A multi-country, multi-year, meta-analytic evaluation of the sex differences in age-specific pertussis incidence rates.

BACKGROUND: Pertussis is frequently reported to be more common in females than in males. However, the variability of the sources of these observations makes it difficult to estimate the magnitude and consistency of the sex differences by age. To address this question, we used meta-analytic methods to analyze pertussis national incidence rates by sex and age group from nine countries between the years 1990 and 2017.
METHODS: For each age group, we used meta-analytic methods to combine the female to male incidence rate ratios (RRs) by country and year. Meta-regression was performed to assess the relative contributions of age, country and time-period to the variation in the incidence RRs.
RESULTS: The pooled female to male incidence RRs (with 95% CI) for ages 0-1, 1-4, 5-9 and 10-14, were 1.03 (1.01-1.06), 1.16 (1.14-1.17), 1.18 (1.15-1.22), 1.15 (1.11-1.18) respectively. For the ages 15-44, 45-64 and 65+ they were 1.65 (1.58-1.72), 1.59 (1.53-1.66), 1.20 (1.16-1.24), respectively. While there were some differences between the countries, the directions were consistent. When including age, country and time in meta-regression analyses, almost all the variation could be attributed to the differences between the age groups.
CONCLUSIONS: The consistency of the excess pertussis incidence rates in females, particularly in infants and very young children, is unlikely to be due to differences in exposure. Other factors that impact on the immune system, including chromosomal differences and hormones, should be further investigated to explain these sex differences. Future studies should consider sex for better understanding the mechanisms affecting disease incidence, with possible implications for management and vaccine development.

Introduction

Despite the availability of an effective vaccine, pertussis caused by (B. pertussis) remains a public health problem in both developing and developed countries [1]. Clinical manifestations of the disease can be mild, severe with occasional fatal outcomes, especially in infants [2]. The appearance of new cases could be, for example, due to low immunization rates [3] inadequate immune responses to vaccine [4] or waning immunity following immunization [5].

Reports from individual countries often mention higher pertussis incidence rates (IR) in females [6-11], not always specifying age groups. These observations have usually based on data from individual countries or using case series data, without denominators for calculating incidence rates (IR). If the excess in females is consistent, it could be due to a number of factors.

These include response to vaccine, behavioral and social factors [12], chromosomal factors, or an interaction between sex hormones and immune function affecting the clinical manifestations of the disease [13]. Establishment of the magnitude and consistency of the sex differences in the disease can provide important clues to the mechanism of disease. In order to study this further, we carried out an in-depth study of the sex differences in pertussis incidence rates at different ages, in different countries and over a number of years, based on national data.

Materials and methods

Source of data and search strategy

In order to guarantee the data quality, we restricted our search strategy to all countries in Europe, North and South America, Australia and New Zealand, with established diagnostic tools and well-organized mandatory reporting systems, that provide data by age and sex for a number of years. National data were obtained either from official internet sites or by contacting representatives of the appropriate country health authorities. The original search was performed from March to June of 2018.

There were nine countries for which the national data were available by age, sex and year—Australia (for years 2001–2016), Canada (for years 1991–2015), Czech Republic (for years 2008–2013), England (for years 1990–2016), Finland (for years 1995–2016), Israel (for years 1998–2016), Netherlands (for years 2001–2017), New Zealand (for years 1997–2015), and Spain (for years 2005–2015).

Data for Australia were obtained from the National Notifiable Diseases Surveillance System (NNDSS), the Department of Health [14], for Canada from the Canadian Notifiable Disease Surveillance System (CNDSS) [15], for the Czech Republic from the Institute of Health Information and Statistics [16], for England, directly from Public Health England (PHE), for Finland, from the National Institute for Health and Welfare (THL) [17], for Israel, from the Ministry of Health, for the Netherlands, directly from the official representative of RIVM, for New Zealand, from the Institute of Environmental Science and Research (ESR) for the Ministry of Health [18], and for Spain, from the Spanish Epidemiological Surveillance Network at the National Centre for Epidemiology [19]. Data on the population size by age, sex and year for the Australian population was obtained from the Australian Bureau of Statistics [20] and for Canada from Statistics Canada [21], for the Czech Republic from the Czech Statistical Office [22], for England, from the Population Estimates Unit, Population Statistics Division, Office for National Statistics [23], and for Finland from the Statistics Finland's PX-Web databases [24]. Data for Israel were obtained from the Central Bureau of Statistics [25], for Netherlands from Statistics Netherlands’ database [26], for New Zealand from Stats NZ, Infoshare, New Zealand [27], and for Spain from the Demographic Statistics Database [28].

Ethical considerations and informed consent

National, open access aggregative and anonymous data were used and there was no need for ethics committee approval.

Statistical analyses

The period under study was between 1990 and 2017. Due to the large amount of data, for presentation purposes, the years were grouped for the graphical presentations. Annual pertussis incidence rates (per 100,000) were calculated by sex and age group, for each country and group of years using the number of reported cases divided by the respective population size and multiplied by 100,000.

The age groups considered were <1 (infants), 1–4 (early childhood), 5–9 (late childhood), 10–14 (puberty), 15–44 or 15–39 (young adulthood), 45–64 or 40–59 (middle adulthood) and 65+/60+ (senior adulthood) years. The surveillance systems in Canada, England, Finland, Netherlands, and New Zealand used similar age groups except for the following: 15–39, 40–59 and 60+. For Australia and Finland, data are missing for ages <1 and 1–4 separately. We made an informed decision not to combine these age groups since there is a difference between infants <1 year old and early childhood. The female to male incidence rate ratio (RR) was calculated by dividing the annual incidence rate in females by that of males, by age group, country, and time periods.

The data were analyzed using meta-analytic methods and meta-regression STATA software version 12.1 (Stata Corp., College Station, TX). For the purpose of applying meta-analytic methods, the national data sets for each age group by country and year were considered as separate “studies” and the outcome variable was the female to male incidence RR. After obtaining pooled incidence RRs separately for each age group, by country and time period, pooled incidence RR’s for each age group were obtained for all countries and time periods together. The results are presented in forest plots. Heterogeneity was evaluated using Cochran's Q statistic, and Tau2 and I2 (to estimate between-study variance) [29]. Where significant heterogeneity was present (if I2≥50% and/or the Q test yielded a p-value <0.1) the random effects model [30] was used to estimate pooled RRs and 95% confidence intervals (CI). Otherwise, the fixed effects model was used.

We performed leave-one-out sensitivity analysis in order to determine how each country and group of years affected the outcome following the recomputed pooled pertussis female to male incidence RR. In order to determine whether there were countries or time periods that are outliers, we created funnel plots and used Egger’s test. In order to explore the contributions of age, countries and time periods to the heterogeneity of the incidence RRs, meta-regression analyses were performed, with incidence RR as the dependent variable.

Results

The summary of male and female pertussis incidence rates (per 100,000 populations) in different countries for each age group and for number of years is presented in Table 1.

Table 1

Details of the countries included in the meta-analysis, by sex and age group—Descriptive data.

			Females		Males
Age	Country	Years	n/N	IR	n/N	IR	RR
<1	Canada	1991–2015	5113/4446799	115	5297/4682619	113.1	1.02
	Czech Republic	2008–2013	76/332712	22.8	83/349195	23.8	0.96
	England	1990–2016	4829/8306732	58.1	4651/8725051	53.3	1.09
	Israel	1998–2016	1397/1410400	99	1587/1486100	106.8	0.93
	Netherlands	2001–2017	1393/1540059	90.5	1378/1616870	85.2	1.06
	New Zealand	1997–2015	1309/548520	238.6	1283/576900	222.4	1.07
	Spain	2005–2015	3169/2514548	126	3318/2679186	123.8	1.02
1–4	Canada	1991–2015	11781/18225737	64.6	10770/19156418	56.2	1.15
	Czech Republic	2008–2013	95/1343670	7.1	77/1410748	5.5	1.30
	England	1990–2016	10149/33207057	30.6	9066/34821935	26	1.17
	Israel	1998–2016	1404/5443300	25.8	1229/5731500	21.4	1.20
	Netherlands	2001–2017	3081/6327474	48.7	2888/6632134	43.5	1.12
	New Zealand	1997–2015	2397/2191980	109.4	2333/2308880	101	1.08
	Spain	2005–2015	2309/10233932	22.6	1996/10880587	18.3	1.23
5–9	Australia	2001–2016	19018/10814642	175.9	17996/11398585	157.9	1.11
	Canada	1991–2015	13502/23469919	57.5	11925/24668602	48.3	1.19
	Czech Republic	2008–2013	204/1450621	14.1	154/1532669	10	1.40
	England	1990–2016	9009/41012194	22	7490/42989082	17.4	1.26
	Finland	1995–2016	1332/3297629	40.4	1109/3440956	32.2	1.25
	Israel	1998–2016	2742/6287700	43.6	2583/6616300	39	1.12
	Netherlands	2001–2017	6953/8108728	85.7	6395/8494005	75.3	1.14
	New Zealand	1997–2015	2823/2752910	102.5	2528/2899540	87.2	1.18
	Spain	2005–2015	2193/12287011	17.8	1899/13017097	14.6	1.22
10–14	Australia	2001–2016	18054/10797396	167.2	17501/11377822	153.8	1.09
	Canada	1991–2015	9632/24391864	39.5	8662/25685783	33.7	1.17
	Czech Republic	2008–2013	829/1339518	61.9	733/1416001	51.8	1.20
	England	1990–2016	3048/40624659	7.5	2787/42597565	6.5	1.15
	Finland	1995–2016	1678/3375446	49.7	1294/3522497	36.7	1.35
	Israel	1998–2016	2659/5807300	45.8	2624/6106400	43	1.07
	Netherlands	2001–2017	10679/8277833	129	10023/8668277	115.6	1.12
	New Zealand	1997–2015	1773/2776650	63.9	1740/2919850	59.6	1.07
	Spain	2005–2015	1968/11627137	16.9	1686/12301238	13.7	1.23
15-39/15-44	Australia	2001–2016	35706/72741755	49.1	23400/75391102	31.8	1.54
	Canada	1991–2015	7924/140453550	5.6	4508/143987472	3.1	1.80
	Czech Republic	2008–2013	1105/12978912	8.5	856/13725818	6.2	1.37
	England	1990–2016	5222/233399206	2.2	3433/234901126	1.5	1.53
	Finland	1995–2016	2036/18050351	11.3	1285/18898064	6.8	1.66
	Israel	1998–2016	5073/29264100	17.3	3014/29586200	10.2	1.70
	Netherlands	2001–2017	14916/44687694	33.4	10414/45667655	22.8	1.46
	New Zealand	1997–2015	4912/13976900	35.1	2586/13546700	19.1	1.84
	Spain	2005–2015	2254/105413400	2.1	1195/110542308	1.1	1.98
40-59/45-64	Australia	2001–2016	37571/42573071	88.3	24409/41988401	58.1	1.52
	Canada	1991–2015	3037/109655649	2.8	1956/110461323	1.8	1.56
	Czech Republic	2008–2013	242/8624880	2.8	107/8403729	1.3	2.20
	England	1990–2016	4433/177644620	2.5	3030/175100277	1.7	1.44
	Finland	1995–2016	1364/16307550	8.4	553/16513241	3.3	2.50
	Israel	1998–2016	2159/13327000	16.2	1377/12368500	11.1	1.46
	Netherlands	2001–2017	11087/40278130	27.5	8018/40902904	19.6	1.40
	New Zealand	1997–2015	3697/10685350	34.6	2349/10201030	23	1.50
	Spain	2005–2015	1030/64340310	1.6	524/63103755	0.8	1.93
60+/65+	Australia	2001–2016	23102/25538457	90.5	17163/21417772	80.1	1.13
	Canada	1991–2015	687/78346403	0.9	477/64590224	0.7	1.19
	Czech Republic	2008–2013	83/5999018	1.4	35/4087584	0.9	1.62
	England	1990–2016	1389/163257756	0.9	1040/129663953	0.8	1.06
	Finland	1995–2016	359/15066114	2.4	182/11159619	1.6	1.46
	Israel	1998–2016	858/7566600	11.3	585/5759300	10.2	1.12
	Netherlands	2001–2017	6791/32663089	20.8	4544/27098379	16.8	1.24
	New Zealand	1997–2015	1618/7386000	21.9	1130/6302700	17.9	1.22
	Spain	2005–2015	340/49879431	0.7	182/37127234	0.5	1.39

IR = incidence rate, IR per 100 000 Male or Female population, incidence RR = female: male incidence Rate Ratio

n- Cumulative total of pertussis cases for given years.

N- Cumulative total of the population for given years.

Infants = age<1 year; early childhood = 1–4 years; late childhood = 5–9 years; puberty = 10–14 years; young adulthood = 15–44 or 15–39 years; middle adulthood = 40–59 or 45–64 years; senior adulthood = 60+ or 65+ years.

Age-specific rates by sex were highest in female infants, in 1–4-year-olds and in age groups of 5–9 and 10–14. There was a decrease in the incidence rate of pertussis in adults, in both groups of males and females.

Results of the study are presented in the forest plots presented by age group in Figs 1–7 (with CI = 95% confidence interval, RR = rate ratio. The right side of the X-axis indicates a higher IR for females and the left side for males).

Fig 1

Forest plot of the female to male pertussis incidence rate ratios (RR) for different years in Canada, Czech Republic, England, Israel, Netherlands, New Zealand, and Spain in infants.

The overall incidence RR in infants was 1.03 (95% CI 1.01–1.06), which indicated a small, but significant increase in incidence of disease in female infants, with low heterogeneity, I2 = 28.9%, and Tau2 = 0.002. The incidence RR in infancy varied from 0.93 in Israel to 1.09 in England. The forest plot for ages 1–4 is shown in Fig 2.

Fig 7

Forest plot of the female to male pertussis incidence rate ratios (RR) for different years in Australia, Canada, Czech Republic, England, Finland, Israel, Netherlands, New Zealand, and Spain in senior adulthood.

Fig 2

Forest plot of the female to male pertussis incidence rate ratios (RR) for different years in Canada, Czech Republic, England, Israel, Netherlands, New Zealand, and Spain in yearly childhood.

The overall incidence RR in ages 1–4 was 1.16 (95% CI 1.14–1.17), which indicated a 16% excess incidence rates in females, with low heterogeneity, I2 = 24.3%, and Tau2 = 0.001. The subtotal incidence RRs varied from 1.08 in New Zealand to 1.30 in Czech Republic. The forest plot for age 5–9 is shown in Fig 3.

Fig 3

Forest plot of the female to male pertussis incidence rate ratios (RR) for different years in Australia, Canada, Czech Republic, England, Finland, Israel, Netherlands, New Zealand, and Spain in late childhood.

The overall incidence RR for age 5–9 was 1.18 (95% CI 1.15–1.22), which indicated 18% excess incidence rates in females, with I2 = 75.8%, and Tau2 = 0.005. The subtotal incidence RRs are significantly greater than 1 in all countries and varied from 1.09 in Australia to 1.4 in Czech Republic. The forest plot for age 10–14 is shown in Fig 4.

Fig 4

Forest plot of the female to male pertussis incidence rate ratios (RR) for different years in Australia, Canada, Czech Republic, England, Finland, Israel, Netherlands, New Zealand, and Spain in puberty.

The overall incidence RR at age 10–14 was 1.15 (95% CI 1.11–1.18), with I2 = 78.3%, and Tau2 = 0.0062. The subtotal incidence RRs varied from 1.07 in Israel and New Zealand to 1.26 in Finland.

Fig 6

Forest plot of the female to male pertussis incidence rate ratios (RR) for different years in Australia, Canada, Czech Republic, England, Finland, Israel, Netherlands, New Zealand, and Spain in middle adulthood.

At age 60+/65+, the overall RR = 1.2, 95% CI 1.16–1.24, I2 = 51.2%, and Tau2 = 0.0034, ranging from RR = 1.12 in England to RR = 1.53 in Czech Republic. The forest plot at age 60+/65+ is presented in Fig 7.

The forest plot for infants (age <1) is shown in Fig 1.

The forest plots for age 15-44/15-39 and 45-64/40-59 are shown in Figs 5 and 6 respectively. For age 15-44/15-39, Fig 5, the overall incidence RR = 1.65 (95% CI 1.58–1.72), with I2 = 90.7%, and Tau2 = 0.0135. Female dominance is significant for every country population, ranging from an incidence RR = 1.33 for Czech Republic to 1.96 for Spain RR = 1.96.

Fig 5

Forest plot of the female to male pertussis incidence rate ratios (RR) for different years in Australia, Canada, Czech Republic, England, Finland, Israel, Netherlands, New Zealand, and Spain in young adulthood.

For age 45-64/40-59 (Fig 6), the overall incidence RR = 1.59, 95% CI 1.53–1.66, I2 = 85.9%, and Tau2 = 0.0106, ranging from an incidence RR = 1.4 in Netherlands to an RR = 2.43 in Finland.

To evaluate the effect of individual country and the group of years on the pooled RR, we performed leave-one-out sensitivity analysis and recomputed the pooled RRs. After omitting one country at a time, the pooled RRs remained very similar (Table 2).

Table 2

Sensitivity analysis, by age group and country.

	Age Group
Country Removed	Infants RR (CI)	Early Childhood RR (CI)	Late Childhood RR (CI)	Puberty RR (CI)	Young Adulthood RR (CI)	Middle Adulthood RR (CI)	Senior Adulthood RR (CI)
Australia	-	-	1.2 (1.15–1.24)	1.16 (1.11–1.21)	1.66 (1.53–1.8)	1.68 (1.52–1.86)	1.22 (1.14–1.3)
Canada	1.03 (0.98–1.09)	1.16 (1.14–1.19)	1.19 (1.13–1.24)	1.15 (1.1–1.2)	1.62 (1.53–1.73)	1.65 (1.53–1.79)	1.2 (1.13–1.27)
Czech Republic	1.03 (0.99–1.08)	1.16 (1.14–1.18)	1.18 (1.14–1.22)	1.15 (1.1–1.19)	1.68 (1.57–1.79)	1.61 (1.5–1.73)	1.19 (1.13–1.26)
England	1.02 (0.98–1.06)	1.15 (1.13–1.17)	1.17 (1.13–1.21)	1.15 (1.1–1.2)	1.66 (1.55–1.78)	1.67 (1.55–1.81)	1.22 (1.15–1.3)
Finland	-	-	1.18 (1.13–1.22)	1.13 (1.09–1.17)	1.64 (1.54–1.76)	1.54 (1.47–1.61)	1.18 (1.12–1.24)
Israel	1.05 (1.02–1.08)	1.15 (1.14–1.17)	1.19 (1.15–1.24)	1.16 (1.11–1.21)	1.64 (1.53–1.75)	1.67 (1.54–1.8)	1.21 (1.14–1.29)
Netherlands	1.02 (0.98–1.07)	1.16 (1.14–1.18)	1.19 (1.14–1.25)	1.16 (1.1–1.21)	1.67 (1.56–1.79)	1.69 (1.55–1.83)	1.19 (1.12–1.26)
New Zealand	1.02 (0.98–1.07)	1.16 (1.14–1.18)	1.19 (1.14–1.24)	1.16 (1.11–1.21)	1.62 (1.52–1.72)	1.66 (1.54–1.8)	1.2 (1.12–1.27)
Spain	1.03 (0.98–1.09)	1.15 (1.13–1.17)	1.18 (1.13–1.23)	1.14 (1.1–1.19)	1.61 (1.52–1.71)	1.61 (1.5–1.72)	1.18 (1.12–1.25)

RR = rate ratio; CI = confidence interval

Similar results were obtained after dropping one group of years at a time (Table 3).

Table 3

Sensitivity analysis, by age group and years.

	Age Group
Years Removed	Infants RR (CI)	Early Childhood RR (CI)	Late Childhood RR (CI)	Puberty RR (CI)	Young Adulthood RR (CI)	Middle Adulthood RR (CI)	Senior Adulthood RR (CI)
1990–1994	1.02 (0.997–1.04)	1.14 (1.12–1.16)	1.15 (1.13–1.17)	1.13 (1.1–1.17)	1.63 (1.55–1.72)	1.56 (1.49–1.64)	1.14 (1.09–1.2)
1995–1999	1.03 (0.998–1.07)	1.16 (1.14–1.18)	1.17 (1.12–1.21)	1.12 (1.1–1.14)	1.6 (1.55–1.66)	1.51 (1.47–1.55)	1.14 (1.09–1.19)
2000–2004	1.03 (1.002–1.07)	1.16 (1.14–1.18)	1.18 (1.13–1.22)	1.15 (1.11–1.2)	1.71 (1.6–1.82)	1.58 (1.49–1.68)	1.15 (1.08–1.21)
2005–2009	1.04 (1.002–1.07)	1.16 (1.14–1.18)	1.17 (1.13–1.22)	1.15 (1.1–1.2)	1.7 (1.58–1.82)	1.58 (1.48–1.68)	1.16 (1.13–1.2)
2010–2014	1.03 (1–1.07)	1.16 (1.14–1.18)	1.18 (1.14–1.22)	1.15 (1.11–1.2)	1.7 (1.57–1.83)	1.6 (1.5–1.71)	1.16 (1.08–1.25)
2015–2017	1.04 (1.01–1.07)	1.15 (1.13–1.17)	1.17 (1.12–1.22)	1.15 (1.1–1.2)	1.7 (1.59–1.81)	1.6 (1.51–1.7)	1.13 (1.08–1.19)

RR = rate ratio; CI = confidence interval

For the funnel plot (Fig 8), Egger's test p value for asymmetry was not significant for all age groups except middle adulthood (from infancy, young childhood, late childhood, puberty, young adulthood, and senior adulthood p value were p = 0.711, p = 0.427, p = 0.217, p = 0.176, p = 0.055 and p = 0.076 respectively). Evidence of asymmetry was observed only for middle adulthood with p = 0.036.

Fig 8

Funnel plots: A) for infants, B) for early childhood, C) late childhood, D) for puberty, E) for young adulthood, F) for middle adulthood and G) for senior adulthood.

In the meta-regression analyses, including age group, country and year, age group contributed almost all the variation in the incidence RRs. For infants the incidence RR was lower than for other age groups and in young and middle adulthood, the incidence RRs were significantly higher than in the other age groups (P < .0001). There was no significant association with reporting time-periods, aside from a borderline negative trend among puberty and middle adulthood groups. In those groups, as the time-periods increased, the incidence RR values decreased (P = 0.05 and P = 0.05 for puberty and middle adulthood respectively). In this group, as the time-periods increased, the incidence RR values decreased, (P = 0.02).

Discussion

In this study, we examined the sex differences in pertussis incidence rates by age group in nine countries over a period of six to 27 years. These results revealed higher pertussis incidence rates in females than in males in all age groups from infancy to older adults. The pooled results varied by age from a 3% excess in infants to an excess of 65% in in young adulthood. These findings were consistent over countries and time periods. The meta-regression results revealed that among the variables, age group contributed almost all the variation in the incidence RRs. The results of this study contrast with the perception that males suffer more than females from infectious diseases [13, 31].

Surveillance data from 1995 onwards in former West German states showed a higher pertussis incidence in females (overall 60% of cases) than in males mainly due to a higher proportion of females among adult cases [4]. In England, slight differences were observed between males and females, with 48.1% incidence rate in male and 51.9% in female during 2011–2012, including in those aged 10–19 years [8]. Skoff et al [9] revealed age specific transmission of pertussis over time and observed that in United States, during 2000–2016, among all pertussis cases reported, the majority (54.7%) were female. In a study in Barcelona, similar incidences of pertussis were observed in males and females under the age 12[10]. In Alberta, Canada, between 2004 and 2015, incidence rates by sex in children under the age 14 were similar between females and males [11].

Unlike in the present study, in general, the sex differences in incidence rates were not reported by age or whether they were consistent over time periods. In some, studies were based on hospital or local data, without population denominators [6-10]. This could be an important source of selection bias.

The current study is based on national data with very large populations, covering a number of years and consequently with large numbers of cases. Selection bias has been minimized by using national data over different time periods, which should be representative of each country. Relevant denominators were available to compute incidence rates as opposed to studies based on a case series. The inclusion of nine countries, with advanced health system, allowed us to evaluate the consistency of the findings over different populations and many years. There is no evidence to suggest that there is selective care or differences in vaccine coverage according to the sex of the child, in any of the countries in this study. Underreporting, as the result of non-specific clinical manifestations of the disease and the lack of laboratory confirmation, may be a source of information bias [32]. However, this is unlikely to be different for females and males. There may be a difference in use of health services by sex in the adult age groups [33], but is unlikely to be a factor in infants and children in the countries in this study. Surveillance systems as well as the diagnostic criteria and proportion of laboratory-confirmed cases are heterogeneous [34], but should not differ between females and males.

There is no clear evidence on differences in response to pertussis vaccine between males and females. Antibody levels have been found to be similar in males and females in infants, children and adults following immunization [35, 36]. As regards exposure differences, in young and middle adulthood, women may have more exposure to cases of pertussis while caring for their own children [12], or through exposure to sick children while working in daycare centers. Adults are a potential reservoir for exposure to pertussis in very young infants [2], although the exposure should be the same regardless of the infant's sex. Such possible sex differences in exposure, vaccination rates [37] or medical services utilization are not relevant explanations for the excess pertussis incidence rates observed in infants and young children.

While this study cannot provide information on the mechanisms underlying the excess incidence rates in females, we can explore some possible explanations. Sex differences in pertussis incidence rates can be due to factors such as biological differences between sexes, such as sex chromosomes and sex hormones. It could be postulated that genetic and/or hormonal differences explain, at least partly, increased pertussis incidence rates in females. Studies indicate that infection with B. pertussis results in an immune response mediated through expansion of Th17 cells [38, 39]. These cells may induce tissue immunopathology [40] via the production of inflammatory cytokines and the creation of an environment contributing to inflammation of the upper respiratory tract, duration of lung tissue pathology and prolonged cough [40, 41].

Differences in the immune responses between males and females are in part attributed to the X chromosome, which contains a high number of immune-related genes and regulatory factors that are involved in both the innate and adaptive immune responses [42, 43]. X-linked mosaicism encourages a highly polymorphic gene expression that could enhance the immune response more in females [43], with consequent more symptomatic pertussis. Thus, a stronger immune response in females could result in more clinical manifestations of pertussis.

Sex hormones may also be implicated in the higher incidence rates of clinical pertussis in females. Higher pertussis incidence and immune response may also be due in part to an estrogen mediated enhanced pro-inflammatory response to B. pertussis invasion via IL-17 and a cytokine storm phenomenon. Progesterone and estrogen are lead to more severe inflammation in respiratory diseases [44, 45] and an increased expression of IL-17, whereas testosterone [46] reduces the generation of Th17 cells. Kuwabara T et al [47] showed that IL-17 plays an important role in chronic inflammation that occurs during the pathogenesis of autoimmune diseases such as human rheumatoid arthritis and multiple sclerosis (MS). Pertussis toxin served as adjuvants to induce sensitization to neural antigens in experimental autoimmune encephalomyelitis, the principle animal model of MS, which is more common in female. It appears that IL-17, the cytokine that is involved in pertussis pathogen eradication [38] and autoimmune diseases pathways [47], along with associated chemokines IL-1β, IL-23R, IL-6 and many others [44, 47] is significant and may be linked to an estrogen-regulated immune overresponse to pertussis infection in female.

The impact of sex hormones on the immune response prior to puberty especially in infancy, is not clear. Maternal hormones that pass through the placenta affect male and female fetuses equally [48]. The mini-puberty phenomenon in infancy results in higher endogenous estrogen levels in female infants [49], which could explain the higher incidence of disease in female infants. In addition, maternal hormones may persist in the infant's circulation for some months after birth and will affect females and males equally [48]. This could mitigate the sex difference in disease and explain the lower female to male incidence ratios in infants than those seen at older ages.

Differences in sex hormone levels continue in childhood [50] and in pre-pubertal children [51].

It is conceivable that, in young adulthood, hormonal and genetic differences continue to exist, but the excess pertussis incidence rates need to be viewed in the context of possible different exposure. It has been noted that IL-17 blood levels increase in pregnancy [52, 53].The available literature [54] indicates that the immune response of aged women may be preserved to a greater extent than in aged men and may contribute to prolonged inflammatory responses and tissue damage in respiratory airways. This could be the reason why women exhibit a higher pertussis incidence rate even in older ages.

Conclusions

This study has provided strong evidence that while the excess female incidence rates for pertussis observed in all age groups differ in magnitude, they are consistent over a number of countries and over different time periods. The mechanism underlying the excess in females is still largely unknown. Behavioral factors may contribute to some of the differences seen in the post-pubertal age groups. However, in infants and children, genetic factors, as well as sex hormones could play a part. Our findings suggest the need to explore further the role of sex differences in the mechanism of pertussis infection, when evaluating the efficacy of pertussis vaccine dosing and schedules especially in adult females for disease prevention and public health promotion.

(XLSX)

Click here for additional data file.

28 Jan 2020

PONE-D-19-34094

A multi-country, multi-year, meta-analytic evaluation of the sex differences in age-specific pertussis incidence rates

PLOS ONE

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Reviewer #2: Yes

**********

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Reviewer #2: Yes

**********

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Reviewer #1: Review of Peer et al PLOS ONE 2020

The authors present a meta analysis of public health surveillance data from 9 countries over a 27 year span to address a persistent question within the pertussis research community: is pertussis more common among females than males? The answer appears to be yes.

Overall, I was very pleased to see this paper and found the methodology and results to be persuasive. Overall, the paper would benefit from a final review to correct small grammatical errors in case and tense, but these were of minor concern.

I had several more substantive comments that I think could be addressed quite easily in a revision as follows:

1. I think that the background section in the abstract does not really get to the main point of the paper very clearly. Yes, sex differences could be an important clue to pathogenesis or exposure risk, but that’s not the starting point for this analysis so much as the relevance of the finding (if confirmed) on the back end. It seems odd to start with justifying the relevance of the finding before confirming that the finding is correct. I would instead start, “Numerous studies over the years have suggested pertussis is more common in females than males, but the quality of these studies and risk for selection biases, make it less clear whether a true sex difference exists. To address this question, we conducted a meta analysis of prospectively collected surveillance data from 9 countries over a 27 year span.”

2. Lines 48-49, maybe simplify sentence to state that pertussis can be mild, severe or potentially fatal, with severe and fatal infections concentrated among infants.

3. Line 66, sentence ends abruptly and without resolution, ‘Facilities and mandatory.’ I assume what you meant is that these data were generated in settings with mandatory reporting requirements for certain infectious diseases, including pertussis.

4. Incidence rates imply time. None of your results report as events/100,000 per unit time. Presumably the incidence rates are per year, but this needs to be stated throughout and in the tables. Please add that dimension to make these rates, not proportions.

5. Table 1. Data from Czech republic stand out with very low reported events. This makes me doubt the validity of the Czech data more than it makes me suspect that pertussis is really so different there than elsewhere. Please comment. Should these data be included? I am skeptical of them.

6. Figures are NOT labeled. There is no figure legend either. Please label the figures. Please provide a legend. Also, quality of figures is very poor. Please upload hi definition Tiff figures.

7. I see no value to the funnel plots. These are used to detect publication bias in the scientific literature. But your data are systematically collected, nationally representative, longitudinal infectious disease surveillance data and are not published in the usual sense when doing a meta-analysis. Personally, I think the funnel plots and Egger’s test play no useful role in this context and should be dropped as irrelevant and more likely misleading.

8. Line 233, authors assert that the 9 countries had similar socioeconomic status. That is really debatable. The Czech republic in 1990 was not comparable in wealth the UK or Canada at that time. Similarly, the data from New Zealand and Australia will be dominated by pertussis events in the aboriginal and Maori populations, who tend to be at the very bottom of wealth quintiles. Basically, I reject your assertion.

9. Not sure of the relevance of citation 9. No one is arguing that more males than females get pertussis.

10. The hormone hypothesis is intriguing and plausible. One facet of your data that supports this is the relatively small F:M gradient among infants compared with all older age groups. That is notable because estrogen levels in a baby are largely reflective of maternal estrogens, and hence tend to be more similar between boy and girl babies during early infancy. As endogenous estrogens rise, the F:M gradient increases. That seems like an important point to buttress your overall argument.

Reviewer #2: General comments:

I enjoyed reading this ms. Looking at sex specific differences is definitely important, due to the differences the authors were able to identify.

For this reason, a figure of the time series for each country by sex would have given the reader a good motivation for the study. I recommend plotting that. This would then be followed by the RR analysis.

There were a few things however that I suggest revising. Please see below.

Introduction:

Line 46 you needs to add references to the possible hypotheses, also you aren’t listing all possible hypotheses so I recommend using “for example” or “among others”

You need to italicize Bordetella pertussis

Line 47-48 do you mean manifestations of “disease”? Disease and infection are two different things.

Line 50 The reports aren’t controversial, some of the assumptions are sometimes controversial. I advise revising this sentence.

Also Skoff et al was a robust study and seminal in understanding age specific transmission, so I believe it warrants less inflammatory remarks.

Line 53- you are forgetting social contacts, which have shown to be a major driver in risk.

Line 57 Are you sure you mean mechanisms of infection or transmission dynamics?

Line 59 – what are these reliable denominators (odd way to finish the introduction) please expand.

Material and methods:

Line 64-65- what do you mean by sophisticated lab facilities?

Line 66-7- Are you making the data you gathered available?

Are these country data resolved by sex?

Line 87-89- I think you can remove this

Line 120 “used”

Results:

Line 239: you mean x axis, right?

What are the grey squares? They make it very hard to look at the variation between years.

While I understand the choice of blocks of 5 years, I wonder if you should have an analysis specific to epidemic years. For instance, in England 2012 was an outbreak year.

In the “leave one out “you should actually consider specific years rather than blocks of years.

Line 153 - you need a space after Fig 3

For table 3 – You should either use individual years or blocks of years. In some countries you have 2015 alone. This is confusing. In blocks of years you might be getting rid of between year variation, especially if you think of epidemic years. Also for instance in the England data set, after 2004 there were some change in reporting this can be a source of further confusion.

Line 196 - So the fact that age group contributes almost all the variation is not surprising. This is due to contact structure and age specific infection risk. This has been shown by Rohani et al 2010, Skoff et al 2015, Bento et al 2018.

Discussion:

Line 230- again what do you mean by “reliable denominators”

Line 237 – I don’t understand this sentence. Revise it.

Line 244- Are you assuming all individuals in your study have been vaccinated? Otherwise this sentence needs revision… Remember that individuals in older age categories (+65) were for the most part not vaccinated. Also the ones younger than 1 many of these cases are kinds younger than 2 mo.

Line 281- your point here is relevant and pertinent. What could be driving these differences prior to puberty?

Also, how about difference investments by sex in terms of reproduction vs immunity?

**********

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Reviewer #1: Yes: Christopher J Gill

Reviewer #2: No

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20 Feb 2020

PONE-D-19-34094

A multi-country, multi-year, meta-analytic evaluation of the sex differences in age-specific pertussis incidence rates

PLOS ONE

Journal Requirements:

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

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

Response: We will structure the article in accordance with PLOS ONE's style requirements.

Comment: At this time, we ask that you please provide the dates on which the original search was performed in the Methods section of your manuscript.

Response: Now we provided this information in the Methods section.

Comment: PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ

Response : I created the new ORCID iD and validated in Editorial Manager.

Comment: Please include a separate caption for each figure in your manuscript.

Response: Done. Captions for each figure are part of RESULTS section.

Response to reviewers

Many thanks to the reviewers for their important and constructive reviews. We have attempted to address all their comments and have corrected the manuscript accordingly. Below are the details of our responses to the reviewers’ comments

Reviewers' comments:

Reviewer's Responses to Questions

Reviewer 1

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: Yes

________________________________________

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

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

3. Comment: 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: No

Response: all raw data underlying the findings described in the manuscript fully available in the table number 1. Due to the large amount of data for presentation purposes group of years together were used.

If needed, we will make available all the data by each calendar year for each country separately and upload as a supplementary table (as a part of The PLOS Data policy requirement).

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: No

Response: Done. Grammatical errors are corrected.

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: Review of Peer et al PLOS ONE 2020

The authors present a meta analysis of public health surveillance data from 9 countries over a 27 year span to address a persistent question within the pertussis research community: is pertussis more common among females than males? The answer appears to be yes.Overall, I was very pleased to see this paper and found the methodology and results to be persuasive. Overall, the paper would benefit from a final review to correct small grammatical errors in case and tense, but these were of minor concern.

I had several more substantive comments that I think could be addressed quite easily in a revision as follows:

Comment 1:

I think that the background section in the abstract does not really get to the main point of the paper very clearly. Yes, sex differences could be an important clue to pathogenesis or exposure risk, but that’s not the starting point for this analysis so much as the relevance of the finding (if confirmed) on the back end. It seems odd to start with justifying the relevance of the finding before confirming that the finding is correct. I would instead start, “Numerous studies over the years have suggested pertussis is more common in females than males, but the quality of these studies and risk for selection biases, make it less clear whether a true sex difference exists. To address this question, we conducted a meta analysis of prospectively collected surveillance data from 9 countries over a 27 year span.”

Response: this part of abstract revised again and rewritten.

Comment 2:

Lines 48-49, maybe simplify sentence to state that pertussis can be mild, severe or potentially fatal, with severe and fatal infections concentrated among infants.

Response: this sentence is simplified and rewritten.

Comment 3:

Line 66, sentence ends abruptly and without resolution, ‘Facilities and mandatory.’ I assume what you meant is that these data were generated in settings with mandatory reporting requirements for certain infectious diseases, including pertussis.

Response: this sentence is rewritten

Comment 4:

Incidence rates imply time. None of your results report as events/100,000 per unit time. Presumably the incidence rates are per year, but this needs to be stated throughout and in the tables. Please add that dimension to make these rates, not proportions.

Response: incidence rates produced are annual incidence rates(We stated it in the Statistical Analyses section)

Comment 5:

Table 1. Data from Czech republic stand out with very low reported events. This makes me doubt the validity of the Czech data more than it makes me suspect that pertussis is really so different there than elsewhere. Please comment. Should these data be included? I am skeptical of them.

Response: We agree with you and understand the problem. We carried out the sensitivity analysis to determine whether the effects we observed were affected by one specific country and did not find this to be so. We believe that very low reported events in Czech Republic are not a source of selection bias and the low rates are unlikely to be different for females and males.

Comment 6:

Figures are NOT labeled. There is no figure legend either. Please label the figures. Please provide a legend. Also, quality of figures is very poor. Please upload hi definition Tiff figures.

Response: All figures are labeled and figures legend will be provided. All labeled figures will be uploaded as TIFF files.

Comment 7:

I see no value to the funnel plots. These are used to detect publication bias in the scientific literature. But your data are systematically collected, nationally representative, longitudinal infectious disease surveillance data and are not published in the usual sense when doing a meta-analysis. Personally, I think the funnel plots and Egger’s test play no useful role in this context and should be dropped as irrelevant and more likely misleading.

Response: funnel plots provide a further examination of whether there are outlier countries related to the size of the incidence RR

Comment 8:

Line 233, authors assert that the 9 countries had similar socioeconomic status. That is really debatable. The Czech republic in 1990 was not comparable in wealth the UK or Canada at that time. Similarly, the data from New Zealand and Australia will be dominated by pertussis events in the aboriginal and Maori populations, who tend to be at the very bottom of wealth quintiles. Basically, I reject your assertion.

Response: We totally accept your comment. We mean that we included countries with advanced health system and facilities. This sentence is rewritten

Comment 9:

Not sure of the relevance of citation 9. No one is arguing that more males than females get pertussis.

Response: We think it is still important to show that there have been publications suggesting the opposite. In fact, in most studies of other infectious diseases, males have higher morbidity. In our study on viral meningitis we showed that the higher incidence rates from viral meningitis in males under the age of 15 is remarkably consistent across countries and time-periods (Peer V, Schwartz N, Green MS. Consistent, Excess Viral Meningitis Incidence Rates in Young Males: A Multi-country, Multi-year, Meta-analysis of National Data. The Importance of Sex as a Biological Variable.EClinicalMedicine. 2019;15:62-71)

Comment 10:

The hormone hypothesis is intriguing and plausible. One facet of your data that supports this is the relatively small F:M gradient among infants compared with all older age groups. That is notable because estrogen levels in a baby are largely reflective of maternal estrogens, and hence tend to be more similar between boy and girl babies during early infancy. As endogenous estrogens rise, the F:M gradient increases. That seems like an important point to buttress your overall argument.

Response: Hormones from the mother pass through the placenta into the baby's blood during pregnancy. Pregnant women produce high levels of the hormone estrogen that can affect the baby.

By the second week after birth, hormones are no longer present in the infant.

(Gevers EF, Fischer DA, Dattani MT. Fetal and neonatal endocrinology. In: Jameson JL, De Groot LJ, de Kretser DM, et al, eds. Endocrinology: Adult and Pediatric. 7th ed. Philadelphia, PA: Elsevier Saunders; 2016:chap 145)

Reviewer #2: General comments:

Comment 1:

I enjoyed reading this ms. Looking at sex specific differences is definitely important, due to the differences the authors were able to identify. For this reason, a figure of the time series for each country by sex would have given the reader a good motivation for the study. I recommend plotting that. This would then be followed by the RR analysis.

Response:We considered the option of a time series analysis, but after consultation felt it would not contribute to the main issue of the paper, since there were no time-related effects.

There were a few things however that I suggest revising. Please see below.

Comment 2:

Introduction:

Line 46 you needs to add references to the possible hypotheses, also you aren’t listing all possible hypotheses so I recommend using “for example” or “among others”

Response: We rewrote this sentence and added the relevant references.

Comment 3:

You need to italicize Bordetella pertussis

Response: Done

Comment 4:

Line 47-48 do you mean manifestations of “disease”? Disease and infection are two different things.

Response: We mean ''disease''. This sentence is rewritten.

Comment 5:

Line 50 The reports aren’t controversial, some of the assumptions are sometimes controversial. I advise revising this sentence.

Response: This sentence is rewritten.

Comment 6:

Also Skoff et al was a robust study and seminal in understanding age specific transmission, so I believe it warrants less inflammatory remarks.

Response: We mentioned Skoff et al as robust study in our manuscript.

Comment 7:

Line 53- you are forgetting social contacts, which have shown to be a major driver in risk.

Response: This sentence is rewritten and reference is added

Comment 8:

Line 57 Are you sure you mean mechanisms of infection or transmission dynamics?

Response: This sentence is rewritten

Comment 9:

Line 59 – what are these reliable denominators (odd way to finish the introduction) please expand.

Response: This sentence is rewritten

Comment 10:

Material and methods:

Line 64-65- what do you mean by sophisticated lab facilities?

Response: This sentence is rewritten

Comment 11:

Line 66-7- Are you making the data you gathered available?

Response: Yes, all the data is available

Comment 12:

Are these country data resolved by sex?

Response: Yes, all the data in these countries is resolved by sex

Comment 13:

Line 87-89- I think you can remove this

Response: We think it's important to clarify the issue of Ethical considerations

Comment 14:

Line 120 “used”

Response: Done

Comment 15:

Results: Line 239: you mean x axis, right?

Response: This error is corrected.

Comment 16:

What are the grey squares? They make it very hard to look at the variation between years.

Response: This is the software default

Comment 17:

While I understand the choice of blocks of 5 years, I wonder if you should have an analysis specific to epidemic years. For instance, in England 2012 was an outbreak year. In the “leave one out” “ you should actually consider specific years rather than blocks of years.

Response: To evaluate the effect of this particular year on the incidence of pertussis, we performed leave-one-out sensitivity analysis by single year and recomputed the pooled RRs. The pooled RRs calculated after leave-one-out sensitivity analysis by single year didn't add to the strong results. We don’t have any reason to believe that the results would differ for years with higher incidence rates.

Comment 18:

Line 153 - you need a space after Fig 3

Response: Done

Comment 19:

For table 3 – You should either use individual years or blocks of years. In some countries you have 2015 alone. This is confusing. In blocks of years you might be getting rid of between year variation, especially if you think of epidemic years.

Response: We agree that some countries database differ in reported years, but have a common period.

In the beginning of results processing we performed the meta-analysis by single year (for every age group and country).it's impossible to perform them in the manuscript as a plot because of huge amount of the data). Year's series allowed us to perform the meta-analysis (forest plots) in appropriate way. Of course, if there are only 2015 data, then the result applies to this particular year.

Table 3 displays the pooled RR per age group for all of the reported years and countries and for the common period as well.

To evaluate the effect of reported year on the incidence of pertussis, we performed leave-one-out sensitivity analysis by single year and recomputed the pooled RRs. The estimated pooled RRs calculated after leave-one-out sensitivity analysis by single year didn't show differences from the primary values of time series, thus no single country or period of time affected the pooled RRs.

Comment 20:

Also for instance in the England data set, after 2004 there were some change in reporting this can be a source of further confusion.

Response: We think that, even the reporting system has changed in England it has not created a selection bias, which would affect differently males and females.

Comment 21:

Line 196 - So the fact that age group contributes almost all the variation is not surprising. This is due to contact structure and age specific infection risk. This has been shown by Rohani et al 2010, Skoff et al 2015, Bento et al 2018.

Response: there are references in the manuscript that address this point.

1. Skoff TH, Hadler S, Hariri S. The epidemiology of nationally reported pertussis in the United States, 2000–2016. Clin Infect Dis. 2019; 68: 1634– 40.

2. Wensley A, Hughes GJ, Campbell H, et al. Risk factors for pertussis in adults and teenagers in England. Epidemiol Infect. 2017;145:1025–36.

Comment 22:

Discussion:

Line 230- again what do you mean by “reliable denominators”

Response: This sentence is rewritten

Comment 23:

Line 237 – I don’t understand this sentence. Revise it.

Response: This sentence is rewritten

Comment 24:

Line 244- Are you assuming all individuals in your study have been vaccinated? Otherwise this sentence needs revision… Remember that individuals in older age categories (+65) were for the most part not vaccinated. Also the ones younger than 1 many of these cases are kinds younger than 2 mo.

Response: This sentence is rewritten.We assume that only the part of population, especially infants and children, is vaccinated. The majority of adults in older ages are not vaccinated.

Comment 25:

Line 281- your point here is relevant and pertinent. What could be driving these differences prior to puberty?

Response: This part is rewritten.

Markedly higher concentrations of estrogen were measured in prepubertal girls as compared to prepubertal boys in whom most values were below the detection limit. Thus, even before any physical signs of pubertal maturation, girls had significantly higher estrogen concentrations compared to boys. (Frederiksen H, Johannsen TH, Andersen SE, Albrethsen J, Landersoe SK, Petersen JH, Andersen AN, Vestergaard ET, Schorring ME, Linneberg A, Main KM, Andersson AM, Juul A. Sex-specific estrogen levels and reference intervals from infancy to late adulthood determined by LC-MS/MS.J Clin Endocrinol Metab. 2019 )

Comment 26:

Also, how about difference investments by sex in terms of reproduction vs immunity?

Response: We addressed the issue of pregnancy in the final part of the discussion. We assume that 5 -10 % of women in the age group 15-44 are pregnant.

Placental immune response for specific viruses and pathogens affect the pregnant woman’s susceptibility to and severity of certain infectious diseases. The generalization of pregnancy as a condition of general immune suppression or increased risk is misleading.( Mor G, Cardenas I. The immune system in pregnancy: a unique complexity. Am J Reprod Immunol. 2010; 63:425-33)

There is growing evidence that the type of response initiated by the placenta might determine the immunologic response of the mother . It is now clear, the placenta represents important immune modulator that affect the global response of the mother to microbial infections. (Racicot K, Kwon JY, Aldo P, Silasi M, Mor G. Understanding the complexity of the immune system during pregnancy. Am J Reprod Immunol. 2014 ;72: 107-16).

Additional amendments from 20-02-2020:

PONE-D-19-34094R1

A multi-country, multi-year, meta-analytic evaluation of the sex differences in age-specific pertussis incidence rates

Thank you for submitting your manuscript entitled "A multi-country, multi-year, meta-analytic evaluation of the sex differences in age-specific pertussis incidence rates" to PLOS ONE. Your manuscript files have been checked in-house but before we can proceed we need you to address the following issues:

1) Please ensure that you refer to Figure 8 in your text as, if accepted, production will need this reference to link the reader to the figure.

Answer: We referred to Figure 8 in the text

2) Please amend your list of authors on the manuscript to ensure that each author is linked to an affiliation.

We note that you have included affiliation numbers 1,¶ and * however only affiliations 1 has authors linked to them. Please amend affiliation 4 to link an author to it or remove if added in error.

Answer: We amended the list of authors and affiliation on the title page

3) Please allow me to provide more insight into how much of your data you are meant to publicly share and what exactly constitutes a minimal data set or underlying data.

We require authors to share the “minimal data set” for their submission. PLOS defines the minimal data set to consist of the data required to replicate all study findings reported in the article, as well as related metadata and methods. Additionally, PLOS requires that authors comply with field-specific standards for preparation, recording, and deposition of data when applicable. For example, authors should submit the following data:

1) The values behind the means, standard deviations and other measures reported;

2) The values used to build graphs;

3) The points extracted from images for analysis.

Authors do not need to submit their entire data set if only a portion of the data were used in the reported study. Also, authors do not need to submit the raw data collected during an investigation if the standard in the field is to share data that have been processed.

Answer: All relevant data are within the manuscript.

Table number 1 contains all the minimal data used for the study ( for meta-analysis).

On this table we performed all the data (“minimal data set” for all reported years together) about pertussis cases by sex (cumulative total n), total population by sex (cumulative total N), calculated incidence rate per 100 000 male or female population, and incidence rate ratio (female: male incidence rate ratio).All this data required for study findings replication.

If needed we could share the all raw data by single year and sex for each country (pertussis cases by sex n by single year and total population by sex N by single year).

Additionally, we require authors to provide sample image data in support of all reported results (e.g. for immunohistochemistry images, fMRI images, etc.), either with the submission files or in a public repository.

Answer: We have no any other image data in support of our findings

At this time, please confirm that the minimal data set are within your paper.

Answer: We confirm that the minimal data set are within the paper.

Submitted filename: Amendments.docx

Click here for additional data file.

16 Mar 2020

PONE-D-19-34094R1

A multi-country, multi-year, meta-analytic evaluation of the sex differences in age-specific pertussis incidence rates

PLOS ONE

Dear Dr Victoria Peer,

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 following minor point to ensure clarity of reporting. In the manuscript text, the authors frequently describe their study using the term 'meta-analysis', we suggest to replace this term by 'meta-analytic methods' to describe their analyses.

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We look forward to receiving your revised manuscript.

Kind regards,

Daniela Flavia Hozbor

Academic Editor

PLOS ONE

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

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Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: All comments have been addressed

**********

2. 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

**********

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

Reviewer #1: Yes

**********

4. 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

**********

5. 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

**********

6. 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: Re. comment 10. While it is true that maternal estrogens disappear from fetal circulation within a few weeks of birth the biological effects of the in utero estrogen exposure will linger for months. Hence my comment about the reduced M:F gradient among infants still stands as a plausible explanation. And as I stated before, I believe that this strengthens your argument.

**********

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Reviewer #1: Yes: Christopher Gill

[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.]

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22 Mar 2020

PONE-D-19-34094R1

A multi-country, multi-year, meta-analytic evaluation of the sex differences in age-specific pertussis incidence rates

PLOS ONE

Dear Dr Victoria Peer,

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 following minor point to ensure clarity of reporting. In the manuscript text, the authors frequently describe their study using the term 'meta-analysis', we suggest to replace this term by ' meta-analytic methods ' to describe their analyses.

Response:

Dear Dr.Daniela Flavia Hozbor,

We replaced the term 'meta-analysis' with term 'meta-analytic methods' throughout the article.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

________________________________________

2. 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

________________________________________

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

Reviewer #1: Yes

________________________________________

4. 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

________________________________________

5. 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

________________________________________

6. 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: Re. comment 10. While it is true that maternal estrogens disappear from fetal circulation within a few weeks of birth the biological effects of the in utero estrogen exposure will linger for months. Hence my comment about the reduced M:F gradient among infants still stands as a plausible explanation. And as I stated before, I believe that this strengthens your argument.

Response: We completely agree with the reviewer and think that the reviewer's comment may be another possible mechanism and explanation for the results. We have now added that to the discussion on lines 289-295 in the revised manuscript.

________________________________________

7. 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: Yes: Christopher Gill

[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.]

Submitted filename: Response to Reviewers.docx.pdf

Click here for additional data file.

27 Mar 2020

A multi-country, multi-year, meta-analytic evaluation of the sex differences in age-specific pertussis incidence rates

PONE-D-19-34094R2

Dear Dr. Victoria Peer,

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.

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With kind regards,

Daniela Flavia Hozbor

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

10 Apr 2020

PONE-D-19-34094R2

A multi-country, multi-year, meta-analytic evaluation of the sex differences in age-specific pertussis incidence rates

Dear Dr. Peer:

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.

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Thank you for submitting your work to PLOS ONE.

With kind regards,

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on behalf of

Dr. Daniela Flavia Hozbor

Academic Editor

PLOS ONE