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Incidence and severity prediction score of COVID-19 in people living with HIV (SCOVHIV): experience from the first and second waves of the pandemic in Indonesia

AIDS Research and Therapy volume 19, Article number: 47 (2022) Cite this article

Abstract

Background

People living with HIV (PLHIV) have higher risk of COVID-19 infection and mortality due to COVID-19. Health professionals should be able to assess PLHIV who are more likely to develop severe COVID-19 and provide appropriate medical treatment. This study aimed to assess clinical factors associated with COVID-19 severity and developed a scoring system to predict severe COVID-19 infection among PLHIV.

Methods

This retrospective cohort study evaluated PLHIV at four hospitals diagnosed with COVID-19 during the first and second wave COVID-19 pandemic in Indonesia. The independent risk factors related to the severity of COVID-19 were identified with multivariate logistic regression.

Results

342 PLHIV were diagnosed with COVID-19, including 23 with severe-critical diseases. The cumulative incidence up to December 2021 was 0.083 (95% CI 0.074–0.092). Twenty-three patients developed severe-critical COVID-19, and the mortality rate was 3.2% (95% CI 1.61%–5.76%). Having any comorbidity, CD4 count of < 200 cells/mm3, not being on ART, and active opportunistic infection were independent risk factors for developing severe COVID-19. SCOVHIV score was formulated to predict severity, with 1 point for each item. A minimum score of 3 indicated a 58.4% probability of progressing to severe COVID-19. This scoring system had a good discrimination ability with the area under the curve (AUC) of 0.856 (95% CI 0.775–0.936).

Conclusion

SCOVHIV score, a four-point scoring system, had good accuracy in predicting COVID-19 severity in PLHIV.

Introduction

The coronavirus-19 (COVID-19) pandemic has impacted many people's health and economic status, especially people living with HIV (PLHIV). A meta-analysis has shown that PLHIV had a higher risk of contracting COVID-19 compared to HIV negative individuals [1]. However, health professionals are still investigating how COVID-19 affects PLHIV. HIV has been associated with an increased risk of death and severe outcomes in some meta-analyses [2,3,4,5]. Understanding the factors related to severe COVID-19 infection, including HIV-specific factors, is important as it will enable doctors to properly assess and prioritize COVID-19 treatment efforts. Furthermore, PLHIV have concerns about the COVID-19 severity risk related to their condition and would benefit from specific information on severity factors [6, 7].

Previous studies on factors related to COVID-19 severity among PLHIV showed conflicting results, except for comorbidities, as seen in the non-HIV population [8,9,10]. Some studies have shown that older PLHIV with lower CD4 count have a higher risk of severe outcomes. Other studies have shown opposing results, indicating that it is unclear whether CD4 count and viral load are indeed factors related to COVID-19 outcomes among PLHIV [11]. Differences in demographic and epidemiologic characteristics between comparison groups must also be taken into consideration. This study aimed to describe the cumulative incidence of SARS-CoV-2 in the first and second waves of the pandemic, as well as COVID-19 outcomes among people living with HIV in Indonesia. The 1st wave started in February 2021 and 2nd wave started in June 2021. Finally, this study attempted to assess clinical factors associated with COVID-19 severity and develop a predictive scoring system for severe COVID-19 infection.

Methods

Study population

This retrospective cohort study evaluated all adult PLHIV engaged in care at four participating HIV treatment centers: Cipto Mangunkusumo National Hospital (Jakarta), Dharmais National Cancer Hospital (Jakarta), Sentra Medika Hospital (West Java), and Universitas Indonesia Hospital (West Java). During the pandemic, COVID-19 diagnoses have been routinely collected at all sites. All cases of PLHIV who had documented SARS-CoV-2 infections as of December 2021 were included in the COVID-19 outcomes evaluation. SARS-CoV-2 infection was defined as a positive SARS-CoV-2 polymerase chain reaction (PCR) or SARS-CoV-2 antigen result.

Data collection

A standardized form containing demographic and clinical information was used to extract data from each hospital’s medical records. Clinical parameters collected included body mass index (BMI), HIV-specific data (opportunistic infection, recent absolute CD4 count and viral load prior to COVID-19 infection, and antiretroviral used), as well as COVID-19 data (severity, hospitalization, and mortality). The BMI category for determining obesity was defined as a body mass index of 30 or higher [12]. Comorbidities evaluated included diabetes mellitus, hypertension, chronic kidney disease, cardiovascular disease, and obesity. Predetermined clinical outcomes of interest were COVID-19 severity and mortality. Severity was divided into several categories as follows: asymptomatic, mild (no pneumonia), moderate (pneumonia without oxygen supplementation), severe (pneumonia with oxygen supplementation), critical (requiring intensive care management or resulting in death by respiratory failure, septic shock, or multiple organ dysfunction or failure) [13, 14]. A severe COVID-19 outcome was a combination of severe and critical categories. Mortality was defined as all causes of mortality during COVID-19 infection.

Statistical analysis

The cumulative incidence from the first clinically detected SARS-CoV-2 infection among PLHIV by key characteristics was calculated from March 1, 2020, until December 31, 2020. Frequencies and percentages were used for categorical variables, and continuous variables were expressed as a mean with SD or median and IQR on the imputed datasets. Missing values were imputed for education (3.5%), working status (0.9%) and recent CD4 count (5.5%). Bivariate analysis was performed to compare factors associated with severe COVID-19 outcomes. Multivariable backward logistic regression identified independent correlations with outcomes and calculated adjusted risk ratios (aRR) with a 95% confidence interval (CI). Differences in p-values of less than 0.05 were considered significant.

Factors that were found to be significant to COVID-19 severity outcomes were put into a predictive scoring system. Scores for each factor were calculated using a stepwise method as follows: (1) dividing risk factor’s coefficient B by its standard error (coefficient B/SE = x); (2) choosing the lowest x value as a reference for the next step; and (3) dividing each x value by the reference value. Calibration of the scoring, using Hosmer-Lemeshow and describing AUCS, was then performed to evaluate the scoring system. For internal validity, calibrations were validated using the bootstrap method. The analysis used STATA/IC for Windows version 14.2 (STATACorp LP).

Results

Incidence and characteristics of PLHIV with COVID-19

During the study period, 342 PLHIV were confirmed to have SARS-CoV-2 infection from a total of 4134 PLHIV. In the same period, 4.6 million Indonesian people were diagnosed with COVID-19. The trend of COVID-19 infection in PLHIV almost mirrored the trend of COVID-19 cases in Indonesian population during the first wave and second wave as presented in Fig. 1. Most of the COVID-19 cases in Indonesia occurred during the second wave of the pandemic. The cumulative incidence of COVID-19 at the end of 2021 was 0.083 (95% CI 0.074–0.092) for adult PLHIV.

Fig. 1
figure 1

Comparison of COVID-19 new cases in PLHIV (in this study) a and in the Indonesian population* b from April 2020 to December 2021. Figure 1b is in the scale of 10,000.

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Most of the PLHIV with a confirmed SARS-CoV-2 infection were male (n = 259, 75.7%), acquired HIV via heterosexual transmission (n = 158, 46.2%), and were actively working (n = 266, 77.8%). Thirty-four PLHIV (9.9%) still had active opportunistic infections and twenty-seven (7.9%) had active tuberculosis infection. Sixty-four of the PLHIV (18.7%) had one or more of the following comorbidities: hypertension (n = 29, 8.5%), obesity (n = 27, 7.9%), diabetes (n = 13, 3.8%), chronic kidney disease (n = 7, 2%), or cardiovascular disease (n = 5, 1.5%).

Of the confirmed COVID-19 cases among the PLHIV in this study, 295 cases (86.3%) were asymptomatic or categorized as mild, 24 (7%) were moderate cases, 13 (3.8%) were severe cases, and 10 (2.9%) were critical cases, as can be seen in Table 1. Fourteen percent of the patients required hospitalization (48 cases) and eleven patients died during the course of their COVID-19 infections (mortality rate 3.2%, 95% CI 1.61–5.76%). Most of the COVID-19 patients self-quarantined (n = 287, 83.9%) while seven patients (2.1%) quarantined at the isolation center. Fifteen patients (4.4%) were unaware of their HIV status before becoming infected with COVID-19, and two patients were newly diagnosed HIV and not on antiretrovirals yet.

Table 1 Demographic and clinical characteristics of COVID-19 patients
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Risk factors for severe COVID-19

Among the PLHIV with COVID-19 infection, bivariate analysis as seen in Table 2, showed that severe COVID-19 infection was associated with not using ART, a recent absolute CD4 count of less than 200 cells/mm3, having an active opportunistic infection, and having any comorbidity. In the adjusted model, not using ART was associated with an increased rate of severe COVID-19 infection (aRR = 3.15, 95% CI 1.616.19), as was a recent absolute CD4 count of less than 200 cells/mm3 (aRR = 2.15, 95% CI 1.12–4.14) and having an opportunistic infection or any comorbidity (aRR = 3.21, 95% CI 1.64–6.27 and aRR = 3.65, 96% CI 1.83–7.28).

Table 2 Bivariate analysis of factors related to severe COVID-19 infection
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SCOVHIV: a scoring model for the prediction of severe COVID-19 among PLHIV

This study established a 4-point scoring system based on the adjusted model, and the results were put into bootstrapping logistic regression. The 4-point scoring system as seen in Table 3, covers the four risk factors included in the final multivariate model, which are ART status, recent absolute CD4 count, opportunistic infection, and any comorbidity. This scoring system had good discrimination ability with area under the curve (AUC) of 0.856 (95% CI 0.775–0.936; p < 0.01) and good calibration (Hosmer–Lemeshow test, 0.201) as seen in Fig. 2. Internal validity was also good (Hosmer–Lemeshow test: 0.201, p < 0.001). A minimum total score of 3 means that the probability of getting severe COVID-19 infection in PLHIV is 58.4%, while total scores of 2 or 1 mean the probability of getting severe COVID-19 infection is 25% and 7.3%, respectively.

Table 3 Derivation of 4-point scoring system to predict severity outcomes of COVID-19 infection from stepwise multivariate analysis (n = 342)
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Fig. 2
figure 2

ROC (Receiver Operating Characteristics) for a 4-point scoring system as a predictor of COVID-19 severity among PLHIV

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Discussion

COVID-19 incidence in this study was 0.083 (95% CI 0.074–0.092), which is higher than a study in Madrid [(0.067 (95% CI 0.057–0.079) [14]. The pooled incidence proportion in 7 other studies was 0.009 (95% CI 0.006–0.011), but this meta-analysis only represented studies in 2020 [15]. Most of the COVID-19 cases observed in this study had no symptoms or mild symptoms (86.3%). Rial-Crestelo et al. also observed a high proportion of patients with asymptomatic or mild disease in Spain (75.3% of 158 patients) [14], while a study in China showed 60% of 2464 PLHIV with COVID-19 infection being asymptomatic [16]. The proportion of severe and critical cases was smaller in our study (6.7%), which showed different patterns, although with limited numbers of COVID-19 cases in PLHIV. Mirzaei et al., which summarized 212 available data from 25 case reports and small studies, described the proportion of severe-critical case as 33.5% of all PLHIV with COVID-19 infection [5].

This study observed a lower mortality rate (3.2%) compared to previous reports. Ssentongo et al. showed pooled mortality rate for studies in 2020 was 12.65% (95% CI 6.81–22.31%) [1], while Liang et al. calculated a pooled mortality rate of 14 studies up to March 2021 was 8.814% [17]. We believe that more participants and longer observation time can better describe the proportion. However, these findings must be interpreted cautiously since a recent meta-analysis still found that PLHIV have a higher risk of death compared to non-HIV COVID-19 patients (HR = 1.76, 95% CI 1.31–2.35) [2].

Moreover, COVID-19 vaccination in PLHIV might change morbidity and mortality rates in the future. This study reported COVID-19 cases from March 2020 to December 2021 while the Indonesian government just started COVID-19 vaccination program to general population in June 2021. Therefore, we believe that not many COVID-19 patients in this study had been vaccinated though the data was not available.

The hospitalization rate of PLHIV with COVID-19 infection was found to be 14%, which is still within the range of other studies (13.8–58%) [9, 11, 14, 15, 18]. This low proportion of hospitalization does not necessarily mean that the number of patients who needed hospitalization was low. As shown in Fig. 1 [19, 20], more than half of the COVID-19 cases occurred during the second wave of the pandemic (June to August 2021) when the delta variant spread, although genomic sequencing was not routinely performed. Some patients may have had difficulties finding hospital care due to the limited capacities of health facilities during the increasing demand for hospitals experienced during waves [21].

Fifteen patients (4.4%) were unaware of their HIV diagnosis before contracting COVID-19. In the newly-diagnosed HIV patients, there were several reports of co-infection of COVID-19 and Pneumocystic jirovecii pneumonia [22, 23]. Some opportunistic pulmonary infections have similar clinical and radiological symptoms to COVID-19, including cytomegalovirus and Pneumocystic jirovecii pneumonia. Thus, other causes of respiratory infection than SARS-CoV-2 must still be considered even during this massive pandemic.

This study identified several factors related to developing severe COVID-19 infection in PLHIV. As in the general population, the severity of COVID-19 infection was found to be associated with either diabetes, hypertension, obesity, chronic kidney disease or cardiovascular disease [1, 11, 17]. This finding contributes to the understanding that comorbidities strongly correlate with severe COVID-19 outcomes [24,25,26,27].

Persistent immune dysfunction may be important in severe COVID-19 infection. This study indicated that a low CD4 count (less than 200 cells/mm3) was associated with COVID-19 severity. This finding was in accordance with other studies. Hoffman et al. showed that a current CD4 count of less than 250 cells/mm3 significantly correlates with the risk of severe COVID-19 [10]. In addition, Nomah et al. found that a low CD4 count (less than 200 cells/ mm3) was associated with worse outcomes from HIV-COVID-19 co-infection [11]. Jassat et al. showed that PLHIV with a history of CD4 count of less than 200 cells/mm3 were twice as likely to die in hospital than those with a CD4 count of 200 cells/mm3 [28]This may be due to the well-described lymphopenia that occurs in severe COVID-19 [29]. Zhang et al. evaluated studies reporting CD4 and CD8 count in severe COVID-19 patients, but not specifically PLHIV, indicating that both CD4 and CD8 T cell counts were significantly lower in the severe group compared to the non-severe group [30]. Therefore, both T cell counts may be considered as biomarkers for predicting severe COVID-19. Not using ART was also found to be a significant predicting factor of COVID-19 severity. HIV infection without ART can be a dangerous comorbidity of COVID-19 infection. Jassat et al. found that PLHIV who were not on ART were more likely to die in hospital than PLHIV who were on ART (aOR = 1.45, 95% CI 1.22–1.72) [28]. A study of T cell dynamics during COVID-19 infection has revealed that COVID-19 leads to a rapid augmentation of the T-cell exhaustion process initially caused by HIV, and this T cell degradation was observed to be the most pronounced in PLHIV not using ART [31].

To the authors’ knowledge, this is the first report on SARS-CoV-2 among PLHIV in Indonesia, and one of a limited number of studies that include PLHIV with a range of opportunistic infections. PLHIV with opportunistic infections are considered as part of the high-risk population for COVID-19 infection and the worse outcome population. In this study, most of the opportunistic infections diagnosed before or during COVID-19 infection were also severe infections, such as extrapulmonary tuberculosis, toxoplasma encephalitis, cryptococcal meningitis, histoplasmosis, and esophageal candidiasis. These kinds of infections also increase the risk of mortality among PLHIV. These findings also contribute important information about managing concurrent AIDS-defining illness during the COVID-19 pandemic. Though not statistically significant, a previous study in three European countries showed that 40% of its PLHIV population who had severe or critical COVID-19 infection had AIDS-defining illness while only 27% of PLHIV with mild to moderate COVID-19 had AIDS-defining illness [10]. The COVID-19 pandemic has disrupted public health priorities, including the fight against HIV. There have been some reports of late presenters of an AIDS-defining life-threatening condition as a result of difficulties accessing hospital care [32]. In addition, during the COVID-19 pandemic, there have been many reports indicating a reduction in HIV testing rates in many countries [33,34,35].

Strength and limitations

This study has some limitations. First, the incidence of infection in the study population could not be precisely assessed because not all participants were tested for SARS-CoV-2. There may have been some symptomatic patients that did not get COVID-19 tested as the price of a SARS-CoV-2 PCR test in 2020–2021 was quite expensive, being almost one-fourth of the minimum wage during a critical epidemiological situation. The occurrence of asymptomatic COVID-19 patients can not be ruled out. However, these issues are reflected in the general population. Another limitation of this study arises from the retrospective design that did not allow for the evaluation of other factors that potentially influence the severity of COVID-19, such as smoking and HIV viral load.

Despite the limitations, a simple clinical scoring system that can predict severe COVID-19 infection among PLHIV was formulated. The 4-point scoring system covers the four risk factors included in the final multivariate model (ART status = 1, recent absolute CD4 = 1, opportunistic infection = 1, and any comorbidity = 1). This scoring system can predict COVID-19 severity outcome once PLHIV get infected with a good discrimination score, good calibration, and good internal validity. A total score of a minimum of 3 indicates a 58.4% probability of progressing to a COVID-19 severe for PLHIV once they have been diagnosed with COVID-19. While previous research focused on well-treated PLHIV, this study included a considerable number of PLHIV who were not on ART and had opportunistic infections, better depicting the situation in low-middle income countries. As both hospitalized and non-hospitalized COVID-19 patients were evaluated, this score can hopefully be used in outpatient and inpatient settings for PLHIV infected with SARS-CoV-2. Future studies will be needed to evaluate the performance of this score in different situations, especially for different SARS-CoV-2 variants and COVID-19 vaccine uptake among PLHIV.

Conclusion

This study has identified four risk factors of severe COVID-19 infection in PLHIV, namely having any comorbidity, a recent absolute CD4 count of < 200 cells/mm3, not being on ART, and having an active opportunistic infection. This 4-point clinical scoring system has the potential to predict the COVID-19 severity progression of PLHIV diagnosed with COVID-19.

Availability of data and materials

The datasets used and/or analyzed during the current study is available from the corresponding author on reasonable request to the author’s email (evy.yunihastuti@gmail.com).

Abbreviations

SARS-CoV-2:

Severe acute respiratory syndrome coronavirus 2

COVID-19:

Coronavirus disease 2019

ART:

Antiretroviral therapy

SCOVHIV:

Severity prediction score of COVID-19 in people living with HIV

PLHIV:

People living with HIV

HIV:

Human immunodeficiency virus

AUC:

Area under the curve

PCR:

Polymerase chain reaction

SD:

Standard deviation

IQR:

Interquartile range

CI:

Confidence interval

AIDS:

Acquired immunodeficiency syndrome

BMI:

Body mass index

aRR:

Adjusted risk ratios

SE:

Standard error

HR:

Hazard ratio

aOR:

Adjusted odds ratio

References

  1. Ssentongo P, Heilbrunn ES, Ssentongo AE, Advani S, Chinchilli VM, Nunez JJ, et al. Epidemiology and outcomes of COVID-19 in HIV-infected individuals: a systematic review and meta-analysis. Sci Rep. 2021;11:1–12. https://0-doi-org.brum.beds.ac.uk/10.1038/s41598-021-85359-3.

    Article  CAS  Google Scholar 

  2. Danwang C, Noubiap JJ, Robert A, Yombi JC. Outcomes of patients with HIV and COVID-19 co-infection: a systematic review and meta-analysis. AIDS Res Ther. 2022;19:1–12. https://0-doi-org.brum.beds.ac.uk/10.1186/s12981-021-00427-y.

    Article  CAS  Google Scholar 

  3. Oyelade T, Alqahtani JS, Hjazi AM, Li A, Kamila A, Raya RP. Global and regional prevalence and outcomes of COVID-19 in people living with HIV: a systematic review and meta-analysis. Trop Med Infect Dis. 2022;7:1–19. https://0-doi-org.brum.beds.ac.uk/10.3390/tropicalmed7020022.

    Article  Google Scholar 

  4. Wang Y, Feng R, Xu J, Shi L, Feng H, Yang H. An updated meta-analysis on the association between HIV infection and COVID-19 mortality. AIDS. 2021;35:1875–80.

    Article  CAS  Google Scholar 

  5. Mirzaei H, McFarland W, Karamouzian M, Sharifi H. COVID-19 among people living with HIV: a systematic review. AIDS Behav. 2020;25:85–92. https://0-doi-org.brum.beds.ac.uk/10.1007/s10461-020-02983-2.

    Article  PubMed Central  Google Scholar 

  6. Honardoost M, Janani L, Aghili R, Emami Z, Khamseh ME. The association between presence of comorbidities and COVID-19 severity: a systematic review and meta-analysis. Cerebrovasc Dis. 2021;50:132–40. https://0-doi-org.brum.beds.ac.uk/10.1159/000513288.

    Article  PubMed  CAS  Google Scholar 

  7. Giri M, Puri A, Wang T, Guo S. Comparison of clinical manifestations, pre-existing comorbidities, complications and treatment modalities in severe and non-severe COVID-19 patients: a systemic review and meta-analysis. Sci Prog. 2021;104:1–24. https://0-doi-org.brum.beds.ac.uk/10.1177/00368504211000906.

    Article  CAS  Google Scholar 

  8. Etienne N, Karmochkine M, Slama L, Pavie J, Batisse D, Usubillaga R, et al. HIV infection and COVID-19: risk factors for severe disease. AIDS. 2020;34:1771–4. https://0-doi-org.brum.beds.ac.uk/10.1097/QAD.0000000000002651.

    Article  PubMed  CAS  Google Scholar 

  9. Pujari S, Gaikwad S, Chitalikar A, Dabhade D, Joshi K, Bele V. Short communication: coronavirus disease 19 among people living with HIV in Western India: an observational cohort study. AIDS Res Hum Retroviruses. 2021;37:620–3. https://0-doi-org.brum.beds.ac.uk/10.1089/aid.2021.0004.

    Article  PubMed  CAS  Google Scholar 

  10. Hoffmann C, Casado JL, Härter G, Vizcarra P, Moreno A, Cattaneo D, et al. Immune deficiency is a risk factor for severe COVID-19 in people living with HIV. HIV Med. 2021;22:372–8. https://0-doi-org.brum.beds.ac.uk/10.1111/hiv.13037.

    Article  PubMed  CAS  Google Scholar 

  11. Nomah DK, Reyes-Urueña J, Diaz Y, Moreno S, Aceiton J. Sociodemographic, clinical, and immunological factors associated with SARS-CoV-2 diagnosis and severe COVID-19 outcomes in people living with HIV: a retrospective cohort study. Lancet HIV. 2021. https://0-doi-org.brum.beds.ac.uk/10.1016/S2352-3018(21)00240-X.

    Article  PubMed  PubMed Central  Google Scholar 

  12. World Health Organization. The Asia – Pacific perspective: redefining obesity and its treatment. Australia: World Health Organization; 2000.

    Google Scholar 

  13. Kementrian Kesehatan RI. Pedoman Pencegahan dan Pengendalian coronavirus disease (COVID-19). Jakarta Selatan: Kementerian Kesehatan RI; 2020. p. 2020.

    Google Scholar 

  14. Rial-Crestelo D, Bisbal O, Font R, De Lagarde M, Pinto A, Arce-García O, et al. Incidence and severity of SARS-CoV-2 infection in HIV-infected individuals during the first year of the pandemic. JAIDS J Acquir Immune Defic Syndr. 2021. https://0-doi-org.brum.beds.ac.uk/10.1097/qai.0000000000002896.

    Article  Google Scholar 

  15. Lee KW, Yap SF, Ngeow YF, Lye MS. Covid-19 in people living with HIV: a systematic review and meta-analysis. Int J Environ Res Public Health. 2021. https://0-doi-org.brum.beds.ac.uk/10.3390/IJERPH18073554.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Overton ET, Weir IR, Zanni MV, Fischinger S, MacArthur RD, Aberg JA, et al. Asymptomatic SARS-CoV-2 infection is common among ART-treated people with HIV. JAIDS J Acquir Immune Defic Syndr. 2022. https://0-doi-org.brum.beds.ac.uk/10.1097/QAI.0000000000003000.

    Article  PubMed  Google Scholar 

  17. Liang M, Luo N, Chen M, Chen C, Singh S, Singh S, et al. Prevalence and mortality due to COVID-19 in HIV Co-infected population: a systematic review and meta-analysis. Infect Dis Ther. 2021;10:1267–85. https://0-doi-org.brum.beds.ac.uk/10.1007/s40121-021-00447-1.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Shapiro A, Rachel BI, Whitney B, Delaney J, Nance R. Factors associated with severity of COVID-19 disease in a multicenter cohort of people with HIV in the United States, March-December 2020. JAIDS J Acquir Immune Defic Syndr. 2022;90(4):369–76. https://0-doi-org.brum.beds.ac.uk/10.1097/QAI.0000000000002989.

    Article  PubMed  CAS  Google Scholar 

  19. World Health Organization. Indonesia: WHO Coronavirus Disease (COVID-19) Dashboard With Vaccination Data | WHO Coronavirus (COVID-19) Dashboard With Vaccination Data. https://covid19.who.int/region/searo/country/id. Accessed on Mar 2, 2022.

  20. COVID-19 Response Acceleration Task Force Republic of Indonesia. Peta Sebaran | Covid19.go.id 2021. https://covid19.go.id/peta-sebaran. Accessed on Mar 3, 2022.

  21. ACT Alliance. ACT alliance alert: Indonesia: Covid-19 crisis - 26 July 2021. ALERT COVID-19 2021. http://www.actalliance.org. Accessed 29 Mar 2022.

  22. Mang S, Kaddu-Mulindwa D, Metz C, Becker A, Seiler F, Smola S, et al. Pneumocystis jirovecii pneumonia and severe acute respiratory syndrome coronavirus 2 coinfection in a patient with newly diagnosed HIV-1 infection. Clin Infect Dis An Off Publ Infect Dis Soc Am. 2021;72:1487–9. https://0-doi-org.brum.beds.ac.uk/10.1093/CID/CIAA906.

    Article  Google Scholar 

  23. Blanco JL, Ambrosioni J, Garcia F, Martínez E, Soriano A, Mallolas J, et al. COVID-19 in patients with HIV: clinical case series. Lancet HIV. 2020;7:e314–6. https://0-doi-org.brum.beds.ac.uk/10.1016/S2352-3018(20)30111-9.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Tan L, Wang Q, Zhang D, Ding J, Huang Q, Tang YQ, et al. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal Transduct Target Ther. 2020;5:16–8. https://0-doi-org.brum.beds.ac.uk/10.1038/s41392-020-0148-4.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Ambrosioni J, Blanco JL, Reyes-Urueña JM, Davies MA, Sued O, Marcos MA, et al. Overview of SARS-CoV-2 infection in adults living with HIV. Lancet HIV. 2021;8:e294-305. https://0-doi-org.brum.beds.ac.uk/10.1016/S2352-3018(21)00070-9.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China. JAMA. 2020;323:1239. https://0-doi-org.brum.beds.ac.uk/10.1001/jama.2020.2648.

    Article  PubMed  CAS  Google Scholar 

  27. Sigel K, Swartz T, Golden E, Paranjpe I, Somani S, Richter F, et al. Coronavirus 2019 and people living with human immunodeficiency virus: outcomes for hospitalized patients in New York City. Clin Infect Dis. 2020;71:2933–8. https://0-doi-org.brum.beds.ac.uk/10.1093/cid/ciaa880.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Jassat W, Cohen C, Tempia S, Masha M, Goldstein S, Kufa T, et al. Risk factors for COVID-19-related in-hospital mortality in a high HIV and tuberculosis prevalence setting in South Africa: a cohort study. Lancet HIV. 2021;8:e554–67. https://0-doi-org.brum.beds.ac.uk/10.1016/S2352-3018(21)00151-X.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Ho H, Peluso MJ, Margus C, Pedro J, Lopes M, Mullen MP. Clinical outcomes and immunologic characteristics of coronavirus disease 2019 in people with human immunodeficiency virus. J Infect Dis. 2021;223:403–8. https://0-doi-org.brum.beds.ac.uk/10.1093/infdis/jiaa380.

    Article  PubMed  CAS  Google Scholar 

  30. Zhang H, Wu T. CD4 + T, CD8 + T counts and severe COVID-19: a meta-analysis. J Infect. 2020. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jinf.2020.06.036.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Sharov KS. HIV/SARS-CoV-2 co-infection: T cell profile, cytokine dynamics and role of exhausted lymphocytes. Int J Infect Dis. 2021;102:163. https://0-doi-org.brum.beds.ac.uk/10.1016/J.IJID.2020.10.049.

    Article  PubMed  CAS  Google Scholar 

  32. Brugnaro P, Morelli E, Cattelan F, Petrucci A, Colombo F, Caputo S, et al. The serious threat of late presenters HIV-infected patients in the context of the COVID-19 pandemic. Infez Med. 2022;30:119–23. https://0-doi-org.brum.beds.ac.uk/10.53854/liim-3001-14.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Shi L, Tang W, Hu H, Qiu T, Marley G, Liu X, et al. The impact of COVID-19 pandemic on HIV care continuum in Jiangsu. China BMC Infect Dis. 2021;21:1–9. https://0-doi-org.brum.beds.ac.uk/10.1186/s12879-021-06490-0.

    Article  CAS  Google Scholar 

  34. Simões D, Stengaard AR, Combs L, Raben D. Impact of the COVID-19 pandemic on testing services for HIV, viral hepatitis and sexually transmitted infections in the WHO European Region, March to August 2020. Eurosurveillance. 2020. https://0-doi-org.brum.beds.ac.uk/10.2807/1560-7917.ES.2020.25.47.2001943.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Adugna A, Azanaw J, Sharew MM. The effect of COVID-19 on routine HIV care services from health facilities in Northwest Ethiopia. HIV/AIDS - Res Palliat Care. 2021;13:1159–68. https://0-doi-org.brum.beds.ac.uk/10.2147/hiv.s341012.

    Article  CAS  Google Scholar 

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Acknowledgements

The authors would like to acknowledge the staff collecting data in the medical record of each hospital; Dede Bahtiar (Cipto Mangunkusumo Hospital), Dyaning Arie Saliring Tyas (Sentra Medika Hospital), Mustika Munthe Kristia (Dharmais National Cancer Hospital).

Funding

No funding was received for this work.

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Authors and Affiliations

  1. Division of Allergy and Clinical Immunology, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia, Cipto Mangunkusumo Hospital, Jl. Diponegoro no. 71, Jakarta, 10430, Indonesia

    Evy Yunihastuti, Teguh Harjono Karjadi, Alvina Widhani, Sukamto Koesnoe & Samsuridjal Djauzi

  2. HIV Integrated Unit, Cipto Mangunkusumo Hospital, Jakarta, Indonesia

    Evy Yunihastuti, Teguh Harjono Karjadi, Alvina Widhani, Salma Sundari & Aljira Fitya Hapsari

  3. Sentra Medika Hospital, Depok, West Java, Indonesia

    Teguh Harjono Karjadi

  4. Department of Internal Medicine, Universitas Indonesia Hospital, Depok, West Java, Indonesia

    Alvina Widhani

  5. Department of Non-Oncology Internal Medicine, Dharmais National Cancer Hospital, Jakarta, Indonesia

    Haridana Indah Setiawati Mahdi & Samsuridjal Djauzi

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  1. Evy Yunihastuti
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Contributions

All authors were involved in the critical revision of the manuscript for important intellectual content and approved the manuscript. EY and AW are responsible for the initial study concept and design. Collection of data was carried out by EY, THK, AW, HISM, SS, AFH. Followed by the acquisition, analysis, or interpretation of data by EY, THK, AW, HISM, SS, AFH. The manuscript was drafted EY, SS, AFH. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Evy Yunihastuti.

Ethics declarations

Ethical approval and consent to participate

This research was approved by the Ethics Committee of Faculty of Medicine University of Indonesia and Cipto Mangunkusumo Hospital, followed by approval from the ethics committee of University Indonesia Hospital, ethics committee of Dharmais National Cancer Hospital, and Director of Sentra Medika Hospital. All personal identifiers were anonymized for confidentiality before data processing was performed. This research and all methods were performed by the Declaration of Helsinki.

Competing interests

The remaining authors (EY, THK, AW, HISM, SS, AFH, SK, SD) declare no conflicts of interest.

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Yunihastuti, E., Karjadi, T.H., Widhani, A. et al. Incidence and severity prediction score of COVID-19 in people living with HIV (SCOVHIV): experience from the first and second waves of the pandemic in Indonesia. AIDS Res Ther 19, 47 (2022). https://0-doi-org.brum.beds.ac.uk/10.1186/s12981-022-00472-1

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Keywords

AIDS Research and Therapy

ISSN: 1742-6405

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