigale Abigayle Abilene Abiranna Abril April Ada Adabel Adalyn Addie Addison Addsyn Adela Albertina Agnes Andreia Angela Arinan Ana Maria Ana Lúcia Ana Ameely Alice Aline Arminda Benedita Bruna Beatriz Barbara Bianca Berenice Bruna Clara Célia Carolina Carol Carla Clarice Cleide Cláudia Carmen Denise Daniella Daniele Diana Débora Emily Esmeralda Elisabeth Flávia Fátima Fabiana Fernanda Geovanna Gláucia Gilda Grace Helena Iraci Iná Irene Janete Juliana Jurema Josefina Joelma Jocilaine Maria Maria Antônia Maria Teresa Martha Mirian Marcela Manoela Michele Mel Melissa Nair Karla Keli Kimberly Keila Lara Laura Lair Lourdes Linda Lívia Lauren Leonora Margareth Mabel Mabinty Mable Macey Maceyn Machele Machelle Maci Macy Madaline Madalyn Madalynn Maddy Mahriana Madeleine Madelene Madeline Madelyn Nádia Núbia Natasha Norma Nina Nataly Noelma Otaviana Paula Patrícia Paloma Paris Regina Roberta Rogéria Raquel Rebeca Raimunda Romana Rose Rosely Ra Raby Raca Rachael Rachaell Racheal Rachelis Rachelle Radella RaDonna Rae Raechel Raegan Raina Solange Sandra Simone Sylvia Sara Telma Tara Uma Vada Val Valarie Valéria Vanity Vanna Vaughnita Velada Velda Velena Velia Velma Quilma Qbilah Qiana Quanda Quaneisha Quaneta Quanishia Queen Queena Queenie Quenisha Quenishia Quentina Quiana Quinn Karen Kelly ] ; ******************************************************************************* ; * Sobrenomes usados para compor o nome dos pacientes ; ******************************************************************************* lastNames = [ Antunes Abbott Adamczyk Agnaudo Araujo Apple da Silva Debrieux Batista Bolivar Brasil Bottino Blue Bachman Bailey Brick Dianno DAvona Dmarco Da neen Da wan Daava Dafne Dagian Dahlia Daija Dantas Dickinson da Costa Copala Cortez Costeau da Costa Saldanha Caliva Callaghan Dutra Boon Bernardes Busht Dabrowski Daecher Edwards Eisenhower Ford Farias Fernandes Fernandez Forest Fancher Fantauzzo Gallet Gomes Gump Greenhill Greece Green Gray Garcia Gusmão Gabriell Gao Gardenia Galena Gallo Garcia Hahn Haley Himan Hill Hutson Highway Julia Jordan Jackson Johnson Xavier Silveira Silva e Souza de Souza Harris Keravec Bastos Lima Lighthouse Lopes Lopez Madilyn Madison Madra Madisyn Maggy Magdalene Maegan Medeiros Marques Moraes Montana Mc Brain Murray Moore Neves Nunes Noodle Noir Maciejewski Mackay Nagy Nelson Paradise de Oliveira Olson Olszewski Pagano Parker Peterson Patterson Pawlak Pawlowski Pena Penn Pennell Perez Perrin Perry Peters Petit Phillps Pierce Pilch Piotrowski Polk Poole Powell Price Quentin Ramirez Ramos Roque Runderberg Rosario Ribeiro Salazar Sanchez Souza da Silva Soares Brito Soares System Smith Snow Speed Taylor Talimbard Tavares Taft Timberland Timoteo Ribeiro da Silva Romeo Usher Visser Valdez Vanburen Wagner Walczak Xylander Yaeger Young Zajac Zakwzewski ]