Study Design and Settings
This ecological study used secondary data provided by the Colombian Ministry of Health.
Colombia has an estimated population of 50,375,594 in a 2,070,408 km2 area. It is divided into five main political regions (the Caribbean, Pacific, Andean, Orinoquia and Amazon regions), 32 provinces and 1,123 municipalities. Colombia borders Brazil and Venezuela in the east, Ecuador and Peru in the south, Panama in the northwest and the Caribbean Sea and the Pacific Ocean in the north and west borders, respectively (Fig. 1). Among the LA countries, Colombia has the fourth highest income distribution inequality (GINI index = 0.50). The majority of people live in poverty without secure jobs, having low educational attainment and poor access to healthcare services.(14-15) Colombia has one of the highest sexual tourism rates compared to other countries worldwide[14], with the Caribbean and ‘coffee belt’ regions being the main tourist destinations. The ‘coffee belt’ comprises the states of Risaralda, Caldas and Quindio.
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The health system adopted in Colombia is the General Health Social Insurance (GHSI) based on managed competition and with two kinds of insurance: a private contributory scheme and the state-subsidised one for low-income people. Also, 92.4% of the Colombian population is covered by the GHSI, with a larger proportion in the subsidized system. Both contribution schemes have different health provider companies, with the public one having a lower coverage of health services and lower accessibility than the private one. The states of the Amazon region have fewer health provider companies compared with other states.[16-17]
Study Population and Variables
This study population is comprised of all HIV/AIDS morbid cases and AIDS-related deaths, reported to the Public Health Surveillance System (PHSS) and to the Department of National Administration and Statistics (DNAS) from the Colombia National Institute of Health. Only information containing Colombian home addresses, occurring between 2008 and 2016, was included in this study.
We collected the following variables: age, sex, year of diagnosis and death, province and city of residence. Data were double-checked to remove inconsistencies and redundancies.
Statistics Analyses
Temporal trend analysis
We employed joinpoint regression to analyse changes in the annual incidence of HIV/AIDS, as well as in AIDS mortality rates, during the period of the study. All analyses were done in the Joinpoint Trend Analysis software 4.8.0.1 (National Cancer Institute, Calverton, MD. USA) and steps have been described previously.[18] Briefly, both incidence and mortality rates were directly age-adjusted following the joinpoint regression model. Joinpoint regression continues to add joinpoints in a linear trend until the number of joints cumulate to distinguish two different periods from one trend. The best-fitting adjusted model was assessed by the Monte Carlo permutation test. Here, we considered annual percent changes (APC), 95% confidence interval (CI 95%) and p-value. Upward and downward trends were considered only with positive or negative APC, respectively, and with p<0.05. If these conditions were not met, it was considered a stationary trend.
All data were grouped by year, sex, counties, region and age range. The age ranges (years) were categorised into four groups: 0–14, 15–44, 45–64 and 65 and over. These categories were made on the basis of available information in the DNAS database for these age groups. The annual incidence of HIV/AIDS and AIDS mortality was calculated on the basis of annual population projections for the whole country and regions, and for the specific age groups and sexes. Both rates were standardised by 100,000 inhabitants.
We considered the incidence and mortality coefficients as the dependent variables. The study years were considered the independent variable.
Spatial analysis
We analysed the spatial distribution of HIV/AIDS incidence and AIDS mortality rates and their spatial autocorrelation and Kernel intensity estimator. To avoid the annual variation in the reported cases of HIV/AIDS and AIDS-related deaths, we grouped the data in 3-year periods: 2008–2010, 2011–2013 and 2014–2016.
Municipal HIV/AIDS incidence and AIDS mortality rates were analysed through choropleth maps. Both rates were calculated for each Colombian city on the basis of their average population projection for each 3-year period. The results were standardised for 100,000 inhabitants.
To assess the overall trend pattern of these variables, we analysed the spatial autocorrelation incidence and mortality rates using Global Moran’s I. The Global Moran’s l index ranges from -1 to 1: an index of -1 means dispersion, 0 is random behaviour and 1 means perfect association. However, to localize the clusters, we used the Local Indicator of Spatial Association (LISA) method. In LISA maps, we can identify four different spatial relationship groups of the analysed variable. High–high and low–low aggregations are areas with a high or low value of the variable under study surrounded by neighbouring areas, which have like values above or below average, respectively, of the specific variable. By contrast, high–low or low–high groups are areas with a high or low value of the analysed variable surrounded by neighbouring areas with opposite values below or above average of the same variable, respectively. We used the standardized first-order queen neighbours and the p-value obtained from 999 permutations as the definition of ‘neighbourhood’. We only considered spatial dependency with Global’s I index (I) above 0 and with p<0.05.[19]
The spatial distribution and the autocorrelation analyses were conducted using the software ArcGis 10.6.1 (ESRI, Redland, CA. USA). The maps were constructed in the Universal Transverse Mercator Coordinate System (UTM), datum D_Bogota, on a scale of 1:12.000.000.
To analyse the direction of expansion or contraction of the HIV epidemic in Colombia, we used the Kernel density estimator. This method allows estimation of a density of an event occurring in one area that can influence its neighbourhood. Events occurring in places closer to each other receive a higher weight than events occurring in distant places.[20] Kernel analysis employs the adaptive influence radius and quartic Kernel smoothing function using the software TerraView 4.2.2 (INPE, Sao Paulo, SP. BR). We considered the municipalities as units of analysis.