http://orcid.org/0000-0003-3005-4184
Figures
Abstract
Aedes aegypti transmitted arboviral diseases are of significant importance in Colombia, particularly since the 2014/2015 introduction of chikungunya and Zika in the Americas and the increasing spread of dengue. In response, the Colombian government initiated the scaling-up of a community-based intervention under inter and multi-sector partnerships in two out of four sectors in Girardot, one of the most hyper-endemic dengue cities in the country. Using a quasi-experimental research design a scaled-up community-led Aedes control intervention was assessed for its capacity to reduce dengue from January 2010 to August 2017 in Girardot, Colombia. Reported dengue cases, and associated factors were analysed from available data sets from the Colombian disease surveillance systems. We estimated the reduction in dengue cases before and after the intervention using, Propensity Score Matching and an Autoregressive Moving Average model for robustness. In addition, the differences in dengue incidence among scaling-up phases (pre-implementation vs sustainability) and between treatment groups (intervention and control areas) were modelled. Evidence was found in favour of the intervention, although to maximise impact the scaling-up of the intervention should continue until it covers the remaining sectors. It is expected that a greater impact of the intervention can be documented in the next outbreak of dengue in Girardot.
Citation: Quintero J, Ronderos Pulido N, Logan J, Ant T, Bruce J, Carrasquilla G (2020) Effectiveness of an intervention for Aedes aegypti control scaled-up under an inter-sectoral approach in a Colombian city hyper-endemic for dengue virus. PLoS ONE 15(4): e0230486. https://doi.org/10.1371/journal.pone.0230486
Editor: Livia Maria Silva Ataide, Universidade Federal de Lavras, BRAZIL
Received: May 25, 2019; Accepted: March 3, 2020; Published: April 1, 2020
Copyright: © 2020 Quintero et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files. All supporting information files are defined as minimal data set.
Funding: The study was supported financially by a grant from the following institutions: Administrative Department of Science, Technology and Innovation of Colombia (Colciencias) Project 622172553374 and by the International Development Research Center of Canada (IDRC) Project 107843-001. The nets employed in this study were donated by the Ministry of Health of Colombia. Colciencias URL: https://www.colciencias.gov.co/ IDRC URL: https://www.idrc.ca/ The funders had no role in the e study design, data collection and analysis, decision to publish, or preparation of the manuscript. JQ and GC received boths awards.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Aedes aegypti is the principal vector of dengue, chikungunya, Zika and yellow fever, and is now found in all continents but Antarctica [1]. Aedes transmitted diseases account for approximately 23% of the estimated global burden of vector-borne diseases [2] and pose a significant economic cost not only for governments in endemic countries that are responsible for case management and cost of vector control activities, but also for households that have their own costs for treatment and protective measures [3–10].
The emergence and resurgence of Aedes-transmitted disease is associated with complex relationships between a variety of ecological, biological, and social factors of urban and peri-urban environments, all of which are particularly challenging for vector control efforts [11–13]. Ecological factors refer to climate (rainfall, humidity, temperature, etc) and the natural and man-made ecological setting (unplanned urbanization). Biological factors relate to the behaviour of the vector, Ae. aegypti, and transmission dynamics of these diseases (i.e the co-circulation of different serotypes) [14]. Social factors incorporate a series of influences relating to health systems, including the weakening of surveillance systems and vector control programmes[15] and health services [16] and their political context (e.g. health sector reforms, decentralization [17]), public and private services such as sanitation and sewage, garbage collection and water supply. Additionally, "macro-social" events are important, including: demographic growth and urbanization, community and household-based practices, knowledge and attitudes and how these are shaped by large-scale forces such as poverty [18,19], social inequality [20] and community dynamics including human movement [21–23].
This complexity highlights the need for setting-specific vector control approaches that combine environmental management practices with community mobilization and engagement, intersectoral and multi-stakeholder partnerships, principles of Integrated Vector Management (IVM) [24] as well as other country-specific policies such as the Integrated Management Strategy (IMS) [25]. Many community led interventions have been conducted in Asia and results indicate that the interventions reduce vector densities but evidence of impact on dengue transmission is lacking [26].
In response to the increasing threat of dengue, the Ecobiosocial/Ecohealth programme was designed by the Special Program for Training and Research in Tropical Diseases (TDR) in partnership with the International Development Research Center from Canada (IDRC) to be implemented in Asia [27] and Latin American countries (Mexico, Ecuador, Colombia, Brazil and Uruguay) over a 4-year period [28]. This initiative carried out a transdisciplinary investigation (Ecohealth approach) of ecological, biological, and social factors of dengue in urban areas, and developed and tested community-based interventions aimed at reducing Aedes breeding sites [29].
Specifically, a Cluster Randomized Controlled Trial (CRCT) was conducted in the dengue- hyperendemic Colombian municipality of Girardot during 2012–2014 [30]. The trial was designed to test the efficacy of long-lasting deltamethrin-treated nets (ITN), used as window/door curtains and covers on water containers, in reducing the Ae. aegypti density as measured through Pupae per Person Index (PPI), a proxy for adult density [30]. The study involved a cluster design comparing ten control and ten intervention areas comprising 100 households each. In control clusters, routine vector control activities (Abate, health education, and occasional public space spraying of an ultra-low volume of Malathion) were conducted. In the intervention clusters, in addition to the routine vector control activities, insecticide-treated curtains were hung over windows and doors and covers were placed over the most Aedes-productive water containers. Results demonstrated that PPI in intervention clusters declined by 60% after the intervention with ITN covers.
In light of the results of this trial, and following the recommendations of the 2017 WHO response strategy [2], the Colombian programme decided to extend the intervention in Girardot with the aim of achieving not only broader reduction in vector densities but also impact on dengue transmission. As a key strategy to reach the institutionalisation of the intervention and long-term viability, an intersectoral action approach was implemented among municipal entities from different sectors (health, social development, tourism, academic and education).
Here, we present the impact of an Aedes-vector control intervention “Girardot Aedes-free” in reducing the number of reported dengue cases in Girardot, Colombia, between 2015 and 2017.
Methods
Setting
The study was conducted in Girardot (4°18′02″N 74°48′27″W), Colombia, 134 km from the capital, Bogotá. Girardot is located 289 meters above sea level, has an annual average maximum temperature of 33.3°C, a relative humidity of 66,38%, a mean annual precipitation of 1,220 mm, with two seasons during the year: the dry season (December-April) and the rainy season (May-October). Approximately, 105,085 inhabitants comprise around 23,885 households (97% of which are urban) distributed over 130.32 km2. The population triples during the weekends, as its main economic revenue is tourism.
Girardot presents an eco-epidemiological and social niche favourable for sustained transmission of dengue [13,31], chikungunya and Zika. The circulation of multiple dengue serotypes has been reported [32,33]. Between 2005 and 2016, 5,928 dengue cases (residents and non-residents) were reported to the surveillance system from which 5.78% were severe. For this same period, an average of more than 500 dengue cases have been reported annually (range 81–1163). In 2013 1,103 cases were reported, 532 in 2014, and 364 in 2015. The age-groups with higher dengue cases were 5–9 and 10–14 years old (SIVIGILA 2005–2018). With respect to chikungunya the first case in Colombia was identified on September 11th of 2014 and in Girardot on December 2014. By the end of 2015 Girardot, reported 8,905 cases of chikungunya representing an annual incidence of 8,416 per 100,000 inhabitants [34] and by the end of 2016, 1,936 cases of Zika with an overall attack rate of 18.43 per 1000 residents [35].
Ae. aegypti has been reported as the principal dengue vector in Girardot [31,36] and in other dengue hyperendemic cities of Colombia (Girardot, Armenia, Arauca, Anapoima). The studies report vector productivity associated with storage of water in large and uncovered low level cement containers known as “albercas”, which are estimated to account for more than 70% of pupae production [36–38].
Study design and data set
An ecological study was conducted to evaluate the impact of an intervention named Girardot Aedes-free in reducing notified dengue cases.
Daily dengue surveillance data for the study period January 2010 to December 2017, were obtained from the Communicable Disease Surveillance System of Girardot, Colombia (SIVIGILA) in which patients with dengue are notified according to a standard case definition of dengue [39]. Dengue cases are identified and reported by the health system as either probable dengue, probable severe dengue or, lab confirmed [39].
Anonymized was provided by the health secretary for analysis. In addition, field site access was approved by the city major (Cesar Fabián Villalba) and by the former health secretary (Manuel Díaz) and current health secretary (Erika Ramírez).
Girardot Aedes-free intervention
The intervention focused on four setting levels (household, school, community and institutional) where diverse actors interact and participate with different intervention components together with the control program activities. This intervention was developed, and scaled-up following an Eco health approach [29]. The scaling-up of the intervention occurred in three distinctive phases: 1. Pre-implementation phase (planning and setting-up of activities), 2. Active implementation (action phase) and 3. Sustainability phase (follow-up activities). Table 1, describes the characteristics of the Girardot Aedes-free and routine vector control interventions.
For household level actions, Girardot, was divided into 4 sectors. A sector was defined as an area that included several neighbourhoods with similar ecological and sociodemographic characteristics. Each sector was divided into intervention, buffer (100 meters), and control zones. The active phase began in Sector 1 followed by Sector 2 between December 4th 2015 until February 24th 2017. During the active implementation phase 3,898 insecticide-treated aluminium covers were distributed in 2,935 households (1.32 covers per household) and 1,774 round covers with elastic band in 965 households (1.84 per household). Sectors 1 and 2 represent 2.52 Km2 of the total of the urban area of Girardot (130.32 km2) (Fig 1).
Data analysis
The effectiveness of the intervention was assessed using different quasi-experimental research designs. Differences were modelled comparing numbers of reported clinically and lab confirmed dengue cases (primary outcome) among intervention implementation points (before–after) using 1. Propensity Score Matching (PSM) [40,41], (see subsection 2 –below) and an Autoregressive Moving Average (ARMA) model and by modelling differences between numbers of dengue cases adjusted by population size of each sector among scaling-up phases (pre-implementation vs sustainability) and between treatment groups (intervention and control areas) (see subsection 3- below). Difference-in-Differences (Diff–in-Diff) method. All statistical analysis was conducted using Stata software version 15 [42].
- Propensity Score Matching (PSM)
The PSM consists of the following steps:
- Estimate the probability that a day would be treated conditional on a set of regressors. The probability is calculated by estimating the coefficients of the model
. The coefficients were calculated by maximizing the following likelihood function
.
- Check if the score is balanced.
- Match each treated day with one not treated. For this, the following matching algorithms were used:
- Nearest neighbour matching: Select a pair of control and treated observations that minimize the following expression
- Radius: Select a pair of control and treated observations that fulfil the following expression
- Kernel (Bartlett): Each observation is matched with several observations as:
Where b is the bandwidth. All alternatives were estimated using common support, a further requirement besides independence.
- Nearest neighbour matching: Select a pair of control and treated observations that minimize the following expression
- Estimate the average impact of treatment on the treated.
2. ARMA.
Because the data have a temporal structure, the estimation of an ARMA model (p, q) was performed. The number of cases is the dependent variable.
Where L is the lag operator, i.e xtLk = xt−k. And the expressions β(L) and α(L) are lag polynomials of order q and p respectively. Using the autocorrelation function, partial autocorrelation and unit root tests, it was determined that the time series has an ARMA structure (3,0,3). To determine the influence of the treatment on the number of cases, a dummy variable was included in the ARMA representation, in the form:
After the estimation several diagnostic tests were performed. The estimate is stable, invertible, and its residuals are not autocorrelated.
3. Diff -in- Diff.
Differences between numbers of dengue cases (primary outcome) among scaling-up phases (pre-implementation–sustainability) and between treatment groups (sectors 1 and 2) were estimated.
Initially dengue cases were geo-localized using the variable “address” using the SIVIGILA data set (78% of the cases were possible to localize). Then the number of dengue cases was identified in the intervention and control areas per sector using the QGIS software (V 2.18). The QGIS command `Join attributes by location´ was used to create a new vector layer containing information on the number of cases per sectors and intervention areas [43].
A linear regression model was used to estimate the effectiveness of the intervention in the presence of associations between sociodemographic factors reported in the SIVIGILA data set (age, sex, ethnic, health insurance as a proxy of socioeconomic status). A descriptive analysis of baseline and follow-up characteristics was performed for each study group and differences between these characteristics were assessed by bivariate analysis using a test of proportions. Categorical variables were summarized as frequencies and numerical variables as means with standard deviations if normally distributed or as the medians with interquartile ranges (IQRs) if not normally distributed. The regression model used the number of cases as parameter estimates grouped per day. The effect of the intervention was tested as the effect-difference from baseline to follow up between the intervention and control areas. Significance was stated at < 0.05 level and 95% confidence intervals are reported.
Ethical considerations
International and National Standard Ethical procedures for obtaining protocol approvals as well as Informed consent were followed. The core research proposal entitled “Ecobiosocial approach for the design and implementation of a sustainable strategy for dengue vector in Colombia” was submitted for ethical clearance through the IRB of Fundación Santa Fe de Bogota. Every year the study was updated for ethics approval. The health secretary of Girardot provided and authorized the use of dengue surveillance data which was anonymized prior to access. In addition, ethical approval was obtained by the London School of Hygiene and Tropical Medicine ethics committee under the reference number 14310.
Results
Description of notified dengue cases in Giradot, 2010–2017
Between 2010 (1st epidemiological week) and 2017 (33rd epidemiological week), 3,193 suspected dengue cases were reported to the surveillance system of Girardot, of which 99.6% were clinically classified as dengue. During this period a mean of 1.93 dengue cases were reported per day (range 1 to 14) although only 198 (6.2%) were laboratory-confirmed. Fig 2A, shows three outbreaks, over the course of 8 years. During 2010, 487 dengue cases were reported, 708 cases in 2013 and 532 in 2014.
A. Number of reported dengue cases in Girardot, Colombia 2010–2017. The solid black line shows the number of dengue cases between 2010 and 2017 in Girardot. The solid gray line shows the number of dengue cases in intervention areas of study sectors 1 and 2. The dashed gray line shows the number of dengue cases in control areas of study sectors 1 and 2. The red square indicates the scaling-up period. B. Number of reported dengue cases in control and interventions areas of study sectors. Girardot, Colombia 2010–2017 The solid black line shows the number of dengue cases between 2015 and 2017 in Girardot. The solid gray line shows the number of dengue cases in intervention areas of study sectors 1 and 2. The dashed gray line shows the number of dengue cases in control areas of study sectors 1 and 2.
Slightly more dengue cases were reported in men than in women (1,690, 52,9%). The mean age was 21.6 years, and 55.3% (1768) among children younger than 16 years old. A greater number was reported in age-groups 0–5 (587) and 6–10 years old (720).
Description of notified cases in Giradot, during scaling-up of the intervention
During the period of March 2015 to August 29 2017 (Setup phase: baseline, Active phase: implementation of intervention and Sustainability phase: follow-up) 702 dengue cases were reported in the SIVIGLA of Girardot. Twenty-eight percent of these (n = 194) were from the study sectors, 69 dengue cases were reported during baseline and follow-up in Sector 1, and 52 cases in Sector 2. During baseline 11 cases were reported in the intervention area of Sector 1, compared to 3 cases in the control area.
A similar situation was observed for Sector 2, where 14 dengue cases were reported in the intervention area, compared to 3 cases in the control area. Dengue incidence was generally higher in Sector 1 (526.2 per 100.000 inhabitants) compared to Sector 2 (381.6 per 100.000 inhabitants). For all sectors the incidence was higher in the control area (529.01 per 100.000 inhabitants) than intervention area (371.32 per 100.000 inhabitants). There was an increase in dengue incidence reported in both intervention and control areas from baseline to follow-up. The increase in dengue incidence per 100.000 inhabitants for all sectors was greater in the control areas (an increase of 396.75 cases per 100.000 inhabitants) than in intervention areas (an increase of 267.02 cases per 100.000 inhabitants). In Sector 1, the increase in dengue incidence was higher in control areas (an increase of 483.33 cases per 100.000 inhabitants) than in intervention areas (an increase of 377.43 cases per 100.000 inhabitants). In Sector 2 the incidence in control areas did not change from baseline to follow-up (236.07), but the incidence from baseline to follow-up in the intervention area increased almost two-fold (Table 2).
Table 2, describes the distribution of dengue reported cases in the intervention and control areas during baseline and follow-up surveys only (n = 122).
Effectiveness
The PSM analysis indicates that the intervention resulted in a decrease of an average of between 0.12 (-0.25,0.01) and 0.26 (-0.42, -0.10) cases of dengue daily (1.82 cases per week or 7.8 cases per month or 95 cases per year) in Girardot (Table 3). By the same means, the time series analysis suggests that the treatment on average decreased the number of dengue cases by 0.27 cases daily (Table 4).
The Diff-in-Diff estimator reports an increase of 0.065 dengue cases daily (0.455 per week, 1.95 per month) (Table 5), but when calculating the differences in incidences rates and rate ratios during sustainability (follow-up phase) among intervention and control areas of both sectors (see Table 2), an incidence rate difference of– 0.0129 (95% CI -0.00179- -0.00078) and an incidence rate ratio of 0.674 (95% CI 0.577–0.786) are observed.
Discussion
The principal goal of any dengue intervention is to reduce disease incidence and preferably transmission by reducing human exposure. Ae. aegypti control remains the primary tool available to achieve the latter goal. Several systematic reviews published in the last decade [26,44–47] have reported the impact of dengue vector control. They concluded that the most effective are community-based interventions that combine community and social mobilization, participation with local government control services, joint collaboration with local services, with environmental management or clean-up campaigns, water covers and window screens using insecticide-treated nets, and use of larvicides.
These reviews indicated that the effect of a dengue vector control interventions is principally measured using entomological parameters (indicators of vector infestation) however these indicators do not always accurately reflect dengue transmission [48,49]. The studies that include epidemiological risk indicators to determine the effect of a dengue vector control intervention mainly use interrupted time series, propensity score matching and classic, spatial, and Bayesian statistical analysis, [50–52]. These are preferably selected as costs, resource demands, and contamination effects are factors that impede the feasibility of conducting alternative experimental designs.
Our study used a quasi-experimental design to assess the impact of a dengue vector control intervention in Girardot, Colombia, developed with local stakeholders and implemented following an ecohealth approach [26].
When analyzing the series of dengue cases, it is observed that outbreaks occur every three years and importantly, the number of cases per season differed, more cases were reported in the dry seasons than in the rainy seasons. Although the dry season includes 7 months (December–February and June–September), and the rainy season includes only 5 (March–May and October–November), the former still has a higher mean number of cases per month. High temperatures, relative humidity, precipitation are well known factors that related to dengue transmission [53,54]. These factors facilitate Ae. aegypti, population growth, but in Girardot the increase in tourism (presence of susceptible populations, higher population density) during the dry seasons is also an important factor that favors virus transmission and can have an effect on the increase of dengue cases reported during this season.
Despite low dengue transmission reported during the intervention phases the results indicate that the areas covered with the intervention reported a reduced dengue incidence over the 6 to 12 months compared to control areas, although in both areas the incidence increased. The difference in dengue incidences seen per sector after the implementation phase may be due to the reduced use of container covers over time. Follow-up of sector 1 was performed 12 months after intervention implementation compared to sector 2, where follow-up was performed after 6 months. A variety of studies argue that the use of an intervention tends to decrease over time [55–57]. As with any vector control measure, a consistent level of compliance is needed by household members to gain sustainability of the intervention [58,59]. There is a need for identifying factors capable of achieving permanent changes in human behavior. Moreover, the percentage of productive container coverage per sector did not reach 100% (Sector 1, 39.54% of coverage and Sector 2, 50.39% of coverage). High coverage is needed for the intervention to have a broader impact [55–57]. The main reason for the limited coverage was the inaccessibility of participant houses, even after three visits. It is important to point out that Girardot is a touristic site and many of the houses are the “second residences” of inhabitants of other cities (primarily Bogotá) for recreational purposes [37].
More than half of the study population comprised schoolchildren who attend school during the daytime. Schoolchildren participated in mobilization activities but not breeding-site interventions nor were screens for classrooms implemented in schools. Another important age group are young men and women who spend significant proportions of time in places of work and in commercials sites, neither of which were included as interventions sites. A growing body of evidence [22,60–62]has shown that human movement is an important consideration when analysing the effectiveness of vector interventions and understanding dengue epidemiology. Previous studies have shown that transmission of dengue virus appears to be largely driven by infections centered in and around the home, with the majority of cases related to one another occurring in people who live less than 200 meters apart, supporting a role for targeted vector control around the residences of detected cases [63].
Furthermore, it is important to consider other locations where individuals tend to gather and spend significant amounts of time, as they may play an important role in the virus cycle. Sites such as schools pose a risk of transmission as there may be abundant breeding containers for Aedes vectors and will contain an aggregation of students during the daytime [64–66].
Defining effectiveness is one of the elements for scaling-up an intervention into public health policies. However, other criteria are equally important, including acceptability, reach, adoption, ease of delivery, alignment with local policies and cost [67–69]. Further analysis of the fidelity [70–72] of the intervention and cost effectiveness are being conducted to have a broader picture for decision making.
Limitations
Using secondary quantitative information of notified dengue cases from SIVIGILA possess several challenges, which has been evident in studies in other countries such as Colombia [32,73–75]. The surveillance system only captures symptomatic patients who sought treatment at health care services, and are registered with a residential address that is not necessary the location of dengue transmission. In addition, no specific serotype information is reported.
Another limitation is the available spatial information (road network, neighbourhoods and blocks) and the address and neighbourhood fields in the SIVIGILA database required for identifying dengue cases. The address and neighbourhood fields are not standardized and an important work of filtering information was needed to decrease the error when localizing each dengue case by sectors and intervention and control areas; but, seventy eight percent of dengue reported cases were able to geo-locate, underestimating the true incidence.
In addition, during intervention dengue incidence decreased during this period following the trend of dengue peaks in Girardot and elsewhere every three years. It would be expected that at higher incidence of dengue the impact of intervention may have been higher than the one reported in our follow up. The decrease of dengue cases during the scaling-up phases can be a long-term result from previous interventions carried out in Girardot since 2012 combined with enhanced vector control actions implemented by the local health authorities due to the re-emergence of chikungunya and Zika viruses.
Conclusion
The aim of dengue vector control is to maintain Ae. aegypti populations below or close to minimal transmission thresholds, slow the force of dengue-virus transmission, and reduce sequential infections with different serotypes. Here an intervention was evaluated for its capacity to reduce notified dengue cases by targeting the most productive dengue vector containers. The results indicate a reduction in dengue incidence compared to matched controls sites, although this is probably an underestimated of the true potential of the intervention. Greater coverage, reaching other sectors and other high-risk transmission areas (Public spaces such as school and commercial sites), and improved surveillance system are required for maximising the effect of the intervention.
Supporting information
S2 Data. Data base for Diff in diff estimation.
https://doi.org/10.1371/journal.pone.0230486.s002
(XLSX)
Acknowledgments
The authors are grateful to the staff members of the Health Secretary of Girardot for their dedicated service during the study, Manuel Diaz and Erica Lorena Ramirez and to various members of the vector control team. We are grateful to Hector Rueda and the field team (Isabel Rodriguez) for the continuous support during field activities. We thank all members of the core Fundación Santa Fe de Bogotá study team: Simon Tomassi and Mauricio Fuentes for their support in GIS, Diana García for expert entomological assistance in the field, carrying out specie identification and processing and entering data into dataset and to Tatiana García as assistant study coordinator. Finally, we are grateful to all residents of Girardot for their participation and assistance throughout the study.
References
- 1. Kraemer MUG, Sinka ME, Duda KA, Mylne AQN, Shearer FM, Barker CM, et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. 2015;1–18.
- 2.
World Health Organization. WHO | Global vector control response 2017–2030 [Internet]. WHO. World Health Organization; 2018 [cited 2018 Oct 19]. Available from: http://www.who.int/vector-control/publications/global-control-response/en/
- 3. Torres J, Castro J. The health and economic impact of dengue in Latin America. Cad Saude Pública, Rio Janeiro [Internet]. 2007;23(S 1):23–31. Available from: http://www.scielo.br/scielo.php?script = sci_arttext&pid = S0102-311X2007001300004&lng = en&nrm = iso&tlng = en
- 4. Shepard DS, Undurraga EA, Halasa YA, Stanaway JD. The global economic burden of dengue: a systematic analysis. Lancet Infect Dis [Internet]. 2016 Aug [cited 2018 Oct 19];16(8):935–41. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27091092 pmid:27091092
- 5. Alfonso-Sierra E, Basso C, Beltrán-Ayala E, Mitchell-Foster K, Quintero J, Cortés S, et al. Innovative dengue vector control interventions in Latin America: what do they cost? Pathog Glob Health [Internet]. 2016 Jan 2;110(1):14–24. Available from: http://www.tandfonline.com/doi/full/10.1080/20477724.2016.1142057 pmid:26924235
- 6. Carrasco LR, Lee LK, Lee VJ, Ooi EE, Shepard DS, Thein TL, et al. Economic impact of dengue illness and the cost-effectiveness of future vaccination programs in singapore. PLoS Negl Trop Dis. 2011;5(12).
- 7. Castro Rodríguez R, Carrasquilla G, Porras A, Galera-Gelvez K, Lopez Yescas JG, Rueda-Gallardo JA. The Burden of Dengue and the Financial Cost to Colombia, 2010–2012. Am J Trop Med Hyg [Internet]. 2016 [cited 2018 Oct 19];94(5):1065–72. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26928834 pmid:26928834
- 8. Gubler DJ. Editorial The Economic Burden of Dengue. Am J Trop Med Hyg [Internet]. 2012 [cited 2018 Oct 19];86(5):743–4. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3335674/pdf/tropmed-86-743.pdf pmid:22556068
- 9. Maria Turchi Martelli C, Bosco Siqueira Junior J, Perpetua Palha Dias Parente M, Laura de Sene Amancio Zara A, Silva Oliveira C, Braga C, et al. Economic Impact of Dengue: Multicenter Study across Four Brazilian Regions. 2015 [cited 2018 Oct 19]; Available from: http://aplicacao.saude.gov.br/plataformabrasil
- 10. Nishikawa AM, Clark OA, Genovez V, Pinho A, Durand L. Economic impact of dengue in tourism in Brazil. Value Heal [Internet]. 2016 [cited 2018 Oct 19];19(3):A216. Available from: https://www.valueinhealthjournal.com/article/S1098-3015(16)01285-7/pdf
- 11. Arunachalam N, Tyagi BK, Samuel M, Krishnamoorthi R, Manavalan R, Tewari SC, et al. Community-based control of Aedes aegypti by adoption of eco-health methods in Chennai City, India. Pathog Glob Health [Internet]. 2012 Dec 12 [cited 2018 Oct 21];106(8):488–96. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23318241 pmid:23318241
- 12. Arunachalam N, Tana S, Espino F, Kittayapong P, Abeyewickreme W, Wai KT, et al. Eco-bio-social determinants of dengue vector breeding: a multicountry study in urban and periurban Asia. Bull World Heal Organ [Internet]. 2010 [cited 2018 Oct 21];88:173–84. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2828788/pdf/09-067892.pdf
- 13. Quintero J, Brochero H, Manrique-Saide P, Barrera-Pérez M, Basso C, Romero S, et al. Ecological, biological and social dimensions of dengue vector breeding in five urban settings of Latin America: A multi-country study. BMC Infect Dis [Internet]. 2014 Dec 21 [cited 2018 Oct 21];14(1):38. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24447796
- 14. Mayer S V, Tesh RB, Vasilakis N. The emergence of arthropod-borne viral diseases: A global prospective on dengue, chikungunya and zika fevers. Acta Trop [Internet]. 2016/11/19. 2017 Feb;166:155–63. Available from: https://www.ncbi.nlm.nih.gov/pubmed/27876643 pmid:27876643
- 15. Horstick O, Runge-Ranzinger S, Nathan MB, Kroeger A. Dengue vector-control services: how do they work? A systematic literature review and country case studies. Trans R Soc Trop Med Hyg [Internet]. 2010 Jun 1;104(6):379–86. Available from: https://doi.org/10.1016/j.trstmh.2009.07.027
- 16. Ardila Pinto F, Martínez S, Fuentes M, Borrero E. Análisis de las demoras en salud en personas que enfermaron de gravedad o fallecieron por dengue en cinco ciudades de Colombia. Vol. 25, Physis: Revista de Saúde Coletiva. scielo; 2015. p. 571–92.
- 17. Schmunis GA, Dias JCP. La reforma del sector salud, descentralización, prevención y control de enfermedades transmitidas por vectores. Vol. 16, Cadernos de Saúde Pública. scielo; 2000. p. S117–23.
- 18. Eisenstein M. Disease: Poverty and pathogens. Nature [Internet]. 2016 Mar 16;531:S61. Available from: https://doi.org/10.1038/531S61a pmid:26981732
- 19. Mulligan K, Dixon J, Sinn C-LJ, Elliott SJ. Is dengue a disease of poverty? A systematic review. Pathog Glob Health [Internet]. 2015 Feb;109(1):10–8. Available from: https://www.ncbi.nlm.nih.gov/pubmed/25546339 pmid:25546339
- 20. Lotufo PA. Zika epidemic and social inequalities: Brazil and its fate. Vol. 134, Sao Paulo Medical Journal. scielo; 2016. p. 95–6.
- 21. Smitha DL, Perkin TA, Reiner RC, Barker CM, Niu T, Chaves LF, et al. Recasting the theory of mosquito-borne pathogen transmission dynamics and control. Trans R Soc Trop Med Hyg. 2014;108(4):185–97. pmid:24591453
- 22. Stoddard ST, Forshey BM, Morrison AC, Paz-Soldan VA, Vazquez-Prokopec GM, Astete H, et al. House-to-house human movement drives dengue virus transmission. Proc Natl Acad Sci U S A [Internet]. 2012/12/31. 2013 Jan 15;110(3):994–9. Available from: https://www.ncbi.nlm.nih.gov/pubmed/23277539 pmid:23277539
- 23. Reiner RC Jr, Stoddard ST, Scott TW. Socially structured human movement shapes dengue transmission despite the diffusive effect of mosquito dispersal. Epidemics [Internet]. 2014/01/08. 2014 Mar;6:30–6. Available from: https://www.ncbi.nlm.nih.gov/pubmed/24593919 pmid:24593919
- 24.
HANDBOOK for Integrated Vector Management.
- 25. San Martín JL, Brathwaite-Dick O. La Estrategia de Gestión Integrada para la Prevención y el Control del Dengue en la Región de las Américas. Rev Panam Salud Pública [Internet]. 2007 Jan [cited 2018 Oct 21];21(1):55–63. Available from: http://www.scielosp.org/scielo.php?script = sci_arttext&pid = S1020-49892007000100011&lng = es&nrm = iso&tlng = es pmid:17439693
- 26. Bowman LR, Donegan S, McCall PJ. Is Dengue Vector Control Deficient in Effectiveness or Evidence?: Systematic Review and Meta-analysis. PLoS Negl Trop Dis. 2016;
- 27. Sommerfeld J, Kroeger A. Eco-bio-social research on dengue in Asia: a multicountry study on ecosystem and community-based approaches for the control of dengue vectors in urban and peri-urban Asia. Pathog Glob Health [Internet]. 2012 Dec [cited 2018 Oct 21];106(8):428–35. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23318234 pmid:23318234
- 28. Sommerfeld J, Kroeger A. Innovative community-based vector control interventions for improved dengue and Chagas disease prevention in Latin America: introduction to the special issue. Trans R Soc Trop Med Hyg [Internet]. 2015 Feb 1 [cited 2018 Oct 21];109(2):85–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25604757 pmid:25604757
- 29. García-Betancourt T, González-Uribe C, Quintero J, Carrasquilla G. Ecobiosocial Community Intervention for Improved Aedes aegypti Control Using Water Container Covers to Prevent Dengue: Lessons Learned from Girardot Colombia. Ecohealth [Internet]. 2014 Sep 25 [cited 2018 Oct 21];11(3):434–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24962850 pmid:24962850
- 30. Quintero J, Garcia-Betancourt T, Cortes S, Garcia D, Alcala L, Gonzalez-Uribe C, et al. Effectiveness and feasibility of long-lasting insecticide-treated curtains and water container covers for dengue vector control in Colombia: a cluster randomised trial. Trans R Soc Trop Med Hyg [Internet]. 2015 Feb 1;109(2):116–25. Available from: https://academic.oup.com/trstmh/article-lookup/doi/10.1093/trstmh/tru208 pmid:25604762
- 31. Quintero J, Carrasquilla G, Suárez R, González C, Olano VA. An ecosystemic approach to evaluating ecological, socioeconomic and group dynamics affecting the prevalence of Aedes aegypti in two Colombian towns. Cad Saude Publica [Internet]. 2009 [cited 2018 Oct 22];25(suppl 1):s93–103. Available from: http://www.scielo.br/scielo.php?script = sci_arttext&pid = S0102-311X2009001300009&lng = en&tlng = en
- 32. Villar LA, Rojas DP, Besada-Lombana S, Sarti E. Epidemiological Trends of Dengue Disease in Colombia (2000–2011): A Systematic Review. PLoS Negl Trop Dis [Internet]. 2015 Mar 19;9(3):e0003499. Available from: https://doi.org/10.1371/journal.pntd.0003499 pmid:25790245
- 33. Padilla JC, Rojas DP, Sáenz-Gómez R. Dengue en Colombia: epidemiología de la reemergencia a la hiperendemia [Internet]. Primera. Bogotá; 2012. 281 p. Available from: https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/INEC/INV/Dengue en Colombia.pdf
- 34.
Instituto Nacional de Salud. Boletín Epidemiológico Semanal, Semana epidemiológica número 40 de 2015 (04 oct. al 10 oct.) [Internet]. Bogotá; 2015 [cited 2018 Oct 22]. Available from: http://www.who.int/mediacentre/factsheets/fs394/es/
- 35. Rojas DP, Dean NE, Yang Y, Kenah E, Quintero J, Tomasi S, et al. The epidemiology and transmissibility of Zika virus in Girardot and San Andres island, Colombia, September 2015 to January 2016. Euro Surveill [Internet]. 2016 Jul 14 [cited 2018 Oct 22];21(28). Available from: http://www.ncbi.nlm.nih.gov/pubmed/27452806
- 36. Alcalá LA, Quintero J, González C, Brochero H, Brochero H. Productividad de Aedes aegypti (L.) (Diptera: Culicidae) en viviendas y espacios públicos en una ciudad endémica para dengue en Colombia. Biomédica [Internet]. 2015 Mar 5 [cited 2018 Oct 22];35(2):258–68. Available from: https://www.revistabiomedica.org/index.php/biomedica/article/view/2567 pmid:26535548
- 37. Fuentes-Vallejo M, Higuera-Mendieta DR, GarcÃ\-a-Betancourt T, AlcalÃ!`-Espinosa LA, GarcÃ\-a-SÃ!`nchez D, MunÃ\copyrightvar-Cagigas DA, et al. Territorial analysis of Aedes aegypti distribution in two Colombian cities: a chorematic and ecosystem approach. Cad SaÃ\textordmasculinede PÃ\textordmasculineblica [Internet]. 2015;31:517–30. Available from: http://www.scielo.br/scielo.php?script = sci_arttext&pid = S0102-311X2015000300517&nrm = iso
- 38. Overgaard HJ, Olano VA, Jaramillo JF, Matiz MI, Sarmiento D, Stenström TA, et al. A cross-sectional survey of Aedes aegypti immature abundance in urban and rural household containers in central Colombia. Parasit Vectors [Internet]. 2017;10(1):356. Available from: https://doi.org/10.1186/s13071-017-2295-1 pmid:28750651
- 39.
Instituto Nacional de Salud. Dengue Surveillance Protocol [Internet]. 2012. Available from: http://www.paho.org/col/index.php?option=com_docman&task=doc_download&gid=1216&Itemid=
- 40. Caliendo M, Kopeinig S. SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHING. J Econ Surv [Internet]. 2008 Feb 1;22(1):31–72. Available from: https://doi.org/10.1111/j.1467-6419.2007.00527.x
- 41.
Bernal R, Peña X. Guía práctica para la evaluación de impacto [Internet]. 1st ed. Universidad de los Andes, Colombia; 2011. Available from: http://www.jstor.org/stable/10.7440/j.ctt1b3t82z
- 42.
StataCorp. Stata Statistical software. Stata: Release 13. 2013.
- 43.
QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org. 2014.
- 44. Horstick O, Runge-Ranzinger S. Protection of the house against Chagas disease, dengue, leishmaniasis, and lymphatic filariasis: a systematic review. Lancet Infect Dis [Internet]. 2018 May 1;18(5):e147–58. Available from: pmid:29074038
- 45.
Bouzid M, Brainard J, Hooper L, Hunter PR. Public Health Interventions for Aedes Control in the Time of Zikavirus–A Meta-Review on Effectiveness of Vector Control Strategies.
- 46. Alvarado-Castro V, Paredes-Solís S, Nava-Aguilera E, Morales-Pérez A, Alarcón-Morales L, Balderas-Vargas NA, et al. Assessing the effects of interventions for Aedes aegypti control: systematic review and meta-analysis of cluster randomised controlled trials. BMC Public Health [Internet]. 2017 May 30;17(Suppl 1):384. Available from: https://www.ncbi.nlm.nih.gov/pubmed/28699552 pmid:28699552
- 47. Ballenger-Browning KK, Elder JP. Multi-modal Aedes aegypti mosquito reduction interventions and dengue fever prevention. Trop Med Int Heal [Internet]. 2009 Dec;14(12):1542–51. Available from: http://doi.wiley.com/10.1111/j.1365-3156.2009.02396.x
- 48. Bowman LR, Runge-Ranzinger S, McCall PJ. Assessing the Relationship between Vector Indices and Dengue Transmission: A Systematic Review of the Evidence. PLoS Neglected Tropical Diseases. 2014.
- 49. Cromwell EA, Stoddard ST, Barker CM, Van Rie A, Messer WB, Meshnick SR, et al. The relationship between entomological indicators of Aedes aegypti abundance and dengue virus infection. PLoS Negl Trop Dis [Internet]. 2017 Mar;11(3):e0005429. Available from: http://europepmc.org/articles/PMC5363802 pmid:28333938
- 50. Wilson AL, Dhiman RC, Kitron U, Scott TW, van den Berg H, Lindsay SW. Benefit of Insecticide-Treated Nets, Curtains and Screening on Vector Borne Diseases, Excluding Malaria: A Systematic Review and Meta-analysis. PLoS Negl Trop Dis. 2014;
- 51. Liyanage P, Rocklöv J, Tissera H, Palihawadana P, Wilder-Smith A, Tozan Y. Evaluation of intensified dengue control measures with interrupted time series analysis in the Panadura Medical Officer of Health division in Sri Lanka: a case study and cost-effectiveness analysis. Lancet Planet Heal [Internet]. 2019;3(5):e211–8. Available from: http://dx.doi.org/10.1016/S2542-5196(19)30057-9
- 52. Kroeger A, Lenhart A, Ochoa M, Villegas E, Levy M, Alexander N, et al. Effective control of dengue vectors with curtains and water container covers treated with insecticide in Mexico and Venezuela: cluster randomised trials. BMJ [Internet]. 2006 May 27 [cited 2018 Oct 21];332(7552):1247–52. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16735334 pmid:16735334
- 53. Brady OJ, Gething PW, Bhatt S, Messina JP, Brownstein JS, Hoen AG, et al. Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Negl Trop Dis. 2012;6(8):e1760. pmid:22880140
- 54. Messina JP, Brady OJ, Pigott DM, Brownstein JS, Hoen AG, Hay SI. A global compendium of human dengue virus occurrence. 2014.
- 55. Kroeger A, Lenhart A, Ochoa M, Villegas E, Levy M, Alexander N, et al. Effective control of dengue vectors with curtains and water container covers treated with insecticide in Mexico and Venezuela: cluster randomised trials. BMJ [Internet]. 2006 May 25;332(7552):1247 LP– 1252. Available from: http://www.bmj.com/content/332/7552/1247.abstract pmid:16735334
- 56. Vanlerberghe V, Villegas E, Oviedo M, Baly A, Lenhart A, McCall PJ, et al. Evaluation of the effectiveness of insecticide treated materials for household level dengue vector control. PLoS Negl Trop Dis. 2011;
- 57. Vanlerberghe V, Villegas E, Jirarojwatana S, Santana N, Trongtorkit Y, Jirarojwatana R, et al. Determinants of uptake, short-term and continued use of insecticide-treated curtains and jar covers for dengue control. Trop Med Int Heal. 2011;16(2).
- 58. Aunger R, Curtis V. Behaviour Centred Design: towards an applied science of behaviour change. Health Psychol Rev. 2016;10(4):425–46. pmid:27535821
- 59. Alison Buttenheim, Michael Z L, Castillo-Neyra R, McGuire M, Toledo Vizcarra AM, Riveros Mollesaca LM, et al. A behavioral design approach to improving vector-control campaigns. SocArXiv. 2018;1–32.
- 60. Stoddard ST, Morrison AC, Vazquez-Prokopec GM, Paz Soldan V, Kochel TJ, Kitron U, et al. The Role of Human Movement in the Transmission of Vector-Borne Pathogens. PLoS Negl Trop Dis [Internet]. 2009 Jul 21;3(7):e481–. Available from: https://doi.org/10.1371/journal.pntd.0000481 pmid:19621090
- 61. Enduri MK, Jolad S. Dynamics of dengue disease with human and vector mobility. Spat Spatiotemporal Epidemiol [Internet]. 2018;25:57–66. Available from: http://www.sciencedirect.com/science/article/pii/S1877584517300229 pmid:29751893
- 62. Reiner RC, Stoddard ST, Scott TW. Socially structured human movement shapes dengue transmission despite the diffusive effect of mosquito dispersal. Epidemics [Internet]. 2014;6:30–6. Available from: http://www.sciencedirect.com/science/article/pii/S1755436513000558 pmid:24593919
- 63. Salje H, Lessler J, Maljkovic Berry I, Melendrez MC, Endy T, Kalayanarooj S, et al. Dengue diversity across spatial and temporal scales: Local structure and the effect of host population size. Science [Internet]. 2017 Mar 24;355(6331):1302–6. Available from: https://www.ncbi.nlm.nih.gov/pubmed/28336667 pmid:28336667
- 64. Hernández-Suárez CM, Mendoza-Cano O. Empirical evidence of the effect of school gathering on the dynamics of dengue epidemics. Glob Health Action [Internet]. 2016 Jan 6;9:28026. Available from: https://www.ncbi.nlm.nih.gov/pubmed/26743450 pmid:26743450
- 65. Ooi EE, Hart TJ, Tan HC, Chan SH. Dengue seroepidemiology in Singapore. Lancet [Internet]. 2001 Mar;357(9257):685–6. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0140673600041374 pmid:11247554
- 66. Endy TP, Nisalak A, Chunsuttiwat S, Libraty DH, Green S, Rothman AL, et al. Spatial and Temporal Circulation of Dengue Virus Serotypes: A Prospective Study of Primary School Children in Kamphaeng Phet, Thailand. Am J Epidemiol [Internet]. 2002 Jul 1;156(1):52–9. Available from: https://doi.org/10.1093/aje/kwf006 pmid:12076888
- 67. Milat AJ, King L, Bauman AE, Redman S. The concept of scalability: Increasing the scale and potential adoption of health promotion interventions into policy and practice. Health Promot Int. 2013;28(3):285–98. pmid:22241853
- 68. Indig D, Lee K, Grunseit A, Milat A, Bauman A. Pathways for scaling up public health interventions. BMC Public Health. 2017;18(1):1–11.
- 69. Barker PM, Reid A, Schall MW. A framework for scaling up health interventions: Lessons from large-scale improvement initiatives in Africa. Implement Sci [Internet]. 2016;11(1):1–11. Available from: http://dx.doi.org/10.1186/s13012-016-0374-x
- 70. Hasson Henna. Systematic Evaluation of Implementation Fidelity of Complex Intervensions in Health and Social Care. Implement Sci. 2010;1–9.
- 71. Moore GF, Evans RE. What theory, for whom and in which context? Reflections on the application of theory in the development and evaluation of complex population health interventions. SSM—Popul Heal [Internet]. 2017;3(December 2016):132–5. Available from: http://dx.doi.org/10.1016/j.ssmph.2016.12.005
- 72. Campbell M, Fitzpatrick R, Haines A, Kinmonth AL, Sandercock P, Spiegelhalter D, et al. Framework for design and evaluation of complex interventions to improve health Framework for trials of complex interventions. Br Med J. 2000;321(7262):694–6.
- 73. Sarti E, L’Azou M, Mercado M, Kuri P, Siqueira JB, Solis E, et al. A comparative study on active and passive epidemiological surveillance for dengue in five countries of Latin America. Int J Infect Dis [Internet]. 2016 Mar 1;44:44–9. Available from: pmid:26836763
- 74. Gómez-Dantés H, Willoquet JR. Dengue in the Americas: challenges for prevention and control. Vol. 25, Cadernos de Saúde Pública. scielo; 2009. p. S19–31.
- 75. Coelho GE, Leal PL, Cerroni M de P, Simplicio ACR, Siqueira JB Jr. Sensitivity of the Dengue Surveillance System in Brazil for Detecting Hospitalized Cases. PLoS Negl Trop Dis [Internet]. 2016 May 18;10(5):e0004705–e0004705. Available from: https://www.ncbi.nlm.nih.gov/pubmed/27192405 pmid:27192405