The effect of HIV status on the frequency and severity of acute respiratory illness.

INTRODUCTION: Antiretroviral therapy has improved the health of people living with HIV (PLW-HIV), though less is known about how this impacts on acute respiratory illness. These illnesses are a common cause of ill health in the general population and any increase in their frequency or severity in PLW-HIV might have significant implications for health-related quality of life and the development of chronic respiratory disease.
METHODS: In a prospective observational cohort study following PLW-HIV and HIV negative participants for 12 months with weekly documentation of any acute respiratory illness, we compared the frequency, severity and healthcare use associated with acute respiratory illnesses to determine whether PLW-HIV continue to have a greater frequency or severity of such illnesses despite antiretroviral therapy.
RESULTS: We followed-up 136 HIV positive and 73 HIV negative participants for 12 months with weekly documentation of any new respiratory symptoms. We found that HIV status did not affect the frequency of acute respiratory illness: unadjusted incidence rates per person year of follow-up were 2.08 illnesses (95% CI 1.81-2.38) and 2.30 illnesses (1.94-2.70) in HIV positive and negative participants respectively, IRR 0.87 (0.70-1.07) p = 0.18. However, when acute respiratory illnesses occurred, PLW-HIV reported more severe symptoms (relative fold-change in symptom score 1.61 (1.28-2.02), p <0.001) and were more likely to seek healthcare advice (42% vs 18% of illnesses, odds ratio 3.32 (1.48-7.39), p = 0.003). After adjustment for differences in baseline characteristics, PLW-HIV still had higher symptom scores when unwell.
CONCLUSIONS: HIV suppression with antiretroviral therapy reduces the frequency of acute respiratory illness to background levels, however when these occur, they are associated with more severe self-reported symptoms and greater healthcare utilisation. Exploration of the reasons for this greater severity of acute respiratory illness may allow targeted interventions to improve the health of people living with HIV. TRIAL REGISTRATION: ISRCTN registry (ISRCTN38386321).

Introduction

The use of antiretroviral therapy (ART) has transformed the lives of people living with HIV (PLW-HIV). In populations with good access to ART and on-going care, PLW-HIV can now have a life expectancy equal to that of the general population.[1, 2]

Effective treatment is changing the demographics of the HIV positive population, who are increasing both in number and age.[3] There is a growing interest, therefore, in the extent to which they may be at greater risk of non-AIDS comorbidities such as chronic cardiovascular, neurological and respiratory diseases.[4-6] Studies evaluating respiratory health have reported a higher prevalence of chronic respiratory illness than the general population, [7] [8] which is only partly explained by greater exposure to established risk factors such as tobacco smoking. [9]

Although there is evidence that ART reduces the incidence of respiratory infections such as Pneumocystis jirovecii pneumonia, bacterial pneumonia and tuberculosis, [10, 11] most studies evaluating respiratory illness in the modern HIV population have been cross-sectional in nature and therefore cannot determine whether acute respiratory illnesses remain more frequent among PLW-HIV.[12, 13] PLW-HIV with Chronic Obstructive Pulmonary Disease (COPD) have a higher exacerbation frequency than equivalent HIV negative individuals.[14, 15] As acute respiratory illnesses are common in the general population and associated with significant morbidity and healthcare utilisation,[16] we postulated that if HIV positive individuals have a higher incidence of these illnesses (or if they were more severe or longer lasting) then this could impact significantly on health-related quality of life, productivity and healthcare costs. A greater frequency of acute respiratory illness might also contribute to the development of chronic lung disease in this population. If specific risk factors for acute respiratory illness could be identified, then interventions to reduce the incidence or severity of these illnesses (such as smoking cessation, immunisations and treatment for respiratory conditions) might be better targeted.

This study aimed to prospectively measure the frequency of acute respiratory illness among HIV positive individuals using antiretroviral therapy compared to HIV negative participants.

Methods

Study design, setting and population

We conducted a prospective cohort study of PLW-HIV and HIV negative individuals. We recruited participants between November 2015 and January 2017 from HIV care services and primary care in London, UK. This is a setting with a high uptake of antiretroviral therapy.[17]

We invited PLW-HIV to participate when attending ambulatory HIV-care appointments: the only eligibility criteria were age over 18 years, consent to participate and absence of symptoms of acute respiratory illness at study entry. We recruited HIV negative adults from primary care, using electronic medical records to invite potential participants with similar age, gender and smoking status to the expected characteristics of the HIV positive population sampled. To achieve this, primary care practice lists were used to identify all patients with age, gender and smoking status suitable for recruitment and individuals were randomly selected to be invited by post from these lists. There were no financial inducements offered, although we reimbursed limited travel expenses where appropriate. All participants provided written informed consent.

The study protocol was reviewed by the London Hampstead Research Ethics Committee (14/LO/1409) and registered with the ISRCTN registry (ISRCTN38386321).

Procedures

At recruitment, participants completed a questionnaire detailing respiratory symptoms, prior illnesses, current treatment, tobacco and recreational drug use. We measured baseline respiratory health status using the St Georges Respiratory Questionnaire (SGRQ) and MRC dyspnoea scores.[18, 19]

Subjects underwent spirometry without bronchodilation, with standard ERS/ATS quality criteria,[20] using the Global Lung Function Initiative equations to calculate predicted values.[21] An obstructed airflow pattern was defined as an FEV1/FVC ratio <0.7, and restrictive pattern as FEV1/FVC >0.7 plus FVC <80% predicted.

We collected naso-pharyngeal swabs to assess the carriage of respiratory viral pathogens at baseline. HIV negative participants had their HIV status confirmed by blood test at recruitment.

Detection of viral respiratory pathogens

The detection of respiratory viral pathogens from nasopharyngeal swabs at baseline and during an acute respiratory illness was undertaken using multiplex PCR testing. In-house real-time RT-PCR assay was performed which was designed to simultaneously detect: rhinovirus, influenza (A & B), parainfluenza (1–4), RSV, adenovirus, enterovirus, coronavirus (NL63, HKU, 229E, OC43), parechovirus and human metapneumovirus.

Follow-up and identification of acute respiratory illnesses

We prospectively followed participants for 12 months and during this each participant was contacted weekly by email or telephone SMS message. In these weekly messages, participants were asked if they had any new respiratory symptoms indicative of an acute respiratory illness.

An acute respiratory illness was defined as the new occurrence (lasting more than 24 hours) of any of the following symptoms: cough, sore throat, blocked or runny nose with or without a sensation of facial pain or pressure, breathlessness or pain on breathing. This definition sought to include both upper and lower respiratory tract illnesses. No assumption was made about whether an illness was caused by an infection, and fever was not included in the illness definition (although individuals were asked about a history of fever).

When participants reported new symptoms, their severity was assessed by supplementary questions including questions on daily activities and treatment. Symptoms were scored on a 0–6 scale and these scores were summarised by calculating a total score (with no weighting applied). We also invited participants to attend for review, (at which we collected samples for the detection of respiratory pathogens) and to complete a daily diary recording symptoms, medication usage and healthcare resource utilisation for the duration of their illness or up to 14 days, whichever was the longer. The questionnaires used on the webform and written bookets are provided as supplementary information.

Sample size calculation

The primary study outcome was the number of acute respiratory tract illnesses occurring over the one-year follow up period. Based on data from the Pulmonary Complications of HIV study, Multicentre AIDS Cohort Study and the Women’s Interagency HIV study we estimated that there would be at least a 50% higher than in HIV uninfected individuals without antiretroviral therapy,[22, 23] and planned the study size to detect lower incidence than this. To estimate the likely number of acute respiratory illnesses seen during follow up, we used data from the FluWatch study,[24] which reported that 44% of those without serological evidence of influenza infection experienced an acute respiratory illness each influenza season (the study was only conducted during influenza seasons). Based on this we conservatively estimated that at least 44% of the HIV negative participants will have an acute respiratory illness over a 12 month period. We planned to have a 2:1 ratio between HIV infected participants and controls. To have an 80% power to detect this difference (i.e. 68% vs. 45%) with a type 1 error of 5% we would need 119 HIV infected and 60 in the HIV uninfected participants in the cohort. We assumed up to a 20% drop out of subjects from the study then we plan to recruit 140 individuals with HIV infection and 70 individuals without HIV infection.

Statistical analysis

The primary outcome was the difference in frequency of acute respiratory illness between HIV positive and negative participants. Pre-defined secondary outcomes were the severity and duration of acute respiratory illness, isolation of respiratory pathogens and healthcare utilisation.

We used Poisson regression models to analyse the frequency of acute respiratory illness (with weeks of data reported as an offset value). Continuous outcomes for symptom severity were analysed using linear regression models. We completed multivariable analysis, adjusting for potential confounding effects of factors chosen a priori based on known risk factors for acute respiratory illness. When participants did not answer the weekly study question, we assumed that they did not have an acute respiratory illness. We undertook data assembly and statistical analysis using Excel (Microsoft Ltd) and Stata v14, (Statacorp).

Results

Participants

209 individuals (136 HIV positive and 73 HIV negative) consented to participate in the study and submitted at least one week of follow-up data. Demographic details of study participants are given in Table 1. There were no significant differences in the age or gender of the HIV positive and negative participants, although there was a trend to suggest that PLW-HIV were more likely to be current smokers (29% vs 16%, p = 0.08), and more likely to report previous or current (within the last 3 months) use of recreational drugs. All HIV positive participants used antiretroviral therapy during the study and 87% had an undetectable blood HIV load at baseline. The median (IQR) CD4 count was 686 (458–848) cells/μL.

Table 1

Participant baseline characteristics.

		HIV Positive N = 136	HIV Negative N = 73	p value
Gender	Female, n (%)	30 (22%)	18 (25%)	0.67*
	Male, n (%)	106 (78%)	55 (75%)
Age, years, mean (SD)		50 (11)	52 (8)	0.11**
Ethnicity	Caucasian, n (%)	103 (76%)	70 (96%)	<0.01 Δ
	Black African / Caribbean, n (%)	23 (17%)	0
	South Asian, n (%)	2 (1%)	2 (3%)
	Other, n (%)	8 (6%)	1 (1%)
Body mass index (BMI), kg/m2 median (IQR)		25 (23–28)	25 (23–26)	0.06
UK Born, n (%)		73 (54%)	54 (74%)	0.07*
Sexuality	Heterosexual, n (%)	37 (27%)	57 (78%)	<0.001Δ
	Homosexual, n (%)	86 (62%)	16 (22%)
	Bisexual, n (%)	12 (9)	0
	Not answered, n (%)	1 (1%)	0
Educational attainment	No qualifications, n (%)	7 (5%)	2 (3%)	0.54Δ
	GCSE or equivalent qualifications at age 16, n (%)	20 (15%)	11 (15%)
	A level or equivalent qualifications at age 18, n (%)	24 (18%)	8 (11%)
	University education, n (%)	72 (53%)	44 (60%)
	Other qualifications, n (%)	13 (10%)	7 (10%)
	Not answered, n (%)	0	1 (1%)
Immunisations (self-report)	Influenza (last 12 months)	90 (66%)	21 (29%)	<0.01*
	Pneumococcal (ever)	50 (37%)	9 (12%)	<0.01*
Comorbid conditions (self-report)	Asthma	22 (16%)	7 (10%)	0.21 Δ
	COPD	3 (2%)	1 (1%)	0.56 Δ
	Heart disease	5 (4%)	2 (3%)	1 Δ
Previous history of respiratory opportunistic infection (HIV positive participants only)		9 (7%) ##
Use of inhaled medications	Any inhaled medication	25 (18%)	5 (7%)	0.07 Δ
	Inhaled corticosteroids	13 (10%)	1 (1%)	0.04 Δ
Tobacco smoking	Current smoker, n (%)	39 (29%)	12 (16%)	0.08*
	Ex-smoker, n (%)	46 (34%)	34 (47%)
	Never smoker, n (%)	41 (37%)	27 (37%)
Tobacco pack-years (median, IQR),◊		6 (2–12)	9 (3–15)	0.17#
Recreational drug use (ever), n (%)		90 (66%)	37 (51%)	<0.005*
Recreational drug use (last 3 months), n (%)		42 (31%)	9 (12%)	0.007*
CD4 count, cells/μL, median (IQR)		686 (458–848)
Use of antiretroviral therapy during study		136 (100%)
HIV load < 40 copies/ml at baseline		118 (87%)

* Chi squared test,

** t—test

# Mann Whitney Test,

Δ Fisher’s exact test

◊ calculated for smokers and ex-smokers only

## consisting of Pneumocystis jirovecii pneumonia (7 cases), tuberculosis (1 case), non-tuberculous mycobacterial infection (1 case)

PLW-HIV reported worse respiratory health status at baseline with higher scores on the St George’s Respiratory Questionnaire (median SGRQ Total score 13 (5–28) vs 6 (2–9), p< 0.001). Using the MRC dyspnoea scale, breathlessness also appeared to be more common: 59 (43%) vs 12 (17%) reporting MRC dyspnoea scale score of 2 or more (p <0.001). Spirometry was normal for most participants, but a greater proportion of PLW-HIV had abnormal spirometry at baseline (19% vs 3% with restrictive spirometry (FEV1/FVC ≥0.7 and FVC< 80% predicted)), and 13% vs 8% with obstructive spirometry (FEV1/FVC <0.7), p = 0.004) (Table 2).

Table 2

Baseline respiratory health status and spirometry.

			HIV Positive (N = 136)	HIV Negative (N = 73)	p value
Baseline St George’s Respiratory Questionnaire score	Symptoms, median (IQR)		30 (8–45)	11 (0–28)	<0.001#
	Activity, median (IQR)		18 (6–36)	6 (0–12)	<0.001#
	Impacts, median (IQR)		4 (0–16)	0 (0–2)	<0.001#
	Total score, median (IQR)		13 (6–29)	6 (2–9)	<0.001#
Baseline MRC Dyspnoea score	1: Not troubled by breathlessness except on strenuous exercise, n (%)		77 (57%)	58 (81%)	0.008Δ
	2: Short of breath when hurrying on a level or when walking up a slight hill, n (%)		43 (32%)	12 (17%)
	3: Walks slower than most people on the level, stops after a mile or so, or stops after 15 minutes walking at own pace, n (%)		6 (4%)	0
	4: Stops for breath after walking 100 yards, or after a few minutes on level ground, n (%)		4 (3%)	0
	5: Too breathless to leave the house, or breathless when dressing/undressing, n (%)		1 (1%)	0
	Not answered		3 (2%)	2 (3%)
Spirometry*	FEV1, L, mean (SD)		3.22 (0.78)	3.53 (0.73)	0.01□
	FEV1% predicted, mean (SD)		91% (14%)	97% (11%)	0.005□
	FVC, L, % predicted, mean (SD)		4.16 (1.01)	4.55 (0.98)	0.02□
	FVC % predicted, mean (SD)		93% (14%)	99% (12%)	0.02□
	Spirometry interpretation	Airflow obstruction, n%**	13 (13%)	5 (8%)	0.004Δ
		Restriction, n%	18 (19%)	2 (3%)
		Normal spirometry, n%	65 (68%)	54(88%)

* 96 HIV positive and 61 HIV negative participants had spirometry results meeting ATS/ERS quality criteria

#Mann-Whitney test

□ t-test

Δ Fisher’s exact test

** FEV1/FVC <0.7

Detection of respiratory viral pathogens at baseline

Viral pathogens were identified in 5 (2%) of baseline nasopharyngeal swabs, all from PLW-HIV. These were: Parainfluenza 2 (two participants), Coronavirus OC43, Influenza A and Parechovirus. All other participants had negative baseline swab results.

Follow-up and completion of weekly responses

The median number of weeks for which each participant provided data during follow-up was 44/52 weeks (85%); this was not significantly different between HIV positive and negative participants. Sixty-six (42%) had 100% response rate to the weekly contacts and 60 (29%) had <80% response rate.

Frequency of acute respiratory illness

One hundred and sixty-eight participants (80%) reported at least one acute respiratory illness during follow up, with the median number per person being 2 (range 0–7 illnesses).

There was no significant difference in the frequency of acute respiratory illness between HIV positive and negative participants. The unadjusted incidence rate per person year of follow-up was 2.08 (95% CI 1.81–2.38) in HIV positive and 2.30 (1.94–2.70) in HIV negative participants; IRR 0.87 (0.70–1.07 p = 0.18).

In univariable regression analyses, smoking status (being an ex-smoker), airflow obstruction (FEV1/FVC <0.7) and the presence of chronic respiratory symptoms (MRC score ≥2) at baseline were associated with a significantly greater frequency of acute respiratory illness. Participants of black ethnicity reported a lower frequency of events than white participants (IRR 0.64 (95% CI 0.42–0.96) p = 0.01) (Table 3).

Table 3

Risk factors for acute respiratory illness among cohort participants.

Characteristic		N	Incidence rate of acute respiratory illness per person year of follow-up	Univariable analysis b		Multivariable analysis b
				IRR (95% CI)	P value	IRR (95% CI)	P value
HIV status	HIV positive	136	2.08 (1.81–2.38)	0.87 (0.70–1.07)	0.18	0.81 (0.61–1.06)	0.13
	HIV negative	73	2.30 (1.94–2.70)	1		1
Gender	Female	48	1.97 (1.56–2.48)	0.84 (0.54–1.23)	0.42	0.88 (0.65–1.21)	0.45
	Male	161	2.21 (1.95–2.47)	1		1
Age (years)	65+	24	1.98 (1.42–2.68)	0.94 (0.67–1.32)	0.40	0.87 (0.58–1.31)	0.90
	55–65	35	2.19 (1.69–2.79)	1.04 (0.79–1.38)		1.03 (0.82–1.46)
	45–55	104	2.19 (1.89–2.53)	1		1
	<45	46	2.11 (1.64–2.67)	0.83 (0.63–1.09)		0.98 (0.68–1.43)
Tobacco Smoking	Current smoker	51	2.25 (1.82–2.76)	1.12 (0.85–1.48)	0.08	1.28 (0.92–1.77)	0.04
	Ex-smoker	80	2.37 (2.01–2.77)	1.29 (1.02–1.62)		1.45 (1.08–1.93)
	Never smoker	78	1.85 (1.53–2.23)	1		1
Spirometry	Obstructive	13	3.33 (2.42–4.47)	1.24 (0.90 1.72)	0.22	1.69 (1.16–2.46)	0.01
	Restrictive	18	1.71 (1.11–2.50)	0.81 (0.52–1.22)		0.83 (0.54–1.29)
	Normal	65	2.07 (1.54–2.23)	1		1
Baseline MRC breathlessness score	3–5	11	2.57 (1.63–3.86)	1.35 (0.82–2.22)	0.01
	2	55	2.64 (2.17–3.17)	1.37 (1.08–1.73)
	1	135	1.88 (1.63–2.15)	1
Recreational drug use, ever	Yes	77	2.19 (1.92–2.50)	1.22 (0.85–1.76)	0.28
	No	127	2.03 (1.68–2.42)	1
Recreational drug use, last 3 months	Yes	51	2.11 (1.69–2.60)	0.92 (0.51–1.37)	0.67
	No	153	2.18 (1.93–2.46)	1
Ethnicity	Black African / Caribbean	23	1.61 (1.06–2.37)	0.64 (0.42–0.96)	0.01	1.07 (0.62–1.88)	0.25
	South Asian / other / mixed	13	3.01 (1.50–5.39)	1.39 (0.98–2.00)		1.40 (0.94–2.07
	White	173	2.13 (1.90–2.38)	1		1
Educational attainment	Other	20	2.27 (1.61–3.12)	0.88 (0.56–1.39)	0.91
	Degree	116	2.38 (2.07–2.73)	0.94 (0.70–1.25)
	A level	32	1.89 (1.41–2.47)	0.91 (0.63–1.31)
	None/GCSE	40	1.64 (1.23–2.13)	1
Baseline SGRQ	>20	42	2.72 (2.17–3.38)	1.39 (0.68–2.24)	0.14
	10–20	28	2.52 (1.99–3.14)	1.41 (0.69–2.89)
	<10	96	1.84 (1.58–2.11)	1
Current CD4a	>500	94	1.98 (1.67–2.33)	0.73 (0.37–1.42)	0.27
	350–500	27	1.86 (1.35–2.51)	0.58 (0.26–1.29)
	<350	15	3.30 (2.23–4.72)	1
Nadir CD4a	500+	17	1.86 (1.19–2.76)	0.75 (0.38–1.48)	0.28
	350–500	20	2.05 (1.40–2.90)	0.84 (0.45–1.58)
	200–350	36	1.84 (1.37–2.41)	0.93 (0.55–1.56)
	<200	63	2.26 (1.78–2.72)	1

a: calculated for HIV positive participants only

b: Poisson regression

In a multivariable model including HIV status, age, gender, ethnicity and the presence of spirometric abnormality, there was again no significant difference in the frequency of acute respiratory illness between HIV positive and negative participants (adjusted IRR 0.80 (0.61–1.06) p = 0.13).

Severity and duration of illness

The web-based symptom questionnaire completed during an acute respiratory illness (by 90% of participants in 361 (97%) illnesses), demonstrated that PLW-HIV had a greater symptom severity score, with a median total score of 14 points (IQR 8–23) compared to 9 (5–14) in HIV negative participants (fold change in score 1.61 (1.28–2.02), p<0.001).

In addition, participants recorded written diaries detailing daily symptoms during 166 (45%) acute respiratory illnesses. In these, PLW-HIV also reported greater symptom scores with median total symptom scores per day of 9.36 points (IQR 5.77–14.95) versus 6.4 points (4.74–9.82) in HIV negative participants (p = 0.008). PLW-HIV also reported more days with at least mild symptoms, with a median duration of 8 (IQR 5.1–10.5) vs 6 (4.25–9.5) days—though this difference was not statistically significant (p = 0.18). The total symptom scores per day based on the written diaries provided by participants are displayed in Fig 1.

Fig 1

Symptom severity during acute respiratory illness.

Effect of participant baseline characteristics on symptom scores during acute respiratory illness

We explored the relationship between baseline characteristics and participant-reported severity of symptoms using the online responses. In univariable log-scale linear regression analyses, HIV status, airflow obstruction (FEV1/FVC <0.7), recent (within 3 months) use of recreational drugs and the presence of increased respiratory symptoms at baseline (MRC dyspnoea score or St George’s Respiratory Questionnaire score) were associated with greater self-reported symptom severity during acute respiratory illness. HIV positive participants had a 61% greater symptom score in univariable analyses (unadjusted fold change in symptom score 1.61, 95% CI 1.28–2.02, p <0.001).

In a multivariable regression model assessing the direct effect of HIV status after adjustment for spirometric impairment, recent recreational drug use, SGRQ score and tobacco smoking, being HIV positive continued to be associated with a greater symptom score during acute respiratory illness, with an adjusted fold-change in symptom score of 1.50 (1.14–1.97, p = 0.004) (Table 4).

Table 4

Relationship between participant baseline characteristics and symptom severity at time of reporting acute respiratory illness, log-scale linear regression analyses.

Characteristic		Median (IQR) symptom score#	Univariable analysis		Multivariableanalysis
			Fold-change in total symptom score*	P value#	Fold-change in total symptom score	P value**
HIV Status	HIV Positive	14 (8–23)	1.61 (1.28–2.02)	<0.001	1.50 (1.14–1.97)	0.02
	HIV Negative	9 (5–14)	1		1
Gender	Female	13 (7–22)	1.08 (0.77–1.66)	0.648	1.30 (0.92–1.83)	0.15
	Male	11 (6–20)	1		1
Spirometry	Restrictive	12 (6–23)	1.00 (0.66–1.51)	0.04	0.96 (0.69–1.35)	0.56
	Obstructive	20 (10–23)	1.51 (1.10–2.07)		1.16 (0.80–1.67)
	Normal	11 (6–19)	1		1
Recreational drugs (ever)	Yes	12 (6–21)	1.64 (0.81–1.36)	0.71
	No	12 (6–20)	1
Recreational drugs, past 3 months	Yes	15 (8–21)	1.34 (1.07–1.67)	0.01	1.27 (0.95–1.72)	0.27
	No	11 (6–20)	1		1
Ethnicity	Black African / Caribbean	8 (4–21)	0.83 (0.41–1.68)	0.47
	South Asian Other/Mixed	13 (9–31)	1.08 (0.48–2.43)
	White / Caucasian	12 (6–20)	1
Qualifications / educational attainment	None	6 (5–24)	0.92 (0.69–1.22)	0.07
	GCSE	8 (4–14)	0.75 (0.55–1.02)
	A level	10 (7–17)	0.78 (0.54–1.14)
	Other	13 (5–22)	1.06 (0.58–1.53)
	University /degree	13 (6–21)	1
Baseline St Georges Respiratory Questionnaire score	>20	14 (9–23)	1.68 (1.30–2.18)	<0.001	1.42 (1.11–1.83)	0.05
	10–20	14 (8–21)	1.48 (1.14–1.93)		1.43 (1.05–1.96)
	<10	9 (4–18)	1		1
Baseline MRC Dyspnoea score	3–5	14 (6–33)	1.75 (1.03–2.98)	0.004◊
	2	13 (7–21)	1.22 (0.53–1.58)
	1	10 (5–18)	1
Tobacco smoking	Current smoker	11 (1–21)	1.12 (0.83–1.51)	0.22	0.91 (0.63–1.33)	0.96
	Ex-smoker	11 (5–20)	0.92(0.70–1.02)		1.06 (0.81–1.38)
	Never smoker	12 (6–19)	1		1

# Univariable log-scale linear regression analysis

* univariable log-scale linear regression model

** multivariable log-scale linear regression including all factors with data in this column

◊ MRC dyspnoea score not included in multivariable model as collinear with SGRQ score

Treatment and healthcare utilisation

Using the diaries completed by participants, PLW-HIV were more likely to obtain advice from a healthcare professional during an acute respiratory illness (42% vs 14%, p = 0.003), and more likely to seek healthcare assessment 32% vs 9%, p = 0.001. There was no significant difference in the proportion of illnesses for which participants took non-prescription medications (59% vs 54%). PLW-HIV used antibiotics in a numerically greater proportion of illnesses (22% vs 11.5%), though this was not statistically significant (OR 2.11 (95% CI 0.75–5.94), p = 0.16).

Isolation of viral and bacterial pathogens during acute respiratory illness

We collected 158 nasopharygeal swabs during an acute respiratory illness. Respiratory viruses were detected in 77 (48%) of these samples, with no significant difference in the likelihood of detection of respiratory viral pathogens between HIV positive and negative groups. A viral pathogen was identified in 52% of illnesses in PLW-HIV (49/94) compared to 45% of HIV negative participants (29/64), p = 0.36. Rhinovirus was the predominant virus identified in both groups, detected in 26% of samples from HIV positive participants and 28% of samples from HIV negative participants. Influenza was detected in 5 samples from PLWH (5%) and 1 sample from an HIV negative participant (2%), seasonal coronavirus was detected in 9 (10%) and 5 (8%) samples respectively, other respiratory viruses detected were enterovirus, Human metapneumovirus, parainfluenza, Respiratory Syncytial Virus, and adenovirus, all in one or two cases only. The relative proportion of different viral results are illustrated in Fig 2.

Fig 2

Respiratory viruses detected during acute respiratory illness.

We obtained sputum samples for bacterial culture during 70 illnesses: pathogens were identified in 10 (19%) of these samples from 9 different participants: Haemophilus influenzae (5 isolates from 4 participants), Streptococcus pneumoniae (3 isolates, each from different participants), Moraxella catarrhalis (2 from different participants). The fungus Aspergillus fumigatus was identified in one participant. All samples from which bacterial or fungal organisms were isolated were from HIV positive participants.

Sensitivity analyses

We conducted several pre-specified sensitivity analyses to ensure that our findings were robust using different analytical methods. These included: a) excluding all participants with less than 80% response rate; b) defining the offset value for the regression analyses as the number of weeks between the first and last response to the weekly messages recorded (rather than the total number of responses) and c) defining the outcome as the proportion of weeks of follow up in which a new respiratory illness was reported (thus giving a continuous rather than count variable) and performing linear regression analyses. The findings of the main analysis were consistent in all these sensitivity analyses. We also repeated the main analyses of frequency of events using a negative binomial model and findings were not significantly different to the Poisson model presented.

Discussion

In this prospective cohort study, we found no evidence of a significant difference in the frequency of acute respiratory illness between HIV positive and negative individuals in a setting with a high uptake of antiretroviral therapy. Our study was powered to detect a difference of 50% or more in the proportion of HIV positive participants experiencing an acute respiratory illness during follow up, however the tight confidence intervals around the incidence rate ratio between the two groups make it unlikely that there is more than a small difference in the frequency of these events.

PLW-HIV did report more severe symptoms during acute illnesses, which was associated with a greater likelihood of seeking healthcare advice and a higher (although not statistically significant) proportion of illnesses treated with antibiotics. This effect persisted after adjustment for important potential confounding factors (such as differences in tobacco smoking), but other differences between the HIV positive and negative groups (such as differences in immunisation frequency) could have influenced this outcome.

As noted elsewhere,[12] PLW-HIV in this population were more likely to report chronic respiratory symptoms at baseline. A greater burden of baseline respiratory symptoms was independently associated with the severity of acute respiratory illnesses during follow-up. However, this did not explain all of the difference between HIV positive and negative participants, as PLW-HIV’s higher symptom scores during acute respiratory illnesses persisted after adjustment for baseline symptoms.

Only a small proportion of the PLW-HIV evaluated had airflow obstruction, in contrast to some other published studies, it should be noted that we only undertook pre-bronchodilator spirometry and this proportion might have been lower still in post-bronchodilator measurements. We did find a greater proportion of the PLW-HIV with lower than predicted vital capacity, a finding which is consistent with some other studies reporting the influence of HIV status on prevalence of lung function impairment, for instance that of Ronit et al evaluating a population in Denmark reported a mean absolute difference in FVC of 395mls,[25] a very similar difference to that found in this study.

There are several possible explanations for the greater burden of respiratory symptoms during acute respiratory illness. These include PLW-HIV having impairments in lung function that are not measured by spirometry (such as low diffusing capacity,[26] or small airways disease [27]). An awareness of the potential for more severe illness might result in an increased perception or concern about physical symptoms in PLW-HIV—which itself may be driven by the higher prevalence of anxiety or depression reported in this population.[28, 29] Finally, there is evidence to suggest that despite immune reconstitution with ART, PLW-HIV may still have greater immune activation than HIV negative individuals [30, 31]–and thus the heightened symptom burden during an acute respiratory illness might reflect a disordered immune response when unwell.

Although, this is the first prospective study of its kind, the greater use of antibiotics during acute illness is consistent with reports of a greater frequency of COPD exacerbations among PLW-HIV with COPD when these are defined by a requirement for treatment.[14, 15] Our study was not powered to address differences in antibiotic usage, though the results suggest that PLW-HIV may be more likely to receive antibiotic treatment for acute respiratory illnesses. This has potential implications for antimicrobial resistance.

We believe our results are relevant to both clinical care and health policy: the greater severity of acute respiratory illness among PLW-HIV suggests that (in addition to the provision of ART) there is a need to implement interventions that can improve the respiratory health. Our data suggest that it is possible to identify individuals at greater risk of more frequent or severe acute respiratory illness who could be the focus of interventions that can reduce their chance of respiratory illness—including smoking cessation, immunisation against influenza and Streptococcus pneumoniae—and improve the diagnosis and treatment of underlying respiratory conditions. A simple risk assessment for respiratory illness could in future be part of routine HIV care assessments in people on antiretroviral therapy.

Our data also highlight the need to understand why PLW-HIV report more severe respiratory symptoms, both at baseline and during acute respiratory illness compared to matched controls. Identifying whether such differences in patient-reported outcomes reflect underlying pathological responses is important; and mechanistic studies which assess immune responses during acute illness might identify causes for the reported differences.

We aimed to document the frequency of all acute respiratory illness, and our study was not powered to measure less common but more severe events such as bacterial pneumonia. Epidemiological data suggest that pneumonia continues to be more frequent among PLW-HIV, and remains an important cause of mortality.[11, 17] Whether HIV positive people with good response to antiretroviral therapy continue to be at greater risk of severe bacterial pneumonia requires further attention.

Our study was based on self-report of acute respiratory illness, and utilised participant-determined measures to record respiratory symptoms, rather than an objective illness severity assessment. A limitation of our methodology was the lack of a standardised Patient Reported Outcome Measure for assessing acute respiratory illness severity, so a questionnaire was specifically created for this study. An illness definition and symptom questions were chosen with the aim of recording all acute respiratory illnesses, but this did not differentiate between upper and lower respiratory tract illnesses. We used intensive active surveillance to identify acute respiratory illnesses, including weekly contact with study participants, thus minimising recall and reporting biases. However, although the response rate to the weekly study contacts was very good (85%), written daily diaries during acute illnesses were only available in 46% of episodes. This may have introduced a degree of bias into our data, though it is reassuring to note that a greater severity of illness was reported from both the online and written diary respondents.

As with any study evaluating an HIV cohort, our findings are not necessarily generalizable to settings where access to HIV care is poor, or to individuals who do not maintain linkage to healthcare. Also, the pattern of utilisation noted by us may be less applicable to regions with different healthcare systems.

Conclusions

In an adult population with a high uptake of antiretroviral therapy, there is no difference in the frequency of acute respiratory illness between HIV positive and negative individuals. When these do occur, people living with HIV report more severe and longer-lasting illnesses and are more likely to seek healthcare advice. This has implications for healthcare resource utilisation and the health-related quality of life of people living with HIV.

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15 Jan 2020

PONE-D-19-35060

The effect of HIV status on the frequency and severity of acute respiratory illness

PLOS ONE

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Reviewer #1: This prospective study by Brown Colleagues exams the incidence and severity of acute respiratory illnesses in an HIV-infected population on ART and compares it to non HIV-infected subjects. They report that the incidence of acute infections is similar in the two groups, but that the severity is worse in the HIV-infected subjects leading to greater seeking of care from the health care system by this group.

In general this is an easy to read, well written manuscript. The size of the two populations is reasonable. Follow up of the subjects over a year is very good for these types of studies. The main concerns relate to how the data was analyzed as described below.

Major Comments:

1. It appears there were two attempts at objective assessment of upper respiratory tract severity. The first was a “web-based symptom questionnaire” graded on a “0-6 scale, including questions on daily activities and treatment.” The second was a subject “diary”, which also appears to have been scored somehow given the data in the results section was also given as “points”. It is unclear how these two measurement tools are different. It would be nice to show these grading systems in supplemental materials to better assess the quality of the data. Furthermore, it needs to be clear in the methods that the first tool is actually web-based. In the methods section it read like the investigators were doing the question asking, which obviously introduces bias if the interviewer knows the subject status. It was not apparent that the first tool was web-based till the first sentence under the “Severity and Duration of Illness” section in the results.

2. There are a lot of differences between the two groups (Table 1). This is important because their adjustment for potential confounding effects was based on “a priori” factors known to be associated with acute respiratory illnesses rather than actual differences between the two groups. Thus, while baseline smoking, baseline respiratory symptoms, and baseline PFTs were appropriately considered, it is not clear that all the significant differences between the groups were taken into account in their univariate and multivariate analysis. Differences that were adjusted for that might be significant include:

a. UK born versus other – relates to different potential prior exposures and cultural responses to illness which might impact assessment of disease severity.

b. Immunizations – much higher in the HIV positive group, which may have lessened the incidence in the HIV positive group.

c. Significantly higher incidence of inhaler use, especially corticosteroids, in the HIV group. These by themselves can affect the incidence and severity of respiratory illnesses.

3. One of the author’s conclusions is that it is “possible to identify individuals at greater risk of more frequent or severe acute respiratory illness (using the degree of airflow obstruction, history of smoking or recreational drug use, and the presence of chronic respiratory symptoms). Does this hold for both HIV-infected subjects and uninfected subjects, or just the HIV population alone? From the tables, it looks like these risk factors were lumped together regardless of HIV status.

Minor Comments:

1. At the end of the Statistical analysis it states that further details are provided in supplemental information. I do not see any supplemental information. Based on major comment 2 above, this is important.

2. While this is in general very well written, for some reason there are multiple grammatical errors in the 4th paragraph of the discussion.

3. Which of the two severity measures is being used in Figure 1?

Reviewer #2: This is a very robust epidemiologic study looking at respiratory symptoms in a cohort of people living with HIV (PLwHIV) and relatively matched controls over a several year period of fairly robust regular communication. Respiratory illness was intentionally loosely defined to capture events that may result in communication with a healthcare professional or treatment. St. Georges Respiratory Questionaire (SGRQ) and the MRC dyspnea scale data was collected at baseline and during any acute illness. In addition an unweighted symptom score was collected daily during acute illness recovery. The PLwHIV were well controlled with all on ART and the majority with suppressed viral load. An attempt was made to identify the common viruses that were representative of the acute illnesses but this may be biased as more severe illness may result in less willingness to return for a swab. The control group is mostly age and gender matched with Ethnicity, sexuality, immunizations and respiratory medication use being different. The subjects had pre-bronchodilator spirometry and both restrictive and obstructive defects were more common in the PLwHIV. Associated with this both the SGRQ and MRC favored more symptoms in PLwHIV. The main outcome of the study, rate of acute illness was the same in both groups with most of the significant findings being related to the subjective measures of respiratory symptom severity. Consistent with would be predicted obstruction, prior smoking, and baseline dyspnea were all associated with greater illness frequency. Although the symptoms scores were higher and PLwHIV contacted medical providers more frequently use of non-prescription medications was equivalent. Overall 158 np swabs were evaluated and there was no difference in the rate of positive swabs between groups or in type of virus recovered. Overall the data is robust and discussed with appropriate notation of the limitations.

1. The incidence of “restrictive” lung disease seems quite large in comparison to prior studies. This would be more appropriately called this PRISm (preserved ratio impaired spirometry) given the individuals only had pre-bronchodilator spirometry. This likely would also be consistent with a predilection for increased symptoms and likely an increased risk of future incident COPD. I do not think this has previously been described. This may be worth adding to the discussion given the findings of the Rotterdam Study would be consistent. From this standpoint it may be important to note who (trained respiratory therapist, research coordinator, nurse) performed the spirometry as an important caveat would be poor effort can give this sort of finding as well but would not explain the association only with PLwHIV.

2. Likey discussion should include the fact that the spirometry was all prebronchodilator and likely over represents the population as having COPD.

3. Inclusion of more information on the symptom score that was utilized at illness initiation and daily diaries would be beneficial. The presumption is that this symptom score was de novo invented and likely has not been confirmed to be of value as an unscaled entity in this disease process. Although this does not lessen the value of this information, knowing that the subscales of the SGRQ were all significantly more in PLwHIV suggests knowing more information about what makes up the Figure 1 results would help.

4. Figure 2 is not particularly well presented. The coloring is based on the percentage of each virus but likely would be improved if both “pie” graphs utilized the same color for same viruses. It is not clear to me if there is any difference in any measured virus as I suspect as the only notation in the text is that Rhinovirus is the most common in both groups and many viral swabs were negative. For instance it seems that influenza vaccination is more common in PLwHIV but yet isolating influenza (probably not significant) was more common in PLwHIV. Given this is probably the most novel piece of data in this study it would be nice if it were slightly better presented.

5. oropharyngeal swabs. Page 4 and 10 states oropharyngeal swabs were utilized all other references suggest these were nasopharyngeal swabs. Likely this is a typo to correct. Suspect they all should be nasopharyngeal given it was a PCR assay.

6. A comment is made that tabulation of “febrile” illness rate is possible and likely it would be worthy to comment how this related to the subsets of acute illnesses and viral recovery from nasopharyngeal swabs.

7. The one factor that may be overlooked is the comfort with which PLwHIV may feel in interacting with health care professionals that may differ from the control population. It is possible that there is a bias towards reporting symptoms in this population given ongoing drug side effects and discussion of them not only establish rapport with health care professionals but may also have resulted in beneficial interactions. There seems to be proof of this in that the use of over the counter medications was equivalent despite the fact that more contact with providers and actual prescribed medications was higher in PLwHIV. Whether this is conditioned by health care providers or as stated a manifestation of mild COPD and PRISm would be nearly impossible to separate as finding a control population with a similar degree of health care exposure would be impossible.

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Reviewer #1: No

Reviewer #2: No

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9 Mar 2020

We thank the reviewers for their detailed evaluation of our manuscript and constructive suggestions for improvement. We have amended the text in light of these comments, and provided responses to each point below.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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2. Please include additional information regarding the questionnaires used in the study and ensure that you have provided sufficient details that others could replicate the analyses. For instance, if you developed a questionnaire as part of this study and it is not under a copyright more restrictive than CC-BY, please include a copy, in both the original language and English, as Supporting Information. If the questionnaires have been published previously, please provide relevant references.

Response: We have included both the baseline and follow-up questionnaires as supplementary material

3. Please provide a sample size and power calculation in the Methods, or discuss the reasons for not performing one before study initiation.

Response: We have included a short section in the methods explaining our sample size calculation

4. Thank you for stating the following beneath the Acknowledgments Section of your manuscript:

'Funding: This study was supported by grants from the National Institute for Health Research (DRF-

2015-08-210) and British HIV association. The funders had no role in the collection, analysis, or

interpretation of data, in the writing of the report or in the decision to submit the paper for

publication.'

Response: we have completed the funding statement in the online submission form.

5. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

Response: We have uploaded anonymised data to the University College London data repository (https://rdr.ucl.ac.uk/) with DOI 10.5522/04/11950284, this will be made available on acceptance of the manuscript for publication.

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Response: we have ensured that all figures have a separate caption.

7. Your ethics statement must appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please also ensure that your ethics statement is included in your manuscript, as the ethics section of your online submission will not be published alongside your manuscript.

Response: Our ethics statement is included in the methods section of the manuscript

Comments to the Author

Major Comments:

1. It appears there were two attempts at objective assessment of upper respiratory tract severity. The first was a “web-based symptom questionnaire” graded on a “0-6 scale, including questions on daily activities and treatment.” The second was a subject “diary”, which also appears to have been scored somehow given the data in the results section was also given as “points”. It is unclear how these two measurement tools are different. It would be nice to show these grading systems in supplemental materials to better assess the quality of the data. Furthermore, it needs to be clear in the methods that the first tool is actually web-based. In the methods section it read like the investigators were doing the question asking, which obviously introduces bias if the interviewer knows the subject status. It was not apparent that the first tool was web-based till the first sentence under the “Severity and Duration of Illness” section in the results.

Response: Thank-you for this suggestion. We have made it clearer in the methods section that follow-up data on the frequency of acute respiratory illness, symptoms during acute respiratory illness and their severity were collected by means of a web-based questionnaire (for most participants) and also written diaries (only completed during illnesses). We have also included these questionnaires as supplementary material.

2. There are a lot of differences between the two groups (Table 1). This is important because their adjustment for potential confounding effects was based on “a priori” factors known to be associated with acute respiratory illnesses rather than actual differences between the two groups. Thus, while baseline smoking, baseline respiratory symptoms, and baseline PFTs were appropriately considered, it is not clear that all the significant differences between the groups were taken into account in their univariate and multivariate analysis. Differences that were adjusted for that might be significant include:

a. UK born versus other – relates to different potential prior exposures and cultural responses to illness which might impact assessment of disease severity.

b. Immunizations – much higher in the HIV positive group, which may have lessened the incidence in the HIV positive group.

c. Significantly higher incidence of inhaler use, especially corticosteroids, in the HIV group. These by themselves can affect the incidence and severity of respiratory illnesses.

Response: We agree with the reviewer that prior exposures and cultural differences, immunisation frequencies and differences in treatments such as inhaled corticosteroids could act as confounders in the analysis. However, we chose not to take the approach of defining adjustment models based on post hoc differences between groups defined by p values (an approach criticised in recent methodological reviews e.g. Lederer et al Annals ATS 2018) and our relatively small cohort lacks the statistical power to allow adjustment for all potential confounding factors. We have added a comment in the discussion that residual confounding (rather than a direct HIV related effect) could influence this finding.

3. One of the author’s conclusions is that it is “possible to identify individuals at greater risk of more frequent or severe acute respiratory illness (using the degree of airflow obstruction, history of smoking or recreational drug use, and the presence of chronic respiratory symptoms). Does this hold for both HIV-infected subjects and uninfected subjects, or just the HIV population alone? From the tables, it looks like these risk factors were lumped together regardless of HIV status.

Response: this comment in the discussion is based on analyses of the cohort as a whole, rather than just the HIV positive participants. On reflection, as implied by the reviewer, this probably goes beyond what can be supported by the data, as using the features identified in the analysis to guide care would in any case require further validation. We have therefore taken out this specific conjecture from the discussion (although continue to make the general point that individuals a high risk of respiratory illness can be identified and prioritised for interventions.

Minor Comments:

1. At the end of the Statistical analysis it states that further details are provided in supplemental information. I do not see any supplemental information. Based on major comment 2 above, this is important.

Response: apologies for this error – there in no supplementary file for the statistical methods, although we have attached copies of our research questionnaires as supplementary files.

2. While this is in general very well written, for some reason there are multiple grammatical errors in the 4th paragraph of the discussion.

Response: we thank the reviewer for this general positive comment and agree that this specific paragraph perhaps became rather convoluted during the drafting process, and have tried to make the prose easier to follow.

3. Which of the two severity measures is being used in Figure 1?

Response: this is based on the scores calculated from the written diaries completed by participants during respiratory illnesses. we have made this clearer in the manuscript.

Reviewer #2: This is a very robust epidemiologic study looking at respiratory symptoms in a cohort of people living with HIV (PLwHIV) and relatively matched controls over a several year period of fairly robust regular communication. Respiratory illness was intentionally loosely defined to capture events that may result in communication with a healthcare professional or treatment. St. Georges Respiratory Questionaire (SGRQ) and the MRC dyspnea scale data was collected at baseline and during any acute illness. In addition an unweighted symptom score was collected daily during acute illness recovery. The PLwHIV were well controlled with all on ART and the majority with suppressed viral load. An attempt was made to identify the common viruses that were representative of the acute illnesses but this may be biased as more severe illness may result in less willingness to return for a swab. The control group is mostly age and gender matched with Ethnicity, sexuality, immunizations and respiratory medication use being different. The subjects had pre-bronchodilator spirometry and both restrictive and obstructive defects were more common in the PLwHIV. Associated with this both the SGRQ and MRC favored more symptoms in PLwHIV. The main outcome of the study, rate of acute illness was the same in both groups with most of the significant findings being related to the subjective measures of respiratory symptom severity. Consistent with would be predicted obstruction, prior smoking, and baseline dyspnea were all associated with greater illness frequency. Although the symptoms scores were higher and PLwHIV contacted medical providers more frequently use of non-prescription medications was equivalent. Overall 158 np swabs were evaluated and there was no difference in the rate of positive swabs between groups or in type of virus recovered. Overall the data is robust and discussed with appropriate notation of the limitations.

1. The incidence of “restrictive” lung disease seems quite large in comparison to prior studies. This would be more appropriately called this PRISm (preserved ratio impaired spirometry) given the individuals only had pre-bronchodilator spirometry. This likely would also be consistent with a predilection for increased symptoms and likely an increased risk of future incident COPD. I do not think this has previously been described. This may be worth adding to the discussion given the findings of the Rotterdam Study would be consistent. From this standpoint it may be important to note who (trained respiratory therapist, research coordinator, nurse) performed the spirometry as an important caveat would be poor effort can give this sort of finding as well but would not explain the association only with PLwHIV.

Response: spirometry was undertaken by a trained respiratory specialist, or clinical research practitioner with specific training in undertaking spirometry. Results were quality assured according to ATS guidelines. The finding of reduced lung volumes, without a difference in prevalence of airflow obstruction, described as restrictive spirometric pattern in our manuscript is consistent with findings of other studies reporting spirometric results in people living with HIV, for instance that of Ronit et al, Thorax 2019. We prefer the term restrictive spirometric pattern, which is more widely recognised than preserved ratio impaired spirometry, and avoids additional use of abbreviations in the text. We have added a paragraph to the discussion discussing the spirometry results in more detail

2. Likey discussion should include the fact that the spirometry was all prebronchodilator and likely over represents the population as having COPD.

Response: we have added this to the discussion

3. Inclusion of more information on the symptom score that was utilized at illness initiation and daily diaries would be beneficial. The presumption is that this symptom score was de novo invented and likely has not been confirmed to be of value as an unscaled entity in this disease process. Although this does not lessen the value of this information, knowing that the subscales of the SGRQ were all significantly more in PLwHIV suggests knowing more information about what makes up the Figure 1 results would help.

Response: We have included the symptom scores and questionnaire as supplementary information. As the reviewer suggests, these questionnaires were devised de novo as we felt that existing scores did not meet the requirements of this study and we did not undertake a separate process of validation.

4. Figure 2 is not particularly well presented. The coloring is based on the percentage of each virus but likely would be improved if both “pie” graphs utilized the same color for same viruses. It is not clear to me if there is any difference in any measured virus as I suspect as the only notation in the text is that Rhinovirus is the most common in both groups and many viral swabs were negative. For instance it seems that influenza vaccination is more common in PLwHIV but yet isolating influenza (probably not significant) was more common in PLwHIV. Given this is probably the most novel piece of data in this study it would be nice if it were slightly better presented.

Response: Thank-you, we agree that this could be better presented. We have revised the text to include the important results in the text and replaced Figure 2 with a simple pie chart for illustrative purposes.

5. oropharyngeal swabs. Page 4 and 10 states oropharyngeal swabs were utilized all other references suggest these were nasopharyngeal swabs. Likely this is a typo to correct. Suspect they all should be nasopharyngeal given it was a PCR assay.

Response: as suggested, we collected nasopharyngeal swabs for respiratory virus detection, this has been corrected in the manuscript.

6. A comment is made that tabulation of “febrile” illness rate is possible and likely it would be worthy to comment how this related to the subsets of acute illnesses and viral recovery from nasopharyngeal swabs.

Response: although we agree with the reviewer that further understanding of the relationship between illness characteristics such as fever and the identification of viral pathogens would be interesting, this is unfortunately beyond the data available – we only have data on self-reported fever, and have viral swab results for only 125 illnesses. Within this group there is no significant relationship between reported fever and detection of a virus (in the 33% of illnesses in which a fever of at least “moderate” severity was reported, a virus was detected 50% of the time; in the 67% of cases in which fever was not significant a virus was detected 41% of the time). The data was not collected with this analysis in mind, and hence we don’ t think this can be taken further.

7. The one factor that may be overlooked is the comfort with which PLwHIV may feel in interacting with health care professionals that may differ from the control population. It is possible that there is a bias towards reporting symptoms in this population given ongoing drug side effects and discussion of them not only establish rapport with health care professionals but may also have resulted in beneficial interactions. There seems to be proof of this in that the use of over the counter medications was equivalent despite the fact that more contact with providers and actual prescribed medications was higher in PLwHIV. Whether this is conditioned by health care providers or as stated a manifestation of mild COPD and PRISm would be nearly impossible to separate as finding a control population with a similar degree of health care exposure would be impossible.

Response: we agree that differences in ease of access to healthcare, and greater concern about symptoms in the PLW-HIV could account for differences in health-seeking behaviour, and have added more explicit discussion of this to the text (although we agree that this something that it would be difficult to fully evaluate)

Submitted filename: PONE-D-19-35060_Responses to reviewers.doc

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27 Apr 2020

The effect of HIV status on the frequency and severity of acute respiratory illness

PONE-D-19-35060R1

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20 May 2020

PONE-D-19-35060R1

The effect of HIV status on the frequency and severity of acute respiratory illness

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