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Factors Associated with Late Diagnosis of HIV/AIDS in a University Hospital in Brazil: Challenges to Achieving the 2030 Target

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18 July 2023

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Abstract
Introduction: This study aimed to identify factors associated with late diagnosis and clinically monitor newly diagnosed HIV/AIDS patients. Method: Retrospective longitudinal study, based on secondary data from a specialized unit at a tertiary hospital. Data collection included sociodemographic, behavioral, clinical, and laboratory data of newly diagnosed HIV patients between 2015-2019. Data analysis adopted inferential statistical tests using the SPSS program, considering α ≤ 0.05. Results: 314 individuals were newly diagnosed with HIV/AIDS. 70.3% (208) had a late diagnosis, and 57.1% (169) were diagnosed very late. There was an association of the very late diagnosis with the variables sex and education and with origin, entry, occurrence of opportunistic diseases, use of ARV therapy, and death, associated with late and very late diagnosis, respectively. The results of the regression model indicate that males had 2.28 [95% CI 1.11 - 4.46] higher chances of having a late diagnosis compared to females. Conclusions: This study evidenced a high prevalence of late and very late diagnoses in newly diagnosed male HIV patients who presented with opportunistic diseases, requiring hospitalization, and having a significant risk of progressing to death.
Keywords: 
Subject: Medicine and Pharmacology  -   Clinical Medicine

1. Introduction

After four decades, human immunodeficiency virus (HIV) infection remains a global public health problem and continues to cause a high number of new infections. The Joint United Nations Programme on HIV/AIDS (UNAIDS) estimates that in 2021, 1.5 million people were diagnosed with HIV1. The World Health Organization (WHO) has defined early diagnosis of HIV infection as one of the priorities for controlling new infections2.
Currently, the combination of clinical and immunological aspects is used as a reference to define late diagnosis, aiming to identify the stage of HIV infection progression. For adults and adolescents, advanced HIV disease is defined as a CD4 cell count below 200 cells/mm3 or clinical stage 3 and/or 43. Brazil adopts European parameters, where individuals with a CD4 count <350 cells/mm3 are considered to have a late diagnosis, and those with a count below 200 cells/mm3 are considered to have a very late diagnosis4,5.
According to late diagnosis estimates by Collins et al. 2022, the European Union and South Africa have 51% and 55% of their diagnosed individuals with CD4 T-cell counts below 350 cells/mm3, respectively. When considering counts below 200 cells/mm3, this percentage drops to 31% in both regions4.
Once early diagnosis is established, starting antiretroviral therapy (ART) and maintaining CD4+ cell counts above 500 cells/mm3 with undetectable viral load, life expectancy similar to that of individuals not living with the virus can be observed6.
In Canada, a study reported that in order to achieve the priority goal of early diagnosis of HIV-infected individuals, it is essential not to miss opportunities in health services, increasing the availability of rapid tests to reach asymptomatic individuals with CD4+ cell counts above 500 cells/mm3 and without opportunistic diseases6,7.
Chone et al. (2022), in Mozambique, observed that despite all the progress in combating the HIV epidemic in the region, some challenges remain, such as the high prevalence of late HIV diagnoses, even after the widespread availability and promotion of free diagnostic tests8.
Late presentation continues to be a major challenge worldwide, despite all efforts made to prevent diseases and mortality associated with late initiation of antiretroviral therapy (ART) and ensure continuous HIV treatment adherence9. In this context, a study in China showed a 57.6% increase (95% CI: 54.5-60.7%) in late HIV diagnosis. Over four years, there was an increase in late presentation of HIV infection, with rates of 52.8% in 2017 and 61.2% in 202010.
Thus, the first CD4 T-cell count allows determining the stage of the disease. In Brazil, a high number of people living with HIV/AIDS (PLWHA) present themselves for the first time to the public health system (SUS) with a CD4 count below 200 cells/mm³ 5. Between 2012 and 2015, there was a decline in the trend of late presentation and advanced disease at healthcare facilities. However, in the historical series from 2015 to 2019, the rate of late diagnoses remained stable, with a 29% increase from September 20225. Therefore, this study aimed to identify factors associated with late diagnosis and clinically monitor newly diagnosed HIV/AIDS patients.

2. Method

This is an observational, analytical study with a quantitative approach, retrospectively based on secondary data collected from the Electronic Patient Record (EPR).
The study was conducted at a specialized unit of a large tertiary hospital in the interior of São Paulo, the Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (HCFMRP-USP). The Specialized Unit for Infectious Diseases Therapy (UETDI) was inaugurated in 1996 with the aim of serving patients diagnosed with HIV/AIDS. It is considered a tertiary reference for the care of PLHIV in the Regional Health Department XIII (DRS XIII), which encompasses 26 municipalities. The healthcare is exclusively provided through the Unified Health System (SUS). The unit comprises three sectors: ward, day hospital, and outpatient clinic for patients requiring medium to high complexity care.
The study population consists of newly diagnosed cases of HIV/AIDS who sought initial care through outpatient appointments or hospitalization in the ward, whether referred from other services or not. Inclusion criteria were adopted as follows: To be aged 18 or older, of both sexes; have available electronic medical records; have been diagnosed with HIV/AIDS in the past 6 months; and be the first visit to UETDI, either through outpatient care or ward admission, between 2015 and 2019. Pregnant individuals were excluded.
Data collection for sociodemographic, behavioral, clinical, and laboratory variables was done using patient record entries through a questionnaire developed for this study (APPENDIX A). The chosen period was 2015 to 2019, as electronic medical records were implemented in 2015, replacing paper documents. A review of all patient records during the selected period who received care initially through the unit’s outpatient clinic and later through the ward was conducted. Data for one year after the initial visit were also collected using the same instrument, aiming to gather information on clinical progress, latest viral load and CD4 T-cell count results, comorbidities during this period, number of recurrent hospitalizations, and deaths.
The following variables were selected: a) sociodemographic: sex (female/male), sexual orientation, date of birth, age, place of birth and origin, marital status, and education level; b) behavioral: smoking, alcohol consumption, illicit drug use, types of illicit drugs, and duration of illicit drug use; c) clinical-epidemiological: date of first visit, patient’s origin, duration of HIV infection diagnosis, use of antiretroviral therapy (ART), duration of ART use, CD4 T-cell count, individual viral load quantification, associated comorbidities, number of recurrent hospitalizations, and treatment abandonment; d) death criteria (death report mentioning AIDS or HIV and cause of death associated with immunodeficiency - without classification by another criteria after investigation; date of death; reason and location), described according to the B20-B24 codes of the International Classification of Diseases (ICD-10) and recorded in the electronic medical records.
The data were entered into an Excel spreadsheet for Windows, with double entry and data validation to identify possible typing errors. Subsequently, the spreadsheet was imported into the Statistical Package for the Social Sciences (SPSS), version 25.0 for Windows, for data analysis. Descriptive statistics, such as mean, median, standard deviation, minimum, and maximum, were used for continuous variables, while relative and absolute frequencies were used for categorical variables. Inferential statistics, such as Fisher’s exact test, paired t-test, and Logistic regression to find the odds ratio was performed, adopting a value of α ≥ 0.05.
The present research project was submitted and approved by the Research Ethics Committee of the Ribeirão Preto School of Nursing, University of São Paulo, under protocol number 4,143,945.

3. Results

A total of 1,120 HIV/AIDS patients were treated at the tertiary hospital during the study period, of whom 314 (28%) were newly diagnosed with HIV infection. Among the total, 70.3% (208) had a late diagnosis, and 57.1% (169) had a very late diagnosis.
Table 1 presents the sociodemographic and behavioral variables associated with receiving a late and very late diagnosis of HIV infection. An association was identified with the variables of gender (p-value = 0.01 and 0.04, respectively) and education level for the very late diagnosis (p-value = 0.03).
Regarding the clinical variables, an association was identified between the variables of origin, admission, and the occurrence of opportunistic diseases [p < 0.001] with late and very late diagnosis. There was also an association between the use of antiretroviral therapy (p-value = 0.04 and 0.01) and death (p-value = 0.01 and <0.001) with late and very late diagnosis, respectively (Table 2).
The results of the regression model indicate that male individuals had 2.28 [1.11 - 4.46] times higher odds of having a late diagnosis compared to females. Having a higher education level (college degree) was a protective factor compared to having only a primary education for late diagnosis. Additionally, not having an opportunistic disease was a protective factor for not having a late diagnosis compared to individuals with an opportunistic disease [OR=0.12, 95% CI 0.06 - 0.24], as shown in Table 3.
Furthermore, individuals who entered through the emergency department had a lower chance [0.11, 95% CI 0.01 - 0.66] of having a very late diagnosis compared to those diagnosed in the ward/inpatient unit. Not having an opportunistic disease [0.6, 95% CI 0.02 - 0.14] and having a higher education level [0.09, 95% CI 0.09 - 0.90] were protective factors for not having a very late diagnosis of HIV infection, as shown in Table 4.

4. Discussion

This study revealed that although patients seen in the initial care arrive as newly diagnosed cases of HIV, the majority can be considered as having a late diagnosis. Of the patients, 66.2% had a CD4+ T-cell count below 350 cells/mm³ (late diagnosis), and 53.8% had a CD4+ T-cell count below 200 cells/mm³ (very late diagnosis). This high rate is similar to a study conducted in a state in Northeast Brazil, which reported 59.1% in 20176, and to other countries such as China, where a percentage greater than 60% was observed from 2015 to 201611, and Ethiopia, with 68.8% in 201412. These data are alarming and higher when compared to national figures, which identified percentages of 42% in 201513 and 48% in 20225 of individuals diagnosed with a CD4+ T-cell count below 350 cells/mm³.
Certain sociodemographic factors, such as educational level and knowledge, may be directly linked to access to healthcare, impacting the prevalence of late or very late diagnosis14. In this study, it was found that 56.29% (n=94) of patients detected with a CD4+ T-cell count below 200 cells/mm³ had only a primary education, similar to findings from a European cohort study conducted from 1996 to 2013, which identified that lower educational attainment was associated with a higher chance of individuals having lower CD4+ T-cell counts and a lower chance of viral suppression15. Another study conducted in Minas Gerais, Brazil, between 2012 and 2018, also found evidence of an association between lower education and a lower chance of achieving viral suppression16.
Regarding gender, this study found that being male was a factor associated with late diagnosis, which is consistent with previous research. In Nigeria, a high percentage of men were also identified as receiving a late HIV diagnosis (90.1% vs. 83.3% for women; P < 0.001) or having advanced disease (70.4% vs. 59.2% for women; P < 0.001)17. Another study conducted in Salvador, Brazil, found that being male increased the chances of delayed diagnosis, with an adjusted odds ratio of 3.02; 95% CI, 2.0-4.618. According to data from the Brazilian Ministry of Health, when stratified by gender, men stand out compared to women. Regarding late diagnosis (CD4+ T-cell count < 350 cells/mm³), as of September 2022, the proportion between sexes was 49% male and 46% female. When it comes to very late diagnosis (CD4+ T-cell count < 200 cells/mm³), this proportion was 29% among men and 27% among women5.
The higher prevalence observed in males may be related to advances and achievements in HIV response policies for women19 and pregnant women, such as individual or group counseling, family planning, regular gynecological care, prenatal care, HIV testing centers, and maternity wards20. Therefore, access to screening for infections like HIV serves as a facilitator for preventive measures and timely diagnosis. A study conducted in Ghana in 2018 confirmed that the prevalence of HIV among pregnant women was lower compared to the general population21. On the other hand, the testing policy during prenatal care has certain effects on the male population, as women discover the presence of HIV infection during pregnancy or through illness, leading to the diagnosis being discovered by their male partners22.
It is worth noting that the majority of HIV infections occur in men who identify as heterosexual, accounting for 59.6% (n=187) of cases. The literature suggests that this population perceives themselves to be at low risk and does not consider themselves part of the at-risk population22. A systematic review conducted in Africa found that factors such as the role of being the head of the family, fear of sexual behavior after a positive result, stigma, and the societal construction of men as strong and self-sufficient contribute to the higher likelihood of late HIV diagnosis among heterosexual men23.
Nationally, despite the increasing prevalence of HIV/AIDS cases among men who have sex with men (MSM) in recent years, the proportion of cases related to heterosexual exposure remains significant. In the southern region of Brazil, for example, heterosexual exposure accounted for 45.2% of AIDS cases in 202124.
A study conducted in the southern region of Brazil in 2019 showed that low risk perception is extremely relevant. Over 90% of individuals interviewed identified themselves as having low or no risk of HIV infection, and over 30% had never been tested for HIV because they did not consider themselves at risk or saw no reason to get tested25.
Regarding the mode of entry, entering the healthcare facility through the ward was associated with late diagnosis compared to patients who came for outpatient consultations. This finding is similar to a study conducted in Malawi in 2020, which found that 53.3% of hospitalized patients newly diagnosed with HIV had a CD4+ T-cell count below 200 cells/mm³, and 32% already had counts below 100 cells/mm³ 26. Another study conducted in South Africa between 2014 and 2015 showed that patients entering the healthcare system through inpatient settings were more likely to have late HIV diagnosis compared to those entering through outpatient settings. Hospitalized patients had very low CD4+ T-cell counts, with a median of 37 cells/mm³, which was higher than the counts observed in outpatient patients27.
Hospitalized patients had very low CD4+ T-cell counts, with a median of 37 cells/mm³, which was higher than the counts observed in outpatient patients. In this study, the use of antiretroviral therapy (ART) was a protective factor against late diagnosis, with a p-value of 0.050 and a 95% confidence interval of 0.228-0.999. The literature highlights the positive impact of immediate initiation of ART after HIV diagnosis28. Viral suppression through ART has both individual and collective health effects, as the treatment serves as prevention of new infections29. In the United Kingdom, a significant number of people living with HIV achieved viral suppression, reaching 97%, further emphasizing the effectiveness of ART treatment30.
A retrospective cohort study conducted in China between 2006 and 2020 demonstrated that the early initiation of ART in people living with HIV significantly reduced the mortality rate in this population, directly impacting overall mortality rates. However, the same study found that factors such as being male, having a very late HIV diagnosis, initiating ART after 12 months of diagnosis, and virologic failure (detectable viral load after 6 months of treatment initiation or modification) influenced a higher mortality rate31,32.
Despite advances in treatment and care for people living with HIV, mortality rates remain high in cases of late diagnosis24. A study conducted in Colombia between 2009 and 2014 with people living with HIV found that 80% of ICU admissions were associated with opportunistic infections, with 57% attributed to respiratory failure as the leading cause of death in this population33. Similarly, another study conducted in French hospitals between 1997 and 2020 showed that although the proportion of admissions for HIV-diagnosed patients and the rate of opportunistic infections significantly decreased, the reasons for admission remained consistent over time, with respiratory failure being the main cause34.
A similar study in Korea between 2004 and 2018 demonstrated that the development of opportunistic diseases within 6 months after HIV diagnosis was more common in men. Additionally, individuals overall had higher mortality rates when they had AIDS-defining illnesses and AIDS-associated cancers35. It is important to highlight the significance of treatment adherence, as higher mortality rates are observed in people living with HIV who are not adequately treated36.
It is known that initiating ART as early as possible leads to health benefits for the individual, reducing the occurrence of severe events, preventing progression to AIDS, and reducing the risk of HIV transmission37. However, it is worth noting that even after immune recovery in individuals who initiated ART with a CD4+ T-cell count below 200 cells/mm³, they may still be at high risk of AIDS-related complications and death, as shown in a study in Greece in 201938. In South Africa, laboratory data from 2010 to 2014 were used to analyze the immune recovery in individuals on ART and it was found that those who initiated ART with a very late diagnosis and a CD4+ T-cell count below 50 cells/mm³ continued to have an average CD4+ T-cell count below 200 cells/mm³ after 12 months of treatment39.
Late HIV diagnosis has consequences for both individual and public health40, as well as economic implications12. Therefore, strategies are needed to improve timely diagnosis, such as targeted campaigns for prevention and diagnosis, promotion of early testing, and care41. Overcoming barriers such as difficulties in accessing tests, lack of awareness of HIV-related risks and diseases, stigma, and prejudice can significantly reduce cases of late diagnosis42.
The implementation of effective strategies that enable early detection of HIV diagnosis is clearly necessary, leading to a reduction in disease transmissibility and consequently decreasing the likelihood of late diagnoses43. In Spain, in 2019, it was found that 16% of patients diagnosed with HIV had missed opportunities for diagnosis in the previous 5 years, as they sought medical attention for other associated illnesses but did not undergo testing for HIV43. Similarly, a study in Canada from 2001 to 2014 showed that 7 to 14% of individuals had one or more missed opportunities for HIV diagnosis, even in a setting with unrestricted healthcare7.
AIDS-related mortality is strongly associated with late presentation, particularly in the first year following HIV diagnosis. It is clear that one of the causes is the missed opportunities for serological testing when individuals seek healthcare services prior to diagnosis44.
As a retrospective cohort study, this study is subject to limitations that include obtaining secondary data from electronic medical records, which means that some information could not be further explored, such as monitoring and clinical outcomes of patients who were transferred to other HIV and AIDS reference centers within and outside Ribeirão Preto, as well as inadequate completion of the available electronic data collection instruments during the initial assessment, resulting in a lack of important social and behavioral information. Another limitation was the emergence of the COVID-19 pandemic, which may have influenced some current significant results in the care and treatment of patients diagnosed with HIV, as the data collection period extended until 2019.

5. Discussion

This study demonstrated a high prevalence of late and very late diagnosis in patients newly diagnosed with HIV within the past 6 months, who predominantly presented with opportunistic diseases, particularly when requiring hospitalization in a tertiary hospital, with a significant risk of progression to death, posing a challenge to public health authorities in the prevention, control, and transmissibility of the disease.
Therefore, attention should be directed towards factors related to late presentation, both behavioral and clinical, such as being male adults with lower education levels, presence of opportunistic diseases, and use of ART, as evidenced in this study. It is known that late presentation is directly correlated with higher mortality rates, as also highlighted by this study. Clinical outcomes are of utmost importance when evaluating diagnosis and treatment adherence, as they reflect the barriers to prevention and the quality of care provided to this population.
The implementation of new protective measures and campaigns is essential in reducing cases of late presentation, not only among high-risk populations but also among those who perceive themselves as low or no risk. However, further research needs to be conducted to fill the gaps identified in this study, including the impact of the COVID-19 pandemic on the diagnosis and treatment of these individuals and professional training, which can be considered a barrier to timely diagnosis and treatment adherence.

Author Contributions

Conceptualization, LMNA and RKR; Methodology, LMNA and RKR; AOP and MGM; Formal Analysis, LMN, AOP and RKR; Investigation, LMNA; Resources, LMNA; Data Curation, MGM, RKR and EG; Writing – Original Draft Preparation, LMNA and RKR; Writing – Review & Editing, LMNA, RKR, AOP, MGM, EG, EPB and ACOS; Visualization, LMNA, RKR, AOP, MGM, EG, EPB and ACOS; Supervision, RKR; Project Administration, RKR.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ribeirão Preto School of Nursing, Ethics Committee of University of Sao Paulo (protocol code 4,143,945 and date of approval: July 8, 2020).

Acknowledgments

The authors are thankful to the Hospital of the Clinics of the Ribeirao Preto Medical School of the University of São Paulo for releasing the data for analysis and making this study possible.

Conflicts of Interest

The authors declare no conflict of interest.

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Table 1. Sociodemographic and behavioral factors of patients with late and very late diagnosis of HIV infection. Ribeirão Preto, SP, Brazil, 2022.
Table 1. Sociodemographic and behavioral factors of patients with late and very late diagnosis of HIV infection. Ribeirão Preto, SP, Brazil, 2022.
Variables Late Diagnosis (CD4 < 350) Very Late Diagnosis (CD4 < 200)
No Yes *p No Yes *p
n(%) n(%) n(%) n(%)
Gender
Female 32 (36.36) 46 (22.12) 0.01 41 (32.28) 37 (21.89) 0.04
Male 56 (63.64) 162 (77.88) 86 (67.72) 132 (78.11)
Nationality
Ribeirão Preto 15 (17.05) 43 (20.67) 0.37 26 (20.47) 32 (18.93) 0.17
Ribeirão Preto Region 17 (19.32) 30 (14.42) 24 (18.90) 23 (13.61)
State of São Paulo 37 (42.05) 75 (36.06) 51 (40.16) 61 (36.09)
Outside the state of São Paulo 19 (21.59) 60 (28.85) 26 (20.47) 53 (31.36)
Origin
Ribeirão Preto 31 (35.23) 74 (36.63) 0.37 41 (32.80) 64 (38.79) 0.11
Ribeirão Preto Region 18 (20.45) 49 (24.26) 29 (23.20) 38 (23.03)
State of São Paulo 39 (44.32) 74 (36.63) 55 (44.00) 58 (35.15)
Outside the State of São Paulo 0 (0) 5 (2.48) 0 (0) 5 (3.03)
Marital State
Single 48 (54.55) 114 (55.07) 0.90 72 (56.69) 90 (53.57) 0.72
Married/Common-law/Living as married 28 (31.82) 61 (29.47) 38 (29.92) 51 (30.36)
Widower 4 (4.55) 8 (3.86) 6 (4.72) 6 (3.57)
Separated/Divorced 8 (9.09) 24 (11.59) 11 (8.66) 21 (12.50)
Education Level
Elementary School 39 (44.83) 108 (52.68) 0.52 53 (42.40) 94 (56.29) 0.03
High School 33 (37.93) 72 (35.12) 48 (38.47) 57 (34.13)
University 12 (13.79) 20 (9.76) 20 (16.00) 12 (7.19)
Illiterate 3 (3.45) 5 (2.44) 4 (3.20) 4 (2.40)
Sexual Orientation
Straight 50 (60.98) 129 (67.89) 0.89 73 (61.34) 106 (69.28) 0.34
Bisexual 8 (9.76) 12 (6.32) 10 (8.40) 10 (6.54)
Homosexual 16 (19.51) 27 (14.21) 24 (20.17) 19 (12.42)
MSM 8 (9.76) 21 (11.05) 12 (10.08) 17 (11.11)
Others 0 (0) 1 (0.53) 0 (0) 1 (0.65)
Smoker
Yes 55 (63.22) 128 (61.84) 0.89 101 (80.16) 127 (76.05) 0.47
No 32 (36.78) 79 (38.16) 25 (19.84) 40 (23.95)
Drug Use
Yes 33 (37.50) 76 (37.07) 1.00 46 (36.22) 63 (37.95) 0.80
No 55 (62.50) 129 (62.50) 81 (63.78) 103 (62.05)
Source: Study Data, Ribeirão Preto/SP, 2022.
Table 2. Clinical factors associated with late and very late diagnosis among people diagnosed with HIV/AIDS infection. Ribeirão Preto, SP, Brazil, 2022.
Table 2. Clinical factors associated with late and very late diagnosis among people diagnosed with HIV/AIDS infection. Ribeirão Preto, SP, Brazil, 2022.
Variable Late Diagnosis (CD4 < 350) Very Late Diagnosis (CD4 < 200)
No Yes *p No Yes *p
n(%) n(%) n(%) n(%)
Time of Diagnosis (month)
< 1 34 (38.64) 103 (49.52) 0.15 54 (42.52) 83 (49.11) 0.53
1 a 3 44 (50.00) 79 (37.98) 56 (44.09) 67 (39.64)
3 a 6 10 (11.36) 26 (12.50) 17 (13.39) 19 (11.24)
Origin
Home 73 (84.88) 98 (48.28) <0.001 103 (82.40) 68 (41.46) <0.001
UBDS 2 (2.33) 27 (13.30) 4 (3.20) 25 (15.24)
Emergency Unit 2 (2.33) 21 (10.34) 5 (4.00) 18 (10.98)
Transfer within the HC 1 (1.16) 13 (6.40) 3 (2.40) 11 (6.71)
Transfer from another city 6 (6.98) 39 (19.21) 7 (5.60) 38 (23.17)
Transfer from an institution in RP 2 (2.33) 5 (2.46) 3 (2.40) 4 (2.44)
Admission
Hospital Ward – Inpatient 13 (14.77) 115 (55.29) <0.001 22 (17.32) 106 (62.72) <0.001
Emergency Care 60 (68.18) 67 (32.21) 86 (67.72) 41 (24.36)
Outpatient Clinic 15 (17.05) 26 (12.50) 19 (14.96) 22 (13.02)
Opportunistic Disease
Yes 33 (37.50) 181 (87.02) <0.001 54 (42.52) 160 (94.67) <0.001
No 55 (62.50) 27 (12.98) 73 (57.48) 9 (5.33)
ART
Yes 10 (11.36) 44 (21.15) 0.04 15 (11.81) 39 (23.08) 0.01
No 78 (88.64) 164 (78.85) 112 (88.19) 130 (76.92)
Time of ART (month)
< 1 3 (33.33) 16 (37.21) 1.00 4 (30.77) 15 (38.46) 0.83
1 a 3 5 (55.56) 19 (44.19) 6 (46.15) 18 (46.15)
3 a 6 1 (11.11) 8 (18.60) 3 (23.08) 6 (15.38)
Death
Yes 5 (5.68) 32 (15.38) 0.01 9 (7.09) 28 (16.57) <0.001
No 59 (67.05) 120 (57.69) 89 (70.08) 90 (53.25)
Transfer 13 (14.77) 44 (21.15) 17 (13.39) 40 (23.67)
Loss of Segment 11 (12.50) 12 (5.77) 12 (9.45) 11 (6.51)
Source: Study Data, Ribeirão Preto/SP, 2022.
Table 3. Factor associated with late diagnosis of HIV infection according to the logistic regression model. Ribeirão Preto, SP, Brazil, 2022.
Table 3. Factor associated with late diagnosis of HIV infection according to the logistic regression model. Ribeirão Preto, SP, Brazil, 2022.
CD4 < 350 * Odds Ratio Bruto CI95% p valor Odds Ratio Ajustado CI95% p valor
Age 1.022 1.001 – 1.044 0.04 1.013 0.987 – 1.039 0.32
Gender
Female Comparison Category Comparison Category
Male 2.01 1.16 – 3.46 0.01 2.28 1.11 – 4.46 0.02
Origin
Home Comparison
Category 		Comparison Category
UBDS 6.4 1.47 – 27.73 0.01 1.32 0.117 -15.046 0.82
Emergency unit 4.8 1.111 – 21.146 0.04 0.79 0.071 – 8.878 0.85
Transfer within the HC 5.8 0.749 – 45.175 0.09 1.85 0.115 – 30.028 0.66
Transfer from another city 3.1 1.289 – 7.799 0.01 0.52 0.073 – 3.749 0.52
Transfer from another unit in Ribeirão Preto 1.0 0.202 – 5.576 0.94 0.21 0.019 – 2.292 0.20
Admission
Hospital Ward - Inpatient Comparison
Category 		Comparison Category
Emergency Care 0.222 0.129 – 0.378 <0.0001 0.238 0.035 – 1.618 0.14
Outpatient Clinic 0.695 0.348 – 1.388 0.30 0.292 0.038 – 2.242 0.24
Opportunistic Disease
Yes Comparison
Category 		Comparison Category
No 0.08 0.04–0.16 <0.0001 0.12 0.06 – 0.24 <0.0001
Viral Load (VL)_1 1 0.99–1.00 0.10 1 0.999 – 1,0 0.49
ART
Yes Comparison
Category 		Comparison Category
No 0.478 0.228–0.999 0.05 0.781 0.310–1.964 0.60
Death
Yes Comparison
Category 		Comparison Category
No 0.670 0.397–1.130 0.13 1.029 0.295 – 3.585 0.96
Transfer 1.548 0.787–3.044 0.21 0.706 0.173 – 2.886 0.63
Loss of Segment 0.429 0.181–1.012 0.05 0.313 0.068 – 1.454 0.14
Table 4. Factor associated with very late diagnosis of HIV infection according to the logistic regression model. Ribeirão Preto, SP, Brazil, 2022.
Table 4. Factor associated with very late diagnosis of HIV infection according to the logistic regression model. Ribeirão Preto, SP, Brazil, 2022.
CD4 < 200 ** Odds Ratio Bruto CI95% p valor Odds Ratio
Adjusted 		CI95% p valor
Age 1.019 0.999 - 1.038 0.06 0.997 0.968 – 1.027 0.84
Gender
Female Comparison Category Comparison Category
Male 1.70 1.01 – 2.86 0.04 1.92 0.91 – 4.05 0.08
Origin
Home Comparison Category Comparison Category
UBDS 5.441 1.842 -16.073 0.002 0.902 0.1005 – 8.098 0.93
Emergency Unit 2.959 1.067 – 8.204 0.04 0.381 0.050 – 2.907 0.35
Transfer within the HC 2.924 0.798 – 10.713 0.10 1.044 0.119 – 9.185 0.97
Transfer from another city 5.084 2.185 – 11.827 <0.0001 0.522 0.083 – 3.284 0.49
Transfer from another unit in Ribeirão Preto 1.017 0.223 – 4.627 0.98 0.261 0.024 – 2.881 0.27
Admission
Hospital Ward - Inpatient Comparison Category Comparison Category
Emergency Care 0.153 0.092 – 0.255 <0.0001 0.11 0.01 – 0.66 0.01
Outpatient Clinic 0.851 0.439 – 1.649 0.63 0.24 0.03- 1.608 0.14
Opportunistic Disease
Yes Comparison Category Comparison Category
No 0.042 0.019 – 0.089 <0.0001 0.06 0.02 – 0.14 <0.0001
Viral Load (VL)_1 1.0 1.0 – 1.0001 0.03 1.0 0.999 – 1.00 0.13
ART
Yes Comparison Category Comparison Category
No 0.446 0.234 – 0.853 0.01 0.629 0.259 – 1.528 0.31
Education Level
Elementary School Comparison Category Comparison Category
High School 0.849 0.521 – 1.385 0.51 0.257 0.034 – 1.957 0.19
University 0.432 0.201 – 0.927 0.03 0.09 0.09 – 0.90 0.04
Technical Education 0.744 0.148 – 3.749 0.72 0.129 0.008 – 2.087 0.15
Death
Yes Comparison Category Comparison Category
No 0.486 0.299 – 0.790 0.004 1.429 0.492 – 4.153 0.51
Transfer 2.006 1.077 – 3.737 0.03 1.159 0.342 – 3.935 0.81
Loss of Segment 0.667 0.284 – 1.565 0.35 0.713 0.162 – 3.129 0.65
* CI = 95% Confidence Interval; Valor-p Source: Study Data, Ribeirão Preto/SP, 2022.
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