Online searches of children’s oseltamivir in public primary and specialized care: Detecting influenza outbreaks in Finland using dedicated databases for health care professionals

Milla Mukka; Samuli Pesälä; Aapo Juutinen; Mikko J Virtanen; Pekka Mustonen; Minna Kaila; Otto Helve

doi:10.1371/journal.pone.0272040

. 2022 Aug 5;17(8):e0272040. doi: 10.1371/journal.pone.0272040

Online searches of children’s oseltamivir in public primary and specialized care: Detecting influenza outbreaks in Finland using dedicated databases for health care professionals

Milla Mukka ^1,^*, Samuli Pesälä ^1,², Aapo Juutinen ³, Mikko J Virtanen ³, Pekka Mustonen ⁴, Minna Kaila ⁵, Otto Helve ^3,⁶

Editor: Ahmad Khanijahani⁷

PMCID: PMC9355218 PMID: 35930527

Abstract

Introduction

Health care professionals working in primary and specialized care typically search for medical information from Internet sources. In Finland, Physician’s Databases are online portals aimed at professionals seeking medical information. As dosage errors may occur when prescribing medication to children, professionals’ need for reliable medical information has increased in public health care centers and hospitals. Influenza continues to be a public health threat, with young children at risk of developing severe illness and easily transmitting the virus. Oseltamivir is used to treat children with influenza. The objective of this study was to compare searches for children’s oseltamivir and influenza diagnoses in primary and specialized care, and to determine if the searches could aid detection of influenza outbreaks.

Methods

We compared searches in Physician’s Databases for children’s oral suspension of oseltamivir (6 mg/mL) for influenza diagnoses of children under 7 years and laboratory findings of influenza A and B from the National Infectious Disease Register. Searches and diagnoses were assessed in primary and specialized care across Finland by season from 2012–2016. The Moving Epidemic Method (MEM) calculated seasonal starts and ends, and paired differences in the mean compared two indicators. Correlation was tested to compare seasons.

Results

We found that searches and diagnoses in primary and specialized care showed visually similar patterns annually. The MEM-calculated starting weeks in searches appeared mainly in the same week. Oseltamivir searches in primary care preceded diagnoses by −1.0 weeks (95% CI: −3.0, −0.3; p = 0.132) with very high correlation (τ = 0.913). Specialized care oseltamivir searches and diagnoses correlated moderately (τ = 0.667).

Conclusion

Health care professionals’ searches for children’s oseltamivir in online databases linked with the registers of children’s influenza diagnoses in primary and specialized care. Therefore, database searches should be considered as supplementary information in disease surveillance when detecting influenza epidemics.

Introduction

Influenza is a serious infectious disease appearing worldwide and is a significant public health concern. Following temporal patterns, influenza occurs in the cold seasons of the year in both adults and children, and infection is spread by two types of influenza viruses, A and B [1]. The global annual attack rate of influenza is 20–30% in children [2]. In a prospective cohort study on the incidence of influenza in Finnish children, an overall influenza attack rate was 19% (252/1,338) [3], and 15–20% of all respiratory infections tested as influenza-positive (3,637 specimens per season) during the epidemic peak [3]. Another Finnish study has shown that the burden of influenza is greatest in children under 3 years in terms of morbidity, complications, and treatment, as well as the significant parental work loss in caring for a sick child [4]. Antiviral agents, such as oseltamivir, are available to treat seasonal or pandemic influenza in children [5–7]. Oseltamivir is a neuraminidase inhibitor that prevents reproduction of the influenza virus and is available as a tablet or in a liquid form (oral suspension). The oral suspension of oseltamivir is typically used to treat children [7].

When using traditional data sources (e.g., diagnoses and laboratory findings) in infectious disease surveillance to detect outbreaks, reporting delays may exist. Online sources, such as general search engines, may provide unreliable information for epidemiological and surveilling purposes stemming from vague user profiles, poor search terms, and the impact of media coverage. Certain searches on Google Flu Trends (GFT) have coincided with medical visits related to influenza-like symptoms [8]; however, one study found that GFT data may not provide reliable surveillance for seasonal or pandemic influenza [9]. Nevertheless, searches have been used in estimating geographical influenza activity, thus making detection and surveillance of influenza epidemics possible [10, 11]. Combining information from several real-time flu predictors (e.g., hospital visits, Google Trends, Twitter posts, FluNearYou, GFT) has been shown to produce more accurate and robust real-time flu predictions [12]. Using machine-learning methods to combine these sources further improves influenza surveillance with more accurate and timely predictions [13, 14]. However, little data exist on searches of dedicated online databases used by health care professionals (HCPs) when detecting influenza outbreaks.

In Finland, a public health care sector comprises public primary and specialized care. Primary care includes health care centers where HCPs encounter first-point-of-contact patients with various symptoms and medical conditions [15, 16]. Specialized care focuses on patients with specific diseases, including medical specialists within their own specialties [15, 16]. Thus, primary care involves patients with unselected and undiagnosed symptoms, while specialized care diagnoses and treats patients with certain medical problems. Different working environments may reflect different information seeking needs in primary and specialized care [17–20]. HCPs seek medical information as part of their clinical work and increasingly use online sources [19, 20]. Pediatricians in specialized care work in emergency departments, outpatient clinics, and hospital wards, handling children during influenza epidemics. Similarly, primary care physicians in health care centers also diagnose and treat children with influenza. In both sectors, physicians prescribe medication to children.

Children spread influenza easily during seasonal epidemics, and specifically those under 5 years of age risk developing severe influenza illness [21]. Oral suspension of oseltamivir is often prescribed in children [7]; however, errors in dosages may occur when calculated based on a child’s weight or age [22, 23]. Therefore, information seeking to verify correct dosages highlights the value of real-time and reliable medical sources used in public health care centers and hospitals. Little is known about this information seeking behavior and its association with seasonal influenza outbreaks. Our previous study [24] found that HCPs’ online searches of oseltamivir and influenza coincide with epidemiological data on influenza, thus we stated that searches could be used as an additional source of information for detecting influenza outbreaks. The aim of our current study is to compare HCPs’ database searches for children’s oseltamivir in public primary and specialized care, and to determine if they could be used as a supplementary source of information for detecting influenza epidemics. We hypothesized that HCPs’ searches for oral suspension of oseltamivir in children would mimic the register data on children’s influenza. This study provides novel information on dedicated online database use by HCPs in distinct health care sectors, and on whether information seeking behavior could predict influenza epidemics, as well as reveal the information needs of HCPs who diagnose and treat children with influenza.

Materials and methods

Setting

When seeking medical information online, HCPs in Finland use dedicated professional databases. Duodecim Medical Publications Ltd (owned by the Finnish Medical Society Duodecim) produces and maintains an online portal, Physician’s Databases (PD) [25], allowing access to medical articles in Finnish. It is heavily used and available throughout the Finnish health care system, including public primary and specialized care. The number of opened articles can be tracked by an Internet protocol address. When searching for information on influenza, HCPs may access PD, which includes a pharmaceutical database with information on oral suspension of oseltamivir. Openings of the page with information on oral suspension of oseltamivir can be tracked in the log files of PD. In our study, we collected weekly online log data on the number of PD searches of oral suspension of oseltamivir (6 mg/mL) in primary and specialized care. In Finland, oral suspension of oseltamivir is the predominant antiviral medication for influenza in children under 7 years, and liquid form is rarely prescribed to any other patient group than children. Therefore, we considered oral suspension of oseltamivir as the most appropriate medication for studying influenza-related online searches related to children.

The Finnish Institute for Health and Welfare is the research and development institute in Finland that maintains the registers of public primary and specialized health care diagnoses and the National Infectious Disease Register (NIDR) [26]. NIDR includes positive laboratory findings of children’s influenza A and B as notified electronically by microbiological laboratories. National registers of public primary and specialized care diagnoses collect information from electronic patient records about doctor visits when a physician reports a child’s influenza diagnosis in the record. We compared the log data on oseltamivir to children’s influenza diagnoses (J09–11 according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision [ICD-10] disease classification code system [27]) in primary and specialized care, as well as positive laboratory findings of children’s influenza A and B (under 7-year-olds) found from NIDR.

Descriptive and statistical analysis

We analyzed the data across Finland from 2012 to 2016, comprising four influenza seasons (i.e., 2012/13, 2013/14, 2014/15, 2015/16) with six indicators (i.e., primary care searches of oral suspension of oseltamivir, specialized care searches of oral suspension of oseltamivir, children’s primary care influenza diagnoses, children’s specialized care influenza diagnoses, and children’s positive laboratory findings of influenza A and B). The log data were analyzed anonymously, thus no individual HCP can be identified. No ethical approval was needed.

We used the Moving Epidemic Method (MEM) to calculate the length of each influenza season with starting and ending weeks and thresholds in four influenza seasons. MEM is a mathematical model assessing the timing of influenza epidemics based on historical data on weekly influenza rates [28]. The World Health Organization and the European Centre for Disease Prevention and Control have implemented MEM to monitor the circulation of influenza in European countries [28–30]. Paired differences in the starting weeks were calculated whether each indicator reaches the epidemic thresholds at similar times, comprising six indicators with a total of 15 pairs. As our data included a small number of searches, diagnoses, and laboratory findings (starting weeks), we used the bootstrapping method to estimate the distribution of observations [31]. The method bootstrapped paired differences consisting of six observations with 1,000 replications that yielded bootstrapped mean, bias-corrected mean, bias-corrected and accelerated (BCa) (adjusted for ties) 95% confidence interval (CI) of the mean, and p-value of the mean. A p-value <0.05 was considered statistically significant. We assessed season-to-season similarity between pairs by using Kendall’s correlation coefficient (τ). R was used to run MEM analyses (R version 3.6.3; packages “mem” and “boot”) [32].

Results

We found visually similar patterns in searches for oral suspension of oseltamivir and children’s influenza diagnoses in primary and specialized health care in Finland during 2012–2016 by season (Fig 1). The number of searches in primary and specialized care remained similar (5,281 vs. 5,658), while the number of influenza diagnoses was only slightly higher (1.3x) in primary care than in specialized care (3,717 vs. 2,837). The number of laboratory reports of influenza A was almost three times higher (2.7x) than of influenza B (4,518 vs. 1,663). The number of searches, diagnoses, and laboratory reports of influenza is shown in Table 1.

Fig 1 — (A) Searches for oral suspension of oseltamivir and children’s influenza diagnoses in primary care. (B) Searches for oral suspension of oseltamivir and children’s influenza diagnoses in specialized care.

Table 1. Total number of searches for oral suspension of oseltamivir, children’s influenza diagnoses in primary and specialized care, and laboratory reports of children’s influenza A and B across Finland 2012–2016 by season.

	Primary care		Specialized care		Laboratory reports
	Number of searches for oral suspension of oseltamivir	Number of children’s influenza diagnoses	Number of searches for oral suspension of oseltamivir	Number of children’s influenza diagnoses	Number of children’s influenza A	Number of children’s influenza B
Season
2012/13	996	867	1340	617	820	276
2013/14	1162	638	1080	373	773	62
2014/15	1314	798	1244	544	969	515
2015/16	1809	1414	1994	1303	1956	810
2012–16	5281	3717	5658	2837	4518	1663

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The starts and ends of influenza seasons were calculated by using MEM. In primary care, searches for oseltamivir oral suspension started in weeks 51–4 and diagnoses in weeks 2–4 (Fig 2). In specialized care, oseltamivir searches started in weeks 50–4 and diagnoses in weeks 50–3 (Fig 3). In primary care, oseltamivir searches ended in weeks 8–14 and diagnoses in weeks 9–13. In specialized care, oseltamivir searches ended in weeks 9–14 and diagnoses in weeks 11–14. The season starts and ends of laboratory reports of influenza A appeared in weeks 50–3 and 9–14, while influenza B appeared in weeks 4–6 and 17–18. The pre-epidemic thresholds of oseltamivir searches in primary and specialized were 9 and 17, while diagnoses were 11 and 11. The post-epidemic thresholds of searches in primary and specialized care were 21 and 23, while diagnoses were 16 and 13. The MEM-calculated season starts and ends are shown in Figs 2 and 3 and Table 2.

Fig 2 — (A) Weekly searches for oseltamivir suspension in primary care. (B) Weekly children’s influenza diagnoses in primary care.

Fig 3 — (A) Weekly searches for oseltamivir suspension in specialized care. (B) Weekly children’s influenza diagnoses in specialized care.

Table 2. MEM-calculated starts and ends of the epidemic seasons by searches for oseltamivir oral suspension and children’s influenza diagnoses in primary and specialized care, and laboratory reports of children’s influenza A and B in Finland 2012–2016.

Primary care

Specialized care

Laboratory reports

Searches for oral suspension of oseltamivir

Children’s influenza diagnoses

Searches for oral suspension of oseltamivir

Children’s influenza diagnoses

Influenza A

Influenza B

Epidemic starts

Season

Week

Date

Week

Date

Week

Date

Week

Date

Week

Date

Week

Date

2012/13

Jan 14

Jan 21

2013/14

Jan 20

Jan 6

Jan 13

Feb 3

2014/15

Dec 29

Jan 5

Dec 8

Jan 19

2015/16

Dec 14

Jan 11

Dec 21

Jan 4

Dec 21

Feb 8

Primary care

Specialized care

Laboratory reports

Searches for oral suspension of oseltamivir

Children’s influenza diagnoses

Searches for oral suspension of oseltamivir

Children’s influenza diagnoses

Influenza A

Influenza B

Epidemic ends

Season

Week

Date

Week

Date

Week

Date

Week

Date

Week

Date

Week

Date

2012/13

Mar 11

Mar 25

Apr 1

Apr 22

2013/14

Mar 10

Mar 24

Apr 21

2014/15

Mar 30

Mar 23

Mar 30

Mar 23

Apr 20

2015/16

Feb 22

Feb 29

Mar 14

Feb 29

May 2

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Primary care searches for oral suspension of oseltamivir preceded children’s influenza diagnoses by −1.0 weeks (95% BCa CI: −3.0, −0.3; p = 0.132) with very high correlation (τ = 0.913), while specialized care oseltamivir searches compared to diagnoses lagged by 0.3 weeks (95% BCa CI: −0.8, 1.3; p = 0.880) with moderate correlation (τ = 0.667). Laboratory reports of influenza A and specialized care influenza diagnoses showed very high correlation (τ = 0.913) with no gap in weeks (mean 0.0, 95% BCa CI: −1.0, 0.5; p = 0.001). However, laboratory reports of influenza A preceded primary care influenza diagnoses by −1.8 weeks (95% BCa CI: −3.5, −0.8; p = 0.006) with high correlation (τ = 0.800). In addition, very high correlations (τ = 0.913) were found between the following pairs: oseltamivir searches in specialized care and influenza diagnoses in primary care, laboratory reports of influenza A and influenza diagnoses in specialized care, and oseltamivir searches in specialized care and laboratory reports of influenza A. Weak or negligible correlations (0.000 < τ < 0.400) were found in pairs related to laboratory reports of influenza B. The pairs, paired differences, and correlations are shown in Table 3.

Table 3. Pairs, paired differences with the mean, bias-corrected and accelerated confidence intervals and p-values, and correlations.

Primary care searches for oseltamivir oral suspension, specialized care searches for oseltamivir oral suspension, children’s influenza diagnoses in primary and specialized care, and laboratory reports of children’s influenza A and B were paired and calculated and bootstrapped according to epidemic starting week.

	Paired differences
Pair	Mean	Bias-corrected and accelerated 95% confidence interval of the mean (adjusted for ties)		p-value of the mean	Kendall’s correlation coefficient (τ)
		Lower	Upper
Oseltamivir searches in primary care–Influenza diagnoses in primary care	−1.0	−3.0	−0.3	0.132	0.913
Oseltamivir searches in specialized care–Influenza diagnoses in primary care	−1.5	−4.0	−0.5	0.124	0.913
Oseltamivir searches in specialized care–Influenza A	0.3	0.0	0.5	0.624	0.913
Influenza A–Influenza diagnoses in specialized care	0.0	−1.0	0.5	0.001	0.913
Influenza A–Influenza diagnoses in primary care	−1.8	−3.5	−0.8	0.006	0.800
Oseltamivir searches in specialized care–Influenza diagnoses in specialized care	0.3	−0.8	1.3	0.880	0.667
Oseltamivir searches in primary care–Oseltamivir searches in specialized care	0.5	−0.8	2.1	0.662	0.667
Influenza diagnoses in specialized care–Influenza diagnoses in primary care	−1.8	−4.0	−0.8	0.010	0.548
Oseltamivir searches in primary care–Influenza A	0.8	−0.5	2.0	0.404	0.548
Oseltamivir searches in specialized care–Influenza B	−3.8	−6.0	−2.3	0.001	0.408
Oseltamivir searches in primary care–Influenza diagnoses in specialized care	0.8	−1.5	2.3	0.474	0.333
Influenza B–Influenza diagnoses in primary care	2.3	1.3	3.3	< 0.001	0.224
Influenza A–Influenza B	−4.0	−6.0	−2.3	0.001	0.224
Influenza B–Influenza diagnoses in specialized care	4.0	1.8	5.3	< 0.001	0.000
Oseltamivir searches in primary care–Influenza B	−3.3	−7.0	−1.8	0.001	0.000

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Discussion

This study has shown that we could compare online searches for oral suspension of oseltamivir in primary and specialized health care to children’s influenza diagnoses and laboratory reports of children’s influenza A and B across Finland during 2012–2016 by season (Fig 1). Searches in primary and specialized care remained similar throughout seasons (Table 1), and starting weeks were calculated by using MEM (Figs 2 and 3). The results we found satisfied our hypothesis, suggesting that searches and diagnoses mimicked each other, mainly appearing in the same week (Table 2). Paired differences showed that searches preceded diagnoses only sometimes, and statistical significance was rarely found. Correlations were high in many pairs (Table 3), meaning that a paired indicator appeared similarly between seasons.

Prior research has shown that HCPs in specialized care use more online sources in their work compared to primary care. In hospitals, physicians and nurses favor online and other electronic sources of medical information [17, 19], while colleagues are the preferred information source among primary care physicians and nurses [20]. Interestingly, primary care physicians’ information seeking from paper sources outnumbered online or electronic sources [18]. In our study, primary health care professionals performed a similar number of medical searches as those in specialized care (5,281 vs. 5,658), probably meaning that HCPs may prescribe oseltamivir similarly in both sectors. However, this needs further research. HCPs search for medical information during clinical work [19, 20], as well as during influenza epidemics [8]. This can be seen in the patterns found, thus highlighting the need for reliable online sources. HCPs likely seek information on oral suspension of oseltamivir to diminish errors in prescribing medication in children [22, 23]. While general search engines cannot characterize their users, searches have been used in influenza surveillance and detection (GFT) [8–11]. Combining flu-related information from online and traditional sources has showed accurate and real-time predictions in influenza surveillance [12–14]. Our study found that the dedicated online medical source, PD [25], aimed at HCPs working in public primary and specialized care, could provide real-time information to be used in daily practice and in surveillance to detect influenza.

The strengths of our study are that HCPs (representativeness) use dedicated professional databases on the Internet (log data, real-time) and that register data were available for comparing indicators of children’s influenza. However, this study has certain limitations. A smaller amount of search- and register data of children’s influenza compared to our previous study on HCP searches [24] may have an effect on the analyses and results. First, our study methodology may include some possible confounders that affect interpretation of the results of online searches, meaning that various HCPs in primary and specialized care may have searched for oseltamivir differently. In our study, we assumed that oral suspension of oseltamivir is mainly used in children. Some of these searches may have been done in connection with treatment of adult or elderly patients unable to swallow tablets, thus liquid oseltamivir is searched for instead. In specialized care, HCPs other than pediatricians may also have searched for information on oseltamivir, and some professionals may have used paper sources and consulted colleagues when verifying the correct dosage of oral suspension of oseltamivir, thus decreasing searches. It is also worth noting that some HCPs may be familiar with the correct oseltamivir dosage once verified, thus a database search is not performed, especially when a physician regularly encounters children with similar weights or ages. Some searches may have been performed by medical students for learning purposes or senior physicians in teaching situations. In addition, HCPs other than physicians (e.g., nurses, pharmacists) may have searched for information on oseltamivir. However, we assume that the majority of oseltamivir searches occur in practice in primary and specialized care by clinical physicians.

Second, HCPs and health care units may report diagnoses and test children differently. We found that influenza diagnoses in primary care appeared slightly higher compared to specialized care (3,717 vs. 2,837) since the searches in primary and specialized care appeared mainly similar (5,281 vs. 5,658) (Table 1). This could mean that HCPs encounter children with influenza and search for oseltamivir in both sectors, but due to the large number of public primary care units (health care centers) in Finland, which more children attend, primary care HCPs may diagnose more children with influenza. However, some specialized care HCPs may have reported children’s influenza-like symptoms in a broader category of infectious diseases, such as acute respiratory infections. It is worth noting that no conclusions of sector differences in laboratory findings can be drawn since we could not distinguish children’s laboratory findings of influenza A (4,518) and B (1,663) between primary and specialized care. The total number of diagnoses (6,554) is slightly higher than total number of laboratory findings (6,181). This means that every child presenting influenza-like symptoms is not tested for the virus (but diagnosed with influenza), especially in primary care where laboratory facilities may be poor quality. Although specialized care may be quicker to test children for the virus and thus find positive results more often, we found a smaller amount of influenza diagnoses in specialized care. There may be a wide variation of reporting diagnoses in distinct primary and specialized care units nationwide. In addition, some HCPs in primary and specialized care units may also report an influenza diagnosis incorrectly in the electronic patient record since laboratory findings have been properly transferred to NIDR by microbiological laboratories. In addition, the oseltamivir search data originated from across the country, including no geographical variations, thus influenza epidemics may begin in different regions of Finland at different times.

Third, while more likely to occur in the general population, the media coverage on the start of influenza season or other influenza-related news during the season may affect information seeking behavior among HCPs, thus showing the increase in oseltamivir searches. However, we did not measure media coverage and its potential impact on searches. We have previously shown [33, 34] searches performed by HCPs to be more specific to recognized outbreaks than searches performed by the general population and therefore consider this to have a minor impact on the results. It is important to note that some parents may request a doctor to prescribe oseltamivir regardless of a test result. Due to these occasional cases, some searches do not accurately indicate true influenza epidemics, but epidemics of fear [11, 35]. This phenomenon may also increase the database searches for oseltamivir, possibly resulting in the biased real-time detection of infectious diseases, such as seasonal influenza outbreaks. However, we do not consider the impact of this on our results to be significant. The databases (PD, the national register of primary and specialized care diagnoses, and NIDR) are separate, thus no searches, diagnoses, and laboratory findings can be connected to one another or the same patient.

Conclusion

To our knowledge, this is the first study to compare HCPs’ online searches for children’s oseltamivir in public primary and specialized care to influenza diagnoses. We found that similar patterns could be seen in oral suspension of oseltamivir searches and children’s influenza diagnoses in primary and specialized health care in Finland during 2012–2016. Searches and diagnoses mainly appeared in the same week during influenza season, but searches preceded diagnoses only sometimes. This emphasizes not only use of online database searches in infectious disease surveillance, but also highlighting various HCPs’ similar searching behavior in health care sectors. The possibility of dosage errors in prescribing medication in children highlights the need for reliable online databases aimed at HCPs who seek medication information. HCPs’ searches for children’s oseltamivir could be used as an additional source of information for disease surveillance when detecting influenza epidemics. It is important to study and understand the characteristics affecting searching behavior to benefit various information sources in the future. Further studies should assess statistical analyses skewed toward the prediction power of the oseltamivir search in actual influenza cases. Future research is needed to focus on the applicability of the results in different infectious diseases, as well as in other health care sectors and medical databases in other countries.

Supporting information

S1 Data

(XLSX)

Click here for additional data file.^{(18.6KB, xlsx)}

Acknowledgments

Preliminary results from this study were presented at the ESPID (European Society for Paediatric Infectious Diseases) conference, May 28–June 2, 2018, Malmö, Sweden.

Abbreviations

GFT: Google Flu Trends
HCPs: health care professionals
ICD-10: International Statistical Classification of Diseases and Related Health Problems, 10th Revision
MEM: Moving Epidemic Method
NIDR: National Infectious Disease Register
PD: Physician’s Databases

Data Availability

All relevant data are within the manuscript and its Supporting information files.

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0272040.r001

Decision Letter 0

Sebastian Shepherd

20 Oct 2021

PONE-D-21-11786

Healthcare professionals’ online searches of children’s oseltamivir in primary and specialized care

PLOS ONE

Dear Dr. Mukka,

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.

The manuscript has been evaluated by two reviewers, and their comments are available below.

The reviewers have raised a number of concerns that need attention. They request additional information on the utility of this data in assessment of influenza severity, the non-qualitative assessment methods used in this study, sources of information for searches, research objectives/rationale, and various other moderate comments on manuscript structure and discussion.

Could you please revise the manuscript to carefully address the concerns raised?

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: Partly

Reviewer #2: Partly

**********

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

Reviewer #1: No

Reviewer #2: No

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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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: There is always a need for new sources of influenza and respiratory virus surveillance data. This study provides evidence of generally good alignment between physician searches related to oseltamivir use in children and other influenza surveillance sources based purely on visual review of time series data. My overarching comments are the following. The authors should make clearer exactly how and in what circumstances this data type may be informative for influenza severity– e.g., to assess severity. I also think some of the results that they highlight are not particularly interesting and it would be best to focus on recommendations for using these data. Their prior work (ref 18) used quantitative methods to assess similar data – it is not clear why they have not employed those methods in this study. Other specific comments are below.

Abstract

I recommend highlighting here that the intent of the study is to assess whether these search data can be used for surveillance purposes. I believe that is the objective of the study and that the text of the objective and rationale sections should be swapped.

The absolute numbers of searches vs diagnoses is not the most important results to highlight. I also don’t think primary vs specialized care based searches are key findings to include in the abstract – unless you make clear that specialized includes hospital care (see comment below).

Introduction

Some editing of the introduction would make the information flow better – either rearrange so influenza and oseltamivir is introduced first, or add paragraph breaks. Right now the first paragraph is muddled.

Lines 51-52 – Seems unnecessary to mention a company (Google) three times – would instead just refer to Google Flu Trends once.

Lines 55-56 – Could be omitted as this is well understood background information.

Since the introduction explains that “specialized care” includes hospital/inpatient based care, and “primary care” seems to only be ambulatory/outpatient, consider calling the specialized care category “specialized and hospital care”. That is an important distinction and makes interpreting the results from the two categories more meaningful.

Much of the information in lines 60-83 should be in the Methods section.

Methods

There are no quantitative methods applied in the study. Visual examination of trends and general comparisons of counts of data is all that is presented, yet they have conducted what appears to be a very similar study previously (ref #18) and applied quite different methods.

Results

Figures 1 and 2 can be combined.

Line 114 – 115 – I’m not sure why a “higher” peak for searches, based on counts, compared to other data sources is important to highlight. Timing of the different data sources is key, as well as what the relative trends are for primary vs specialized and hospital based care – i.e., severity of the season. If the specialized searches for oseltamivir start to appear sooner than other indicators of activity, that is important to highlight. Commenting on what is known about the severity of the seasons you studied would also be useful – to help readers understand how such data can help elucidate influenza activity per season, in “real time”.

The peak of specialized searches in the spring of the 2013-14 season does not align with diagnoses or lab reports – what is your hypothesis? This is mentioned in the results but never discussed. This may just be a data anomaly but it is quite evident and needs to be addressed in some way.

Reviewer #2: Dear editor,

Thank you for providing me with this opportunity to review the mmanuscript titled “Healthcare professionals’ online searches of children’s oseltamivir in primary and specialized care” submitted to Plos One journal.

In their interesting work, authors have focused on trying to establish a relationship between the number of searches in the primary and specialized care for oseltamivir and the recorded cases of flu cases in children. This study is valuable because the findings can potentially contribute to the effective prediction of flu pandemics using the frequency of searches for a specific term/phrase.

I believe that this manuscript can be considered for further review with additional work, and I do not suggest publication in its current form. I have made detailed comments related to each section of the manuscript below. This manuscript presents a lack of rigorous statistical analysis and an underdeveloped methodology, which need special attention.

Title:

Suggestions: Change the title to something more generalizable that shows the usefulness of online search results for flu outbreak detection/prediction

Keywords

Correct “speralized care” to “Specialized care”

Abstract

Please consider these comments for the abstract and apply those to the body of the manuscript wherever applicable.

Well-constructed and organized, overall. Important notes:

Lines 24-30: The introductory sentences in the “Objective” section do not align correctly with the research objective. More specifically, the last sentence needs to be changed to reflect the objective of this research (closer to the “Rationale”).

Lines 34-38: Include the source of information/ data for both online searches and the recorded number of cases.

Lines 39-44: Results need some rigorous statistical analysis. At least, the correlation between the number of searches and the number of recorded cases should be considered. Moreover, if possible additional variables for regression analysis are suggested. Probably a formula can be developed to predict the number of cases using the frequency of searches.

Lines 45-46: The conclusion is stated in a form that suggests a binary outcome/incident as yes/no. It should be rephrased as a directional association. More searches= more reported cases (A positive correlation between the frequency of searches and the # of reported cases)

Introduction

Overall the content of the introduction section is satisfactory. However, the organization and flow are relatively heavy to digest. Please consider using different paragraphs for each of the following content in the following order:

1- Influenza (include # of annual cases in Finland (or Prevalence rate), deaths, plus the burden of disease in terms of cost to the healthcare system.

2- Different options for Online searches, pros cons.

3- Details about the setting (Finish healthcare system) relevant to the topic.

4- The rationale for the need for this research and its expected value added to literature/practice.

Material and Methods

This section is poorly developed and need much work, including:

1- Suggest adding/ or moving some content from the introduction and adding a “Setting” sub-heading to discuss the study setting.

2- The data sources for online searches and recorded cases should be included here.

3- The rationale and logic behind choosing this medication (oseltamivir) and why this one(and not other medications/ keywords?) should be clarified.

4- Important: The analysis is insufficient. At least, the correlation between the number of searches and the number of recorded cases should be considered. Moreover, if possible additional variables for regression analysis are suggested. Probably a formula can be developed to predict the number of cases using the frequency of searches.

Line 102: change “laboratory positive findings” to “positive laboratory findings”

Results

The results section needs additional work.

Important: As a result of substantial changes suggested to the “Methods” section, the “Results” section also needs more development. Begin by including the descriptive statistics about the # of searches. Then, expand with correlation/regression findings. Some charts can be useful here

Line 114 and elsewhere: The season 2012/13 needs to be written transparently. For example, the phrase “In specialized care 2012/13 and 2013/14,” is confusing. Please use year and season to make these statements easy to understand.

Line 115: Please rewrite this phrase “a wider scale of months before and after these seasons”. It’s a bit unclear.

Discussion

The discussion section talks about the potential reasons behind why searches are more/less in primary/specialized settings and probably why the reported cases are different in these settings. However, there is no discussion about the uses of these search result for prediction and potential confounders that can impact the accuracy/ issues of using these search results to predict the number of cases. Moreover, other studies/ applications similar to this research should be included and discussed here.

Lines 140-141: This statement does not reflect the relative size of the primary and specialized care. The total number of practitioners in different levels (primary/specialized care) should be included to make a legitimate comparison. More can be discussed here by knowing the size of each care level. Needs to be corrected “However, our study found that professionals in primary care use PD as much as their colleagues in specialized care”

Conclusions

Change based on the results from additional statistical analysis leaned toward the prediction power of the search for oseltamivir in actual flu cases.

The suggestion for future research in other diseases is proper.

**********

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Reviewer #1: No

Reviewer #2: Yes: Ahmad Khanijahani

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Attachment

Submitted filename: Review_PONE-D-21-11786.docx

Click here for additional data file.^{(18.1KB, docx)}

PLoS One. 2022 Aug 5;17(8):e0272040. doi: 10.1371/journal.pone.0272040.r002

Author response to Decision Letter 0

24 Jan 2022

Response: Thank you for your general comment on our study.

Abstract

Response: Thank you for your comment. We have now used the following sections in Abstract: Introduction, Methods, Results, and Conclusion. Also, we have revised the objective and rational as suggested, and included them in the Introduction section. We also embedded further information to introduce the study background. In Abstract/Conclusion, we mention that “…the searches should be considered as a supplementary source of information for disease surveillance when detecting influenza epidemics.” The Abstract section is also revised due to the words allowed (max 300 words).

Response: Thank you for this comment. We have now used statistical analyses in our study, and added statistical data in Abstract/Results. Also, hospitals are now mentioned in Abstract.

Introduction

Response: Thank you for your suggestion. We have now rearranged Introduction and divided it into four paragraphs: influenza/oseltamivir, online searching, Finnish healthcare system/professionals, and rationale/objective.

Lines 51-52 – Seems unnecessary to mention a company (Google) three times – would instead just refer to Google Flu Trends once.

Response: We have omitted Google (mentioned twice) as suggested. We mention GFT only.

Lines 55-56 – Could be omitted as this is well understood background information.

Response: Thank you for this suggestion. However, we have now reorganized the paragraphs of the Introduction section with additional information on influenza, and therefore we have left this sentence in the text.

Response: Thank you for your important comment. In Finland, public specialized care is comprised of both outpatient clinics and hospitals, and our data on specialized care include both. The point is that specialists (such as pediatricians) work in public specialized care, while general practitioners work in public primary care (such as in healthcare centers). In Finland, the term “hospital” comprises outpatient/inpatient in distinct specialty, meaning that a specialist works in outpatient clinics encountering patients within their own specialty, for example in pediatrics. We mention in Introduction: “Pediatricians in specialized care work in emergencies, outpatient clinics, and hospital wards…”

Much of the information in lines 60-83 should be in the Methods section.

Response: We have now relocated much of the information from Introduction to Methods as suggested. Also, Introduction is now reorganized by dividing it into four paragraphs.

Methods

Response: Thank you for your comment. We have now added statistical analyses in our study and described it in Materials and Methods. We used the Moving Epidemic Method (MEM) to calculate the starting and ending weeks with pre-epidemic and post-epidemic thresholds. The MEM model is the same method we used in our previous study (ref #18). Also, paired differences with correlations were assessed.

Results

Figures 1 and 2 can be combined.

Response: Thank you for your suggestion. Figures were meant to be combined during the layout processing, so that they are set in one figure in order to be seen at a glance. Also, more figures have been added in order to be set into three panels (Figures 1A-B, Figures 2A-B, and Figures 3A-B).

Response: Thank you for pointing out this important detail. It is true that timing is key in terms of comparing different data. However, higher peaks for searches (done by healthcare professionals) may mirror different information seeking behaviors among primary and specialized care professionals, not just comparing data between indicators, but also between professionals working in different healthcare sectors (specialized care pediatricians vs. primary care general practitioners; both encountering children with influenza and prescribing oral suspension of oseltamivir [In Finland, this is also legitimated for general practitioners]). In this study, we wanted to showcase how searches, influenza diagnoses, and laboratory findings could coincide with one another. Along with this, the number of professionals’ searches in primary and specialized care was the aim of our research, and thus we found it important to study. However, we have now omitted information on peaks.

Of note, we have run statistical analyses from our data in order to highlight the timing of different indicators. We used the Moving Epidemic Method (MEM model) to calculate the starting and ending weeks of an epidemic with pre-epidemic and post-epidemic thresholds. Paired differences were assessed as well.

Response: Thank you for your comment. In Figure 2, three peaks are shown during the 2013/14 season (February-March). Searches are align with laboratory reports (influenza A) (searches peak higher instead), but diagnoses stay behind. However, we have omitted information on peaks, because additional statistical analyses were added into this study.

Reviewer #2: Dear editor,

Thank you for providing me with this opportunity to review the manuscript titled “Healthcare professionals’ online searches of children’s oseltamivir in primary and specialized care” submitted to Plos One journal.

Response: Thank you for your general comment on our study.

Title:

Suggestions: Change the title to something more generalizable that shows the usefulness of online search results for flu outbreak detection/prediction

Response: Thank you for this suggestion. We have now revised the title as follows: “Online searches of children’s oseltamivir in public primary and specialized care - Detecting influenza outbreaks in Finland using dedicated databases for health care professionals.”

Keywords:

Correct “speralized care” to “Specialized care”

Response: Sorry, a typo. We have corrected this keyword.

Abstract:

Please consider these comments for the abstract and apply those to the body of the manuscript wherever applicable.

Well-constructed and organized, overall. Important notes:

Lines 34-38: Include the source of information/ data for both online searches and the recorded number of cases.

Response: Thank you for this comment. We have added the information sources as suggested.

Response: We have now added a rigorous statistical analysis (Moving Epidemic Method (MEM)) in our study. Also, paired differences with correlations were calculated.

Response: Thank you for your comment. We have now rephrased the conclusion sections of Abstract and Conclusion according to our results/findings we conclude. Correlations calculated from MEM results (starting weeks) are mentioned in Abstract and Results.

Introduction:

Response: Thank you for your comment. We have now revised the Introduction section by dividing the section into four paragraphs as suggested.

1- Influenza (include # of annual cases in Finland (or Prevalence rate), deaths, plus the burden of disease in terms of cost to the healthcare system.

Response 1: Thank you for this important comment. We have now added a couple of sentences about these issues you suggest. Unfortunately, we have no numerical data available on deaths and costs of children’s influenza in Finland. However, we have described the burden of influenza on the society and how it affects healthcare system and families. Also, we found two studies on incidence of influenza in Finnish children, and we described the results in the first paragraph of Introduction.

2- Different options for Online searches, pros cons.

Response 2: We have now added different options for online searches and described the challenges with additional literature embedded.

3- Details about the setting (Finish healthcare system) relevant to the topic.

Response 3: The Finnish healthcare system (details about public primary and specialized care) is introduced here.

4- The rationale for the need for this research and its expected value added to literature/practice.

Response 4: We have added the aim and rationale of this study, and the expected value to added literature/practice.

Material and Methods:

This section is poorly developed and need much work, including:

1. Suggest adding/ or moving some content from the introduction and adding a “Setting” sub-heading to discuss the study setting.

Response 1: We have relocated some content from the Introduction section to Material and Methods and added the “Setting” subheading as suggested. Please see below (number 2).

2. The data sources for online searches and recorded cases should be included here.

Response 2: We mention the data sources for online searches (Physician’s Databases, PD) and for recorded cases (National Infectious Disease Register, NIDR) in Materials and Methods/Setting. We have now relocated the content (details on PD and NIDR), from the Introduction section as suggested.

3. The rationale and logic behind choosing this medication (oseltamivir) and why this one (and not other medications/ keywords?) should be clarified.

Response 3: Thank you for your comment. We have now clarified in the “Setting” section why we chose oseltamivir in our study.

4. Important: The analysis is insufficient. At least, the correlation between the number of searches and the number of recorded cases should be considered. Moreover, if possible additional variables for regression analysis are suggested. Probably a formula can be developed to predict the number of cases using the frequency of searches.

Response 4: We have added statistical analyses in our study. We used the Moving Epidemic Method (MEM model) to calculate the starting and ending weeks of an epidemic with pre-epidemic and post-epidemic thresholds. Also, correlations were calculated (paired differences). We have also added the “Descriptive and Statistical Analysis” subheading in Materials and Methods.

Line 102: change “laboratory positive findings” to “positive laboratory findings”

Response: We have changed this as suggested.

Results:

The results section needs additional work.

Response: Thank you for your suggestion. We have now reorganized the Results section (by beginning the number of searches) as suggested. Statistical analyses, including the Moving Epidemic Method (MEM), have been added and the results tabled.

Response: We have revised years and seasons as suggested. However, we have omitted information on seasons due to changes in analyses used in this study (MEM).

Line 115: Please rewrite this phrase “a wider scale of months before and after these seasons”. It’s a bit unclear.

Response: Due to statistical analyses added in our study (MEM), we have omitted the sentence as being unnecessary.

Discussion:

Response: Thank you for this comment. We have described the limitations of this study and also added more information on confounders that may impact the results of our study (searches), also from the view of predicting/detecting the number of cases. We have also included other studies similar to this research, and some sentences are relocated in the paragraph of limitations.

Response: Thank you for your important comment. This is true that the statement does not reflect the relative size of primary and specialized care. We have now omitted the statement since we have no accurate information on the professionals working in primary and specialized care during those years.

Conclusions:

Change based on the results from additional statistical analysis leaned toward the prediction power of the search for oseltamivir in actual flu cases. The suggestion for future research in other diseases is proper.

Response: We have added that future studies should use additional statistical analyses leaned toward the prediction power of the search of oseltamivir in actual influenza cases.

Attachment

Submitted filename: Comments and responses.docx

Click here for additional data file.^{(30KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0272040.r003

Decision Letter 1

Ahmad Khanijahani

28 Apr 2022

PONE-D-21-11786R1Online searches of children’s oseltamivir in public primary and specialized care ― Detecting influenza outbreaks in Finland using dedicated databases for health care professionalsPLOS ONE

Dear Dr. Mukka,

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

We have the response from the reviewers assigned for your revised manuscript. Overall, reviewers indicated that the revisions made by the authors addressed most of the questions raised by the first two reviewers. Moreover, I reviewed your revised manuscript, and I believe that with minor revisions, it can be considered for publication

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Reviewer #3: (No Response)

Reviewer #4: This article provides the use of prescription antiviral dose look-up as a novel method for surveillance of influenza in children, and is an important and potentially helpful use of understanding influenza in our young populations.

There could be more caveats provided around the possible confounders in these methodology: what other conclusions could be drawn from these data? are there any biases which arise from these data which have not been shared? and if so , what direction do they draw the results in? For example the difference in 1.7 in lab diagnostics between settings, but the dose checking was the same- what does this mean? what other explanations are there for this? Could anyone be looking up the does of osteltamivir for other reasons (ie teaching/learning?) Could people be prescribing without looking up the does once they know it?

The abstract doesn't read properly, and could do with checking for basic English.

However, this is a novel and important finding, and useful for surveillance of influenza in children, and is useful to share as helpful methods for public health practice.

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PLoS One. 2022 Aug 5;17(8):e0272040. doi: 10.1371/journal.pone.0272040.r004

Author response to Decision Letter 1

24 May 2022

REBUTTAL LETTER

Academic editor: We have the response from the reviewers assigned for your revised manuscript. Overall, reviewers indicated that the revisions made by the authors addressed most of the questions raised by the first two reviewers. Moreover, I reviewed your revised manuscript, and I believe that with minor revisions, it can be considered for publication

Response: Thank you for your comment. We have answered the points made by the reviewer #4 (please see below). The text has been language-checked, edited, and proofread in terms of proper English language.

Academic editor: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Response: We have checked the Reference list to ensure that it is complete and correct. Due to additional information embedded in Discussion, three references [33,34,35] have been added on the Reference list.

Reviewers' comments

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: (No Response)

Reviewer #4: All comments have been addressed

Response 1: Thank you for your comment.

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

Reviewer #3: (No Response)

Reviewer #4: Partly

Response 2: We have now added more information on conclusions supported by the data in the Discussion section. Please see below (Response 6).

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

Reviewer #3: (No Response)

Reviewer #4: Yes

Response 3: Thank you for your comment.

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

Reviewer #3: (No Response)

Reviewer #4: Yes

Response 4: Thank you for your comment.

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

Reviewer #3: (No Response)

Reviewer #4: No

Response 5: Thank you for pointing out your concern. The text has been language-checked, edited, and proofread in terms of proper English language.

6. Review Comments to the Author

Reviewer #3: (No Response)

The abstract doesn't read properly, and could do with checking for basic English.

However, this is a novel and important finding, and useful for surveillance of influenza in children, and is useful to share as helpful methods for public health practice.

General response 6: Thank you for your comments. We have now added more information regarding your suggestions and answered them (and added in the manuscript) in detail below. Also, we have restructured the Discussion section due to the additional information embedded.

Comment: What other conclusions could be drawn from these data? Are there any biases which arise from these data which have not been shared? And if so, what direction do they draw the results in?

Response: Thank you for your important comment. We have gone through the discussion among co-authors, and we believe that the confounders and biases are now described in the Discussion section. We have now added the following sentences about these possible confounders in our study:

“In addition, HCPs other than physicians (e.g., nurses, pharmacists) may have searched for information on oseltamivir. However, we assume that the majority of oseltamivir searches occur in practice in primary and specialized care by clinical physicians.”

“There may be a wide variation of reporting diagnoses in distinct primary and specialized care units nationwide.”

“Third, while more likely to occur in the general population, the media coverage on the start of influenza season or other influenza-related news during the season may affect information seeking behavior among HCPs, thus showing the increase in oseltamivir searches. However, we did not measure media coverage and its potential impact on searches.”

“It is important to note that some parents may request a doctor to prescribe oseltamivir regardless of a test result. Due to these occasional cases, some searches do not accurately indicate true influenza epidemics, but epidemics of fear [11,35]. This phenomenon may also increase the database searches for oseltamivir, possibly resulting in the biased real-time detection of infectious diseases, such as seasonal influenza outbreaks. However, we do not consider the impact of this on our results to be significant.”

Comment: For example the difference in 1.7 in lab diagnostics between settings, but the dose checking was the same- what does this mean? What other explanations are there for this?

Response: Thank you for your comment. We have re-checked the absolute numbers of diagnoses in primary and specialized care, and there are typos in the specialized care diagnoses (Table 1). Diagnoses in years 2012-16 equal 2,837 (not 6,181). Specialized care diagnoses in the whole column are the sum of influenza A and B columns (this is incorrect). These have now been revised. However, the MEM analysis has been run by using the corrected data as can be seen in Figures 1-3 (number of diagnoses on y axes similar). This typo in Table 1 does not affect the MEM results and MEM conclusions, but we have now revised the Discussion section that specialized care diagnoses would have appeared too high (6,181) compared to primary care (3,717). New conclusions have been added. We thank the reviewer for rigorous evaluation of the text.

We have revised the text as follows: “We found that influenza diagnoses in primary care appeared slightly higher compared to specialized care (3,717 vs. 2,837) since the searches in primary and specialized care appeared mainly similar (5,281 vs. 5,658) (Table 1). This could mean that HCPs encounter children with influenza and search for oseltamivir in both sectors, but due to the large number of public primary care units (health care centers) in Finland, which more children attend, primary care HCPs may diagnose more children with influenza.”

Comment: Could anyone be looking up the dose of oseltamivir for other reasons (ie teaching/learning?)

Response: This is an important point. We have added the following sentence in the Discussion section: “Some searches may have been performed by medical students for learning purposes or senior physicians in teaching situations. In addition, HCPs other than physicians (e.g., nurses, pharmacists) may have searched for information on oseltamivir. However, we assume that the majority of oseltamivir searches occur in practice in primary and specialized care by clinical physicians.”

Comment: Could people be prescribing without looking up the dose once they know it?

Response: This is possible. We have added the following sentence in Discussion: “It is also worth noting that some HCPs may be familiar with the correct oseltamivir dosage once verified, thus a database search is not performed, especially when a physician regularly encounters children with similar weights or ages.”

Comment: The abstract doesn't read properly, and could do with checking for basic English.

Response: We have proofread the manuscript throughout, especially the abstract. The text has been language-checked, edited, and proofread in terms of proper English language. Revisions in English language can be seen in the file labeled “Revised Manuscript with Tracked Changes”.

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PLoS One. doi: 10.1371/journal.pone.0272040.r005

Decision Letter 2

Ahmad Khanijahani

13 Jul 2022

Online searches of children’s oseltamivir in public primary and specialized care: Detecting influenza outbreaks in Finland using dedicated databases for health care professionals

PONE-D-21-11786R2

Dear Dr. Mukka,

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

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PLoS One. doi: 10.1371/journal.pone.0272040.r006

Acceptance letter

Ahmad Khanijahani

27 Jul 2022

PONE-D-21-11786R2

Online searches of children’s oseltamivir in public primary and specialized care: Detecting influenza outbreaks in Finland using dedicated databases for health care professionals

Dear Dr. Mukka:

I'm 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.

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Data Availability Statement

All relevant data are within the manuscript and its Supporting information files.

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PERMALINK

Online searches of children’s oseltamivir in public primary and specialized care: Detecting influenza outbreaks in Finland using dedicated databases for health care professionals

Milla Mukka

Samuli Pesälä

Aapo Juutinen

Mikko J Virtanen

Pekka Mustonen

Minna Kaila

Otto Helve

Roles

Abstract

Introduction

Methods

Results

Conclusion

Introduction

Materials and methods

Setting

Descriptive and statistical analysis

Results

Fig 1. Oseltamivir searches, influenza diagnoses, and laboratory reports of children’s influenza A and B across Finland during 2012–2016 by season.

Table 1. Total number of searches for oral suspension of oseltamivir, children’s influenza diagnoses in primary and specialized care, and laboratory reports of children’s influenza A and B across Finland 2012–2016 by season.

Fig 2. Primary care searches, diagnoses, and starts and ends of influenza epidemic periods (underlined weeks) across Finland during 2012–2016 by season, including pre-epidemic and post-epidemic influenza thresholds.

Fig 3. Specialized care searches, diagnoses, and starts and ends of influenza epidemic periods (underlined weeks) across Finland during 2012–2016 by season, including pre-epidemic and post-epidemic influenza thresholds.

Table 2. MEM-calculated starts and ends of the epidemic seasons by searches for oseltamivir oral suspension and children’s influenza diagnoses in primary and specialized care, and laboratory reports of children’s influenza A and B in Finland 2012–2016.

Table 3. Pairs, paired differences with the mean, bias-corrected and accelerated confidence intervals and p-values, and correlations.

Discussion

Conclusion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Sebastian Shepherd

Roles

Author response to Decision Letter 0

Decision Letter 1

Ahmad Khanijahani

Roles

Author response to Decision Letter 1

Decision Letter 2

Ahmad Khanijahani

Roles

Acceptance letter

Ahmad Khanijahani

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

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