Admission diagnoses and outcomes among hospitalized people living with HIV during pre-COVID-19,COVID-19 and post-COVID-19 pandemic periods

Abstract

Background

Data on the impact of coronavirus disease 2019 (COVID-19) on hospitalization and outcomes among people living with HIV (PLHIV) are limited.

Methods

A retrospective cohort study was conducted among PLHIV hospitalized during the pre-COVID-19, COVID-19, and post-COVID-19 periods.

Results

Of the 310 PLHIV included, 117, 125 and 68 were admitted during the three periods, respectively and 115 (37%) were newly diagnosed with HIV. Median CD4 cell counts and proportions of those with antiretroviral therapy (ART) adherence rates ≥95% at admission were different between the three periods [(206, 97 and 138 cells/mm³ (p = .02) and 97%, 89% and 100% (p = .06), respectively]. Of the 310 PLHIV, admission diagnoses were non-AIDS-related (62%) and AIDS-related (38%). Most of the non-AIDS-related diagnoses were infections other than opportunistic infections (OIs) (40%) while OIs were the most common for AIDS-related diagnoses (88%). The types of admission diagnoses were comparable between the three periods. Hospital mortality rates were 10%, 13% and 16% during pre-COVID-19, COVID-19 and post-COVID-19 periods, respectively (p = .80). By multivariable analysis, intensive care unit admission, underlying malignancy, monthly income less than $USD 400, and admission CD4 less than 50 cells/mm³ were independently associated with hospital mortality.

Conclusions

Although admission during COVID-19 pandemic period was not associated with increased mortality, we observed the impact of the pandemic on the lower CD4 cell count and ART adherence at admission among hospitalized PLHIV. Interventions to improve early care engagement, ART adherence, and close monitoring for those with identified mortality risks are needed for better HIV care, especially during pandemics.

Keywords

Hospitalized COVID-19 pandemic people living with HIV human immunodeficiency virus Thailand

Introduction

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was declared a public health emergency of international concern in January 2020 and was classified as a global pandemic in March 2020.¹ SARS-CoV-2 can readily spread through large droplets from coughing, sneezing and talking and through small aerosol particles and contact with bodily fluids such as mucus, saliva, and feces.² Given the broad range of COVID-19 manifestations, asymptomatic transmission did occur during the pandemic.² To control and mitigate the impact of COVID-19, countries worldwide had implemented various measures, particularly social distancing measures such as quarantine, travel restrictions, closure of services, and total lockdowns. These measures had led to limited mobility and increased difficulty in accessing medical care in general population. In addition, COVID-19 affected healthcare resources and administration in Thailand. In our tertiary-care hospital, the average COVID-19-related absenteeism rate for healthcare professionals (HCP) in each department during the pandemic was 15% while there was a 10% increase in number of beds to accommodate hospitalized COVID-19 patients resulting in high workload and task shifting among HCP.

Individuals with comorbidities, including hypertension, diabetes, obesity, chronic heart failure, chronic lung disease, chronic kidney disease, cancer, organ transplant recipients, and people living with human immunodeficiency virus (PLHIV), are at increased risk of COVID-19 acquisition, and COVID-19 related hospitalization and mortality.^3,4 PLHIV hospitalized with COVID-19 were more likely to require intensive care unit (ICU) admission and respiratory or circulatory support compared to those without HIV.⁵ Given the implemented infection control measures and diversion of general care to COVID-19 care among healthcare professionals, accessing HIV care during the COVID-19 pandemic had been challenging, leading to missed appointments and decreased adherence to antiretroviral therapy (ART) and complicating PLHIV’s health outcomes.⁶

During the pre-COVID-19 period, the most common reasons for hospitalization among PLHIV were opportunistic infections (OIs), such as tuberculosis, pneumocystosis, cryptococcosis, toxoplasmosis, and candidiasis.^7–14 The in-hospital mortality rates were reported to be 5–40% depending on settings, severity of illnesses and period in relation to the combined ART era.^7–10,12,14 The proportion of OIs as the admission diagnosis and in-hospital mortality rate had decreased while the proportion of non-HIV-related illnesses had increased from the early to the late combined ART eras.^9,11,12 In Thailand, there were significant declines in the proportion of OIs as admission diagnoses (100%–84%) and the mortality rate (28%–10%) during the period from 2002 to 2010.^7,8 Nonetheless, existing data are limited in regards to admission diagnoses and outcomes during COVID-19 and post-COVID-19 periods and no study has evaluated inpatient complications, ART management during hospitalization, and linkage to care after discharge during these periods. To investigate the impact of the COVID-19 pandemic on hospitalization among PLHIV, this study aimed to compare PLHIV’s characteristics, admission diagnoses, and relevant treatment outcomes among those admitted in the periods before, during, and after the COVID-19 pandemic in Thailand.

Methods

Study design and participants

A retrospective cohort study was conducted among PLHIV who were hospitalized at Thammasat University Hospital (TUH), a 700-bed tertiary-care medical center serving populations in central Thailand, during the period from September 1, 2017, to October 31, 2023. Participants must be aged 18 years or older and diagnosed with HIV prior or at the time of hospitalization. Exclusion criteria include persons who were hospitalized for scheduled elective surgery, procedure or medical treatment, such as chemotherapy, those in the terminal stage of any illnesses who had received palliative care before hospitalization, and those with missing data required for the study, lost data, or data inaccessible for retrieval. Ethical approval was obtained from the Human Research Ethics Committee of Thammasat University (Medicine), Thailand (protocol approval number 006/2567). The study was conducted in accordance with the amended Declaration of Helsinki, the Belmont Report, the Council for International Organizations of Medical Sciences (CIOMS) Guidelines, and the International Conference on Harmonization-Good Clinical Practice (ICH-GCP). Informed consent was waived in this study due to the nature of retrospective design and minimal risk to the participants.

Study protocol

PLHIV hospitalized during the study periods were identified from the database of the HIV/AIDS Care Unit of TUH, which has collected information about hospital care among all PLHIV since 2014. Some additional data were collected from the TUH paper and electronic medical records to ensure the completeness of the data. Participants were categorized into three groups according to the periods they were hospitalized as 1) Period 1: The pre-COVID-19 pandemic period: September 30, 2017, to March 25, 2020 (before the enforcement of the first emergency decree for COVID-19 in Thailand) 2) Period 2: The COVID-19 pandemic period: March 26, 2020, to September 30, 2022 (during the emergency decree for COVID-19 in Thailand) and 3) Period 3: The post-COVID-19 pandemic period: October 1, 2022, to October 31, 2023 (after the lifting up of the emergency decree for COVID-19 in Thailand). Data were collected and recorded in a case report form, which included basic demographics, HIV clinical data, comorbidities, health status, diagnosis made and complications occurring during hospitalization, treatment outcomes, and continuity of care after hospital discharge. The primary outcome was the diagnosis leading to hospitalization of each PLHIV admitted during the study periods. The secondary outcomes included the treatment outcomes, occurrence of complications and characteristics of ART before and during admission and at the time of discharge, and linkage to care comparing between the 3 study periods.

Statistical analysis

According to our review, the in-hospital mortality rate among PLHIV in our hospital was 10% prior to the COVID-19 pandemic period; we anticipated this in-hospital mortality rate to be 25% during the COVID-19 pandemic period. To detect this difference in the mortality rate, with a statistical power of 80% at a significant level of 95%, the required sample size was 100 per group. Descriptive data were reported in numbers (percentage) and median (interquartile range, IQR). Categorical data were compared using Chi-square test, while continuous data were compared using Kruskal-Wallis Test between PLHIV admitted during the three study periods (due to non-normal distribution of the data). Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were calculated through multivariable logistic regression analysis to identify factors associated with hospital mortality among the study participants. A p-value <.05 was considered statistically significant. All data analyses were conducted using SPSS version 29 (Chicago, IL, USA).

Results

Baseline and HIV clinical characteristics of study participants

There were 375 PLHIV who were hospitalized during the all study periods and 65 were excluded due to hospitalization for scheduled elective surgery (N = 25), undergoing procedure or medical treatment, such as chemotherapy (N = 22), being in the terminal stage of any illnesses who had received palliative care before hospitalization (N = 8), and missing data required for the study, lost data, or data inaccessible for retrieval (N = 10). The remaining 310 PLHIV were included in this study of which 117, 125 and 68 were hospitalized during pre-COVID-19, COVID-19 and post-COVID-19 periods, respectively. Most of the participants were male (67.7%) with median age of 42 years (range 16–87 years) (Table 1). Over half were single (55.5%), had the highest education level of bachelor’s degree (58.7%), and had a monthly income ≥ $US450 (75.5%). Comparing the participants in the three periods, those admitted in the pre-COVID-19 period were significantly younger, otherwise, there were no significant differences in other baseline characteristics and comorbidities (Table 1). Regarding the HIV clinical characteristics (Table 2), 115 (37.1%) were newly diagnosed with HIV during this hospitalization. The majority of all participants had been known to have HIV infection for less than 1 year (53.2%). Median CD4 cell count on admission for the whole cohort was 141.5 cells/mm³, with 48.4% unaware of their viral load and 36.1% having suppressed a viral load. Most of the PLHIV with established care prior to admission had follow-up visit rates of over 95% and most of the PLHIV previously on ART had adherence rates of over 95%. When comparing between PLHIV admitted during the three periods, those admitted during the COVID-19 period had significantly lower CD4 counts and a significantly higher proportion of unsuppressed viral load compared to the other two groups. There was a significantly higher proportion of PLHIV not receiving ART on admission in both COVID-19 and post-COVID-19 periods compared to the pre-COVID-19 period. The most common ART regimen received prior to admission was significantly different between the three groups due to changes in Thailand National Guidelines recommendations.¹⁵ The most common regimen used for PLHIV admitted during the pre-COVID-19 and COVID-19 periods was a non-nucleoside reverse transcriptase inhibitor (NNRTI) + 2 nucleotide reverse transcriptase inhibitors (NRTIs) regimen, while an integrase strand transfer inhibitor (INSTI) +2 NRTIs regimen was the most common received regimen for PLHIV admitted during the post-COVID-19 period.

Table 1.

Baseline demographic and clinical characteristics of the 310 study participants stratified by admission periods.

Characteristic	Total (N = 310)	Pre-COVID (N = 117)	COVID (N = 125)	Post-COVID (N = 68)	p a
Age (year, median, IQR)	42 (32–53)	38 (29–51)	46 (34–58)	45.5 (32–51)	.019
Gender					.198
Male	210 (67.7)	73 (62.4)	86 (68.8)	51 (75.0)
Female	100 (32.3)	44 (37.6)	39 (31.2)	17 (25.0)
Marital status					.103
Single	172 (55.5)	65 (55.6)	65 (52.0)	42 (61.8)
Married	121 (39.0)	49 (41.9)	52 (41.6)	20 (29.4
Divorced	11 (3.5)	0 (0)	7 (5.6)	4 (5.9)
Widowed	2 (0.6)	1 (0.9)	1 (0.8)	0 (0)
Living together without marriage	4 (1.3)	2 (1.7)	0 (0)	2 (2.9)
Nationality					.229
Thai	283 (91.3)	110 (94.0)	110 (88.0)	63 (92.6)
Othersb	27 (8.7)	7 (6.0)	15 (22.0)	5 (7.4)
Occupation					.06
Government officer	68 (21.9)	22 (18.8)	31 (24.8)	15 (22.1)
Company employee	80 (25.8)	44 (37.6)	22 (17.6)	14 (20.6)
Self-employed	71 (22.9)	23 (19.7)	33 (26.4)	15 (22.1)
Agriculturist	9 (2.9)	3 (2.6)	4 (3.2)	2 (2.9)
Housekeeper	13 (4.2)	5 (4.3)	3 (2.4)	5 (7.4)
Student	15 (4.8)	7 (6.0)	1 (0.8)	7 (10.3)
Unemployed	28 (9.0)	8 (6.8)	15 (12.0)	5 (7.4)
Othersc	26 (8.4)	5 (4.3)	16 (12.8)	5 (7.4)
Highest education					.149
Primary school	3 (1.0)	2 (1.7)	1 (0.8)	0 (0)
Secondary school	125 (40.3)	39 (33.3)	60 (48.0)	26 (38.2)
Bachelor’s degree	182 (58.7)	17 (65.0)	64 (51.2)	42 (61.8)
Monthly household income (USD)					.323
<150	9 (2.9)	3 (2.6)	2 (1.6)	4 (5.9)
150-<450	67 (21.6)	20 (17.1)	32 (25.6)	15 (22.1)
450-<900	156 (50.3)	60 (51.3)	65 (52.0)	31 (45.6)
≥900	78 (25.2)	34 (29.1)	26 (20.8)	18 (26.5)
Current substance use
Tobacco	135 (43.5)	49 (41.9)	57 (45.6)	29 (42.6)	.831
Alcohol	174 (55.5)	63 (53.8)	67 (53.6)	42 (61.8)	.499
Other illicit drugs	11 (3.5)	3 (2.6)	3 (2.4)	5 (7.8)	.158
Comorbidities
Hypertension	35 (11.3)	12 (10.3)	14 (11.2)	9 (13.2)	.826
Diabetes mellitus	20 (6.5)	7 (6.0)	8 (6.4)	5 (7.4)	.935
Obesity	1 (0.3)	0 (0)	0 (0)	1 (1.5)	.168
Lung disease	6 (1.9)	1 (0.9)	3 (2.4)	2 (2.9)	.542
Liver disease	4 (1.3)	0 (0)	3 (2.4)	1 (1.5)	.252
Kidney disease	13 (4.2)	7 (6.0)	4 (3.2)	2 (2.9)	.471
Cardiovascular disease	10 (3.2)	3 (2.6)	3 (2.4)	4 (5.9)	.373
Malignancy	9 (2.9)	3 (2.6)	4 (3.2)	2 (2.9)	.957
Othersd	29 (9.4)	6 (5.1)	16 (12.8)	7 (10.3)	.117

Note: Data are in numbers (%), unless indicated otherwise.

IQR: interquartile range.

^aComparing between the three admission periods.

^bIncluded Burmese, Cambodian, American and South African.

^cIncluded freelancer, monk and retired civil servant.

^dIncluded anti-synthetase syndrome, epilepsy, factor VII inhibitor, gout, hyperthyroidism, major depressive disorder, stroke, obstructive sleep apnea, osteoporosis, Parkinson disease, reactive arthritis, schizophrenia, systemic lupus erythematosus and thalassemia.

Table 2.

HIV clinical characteristics of the 310 study participants stratified by admission periods.

Characteristic	Total (N = 310)	Pre-COVID (N = 117)	COVID (N = 125)	Post-COVID (N = 68)	Pa
Status of HIV infection					.121
New case	115 (37.1)	35 (29.9)	51 (40.8)	29 (42.6)
Known HIV case	195 (62.9)	82 (70.1)	74 (59.2)	39 (57.4)
Previously untreated or lost to follow up known HIV case	44/195 (22.6)	13/82 (15.9)	18/74 (24.3)	13/39 (33.3)	.089
Year since HIV diagnosis					.403
<1	165 (53.2)	54 (46.2)	71 (56.8)	40 (58.8)
1–5	35 (11.3)	16 (13.7)	12 (9.6)	7 (10.3)
>5	110 (35.5)	47 (40.2)	42 (33.6)	21 (30.9)
Admission CD4 cell count (cell/mm³, median, IQR)	141.5 (38.5–331.5)	206 (59–400)	97 (27–300)	137.5 (45.5–273.3)	.022
Admission HIV RNA					.010
Suppressed	112 (36.1)	55 (47.0)	37 (29.6)	20 (29.4)
Unsuppressed	48 (15.5)	12 (10.3)	27 (21.6)	9 (13.2)
Unknown	150 (48.4)	50 (42.7)	62 (48.8)	39 (57.4)
Admission ART status					.013
Currently on ART	151 (48.7)	69 (59.0)	56 (44.8)	26 (38.2)
No ART	159 (51.3)	48 (41.0)	69 (55.2)	42 (61.8)
ARV regimen of those currently on ART					.001
INSTI + 2NRTI	31/151 (20.5)	0/69 (0)	14/56 (25.0)	17/26 (65.4)
NNRTI + 2NRTI	111/151 (73.5)	64/69 (92.8)	39/56 (69.6)	8/26 (30.8)
PI + 2NRTI	8/151 (5.3)	5/69 (7.2)	3/56 (5.4)	0/26 (0)
3TC + LPV/r	1/151 (0.7)	0/69 (0)	0/56 (0)	1/26 (3.8)
Adherence to ART ≥95% of those currently on ART	143/151 (94.7)	67/69 (97.1)	50/56 (89.3)	26/26 (100)	.063

Note: Data are in numbers (%), unless indicated otherwise.

3TC: lamivudine; ART: antiretroviral therapy; ARV: antiretroviral; HIV: human immunodeficiency virus; INSTI: integrase strand transfer inhibitor; LPV/r: lopinavir/ritonavir; NNRTI: non-nucleoside reverse transcriptase inhibitor NRTI: nucleoside reverse transcriptase inhibitor; PI: protease inhibitor; RNA: ribonucleic acid.

^aComparing between the three admission periods.

Admission characteristics and outcomes

Of the 310 PLHIV, 119 (38.4%) were diagnosed with acquired immune deficiency syndrome (AIDS)-related diseases (Table 3). The most common AIDS-related diseases were opportunistic infections (OIs), followed by malignancies, while the most common non-AIDS-related diseases were infections other than OIs, followed by malignancies, trauma, and COVID-19 (Table 3). Sixty-two patients (20%) experienced complications during admission, of which nosocomial infections and adverse drug events were the two most common complications and 17 (5.5%) required intensive care unit (ICU) admission. Other admission characteristics were comparable between the three groups. In terms of hospitalization outcomes, 268 patients (86.5%) improved, 39 (12.6%) died, and 3 (1.0%) had become disabled. For ART management upon admission, 2 (1.3%) of 151 PLHIV previously on ART had their ART disrupted due to drug allergy. Twenty-four of 44 PLHIV who were untreated or lost to follow-up (54.5%) were started/restarted on ART and 74 out of 115 newly diagnosed with HIV participants (64.3%) were able to initiate ART during hospitalization. 16 of these 74 PLHIV (21.6%) had ART started within 7 days. After discharge from the hospital, 252 out of 271 surviving PLHIV (93%) were linked to outpatient care within 30 days. The hospitalization outcomes, ART management and linkage to continuity care were comparable between the three groups (Table 3).

Table 3.

Admission characteristics and outcomes among the 310 study participants stratified by admission periods.

Characteristic and outcome	Total (N = 310)	Pre-COVID (N = 117)	COVID (N = 125)	Post-COVID (N = 68)	p ^a
Admission diagnosis					.156
AIDS-related	119 (38.4)	39 (33.3)	56 (44.8)	24 (35.3)
Non-AIDS-related	191 (61.6)	78 (66.7)	69 (55.2)	44 (64.7)
AIDS-related diagnosis category					.272
Opportunistic infection	105/119 (88.2)	32/39 (82.1)	52/56 (92.2)	21/24 (87.5)
Malignancy	14/119 (11.8)	7/39 (17.9)	4/56 (7.1)	3/24 (12.5)
Non-AIDS-related diagnosis category					.577
Infection	77/191 (40.3)	32/78 (41.0)	26/69 (37.7)	19/44 (43.2)
Malignancy	21/191 (11.0)	7/78 (9.0)	8/69 (11.6)	6/44 (13.6)
Trauma	13/191 (6.8)	4/78 (5.1)	5/69 (7.2)	4/44 (9.1)
COVID-19	5/191 (2.6)	0/78 (0)	3/69 (4.3)	2/44 (4.5)
Otherb	72/191 (39.3)	35/78 (44.9)	27/69 (39.1)	13/44 (29.5)
Having complication during hospitalization	62 (20.0)	18 (15.4)	25 (20.0)	19 (27.9)	.120
Type of complication					.292
Drug-related complication	24/62 (38.7)	5/18 (27.8)	12/25 (48.0)	7/19 (36.8)
Nosocomial infection	29/62 (46.8)	12/18 (66.7)	9/25 (36.0)	8/19 (42.1)
Otherc	9/62 (14.5)	1/18 (5.6)	4/25 (16.0)	4/19 (21.1)
Length of hospital stay (day, median, IQR)	13 (6–22)	12 (2–21.5)	14 (7.5–24)	14 (6–22.75)	.30
ICU admission	17 (5.5)	9 (7.7)	6 (4.8)	2 (2.9)	.357
Length of ICU stay (day, median, IQR)d	15 (3.5–25)	7 (2.5–17.5)	27 (15.5–33.5)	9.5 (4–15)	.085
In-hospital outcomes					.800
Improved	268 (86.5)	104 (88.9)	108 (86.4)	56 (82.4)
Disability	3 (1.0)	1 (0.9)	1 (0.8)	1 (1.5)
Death	39 (12.6)	12 (10.3)	16 (12.8)	11 (16.2)
ART-related outcomes
ART disruption among those on ART	2/151 (1.3)	1/69 (1.4)	0/56 (0)	1/26 (3.8)	.363
ART initiation among those previously untreated or lost to follow-up	24/44 (54.5)	7/13 (53.8)	10/18 (55.6)	7/13 (53.8)	.99
ART initiation among those newly HIV diagnosed	74/115 (64.3)	21/35 (60.0)	32/51 (62.7)	21/29 (72.4)	.558
Time to ART initiation among newly diagnosed persons					.283
Within 7 days	16/74 (21.6)	3/21 (14.3)	6/32 (18.8)	7/21 (33.3)
More than 7 days	58/74 (78.4)	18/21 (85.7)	26/32 (81.2)	14/21 (66.7)
Linkage to outpatient care among survivors					.693
Within 30 days	252/271 (93.0)	97/105 (92.4)	103/109 (94.5)	52/57 (91.2)
More than 30 days	3/271 (1.1)	2/105 (1.9)	0/109 (0)	1/57 (1.8)
Lost to follow-up	16/271 (5.9)	6/105 (5.7)	6/109 (5.5)	4/57 (7.0)

Note: Data are in numbers (%), unless indicated otherwise.

ART: antiretroviral therapy; ICU: intensive care unit; IQR: interquartile range.

^aComparing between the three admission periods.

^bIncluded acute limb ischemia, acute myocardial infarction, acute pancreatitis, acute psychosis, alcohol withdrawal seizure, anemia, aortic dissection, cardiac arrest, cesarean section, chronic limb ischemia, Drug rash with eosinophilia and systemic symptoms (DRESS) syndrome, gastroduodenal ulcer, gut obstruction, Hemolysis, elevated liver enzymes and low platelets (HELLP) syndrome, heart failure, hepatitis, hyponatremia, immune thrombocytopenia, intracranial hemorrhage, normal delivery, neuromyelitis optica spectrum disorder, acetaminophen overdose, renal failure, ruptured cavernoma, severe aortic stenosis, simple hyperglycemia, spontaneous pneumothorax, status epilepticus, Stephen-Johnson syndrome, thyroid storm and retained placenta.

^cIncluded acute liver failure, cardiac arrest, mechanic valve thrombosis and pneumothorax.

^dLength of ICU stay among those admitted to ICUs.

Factors associated with in-hospital mortality

The comparison of characteristics between 39 PLHIV who died and 271 PLHIV who survived during the hospitalization is shown in Table 4. PLHIV who died were more likely to have monthly income lower than $US450, have comorbid chronic liver disease or malignancy, have CD4 cell counts at admission lower than 50 cells/mm³, and be admitted to an ICU. By multivariable logistic regression analysis (Table 5), independent factors associated with in-hospital mortality included ICU admission (aOR 12.81; 95% CI 4.31–38.05: p < .001), underlying malignancy (aOR 9.19; 95% CI 2.13–39.54: p = .003), monthly income less than $US450 (aOR 2.69; 95% CI 1.26–5.73: p = .01) and admission CD4 cell count less than 50 cell/mm³ (aOR 2.60; 95% CI 1.22–5.54: p = .01).

Table 4.

Comparing characteristics between the study participants who survived and those who died during hospitalization.

Characteristic	Those who survived (N = 271)	Those who died (N = 39)	p ^a
Period of admission			.502
Pre-covid	105 (38.7)	12 (30.8)
Covid	109 (40.2)	16 (41.0)
Post-Covid	57 (21.0)	11 (28.2)
Age (year, median, IQR)	42 (32–52)	42 (31–50)	.504
Male gender	183 (67.5)	27 (69.2)	.832
Nationality			.11
Thai	250 (92.3)	33 (84.6)
Othersb	21 (7.7)	6 (15.4)
Highest education			.157
Primary school	2 (0.7)	1 (2.6)
Secondary school	105 (38.7)	20 (51.30
Bachelor’s degree	164 (60.5)	18 (46.2)
Monthly household income (USD)			.003
<150	5 (1.80)	4 (10.3)
150-<450	54 (19.9)	13 (33.3)
450-<900	143 (52.2)	13 (33.3)
≥900	69 (25.5)	9 (23.1)
Current substance use
Tobacco	116 (42.8)	19 (48.7)	.495
Alcohol	150 (55.4)	22 (56.4)	.901
Other illicit drugs	11 (4.1)	0 (0)	.371
Comorbidities
Hypertension	31 (11.4)	4 (10.3)	.827
Diabetes	18 (6.6)	2 (5.1)	.719
Obesity	1 (0.4)	0 (0)	.704
Lung disease	5 (1.8)	1 (2.6)	.761
Liver disease	2 (0.7)	2 (5.1)	.023
Kidney disease	13 (4.8)	0 (0)	.162
Cardiovascular	7 (2.6)	3 (7.7)	.091
Malignancy	5 (1.8)	4 (10.3)	.017
Othersc	23 (8.5)	20 (15.4)	.167
Status of HIV infection			.868
New case	101 (37.3)	14 (35.9)
Known HIV case	170 (62.7)	25 (64.1)
Year since HI diagnosis			.212
<1	146 (53.9)	19 (48.7)
1–5	33 (21.2)	2 (5.1)
>5	92 (22.9)	18 (46.2)
Admission CD4 cell count			.012
<50 cell/mm³	72 (26.6)	18 (46.2)
≥50 cell/mm³	199 (73.4)	21 (53.8)
Admission HIV RNA			.063
Suppressed	100 (36.9)	12 (30.8)
Unsuppressed	37 (13.7)	11 (28.2)
Unknown	134 (49.4)	16 (41.0)
Admission ART status			.305
Currently on ART	135 (49.8)	16 (41.0)
No ART	136 (50.2)	23 (59.0)
Adherence to ART ≥95% of those currently on ART	128/135 (94.8)	15/16 (93.8)	.857
Admission diagnosis			.156
AIDS-related	100 (36.9)	19 (48.7)
Non-AIDS-related	171 (63.1)	20 (51.3)
Admission diagnosis categories			.348
Opportunistic infection	90 (33.2)	15 (38.5)
Other infections	65 (24.0)	12 (30.8)
Malignancy	29 (10.7)	6 (15.4)
Trauma	13 (4.8)	0 (0)
COVID-19	5 (1.8)	0 (0)
Otherd	69 (25.5)	6 (15.4)
ICU admission	8 (3.0)	9 (23.0)	<.001

Note: Data are in numbers (%), unless indicated otherwise.

3TC: Lamivudine; ART: antiretroviral therapy; ARV: antiretroviral; HIV: human immunodeficiency virus; ICU: intensive care unit; INSTI: integrase strand transfer inhibitor; IQR: interquartile range; LPV/r: lopinavir/ritonavir; NNRTI: non-nucleoside reverse transcriptase inhibitor; NRTI: nucleoside reverse transcriptase inhibitor; PI: protease inhibitor; RNA: ribonucleic acid.

^aComparing between the study participants who survived and those who died.

^bIncluded Burmese, Cambodian, American and South African.

^cIncluded anti-synthetase syndrome, epilepsy, factor VII inhibitor, gout, hyperthyroidism, major depressive disorder, stroke, obstructive sleep apnea, osteoporosis, Parkinson disease, reactive arthritis, schizophrenia, systemic lupus erythematosus and thalassemia.

^dIncluded acute limb ischemia, acute myocardial infarction, acute pancreatitis, acute psychosis, alcohol withdrawal seizure, anemia, aortic dissection, cardiac arrest, cesarean section, chronic limb ischemia, Drug rash with eosinophilia and systemic symptoms (DRESS) syndrome, gastroduodenal ulcer, gut obstruction, Hemolysis, elevated liver enzymes and low platelets (HELLP) syndrome, heart failure, hepatitis, hyponatremia, immune thrombocytopenia, intracranial hemorrhage, normal delivery, neuromyelitis optica spectrum disorder, acetaminophen overdose, renal failure, ruptured cavernoma, severe aortic stenosis, simple hyperglycemia, spontaneous pneumothorax, status epilepticus, Stephen-Johnson syndrome, thyroid storm and retained placenta.

Table 5.

Factors associated with in-hospital mortality among the study participants.

Factor	Univariable analysis		Multivariable analysis
Factor	OR (95% CI)	p	Adjusted OR (95% CI)	p
Intensive care unit admission	9.86 (3.54–27.46)	<.001	12.81 (4.31–38.05)	<.001
Underlying malignancy	6.08 (1.56–23.72)	.009	9.19 (2.13–39.54)	.003
Monthly income less than $US 450	2.78 (1.39–5.57)	.004	2.69 (1.26–5.73)	.01
Admission CD4 count less than 50 cells/mm³	2.37 (1.19–4.70)	.01	2.60 (1.22–5.54)	.01

Note: CI: confidence interval; OR: odds ratio.

Discussion

The study findings revealed that most of the admission diagnoses among PLHIV throughout the three periods were non-AIDS-related and predominantly due to infections other than OIs. These were consistent with the most common diagnoses among general patient population admitted to TUH during the same periods. However, there was non-significant higher proportion of admissions due to AIDS-related diseases due to either OIs or malignancies during the COVID-19 period than the other two periods. These might be explained by the significantly lower CD4 cell count and higher proportion of those with detectable HIV viral load at admission among PLHIV hospitalized during the COVID-19 period compared to the other periods. The findings were consistent with those of a previous study, which found that the median CD4 count was lower, and HIV viral load were doubled in PLHIV hospitalized during the COVID pandemic.¹⁶ These unfavorable baseline characteristics of PLHIV could represent the greater delay in HIV diagnosis and access to ART or ART disruption as a result of difficulties in accessing HIV screening, care engagement, and continuity care services among those at-risk for HIV acquisition and PLHIV caused by the COVID-19 pandemic.^17–19 Nonetheless, this study is the first to show that the baseline CD4 cell count and the rate of virologic suppression were still lower among hospitalized PLHIV during the first year of post-COVID-19 period compared to the pre-COVID-19 period, indicating the need for interventions to maintain comprehensive HIV screening and care through the pandemic period.

When comparing the treatment outcomes between PLHIV admitted during the three periods, no differences were found in terms of complications, length of hospital stay, rate of ICU admission, length of ICU stay, and hospital mortality. The results might represent the standard of inpatient care provided in TUH, which remained effective and consistent, despite the diversion of hospital resources to care for COVID-19 patients, in contrast with a study by Kazibwe et al.,²⁰ which found a threefold increase in mortality rates among PLHIV admitted to hospitals during the COVID-19 pandemic. This increased mortality could largely be attributed to the different healthcare settings and the vulnerability of the PLHIV to disruptions in the health system, potentially stemming from interruptions in ART in that study. These differences emphasize the importance of maintaining standard of care and appropriate administrative management during pandemics.

In this study, we found only a 1.3% disruption rate of ART among PLHIV currently on ART during the time of admission, which might be attributed to our intensive and regular HIV inpatient care round conducted by a multidisciplinary team involving infectious diseases specialist, HIV nurses, pharmacists and social workers. This inpatient round included detailed assessment of the patients’ baseline characteristics, a 20-min education session provided to each patient at the bedside about important HIV topics, facilitating the process of selecting an HIV care facility based on the patients’ medical coverage and preferences and contacting the chosen facility for them to schedule a follow-up appointment and telephone calls to remind patients of their upcoming appointment.²¹ This had also led to a high linkage to outpatient care among survivors in this study. However, the ART initiation rate during admission for previously untreated or lost to follow-up PLHIV and those newly diagnosed with HIV remained low to moderate, especially for the ART initiation rate within 7 days among newly diagnosed PLHIV. This was mainly due to the necessity to avoid immune reconstitution inflammatory syndrome while treating some OIs, which delayed ART initiation. Some PLHIV were also in emergency conditions or experiencing clinical worsening to the extent that they could not be started on ART or even passed away prior to ART initiation. Consistent with other study findings, which demonstrated that factors associated with failure to initiate ART during hospitalization were being diagnosed with OIs, being too sick or dying before starting ART, and refusing to start ART during hospitalization.^22,23 These study findings underscore the importance of interventions to facilitate prompt initiation of ART if once PLHIV are ready and no contraindications exist.

Our study demonstrated an in-hospital mortality rate of 12.6% among PLHIV, which was slightly higher than those reported in previous studies (mortality rate range 5.4–10.3%).^8,24,25 It was possible that there were higher proportion of PLHIV with underlying malignancies or those presenting with respiratory failure in our study than those studies. However, a cross-sectional study among African PLHIV reported a hospital mortality rate of 26%, which was higher than ours. The higher mortality rate in the African study was possibly due to the higher ART interruption rate during admission compared to our study (19% vs 1.3%).²⁶ Furthermore, we identified monthly income less than $US 450, underlying malignancy, admission CD4 cell count less than 50 cells/mm³, and intensive care unit admission as the independent factors associated with in-hospital mortality. The lower income among PLHIV could lead to limited access to standard HIV care prior to admission, delayed diagnosis and presentation at a hospital, leading to increased hospital mortality,²⁷ while PLHIV with underlying malignancies or low CD4 cell count at admission were considered immunocompromised and at-risk for OIs, comorbidities and mortality as demonstrated in the previous studies.^28–32 Admission to ICU could represent the severity of illnesses and thus was associated with mortality in our study.

There were some recognizable limitations in this study. First, the retrospective design may be associated with incomplete or missing data and subjected to misinterpretation biases. Second, the study was conducted in a single tertiary care setting, which may limit generalizability of the findings to other settings. Third, a small sample size in the post-COVID group may limit detection of differences between characteristics and outcomes of interest between the three study groups. Fourth, given the difference in ART regimens which were prescribed according to the national guidelines during each study period, this might affect the PLHIV’s clinical and ART-related outcomes in the study. Lastly, although different strains of SARS-CoV-2 had caused COVID-19 with different severity and burdens on healthcare resources, we did not evaluate and differentiate the impact of COVID-19 by the predominant causative strains during the COVID-19 period.

In conclusion, admission diagnoses and outcomes did not differ significantly between PLHIV who were hospitalized before, during, and after COVID-19 pandemic periods in our setting, where standard of inpatient care was maintained despite the diversion of hospital resources to care for COVID-19 patients. Nonetheless, our study highlights the impact of the COVID-19 pandemic on delayed HIV infection diagnosis and poor adherence to HIV treatment and continuity care. Measures should be implemented to promote early care engagement and improve adherence to care during future pandemics. Apart from appropriate treatment of the presenting illness, caring for hospitalized PLHIV should focus on continuous ART, early initiation of ART among newly-diagnosed persons, linkage to continuity care, and close monitoring of individuals who had identified risk factors associated with hospital mortality. Further studies conducted in different settings and with a larger sample size are required to confirm this study’s findings.

Footnotes

Author contributions

P.P: Conceptualization, Methodology, Data Curation, Formal analysis, Writing-Original draft preparation, Reviewing and Editing; T.K.: Conceptualization, Methodology, Formal analysis, Writing-Reviewing and Editing.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Thana Khawcharoenporn

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