Distribuição dos atendimentos de tratamento contínuo
Stats4Health
- Walmes M. Zeviani (
walmes@ufpr.br)
- Wagner H. Bonat (
wbonat@ufpr.br)
- Guilherme Hathy (
guilherme.hathy@miti.com.br)
- Maria Helena Oliveira (
mariaholiveira34@gmail.com)
walmes@ufpr.br)wbonat@ufpr.br)guilherme.hathy@miti.com.br)mariaholiveira34@gmail.com)3 de Dezembro de 2017
Problematização
- Alvo: Distribuição do atendimento de doenças de tratamento contínuo.
- Caso: Dados de atendimentos de hemodiálise em Curitiba/2016.
- Demanda VS oferta.
- OPORTUNIDADES:
- 34% dos atendimentos foram na unidade mais próxima.
- Economia de R$ 1.46 por atendimento apenas em Curitiba.
- Ganho em qualidade de vida.
- Objetivos da aplicação
- Subsidiar o gestor público na tomada de decisão visando
- Orientar a distribuição dos pacientes.
- Redução de custos com transporte.
- Alocação e/ou ampliação da rede de unidades de atendimento.
- Metodologia
- Dados do SIASUS.
- Conhecimento em Estatística.
- Análise exploratória gráfica e descritiva.
- Software R de Computação Científica (livre de código aberto).
- Enriquecimento dos dados com lat & long e distâncias.
Análise dos dados
Configuração do ambiente
#-----------------------------------------------------------------------
# Limpa ambiente de trabalho.
rm(list = ls())
# Pacotes.
library(lattice) # Gráficos.
library(latticeExtra)
library(RColorBrewer) # Esquemas de cores.
library(plyr) # Manipulação de dados.
# https://www.shanelynn.ie/massive-geocoding-with-r-and-google-maps/
library(ggmap) # CEP para lat & lon.
# https://rstudio.github.io/leaflet/
library(leaflet) # Gráficos interativos.
#-----------------------------------------------------------------------
# Configurações da lattice.
mycol <- c("#E41A1C", "#377EB8", "#4DAF4A",
"#984EA3", "#FF7F00", "#FFFF33")
# Trellis graphical style.
ps <- list(box.rectangle = list(col = 1, fill = c("gray70")),
box.umbrella = list(col = 1, lty = 1),
dot.symbol = list(col = 1, pch = 19),
dot.line = list(col = "gray50", lty = 3),
plot.symbol = list(col = 19, cex = 0.8),
plot.line = list(col = 1),
plot.polygon = list(col = "gray50"),
superpose.line = list(col = mycol, lty = 1),
superpose.symbol = list(col = mycol, pch = 19, fill = mycol),
superpose.region = list(col = mycol, pch = 19),
superpose.polygon = list(col = mycol),
strip.background = list(col = c("gray80", "gray50")),
axis.text = list(cex = 0.8))
trellis.par.set(ps)
# lattice.options(default.args = list(as.table = TRUE))
# show.settings()Importação dos dados
#-----------------------------------------------------------------------
# Importa dados de hemodialise.
# save.image("seguranca.RData")
# Exibe os arquivos no diretório de trabalho.
list.files()
# Importa a tabela.
nef <- read.csv2(file = "baseSIAOK.csv",
header = TRUE,
sep = ",",
stringsAsFactors = FALSE)
str(nef)cat("Dimensões da tabela.\n")
dim(nef)
cat("Nome das colunas (variáveis).\n")
names(nef)#-----------------------------------------------------------------------
# Gráfico de pareto do número de ocorrências por MUN.
barchart(sort(table(nef$AP_UFMUN),
decreasing = FALSE),
xlab = "Número de atendimentos",
ylab = "Código do município") +
latticeExtra::layer(panel.abline(v = seq(0, max(x), by = 1000),
col = "gray70"),
under = TRUE)# Códigos para filtrar os registros de Curitiba.
mun <- c(Curitiba = "410690")
# Aplicando o filtro.
nef <- subset(nef, AP_UFMUN %in% mun)
# Dimensões depois de filtrar.
dim(nef)
# Frequencias de dados ausentes.
table(is.na(nef$AP_CEPPCN))Aquisição das coordenadas do CEP
#-----------------------------------------------------------------------
# Buscar o lat & lon a partir dos CEPs.
# Número de digitos do CEP.
table(nchar(nef$AP_CEPPCN))
# Formatando os CEPs para fazer a busca na API do Google.
cep <- gsub(x = nef$AP_CEPPCN,
pattern = "(\\d{2})(\\d{3})(\\d{3})",
replacement = "\\1.\\2-\\3")
cep <- sprintf("CEP %s, Curitiba-PR", cep)
# head(cep)
# Quantidade de CEPs únicos e duplicados.
table(duplicated(cep))
# Para recortar e colar no RStudio Web do LEG: rstudio.leg.ufpr.br.
# Com servidor do LEG a conexão com Google é mais rápida.
# dput(unique(cep))
# latlon <- geocode(location = cep)
# str(latlon)
# Valores de lat & lon retornados para API para cada CEP enviado.
latlon <-
structure(list(lon = c(-49.3069611, -49.291748, -49.291748, -49.2360011,
-49.2664018, -49.3602383, -49.2511995, -49.2778063, -49.291748,
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-49.3018896, -49.3069611, -49.3221781, -49.3323251, -49.2594132,
NA, -49.3323251, -49.224605, -49.2461329, -49.2562665, -49.2816082,
-49.2988176, -49.1967573, -49.291748, -49.2664018, -49.1777779,
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-49.2182748, -49.1904301, -49.3526243, -49.2056164, NA, -49.2562665,
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-49.2892129, -49.325983, -49.2664018, -49.2499328, -49.2664018,
NA, -49.3526243, -49.2660851, -49.291748, -49.2220728, -49.1862508,
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-25.5570359, -25.4297854, -25.5025633, -25.5441946, -25.4301074,
-25.4158258, -25.3597747, -25.4632669, -25.3241055, -25.4237277,
NA, -25.4023021, -25.4712653, -25.3877968, -25.4311031, -25.4712796,
-25.4023021, -25.3172938, -25.461021, -25.4237277, -25.3585076,
-25.4745672, -25.4798276, -25.4195501, -25.4195501, -25.4414357,
-25.404689, -25.5441946, -25.4237277, -25.3877968, -25.5488845,
-25.4417996, -25.5488845, -25.5176381, -25.5123456, -25.4128549,
-25.3408298, -25.5588709, -25.4237277, -25.4621431, -25.4338299,
-25.4406557, -25.3772995, -25.533311, -25.5129471, -25.5488845,
-25.461021, -25.5263222, -25.5176381, -25.5437894, -25.3712508,
-25.5348061, -25.401382, -25.5163403, -25.4568622, -25.4389499,
-25.5163403, -25.4893903, -25.4997715, -25.4309125, -25.520314,
-25.3585076, -25.4237277, -25.4712653, -25.4778939, -25.4311031,
-25.4788979, -25.5125457, -25.4694879, -25.4568622, -25.3669676,
-25.4007895, -25.4137719, -25.448836, -25.4977191, -25.4128549,
-25.4237277, -25.5176381, -25.503554, -25.4147371, -25.4237277,
-25.4447841, -25.5529765, -25.401382, -25.5584612, -25.511838,
-25.3708973, -25.3737343, -25.3972295, -25.4715469, -25.4997715,
-25.533311, -25.4798276, -25.3829477, -25.1495815, -25.5025633,
-25.4237277, -25.4863988, -25.4354136, -25.4256165, -25.4967722,
-25.3945546, -25.5954269, -25.6602153, -25.3923531, -25.4237277,
-25.4778939, -25.4741948, -25.441814, -25.5257215, -25.511838,
-25.527821, -25.3855732, -25.4237277, -25.5709555, -25.4761145,
-25.350195, -25.4355706, -25.4788979, -25.4406557, -25.4568622,
-25.4608322, -25.4893903, -25.5227299, -25.5404629, -25.4608322,
-25.371596, -25.4301074, -25.3288944, -25.3850202, -25.445388,
-25.5437894, -25.3408298, -25.4070604, -25.542697, -25.4354136,
-25.4975339, -25.3804118, -25.4798276, -25.4761145, -25.4691128,
-25.4608322, -25.4237277, -25.4715469, -25.4406557, -25.5954269,
-25.4607546, -25.5437894, -25.645646, -25.4607546, -25.4997715,
-25.3693852, -25.3737343, -25.3693852, -25.4447841, -25.4061214,
-25.6426652, -25.6426652, -25.4715469, -25.5835712, -25.5253105,
-25.5529765, -25.4941462, -25.5954269, -25.5381718, -25.4564638,
-25.5129471, -25.3669676, -25.4564638, -25.4447841, -25.4237277,
-25.4835974, -25.4741948, -25.3949874, -25.4237277, -25.4253119,
-25.3796091, -25.5709555, -25.4608322, -25.4570094, -25.5954269,
-25.4297854, -25.4237277, -25.527821, -25.4734736, -25.4570094,
-25.4019404, -25.4712796, -25.4370981, -25.4568622, -25.5954269,
-25.4745672, -25.4863988, -25.5129471, -25.5437894, -25.667025,
-25.4297854, -25.5147985, -25.3395906, -25.4256165, -25.4301074,
-25.4237277, -25.4733752, -25.4447841, -25.5709555, -25.4237277,
-25.520314, -25.5488845, -25.4128549, -25.3899986, -25.5441946,
-25.4568622, -25.4712796, -25.5437894, -25.667025, -25.4997715,
-25.3541729, -25.4835974, -25.4297854, -25.5529765, -25.5954269,
-25.4788979, -25.5441946, -25.3899986, -25.5488845, -25.3597747,
-25.3241055, -25.4382479, -25.4922636, -25.533311, -25.3935639,
-25.441814, -25.4237277, -25.3541729, -25.5125457, -25.4579084,
-25.538389, -25.465143, -25.5257215, -25.3669676, -25.4237277,
-25.4237277, -25.5042838, -25.4237277, -25.5954269, -25.4632669,
-25.5709555, -25.3585076, -25.404689, -25.4237277, -25.4977191,
-25.5054406, -25.5435704, -25.5216248, -25.5441946, -25.4730761,
-25.445388, -25.4505217, -25.4309125, -25.4863988, -25.5105246,
-25.5176381, -25.4237277, -25.4237277, -25.4798276, -25.538389,
-25.4237277, -25.5105246, -25.5257215, -25.5437894, -25.38426,
-25.4316868, -25.4237277, -25.5106183, -25.4835974, -25.5025633,
-25.3415442, -25.4237277, -25.3855732, -25.4860503, -25.3710218,
-25.5257215, -25.4549875, -25.4070604, -25.4237277, -25.4824311,
-25.4237277, -25.3923531, -25.485098, -25.538389, -25.3355143,
-25.5025633, -25.520314, -25.4195501, -25.4237277, -25.485098,
-25.3804118, -25.4735715, -25.4975339, -25.3241055, -25.4370981,
-25.4893903, -25.4568622, -25.464945, -25.4237277, -25.3659813,
NA, -25.4468889, -25.4237277, -25.542697, NA, NA, -25.4920729,
-25.4237277, -25.3693852, -25.4237477, -25.4237277, NA, -25.4237277,
-25.511838, -25.5832769, -25.533311, -25.4301074, -25.4745672,
-25.4237277, -25.4607546, -25.4237277, -25.483406, -25.5954269,
-25.5348061, -25.5584612, -25.3597747, -25.4237277, -25.5441946,
-25.4607546, -25.4497275, -25.538389, -25.4237277, -25.4128549,
-25.4941462, -25.4694879, -25.3402858, -25.5570359, -25.5004538,
-25.5416763, -25.4237277, -25.5954269, -25.4335813, -25.4128549,
NA, -25.4237277, -25.4370981, -25.5441946, -25.4715469, -25.4297854,
-25.503554, -25.4237477, -25.4612077, -25.5709555, -25.5441946,
-25.5163403, NA, -25.4570094, NA, -25.4608322, -25.4549875, -25.3395906,
-25.4920729, -25.3541729, -25.5123456, -25.4237277, NA, -25.511838,
-25.511838, -25.4237277, -25.3408298, -25.461021, -25.4735715,
-25.4370981, -25.4237277, -25.404689, -25.4863988, -25.3395906,
-25.4761145, -25.4632669, -25.4897917, -25.4461553, -25.3218022,
-25.4997715, -25.4237277, -25.4301074, -25.4608322, -25.314294,
-25.38426, -25.4691128, -25.3241055, -25.4497275, -25.4632669,
-25.4979285, -25.5348061, -25.5105246, -25.4564638, -25.4237277,
-25.4237277, NA, -25.4893903, -25.4154713, -25.4237277, -25.520314,
-25.4765441, -25.4461553, -25.430016, -25.4417996, -25.4237277,
-25.4997715, -25.5588709, -25.503554, -25.3943643, -25.4237277,
-25.4237277, -25.3597747, -25.5129471, -25.4195501, NA, -25.4860503,
-25.5570359, -25.527821, -25.314294, -25.4389499, -25.38426,
-25.4256165, -25.3693852, -25.5216248, -25.5709555, -25.4975339,
-25.4370769, -25.401382, -25.533311, -25.4237277, NA, -25.4007895,
-25.4778939, -25.4745672, -25.5441946, -25.4237277, -25.4632669,
-25.4920729, -25.4920729, -25.4237277, -25.5529765, -25.3945546,
-25.4237277, -25.5176381, -25.503554, -25.4237277, -25.6139081,
-25.4691128, -25.5216248, -25.4918548, -25.4237277, -25.314294,
-25.4237277, -25.4461553, -25.4389499, -25.4447841, -25.4417996,
-24.3287937, -25.3388506, -25.5437894, -25.4237277, -25.5163403,
-25.4237277, -25.3387476, -25.4256165, -25.4237277, NA, -25.5441946,
-25.5404629, -25.5123456, -25.5488845, -25.4144905, -25.4406557,
-25.5039825, -25.4367121, -25.4941462, -25.4370769, -25.4237277,
-25.5348061, -25.4548053, -25.4788979, -25.3855732, -25.4557498,
-25.4237277, -25.5147985, -25.4632669, -25.4237277, -25.4237277,
-25.5954269, -25.4975339, -25.4237277, -25.3712508, -25.3829477,
-25.4237277, -25.4975339, -25.4237277, -25.5488845, -25.4237277,
-25.5441946, -25.4237277, -25.4835974, -25.464945, -25.4237277,
-25.3241055, -25.4975339, -25.3531282, -25.3408298, -25.3772995,
-25.4070604, NA, -25.4237277, -25.5257215, -25.4237277, -25.4920729,
-25.461021, -25.5381718, -25.4128549, -25.3659813, -25.538389,
-25.4061214, -25.4920729, -25.4158258, -25.4237277, -25.3395906,
-25.4237277, -25.3796091, -25.3820079, -25.4367711, -25.5123456,
-25.38426, -25.4893903, -25.533311, -25.4497275, -25.4237277,
-25.4821393, -25.4237277, -25.4237277)),
.Names = c("lon", "lat"), class = "data.frame", row.names = c(NA, -1271L))
# Mantém apenas os CEPs dentro da delimitação.
latlon <- cbind(cep = unique(nef$AP_CEPPCN),
latlon)
head(latlon)
# Dois cliques para delimitar um retangulo só com dados de CWB.
# plot(lat ~ lon, data = latlon)
# bb <- locator(n = 2, type = "p", pch = 3)
# dput(bb)
# Coordenadas que definem um retângulo contendo dados de Curitiba.
bb <- structure(list(x = c(-49.4651796798985, -48.9478976959142),
y = c(-25.2595489355597, -25.7423278381743)),
.Names = c("x", "y"))
# Exibe a delimitação.
plot(lat ~ lon, data = latlon)
rect(xleft = bb$x[1],
xright = bb$x[2],
ybottom = bb$y[1],
ytop = bb$y[2])
# Mantém apenas as coordenadas dentro da delimitação.
latlon <- subset(latlon,
lon >= min(bb$x) &
lon <= max(bb$x) &
lat >= min(bb$y) &
lat <= max(bb$y))
str(latlon)
# Verifica.
# plot(lat ~ lon, data = latlon)#-----------------------------------------------------------------------
# Acrescentar o lat & lon nos registros da tabela.
dim(latlon)
dim(nef)
# Faz o pareamento.
i <- match(x = nef$AP_CEPPCN, table = latlon$cep)
length(i)
# Adiciona o lat & lon.
nef$lat <- latlon$lat[i]
nef$lon <- latlon$lon[i]
# Remove registros do hospital Pequeno Príncipe.
nef <- subset(nef, nef$AP_CODUNI != 15563)
# nef$AP_PRIPAL
# Troca código da causa principal pelo significado.
pp <- c("305010107" = "Hemodiálise",
"305010115" = "Hemodiálise - HIV",
"305010166" = "Acompanhamento domiciliar",
"305010182" = "Treinamento de paciente ")
nef$AP_PRIPAL <- pp[match(nef$AP_PRIPAL, as.integer(names(pp)))]Dados dos hospitais que atendem hemodialise
#-----------------------------------------------------------------------
# Dados com coordenadas das unidades de atendimento de Curitiba.
hos <- read.csv2(file = "hosp.txt",
header = TRUE,
sep = ";",
fileEncoding = "latin1",
dec = ".",
colClasses = c(NA,
"character",
"numeric",
"numeric"))
# Remove hospital Pequeno Príncipe.
hos <- hos[-3, ]
hos
# Adiciona uma coluna com o nome do hospital do atendimento.
nef$hospname <- hos$name[match(x = nef$AP_CODUNI, table = hos$cod)]
# Gráfico de pareto de casos por hospital.
barchart(~sort(table(nef$hospname)),
xlab = "Número de atendimentos",
ylab = "Unidade de atendimento") +
latticeExtra::layer(panel.abline(v = seq(0, max(x), by = 1000),
col = "gray70"),
under = TRUE)
Distribuição cronológica dos atendimentos
#-----------------------------------------------------------------------
# Série temporal de atendimentos mensais.
# tbd <- as.data.frame(xtabs(~hospname + AP_CMP,
# data = subset(nef, AP_CMP > 201600)))
# str(tbd)
xtabs(~AP_CMP, data = subset(nef, AP_CMP > 201600))
tbd <- as.data.frame(xtabs(~hospname + AP_CMP + AP_PRIPAL,
data = subset(nef, AP_CMP > 201600)))
str(tbd)
xyplot(Freq ~ AP_CMP | AP_PRIPAL,
groups = hospname,
data = tbd,
xlab = "Meses",
ylab = "Número de atendimentos",
as.table = TRUE,
scales = list(x = list(rot = 90)),
layout = c(NA, 2),
auto.key = list(title = "Unidade de atendimento",
cex.title = 1.1,
columns = 2),
type = "o")xyplot(Freq ~ AP_CMP | hospname,
groups = AP_PRIPAL,
data = tbd,
jitter.x = TRUE,
layout = c(NA, 2),
xlab = "Meses",
ylab = "Número de atendimentos",
as.table = TRUE,
scales = list(x = list(rot = 90)),
auto.key = list(title = "Causa principal",
cex.title = 1.1,
columns = 2),
type = "o")Distribuição espacial dos atendimentos
#-----------------------------------------------------------------------
# Distribuição espacial dos atendidos e hospitais.
# Define o esquema de cores.
# display.brewer.all()
nh <- unique(nef$AP_CODUNI)
col <- brewer.pal(n = length(nh), name = "Set1")
pal <- colorFactor(palette = col, domain = nh)
mymap <- leaflet(data = subset(nef,
complete.cases(cbind(lat, lon)))) %>%
addTiles() %>%
addMarkers(lng = hos$lon,
lat = hos$lat,
popup = hos$name) %>%
addCircleMarkers(
radius = 6,
color = ~pal(AP_CODUNI),
stroke = FALSE,
fillOpacity = 1) %>%
addCircleMarkers(
lng = hos$lon, lat = hos$lat,
radius = 15,
color = ~pal(hos$cod),
stroke = TRUE,
fillOpacity = 1)
mymap#-----------------------------------------------------------------------
# Remove atendimentos sem lat lon.
table(is.na(nef$lat))
nef <- subset(nef, !is.na(nef$lat))
table(duplicated(nef[, c("lat", "lon")]))
table(duplicated(nef$AP_CNSPCN))
aten <- table(nef$AP_CNSPCN)
hist(aten,
breaks = seq(min(aten, na.rm = TRUE),
max(aten, na.rm = TRUE) + 1,
by = 1) - 0.5,
col = "seagreen",
xlab = "Número de atendimentos no ano",
ylab = "Frequência absoluta")
box()Distâncias percorridas
#-----------------------------------------------------------------------
# Distâncias.
# Calcula a distância de cada paciente para hospital em que fez o
# procedimento.
nef$unidist <- NA
for (i in seq_len(nrow(nef))) {
coordh <- hos[match(x = nef$AP_CODUNI[i], table = hos$cod),
c("lat", "lon")]
coordp <- nef[i, c("lat", "lon")]
if (!any(is.na(coordp))) {
nef$unidist[i] <- dist(rbind(coordh, coordp)) * 100
} else {
nef$unidist[i] <- NA
}
}
hist(nef$unidist,
col = "gray50",
main = NA,
xlab = "Distância percorrida (km)",
ylab = "Frequência absoluta")
rug(nef$unidist)
box()plot(ecdf(nef$unidist), main = NA)
q <- c(0.5, 0.7, 0.9, 0.95)
qq <- quantile(nef$unidist, probs = q, na.rm = TRUE)
segments(y0 = 0, x0 = qq, y1 = q, x1 = qq, lty = 2)
segments(y0 = q, x0 = par()$usr[1], y1 = q, x1 = qq, lty = 2)#-----------------------------------------------------------------------
# Calcula a distância de cada paciente para cada hospital.
alldist <- matrix(NA, nrow = nrow(nef), ncol = nrow(hos))
coordh <- hos[, c("lat", "lon")]
# i <- 10
for (i in seq_len(nrow(nef))) {
coordp <- nef[i, c("lat", "lon")]
if (!any(is.na(coordp))) {
alldist[i, ] <-
100 * apply(coordh,
MARGIN = 1,
FUN = function(x) {
dist(rbind(coordp, x))
})
}
}
str(alldist)
# Menor distância.
nef$mindist <- apply(alldist, MARGIN = 1, min, na.rm = TRUE)
nef$mindist[!is.finite(nef$mindist)] <- NA
head(nef$mindist, n = 30)
# Distância mediana.
nef$meddist <- apply(alldist, MARGIN = 1, median, na.rm = TRUE)
nef$meddist[!is.finite(nef$meddist)] <- NA
head(nef$meddist, n = 30)
# d <- d[is.finite(d)]
# Custos.
nrow(nef)
a <- sum(nef$mindist) * 0.76
b <- sum(nef$unidist) * 0.76
b/a
(b - a)/nrow(nef)
# plot(ecdf(d))
# lines(ecdf(nef$unidist), col = 2)
# ecdfplot(~unidist + mindist + meddist | factor(AP_PRIPAL),
# # layout = c(NA, 1),
# data = nef,
# xlab = "Distância (km)",
# ylab = "Proporção acumulada",
# xlim = c(0, 30),
# auto.key = list(title = "Distância percorrida",
# cex.title = 1.1,
# text = c("Real", "Mínima", "Mediana")),
# as.table = TRUE) +
# latticeExtra::layer(panel.abline(v = seq(5, 25, 5),
# h = seq(0, 1, 0.2),
# col = "gray50", lty = 2),
# under = TRUE)
# ecdfplot(~unidist + mindist + meddist,
# # layout = c(NA, 1),
# data = nef,
# xlab = "Distância (km)",
# ylab = "Proporção acumulada",
# xlim = c(0, 30),
# lwd = c(2, 1, 1),
# auto.key = list(title = "Distância percorrida",
# cex.title = 1.1,
# text = c("Real", "Mínima", "Mediana")),
# as.table = TRUE) +
# latticeExtra::layer(panel.abline(v = seq(5, 25, 5),
# h = seq(0, 1, 0.2),
# col = "gray50", lty = 2),
# under = TRUE)
ecdfplot(~unidist + mindist,
# layout = c(NA, 1),
data = nef,
xlab = "Distância (km)",
ylab = "Proporção acumulada",
xlim = c(0, 30),
lwd = c(2, 1),
auto.key = list(title = "Distância percorrida",
cex.title = 1.1,
text = c("Real", "Mínima")),
as.table = TRUE) +
latticeExtra::layer(panel.abline(v = seq(5, 25, 5),
h = seq(0, 1, 0.2),
col = "gray50",
lty = 2),
under = TRUE)
# Razão da soma das distância.
with(nef, {
sum(unidist, na.rm = TRUE)/sum(mindist, na.rm = TRUE)
})
# Razão entre a distância atual e a distância mínima.
nef$ratiodist <- with(nef, {
unidist/mindist
})
# Percentual de pessoas que vão no hospital mais próximo.
mean(nef$ratiodist <= 1, na.rm = TRUE)
# plot(ecdf(nef$ratiodist))
# ecdfplot(~ratiodist,
# groups = AP_PRIPAL,
# data = nef,
# auto.key = TRUE,
# as.table = TRUE)
# Média por principal causa da hemodialise.
ddply(nef,
.(AP_PRIPAL),
summarise,
md = mean(ratiodist <= 1, na.rm = TRUE),
N = length(ratiodist))Alocação dos atendimentos
#-----------------------------------------------------------------------
# Matriz de confusão.
# xt <- xtabs(~AP_CMP + AP_CODUNI, data = nef)
# mosaicplot(t(xt))
# barplot(sort(prop.table(xtabs(~hospname, data = nef))))
# dim(alldist)
# dim(nef)
# Para onde deveria ter ido.
y <- hos$cod[apply(alldist,
MARGIN = 1,
FUN = function(x) {
i <- which.min(x)
if (length(i)) {
i
} else {
NA
}
})]
# Para onde ele foi.
x <- nef$AP_CODUNI
length(x) == length(y)
# sum(is.na(nef$AP_CODUNI))
# sum(is.na(y))
xtb <- table(x, y)
# Matriz de confusão.
xtb
dim(xtb)
rownames(xtb) <-
hos$name[match(rownames(xtb), hos$cod)]
colnames(xtb) <-
hos$name[match(colnames(xtb), hos$cod)]
# dim(xtb)
# xtb <- xtb[, -3]
# mosaicplot(xtb, col = col, off = 2)
sum(diag(xtb))
sum(xtb)
sum(diag(xtb))/sum(xtb)
xtb
#-----------------------------------------------------------------------
tbx <- merge(as.data.frame(table(x)),
as.data.frame(table(y)),
by.x = "x",
by.y = "y")
tbx$hosname <- hos$name[match(tbx$x, hos$cod)]
# tbx$hosname <- hos$name
barchart(Freq.x + Freq.y ~ hosname,
data = tbx,
xlab = "Unidades de atendimento",
ylab = "Número de atendimentos",
scales = list(x = list(rot = 15)),
auto.key = list(title = "Alocação",
cex.title = 1.1,
columns = 2,
text = c("Real", "Ideal"))) +
latticeExtra::layer(panel.abline(h = seq(0, max(y), by = 500),
col = "gray50",
lty = 2),
under = TRUE)
# sum(with(tbx, Freq.x - Freq.y))
# sum(tbx$Freq.x) == sum(tbx$Freq.y)
# sum(tbx$Freq.x) == nrow(nef)
#-----------------------------------------------------------------------
# mosaicplot(xtb, off = FALSE)
# barplot(xtb, beside = TRUE)
xtbd <- as.data.frame(xtb)
str(xtbd)
barchart(Freq ~ y | x,
data = xtbd,
as.table = TRUE,
scales = list(x = list(rot = 30)),
ylab = "Total de atendimentos",
xlab = "Unidades de atendimento",
key = list(title = "Alocação",
cex.title = 1.1,
columns = 2,
text = list(c("Inadequado", "Adequado")),
rect = list(col = c("gray90", "gray50"))),
panel = function(...) {
col <- rep("gray90", 7)
col[which.packet()] <- "gray50"
panel.barchart(..., col = col)
}) +
latticeExtra::layer(panel.abline(h = seq(0, max(xtbd$Freq),
by = 500),
col = "gray50",
lty = 2),
under = TRUE)
#-----------------------------------------------------------------------
z <- nef$AP_PRIPAL
xtb <- table(x, y, z)
xtb <- as.data.frame(xtb)
str(xtb)
xtb$x <- hos$name[match(xtb$x, hos$cod)]
xtb$y <- hos$name[match(xtb$y, hos$cod)]
str(xtb)
xtb$x <- factor(xtb$x, levels = hos$name)
xtb$y <- factor(xtb$y, levels = hos$name)
# combineLimits(
# useOuterStrips(
# barchart(Freq ~ y | x + z,
# data = xtb,
# as.table = TRUE,
# scales = list(relation = "free",
# x = list(rot = 30)),
# ylab = "Total de atendimentos",
# xlab = "Unidades de atendimento",
# key = list(title = "Alocação",
# cex.title = 1.1,
# columns = 2,
# text = list(c("Inadequado", "Adequado")),
# rect = list(col = mycol[1:2])),
# panel = function(...) {
# col <- rep(mycol[1], nrow(hos))
# col[which.packet()[1]] <- mycol[2]
# panel.barchart(..., col = col)
# })))
#-----------------------------------------------------------------------Considerações finais
- Subsidiar a distribuição dos pacientes nas UA.
- Distribuição otimizada:
- Redução de custos e riscos com transporte.
- Aumento da qualidade e expectativa de vida.
- Orientar investimentos:
- Ampliação da capacidade das UA.
- Criação de novas UA.
- Aplicação portável e baseada em software livre.
- Soluções de problemas sociais com Estatística.