Data collection
Colombia is divided into 32 departments and 1119 municipalities. According to the 2018 national census, 77.1% of population lives in urban areas and approximately 25% residing in four main cities (Bogotá, Medellín, Cali, and Barranquilla). (15) The central region is the territory where the largest population is located with equivalent to 70.1%, where high complexity/specialized university hospitals area also located. Followed by 21.9% inhabit the north region, the 3% in the east and the remained in the west and the south (2.7% and 2.4%, respectively). (16)
The Individual health records system (Registros Individuales de Prestación de Servicios de Salud – RIPS, by its Spanish acronym) is a data repository for the management, regulation, and control processes for the health services required by the General System of Social Security in Colombia (17). Their aim is to follow up on the health services provided, evaluate service coverage, formulate health policies as well as allocate financial and human resources. The information must be organized according to guidelines proposed by the Ministry of Health. Data registration is mandatory and performed monthly by health-providing institutions and independent professionals. Data analysis reports are sent to insurers for validation and verification. Finally, records are consolidated and a report is generated. (17) Information is available for public consultation through an Open Database Connectivity (ODBC).
For the present study we reviewed data collected between January 2015 to December 2019. All registered patients diagnosed with renal malformations (Q600, Q601, Q602, Q603, Q604, Q605, Q610, Q611, Q612, Q613, Q614, Q615, Q618, Q619, Q620, Q621, Q622, Q623, Q624, Q625, Q626, Q627, Q628, Q630, Q631, Q632, Q633, Q638, Q639), male genital malformations (Q530, Q531, Q532, Q539, Q540, Q541, Q542, Q543, Q544, Q548, Q549, Q550, Q551, Q552, Q553, Q554, Q555, Q556), and exstrophy-epispadias complex (Q640, Q641), according to the International Statistical Classification of Diseases and Health Problems 10th revision (ICD-10) (18) were included for analysis. No identifying variables were collected. We applied the diagnostic filters “confirmed new cases” and “confirmed repeated cases”. Diagnostic impression and unspecified cases were excluded. With the purpose of quantifying the number of patients with these diagnoses, the function “people attended” was used, which includes each patient only once even if was attended multiple times during the time of the study.
The National Administrative Department of Statistics (Departamento Administrativo Nacional de Estadistica – DANE, by its Spanish acronym) is the entity responsible for collecting, processing, analysing official statistics in Colombia (19). The Vital Statistics Subsystem collects and processes information about all births and deaths that occur in the country. Live birth and death certificates are filled out by doctors, nurses, or authorized health personnel who attended the event in the institutions providing health services throughout the country. Live births are defined as a product of gestation after the expulsion or removal of the mother’s body, regardless of the duration of the pregnancy. They must be able to breathe or give any other sign of life, such as heartbeat or umbilical cord pulsation (20). For the present study, we included all registered live births during 2015 – 2019 in each department of the country and discriminating by gender.
Geographical variables (latitude and longitude) of each department were extracted using the DANE geoportal. This tool collects the information of georeferenced data and allow access to geographic limits and official maps of the Colombian territory.
Statistical analysis
Cluster detection was performed using SaTScan v9.6 (21) for macOS (Satscan, 2018) to identify clusters with either high or low rates of medical assessments over time. The spatial-temporal statistical analysis using a Poisson probability model was conducted for each diagnostic group. In the case file, we included all people with the specified diagnoses and in the population file, the live births were added. Latitude and longitude coordinates were entered for each department. The study period started 01/01/2015 and ended 31/12/2019. Each analysis was run using a time aggregation of 1 year of length and the option scan for areas with high or low rates was selected. The results obtained were visualized in a map from Google Earth. Areas with the highest concentration of cases are shown in red. Meanwhile, clusters in blue show those municipalities below the average value assessed for the years analyzed.