288 Comparison of Stigma in Persons with Mental Illness and People Living with HIV in Sri Lanka

2011 ◽  
Vol 4 ◽  
pp. S42
Author(s):  
S.M. Fernando ◽  
P. Abeykoon ◽  
S. Perera ◽  
F.P. Deane ◽  
H.J. McLeod
2020 ◽  
Author(s):  
Jessica P Ridgway ◽  
Arno Uvin ◽  
Jessica Schmitt ◽  
Tomasz Oliwa ◽  
Ellen Almirol ◽  
...  

BACKGROUND Mental illness and substance use are prevalent among people living with HIV and often lead to poor health outcomes. Electronic medical record (EMR) data are increasingly being utilized for HIV-related clinical research and care, but mental illness and substance use are often underdocumented in structured EMR fields. Natural language processing (NLP) of unstructured text of clinical notes in the EMR may more accurately identify mental illness and substance use among people living with HIV than structured EMR fields alone. OBJECTIVE The aim of this study was to utilize NLP of clinical notes to detect mental illness and substance use among people living with HIV and to determine how often these factors are documented in structured EMR fields. METHODS We collected both structured EMR data (diagnosis codes, social history, Problem List) as well as the unstructured text of clinical HIV care notes for adults living with HIV. We developed NLP algorithms to identify words and phrases associated with mental illness and substance use in the clinical notes. The algorithms were validated based on chart review. We compared numbers of patients with documentation of mental illness or substance use identified by structured EMR fields with those identified by the NLP algorithms. RESULTS The NLP algorithm for detecting mental illness had a positive predictive value (PPV) of 98% and a negative predictive value (NPV) of 98%. The NLP algorithm for detecting substance use had a PPV of 92% and an NPV of 98%. The NLP algorithm for mental illness identified 54.0% (420/778) of patients as having documentation of mental illness in the text of clinical notes. Among the patients with mental illness detected by NLP, 58.6% (246/420) had documentation of mental illness in at least one structured EMR field. Sixty-three patients had documentation of mental illness in structured EMR fields that was not detected by NLP of clinical notes. The NLP algorithm for substance use detected substance use in the text of clinical notes in 18.1% (141/778) of patients. Among patients with substance use detected by NLP, 73.8% (104/141) had documentation of substance use in at least one structured EMR field. Seventy-six patients had documentation of substance use in structured EMR fields that was not detected by NLP of clinical notes. CONCLUSIONS Among patients in an urban HIV care clinic, NLP of clinical notes identified high rates of mental illness and substance use that were often not documented in structured EMR fields. This finding has important implications for epidemiologic research and clinical care for people living with HIV.


2014 ◽  
Vol 18 (6) ◽  
pp. 1133-1141 ◽  
Author(s):  
Erica Breuer ◽  
Kevin Stoloff ◽  
Landon Myer ◽  
Soraya Seedat ◽  
Dan J. Stein ◽  
...  

2016 ◽  
Vol 3 (3) ◽  
pp. 213-217
Author(s):  
Endah Tri Suryani

The spread of HIV and AIDS in Indonesia over the last five years is quite high. United NationsAIDS (UNAIDS) even dub Indonesia as an Asia’s country with most spread of HIV/AIDS. However thefear of stigma and discrimination against PLWHA (people living with HIV/AIDS) remains a majorobstacle. The purpose of this research was to describe self stigma of HIV/AIDS in poly Cendana NgudiWaluyo Hospital Wlingi based questionnaire ISMI (Internalized Stigma of Mental Illness) that includeda portrait of alienation, acceptance of stereotypes, experience of discrimination, social withdrawal,and rejection of stigma. The samples were 27 people with HIV/AIDS. The results showed that generallyself stigma of HIV/AIDS were low 44.4% (12 patients). This result, indicated that the motivation ofpeople living with HIV/AIDS as well as their moral support was instrumental in lowering self-stigma.Recommendations from this study were expected for health care to prevent and overcome self stigma ofHIV/AIDS.


Author(s):  
Rudramma J. ◽  
Jannatbi Iti

Background: HIV infection is one of the raising public health problems. HIV diagnosis is usually associated with stigma and often results in mental illness among the people infected. Depression is the most common mental illness in HIV patients as found by various studies. Hence the present study aimed to determine the proportion of depression and its socio-demographic and clinical predictors among people living with HIV/AIDS (PLHA).Methods: A hospital based cross sectional study was done among 322 PLHA on Antiretroviral therapy attending ART centre at GIMS Teaching Hospital, Gadag. After taking written informed consent from the patients, a predesigned proforma which included socio-demographic variables, clinical details, and CD-4 count, along with patient health questionnaire (PHQ) 9 was administered to assess depression in PLHA.Results: Out of the 322 people living with HIV/AIDS, 108 (33.5%) had depressed. According to PHQ 9 questionnaire, 19.9% had mild depression, 10.6% moderate depression and 3.1% had moderate severe depression. It was noted that 40.3% of females had depression compared to 24.8% of males. PHLA who were on ART for less than one year had higher proportion of depression (61.1%) compared to those with 5 years duration of ART (28.6%) and it was statistically significant.Conclusions: In the study 33.5% of PHLA had depression. Socio-economic status, gender, duration of ART had significant association with depression whereas age, education, place of residence, CD4 count were not associated with depression. Depression screening among PHLA can be done at regular follows ups at ART centres.


10.2196/23456 ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. e23456
Author(s):  
Jessica P Ridgway ◽  
Arno Uvin ◽  
Jessica Schmitt ◽  
Tomasz Oliwa ◽  
Ellen Almirol ◽  
...  

Background Mental illness and substance use are prevalent among people living with HIV and often lead to poor health outcomes. Electronic medical record (EMR) data are increasingly being utilized for HIV-related clinical research and care, but mental illness and substance use are often underdocumented in structured EMR fields. Natural language processing (NLP) of unstructured text of clinical notes in the EMR may more accurately identify mental illness and substance use among people living with HIV than structured EMR fields alone. Objective The aim of this study was to utilize NLP of clinical notes to detect mental illness and substance use among people living with HIV and to determine how often these factors are documented in structured EMR fields. Methods We collected both structured EMR data (diagnosis codes, social history, Problem List) as well as the unstructured text of clinical HIV care notes for adults living with HIV. We developed NLP algorithms to identify words and phrases associated with mental illness and substance use in the clinical notes. The algorithms were validated based on chart review. We compared numbers of patients with documentation of mental illness or substance use identified by structured EMR fields with those identified by the NLP algorithms. Results The NLP algorithm for detecting mental illness had a positive predictive value (PPV) of 98% and a negative predictive value (NPV) of 98%. The NLP algorithm for detecting substance use had a PPV of 92% and an NPV of 98%. The NLP algorithm for mental illness identified 54.0% (420/778) of patients as having documentation of mental illness in the text of clinical notes. Among the patients with mental illness detected by NLP, 58.6% (246/420) had documentation of mental illness in at least one structured EMR field. Sixty-three patients had documentation of mental illness in structured EMR fields that was not detected by NLP of clinical notes. The NLP algorithm for substance use detected substance use in the text of clinical notes in 18.1% (141/778) of patients. Among patients with substance use detected by NLP, 73.8% (104/141) had documentation of substance use in at least one structured EMR field. Seventy-six patients had documentation of substance use in structured EMR fields that was not detected by NLP of clinical notes. Conclusions Among patients in an urban HIV care clinic, NLP of clinical notes identified high rates of mental illness and substance use that were often not documented in structured EMR fields. This finding has important implications for epidemiologic research and clinical care for people living with HIV.


2016 ◽  
Vol 92 (Suppl 1) ◽  
pp. A45.1-A45
Author(s):  
A.A.I.N Jayasekara ◽  
D.A.C.L Dalugama ◽  
W.M.S.N.K Nawarathne ◽  
K.M.N.G.N Dias ◽  
S.D Dharmarathne

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