
; *******************************************************************************
; * 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
]

