Analyzing demographic data with epiCo

1. Navigating the Codification of the Political Administrative Division of Colombia (DIVIPOLA)

DIVIPOLA is a standardized nomenclature designed by the DANE for the identification of territorial entities (departments, districts, and municipalities), non-municipalized areas, and populated centers by assigning a unique numerical code to each of these territorial units.

Colombia has:

• 32 Departments (Administrative Division Level 1)
• 1102 Municipalities (Administrative Division Level 2)
• 1 Island
• 18 Non-municipalized areas
• 6678 Populated centers

Two digits are used for the codification of departments, and five digits are used for the codification of municipalities (the first two being the department where they are located).

epiCo provides the complete list of departments and municipalities codes through a built-in dataset.

library(epiCo)
library(incidence)

data("divipola_table")

2. Population pyramids

epiCo provides a built-in dataset with the population projections of Colombia at the national, departmental, and municipality levels (provided by the DANE). These datasets contains the population projections from 2012 to 2024 for ages from 0 to 100 years. However, for the municipal projections it has the ages from 0 to over 85 years old.

Users can perform queries on this data by using the population_pyramid function, providing the DIVIPOLA code of the territory of interest and the year to consult.

ibague_code <- "73001" # DIVIPOLA code for the city of Ibagu<U+00E9>
year <- 2016 # Year to consult
ibague_pyramid_2016 <- population_pyramid(ibague_code, year) # Population
# pyramid (dataframe) for the city of Ibagu<U+00E9> in the year 2019
# dissagregated by sex
knitr::kable(ibague_pyramid_2016[1:5, ])

Definitions of age ranges and plotting are also provided for both: total number of individuals, or proportion of individuals

ibague_code <- "73001" # DIVIPOLA code for the city of Ibagué
year <- 2019 # Year to consult
age_range <- 5 # Age range or window
ibague_pyramid_2019 <- population_pyramid(ibague_code, year,
  range = age_range,
  sex = TRUE, total = TRUE,
  plot = TRUE
)

Population pyramid for the city of Ibagué in 2019

3. Demographic variables

Events of epidemiological relevance are reported to the SIVIGILA using an official notification form (see link).

epiCo provides a function to consult the dictionaries for the ethnicity categories, special population groups, and occupation codifications used by the SIVIGILA. As shown in the following example:

demog_data <- data.frame(
  id = c(0001, 002, 003, 004, 005, 006, 007, 008),
  ethnicity_label = c(3, 4, 2, 3, 3, 3, 2, 3),
  occupation_label = c(6111, 3221, 5113, 5133, 6111, 23, 25, 99),
  sex = c("F", "M", "F", "F", "M", "M", "F", "M"),
  stringsAsFactors = FALSE
)


ethnicities <- describe_ethnicity(demog_data$ethnicity_label)
knitr::kable(ethnicities)

occupations <- describe_occupation(
  isco_codes = demog_data$occupation_label,
  sex = demog_data$sex,
  plot = "treemap"
)
#> 2 codes are invalid.

Treemap plot of the distribution of occupations reported in the line list

knitr::kable(occupations$data)

4. Epidemiological data

epiCo is a tool that produces analyses based on epidemiological data extracted from SIVIGILA or provided by the user. epi_data is a built-in file that shows an example of the structure used by the package, which is the same as the one reported by SIVIGILA. This file contains the cases of all the municipalities in Tolima for the years 2015-2021.

The following analyses use the dengue cases reported in Tolima in 2019.

data("epi_data")

data_tolima <- epi_data[lubridate::year(epi_data$fec_not) == 2019, ]
knitr::kable(data_tolima[1:5, 4:12])

5. Estimation of incidence rates

The incidence rate feature of epiCo requires the incidence package to produce a modified incidence object. Instead of a count vector (or matrix), it transforms the object to provide a rate element accounting for the number of cases in the time period divided by the total number of inhabitants in the specific region and year.

epiCo uses the DANE population projections as denominators; therefore, it is necessary to provide the administration level at which incidences are calculated.

incidence_object <- incidence(
  dates = data_tolima$fec_not,
  groups = data_tolima$cod_mun_o,
  interval = "1 epiweek"
)
incidence_rate_object <- incidence_rate(incidence_object, level = 2)
knitr::kable(incidence_rate_object$counts[1:5, 1:12])

If groups in the incidence object are not within the DIVIPOLA coding for municipalities (level 2) or departments (level 1), or a national estimation is intended (level 0), the function will not be able to estimate an incidence rate.

6. Estimation of risk by age group

Normalization of data is a key aspect of epidemiology. epiCo allows for the age distribution of cases and normalizes the epidemiological data with the age structure of a population. This normalization allows us to estimate the age risk of a disease according to the age structure of the general population in a municipality, department, or country in a certain year.

data_ibague <- data_tolima[data_tolima$cod_mun_o == 73001, ]

age_risk_data <- age_risk(
  age = data_ibague$edad,
  population_pyramid = ibague_pyramid_2019$data,
  sex = data_ibague$sexo, plot = TRUE
)

Age risk plot for the city of Ibagué in 2019