TY - JOUR
T1 - Analysis of HIV/AIDS DRG in Portugal
T2 - A hierarchical finite mixture model
AU - Dias, Sara Simões
AU - Andreozzi, Valeska
AU - Martins, Rosário O.
PY - 2013
Y1 - 2013
N2 - Inpatient length of stay (LOS) is an important measure of hospital activity, but its empirical distribution is often positively skewed, representing a challenge for statistical analysis. Taking this feature into account, we seek to identify factors that are associated with HIV/AIDS through a hierarchical finite mixture model. A mixture of normal components is applied to adult HIV/AIDS diagnosis-related group data (DRG) from 2008. The model accounts for the demographic and clinical characteristics of the patients, as well the inherent correlation of patients clustered within hospitals. In the present research, a normal mixture distribution was fitted to the logarithm of LOS and it was found that a model with two-components had the best fit, resulting in two subgroups of LOS: a short-stay subgroup and a long-stay subgroup. Associated risk factors for both groups were identified as well as some statistical differences in the hospitals. Our findings provide important information for policy makers in terms of discharge planning and the efficient management of LOS. The presence of "atypical" hospitals also suggests that hospitals should not be viewed or treated as homogenous bodies.
AB - Inpatient length of stay (LOS) is an important measure of hospital activity, but its empirical distribution is often positively skewed, representing a challenge for statistical analysis. Taking this feature into account, we seek to identify factors that are associated with HIV/AIDS through a hierarchical finite mixture model. A mixture of normal components is applied to adult HIV/AIDS diagnosis-related group data (DRG) from 2008. The model accounts for the demographic and clinical characteristics of the patients, as well the inherent correlation of patients clustered within hospitals. In the present research, a normal mixture distribution was fitted to the logarithm of LOS and it was found that a model with two-components had the best fit, resulting in two subgroups of LOS: a short-stay subgroup and a long-stay subgroup. Associated risk factors for both groups were identified as well as some statistical differences in the hospitals. Our findings provide important information for policy makers in terms of discharge planning and the efficient management of LOS. The presence of "atypical" hospitals also suggests that hospitals should not be viewed or treated as homogenous bodies.
KW - Diagnosis related group
KW - Hierarchical modelling
KW - Length of stay
KW - Mixture regression
UR - http://www.scopus.com/inward/record.url?scp=84893677414&partnerID=8YFLogxK
U2 - 10.1007/s10198-012-0416-5
DO - 10.1007/s10198-012-0416-5
M3 - Article
C2 - 22864565
AN - SCOPUS:84893677414
SN - 1618-7598
VL - 14
SP - 715
EP - 723
JO - European Journal Of Health Economics
JF - European Journal Of Health Economics
IS - 5
ER -