The health and economic burden of respiratory syncytial virus associated hospitalizations in adults.

BACKGROUND: Respiratory syncytial virus (RSV) is increasingly recognized as an important cause of illness in adults; however, data on RSV disease and economic burden in this age group remain limited. We aimed to provide comprehensive estimates of RSV disease burden among adults aged ≥18 years.
METHODS: During 2012-2015, population-based, active surveillance of acute respiratory infection (ARI) hospitalizations enabled estimation of the seasonal incidence of RSV hospitalizations and direct health costs in adults aged ≥18 years in Auckland, New Zealand.
RESULTS: Of 4,600 ARI hospitalizations tested for RSV, 348 (7.6%) were RSV positive. The median (interquartile range) length of hospital stay for RSV positive patients was 4 (2-6) days. The seasonal incidence rate (IR) of RSV hospitalizations, corrected for non-testing, was 23.6 (95% confidence intervals [CI] 21.0-26.1) per 100,000 adults aged ≥18 years. Hospitalization risk increased with age with the highest incidence among adults aged ≥80 years (IR 190.8 per 100,000, 95% CI 137.6-244.0). Being of Māori or Pacific ethnicity or living in a neighborhood with low socioeconomic status (SES) were independently associated with increased RSV hospitalization rates. We estimate RSV-associated hospitalizations among adults aged ≥18 years to cost on average NZD $4,758 per event.
CONCLUSIONS: RSV infection is associated with considerable disease and economic cost in adults. RSV disproportionally affects adult sub-groups defined by age, ethnicity, and neighborhood SES. An effective RSV vaccine or RSV treatment may offer benefits for older adults.

Introduction

Respiratory syncytial virus (RSV) is well established as a major cause of acute respiratory infections (ARI) in children, but the burden of disease in adults has been less completely studied. Recent studies have reported RSV as a considerable cause of morbidity and mortality in adults [1] sometimes equalling or exceeding rates caused by seasonal influenza [2-4].

While previous studies have been informative, uncertainties about RSV disease burden among adults remain. Most estimates of RSV-associated hospitalization rates are from studies conducted in the USA and appear to vary by location and calendar year. The methods used to estimate RSV disease burden have also varied, with a few studies using active surveillance [2-6], while others have been based on statistical models correlating clinical data with viral activity captured through passive surveillance [7-9]. Moreover, laboratory methods used to confirm RSV have changed over time, with molecular methods having higher sensitivity than previously used serologic and virus isolation techniques [10]. Finally, few studies among adults report RSV hospitalization rates by fine age strata or other demographic characteristics [1].

No licensed RSV vaccine is currently available; however, several adult RSV vaccines candidates are in development [11, 12]. More comprehensive estimates of RSV disease burden in adults will help inform the introduction of such interventions.

In this study, we utilised ARI surveillance data to estimate the incidence of RSV-associated hospitalizations and direct healthcare associated costs among adults aged ≥18 years in Auckland, New Zealand (NZ). We present RSV-associated hospitalization rates estimated by two complementary methods. Our findings provide evidence to inform RSV treatment and preventative strategies in adult populations.

Methods

In this study, we retrospectively established a cohort of adult residents aged ≥18 years and identified RSV positive ARI hospitalizations within this cohort using data from the Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) project (Fig 1). SHIVERS was an active ARI surveillance project conducted in two public hospitals which provide all inpatient services for the population residing in the central, southern, and eastern regions of Auckland [13, 14]. The study area is predominantly urban with an estimated 734,530 adults aged ≥18 years in 2015, of whom 9% are Māori (NZ’s indigenous population), 16% Pacific (including ethnic groups from Samoa, Cook Islands, Tonga, Niue, Fiji, Tokelau, Tuvalu, and Kiribati), 23% Asian, and 52% of European or other ethnicities [15]. Ethical approval for the SHIVERS project was obtained from the NZ Ministry of Health, Health and Disability Ethics Committee (NTX/11/11/102).

Fig 1

Weekly counts of acute respiratory infection (ARI) hospitalizations and respiratory syncytial virus (RSV) laboratory-confirmed hospitalizations among adults aged 50 years or older in Auckland, New Zealand, 2012–2015.

Hospital surveillance

From 30 April 2012 to 31 December 2015, research nurses evaluated adults admitted to inpatient wards for suspected ARI. Suspected ARI cases were identified through a combination of reviewing admission diagnoses and interviewing patients about their presenting signs and symptoms. Among ARI patients, those meeting the WHO severe ARI (SARI) case definition of cough and measured or reported fever, within the last 7 days in 2012, and then within 10 days from 2013 onwards were enrolled. Study nurses obtained consent from eligible patients; completed detailed case report forms on a range of factors including comorbidities, influenza vaccination, antibiotic treatment and clinical outcomes; and collected nasopharyngeal swabs.

To provide an understanding of the respiratory virus hospitalization burden among ARI patients not meeting the SARI definition, in 2013–2015 study nurses enrolled a sample of non-SARI respiratory patients. Sampling in 2013 was during the peak winter period (12 August to 6 October) and included a weekly random selection of four adult inpatients who fitted the non-SARI respiratory definition. In 2014 and 2015, this surveillance was extended to enrol approximately twelve adult non-SARI respiratory patients weekly for a broader winter season (end of April to end of September, week 18–39).

In addition to respiratory virus test results generated by SHIVERS surveillance, hospital laboratories also provided results from clinically ordered tests performed on SARI and non-SARI respiratory patients during the study period. These results were included after validation of the hospital respiratory virus PCR assay. This clinician identified sample was especially valuable in expanding the number of laboratory tested non-SARI patients including in the analysis, since fewer non-SARI patients were systematically enrolled in the SHIVERS study.

Full-year surveillance data showed that RSV follows a well-defined seasonal pattern with 89.5% of RSV positive hospitalizations detected during the broad winter surveillance period (Fig 1). As SHIVERS non-SARI respiratory testing was only conducted during winter seasons, we restricted our analyses to weeks 18–39 of 2012–2015.

Laboratory methods

Collected specimens were tested for RSV, influenza, rhinovirus, adenovirus, and human metapneumovirus using the United States Centers for Disease Control and Prevention real-time reverse transcription (RT)-PCR protocol [16, 17] at the Institute of Environmental Science and Research, or using the AusDiagnostic PCR protocol and real-time PCR assays at hospital laboratories [18]. A sample of positive specimens were further sub-typed. Further information on performance of hospital assays compared to CDC’s real-time RT-PCR as a gold standard are provided in supplementary material (S1 Table).

Incidence rate denominator data sources

We used national administrative datasets including hospital discharges from the National Minimum Dataset, primary health care enrolments from Primary Health Organizations, births from the National Maternity Collection, deaths from the National Mortality Collection, and specialist visits from the National Non-Admitted Patient Collection [19] to identify and obtain individual level demographic data on adults aged ≥18 years residing in the SHIVERS study area during surveillance periods. These datasets use a unique patient identifier (National Health Index (NHI) numbers) enabling linkage. This population data was linked to SHIVERS data using NHI numbers to identify those with ARI and RSV confirmed hospitalizations.

Cost estimation

Each inpatient event in the National Minimum Dataset is allocated a Diagnosis Related Group (DRG) code [20]. The DRG coding system categorises hospitalizations into clinically similar events with comparable resource use. These codes, together with information on hospital length of stay and additional interventions such as mechanical ventilation, are used to calculate a cost weight and therefore a cost for each inpatient hospital admission. DRGs are the principal means of reimbursing hospitals for inpatient care in most high-income countries including New Zealand [21]. We calculated hospitalization costs by multiplying each cost weight provided in the NMDS with the NZ fixed cost multiplier applicable for the 2017/18 financial year [20].

Statistical analysis

Chi-square tests were used to test associations between categorical variables and Student’s t-tests for continuous variables. Among hospitalized cases, logistic regression with adjustment for age and ethnicity was used to test for association of comorbidities, influenza vaccination, and antibiotic treatments with RSV positivity.

For incidence calculations, if a RSV positive patient was transferred to another hospital and/or had multiple RSV positive hospitalizations within 14 days of discharge from their first hospitalization, they were considered a singular illness episode. Incidence rates were calculated by dividing the number of RSV-associated ARI hospitalizations (singular episodes) by the number of adults residing in the study area. Confidence intervals for incidence rates and rate ratios were based on the Poisson distribution. Rates were stratified by Auckland sub-region, age, sex, ethnicity, and SES as they are considered key modifiers of ARI hospitalization risk. SES was quantified using New Zealand’s small area measure of neighborhood deprivation derived from the national census (NZ Deprivation Index 2013) [22]. This SES measure was used to divide the study sample into SES quintiles (quintile 1 –least deprived to quintile 5 –most deprived).

In New Zealand, Māori and Pacific peoples have lower reported life expectancy than other ethnic groups [23], and are also overrepresented in the lower SES groups [24]. To control for confounding and evaluate the independent effects of these factors, RSV hospitalization rates and rate ratios for age group, ethnicity, and SES were adjusted for each other.

The correction of incidence rate calculations for non-testing for RSV among ARI patients was done using two methods. First, we multiplied the proportion of those tested who were positive for RSV by the number of non-tested ARI patients within strata formed by study year, sub-region, age group, SES quintile, and ethnicity (multiplier method). Second, we corrected test results using the multivariate imputation by chained equations (MICE) method [25], which has been shown to yield unbiased estimates when accounting for missing outcome data [26]. For imputation, we verified that non-tested patients were missing at random, using a Missing Completely at Random Test [27], before creating 30 imputed datasets of RSV results with age, ethnicity, SES, SARI case definition, sex, week of hospitalization, and specimen type (clinician-ordered versus SHIVERS systematic sampling) included as predictors of missingness. To ensure correction of non-testing was only done for singular RSV illness episodes, patients with two or more untested ARI hospitalizations within 14 days of each other were considered a singular episode. All analyses were performed using Stata 14 (College Station, TX:StataCorp LP).

Results

Study population

Based on our linked data methodology, we identified an annual average of 731,204 adults aged ≥18 years residing in the study area during 2012–2015. This was similar to the 2015 Statistics NZ population estimates (734,530) [15].

Hospitalized patients

The temporal distribution of RSV hospitalizations among adults demonstrated a consistent peak during the New Zealand winter (May-September) (Fig 2). Over the four winter seasons, there were 8,776 ARI hospitalizations among adults aged ≥18 years, including 3,804 (43.4%) SARI events and 4,972 (56.7%) non-SARI events. Of ARI hospitalizations, 4,600 (52.4%) were tested for RSV (Fig 2). Of the 348 unique RSV-associated hospitalization episodes identified, there were 226 (7.4%) from the 3,046 SARI tested hospitalizations and 122 (7.9%) from the 1,554 non-SARI tested hospitalizations.

Fig 2

Flowchart detailing retrospective cohort of adults aged ≥50 years in Auckland, New Zealand in 2012–2015 and number of acute respiratory infection (ARI) and respiratory syncytial virus (RSV)-tested hospitalizations.

*For incidence rate calculations, correction of non-testing among ARI patients was done using two methods; first by multiplying the proportion positive for RSV in each demographic strata to non-tested ARI patients in each group; and second by using the multivariate imputation by chained equations (MICE) method of imputation in STATA [25].

Non-tested cases were older and a smaller proportion of them were of Asian ethnicity (S2 Table). The patients tested through the SHIVERS study and those tested based on clinician’s orders differed by age, SES, ethnicity, SARI case definition, and length of hospital stay (S3 Table).

Among RSV positive samples, 103 (29.6%) were subtyped of which 54 (52.4%) were RSV-A and 49 (47.5%) RSV-B. There were 47 (18.0%) co-detections of RSV with other viruses. Of the 261 RSV positive samples tested for other respiratory viruses, 26 (10.0%) were influenza co-detections, 13 (5.0%) rhinovirus, 6 (2.3%) were adenovirus, and 3 (1.1%) were human metapneumovirus co-detections.

The median (interquartile range [IQR]) hospital length of stay in days among all ARI hospitalizations was 3 (2–6). Of ARI hospitalizations, 179 (2.0%) were admitted to the intensive care unit (ICU), 244 (2.8%) died during their hospital stay, and 312 (3.6%) died within 30 days of discharge. The median (interquartile range [IQR]) hospital length of stay for RSV positive adults was 3 (2–6) days. Among RSV positive cases, 4 (1.1%) died during hospitalization and 11 (3.2%) died within 30 days of hospital discharge.

Hospital length of stay did not differ by RSV positivity (p-value = 0.590). The proportion of ARI hospitalizations requiring ICU admission also did not differ significantly by RSV positivity (RSV positive = 8/348 (2.3%) vs RSV negative = 132/4252 (3.1%); p-value = 0.400).

Of 8,776 ARI hospitalizations, 3,421 (39.0%) had additional information on comorbidities, current influenza vaccination status, and antibiotic treatment following hospital admission (Fig 3). Of these cases, 1,740 (50.9%) were regular smokers, 1,970 (57.6%) patients had a current influenza vaccination, and 3,035 (88.7%) received antibiotics during the hospital admission. In terms of comorbidities, 1,523 (44.5%) cases had a chronic lung disease, and 1,284 (37.5%) had a cardiovascular condition. No factors were significantly associated with RSV positivity following adjustment for age and ethnicity.

Fig 3

Risk factors among acute respiratory infection (ARI) hospitalizations in adults aged 18 years or more in Auckland, New Zealand, 2012–2015 by respiratory syncytial virus (RSV) result.

Among 3,421 patients with complete information on comorbidities, influenza vaccination status, and antibiotic treatment. There was no significant difference in risk factors by RSV positivity status after adjusting for age and ethnicity. * Includes asthma, COPD, and bronchiectasis.

Seasonal incidence of RSV confirmed hospitalizations

The seasonal incidence of RSV hospitalization without accounting for non-tested adults was 11.9 (95% confidence interval [CI] 10.7–13.2) per 100,000 adults (Table 1). Following correction for non-testing, the incidence of RSV-associated ARI hospitalizations was 22.7 (95% CI: 21.0–24.5) per 100,000 persons using the multiplier method and 23.6 (95% CI 21.0–26.1) per 100,000 adults using multiple imputation (Table 1). Rates were not significantly different based on the method used for correction, however multiple imputation estimates had slightly wider confidence intervals due to the incorporation of uncertainty inherent in imputation. Subsequent presentation of incidence estimates used data derived from multiple imputation.

Table 1

Seasonal incidence rates (IR) of acute respiratory infection (ARI) and laboratory-confirmed respiratory syncytial virus (RSV)-associated ARI hospitalizations among adults 18 years and older by year, Auckland sub-region, demographic factors in Auckland, New Zealand, 2012–2015.

		ARI hospitalizations				Incidence rates (IR) per 100,000 adults
						Crude—Not corrected for non-testing	Corrected for non-testing*
							Multiplier method	Multiple imputation
	No. of adults	Total	No. tested for RSV	No. confirmed with RSV	(%) of tested	IR (95% CI)	IR (95% CI)	IR (95% CI)
Total	2924815	8776	4600	348	(7.6)	11.9 (10.7–13.2)	22.7 (21.0–24.5)	23.6 (21.0–26.1)
Season
2012	708332	2637	951	59	(1.3)	8.2 (6.1–10.3)	23.2 (19.8–26.9)	25.5 (18.2–32.9)
2013	726127	2036	932	83	(1.8)	11.3 (8.8–13.7)	24.9 (21.4–28.8)	29.2 (23.2–35.2)
2014	736724	2023	1315	86	(1.9)	11.7 (9.2–14.1)	17.9 (15.0–21.3)	19.1 (15.2–23.0)
2015	753632	2080	1402	120	(2.6)	15.9 (13.1–18.8)	23.6 (20.3–27.4)	25.1 (20.4–29.7)
Region
Central Auckland	1435862	4061	2063	158	(3.4)	11.0 (9.3–12.8)	21.6 (19.3–24.2)	22.7 (19.3–26.0)
South East Auckland	1488953	4715	2537	190	(4.1)	12.8 (10.9–14.7)	23.7 (21.3–26.3)	24.4 (21.1–27.8)
Age Group (years)
18–49	1838952	2117	1367	64	(1.4)	3.5 (2.6–4.3)	5.4 (4.4–6.6)	5.9 (4.3–7.5)
50–64	658708	1974	1129	93	(2.0)	14.2 (11.3–17.1)	24.8 (21.2–28.9)	24.2 (18.2–30.2)
65–79	327234	2677	1308	118	(2.6)	36.4 (29.6–43.2)	74.0 (64.9–83.8)	72.9 (57.4–88.3)
≥65	427155	4685	2104	191	(4.2)	44.7 (38.6–51.5)	99.5 (90.3–10.9)	99.2 (82.4–115.9)
≥80	99921	2008	796	73	(1.6)	74.5 (57.4–91.5)	184.1 (158.5–212.8)	190.8 (137.6–244.0)
Sex
F	1543948	4682	2528	198	(4.3)	12.9 (11.1–14.7)	23.8 (21.4–26.3)	23.9 (20.7–27.1)
M	1380867	4094	2072	150	(3.3)	10.9 (9.1–12.7)	21.4 (19.1–24.0)	23.3 (19.8–26.7)
SES (unadjusted) †
1	525511	931	474	35	(0.8)	6.7 (4.5–8.9)	13.1 (10.2–16.6)	13.9 (9.0–18.7)
2	566525	1216	624	49	(1.1)	8.5 (6.1–10.9)	16.7 (13.6–20.5)	17.3 (12.7–22.0)
3	512159	1335	676	52	(1.1)	10.6 (7.8–13.4)	20.1 (16.4–24.4)	22.1 (16.5–27.7)
4	383620	1258	639	57	(1.2)	13.6 (9.9–17.3)	29.2 (24.0–35.1)	28.1 (20.2–36.0)
5	937001	4036	2187	155	(3.4)	17.1 (14.3–19.8)	30.5 (27.1–34.3)	31.7 (26.9–36.6)
Ethnicity (unadjusted)
Māori	249970	1585	826	43	(0.9)	17.3 (12.2–22.5)	33.3 (26.5–41.2)	34.3 (24.7–43.9)
Pacific	482596	2552	1452	121	(2.6)	25.2 (20.6–29.9)	44.1 (38.4–50.5)	45.3 (37.2–53.4)
Asian	684059	811	440	32	(0.7)	4.7 (3.1–6.3)	8.6 (6.6–11.1)	9.2 (6.0–12.4)
European/Other	1508190	3828	1882	152	(3.3)	10.1 (8.5–11.7)	20.5 (18.3–22.9)	21.4 (17.8–25.0)

* Correction for non-testing among ARI patients was done using two methods; first by multiplying the proportion positive for RSV in each demographic strata to non-tested ARI patients identified in each group; and second by using the multivariate imputation by chained equations (MICE) method of imputation [25].

† SES quantified using a small area level measure of neighborhood deprivation derived from the national census (NZDep2013) [22].

Following adjustment for age group, SES, and ethnicity; incidence increased by age, with adults aged 80 years or older being approximately 30 times as likely to have a RSV-associated hospitalization compared to those aged 18–49 years (Rate ratio [RR] 31.3, 95% CI 22.3–44.0; Fig 4). Adults from SES quintiles 3–5 were at higher risk of a RSV-associated hospitalization compared to adults in the least-disadvantaged SES group (quintile 1), following adjustment for age group and ethnicity (Fig 4). Similarly, RSV hospitalization rates adjusted for age group and SES were higher in Māori (RR 2.8, 95% CI 2.0–4.0) and Pacific adults (RR 3.5, 95% CI 2.6–4.7) compared to those of European or other ethnicities (Fig 4).

Fig 4

Adjusted incidence rate ratios for age group (referent 18–49 years old), socio-economic status (referent–quintile 1), and ethnicity (referent–European/other) of respiratory syncytial virus (RSV) associated acute respiratory infection (ARI) hospitalizations among adults 50 years or older in Auckland, New Zealand, 2012–2015.

*Incidence rate ratios for age, socio-economic status and ethnicity have been adjusted for each other. † SES quantified using a small area level measure of neighborhood deprivation derived from the national census (NZDep2013) with SES 1 as least deprived and SES 5 as most deprived [22].

Direct health-care costs

Based on the DRG costing methodology, the median (IQR) cost per RSV hospitalization among adults was NZD $3,723.84 ($2500.44-$5,028.44) while among non-RSV hospitalizations the median (IQR) cost per hospitalization was $3,950.71 ($2423.67-$5028.44). Hospitalization costs were not significantly different by RSV positivity in adults (p-value = 0.254). After accounting for non-testing, the annual direct health-care cost of RSV-confirmed hospitalizations in Auckland was NZD $818,398.78 or an average NZD $4,758.13 per hospitalization. This is equivalent to USD $525,137.85 or an average USD $3,053.13 per hospitalization, using November 2019 currency exchange rates.

Discussion

We present estimates of RSV hospitalizations rates among adults aged 18 years or more from population based ARI surveillance. We show RSV to be associated with approximately 8% of ARI hospitalizations during winter seasons in this age group and to cost an average NZD $4,758 per episode. Increasing age, Māori or Pacific ethnicity and low neighborhood SES were all independently associated with an increased risk of RSV-associated hospitalization in this population.

Of year-round RSV-associated SARI hospitalizations, 90% of RSV occurred during the winter season suggesting that seasonal incidence approximates annual incidence. We observed disparities in RSV-associated hospitalization rates by age, ethnicity and SES. Such disparities have been reported for RSV-associated hospitalization rates in children [28], but to our knowledge this is the first study to demonstrate such inequalities among adults. Ethnic differences in respiratory infectious diseases are thought primarily to be due to social and economic factors, as ethnic groups with increased risk of infection and disease are also overrepresented in lower socio-economic groups. However in our study we found both ethnicity and SES to have independent effects on RSV-associated hospitalization risk. A potential explanation is that our neighborhood-level measure of SES may not accurately capture individual SES. Consequently, social and economic factors associated with RSV disease risk such as smoking, housing density, presence of comorbidities, health care seeking behaviour, and poor nutrition are potentially being better captured by ethnicity. As our investigation is exploratory, further investigation is warranted to guide policy development. Nevertheless our findings highlight the value of assessing RSV related health disparities among different ethnic populations in other countries.

Our estimates of RSV positivity among adult ARI hospitalizations is comparable to one US study using active ARI surveillance and laboratory confirmation among hospitalized adults aged ≥65 years [2]. When comparing our estimates to studies reporting RSV hospitalization rates using hospital surveillance [3, 4], we found our rates (23.6 per 100,000 adults aged ≥18 years and 99.2 per 100,000 adults aged ≥65 years) to be lower than those reported in these studies (55 per 100,000 adults aged ≥18 years and 189–254 per 100,000 adults aged ≥65 years). One possible reason for differences could be the approach used to extrapolate RSV positivity in untested patients. In our study, all ARI patients were actively identified by study nurses during a broad winter season, and RSV positivity for those without study testing was estimated while accounting for demographic and clinical differences using a traditional multiplier method and a multiple imputation method. In the US studies, RSV hospitalization rates were estimated by multiplying the proportion of enrolled patients positive for RSV by the total number of residents with an ICD-9 classified ARI hospitalization during influenza seasons [3, 4]. Additionally, one of the studies took place during a single year when there was a novel influenza A H1NI pandemic [3], thus rates estimated that year may not be representative of all years

The relative burden of influenza compared to that due to RSV also differed in comparison to these earlier studies. In the studies by Falsey and Widmer et al, the proportion of ARI hospitalizations in adults positive for influenza were found to be similar to those for RSV. In our study, the proportion of hospitalized adults with influenza (approximately 24%) and the influenza-related hospitalization rate (approximately 72 per 100,000) [29-31] were higher than those for RSV, despite influenza vaccination coverage in adult populations in NZ being similar to those of the US study locations. It is possible that these differences are due to the effects of different circulating influenza strains. In our study, influenza H3N2 was the predominant strain every year except 2014, and has been shown to result in more severe illness than RSV [32], whereas in the US studies, influenza H1N1 was generally the predominant strain.

The RSV-associated hospitalization rates among adults aged ≥65 years (99 per 100,000 adults) reported in this study were similar to estimates reported by studies using indirect statistical modelling. Zhou et al modelled hospitalization and viral surveillance data in 13 US states from 1993–1994 through 2007–2008 and estimated a RSV hospitalization rate of 86 per 100,000 adults aged ≥65 years [9]. Similarly, Fleming et al modelled viral surveillance and hospitalization data from the UK from 1995–2009 and estimated a RSV hospitalization rate of 156 per 100,000 adults aged ≥65 years [7]. Finally, similar to our findings, Zhou et al estimated hospitalization rates due to influenza in adults to be considerably higher than that for RSV, particularly in years dominated by influenza H3N2 [9].

When carrying out analyses among hospitalized cases, we did not find any assessed comorbidity to be significantly associated with RSV positivity following adjustment for age and ethnicity. This lack of association is likely due to the higher risk of hospitalization for non-RSV illnesses in adults with comorbidities i.e. selection bias that occurs when both exposure and outcome are associated with hospitalization. Thus in order to accurately measure the effect of comorbidity on RSV disease risk, population level comorbidity data should be used to estimate RSV-associated hospitalization rates in specific co-morbidity strata. Such estimates will be valuable in identifying adult groups who are at particularly high-risk from RSV-associated disease and priority groups for future RSV vaccines and treatments. Unfortunately, we did not have population-level comorbidity data to carry out such analyses in the current study, however we intend to do so in future work.

A strength of this study is its use of ARI surveillance across multiple seasons linked with individual-level population data, to estimate RSV hospitalization rates by demographic characteristics important for New Zealand. Nonetheless our study had important limitations. First of which was the lack of RSV testing for all ARI patients and differences in testing by SARI case definition. However, our estimates were similar using two different correction methods for non-testing. Additionally, by using multiple imputation we were able to provide more realistic measures of uncertainty for our rate estimates. Secondly, our estimations of cost are based on direct health care associated cost and do not account for indirect costs associated with loss of work hours and out-of-pocket expenses. Finally, the lack of systematic bacterial and viral co-infection data prevented detailed assessments of the causal role of RSV infection in adult ARIs. However a recent meta-analysis suggests strong evidence for this association, showing that among individuals with RSV-ARI, ARI is causally attributable to RSV in about 88% of cases [33].

Conclusion

In a setting outside of the United States, where most studies assessing the burden of RSV in older have been conducted, we confirm that RSV has a considerable hospitalization burden and associated economic cost in adults. In our study from 2012–2015, RSV burden was less than that for influenza. RSV disproportionally affects adults by age. Being of Indigenous Māori or Pacific ethnicity or from a low socio-economic area pose independent risks for RSV-associated hospitalization. An effective RSV vaccine or treatment may offer benefits for older adults.

Performance of hospital assays (clinician ordered tests) in detecting different respiratory viruses 2012–2016.

(DOCX)

Click here for additional data file.

Comparison of tested and non-tested ARI hospitalizations stratified by SARI case definition among adults aged 18 or older in Auckland, New Zealand 2012–2015.

(DOCX)

Click here for additional data file.

Comparison of SHIVERS systematic and clinical ordered testing among ARI hospitalizations in adults aged 18 or older in Auckland, New Zealand 2012–2015.

(DOCX)

Click here for additional data file.

6 Apr 2020

PONE-D-19-32957

The health and economic burden of respiratory syncytial virus associated hospitalizations in adults

PLOS ONE

Dear Ms. Prasad,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR:

Both reviewers found merit in your work and the quality of the manuscript - each has specific issues that require detailed review at this stage. Reviewer 2 has made an important comment on associations founded on ethnicity which is an increasingly important issue globally.

==============================

We would appreciate receiving your revised manuscript by May 21 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Richard van Zyl-Smit

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

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

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for stating the following in the Competing Interests section:

"NP, SH, and ECN are currently contracted by GlaxoSmithKline for an RSV surveillance project."

Please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials, by including the following statement: "This does not alter our adherence to  PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests).  If there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include your updated Competing Interests statement in your cover letter; we will change the online submission form on your behalf.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

Additional Editor Comments (if provided):

Thank you for your submission - both reviewers have comments that require some thought and review.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors retrospectively review a cohort of Auckland, New Zeland adult persons aged ≥18 years and identified RSV positive among ARI hospitalizations, from 2012 to 2015, using data from the Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) project. They aimed to provide estimates of RSV disease and economic burden in this cohort.

Of 4600 ARI hospitalizations tested for RSV, 348 (7.6%) were RSV positive. They found a hospitalization risk increased with: age (highest incidence among adults > 80 years), Maori or Pacific ethnicity or living in a neighborhood considered with low socioeconomic status. The average direct cost associated with each RSV hospitalization was NZD $ 4758. They conclude that an effective RSV vaccine or treatment may offer benefits for older adults.

The manuscript is well written, and easy to read, data support the conclusions, methodology is comprehensive. I will not comment the statistical analysis in detail as my knowledge in the topic is not deep.

Concerning discussion:

My first question is related to the causal attribution for RVS in patients presenting with ARI that is not discuss in the manuscript. In 47 (18%) of the cases other virus were co- detected (influenza, rhinovirus, adenovirus and human metapneumovirus). How did Authors deal with the issue of causal attribution? And what about other etiologies as such as bacterial infection? We know asymptomatic RSV infection is uncommon, but still possible. Among individuals with RSV-ARI, ARI is causally attributable to RSV in about 88% (Shi T, et al, 2019).

Concerning ARI severity (resulting in UCI admission) and co-morbidities there was no significant difference by RSV positivity status after adjusting for age and ethnicity. Any comments about this? The authors suggest a few lines below that co-morbidities may increase the risk of RSV infection in ethnic and SES populations (page 15, lines 267-8)

Due to winter seasonal incidence that approximates annual incidence on RSV infection due Authors recommend RSV screening only during winter season?

Reviewer #2: There are two issues I believe require some clarity.

The economic costs are well documented - but don't mean much to anyone outside of the NZ system and it would be helpful for readership to understand what a day in the ward costs or what the costs of a bacterial pneumonia admission would be for example.

The second and Major/critical issue is the association with Ethnicity. I understand from your description that you had adjusted ethnicity for SES - I am not happy that this is sufficient.

Unless you can provide a biological plausible reason why having Maori Genes places you at higher risk for RSV than Caucasian genes, I do not accept that you have adjusted adequately. I am not aware if there are genetic susceptibilities in Maori or pacific islanders that predispose them to viral infections?

Before concluding that "maori" is an association, I would like to see your model adjusted for SES, tobacco smoking, housing density, number of children, vaccination, nutrition, etc etc. otherwise you are merely using ethnic background as a proxy for SES which then implies that Maori has nothing to do with being Maori - but just being disadvantaged.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

6 May 2020

Reviewer #1:

Comment: The authors retrospectively review a cohort of Auckland, New Zeland adult persons aged ≥18 years and identified RSV positive among ARI hospitalizations, from 2012 to 2015, using data from the Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) project. They aimed to provide estimates of RSV disease and economic burden in this cohort.

Of 4600 ARI hospitalizations tested for RSV, 348 (7.6%) were RSV positive. They found a hospitalization risk increased with: age (highest incidence among adults > 80 years), Maori or Pacific ethnicity or living in a neighborhood considered with low socioeconomic status. The average direct cost associated with each RSV hospitalization was NZD $ 4758. They conclude that an effective RSV vaccine or treatment may offer benefits for older adults.

The manuscript is well written, and easy to read, data support the conclusions, methodology is comprehensive. I will not comment the statistical analysis in detail as my knowledge in the topic is not deep.

Response: We thank the reviewer for their positive comments on this paper.

Concerning discussion:

My first question is related to the causal attribution for RVS in patients presenting with ARI that is not discuss in the manuscript. In 47 (18%) of the cases other virus were co- detected (influenza, rhinovirus, adenovirus and human metapneumovirus). How did Authors deal with the issue of causal attribution? And what about other etiologies as such as bacterial infection? We know asymptomatic RSV infection is uncommon, but still possible. Among individuals with RSV-ARI, ARI is causally attributable to RSV in about 88% (Shi T, et al, 2019).

Response: Thank you for highlighting this important point. As the study reported in this manuscript estimated RSV hospitalization rates in adults by linking national administrative datasets with an ARI surveillance project, we did not have data on RSV cases among non-ARI patients. We state in the paper that our aim was to estimate ARI associated RSV hospitalisation rates and have made edits to emphasize this point throughout.

Unfortunately we did not have systematic data on bacterial infections and could not account for this in our methods. Finally as indicated in Pg 9 line 188, not all adults were tested for all respiratory viruses. As such, we were only able to describe the type of co-infections among adults that were tested for other viruses but not account for them within our incidence estimations. We have now added a point to our discussion regarding this important limitation as shown below.

Finally, the lack of systematic bacterial and viral co-infection data prevented detailed assessments of the causal role of RSV infection in adult ARIs. However a recent meta-analysis suggests strong evidence for this association, showing that among individuals with RSV-ARI, ARI is causally attributable to RSV in about 88% of cases.

Concerning ARI severity (resulting in UCI admission) and co-morbidities there was no significant difference by RSV positivity status after adjusting for age and ethnicity. Any comments about this? The authors suggest a few lines below that co-morbidities may increase the risk of RSV infection in ethnic and SES populations (page 15, lines 267-8)

Response: Thank you for highlighting this point. We agree this result should have been mentioned in the discussion. The lack of association between assessed comorbidities is likely driven by the fact that adults with comorbidities have a higher risk of hospitalization for non-RSV illnesses compared to those without. This is a common selection bias when carrying out analysis in hospitalized cases. We now mention this in the discussion and highlight ways in which this selection bias can be addressed as shown below

When carrying out analyses among hospitalized cases, we did not find any assessed comorbidity to be significantly associated with RSV positivity following adjustment for age and ethnicity. This lack of association is likely due to the higher risk of hospitalization for non-RSV illnesses in adults with comorbidities i.e. selection bias that occurs when both exposure and outcome are associated with hospitalization. In order to accurately estimate the effect of comorbidity on RSV disease risk, population level comorbidity data should be used to estimate RSV-associated hospitalization rates in specific co-morbidity strata. Such estimates will be valuable in identifying adult groups who are at particularly high-risk from RSV-associated disease and priority groups for future RSV vaccines and treatments.

Due to winter seasonal incidence that approximates annual incidence on RSV infection due Authors recommend RSV screening only during winter season?

Response: In Auckland, New Zealand, 90% of RSV positive nasal swab samples were detected during the winter season [Pg 5, line 97]), indicating that our seasonal rates would approximate to annual incidence. While this suggests that only seasonal surveillance for RSV could be sufficient for estimating RSV-associated disease burden – more data on RSV seasonality at the regional level and over a longer period of time is required before such a recommendation can be made.

Reviewer #2: There are two issues I believe require some clarity.

Comment: The economic costs are well documented - but don't mean much to anyone outside of the NZ system and it would be helpful for readership to understand what a day in the ward costs or what the costs of a bacterial pneumonia admission would be for example.

Response: Thank you for this comment. We agree it would be helpful for readers to know the cost of non-RSV hospitalizations in adults. We could not find published information on average cost or bacterial pneumonia associated admission costs among adults – however we now provide additional detail on costs using the data we have – as shown below.

Based on the DRG costing methodology, the median (IQR) cost per RSV hospitalization among adults was NZD $3,723.84 ($2500.44-$5,028.44) while among non-RSV hospitalizations the median (IQR) cost per hospitalization was $3,950.71 ($2423.67-$5028.44). Hospitalization costs were not significantly different by RSV positivity in adults (p-value = 0.254).

Comment: The second and Major/critical issue is the association with Ethnicity. I understand from your description that you had adjusted ethnicity for SES - I am not happy that this is sufficient.

Unless you can provide a biological plausible reason why having Maori Genes places you at higher risk for RSV than Caucasian genes, I do not accept that you have adjusted adequately. I am not aware if there are genetic susceptibilities in Maori or pacific islanders that predispose them to viral infections?

Before concluding that "maori" is an association, I would like to see your model adjusted for SES, tobacco smoking, housing density, number of children, vaccination, nutrition, etc etc. otherwise you are merely using ethnic background as a proxy for SES which then implies that Maori has nothing to do with being Maori - but just being disadvantaged.

Response: As mentioned in the manuscript, our study used national administrative datasets linked to ARI surveillance to estimate RSV hospitalisation rates. The advantage of using such administrative datasets is that we had individual level demographic data (age, sex, ethnicity, SES status based on residential area) on all Auckland resident adults. This enabled estimation of rates by fine demographic strata. It also enabled us to carry out adjustments if necessary. We agree investigating other factors such as tobacco smoking, housing density, number of children, vaccination, and nutrition would be very valuable in better understanding RSV-associated disease risk. However, we did not have this data available at a population level and therefore could not include variables describing these exposures in our models.

In the methods section (Pg 7, line 146) we state with references that in New Zealand, Māori and Pacific peoples have lower reported life expectancy than other ethnic groups, and are also over-represented in the lower SES groups.

This means that ethnicity acts as a confounder on the relationship between age and RSV hospitalisation and on the relationship between SES and RSV hospitalisation and vice-versa. Thus to control for confounding and evaluate the independent effects of age, ethnicity and SES on RSV hospitalization; rates and rate ratios for age group, ethnicity, and SES were adjusted for each other.

We found that even after adjustment for age and SES, Māori and Pacific peoples still had higher rates of RSV hospitalisation compared to other ethnic groups. This finding does not necessarily mean that Māori and Pacific peoples have genetic susceptibilities that predispose them to viral infections it suggests that there are factors associated with being Māori or Pacific that increase the risk of RSV disease that are not being captured by age and area-level measure of SES.

In the discussion (Paragraph 3) we state these higher rates of RSV hospitalisations among Māori and Pacific peoples are likely driven by differences in access to health care and health care seeking behavior as well as co-morbidities that increase the risk of RSV infection. We realize this may not have been sufficient or clear enough and have edited the paragraph as shown below.

We observed disparities in RSV-associated hospitalization rates by age, ethnicity, and SES. Such disparities have been reported for RSV-associated hospitalization rates in children [28], but to our knowledge this is the first study to demonstrate such inequalities among adults. Ethnic differences in respiratory infectious diseases are thought primarily to be due to social and economic factors, as ethnic groups with increased risk of infection and disease{Shi, 2019 #551}se are also overrepresented in lower socio-economic groups. However in our study we found both ethnicity and SES to have independent effects on RSV-associated hospitalization risk. A potential explanation is that our neighborhood-level measure of SES may not accurately capture individual SES. Consequently, social and economic factors associated with RSV disease risk such as smoking, housing density, presence of comorbidities, health care seeking behavior, and poor nutrition are potentially being better captured by ethnicity. As our investigation is exploratory, further investigation is warranted to guide policy development. Nevertheless our findings highlight the value of assessing RSV related health disparities among different ethnic populations in other countries.

Submitted filename: RSV burden cost_adults_Plos One_Responses_20200422.docx

Click here for additional data file.

22 May 2020

The health and economic burden of respiratory syncytial virus associated hospitalizations in adults

PONE-D-19-32957R1

Dear Dr. Prasad,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Richard van Zyl-Smit

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Thank you for your detailed responses to the concerns raised and changes made to the manuscript

no further major issues

Reviewers' comments:

29 May 2020

PONE-D-19-32957R1

The health and economic burden of respiratory syncytial virus associated hospitalizations in adults

Dear Dr. Prasad:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof Richard van Zyl-Smit

Academic Editor

PLOS ONE