Morbi-Mortality of the Victims of Internal Conflict and Poor Population in the Risaralda Province, Colombia

Abstract

This work studies the health status of two populations similar in most social and environmental interactions but one: the individuals from one population are victims of an internal armed conflict. Both populations are located in the Risaralda province, Colombia and the data for this study results from a combination of administrative records from the health system, between 2011 and 2016. We implemented a methodology based on graph theory that defines the system as a set of heterogeneous social actors, including individuals as well as organizations, embedded in a biological environment. The model of analysis uses the diagnoses in medical records to detect morbidity and mortality patterns for each individual (ego-networks), and assumes that these patterns contain relevant information about the effects of the actions of social actors, in a given environment, on the status of health. The analysis of the diagnoses and causes of specific mortality, following the Social Network Analysis framework, shows similar morbidity and mortality rates for both populations. However, the diagnoses' patterns show that victims portray broader interactions between diagnoses, including mental and behavioral disorders, due to the hardships of this population.

Keywords: cardiovascular disease; mental health; morbidity; mortality; social network.

Figure 1

Structure of the Morbidity Network.

Figure 2

Graph of the Morbidity Network of Diagnoses for all medical records of residents in the Risaralda province, from 2011 to 2016. Source: authors’ own calculations.

Figure 3

Nodal degree and nodal strength for the population residing in the Risaralda province. Complementary cumulative distribution functions for (a) the nodal degree probability distribution and (b) the nodal strength of the Morbidity Network of Diagnoses for the total population resident in the Risaralda province, from 2011 to 2016. Source: authors’ own calculations.

Figure 4

Detection algorithm for k-communities. (a) Cliques detection with size k (b) Detection of the sequence of Cliques (c) Selection of the Clique’s neighbors with size k-1 (d) Formation of K – Communities. Source: authors’ own calculations.

Figure 5

Structure of the Mortality Network. The nodes or vertices represent the causes of death: $C_1$ is the main cause, while $C_2$ and $C_3$ are the secondary causes. The edges or links represent the number of cooccurrences of both primary and secondary causes for every individual. Source: authors’ scheme.

Figure 6

Nodal degree and nodal strength for the victims of the armed conflict in Colombia, resident in the Risaralda province. Complementary cumulative distribution functions of (a) the nodal degree K, and (b) the nodal strength $F_K$ for the Morbidity Network of Diagnoses for the victims of the armed conflict in Colombia, residing in the Risaralda province, from 2011 to 2016. Source: authors’ own calculations.

Figure 7

Motif coherence for cliques and communities. Motif coherence for (a) cliques and for (b) communities for the Morbidity Network of Diagnoses of the victims of the armed conflict in Colombia, residing in the Risaralda province, from 2011 to 2016. Source: authors’ own calculations.

Figure 8

Motifs of diagnoses with the highest coherence level. Morbidity Network of Diagnoses for the victims of the armed conflict in Colombia, residing in the Risaralda province, from 2011 to 2016. Source: authors’ own calculations. Note that the ICD-X codes correspond to: E782: mixed hyperlipidaemia, I119: hypertensive heart disease without heart failure, E119: type 2 diabetes mellitus without complications, E039: hypothyroidism, unspecified, E781: pure hyperglyceridemia, F341: dysthymic disorder, and F412: mixed anxiety and depressive disorder.

Figure 9

Clique with the highest overlap in morbidity and mortality networks. Mortality Network of Diseases for the victims of the armed conflict in Colombia, resident in the Risaralda province, from 2011 to 2016. Source: authors’ own calculations. Note that the ICD-X codes correspond to: E119: type 2 diabetes mellitus without complications, I119: hypertensive heart disease without heart failure, and E107: diabetes mellitus insulin dependent, with multiple complications.

Figure 10

Nodal degree and nodal strength for the SISBEN I and II population resident in the Risaralda province. Complementary cumulative distribution functions of (a) the nodal degree K, and (b) the nodal strength, $F_K$ for the Morbidity Network of Diagnoses of the SISBEN I and II population, residing in the Risaralda province, from 2011 to 2016. Source: authors’ own calculations.

Figure 11

Motif coherence for cliques and SISBEN I and II. Motif coherence for (a) cliques and (b) SISBEN I and II for the Morbidity Network of Diseases for victims of the armed conflict in Colombia, residing in the Risaralda province, from 2011 to 2016. Source: authors’ own calculations.

Figure 12

Highest overlapping cliques in morbidity and mortality for SISBEN I and II. Mortality Network of Diseases in the SISBEN I and SISBEN II population residing in the Risaralda province, from 2011 to 2016. Source: authors’ own calculations. Note that the ICD-X codes correspond to: E781: pure hyperglyceridemia, I10X: essential (primary) hypertension, and E119: type 2 diabetes mellitus without complications.