Dengue epidemiology in selected endemic countries: factors influencing expansion factors as estimates of underreporting.

Tropical Medicine and International Health

doi:10.1111/tmi.12498

volume 20 no 7 pp 840–863 july 2015

Review

Dengue epidemiology in selected endemic countries: factors influencing expansion factors as estimates of underreporting* Nguyen T. Toan1, Stefania Rossi2, Gabriella Prisco3, Nicola Nante4 and Simonetta Viviani4 1 2 3 4

Clinical Research Unit, Pasteur Institute, Ho Chi Minh City, Vietnam Department of Molecular and Developmental Medicine, University of Siena, Siena, Italy Postgraduate School of Public Health, University of Siena, Siena, Italy Department of Molecular and Developmental Medicine, Postgraduate School of Public Health, University of Siena, Siena, Italy

Abstract

objective Dengue fever is globally considered underestimated. This study provides expansion factors (EFs) for dengue endemic selected countries and highlights critical issues in the use of EFs. methods We identified dengue epidemiological cohort studies from 2000 to July 2013 through a literature search using PubMed, Web of Science and Lilacs (Latin American and Caribbean Health Sciences Database), pre-defined keywords and inclusion/exclusion criteria, and included Brazil, Colombia, Nicaragua, Peru, Puerto Rico, Venezuela, Bangladesh, Cambodia, India, Indonesia, Philippines, Singapore, Sri Lanka, Thailand and Vietnam. Dengue national and local passive surveillance data were derived from WHO regional websites, PAHO, SEARO and WPRO. EFs were calculated as CI cohort studies/CI passive data for both national and local levels. results Cohort studies differed in case definition, laboratory test used and surveillance methods. The information on SEARO, PAHO and WPRO websites differed in terms of dengue epidemiological variables, population denominators and completeness. The highest incidence was reported by PAHO countries followed by WPRO and SEARO countries. EFs may vary for the different variables and denominators used for calculation. EFs were the highest in SEARO countries and lowest in PAHO countries. A trend for lower local EFs was observed. conclusions The use of EFs for quantifying dengue underreporting may be problematic due to lack of uniformity in reporting dengue both active and passive surveillance data. Quality dengue surveillance data are urgently needed for a better estimate of dengue disease burden and to measure the impact of preventive intervention. keywords dengue, epidemiology, endemic countries, underreporting, expansion factors

Introduction Dengue fever (DF) is caused by infection with the dengue virus (DENV), a RNA virus that occurs as four recognised serotypes: DENV-1, DENV-2, DENV-3 or DENV-4, which belong to the Flavivirus genus of the virus family Flaviviridae. These viruses are transmitted in humans by mosquitoes (primarily Aedes aegypti). Infection with a DENV can result in a range of symptoms, from subclinical disease to debilitating but transient dengue fever (DF) to life-threatening dengue hemorrhagic fever (DHF) or dengue shock syndrome (DSS) [1, 2].

*Full free access from www.tmih.com.

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The most severe forms of dengue disease – DHF and DSS – are life threatening, and children with DENV infection are particularly at risk of progressing to severe dengue (DHF/DSS) [2, 3]. There is no specific treatment for dengue. Supportive care includes control of fever and pain with antipyretics/analgesics and adequate fluid intake. In countries with case management programmes in place, the case fatality rate can be reduced to close to zero [4]. Routine laboratory diagnosis of dengue infections is based on one or more of the following: the detection of DENV-specific antibodies (by plaque reduction neutralisation test, ELISA IgM or IgG); virus isolation; detection of viral RNA by quantitative reverse transcription polymerase chain reaction (qRT-PCR); or detection of viral non-structural protein (NS1) antigen by enzyme-

© 2015 John Wiley & Sons Ltd



N. T. Toan et al. Expansion factors for dengue

linked immunosorbent assay (ELISA) [1–3]. The geographical spread of both the mosquito vectors and the viruses has led to a global resurgence of epidemic DF and emergence of DHF in the past 25 years, with the development of hyperendemicity in many urban and periurban centres of the tropics. Dengue is hyperendemic in Asia, the Pacific area and Central and South America (including the Caribbean) [5–7]. However, dengue is considered globally underreported: with 2.5 billion people are at risk of a dengue infection, at least 50 million people infected, >500 000 cases of DHF and approximately 22 000 deaths annually are likely attributable to dengue (available at http://www.cdc.gov/Dengue/. Accessed 23 July 2014) [3, 6]. Routine surveillance performed through notification of dengue cases by healthcare providers and ‘enhanced surveillance’, a form of surveillance usually in response to an epidemic alert [2], are in place in most dengue endemic countries to detect dengue fever. In the last decade, ‘enhanced surveillance’ has been used more generally to increase dengue awareness [8]. However, data reported through this system are considered to underestimate the true DF incidence [3, 6]. DF incidence estimates can also be derived from ad hoc epidemiological studies (active surveillance) that over the past 30 years have been conducted in some of the dengue high-endemic regions [9–11]. These studies provide the best estimate of the epidemiology of dengue and show how the sensitivity of a surveillance system can be improved by conducting active surveillance [12, 13]. WHO headquarters (HQ) and WHO regional offices [1, 2, 14, 15] issued a number of guidelines and recommendations for reporting dengue fever that are important to consider as dengue is endemic in three WHO regions: WHO South-East Asia Regional Office (SEARO), WHO Western Pacific Regional Office (WPRO) and the Pan American Health Organization (PAHO). Although recent estimates for the African region show that dengue is endemic in many parts of the continent [6], Africa was not included in this review for scarcity of systematic data collection. The lack of reliable and precise data on dengue incidence renders problematic the true estimate of the disease burden, that is the health, social and economic impact for the population affected by dengue [2, 3]. Recent studies [16–18] gave quantitative estimates of the degree of underreporting (expansion factor, EF) in the Americas and South-East Asia, and in 2013, a report with EFs for the Americas and South-East Asia was published as the result of a collaboration between Malaysian Universities and the Ministry of Health of Malaysia [19, 20]. Officially reported dengue episodes need to be adjusted for underreported to obtain a more accurate estimate of


the number of apparent dengue episodes occurring in a specific population. These adjustment factors, sometimes called expansion factors (EFs), are ideally based on empirical data (e.g. cohort or capture–recapture studies), but have been also obtained from expert opinion [21]. The EF is calculated as the ratio of ‘the best estimate of the number of cases and reported number of cases in a specific population in one year’, where ‘an EF=1 indicates perfect reporting of dengue and EF>1 represents the degree of underreporting’ [19]. The aim of this study was twofold: first, to compare dengue epidemiological data reported through the national and local routine surveillance systems and data from ad hoc epidemiological studies performed in dengue endemic selected countries from PAHO, SEARO and WPRO regions and, second, to identify critical factors able to influence the calculation of the EFs as an estimate of the extent of underreporting.

Methods Search strategy and selection criteria We performed a literature review using PubMed, Web of Science and Lilacs (Latin American and Caribbean Health Sciences Database) using the research categories ‘Dengue diseases’, ‘Active and Passive Surveillance’ and ‘Selected endemic countries’ (Brazil, Colombia, Nicaragua, Peru, Puerto Rico, Venezuela, Bangladesh, Cambodia, India, Indonesia, Philippines, Singapore, Sri Lanka, Thailand, Vietnam) with the terms ‘dengue’, ‘burden’, ‘incidence’, ‘cohort’, ‘outpatient’ and ‘active surveillance’. The search period was January 2000 to July 2013, and we considered publications in English, French, Spanish and Portuguese. We also reviewed bibliographies of pertinent articles and searched for relevant unpublished studies in the grey literature databases: Systems for Information in Grey Literature (SIGLE), EThOS – Electronic theses online service, Inside Conferences and Dissertation Abstracts. We included studies designed as cohort prospective (community based or school based) or designed as capture–recapture (community based or school based) and studies having active data of dengue incidence referred from 2000 to 2013. We excluded data derived from estimate or modelling studies, studies not reporting case definition or laboratory confirmation, studies recruiting suspected or confirmed cases, entomological surveillance data or health economic data, studies related to clinical findings or viral molecular characterisations, and data not referred to selected endemic countries (Thailand, Vietnam, Singapore, Philippines, India, Sri Lanka, Bangladesh, Indonesia, Venezuela, Nicaragua, Puerto Rico, 841




Colombia, Brazil, Cambodia, Per u). Two investigators (SR and GP) independently reviewed the title and the abstracts of the articles identified using the search strategies described and retained those articles that met the inclusion/exclusion criteria. Disagreements were resolved through discussions with a third investigator (SV). Statelevel passive data reported to WHO Regional Offices, PAHO, SEARO and WPRO, were accessed at the respective websites (available at http://www.paho.org/hq, http://www.searo.who.int/en, http://www.wpro.who.int/ en. Accessed 25 July 2013) for the same time period. As our aim was to calculate EFs also for the local level, to retrieve local dengue routine surveillance data for each selected country, a number of national organisations website were consulted: MoH/MoHp, National Statistical Office/Dept of Statistics, Bureau of epidemiology, Lardidev and DengueNet (available at www.moh.gov.kh/, www.minsa.gov.pe/; www.minsalud.gov.co; www.mpps.gob.ve; www.moph.go.th; www.fundabiomed.fcs.uc.edu.ve/lardidev.html; www.who.int/csr/disease/ dengue/denguenet/en/. Accessed 10 February 2014). Expansion factor calculation EFs were calculated as CIcohort studies/CIpassive data for both national and local level and for each year. In addition, we calculated lEFs to explore factors that can potentially influence EFs such as laboratory confirmation of cases, incidence type [cumulative or incidence density (ID)], cases from urban or rural area, age group of cases and collection of cases during the dengue season or annually. Epidemic years To identify the ‘epidemic years’, we calculated the mean of the annual number of dengue cases in the studied period for each given country and then defined up to three epidemic years for a given country as those corresponding to the years which reported the highest peaks of dengue cases above the mean.

Results Literature search of cohort surveillance studies (1 January 2000–25 July 2013) All cohort studies identified during the study period and included in this analysis according to the pre-defined inclusion/exclusion criteria were cohort prospective studies (Table 1) [8, 22–36]. Secondary publications of the main study were also included [37–47]. 842

The studies show some degree of variability with respect to (i) incidence measures; (ii) age of subjects enrolled in the cohorts; (iii) active surveillance strategies; (iv) specific area of the country where incidence of dengue is measured, that is rural, urban, or rural and urban combined; (v) laboratory assays for case confirmation; and (vi) annual or seasonal dengue cases reporting (Table 1). Some studies calculated cumulative incidence (CI) or provided information to calculate it, while others reported the ID with different measure units: person-year, person-season and person-days. Moreover, different denominators of CI were used: ‘cohort size at the end of surveillance season’ [36], ‘all subjects active in the cohort for at least half of the study year, whether or not an annual sample was taken at the end of the year’ [33], ‘total population under study, with paired annual samples available’ [22], ‘cohort of schoolchildren at the end of the study, who gave a complete set of three consecutive samples (one baseline and two follow-ups)’ [26], ‘total number of people who began the study’ [28]. With the purpose of obtaining uniformity of incidence measures, for each study, we calculated the CI expressed as the number of confirmed symptomatic cases/population cohort size. For one study in Thailand (Ratchaburi) [32], the original ID reported in the article was used, as no information was provided for CI calculation. Most cohorts were composed of children and/or adolescents, only one included infants and toddlers (2– 15 months) [28]. The majority of studies adopted a community-based or school-based surveillance strategy, while enhanced community-based surveillance was used only in two studies in Nicaragua and Puerto Rico [8, 33]. A study in Indonesia was based on sentinel active community surveillance [9], that is ‘the study of disease rates in a specific cohort, geographic area or population subgroup’ [2]. In Peru, both school-based and community-based surveillance were used in the same time period for the same population [31], whereas school-based and community-based surveillance were used in Vietnam for the same cohort, but during different seasons [34], i.e. during, respectively, school term (September–May) and school vacation (June-August, peak dengue season). The studies employed different strategies in identifying and sampling febrile cases. In school-based studies, the identification of the febrile cases corresponded to the days of school absenteeism. For example, in Nicaragua, ‘the resource constraints permitted the study nurse to visit the absent child’s house only on the third day of absence’ [22], while in Thailand, in Ratchaburi, active surveillance for school absence was conducted daily [32]. Otherwise, community-based studies appeared to be uniform with respect





Table 1 Summary of active dengue surveillance: prospective cohort and capture–recapture studies

PAHO countries Colombia‡

Nicaragua‡


Active surveillance for febrile detection in prospective cohort (Study population, age)

Cumulative incidence – CI* (Incidence density – ID)†

School based (5–19 years old) Dengue season cases – urban area: Students from primary and secondary schools in Medellin (three different sites: districts of San Javier, Poblado and Laureles); cases were identified by school absenteeism due to a febrile episode of less than 7 days of duration A study nurse visited each school every 4 days. If the schoolchild met the inclusion criteria, a study physician performed a complete medical examination A clinically suspected dengue episode with virological or serological confirmation. Specific IgM antibodies detected by DEN IgM capture ELISA. Detection of dengue virus RNA by RTPCR School based (4–16 years old) Annual cases – urban area: Daily monitoring school absenteeism Monitoring fever at school by study nurse at third day Self-approach by subject when they have got fever Children with fever and dengue-like symptoms were identified as suspected cases of dengue. Acute (first day of identification) and convalescent phase serum samples were collected. Reverse transcriptase polymerase chain reaction (RT-PCR) and virus isolation were performed on acute phase samples Community-based enhanced surveillance (2–9 years old) Annual cases* – urban area: Household visit by study team, using GPS device Febrile children approach health centre/ hospital Dengue detection and classification into four categories (A, B, C, D) Confirmed case by IgM/IgG MAC-ELISA (ME), VI, RT-PCR, HI Seroconversion test: IgM/IgG ME Confirmed case by IgM/IgG MAC-ELISA (ME), VI, RT-PCR, HI Seroconversion test: IgM/IgG ME59,75,94,545 *Note: ‘Annual incidence and expansion factors were calculated in relation to the annual dengue season, beginning in July of each year’.

May–December 2010: CI = 5.9/103 (14/2379) June–December 2011: CI = 4.9/103 (9/1840)

May 2001–May 2002: CI Symptomatic= 8.5/103 (4/467) CI Total = 118/103 (55/467) May 2002–May 2003: CI Symptomatic =8.3/103 (6/719) CI Total 57/103 (41/719)

August 2004–June 2005: CI symptomatic = 4.6/103 (17/3713) CI total = 86/103 (319/3713) August 2005–June 2006: CI symptomatic = 17.6/103 (65/3698) CI total = 111/103 (409/3689) August 2006–June 2007: CI Symptomatic = 3.6/103 (13/3563) CI total = 58/103 (207/3563) August 2007–June 2008: CI Symptomatic = 17.4/103 (64/3676) CI total = 73/103 (268/3676)

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Table 1 (Continued)

PAHO countries Peru

Puerto Rico§

844



School-based surveillance (5- to 17-year-old children and their adult family members, all age) Annual cases – urban area: Monitoring school absenteeism in school term and weekly home visit (from one to three times per week) in school holiday Confirmed case by virus isolation, RT-PCR, IgM/IgG MAC-ELISA (ME). Seroconversion: PRNT Community-based surveillance (All age) Annual cases – urban area: Door-to-door febrile illness surveillance programme. Household visit three times per week to detect febrile illness Hospitalisation in suspected dengue case Two blood samples (acute and convalescent) Active dengue cases were identified by viral isolation, IgM serology (elevated IgM antibody titres > 1:400) in the acute sample, convalescent sample or both), or a fourfold rise in IgG antibody titres between acute and convalescent samples (ELISA) Community-based, enhanced surveillance (All age) Annual cases – urban area: Active case finding by support healthcare provider’s identification and reporting of symptomatic dengue cases among residents Confirmed case by RT-PCR, IgM MAC-ELISA (ME). Seroconversion by MN or quantitative IgG ELISA in case PCR (+) or IgM seroconversion A prospective follow-up study in a cohort of schoolchildren (5–13 years old), who gave a complete set of three consecutive samples (one baseline and two follow-ups) Dengue Season cases – urban area: ‘We assumed that the cohort was representative of the population at risk for contracting dengue, which state surveillance data showed to be 5–13year-old children and adolescents’ (LARDIDEV, unpublished observations) Consecutive samples were assayed by PRNT

January 2000–August 2004: CI = 26.1/103 (annul mean data)

April 2004–December 2005: School based (5–17 years) CI = 9.7/103 (11/1135) ID = 12.9/103 person-years Community based (5–17 years) CI = 16.3/103 (25/1537) ID = 23.5/103person years Community (0–98 years) CI = 11.5/103 (56/4850) ID = 17.1/103 person-years

June 2005–May 2006: CI = 7.7/103 (156/20 152)

May–November 2002: CI = 25.8/103 (183/710) May–November 2003: CI = 16.9/103 (120/710)





Table 1 (Continued)

PAHO countries Venezuela§

SEARO countries Indonesia

Thailand




Community based (5–94 years old) In this study, active surveillance was incorporated as a part of the prospective study design. The participants’ houses were visited three times a week A biannual blood sample was taken for each study participants, to establish the prevalence and 6-month incidence of dengue infection Dengue season cases – urban area: Confirmed case by IgM/IgG MAC-ELISA (ME), PRNT, RT-PCR

September–December 2007: CI = 18.7/103 (47/2509)

Active surveillance for febrile detection in prospective cohort (Study population, age) Sentinel active community surveillance (18–66 years old) Annual – urban area: Factory personal office notifies the event (failed to show up for work) Physician evaluation Mobile team to contact 24 h when absenteeism Confirmed case by IgM/IgG MACELISA (ME), RT-PCR, VI; Seroconversion by HI and PRNT Prospective, multicentre, active fever surveillance, cohort study (community, schools, health centres and/or private health clinics, depending on each study site’s setting). Children 2–14 years old Annual cases – rural and urban areas: Laboratory-confirmed dengue: NS1 positive. Probable dengue: IgM positive and/ or fourfold rise in IgG Prospective, multicentre, active fever surveillance, cohort study (community, schools, health centres and/or private health clinics, depending on each study site’s setting. Children 2–14 years old Annual cases – rural and urban areas: Laboratory-confirmed dengue: NS1 positive Probable dengue: IgM positive and/ or fourfold rise in IgG

Cumulative incidence – CI* (Incidence density – ID)† 2000–2002: ID Symptomatic = 18/103 person-years ID Asymptomatic = 56/103 person-years CI = 37.9/103 (90/2374)

June 2010–July 2011: CI = 35.9/103 (16/446)

June 2010–July 2011: CI = 20.1/103 (6/299)

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Table 1 (Continued) Active surveillance for febrile detection in prospective cohort (Study population, age)

SEARO countries Thailand Kamphaeng Phet

School-based surveillance from Jun to Nov (4–15 years old) Dengue season cases – urban area: School absenteeism report and then home visit by village health worker (four persons in mobile team) Confirmed case by IgM/IgG MACELISA (ME), RT-PCR, VI, HI

Thailand Ratchaburi‡

School-based surveillance (3–14 years old) Annual cases – rural area: Self-reporting febrile episode at home and school absenteeism report. Project field coordinator visit household, two times per week in vacation Confirmed case by IgM/IgG MACELISA (ME), RT-PCR, VI

WPRO countries Cambodia†,‡ Kampong Cham

2000 June–November: CI = Total 21.6/103 (37/1713) CI = Sympt 7.6/103 (13/1737) CI = Inapp. 14/103 (24/1713) 2004: CI = Total 59.3/103 (120/2023) CI = Sympt 16.3/103 (33/2023) CI = Inapp. 40/103 (81/2023) 2005: CI = Total 52.4/103 (106/2021) CI = Sympt 13.4/103 (27/2021) CI = Inapp. 38.1/103 (77/2021) 2006: CI = Total 98.6/103 (201/2039) CI = Sympt 44.1/103 (90/2,039) CI = Inapp. 50.5/103 (103/2039) 2007: CI = Total 63.8/103 (128/2007) CI = Sympt 19.4/103 (39/2007) CI = Inapp. 42.4/103 (85/2007) 2006 ID = 17.7/103 person-years 2007: ID = 35.8/103 person-years 2008: ID = 57.4/103 person-years 2009: ID = 32.9/103 person-years



Active community-based surveillance (0–19 years old) Mainly during the rainy season – rural and urban areas combined:

2006 May–November: CI = 13.4/103 (89/6657) ID = 13.4/103 person-season 2007 June–December: CI = 52.5/103 (530/10 086) ID = 57.8/103 person-season 2008 April–December: CI = 15.2/103 (117/7673) ID = 17.6/103 person-season

• • •

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Fever was detected by notification of trained mother or weekly visit of village team. Village team inform to investigation team Investigation teams visit household and check, take blood samples (acute and convalescent) if dengue is suspected Confirmed case by IgM/IgG, and then, RT-PCR was used when IgM (+) and VI was used when RT-PCR (+)





Table 1 (Continued)

WPRO countries Philippines§

Vietnam‡ Long Xuyen



Prospective community-based study (infants 2–15 months) Annual cases – semi-urban area: During the rainy season (June–November 2007), mothers were encouraged to bring their infants to the San Pablo City Health Office for the evaluation of outpatient febrile illnesses. Acuteand convalescent-phase (day 14) blood samples were obtained from study infants with febrile illnesses that did not have an obvious source at time of presentation. Clinical data and blood samples were collected, and surveillance was performed for symptomatic and inapparent DENV infections. Confirmed case by IgM/IgG ELISA, RT-PCR Prospective, multicentre, active fever surveillance, cohort study (community, schools, health centres and/or private health clinics, depending on each study site’s setting). (Children 2–14 year) Annual cases – rural and urban areas: Laboratory-confirmed dengue: NS1 positive Probable dengue: IgM positive and/or fourfold rise in IgG School-based and community-based surveillance, (2–15 years old) Annual cases – Urban areas:

January 2007–January 2008: CI = 9.0/103 (40/4441) 2007: ID = Total: 119/103 person-ys ID Inapparent = 103/103 person-ys ID Apparent = 16/103 person-ys ID Hospitalised ID

•

•

In school term (September–May): Daily absenteeism report at school Household visit by collaborators Hospital or mobile team’s examination Two blood samples (acute and convalescent) In school vacation (June–August, peak dengue season): Same procedure but household visit three times per week instead of daily absenteeism report Confirmed case by IgM/IgG Mac-Elisa (ME), VI. Retrospective samples were tested by NS1, real-time RT-PCR. Seroconversion by IgM/IgG MAC-ELISA (ME)


Outpatients = 16/103 person-ys

June 2010–July 2011: CI = : 20.2/103 (6/297)

Case definition A: serological or virological confirmation: (i) positive dengue virus isolation test result or (ii) IgM antidengue antibodies detected in acute or convalescent serum or (iii) an increase in antidengue IgG titre of at least fourfold between acute and convalescent sera 2004: CI = 33.3/103 (73/2194) 2005: CI = 15.7/103 (51/3239) 2006: CI = 24.5/103 (77/3146) 2007: CI = 35.4/103 (109/3081) Case definition B: virological confirmation: positive test result by either virus isolation or NS1 antigen or qRT-PCR assays 2004: CI = 24.6/103 2005: CI = 11.7/103 2006: CI = 24.7/103 2007: CI = 37.0/103

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Table 1 (Continued)

WPRO countries



Prospective, multicentre, active fever surveillance, cohort study (community, schools, health centres and/or private health clinics, depending on each study site’s setting). Children 2–14 years Annual cases – rural and urban areas: Laboratory-confirmed dengue: NS1 positive Probable dengue: IgM positive and/or fourfold rise in IgG

June 2010–July 2011: CI = 20.5/103 (3/146)

*Calculated by author when data available from article: CI = confirmed symptomatic cases/cohort size (otherwise, CI Total = asymptomatic and/or inapparent + symptomatic cases/cohort size). †ID were taken from the article, if published, whenever data for CI calculation were not available. ‡Local passive surveillance data available from article or reference of the article. §Specific study design features: Venezuela (A prospective follow-up study in a cohort of schoolchildren, 5–13 years old) and Philippines (A prospective nested case–control study of DENV infections during infancy).

to febrile case surveillance. Most of the cohort studies were conducted in urban areas, with only few exceptions in rural [32, 45] and rural–urban combined [24, 25] areas. In almost all studies, the primary endpoint was fever with laboratory confirmation for dengue. Laboratory confirmation was reached in the vast majority of studies by reverse trascriptase polymerase chain reaction (RT-PCR) with additional ELISA for IgM and IgG and plaque reduction neutralisation test (PRNT). One study [24] used the detection of non-structural protein (NS1) by ELISA [2], and another performed in Venezuela was designed as a prospective follow-up study with seroconversion to PRNT as primary endpoint [26]. In Appendix 1, dengue serotype distribution detected by active surveillance studies is reported. Almost all four serotypes circulated in the same year in the study locations, and the proportion of isolates by serotype is consistently reported. In some studies, data were collected in the dengue epidemiological season [26–28, 30, 35, 36, 47] – as in Thailand, where active case surveillance of study participants was limited to the dengue season from June to November each year [27, 36]. In other studies, data were collected throughout the calendar year [8, 22, 24, 25, 29, 31–34] (Table 1). For the purpose of calculating EFs, we considered data collected during the dengue season as representative of the calendar year. Incident cases outside the dengue season comprise no more than 5% of all cases of the year [33, 35]. We identified six previous cohort studies – one in the Philippines [25], four in the Americas (Colombia [30], Peru [31], Venezuela [26, 28]) and one in multisite locations in five Asian countries (Indonesia, Philippines, Thailand, Malaysia and Vietnam) [24]. In the period 848

considered, we did not find any publication reporting of active surveillance data for India, Sri Lanka, Bangladesh and Singapore. For Brazil, we found a cohort study [48] that did not meet our selection criteria. Another multisite active surveillance study has completed recruitment in Brazil in 2012. Results were not published at the time of this review (available at http://clinicaltrials.gov/show/ NCT01293331. Accessed 10 September 2013). Dengue cases on PAHO, SEARO and WPRO websites Dengue cases reported by selected endemic countries to the respective WHO Regional Offices during the study period 2000–2013 are published in PAHO, SEARO and WPRO websites (available at http://www.paho.org/hq/, http://www.searo.who.int/en/, http://www.wpro.who.int/ en/. Accessed 25 July 2013) and presented in Table 2. On the 25th of July 2013, PAHO data were updated at 13th of July 2013, SEARO at 31st of December 2012 and WPRO at 31st of December 2011. PAHO and WPRO countries reported incidence data on denominator 105 that we transformed in 103 as more convenient for comparison. As SEARO countries reported only absolute numbers, for each country and each year, we calculated the incidence by 103 using absolute number of dengue cases as in SEARO website and respective country population as reported in www.indexmundi.com (Accessed 31 July 2013) (2000–2001) and SEARO country population (2002–2012). SEARO selected countries did not report laboratoryconfirmed data in the period considered; PAHO countries did since 2003, and no WPRO countries reported laboratory-confirmed data except Cambodia in 2009 (Table 2).





Appendix 3 Table A3. Comparison of Dengue Incidence from active surveillance and local passive surveillance (CI-active vs CI -passive) Country

Active Surveillance

Cumulative Incidence§

LOCAL Passive#

Expansion Factor

Surveillance

(EF)^

Cumulative Incidence Clinical

Laboratory

Active/

Active/

notified

confirmed

Passive

Passive

Clinical

Confirmed

Cases (All age) PAHO COLOMBIA

2010* (5-19 years old):

CI = 5·9/103 (14/2,379)

-

-

-

-

Medellin

2011* (5-19 years old):

CI = 4·9/103 (9/1,840)

0·35/103

-

14

-

2001* (4-16 years old):

CI = 8·5/103 (4/467)

0·8/103

-

11

-

2002* (4-16 years old):

CI =8·3/103 (6/719)

0·3/103

-

28

-

3

-

22

3

-

28

3

-

14

3

NICARAGUA Managua

2004** (2-9 years old):

3

CI = 4·6/10 (17/3,713)

-

2005** (2-10 years old):

CI = 17·6/103 (65/3,698)

-

0·63/10

-

0·25/10

-

0·77/10

-

22

-

4

-

-

5

-

-

6

-

-

8

-

-

4

-

-

6

-

2006** (2-11 years old): CI = 3·6/103 (13/3,563) 2007** (2-12 years old):

CI = 17·4/103 (64/3,676) CI: 9·7/103 (11/1,135)

2004* (5-17 years):

0·21/10

2.8/103

3

ID:12.9/10 person years PERU

3

2004** (5-17 years): Dept Loreto

CI:16·3/10 (25/1,537)

2·8/10

3

ID:23·5/103 person years

(Iquitos)

CI:11·5/103 (56/4,850)

2004** (0-98 years)

2·8/10

3


VENEZUELA

2002**** (5–13 years old)

CI: 25·8 /103 (183/710)

Aragua State

2003**** (5–13 years old)

3

(Maracay)

2007*** (5–94 years old) CI: 18·7 /103 (47/2,509)

CI: 16·9/10 (120/710)

0·6/10

3

-

43

-

0·5 /10

3

-

34

-

2·3/10

3

-

8

1·6 /10

3

-

11

-

2·0 /10

3

-

18

-

-

16

-

SEARO ID: 17·7/103 person-years

THAILAND

2006* (3-14 years)

Ratchaburi

2007* (3-14 years)

ID: 35·8/103 person-years

Province (Muang

2008* (3-14 years)


District)

2009* (3-14 years)

3·6/10

3


1·6/10

3

-

21

-

CI: 13·4/103 (89/6,657)

3·6/10

3

-

4

-

WPRO 2006** (0-19 years old)

ID: 13·4/103 person-season

CAMBODIA Kampong Cham

2007** (0-19 years old)

CI: 52·5/103 (530/10,086)

4 5·1/10

3

-


Province 2008** (0-19 years old)

CI: 15·2/103 (117/7,673)

10

-

11 1·9/10

3

-

8

-

9

3


Case def. ‘A'(see Table 1) VIETNAM

5·8/10

3

3

2004*/** (2-15 years old) CI: 33·3 /103 (73/2,194)

-

6

-

Giang Province

2005*/** (2-15 years old)

CI: 15·7 /10 (51/3,239)

3·4/10

3

-

5

-

(Long Xuyen)

2006*/** (2-15 years old)

CI: 24·5 /103 (77/3,146)

4·5/10

3

-

5

-

2007*/** (2-15 years old)

CI: 35·4 /103 (109/3,081)

6·2 /10

3

-

6

-

§

3

CIActive: Cumulative Incidence = [(n°symptomatic laboratory confirmed cases/cohort size)/10 ]

CIPassive: Cumulative Incidence = [(n° notified clinical cases/Local or Regional population, all age)/103] ^

An EF can be calculated a: “the analyst’s best estimate of the total number of dengue episodes in a specified population divided by the episodes

reported (whether or not they actually were laboratory-confirmed dengue” (CI from active surveillance/CI from passive surveillance) *School-based surveillance; **Community based enhanced surveillance; ***Community-based surveillance; ****Follow up study or Nested Case-

control study # local data in italic


859




8

Brazil Colombia Nicaragua Perù Puerto Rico Venezuela

7 6 5

PAHO (2000-2013)

4 3 2 1

13

12

20

20

20

20

11

10

09

08

20

06

07

20

20

20

05 20

20

04

03

02

20

01

20

20

8 SEARO (2000-2012) 7 6

Bangladesh/ India Indonesia Sri Lanka Thailand

5 4 3 2 1

12 20

20 11

20

10

09 20

20

20

08

07

06 20

05 20

04 20

03 20

02 20

20

20

01

0

00

Passive cumulative incidence (CI/103 population)

20

00

0

8 WPRO (2000-2011)

7 6

Cambodia Philippines

5

Singapore Vietnam

4 3 2 1

850

20 11

10 20

20 09

08 20

20 07

6 20 0

05 20

4 20 0

20 03

2 20 0

20 01

20 0

0

0 Figure 1 Dengue Incidence in selected endemic countries by WHO Region.





study with three peaks identified in 2001, 2007 and 2010–2012. Venezuela reported the highest incidence and Brazil the lowest. Within the WPRO’s countries, Vietnam and Singapore reported the highest incidence with peaks in 2005 and 2007. Within SEARO’s countries, Thailand and Sri Lanka were the highest reporting countries with an appreciable peak in 2010 and 2012 (Figure 1). During the study period, Indonesia reported incidence between 0.1 and 0.7/103. Bangladesh and India reported so few dengue cases in relation to population size that the incidence is close to zero (Table 2).

Indonesia 2000 2001 36 2002 2010 25 Thailand K. P. 2000 27 2004 19 2005 2006 19 2007 29.5 Thailand R. 2006 36 2007 41 2008 2009 Thailand 2010 11

As the purpose of this study was also to estimate the local EFs by comparing active data with passive data

Expansion factor (EF)

Colombia/Medellin 2011

14

Nicaragua/Managua 2001

11


Epidemic 59


58

Nicaragua/Managua 2005 Nicaragua/Managua 2006

PAHO

Colombia 2010 1 2011 3·5 21 Nicaragua 2001 21 2002 23 2004 2005 2006 12 2007 24 Peru 2004 41 2004 29 2004 Puerto Rico 2005 5·5 Venezuela 2002 17 2003 15 6·5 2007

Dengue cases reported by regional or provincial level to national routine surveillance

Peru/Dept Loreto (Iquitos) 2004

4


6

73·5

Thai/Ratchaburi Muang District 2006 Thai/Ratchaburi Muang District 2007

WPRO

Active Communitybased Surveillance

43

Active School and Community-based Surveillance

34 8 11 18 16

Thai/Ratchaburi Muang District 2009

21

82

WPRO 40

Active Community-based Enhanced Surveillance

4

Thai/Ratchaburi Muang District 2008

Cambodia/Kampong Cham 2006

20

14 22

Venezuela/Aragua (Maracay) 2007

Active School-based Surveillance

28

Venezuela/Aragua (Maracay) 2003

60

Cambodia 2006 11 19 2007 22 2008 Philippines 2007 15 2010 14 33 Vietnam 2004 22 2005 31 2006 29.5 2007 2010 15 0

22

Venezuela/Aragua (Maracay) 2002 126

Epidemic year

28


Peru/Dept Loreto (Iquitos) 2004 55

SEARO

SEARO

PAHO

Dengue cases were collected from different health institutions. For some countries such as Thailand, Cambodia, Philippines, Nicaragua and Puerto Rico [12], dengue cases were collected exclusively from hospital surveillance, whereas for the majority of countries, dengue cases were collected from both outpatient clinics and hospitals. All PAHO countries reported serotypes distribution by year. WPRO countries report serotype distribution since 2004, whereas the information on serotype distribution is not available for SEARO countries (Table A1, Appendix 1). Overall, we observed that all four dengue serotypes circulated in the same year in each country. While active surveillance ‘ad hoc studies’ consistently reported the proportion of isolated serotypes, only Vietnam reported the proportion of isolates by serotype in 2004– 2007 and 2011 as part of the passive surveillance system. The highest incidence of dengue was observed in PAHO’s countries consistently across the period under

60

80

100

120

140

nEF = CI-active data/CI passive National data) CIActive: Cumulative Incidence = [(n° symptomatic–not hospitalized laboratory confirmed cases/cohort size)/103] CIPassive: Cumulative Incidence = [(n° symptomatic notified clinical cases/NATIONAL population, all age)/103]

4


10

Cambodia/Kampong Cham 2008 Vietnam/Guang (LongXyyen) 2004

8 6

Vietnam/Guang (LongXyyen) 2005

5


5


6

–10

40

90

140

lEF = CI-active/CI passive Local data) CIActive: Cumulative Incidence = [(n° symptomatic–not hospitalized laboratory confirmed cases/cohort size)/103] CIPassive: Cumulative Incidence = [(n° symptomatic notified clinical cases/LOCAL population, all age)/103]

Figure 2 Comparison of Dengue Incidence from active and passive surveillance : nEf s vs. lEFs.


851




obtained from both national and local level, we identified passive surveillance data referring to the most representative geographical location of the active surveillance sites in the websites mentioned in the methods and to articles [22, 33–35, 47] where passive data were reported (Table A2, Appendix 2 & Table A3, Appendix 3). Dengue cases or incidence were transformed in CI/103 population as mentioned above. Expansion Factors (EFs): comparison between active and passive incidence Figure 2 shows EFs by national (nEFs) or local (lEFs) level, year and WHO regions. EFs were calculated for those countries and regions/province for which active surveillance data were available from cohort study as well as passive surveillance data for both national and local level. The highest national expansion factors found were nEF = 126 for Indonesia in 2001 (Figure 2) followed by Thailand (Ratchaburi) in 2009 with nEF = 82, Thailand (Kamphaeng Phet) in 2006 with nEF = 73, Indonesia in 2010 with nEF = 60, Nicaragua in 2005 and 2007 with nEF = 58 and 59, respectively, and Indonesia in 2006 with nEF = 55. The lowest nEFs are observed for Colombia in 2010 and 2011 with nEF = 1 and 3, respectively, Puerto Rico in 2005 with nEF = 5, Venezuela in 2007 with nEf = 6, and Cambodia in 2006 and Thailand in 2010 with nEF = 11. Overall, the lowest nEFs were found for WPRO countries, the highest in SEARO countries. There was a trend for lower lEFs than nEFs in all countries with the exception of Venezuela (Aragua) in 2002 and 2003, and Colombia (Medellin) in 2011 (Figure 2). In locations where both active school-based surveillance and active community-based surveillance were in place at the same time, lEF was lowest (Figure 2). Among the factors that can potentially influence EFs, we did not find a substantial difference in lEFs if cases who were reported through passive surveillance are laboratory-confirmed or only clinical diagnosis (Figure A1 in Appendix 4), and if incidence measures were differently expressed (Figure A2 Appendix 4). lEFs are substantially different if cases compared are from rural or urban area (Figure A3 in Appendix 4), if their age or age group is different (Figure A4 in Appendix 4) and if they are collected during the dengue season or annually (Figure A5 in Appendix 4). The serological study (active study) performed in Maracay, Venezuela, in 2002 and 2003 showed the highest lEFs (Figure 2 and Figure A4 in Appendix 4) [26]. We did not observe any trend in EFs according to the epidemic year (Figure 2).

852

Discussion We found high levels of dengue underreporting in selected endemic countries as estimated by EFs. First, nEFs were substantially higher in SEARO countries than PAHO and WPRO countries with an outlier value for Indonesia in 2001. Second, lEFs were generally lower than nEFS with few exceptions (lEFs were higher than nEFS in Medellin, Colombia in 2011 and Maracay, Venezuela in 2003 and 2007) (Figure 2). When an ‘ad hoc’ active surveillance study is conducted in a given area, the passive reporting system of that area may benefit with more cases reported through the routine surveillance system. Another explanation can be due to the site selection bias in cohort studies. As they are time and resource consuming, they tend to be performed in areas where high DENV transmission is expected to occur. Therefore, on average, incidence of dengue in areas with cohort studies is higher than the incidence at national level. Third, routine surveillance data as published in SEARO, PAHO and WPRO are expressed differently in terms of numerator, denominator, source of surveillance, serotype reporting and method for laboratory confirmation of dengue cases. Similarly, active surveillance studies used different methods for case detection, case definition, laboratory methods, seasonality definition and serotype reporting. Fourth, we found that several factors influenced lEFs estimates, such as the area of surveillance (urban or rural), the age group and the period of data collection (seasonal vs. annual). Our study has some limitations. For Brazil, India and Bangladesh, EFs were not calculated. For Brazil, we did not find any active surveillance data meeting study inclusion criteria, although routine surveillance data were found in the PAHO website for the study period. For India and Bangladesh, also no active data were found for the study period, and very few dengue cases were reported on the SEARO website, although dengue is considered to be endemic in both countries.[2] The exclusion of such a large population exposed to dengue has limited the findings of our study. Another limitation is that we did not further elaborate on circulation of the predominant serotype, as only qualitative data were available from passive surveillance data with the exception of Vietnam. We propose a systematic analysis of EFs by comparing cohort studies with national and local routine data. Because dengue transmission is usually higher in areas where cohort studies are conducted than in the wider geographical area, the nEFs may have been overestimated. No clear trend in EFs was identified during epidemic years. A possible explanation is that during epidemic





years, there might be more awareness of dengue and some non-dengue febrile illnesses may be reported as dengue, which would increase the total cases reported. However, particularly in regions with lower quality or less access to health care, there is substantial health-system congestion during outbreaks, which may reduce the number of reports. EFs for dengue have been reported in other recent studies as an estimate of underreporting based on the annual average for 10 years, [13] with the intent to provide stable values over the period considered. However, it is important to consider that dengue transmission has a marked geographical and temporal heterogeneity [43, 44, 49, 50] and that the rate of reporting and health quality systems [18] or health systems accessibility [51] are correlated. We showed that not only nEF and lEF differ (Figure 2), but that EFs are influenced by the yearly epidemiological fluctuation of dengue and by the different parameters used for EFs calculation such as the denominator, the surveillance methods used, the age group and the area where surveillance is performed (Figure 2, Appendix 4). All or some of these factors should be considered when estimating EFs as it is important to obtain as much as possible unbiased values. On the other hand, it may be complex to deal with the extreme variability and uncertainty of some estimates. The use of extensive sensitivity analysis should be considered as a possible approach to minimise variation. We found that the existing passive surveillance system is not optimal in providing reliable dengue data. Although we cannot expect that true dengue incidence data are provided through the surveillance system, it would be desirable to have some standardisation (i.e. case definition, disease severity – DSS/DHS, serotype) among the WHO regional offices on the data requested to the individual countries. Some monitoring and active solicitations should be in place for overseeing the qualitative and quantitative aspects of reporting. If surveillance is not improved at both country and WHO regional office level, the use of the data generated will not be even helpful for monitoring trends in dengue transmission. Accurate estimates of dengue disease burden are important because they allow informed policy decisions, increase dengue awareness and help define funding and research priorities from different institutions (governments, donors, NGOs, corporations). Cohort studies are probably the most efficient and most popular in providing true dengue disease incidence data. As they are complex studies to implement, and resource and time consuming, the method to be used (case definition area and population selection, laboratory assay, time, duration) must be carefully considered. Sero-epidemiological


studies can also provide information on dengue disease incidence, although we identified only one [23]. They should be explored further. EFs were developed to measure the economic cost of dengue in countries. The use of this metric at the national or multiple regional levels should be cautious, particularly when comparing cohort studies conducted in highendemic areas. The use of time-aggregated data [17, 18] to calculate EFs is probably acceptable to estimate the cost of dengue disease. As we have shown, EFs estimate varies by year due to a number of factors that make their use questionable in estimating the extent of underreporting at national level. lEFs may be more reliable in estimating underreporting at the local or regional level. Reliable dengue incidence data are urgently needed to evaluate interventions that aim to prevent and control dengue epidemics, including new innovative approaches to control mosquitoes [52] and widespread use of dengue vaccines [53, 54]. Acknowledgements This work originated by the thesis dissertation of NTT to the MSc course in Vaccinology and Pharmaceutical Clinical Development (Novartis Vaccines Academy and University of Siena – A.A. 2010–2011). The authors thank Dr. Sue Ann Costa Clemens, M.D., PhD. Prof. Pediatric Infectious Diseases – Director Novartis Vaccines Academy, Dr. Audino Podda Audino, M.D., PhD – Head of Clinical Development & Regulatory Affairs at Novartis Vaccines Institute for Global Health, Siena, Italy, and Prof. Emanuele Montomoli BSc, MSc – President Master Technical Scientific Committee, Master in Vaccinology and Pharmaceutical Clinical Development, Novartis Academia & University of Siena, Italy. We are grateful to the librarians Cristina Costantini and Roberto Faleri of the Medical Faculty Library, University of Siena, Italy, for their help in the search strategy. References 1. World Health Organization (WHO). Dengue Haemorrhagic Fever: Diagnosis, Treatment, Prevention and Control (2nd edn). WHO:Geneve, 1997. 2. World Health Organization (WHO). Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control: New Edition. WHO: Geneva, 2009. 3. Simmons CP, Farrar JJ, Nguyen vV, Wills B. Dengue. N Engl J Med 2012: 366: 1423–1432. 4. Mayurasakorn S, Suttipun N. The impact of a program for strengthening dengue hemorrhagic fever case management on the clinical outcome of dengue hemorrhagic fever

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Appendix 1 Table A1. Prevalent serotypes by years: information from active (bold characters) and passive surveillance PAHO 2001

2000

2002

2003

2004

2005

2007

2008

2009

2010

D1, D2, D3

D1, D2, D3

D1, D2, D3

D1, D2, D3,D4

2006

2011

2012

2013

D1, D2,

D1, D2,

D1, D2,

D3,D4

D3,D4

D3,D4

Countries

D1, D2,

D1, D2, Brazil

D1, D2, D3

D1, D2, D3

D1, D2, D3

D1, D2, D3

D1, D2, D3

D3

D3

D1 42% (5/12)

D3 17% (2/12)

D2 33% (4/12) N.A

D1, D3, D1, D2, D4

Colombia

D1, D2, D3, D1, D2, D3

D4

D1, D2,

D1, D2, D3,

D3, D4

D4

D1, D2, D1, D2, D3

D1, D2, D3 D4

D1, D2, D1, D3, D4

D1, D2, D3, D4 D3, D4

D3, D4

D4 8% (1/12)

D1, D2, D3, D4

D2 66% (106/161) D1 22% (35/161)

D1 17% D2 25% (1/4)

D1, D2, D3

D1, D2, D3

D1, D3

D1, D2, D3

D1, D3

D1, D2, D3,

D1, D2,

D1, D2, D3,

D1, D2,

D4

D3, D4

D4

D3, D4

D1, D2,

D1, D2, D3,

D4

D4

D1, D2, D3,

D1, D2,

D1, D2, D3,

D1, D2,

D4

D3, D4

D4

D3, D4

D3 12% (19/161)

(1/6)

Nicaragua D4 0·6% (1/161)

D1

D2, D4 D2, D3

D1, D3, D1, D2, D3,

D1, D2, D4

D1, D2, D4

D1, D2, D4

D1, D2, D3 D4

Peru

D1, D2, D3,

D1, D2, D3,

D1, D2, D3, D1, D3

D1, D2, D4

D1, D2, D3

D4

D4

D3 D1, D2, D3

D4

D1, D3, D4

D1, D2, D3, D4

D4

D2 49% Puerto Rico

(77/156) D1, D2, D3

D2, D3

D2, D3

D1, D2, D3

D1, D2,

D1, D2, D3,

D3

D4

D2, D3, D4

D1, D2, D3

D1, D2, D3, D4

D1, D2, D4

D1, D4

D2, D3, D4 D1 39% (157/401) D1 6% D1 5% (5/89) (2/37)

D2 41%

D2 11%

(166/401)

D2 6% (5/89) (4/37) D3 74%

D3 17%

D3 59% D1, D2, D3,

D1, D2, D3,

(66/89)

D4

D4

D4 5%

Venezuela

D1, D2, D3,

D1, D2, D3,

D1, D2,

D4

D4

D3, D4

(22/37)

(70/401)

D1, D2, D3, D1, D2, D3, D4 D4

D4 16% (4/89)

D4 2%

(6/37)

(8/401) D2, D3, D1, D2, D3 D4 D1, D2, D3, D4

SEARO 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Countries D2 27% (24/90) D1 12% (11/90) Indonesia D4 11% (10/90) D3 9% (8/90) D4 75% (15/20)

D1 68% (21/31)

D4 57%

D1 68%

(16/28)

(46/68) D110%

D2 26%

D2 was the Thailand

(2/20) only dengue

KamphaengPhet

NA serotype

NA

(8/31)

D2 32%

D4 30%

(9/28)

(21/68)

NA D2 10%

NA

D4 6%

isolated (2/20)

(2/31)

D3 11%

D2 1%

(3/28)

(1/68) D3 5% (1/20)

856





D1 50% (17/34) D1 34% D4 38%

D1 51%

D1 38%

(68/134)

(28/73)

D2 37%

D3 34%

(49/134)

(25/73)

D3 10%

D2 25%

(14/134)

(18/73)

D4 2%

D4 4%

(3/134)

(2/73)

NA

NA

2008

2009

(31/92)

(13/34) D2 33% D3 9%

(30/92)

(3/34) Thailand D3 26% Ratchaburi

NA

NA

NA

NA

NA

NA

D2 3%

(24/92)

(1/34) D4 8% (7/62)

NA

NA

NA

NA

2010

2011

2012

2013

NA

NA

NA

NA

NA

NA

NA

NA

WPRO 2000

2001

2002

2003

2004

2005

2006

2007

Countries D1 78% D3 68% (25/32) (217/320) D2 78% D3 16% D1 28%

(56/72)

(5/32) (90/320) D3 17%

D2 75%

(12/72)

D4 16%

D1, D2, D3,

D1 6%

D4

D1 77%

D2 6% Cambodia

D4 4% NA

NA

NA

NA

D2, D3

NA

D2 19%

(2/32) (12/320) D4 6% D2 0.3%

D3 3%

D3 2% D4 2%

(4/72)

(1/320) NA NA NA

D3 84% (52/62)

D2 15% D1 44%

(9/62) Philippines NA

NA

NA

NA

D1, D2, D3,

D1, D2,

D4

D3, D4

D1, D2, D3,

D1, D4

NA NA

D3 43% D4 D2 13%

D1 2% (1/62)

D1, D2, D3, D4 D1 55% (122/223) D2 28% (62/223) D3 8% (17/223)

D1 42% D1,

D4 0% (22/223)

Vietnam NA

NA

NA

NA

NA D2 62%

D2 69%

D2 52%

D1, D2, D3,

D 32%

D4

D4 18%

D2,

D1 56% D3


D1 23%

D1 19%

D1 42%

D2 25%

D3 10%

D3 10%

D3 3%

D3 15%

D4 4%

D4 2%

D4 3%

D4 3%

D3 8%

857




Appendix 2 Table A2. Comparison of Dengue Incidence from active surveillance and national passive surveillance (CI-active vs CI -passive) Country

National Passive Surveillance CumulativeIncidence Clinical Laboratory notified confirmed Cases (All age)

Active Surveillance Cumulative Incidence§

Expansion Factor (EF) ^ Active/ Passive Clinical

Active/ Passive Confirmed

PAHO Colombia

Nicaragua

CI = 5·9/103 (14/2,379) CI = 4·9/103 (9/1,840)

6·9/103 1·4/103

3·3/103 0·4/103

1 3·5

1 1

2001* (4-16 years old): CI = 8·5/103 (4/467) 2002* (4-16 years old):CI = 8·3/103 (6/719)

0·4/103 0·4/103

-

21 21

-

2004** (2-9 years old):CI = 4·6/103 (17/3,713) 2005** (2-9 years old):CI = 17·6/103 (65/3,698) 2006** (2-9 years old):CI = 3·6/103 (13/3,563) 2007** (2-9 years old):CI = 17·4/103 (64/3,676)

0·2/103 0·3/103 0·3/103 0·3/103

0·1/103 0·2/103

23 59 12· 58

46 87

0·4/103 0·4/103 0·4/103

-

24 41 29

-

2010* (5-19 years old): 2011* (5-19 years old):

CI: 9·7/103 (11/1,135) CI:16·3/103 (25/1,537) CI:11·5/103 (56/4,850)

Peru

2004* (5-17 years): 2004**(5-17 years): 2004**(0-98 years)

Puerto Rico

2005*** (All age)

CI: 7.7/103 (156/20,152)

1·4/103

-

5·5

-

Venezuela

2002**** (5–13 years old) 2003**** (5–13 years old)

CI: 25·8 /103 (183/710) CI: 16·9/103 (120/710)

1·5/103 1·1/103

-

17 15

-

2007*** (5–94 years old) CI: 18·7 /103 (47/2,509)

2·9/103

1·8/103

6·5

10

0·1/103 0·2/103 0·2/103

-

55 126 36

-

0·6/103

-

60

-

1·8/103

-

11

-

SEARO

Indonesia

2000**(18-66 years): 2001**(18-66 years): 2002**(18-66 years): 2010** (2-14 years)

CI: 5·5/103 (13/2,374) CI: 25·3/103 (60/2,374) CI: 7·2/103 (17/2,374) CI: 35·9/103 (16/446) 20·1/103

(6/299)

Thailand

2010** (2-14 years)

CI:

Thailand KamphaengPhet

2000* 2004* 2005* 2006* 2007*

(4-15 years) (4-15 years) (4-15 years) (4-15 years) (4-15 years)

CI: 7·6/103 (13/1,737) CI: 16·3/103 (33/2,023) CI: 13·4/103 (27/2,021) CI: 44·1/103 (90/2,039) CI: 19·4/103 (39/2,007)

0·3/103 0·6/103 0·7/103 0·6/103 1·0/103

-

25 27 19 73·5 19

-

Thailand Ratchaburi

2006* 2007* 2008* 2009*

(3-14 years) (3-14 years) (3-14 years) (3-14 years)

ID: 17·7/103 person-years ID: 35·8/103 person-years ID: 57·4/103 person-years ID: 32·9/103 person-years

0·6/103 1·0/103 1·4/103 0·4/103

-

29·5 36 41 82

-

1·2/103 2·8/103 0·7/103

-

11 19 22

-

WPRO Cambodia

Philippines

Vietnam

2006*** (0-19 years old)CI: 13·4/103 (89/6,657) 2007***(0-19 years old) CI: 52·5/103 (530/10,086) 2008***(0-19 years old)CI: 15·2/103 (117/7,673) 2007****(2-15 monthsold)CI: 9·0/103 (40/4,441) 20·2/103

0·6/103

-

15

-

1·4/103

-

14

-

2010** (2-14 years)

CI:

Case def. ‘A' 2004*/** (2-15 years old) 2005*/** (2-15 years old) 2006*/** (2-15 years old) 2007*/** (2-15 years old)

CI: 33·3 /103 (73/2,194) CI: 15·7 /103 (51/3,239) CI: 24·5 /103 (77/3,146) CI: 35·4 /103 (109/3,081)

1·0/103 0·7/103 0·8/103 1·2/103

-

33 22 31 29·5

-

2010** (2-14 years)

CI: 20.5/103 (3/146)

1·4/103

-

15

-

(6/297)

§CI

3 Active: Cumulative Incidence = [(n° laboratory confirmed cases/cohort size)/10 ] CIPassive: Cumulative Incidence = [(n° notifiedclinical cases/national population, all age)/103]; (n° confirmedclinical cases/national population) ^An EF can be calculated a: “the analyst’s best estimate of the total number of dengue episodes in a specified population divided by the episodes reported (whether or not they actually were laboratory-confirmed dengue” (CI from active surveillance/CI from passive surveillance) *School-based surveillance; **Community based enhanced surveillance; ***Community-based surveillance; ****Follow up study or Nested Casecontrol study

858





Appendix 3 Table A3. Comparison of Dengue Incidence from active surveillance and local passive surveillance (CI-active vs CI -passive) Country

Active Surveillance

Cumulative Incidence§

LOCAL Passive#

Expansion Factor

Surveillance

(EF)^

Cumulative Incidence Clinical

Laboratory

Active/

Active/

notified

confirmed

Passive

Passive

Clinical

Confirmed

Cases (All age) PAHO COLOMBIA

2010* (5-19 years old):

CI = 5·9/103 (14/2,379)

-

-

-

-

Medellin

2011* (5-19 years old):

CI = 4·9/103 (9/1,840)

0·35/103

-

14

-

2001* (4-16 years old):

CI = 8·5/103 (4/467)

0·8/103

-

11

-

2002* (4-16 years old):

CI =8·3/103 (6/719)

0·3/103

-

28

-

3

-

22

3

-

28

3

-

14

3

NICARAGUA Managua

2004** (2-9 years old):

3

CI = 4·6/10 (17/3,713)

-

2005** (2-10 years old):

CI = 17·6/103 (65/3,698)

-

0·63/10

-

0·25/10

-

0·77/10

-

22

-

4

-

-

5

-

-

6

-

-

8

-

-

4

-

-

6

-

2006** (2-11 years old): CI = 3·6/103 (13/3,563) 2007** (2-12 years old):

CI = 17·4/103 (64/3,676) CI: 9·7/103 (11/1,135)

2004* (5-17 years):

0·21/10

2.8/103

3

ID:12.9/10 person years PERU

3

2004** (5-17 years): Dept Loreto

CI:16·3/10 (25/1,537)

2·8/10

3


(Iquitos)

CI:11·5/103 (56/4,850)

2004** (0-98 years)

2·8/10

3


VENEZUELA

2002**** (5–13 years old)

CI: 25·8 /103 (183/710)

Aragua State

2003**** (5–13 years old)

3

(Maracay)

2007*** (5–94 years old) CI: 18·7 /103 (47/2,509)

CI: 16·9/10 (120/710)

0·6/10

3

-

43

-

0·5 /10

3

-

34

-

2·3/10

3

-

8

1·6 /10

3

-

11

-

2·0 /10

3

-

18

-

-

16

-

SEARO ID: 17·7/103 person-years

THAILAND

2006* (3-14 years)

Ratchaburi

2007* (3-14 years)


Province (Muang

2008* (3-14 years)


District)

2009* (3-14 years)

3·6/10

3


1·6/10

3

-

21

-

CI: 13·4/103 (89/6,657)

3·6/10

3

-

4

-

WPRO 2006** (0-19 years old)


CAMBODIA Kampong Cham

2007** (0-19 years old)

CI: 52·5/103 (530/10,086)

4 5·1/10

3

-


Province 2008** (0-19 years old)

CI: 15·2/103 (117/7,673)

10

-

11 1·9/10

3

-

8

-

9

3


Case def. ‘A'(see Table 1) VIETNAM

5·8/10

3

3

2004*/** (2-15 years old) CI: 33·3 /103 (73/2,194)

-

6

-

Giang Province

2005*/** (2-15 years old)

CI: 15·7 /10 (51/3,239)

3·4/10

3

-

5

-

(Long Xuyen)

2006*/** (2-15 years old)

CI: 24·5 /103 (77/3,146)

4·5/10

3

-

5

-

2007*/** (2-15 years old)

CI: 35·4 /103 (109/3,081)

6·2 /10

3

-

6

-

§

3

CIActive: Cumulative Incidence = [(n°symptomatic laboratory confirmed cases/cohort size)/10 ]

CIPassive: Cumulative Incidence = [(n° notified clinical cases/Local or Regional population, all age)/103] ^

An EF can be calculated a: “the analyst’s best estimate of the total number of dengue episodes in a specified population divided by the episodes

reported (whether or not they actually were laboratory-confirmed dengue” (CI from active surveillance/CI from passive surveillance) *School-based surveillance; **Community based enhanced surveillance; ***Community-based surveillance; ****Follow up study or Nested Case-

control study # local data in italic


859




Appendix 4


11

Clinical notified passive Lab Confirmed passive data


28


22


28


14


22 0

10

20

30

40

50

60

70

EF -Expansion factor

Figure A1 EF calculated from local data, for both active and passive surveillance: clinical notified passive data 2001–2002 vs. laboratory-confirmed passive data (2004–2007).


4

EF = CI active/CI passive EF = ID active/CI passive

6 Cambodia/Kampong Cham 2006

4 4


10 11


8 9 0

10

20

30

40

50

60

70

EF -Expansion afactor

Figure A2 EF calculated from Local data, for both Active and Passive surveillance: CI active/CI passive vs ID active/CI passive (Active and Passive cases = same age).

860





SEARO-Thai/Ratchaburi Muang District 2006

11


16


18


21

PAHO-Peru/Dept Loreto (Iquitos) 2004

4


4

WPRO-Vietnam/Guang (LongXyyen) 2005

5


5


6


6


6

PAHO-Venezuela/Aragua (Maracay) 2007

Rural Area Urban Urban & Rural Area

8

PAHO-Nicaragua/Managua 2001

11

PAHO-Colombia/Medellin 2011

14


14

PAHO_Nicaragua/Managua 2007

22


22


28


28 34

PAHO-Venezuela/Aragua (Maracay) 2003 PAHO-Venezuela/Aragua (Maracay) 2002

* 43

WPRO-Cambodia/Kampong Cham 2006

*

4


8


10 0

10

20 30 40 50 EF -Expansion factor (*serological study-urban)

60

70

Figure A3 EF calculated from Local data, for both Active and Passive surveillance :clinical notified Urban area vs Rura larea vs Urban&Rural area.


861





4


4


4


5


5

age active cases = age passive cases age active cases ≠ age passive cases


6

WPRO- Vietnam/Guang (LongXyyen) 2004

6


6


8


8 Median


10


11


11


16


18


21


28


34


*

43 0

10

20

*

30

40

50

60

70

EF -Expansion factor (*serologicalstudy – differentages)

Figure A4 EF calculated from local data, for both active and passive surveillance: age active cases = age passive cases vs. age active cases ≠ age passive cases.

862





PAHO_Nicaragua/Managua 2007

22


11


28


22


28


14

PAHO-Peru/Dept Loreto (Iquitos) 2004 4 PAHO-Peru/Dept Loreto (Iquitos) 2004

6

Active annualcases

PAHO-Peru/Dept Loreto (Iquitos) 2004 4 SEARO-Thai/Ratchaburi Muang District 2006

11


Active seasonal cases

18


16


21


6


5


5


6

PAHO-Colombia/Medellin 2011

14


43


*

34


*

8


4


10


8 0

10

20

30

40

50

60

70

EF-Expansion factor (*serologicalstudy- seasonal)

Figure A5 EF calculated from local data, for both active and passive surveillance: active annual cases vs. active seasonal cases.

Corresponding Author Simonetta Viviani, Department of Molecular and Developmental Medicine, Postgraduate School of Public Health, University of Siena, 53100 Siena, Italy. Tel.: +39 0577 301127; Fax: 0039 0577 234190; E-mail: [email protected]


863

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