CONSTRUCCIÓN DE UN ÍNDICE MULTIDIMENSIONAL PARA MEDIR EL ACCESO A LOS BIENES Y SERVICIOS DE SALUD DE LAS PERSONAS MAYORES EN UNA LOCALIDAD DE LA PROVINCIA DE BUENOS AIRES

GONZALEZ GISELA PAULA; GERI MILVA; MOSCOSO NEBEL SILVANA; LAGO FERNANDO

SAnta Rosa, La Pampa

Congreso; XXXVI Encuentro Nacional de Docentes en Investigación Operativa: XXXIV Escuela de Perfeccionamiento en Investigación Operativa; 2023

Escuela de Perfeccionamiento en Investigación Operativa

The purpose of this paper is to build multidimensional indices of access to healthgoods and services for those over 60 years of age in the city of Bahía Blanca (BuenosAires Province, Argentina) incorporating all dimensions of this phenomenon. The datacomes from a survey carried out between December 2021 and March 2022 on asample of 200 older adults from the Bahía Blanca district affiliated with an insurer. Forthe processing of the indices, the Categorical Principal Component Analysis(CATPCA)technique was used, whose main advantage has to do with the fact that itallows dealing with variables of a different nature. Likewise, it does not requireassuming that the relationship between the variables analyzed is necessarily linear.Taking into consideration the theoretical framework of reference, 21 variableswere selected from the database to reflect the different dimensions of the phenomenon.They were treated according to their nature (nominal categorical and ordinalcategorical). Four main components were analyzed based on the consideration of theeigenvalues and percentage of explained variance, each of which was then interpretedas a multidimensional index. Those variables whose sum of assumed factor loads ineach squared component (VAF) was less than 0.25 were sequentially eliminated (16variables were finally selected). Once it was verified that the VAF of all the variableswas equal to or higher than 0.25, it was determined in which principal component eachof them presented the highest factorial load. Then, each of the four multidimensionalindices was interpreted. For each main component, the categories of the variables thatpresented the highest relative weight with respect to the other components wereeliminated from the analysis. Immediately after, the assumed scores were multipliedby the transformed value that each category assumes by the factorial load of thevariable to which they correspond in the component under study. All the categoriesthat scored the highest in each case were analyzed, determining whether they areindices of obstacles or facilitators of access to health goods and services. This is thetext.Two indices of facilitators and two of barriers to healthcare access wereformulated, of which only two (one of each type) were validated. The validatedfacilitator index involves aspects related to the dimensions of accessibility (in its threesubdimensions) and adequacy. It is negatively correlated with the presence ofobstacles to access. The other validated index refers to barriers to healthcare accessand is linked to the stages of seeking and using health care. Likewise, it is relatedto the dimensions of adaptation and accessibility (both administrative, organizationaland economic). This index is positively correlated with the presence of barriers toaccess.Keywords: STATISTICS - MULTIDIMENSIONAL INDEX - ACCESS TO HEALTHSERVICES - OLDER ADULTS.Estadística 64REFERENCIASADAY, L. A., & ANDERSEN, R. (1974): “A framework for the study of access to medicalcare”. Health services research, 9(3), 208.CHANDRA, R (2016): “Cities and the questions of health equity: A study ofmultidimensional healthcare access in India”. In European Journal of Public Health (Vol26, pp 371-371).GRECO, S., ISHIZAKA, A., TASIOU, M., & TORRISI, G. (2019): “On the methodologicalframework of composite indices: A review of the issues of weighting, aggregation, androbustness”. Social indicators research, 141(1), 61-94.LINTING, M., & VAN DER KOOIJ, A. (2012): “Nonlinear principal components analysiswith CATPCA: a tutorial”. Journal of personality assessment, 94(1), 12-25.SAUKANI, N., & ISMAIL, N. A. (2019): “Identifying the components of social capital bycategorical principal component analysis (CATPCA)”. Social Indicators Research,141(2), 631-655.