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Estimating effectiveness of case-area targeted response interventions against cholera in Haiti

  1. Edwige Michel
  2. Jean Gaudart
  3. Samuel Beaulieu
  4. Gregory Bulit
  5. Martine Piarroux
  6. Jacques Boncy
  7. Patrick Dely
  8. Renaud Piarroux  Is a corresponding author
  9. Stanislas Rebaudet  Is a corresponding author
  1. Ministry of Public Health and Population, Directorate of Epidemiology Laboratory and Research, Haiti
  2. Aix-Marseille Université, APHM, INSERM, IRD, SESSTIM, Hop Timone, BiSTIC, Biostatistics and ICT, France
  3. United Nations Children's Fund, Haiti
  4. Centre d'Épidémiologie et de Santé Publique des Armées, Service de Santé des Armées, France
  5. Ministry of Public Health and Population, National Laboratory of Public Health, Haiti
  6. Sorbonne Université, INSERM, Institut Pierre-Louis d’Epidémiologie et de Santé Publique (IPLESP), AP-HP, Hôpital Pitié-Salpêtrière, France
  7. APHM, Hôpital Européen, Aix Marseille Université, INSERM, IRD, SESSTIM, IPLESP, France
  8. Sorbonne Université, INSERM, Institut Pierre-Louis d’Epidémiologie et de Santé Publique, France
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Cite this article as: eLife 2019;8:e50243 doi: 10.7554/eLife.50243

Abstract

Case-area targeted interventions (CATIs) against cholera are conducted by rapid response teams, and may include various activities like water, sanitation, hygiene measures. However, their real-world effectiveness has never been established. We conducted a retrospective observational study in 2015–2017 in the Centre department of Haiti. Using cholera cases, stool cultures and CATI records, we identified 238 outbreaks that were responded to. After adjusting for potential confounders, we found that a prompt response could reduce the number of accumulated cases by 76% (95% confidence interval, 59 to 86) and the outbreak duration by 61% (41 to 75) when compared to a delayed response. An intense response could reduce the number of accumulated cases by 59% (11 to 81) and the outbreak duration by 73% (49 to 86) when compared to a weaker response. These results suggest that prompt and repeated CATIs were significantly effective at mitigating and shortening cholera outbreaks in Haiti.

Introduction

On October 2017, Global Task Force on Cholera Control (GTFCC) partners committed to reduce cholera deaths by 90% and to eliminate the disease in 20 countries by 2030, through a multi-sectoral approach (Anon, 2017). This new global strategy planned to combine long-term disease prevention in cholera hotspots with sustainable WaSH (water sanitation and hygiene) solutions and large-scale use of oral cholera vaccine (OCV), with the short-term strengthening of early detection of outbreaks and immediate and effective response through reactive OCV campaigns and rapid response teams (RRTs) (Anon, 2017). RRTs, also referred as mobile teams, have been successfully implemented against polio or Ebola outbreaks (Global Polio Eradication Initiative, 2017; World Health Organization (WHO), 2014). However, response interventions targeted to neighbours of cholera cases (case-area targeted interventions [CATIs]) using combinations of water, sanitation, and hygiene measures, and/or prophylactic antibiotics have rarely been documented, evaluated or promoted against cholera in the published literature (Voelckel, 1971; Piarroux and Bompangue, 2011; Deepthi et al., 2013; Taylor et al., 2015; Mwambi et al., 2016; Finger et al., 2018).

In practice, CATIs are supported by the frequent household transmission of Vibrio cholerae O1 (Weil et al., 2009; Weil et al., 2014; Taylor et al., 2015; Domman et al., 2018), the increased cholera risk among neighbours living within a few dozen meters of cases during the few days following disease onset (Debes et al., 2016; Azman et al., 2018), and the significant protection of household contacts of cholera patients by promoting hand washing with soap and treatment of water (George et al., 2016). A micro-simulation modelling study suggests that early CATIs can be more resource-efficient than mass interventions against cholera (Finger et al., 2018). However, CATI effectiveness has never been evaluated in a real-world setting.

Haiti has implemented CATIs as a national coordinated strategy against cholera since July 2013 (Rebaudet et al., 2019a). After the disease was accidentally imported in October 2010 (Piarroux et al., 2011), the country experienced a massive epidemic, with a total of 820,085 suspected cases and 9792 cholera-related deaths recorded by April 20, 2019 by the Haitian Ministry of Public Health and Population (MSPP) (http://mspp.gouv.ht/newsite/documentation.php, accessed Jul 1, 2019). In 2013, only 68% of Haitian households drank from improved water sources, 26% had access to improved sanitation facilities and 34% had water and soap available for hand washing (République d’Haïti, Ministère de la Santé Publique (MSPP), 2013). But little of the $1.5 billion USD designated by the Plan for the Elimination of Cholera in Haiti 2013–2022 to develop water and sanitation infrastructures has been expended or pledged so far (Ministry of Public Health and Population, National Directorate for Water Supply and Sanitation, 2013). Two pilot OCV reactive campaigns vaccinated approximately 100,000 people in 2012 and to date, additional campaigns have targeted about 10% of the Haitian population (Ivers, 2017; Poncelet, 2015). UNICEF thus backed the MSPP and the Haitian National Directorate for Water and Sanitation (DINEPA) to launch a complementary nationwide coordinated cholera alert-response strategy aiming to interrupt local cholera outbreaks at an early stage (Rebaudet et al., 2019a). This program planned to rapidly send multisectoral rapid response teams to every patient household and neighbourhood in order to identify additional cases, to decontaminate patient premises, to educate on risk factors and methods of prevention and management, to distribute soap and oral rehydration salts (ORS), to chlorinate water at the household level or directly at collection points, and to propose prophylactic antibiotics to close contacts of cholera cases.

This response CATI strategy was implemented gradually from mid-2013 and became an essential pillar of the fight when the national cholera elimination plan was updated in mid-2016 (République d’Haïti, Ministère de la Santé Publique et de la Population, Direction Nationale de l’Eau Potable et de l’Assainissement, 2016). Implementation of this strategy offers a unique opportunity to evaluate the effectiveness of CATIs against cholera outbreaks. Based on available data, we conducted a retrospective observational study over 3 years in the Centre department of Haiti addressing the outcome of local cholera outbreaks according to the response promptness and intensity. We present here the first effectiveness estimates for rapid and targeted response interventions against cholera.

Results

Outbreak and response characteristics

From January 1, 2015, to December 31, 2017, the line-listing of the Centre department reported a total of 10,931 patients with suspected cholera, including 10,428 with a comprehensive location. Details on cholera cases are summarized in Appendix 1—table 1. Intravenous (IV) rehydration was mentioned for 2144 of them. These patients originated from 1497 localities and their time distribution exhibited a marked seasonality (Figure 1A and B). Concomitantly, 1070 stools sampled in Centre department were cultured for V. cholerae O1, of which 509 (48%) were positive (Figure 1A), including 360 with a comprehensive location. Additional details on cholera cultures are summarized in Appendix 1—table 1.

Figure 1 with 1 supplement see all
Daily evolution of (A) suspected cholera cases, cases with severe dehydration and stool cultures positive for V. cholerae O1, (B) accumulated rainfall, (C) localities with a current cholera outbreak, and (D) case-area targeted interventions (CATIs), in the Centre department of Haiti between January 2015 and December 2017.

Defining outbreaks by the occurrence of at least two suspected cholera cases with at least one severely dehydrated case or positive culture, within the same locality, during a three-day time window, and after a refractory period of at least 21 days with no case, we identified 452 cholera outbreaks (Figure 2), which mainly occurred during case incidence peaks (Figure 1C) and were distributed across 290 localities (Figure 3). The median cumulative number of cases per outbreak was 3 (Interquartile range [IQR], 4), and the median duration of outbreaks was 5 days (IQR, 18).

Identification of outbreaks and stratification of outbreaks according to response promptness and response intensity.
Cholera outbreaks in the Centre department, Haiti, between January 2015 and December 2017: spatial distribution and number of identified outbreaks (size of pie charts); proportion of outbreaks that were and were not responded with at least one complete CATI (angle of green and red slices, respectively).

Over the same period, 3,887 CATIs were notified in the Centre department by non-governmental organization (NGO) rapid response teams, including 3,533 CATIs (91%) with a comprehensive location, and 2719 (70%) conducted in tandem with staff of EMIRA (Equipe mobile d’intervention rapide, Rapid intervention mobile team, i.e. cholera rapid response team of the MSPP) (Figure 1D). Based on CATI activities summarized in Appendix 1—table 1, a total of 3,596 CATIs (93%) were categorized as complete (at least decontamination, education and distribution of chlorine tablets), and 1922 (49%) also included a reported antibiotic prophylaxis. Overall, 633 complete CATIs (18%) were conducted in localities experiencing an identified cholera outbreak (Figure 1D).

Analysis of confounders

Baseline characteristics of outbreaks and comparisons between the four classes of response promptness are presented in Table 1. The time to the first complete CATI (response promptness) significantly improved during the six semesters of the study, was significantly higher in more densely populated localities, and was lower in localities targeted by a previous OCV campaign. Outbreaks with prompter responses exhibited significantly more positive cultures during the first three days than outbreaks with delayed responses. None of the other covariates were significantly associated with response intensity (Table 1).

Table 1
Baseline characteristics of outbreaks that were responded to, according to the response promptness (time to the first complete case-area targeted intervention).
All outbreaksOutbreaks responded to with ≥ 1 complete CATIClass of response promptness
(time to the first complete CATI)
Comparison between classes of promptness
>7 days3 to 7 days2 days≤1 dayHazard ratio
(95% CI)
p-value
No. of outbreaks452238
(53%)
48
(20%)
40
(17%)
43
(18%)
107
(45%)
Semester sinceJanuary 20151.10e7
(1.64e6 to 7.40e7)
<0.0001*
Population density, median (IQR; inhab./km2)3.5
(6.5)
3.6
(11.5)
4.3
(10.6)
2.8
(4.6)
3.7
(8.9)
3.8
(12.6)
1.01
(1 to 1.02)
0.0039*
Travel time to thenearest town, median (IQR;minutes)26.7
(33.2)
24.9
(31.8)
30 (3
4.3)
27.1
(42.1)
24.8
(28.4)
22
(32)
1
(0.99 to 1)
0.274
Accumulated incidence between 2010 and 2014, median (IQR; per 1000 inhabitants)103.8
(77.5)
103.8
(77.5)
103.8
(131.4)
103.8
(49.1)
103.8
(56.6)
103.8
(77.5)
0.4
(0.09 to 1.83)
0.237
Coverage of OCVcampaigns between 2012 and 2014, median (IQR;%) [mean, SD]0% (86)
[25%, 40]
0% (0)
[21%, 38]
0% (86)
[30%, 42]
0% (0)
[18%, 36]
0% (0)
[15%, 33]
0% (0)
[21%, 38]
0.61
(0.38 to 0.98)
0.0393*
Previous cases inthe same localityduring the study, median (IQR; no.per year)4.3
(10.1)
5.2
(10.5)
7.2
(10.1)
5
(11.5)
6.9
(11.1)
5
(8.5)
0.99
(0.97–1.02)
0.6540
Previous completeCATIs in the samelocality during thestudy, median(IQR; no. per year)0.2
(1.9)
0.9
(2.7)
0.7
(2.2)
0.5
(2.3)
1.2
(2.9)
1.4
(2.7)
0.98
(0.91–1.06)
0.6500
Daily rainfall during outbreak,median (IQR; mm)6.6
(13.3)
7.7
(13.3)
12
(6)
6.9
(10.8)
10
(13.7)
3.6
(14.4)
0.99
(0.96 to 1.03)
0.638
No. of cases duringthe first 3 daysofoutbreak, median (IQR) [mean, SD]2 (1)
[2.5, 1.5]
2 (1)
[2.7, 1.9]
2 (1)
[2.5, 1.0]
2.5 (1)
[3.4, 2.1]
2 (0.5)
[2.8, 2.4]
2 (1)
[2.5, 1.9]
1.04
(0.93 to 1.16)
0.488
No. of positiveculture during thefirst 3 daysof outbreak, median(IQR) [mean, SD]0 (0)
[0.2, 0.6]
0 (0)
[0.3, 0.7]
0 (0)
[0.1, 0.3]
0 (0)
[0.2, 0.5]
0 (1)
[0.5, 0.9]
0 (1)
[0.4, 0.7]
2.03
(1.3 to 3.17)
0.0018*
  1. CATI, case-area targeted intervention; IQR, interquartile range; SD, standard deviation.

    Univariate comparisons between classes of response promptness using Cox models for Andersen-Gill counting process (AG-CP), with time to the first complete CATI modelled as a recurrent time-to-event outcome.

  2. *Significant p-value.

Baseline characteristics of outbreaks and comparisons between the four classes of response intensity are presented in Table 2. The numbers of complete CATIs per week and per case (response intensity) significantly improved during the six semesters of the study. Outbreaks receiving more CATIs per case exhibited significantly fewer cases during the first three days than outbreaks receiving less intense responses (Table 2).

Table 2
Baseline characteristics of outbreaks that were responded to, according to the response intensity (number of complete case-area targeted interventions per week or per case).
No. of complete CATIs per weekComparison between classes of CATIs per week
<0.250.25 to 0.50.5 to 1≥1OR
(95% CI)
p-value
No. of outbreaks25
(11%)
43 (18%)120 (50%)50 (21%)
Semester since January 20151.14
(1.03 to 1.25)
0.0111*
Population density, median (IQR;inhab./km2)3.9
(11.8)
3.4
(3.8)
3.4
(12)
3.7
(11.8)
1
(1 to 1.01)
0.4093
Travel time to the nearest town, median (IQR; minutes)30
(26.8)
33.5
(41.9)
22.1
(28)
25.4
(34.2)
1
(1 to 1.01)
0.8379
Accumulated incidencebetween 2010 and 2014, median (IQR; per 1000 inhab)125.8
(250.9)
99.5
(97.8)
103.8
(49)
103.8
(79.6)
0.64
(0.37 to 1.1)
0.1037
Coverage of OCVcampaigns between2012 and 2014, median(IQR; %) [mean, SD]0% (86)
[43%, 44]
0% (0)
[11%, 29]
0% (0)
[19%, 36]
0% (86)
[25%, 40]
1.03
(0.74 to 1.44)
0.8464
Previous cases in thesame locality during thestudy, median (IQR; no.per year)10.1
(9.4)
6
(8.8)
4
(9)
5.9
(11.7)
1.01
(0.99–1.02)
0.5011
Previous complete CATIsin the same localityduring the study, median(IQR; no. per year)0.7
(2.1)
0.4
(2.5)
1
(2.7)
1.5
(3.2)
1.04
(1–1.08)
0.0763
Daily rainfall duringoutbreak, median (IQR;mm)12
(4)
8
(11.5)
6.6
(16.3)
6.2
(11.1)
0.99
(0.98 to 1.01)
0.331
No. of cases during thefirst 3 daysof outbreak,median (IQR) [mean, SD]2 (0)
[3.1, 2.7]
2 (1)
[2.7, 1.5]
2 (0)
[2.3, 1.4]
3 (2)
[3.4, 2.5]
0.81
(0.71 to 0.93)
0.3806
No. of positive cultureduring the first 3 daysofoutbreak, median (IQR) [mean, SD]0 (0)
[0.2, 0.6]
0 (0)
[0.2, 0.4]
0 (1)
[0.4, 0.8]
0 (0)
[0.2, 0.6]
1.03
(0.85 to 1.25)
0.7569
No. of complete CATIs per caseComparison between classes of CATIs per case
<0.250.25 to 0.50.5 to 1≥1OR
(95% CI)
p-value
No. of outbreaks47 (20%)56 (24%)81 (34%)54 (23%)
Semester since January20151.24
(1.13 to 1.37)
<0.0001*
Population density,median (IQR;inhab./km2)3
(5)
4.2
(14.7)
3.3
(3.6)
3.7
(12.9)
1
(1 to 1.01)
0.468
Travel time to thenearest town, median(IQR; minutes)31.2
(42.1)
17.3
(44.4)
25.5
(27.3)
18.9
(23.7)
1
(0.99 to 1)
0.344
Accumulated incidencebetween 2010 and 2014, median (IQR; per 1000 inhabitants)125.8
(77.5)
103.8
(93.5)
103.8
(64.6)
103.8
(43.3)
1.01
(0.42 to 2.43)
0.981
Coverage of OCVcampaigns between 2012 and 2014, median(IQR; %) [mean, SD]0% (86)
[30%, 42]
0% (0)
[13%, 31]
0% (0)
[21%, 38]
0% (0)
[22%, 38]
0.96
(0.6 to 1.54)
0.881
Previous cases in thesame locality during the study, median (IQR; no. per year)4.4
(9.9)
8.4
(16.3)
6.2
(7.9)
3.5
(6.4)
1
(0.98–1.02)
0.7730
Previous complete CATIsin the same locality during the study, median(IQR; no. per year)0
(1)
1.4
(4.3)
1.4
(2.7)
1
(2.6)
1.03
(0.97–1.08)
0.3550
Daily rainfall duringoutbreak, median (IQR;mm)12
(4.3)
6.1
(13.7)
5.3
(13.6)
6.1
(13.7)
1
(0.98 to 1.02)
0.983
No. of cases during thefirst 3 daysof outbreak,median (IQR) [mean, SD]3 (3)
[4.3, 3.2]
2 (1)
[2.6, 1.1]
2 (0)
[2.5, 1.3]
2 (1)
[1.8, 0.8]
0.81
(0.71 to 0.93)
0.0019*
No. of positive cultureduring the first 3 daysofoutbreak, median (IQR)[mean, SD]0 (0)
[0.2, 0.9]
0 (0)
[0.3, 0.5]
0 (0)
[0.2, 0.6]
0 (1)
[0.6, 0.7]
1.14
(0.92 to 1.42)
0.232
  1. CATI, case-area targeted intervention; IQR, interquartile range; SD, standard deviation; OR (95% CI), Odds ratio (95%-confidence interval).

    Univariate comparisons using generalized linear mixed models with CATIs/weeks ratio or CATIs/cases ratio as model outcome and a negative-binomial distribution.

  2. *Significant p-value.

CATI effectiveness according to the response promptness

There was a positive association between the time to the first complete CATI after outbreak onset, and the number of cases recorded from the fourth day of the outbreak (Figure 4A, Table 3). Consequently, the prompter the response, the higher the CATI effectiveness on the reduction of outbreak size (Table 3). Compared to a first complete CATI > 7 days after outbreak onset, the crude effectiveness of a first complete CATI ≤ 1 day (cCE1) was 83% (95% CI, 71 to 90), and after adjusting for potential confounders (aCE1), 76% (59 to 86).

Outbreak outcome according to the class of response promptness.

(A and B) and to the class of response intensity (C and D): (A and C) comparison of the outbreak size (number of suspected cholera cases from the fourth day of outbreak) and (B and D) Kaplan-Meier comparison of the outbreak duration (in days), according to the time to the first complete CATI (A and B), to the number of complete CATIs per week (C) and to the number of complete CATIs per case (D).

Table 3
CATI effectiveness (CE1) of the response promptness (time to the first complete CATI) on outbreak size (number of cases from the fourth day of outbreak).
No. of cases from the 4th day of outbreakCrude estimate of CATI effectiveness (cCE1)Adjusted estimate of CATI effectiveness (aCE1)
NMedian
(IQR)
%
(95% CI)
p-value%
(95% CI)
p-value
Time to the first complete CATI
>7 days484.5
(9.25)
RefRefRefRef
3 to 7 days402.5
(9.25)
49%
(6 to 72)
0.0318*50%
(9 to 72)
0.0222*
2 days431
(3)
76%
(55 to 87)
<0.0001*68%
(40 to 83)
0.0004*
≤1 day1070
(2)
83%
(71 to 90)
<0.0001*76%
(59 to 86)
<0.0001*
  1. CATI, case-area targeted intervention; IQR, interquartile range.

    *Significant p-value.

  2. Crude CATI effectiveness (cCE1) was estimated on the No. of cases from the fourth day of outbreak, using generalized linear mixed models with a negative-binomial distribution, as (1 – Incidence ratio).

    Estimates of CATI effectiveness (aCE1) were adjusted according to covariates for which p-values were less than 0.25 at the initial univariate step (Table 1): number of positive cultures during the first 3 days of outbreak, population density, accumulated case incidence between 2010 and 2014, coverage of OCV campaigns between 2012 and 2014 and semester.

Similarly, there was a positive association between the time to the first complete CATI after outbreak onset, and the duration of outbreaks (Figure 4B, Table 4). Consequently, the prompter the response, the higher the CATI effectiveness on the reduction of outbreak duration (Table 4). Compared to a first complete CATI > 7 days after outbreak onset, the crude effectiveness of a first complete CATI ≤ 1 day (cCE2) was 59% (36 to 74), and after adjusting for potential confounders, (aCE2) 61% (41 to 75).

Table 4
CATI effectiveness (CE2) of the response promptness (time to the first complete CATI) on outbreak duration (in days).
Duration of outbreakCrude estimate of CATI effectiveness (cCE2)Adjusted estimate of CATI effectiveness (aCE2)
NMedian
(IQR; days)
%
(95% CI)
p-value%
(95% CI)
p-value
Time to the first complete CATI
>7 days4826
(39)
RefRefRefRef
3 to 7 days4013
(33)
45%
(17 to 64)
0.0046*53%
(29 to 69)
0.0004*
2 days439
(25)
37%
(−6 to 62)
0.081027%
(−22 to 56)
0.2322
≤1 day1073
(15.5)
59%
(36 to 74)
<0.0001*61%
(41 to 75)
<0.0001*
  1. CATI, case-area targeted intervention; IQR, interquartile range.

    *Significant p-value.

  2. Crude CATI effectiveness (cCE2) was estimated on the duration of outbreak, using Cox models for Andersen-Gill counting process (AG-CP), as (1–1/hazard ratio).

    Estimates of CATI effectiveness (aCE2) were adjusted according to covariates for which p-values were less than 0.25 at the initial univariate step (Table 1): number of positive cultures during the first 3 days of outbreak, population density, accumulated case incidence between 2010 and 2014, coverage of OCV campaigns between 2012 and 2014 and semester.

CATI effectiveness according to the response intensity

In addition, there was a negative association between the number of complete CATIs per week of outbreak, and the number of cases recorded from the fourth day of outbreak (Figure 4C, Table 5). Consequently, the more intense the response, the significantly higher the CATI effectiveness was estimated to be on the reduction of outbreak size (Table 5). Compared to a number of complete CATIs < 0.25 per week, the crude effectiveness of a number of complete CATIs ≥ 1 per week (cCE3) was 74% (95% CI, 44 to 88), and after adjusting for potential confounders (aCE3), 59% (11 to 81).

Table 5
CATI effectiveness (CE3) of the response intensity (number of complete CATIs per week) on outbreak size (number of cases from the fourth day of outbreak).
No. of cases after the 4th day of outbreakCrude estimate of CATI effectiveness (cCE3)Adjusted estimate of CATI effectiveness (aCE3)
NMedian
(IQR)
%
(95% CI)
p-value%
(95% CI)
p-value
No. of complete CATIs per week
<0.25259
(8)
RefRefRefRef
0.25 to 0.5433
(3)
55%
(1 to 79)
0.0457*45%
(−17 to 74)
0.1206
0.5–11200
(3)
79%
(59 to 89)
<0.0001*70%
(42 to 84)
0.0003*
≥1501
(2.75)
74%
(44 to 88)
0.0006*59%
(11 to 81)
0.0235*
  1. CATI, case-area targeted intervention; IQR, interquartile range.

    *Significant p-value.

  2. Crude CATI effectiveness (cCE3) was estimated on the No. of cases from the fourth day of outbreak, using generalized linear mixed models with a negative-binomial distribution, as (1 – Incidence ratio).

    Estimates of CATI effectiveness (aCE3) were adjusted according to covariates for which p-values were less than 0.25 at the initial univariate step (Table 2): accumulated case incidence between 2010 and 2014, and semester.

Similarly, there was a negative association between the number of complete CATIs per case, and the duration of outbreaks (Figure 4D, Table 6). Consequently, the more intense the response, the significantly higher the CATI effectiveness on the reduction of outbreak duration (Table 6). Compared to a number of complete CATIs < 0.25 per case, the crude effectiveness of a number of complete CATIs ≥ 1 per case (cCE4) was 76% (95% CI, 54 to 88), and after adjusting for potential confounders (aCE4), 73% (49 to 86).

Table 6
CATI effectiveness (CE4) of the response intensity (number of complete CATIs per case) on outbreak duration (in days).
Duration of outbreakCrude estimate of CATI effectiveness (cCE4)Adjusted estimate of CATI effectiveness (aCE4)
NMedian
(IQR; days)
%
(95% CI)
p-value%
(95% CI)
p-value
No. of complete CATIs per case
<0.254725
(32)
RefRefRefRef
0.25 to 0.55619.5
(30.75)
8%
(−35 to 37)
0.67381%
(−45 to 32)
0.9759
0.5 to 1813
(16)
59%
(35 to 75)
0.0002*57%
(30 to 74)
0.0007*
≥1542
(5.75)
76%
(54 to 88)
<0.0001*73%
(49 to 86)
<0.0001*
  1. CATI, case-area targeted intervention; IQR, interquartile range.

    *Significant p-value.

  2. Crude CATI effectiveness (cCE4) was estimated on the duration of outbreak, using Cox models for Andersen-Gill counting process (AG-CP), as (1–1/hazard ratio).

    Estimates of CATI effectiveness (aCE4) were adjusted according to covariates for which p-values were less than 0.25 at the initial univariate step (Table 2): number of cases and number of positive cultures during the first 3 days of outbreak, yearly number of previous complete CATIs during the study, and semesters.

Several sensitivity analyses using alternative definitions of cholera outbreak, alternative definitions of CATIs and alternative methods of covariate selection for adjustment yielded consistent estimates of CATI effectiveness according to response promptness and response intensity (Appendix 3).

Effectiveness of antibiotic prophylaxis

Finally, stratified analyses showed that three estimates of CATI effectiveness out of four appeared higher in the subgroup of outbreaks that were only responded to by complete CATIs with antibiotic prophylaxis (ATB) than in the subgroup of outbreaks only responded to by complete CATIs that never included ATB (Table 7). More precisely, the adjusted effectiveness of a prompt response on outbreak size (aCE1) was 63% (24 to 82) when all CATIs included antibiotic prophylaxis, and 39% (-38 to 73) when no CATI did. The adjusted effectiveness of a prompt response on outbreak duration (aCE2) was 74% (43 to 88) when all CATIs included antibiotic prophylaxis, and 58% (11 to 80) when no CATI did. Similarly, the adjusted effectiveness of an intense response on outbreak duration (aCE4) was 90% (72 to 96) when all CATIs included antibiotic prophylaxis, and 79% (46 to 92) when no CATI did. Conversely, the adjusted effectiveness of an intense response on outbreak size (aCE3) was 62% (3 to 85) when all CATIs included antibiotic prophylaxis, and 76% (12 to 94) when no CATI did (Table 7).

Table 7
Effectiveness of complete CATIs stratified by antibiotic prophylaxis.
Outbreak subgroupAll outbreaks responded to by any complete CATIs
(Tables 36)
Outbreaks only responded to by complete CATIs with ATBOutbreaks only responded to by complete CATIs without ATB
No. of outbreaks that were responded to (%)238 (53%)115 (25%)78 (17%)
%
(95% CI)
p-value%
(95% CI)
p-value%
(95% CI)
p-value
CATI effectiveness according to the response promptness
≤1 day vs > 7 days adjusted estimate of CATI effectiveness on accumulated cases (aCE1)76%
(59 to 86)
<0.0001*63%
(24 to 82)
0.007*39%
(−38 to 73)
0.2369
≤1 day vs > 7 days adjusted estimate of CATI effectiveness on outbreak duration (aCE2)61%
(41 to 75)
<0.0001*74%
(43 to 88)
0.0009*58%
(11 to 80)
0.0237*
CATI effectiveness according to the response intensity
≥1 vs<0.25 completeCATIsper week adjusted estimateof CATI effectivenessonaccumulated cases (aCE3)$59%
(11 to 81)
0.0235*62%
(3 to 85)
0.04276%
(12 to 94)
0.0312
≥1 vs<0.25 completeCATIs per case adjusted estimateof CATI effectivenessonoutbreak duration (aCE4)£73%
(49 to 86)
<0.0001*90%
(72 to 96)
<0.0001*79%
(46 to 92)
0.0012*
  1. CATI, case-area targeted intervention.

    ATB, antibiotic prophylaxis.

  2. Estimates of CATI effectiveness (aCE1) were adjusted according to covariates for which p-values were less than 0.25 at the initial univariate step (Table 1): number of positive cultures during the first 3 days of outbreak, population density, accumulated case incidence between 2010 and 2014, coverage of OCV campaigns between 2012 and 2014 and semester.

    Estimates of CATI effectiveness (aCE2) were adjusted according to covariates for which p-values were less than 0.25 at the initial univariate step (Table 1): number of positive cultures during the first 3 days of outbreak, population density, accumulated case incidence between 2010 and 2014, coverage of OCV campaigns between 2012 and 2014 and semester.

  3. $Estimates of CATI effectiveness (aCE3) were adjusted according to covariates for which p-values were less than 0.25 at the initial univariate step (Table 2): accumulated case incidence between 2010 and 2014, and semester.

    £Estimates of CATI effectiveness (aCE4) were adjusted according to covariates for which p-values were less than 0.25 at the initial univariate step (Table 2): number of cases and number of positive cultures during the first 3 days of outbreak, yearly number of previous complete CATIs during the study, and semesters.

  4. *significant after Bonferroni correction.

Discussion

Our quasi-experimental study, based on epidemiological and intervention records over three years in one administrative department of Haiti, showed that prompt and repeated response CATIs conducted by rapid response teams were significantly associated with shortening of cholera outbreaks and mitigating of outbreak case load. Of note, numerous suspected cholera outbreaks spontaneously ended before any response could be conducted. But when taking into account this significant confounding by indication (Remschmidt et al., 2015), the prompter the first complete CATI was implemented and the more complete CATIs were conducted, the fewer cases were recorded and the shorter the outbreak lasted.

While many mild suspected outbreaks may spontaneously end without any response intervention, prompt and repeated CATIs appear difficult to sustain during the largest outbreaks. As suggested by the slow increase in the number of CATIs observed during the study period, CATIs may be logistically complex to implement, and response teams can become overwhelmed when they try to simultaneously address a large number of cases (Finger et al., 2018; Rebaudet et al., 2019a). Such strategy certainly is most relevant at the beginning of epidemics, or during trough periods or tails of epidemics (Finger et al., 2018; Rebaudet et al., 2013).

Our study comes with a number of limitations. Because CATIs were not randomized, response effectiveness may have been biased by unmeasured confounders. As we observed a significant confounding by indication on the probability for an outbreak to receive a CATI response, we limited our analysis on outbreaks that were responded to. We subsequently did not observe any consistent residual difference of initial severity between classes of response promptness and response intensity. However, our models were adjusted for potential confounders and took into account the heterogeneity between localities. This quasi-experimental study was also stratified on response promptness and on response intensity, which yielded consistent response effectiveness estimates (Shadish et al., 2002).

Analyses may also have been biased by missing epidemiological data. Indeed, some patients do not seek care, even when they experience severe dehydration. Besides, stool sampling for confirmation culture was not systematic, which certainly led us to overlook several authentic outbreaks and mis-select clusters of non-cholera diarrhoeas. It may have led us to misdate several outbreak onset and outbreak end. Depending on the differential distribution of these potential biases among classes of response promptness and intensity, these limits could have led to over- or under-estimation of the effectiveness of prompt and intense CATIs. Nevertheless, our outbreak definition aimed to deal with those missing data and be specific in order to analyse CATI effectiveness on definite outbreaks. Like for many diseases, no standardized cholera outbreak criteria exists, and several definitions may be more or less suitable depending on interventions and analyses objectives (Brady et al., 2015). Our retrospectively defined outbreaks may be an approximate unit of analysis in terms of space, time and population, which may also have biased effectiveness results. We therefore conducted a sensitivity analysis using alternative definitions, including systematically lab-confirmed cholera outbreaks, which showed consistent and robust estimates (Appendix 3.1). We also used mixed models in order to take into account heterogeneity between localities in the random effect (Berridge and Crouchley, 2011). Additional CATI effectiveness studies at the household and at the administrative commune levels are underway in Haiti.

Our study analysed 3887 CATIs prospectively notified by rapid response teams to UNICEF. But some additional CATIs may have been omitted, while other CATIs remained unrecorded because they were implemented by the EMIRA alone. Nevertheless, many of their respective CATIs actually overlapped, and we thus believe our response database to be reasonably exhaustive. Conversely, only 16% of complete CATIs were conducted in a locality experiencing a current outbreak. The remaining CATIs were implemented in response to sporadic cases that did not meet outbreak definition criteria, as illustrated by much higher rates with less stringent outbreak definitions (Appendix 3.1). Sporadic CATIs may have prevented, delayed or attenuated the emergence of outbreaks. They may also be associated with the propensity of future outbreak response. We thus included the frequency of previous complete CATIs in our analysis but found no significant association with response promptness or intensity.

Our study aimed to assess the overall effectiveness of a CATI strategy. It neither aimed to estimate the respective effectiveness of each response components, nor the optimal radius of intervention, which would warrant dedicated field studies comparing different types of interventions. We thus chose a conservative definition of complete CATIs and performed a sensitivity analysis with alternative CATI definitions that exhibited consistent results (Appendix 3.2). Because nearly all CATIs included house decontamination, education and chlorine distribution, stratified analyses on these activities were not possible. However, three effectiveness estimates out of four appeared higher when all CATIs included antibiotic prophylaxis than when no CATI did. Several trials have also suggested that chemoprophylaxis has a protective effect among household contacts of people with cholera (Reveiz et al., 2011), and a micro-simulation model suggested that administration of antibiotics in CATIs could effectively avert secondary cases (Finger et al., 2018). But considering the risk of resistance selection (Mhalu et al., 1979; Dromigny et al., 2002), the selected distribution of antibiotic prophylaxis to close contacts is usually not recommended (Global Task Force on Cholera Control (GTFCC), 2018) and must, at the minimum, be used with caution and close monitoring of antibiotic susceptibility. In Haiti, all clinical V. cholerae O1 isolates have remained susceptible to doxycycline between 2013 and 2019 (Haitian Ministry of Public Health and Population, MSPP). As suggested by previous field or modelling studies (Ali et al., 2016; Parker et al., 2017b; Parker et al., 2017a; Finger et al., 2018), adding the administration of a single-dose OCV during CATIs could be an effective, but likely logistically complex, strategy.

Overall, our results suggest that case-area targeted interventions are significantly effective to mitigate and shorten local cholera outbreaks. Household water treatment, sanitation and hygiene promotion, as well as antibiotic prophylaxis theoretically prevent both human-to-human and environment-to-human cholera transmission pathways. Regardless of their respective role, which has been much debated (Morris, 2011; Kupferschmidt, 2017; Rebaudet et al., 2019b), our results thus confirm the relevance of promoting rapid response teams as a key component of the new global strategy for cholera control (Global Task Force on Cholera Control (GTFCC), 2017; Anon, 2017). Such findings need to be replicated in other settings and at other spatial and time scales. It will be critical to understand where CATIs should be prioritized, which radius is optimal, and which intervention components are most effective.

Materials and methods

Study design, setting and cholera surveillance

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To assess CATI effectiveness, we conducted a retrospective observational study, which compared the outcome of cholera outbreaks according to the promptness or intensity of response CATIs. This corresponded to a quasi-experimental study using a post test-only design with stratified groups (Shadish et al., 2002). The study was conducted from January 1, 2015, to December 31, 2017 in the Centre department, one of the 10 administrative districts of Haiti. Centre department covers an area of 3487 km2, with an altitude ranging from 69 m to 1959 m, and is administratively subdivided in 12 communes. In 2015, the Centre population was estimated to be 746,236 inhabitants, including 20% living in urban neighbourhoods, and 80% in numerous rural settlements (Institut Haitien de Statistique et d’Informatique (IHSI), 2015). For the purpose of this study, we designate urban neighbourhoods and rural settlements as ‘localities’.

In 2015–2017, 17 cholera treatment centres, cholera treatment units and acute diarrhoea treatment centres officially treated and recorded suspected cholera cases and associated deaths to the MSPP. A probable suspected cholera case was defined as a patient who develops acute watery diarrhoea with or without vomiting. Daily cases and deaths tolls aged <or ≥ five years old were separately notified to the department health directorates. From 2014, the health directorate of the Centre department established a line-listing of all suspected cholera cases, mentioning sex, age, date of admission, address and use of IV rehydration (a surrogate for severe dehydration). Finally, routine bacteriological confirmation of a subset of suspected cholera cases was performed at the National Laboratory of Public Health (LNSP) in Port-au-Prince Metropolitan Area, using stool sampling with Carry-Blair transport medium and standard culture and phenotyping methods (Centers for Disease Control and Prevention (CDC), 1999).

Procedures: rapid case-area targeted interventions (CATIs)

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From July-2013, the nationwide case-area targeted rapid response strategy to eliminate cholera in Haiti was laboriously but increasingly implemented throughout the country (Rebaudet et al., 2019a). In the Centre department between 2015 and 2017, UNICEF established a partnership with Zanmi Lasante, Oxfam, ACTED and IFRC (International Federation of Red Cross and Red Crescent), four NGOs that hired WaSH rapid response teams composed of local Haitian staff. MSPP also established its own teams called EMIRAs, which included healthcare workers (nurses, auxiliary nurses). Staff of the NGO rapid response teams and EMIRA worked together and deployed mixed teams, which were requested to respond to every suspected cholera case or death within 48 hr after admission at healthcare facility. For this purpose, rapid response teams were encouraged to get epidemiological cholera data on a daily basis from departmental health directorates and treatment centres (Rebaudet et al., 2019a). The core methodology of response CATIs had been established with the MSPP and its partners and included: (i) door-to-door visits to affected families and their neighbours (minimum five households depending on the local geography), who were proposed house decontamination by chlorine spraying of latrines and other potentially contaminated surfaces; (ii) on-site organization of education sessions about cholera and hygiene promotion; (iii) and distribution of one cholera kit per household (composed of five soaps, five sachets of ORS, and chlorine tablets [80 Aquatabs33 mg in urban settings or 150 Aquatabs in rural areas]). EMIRA staff also provided (iv) prophylactic antibiotics to contacts living in the same house as cholera cases with one dose of doxycycline 300 mg for non-pregnant adults only. When appropriate, rapid response teams also: (v) established manual bucket chlorination at drinking water collection points during one or more weeks, by hiring and instructing local volunteers; (vi) chlorinated water supply systems and reported potential malfunctions to DINEPA; (vii) supervised safe funeral practices for cholera casualties; and (viii) provided primary care to cholera cases found in the community. CATIs were prospectively documented and transmitted by WaSH rapid response teams to UNICEF with date, location (i.e., commune, communal section, locality) and implemented activities, including specific activities of embedded EMIRA staff.

Response CATIs were defined as complete if rapid response teams reported at least education, decontamination and distribution of chlorine tablets. A sensitivity analysis of CATI effectiveness estimates using alternative CATI definitions is provided in Appendix 3.2.

Outbreaks identification and characterization

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In order to identify cholera outbreaks, we first cleaned the anonymised case line-listing provided by the health directorate of the Centre department, the anonymised stool culture database provided by the LNSP and the response database provided by UNICEF. We manually corrected date errors and duplicates. Using repeated field investigations, GPS coordinates provided by rapid response teams, and several geographic repositories (http://ihsi.ht/publication_cd_atlas.htm, https://www.indexmundi.com/zp/ha/, https://www.openstreetmap.org/, https://www.google.fr/maps, accessed Jul 1, 2019), we corrected case, culture and response addresses with unified and geolocated locality names. We included every suspected case, every stool culture positive for V. cholerae O1 and every complete CATI of a WaSH rapid response team reported in the Centre department between January 2015 and December 2017.

To assess response effectiveness, we needed to escape the double pitfall of an overly restrictive definition of outbreaks, for example by requiring a bacteriological documentation for each suspected case and, on the contrary, of an unspecific definition, in which a large number of non-cholera diarrhoea cases would have been included. In addition, we had to deal with the fact that some patients with a positive culture were missing in the line-listing. Considering the median and the maximum O1-serogroup cholera incubation period are about 1.5 and 7 days, respectively (Azman et al., 2013), we thus defined outbreaks by the occurrence of at least two suspected cholera cases with at least one severely dehydrated case or positive culture, within the same locality, during a three-day time window, and after a refractory period of at least 21 days with no case. Outbreak onset was defined as the date of the first suspected case or positive culture, and outbreak end as the date of the last case or positive culture before a refractory period of at least 21 days. We conducted a sensitivity analysis using alternative outbreak definitions (Appendix 3.1).

For each identified outbreak, we then counted the numbers of cases and positive culture during the first three days as surrogates of initial severity. With a median incubation period of 1.5 days (Azman et al., 2013), we considered that a response – even a prompt one – would have little impact on the occurrence of additional cases during the two days following detection of the first case. Using a geographic information system (GIS), we extracted locality characteristics such as median population density (Sorichetta et al., 2015) and travel time to the nearest town (Weiss et al., 2018), using 1000 m radius buffer zones. Because cholera transmission and CATI response against cholera in Haiti were found to be influenced by rainfall (Eisenberg et al., 2013; Rebaudet et al., 2019a), we obtained NASA satellite estimates of daily-accumulated rainfall (TRMM_3B42_daily v7, area-averaged with 0.25° x 0.25° accuracy) (https://giovanni.gsfc.nasa.gov/giovanni/, accessed Jul 1, 2019). We gathered vaccine coverage of OCV campaigns conducted between 2012 and 2014, and accumulated incidence rates of suspected cholera cases between 2010 and 2014 (Haitian Ministry of Public Health and Population, MSPP), as surrogates of the population immunity against cholera. In order to better take into account the propensity of localities to experience outbreaks and receive response CATIs, we also counted the number of previous cases per year and the number of previous complete CATIs per year in the same locality since the beginning of the study. To take into account the possible variation of CATI implementation and effectiveness over time, we divided the three-year study period into six semesters (first and last six months of every year).

We then considered that outbreaks were responded to if at least one complete CATI was implemented within seven days after the last recorded case of the outbreak. In order to characterize the response promptness in this subgroup of outbreaks, we first counted the number of days between outbreak onset and the first complete CATI, and split outbreaks that were responded to between four classes of response promptness: >7 days, 3 to 7 days, 2 days and ≤1 day. In order to characterize the response intensity in the subgroup of outbreaks that were responded to, we also counted the number of complete CATIs per outbreak, divided this number by the outbreak duration (in week), and split outbreaks that were responded to between four classes of response intensity: <0.25, 0.25 to 0.5, 0.5 to 1 and ≥1 CATIs per week. We also divided the number of complete CATIs per outbreak by the number of accumulated cases per outbreak, and split outbreaks that were responded to between four classes of response intensity: <0.25, 0.25 to 0.5, 0.5 to 1 and ≥1 CATIs per case. Finally, we calculated two surrogates of outbreak outcome: the number of accumulated suspected cases from the fourth day of outbreak (outbreak size), and the number of days between the first and the last reported case or culture (outbreak duration). A sensitivity analysis of CATI effectiveness using alternative response time windows and categories is provided in Appendix 3: Sensitivity analyses of CATI effectiveness.

Statistical analysis

Analysis of confounders

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The assessment of a possible confounding by indication is detailed in Appendix 2 (Remschmidt et al., 2015). We found that CATI response was more likely in more severe outbreaks. To handle this major bias, we therefore assessed CATI effectiveness (CE) by analysing the outcome of outbreaks that were responded to, according to the response promptness and according to response intensity. In two separate analyses, we compared two endpoints between the four classes of response promptness and between the four classes of response intensity (exposure): the number of cases from the fourth day of outbreak (CE represented the proportion of averted cases); and the outbreak duration (CE represented the proportion of averted days).

As an initial univariate step, we looked for possible confounders among baseline outbreak characteristics of response groups. First, each possible confounder was modelled as an independent variable, and time to the first complete CATI as a recurrent time-to-event outcome, using Cox survival models for Andersen-Gill counting process (AG-CP). This AG-CP survival model was chosen to take into account the correlated repetitions of outbreaks within localities (Andersen and Gill, 1982). Each possible confounder was also modelled as a fixed effect variable, the number of CATIs per week or the number of CATIs per case as dependent variables, and localities as a random effect, using generalized linear mixed models (GLMMs) with a negative-binomial distribution. The mixed model approach aimed to take into account the homogeneous pattern within localities, and the negative-binomial distribution to take into account overdispersion (Berridge and Crouchley, 2011).

CATI effectiveness according to response promptness

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The first evaluation of CATI effectiveness (CE1) was then performed by comparing the outbreak size (number of cases from the fourth day of outbreak) between the four classes of response promptness (time to the first complete CATI). For this, we used GLMMs with cases from the fourth day of outbreak as a dependent variable, localities as a random effect, and a negative-binomial distribution (Berridge and Crouchley, 2011). For each class of response promptness, we estimated the crude CATI effectiveness (cCE1) as: 1 – Incidence ratio. We then obtained adjusted estimates of CATI effectiveness (aCE1) by adjusting for confounders for which p-values were less than 0.25 at the initial univariate step (Mickey and Greenland, 1989). A sensitivity analysis of CATI effectiveness using alternative methods of covariate selection is provided in Appendix 3.3.

A second evaluation of CATI effectiveness (CE2) was performed by comparing the outbreak duration between the four classes of response promptness, using survival analyses censoring outbreak extinction. We assessed time-to-event by Kaplan-Meier analysis to illustrate the cumulative probability of outbreak end between the different response promptness classes. In order to estimate CATI effectiveness according to response promptness and take into account the correlated repetitions of outbreaks within localities, we then fitted Andersen-Gill (AG-CP) survival models (Andersen and Gill, 1982). For each class of response promptness, we estimated the crude CATI effectiveness (cCE2) as: 1 – (1/Hazard ratio). We then obtained adjusted estimates of CATI effectiveness (aCE2) using the same methodology.

CATI effectiveness according to response intensity

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We then estimated the effectiveness of the response intensity, by comparing the outbreak size or duration between different classes of response intensity, using the same methodology as for the effectiveness according to response promptness. In order to avoid that cases or duration be included both within outcome and exposure variables, we approximated response intensity by the number of complete CATIs per week ratio when comparing the number of cases accumulated from the fourth day of outbreak (CE3). Conversely, we used the number of complete CATIs per case ratio when comparing the duration of outbreak (CE4).

For all effectiveness analyses, a p-value of less than 0.05 (two-sided) was considered to indicate statistical significance.

Effectiveness of antibiotic prophylaxis

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In order to assess the effectiveness of antibiotic prophylaxis, we conducted similar comparisons of outbreak size using GLMMs or outbreak duration using Andersen-Gill (AG-CP) survival models according to the response promptness or to the response intensity, stratified by whether all complete CATIs or none of the complete CATIs included antibiotic prophylaxis. We adjusted estimates of CATI effectiveness for the same confounders as in previous analyses. Using a Bonferroni correction for multiple comparisons, a p-value of less than 0.025 (two-sided) was considered to indicate statistical significance.

Software

The GIS and the map were done using QGIS software v3.03 and layers obtained from Haiti Centre National de l’Information Géospatiale (CNIGS) (http://cnigs.ht/, accessed Jul 1, 2019). Analyses and graphs were done using RStudio version 1.0.136 for Mac with R version 3.4.2 and the {ggplot2}, {lme4}, {survival} and {survminer} packages.

Appendix 1

Data characteristics

The study was conducted from January 1, 2015, to December 31, 2017 in the Centre department, where the cholera started in October 2010 (Piarroux et al., 2011). Like the rest of the country, this administrative district experienced a massive epidemic in 2010–2011 (Gaudart et al., 2013; Figure 1—figure supplement 1). Incidence then gradually decreased from 2012 to 2014, but in the following years, the Centre department remained one of the most affected area of the country (Rebaudet et al., 2019a). In order to better guide the response CATI strategy, the health directorate of the Centre department established case line-listing from 2014. This gave us the opportunity to retrospectively assess the impact of these interventions at the level of localities between 2015 and 2017 (Figure 1—figure supplement 1).

We thus analysed suspected cholera cases, cholera stool cultures and case-area targeted interventions (CATIs) recorded between January 1, 2015, and December 31, 2017. Baseline characteristics of these data are summarized in Appendix 1—table 1.

Appendix 1—table 1
Baseline characteristics of suspected cholera cases, cholera stool cultures and case-area targeted interventions (CATIs) from January 1, 2015, to December 31, 2017.
Suspected cholera cases
Total no. of cases10931
Median age (IQR)18 (35)
Sex ratio (M/F)1.0
No. of cases with a comprehensive location (%)10428 (95%)
No. of different localities1497
No. of cases with IV rehydration (%)2301 (21%)
No. of cases with a comprehensive location and IV rehydration (%)2144 (20%)
Stool cultures
Total no. of stool samples cultured1070
No. of stool cultures positive for V. cholerae O1 (%)509 (48%)
No. of positive cultures with a comprehensive location (%)360 (34%)
No. of different localities176
Case-area targeted interventions (CATIs)
Total no. of CATIs3887
No. of CATIs conducted with EMIRA staff (%)2719 (70%)
No. of CATIs with a comprehensive location (%)3533 (91%)
No. of different localities815
No. of CATIs with reported house decontamination (%)3655 (94%)
No. of decontaminated houses per CATI, median (IQR)4 (5)
No. of CATIs with reported education (%)3815 (98%)
No. of educated people per CATI, median (IQR)30 (47)
No. of CATIs with reported chlorine distribution (%)3748 (96%)
No. of household receiving chlorine per CATI, median (IQR)7 (8)
No. of CATIs with reported antibiotic prophylaxis (%)2002 (52%)
No. of people receiving antibiotic prophylaxis per CATI, median (IQR)20 (19)
No. of complete CATIs (%)3596 (93%)
No. of complete CATIs with antibiotic prophylaxis (%)1922 (49%)
  1. EMIRA, cholera rapid response team of the Ministry of health; IQR, interquartile range.

    *Complete CATI, at least decontamination, education and distribution of chlorine tablets.

Appendix 2

Assessment of confounding by indication

In the event that case-area targeted interventions (CATIs) were significantly more likely implemented in more severe outbreaks, estimates of CATI effectiveness could be underestimated by a confounding by indication (Remschmidt et al., 2015).

Therefore, we initially compared baseline characteristics of outbreaks that were responded to (≥1 complete CATI implemented within 7 days after the last recorded case of the outbreak) and outbreaks that were not. This outcome variable following a binomial distribution, univariate logistic mixed models were used to estimate odds ratios associated with each covariate. Localities were modelled as a random effect variable, in order to take into account the heterogeneity between localities.

We then evaluated CATI effectiveness (CE5) by comparing the number of cases from the 4th day of outbreak between outbreaks that were and were not responded to. For this, we used logistic mixed models with cases from the 4th day of outbreak as a dependent variable (binomial distribution) (Berridge and Crouchley, 2011). We estimated the crude CATI effectiveness (cCE5) as: 1 – Odds ratio. We then obtained adjusted estimates of CATI effectiveness (aCE5) by adjusting for the following covariates: the number of cases and the number of positive cultures during the first 3 days of outbreak, rainfall, population density, travel time to the nearest town, accumulated case incidence between 2010 and 2014, coverage of OCV campaigns between 2012 and 2014 OCV campaigns, and the number of semesters since the beginning of the study.

A second evaluation of CATI effectiveness (CE6) was performed by comparing the duration of outbreaks between outbreaks that were and were not responded to, using survival analyses censoring outbreak extinction. We fitted Cox models for Andersen-Gill counting process (AG-CP) (Andersen and Gill, 1982). We estimated the crude CATI effectiveness (cCE6) as: 1 – (1/Hazard ratio). We then obtained adjusted estimates of CATI effectiveness (aCE6) by adjusting for confounders for which p-values were less than 0.25 at the initial univariate step (Mickey and Greenland, 1989).

For all effectiveness analyses, a p-value of less than 0.05 (two-sided) was considered to indicate statistical significance.

Overall, 238 identified outbreaks (53%) received a complete CATI within 7 days after the last recorded case, while 214 did not (Figure 2, Appendix 2—table 1). The proportion of outbreaks that were responded to progressively increased along the study semesters. These outbreaks occurred in localities which were significantly more densely populated, were significantly closer to a town, had significantly been less targeted by a previous mass OCV campaign than localities of outbreaks were non-responded to (Appendix 2—table 1). Outbreaks that were responded to exhibited a more severe onset (numbers of suspected cholera cases and positive cultures during the first 3 days were significantly higher), and they tended to be preceded by more cases than outbreaks that received no response (Appendix 2—table 1). This indicated a significant confounding by indication. Outbreaks that were responded to appeared to be more frequently preceded by complete CATIs than outbreaks that received no response (Appendix 2—table 1), which suggested a higher propensity of response.

Appendix 2—table 1
Baseline characteristics of outbreaks that were and were not responded to.
All outbreaksOutbreaks with no complete CATIOutbreaks with ≥ 1 complete CATIOdds radio
(95% CI)
p-value
No. of outbreaks452214
(47%)
238
(53%)
Semester since January 20152.03
(1.63 to 2.51)
<0.0001*
Population density,median (IQR; inhab./km2)3.5
(6.5)
3.4
(4.8)
3.6
(11.5)
1.01
(1 to 1.02)
0.0308*
Travel time to thenearest town, median(IQR; minutes)26.7
(33.2)
30.2
(31.9)
24.9
(31.8)
0.99
(0.98 to 1)
0.0143*
Accumulated incidencebetween 2010 and 2014,median (IQR; per 1000inhabitants)103.8
(77.5)
103.8
(293.4)
103.8
(77.5)
0.43
(0.08 to 2.33)
0.327
Coverage of OCVcampaigns between 2012 and 2014, median(IQR; %) [mean, SD]0% (86)
[25%, 40]
0% (86)
[30%, 42]
0% (0)
[21%, 38]
0.52
(0.3 to 0.87)
0.0137*
Previous cases in thesame locality during the study, median (IQR; no.per year)4.3
(10.1)
4
(9)
5.2
(10.5)
1.01
(0.99–1.04)
0.2320
Previous complete CATIs in the same localityduring the study, median(IQR; no. per year)0.2
(1.9)
0
(0.8)
0.9
(2.7)
1.23
(1.11–1.36)
<0.0001*
Daily rainfall duringoutbreak, median (IQR;mm)6.6
(13.3)
5.2
(12.5)
7.7
(13.3)
1.01
(0.99 to 1.03)
0.359
No. of cases during thefirst 3 daysof outbreak, median (IQR) [mean, SD]2 (1)
[2.5, 1.5]
2 (0)
[2.3, 0.9]
2 (1)
[2.7, 1.9]
1.22
(1.04 to 1.43)
0.0156*
No. of positive cultureduring the first 3 daysofoutbreak, median (IQR) [mean, SD]0 (0)
[0.2, 0.6]
0 (0)
[0.2, 0.5]
0 (0)
[0.3, 0.7]
1.64
(1.12 to 2.39)
0.0101*
  1. CATI, case-area targeted intervention; SD, standard deviation.

    Univariate comparisons using univariate logistic mixed models with response as model outcome and a binomial distribution, and outbreaks with no complete CATI as the reference class.

  2. *Significant p-value.

Outbreaks that were responded to exhibited a paradoxically worse outcome than outbreaks that received no response. The median number of cases from the 4th day was 1 (interquartile range [IQR], 5) and 0 (IQR, 2) in outbreaks that were and were not responded to, respectively (Appendix 2—figure 1A, Appendix 2—table 2). Whereas the median duration of outbreaks that received at least one complete CATI was 11 days (IQR, 26.8), it was 3 days (IQR, 8) for outbreaks that were not responded to (Appendix 2—figure 1B, Appendix 2—table 3). The distribution of the number of cases from the 4th day of outbreaks that received no response (Appendix 2—figure 1A) looked like the distribution of the number of cases from the 4th day of outbreaks that were responded to within ≤ 1 days (Figure 4A), and looked like the distribution of the number of cases from the 4th day of outbreaks that received ≥ 1 CATI per week (Figure 4C). The Kaplan-Meier curve of outbreaks that received no response (Appendix 2—figure 1B) also looked like the Kaplan-Meier curve of outbreaks that were responded to within ≤ 1 days (Figure 4B), and looked like the Kaplan-Meier curve of outbreaks that received ≥ 1 CATI per case (Figure 4D). This illustrates the effect of the confounding by indication: because outbreaks that were not responded to were initially less severe, their outcome appeared better than the outcome of outbreaks that were responded to; the outcome of outbreaks that were not responded to also appeared close to the outcome of outbreaks that received a prompt and intense response.

Appendix 2—figure 1
Outbreak outcome of outbreaks that were and were not responded to: (A) comparison of the outbreak size (number of suspected cholera cases from the 4th day of outbreak) and (B) Kaplan-Meier comparison of the outbreak duration (in days).
Appendix 2—table 2
Protective effectiveness of response: comparison of the number of suspected cholera cases from the 4th day of outbreak between outbreaks that were and were not responded to (CE5).
No. of cases from the 4th day of outbreakCrude estimate of CATI effectiveness (cCE5)Adjusted estimate of CATI effectiveness (aCE5)
NMedian (IQR)% (95% CI)p-value% (95% CI)p-value
No. of CATIs during outbreak
No CATI*2140
(2)
RefRefRefRef
≥1 CATIs2381
(5)
−228%
(−353 to −138)
<0.0001−411%
(−638 to −254)
<0.0001
  1. CATI, case-area targeted intervention; IQR, interquartile range.

    *Reference class.

  2. Crude CATI effectiveness (cCE3) was estimated on the No. of cases from the 4th day of outbreak, using logistic mixed models, as (1 – Odds ratio).

    Estimates of CATI effectiveness (aCE3) were adjusted according to covariates for which p-values were less than 0.25 at the initial univariate step (Appendix 2—table 1): number of cases and number of positive cultures during the first 3 days of outbreak, population density, travel time to the nearest town, coverage of OCV campaigns between 2012 and 2014 OCV campaigns, and semesters.

Appendix 2—table 3
Protective effectiveness of response: comparison of the duration of outbreaks between outbreaks that were and were not responded to (CE6).
Duration of outbreakCrude estimate of response effectiveness (cCE6)Adjusted estimate of response effectiveness (aCE6)
NMedian (IQR; days)% (95% CI)p-value% (95% CI)p-value
No. of CATIs during outbreak
No CATI*2143
(8)
RefRefRefRef
≥1 CATIs23811
(26.75)
−319%
(−457 to −216)
<0.0001−300%
(−441 to −196)
<0.0001
  1. CATI, case-area targeted intervention; IQR, interquartile range.

    *Reference class.

  2. Crude response effectiveness (CCE4) was estimated on the duration of outbreak, using Cox models for repeated events with Anderson-Gills correction (AGCP), as (1–1/hazard ratio).

    Estimates of CATI effectiveness (aCE4) were adjusted according to covariates for which p-values were less than 0.25 at the initial univariate step (Appendix 2—table 1): number of cases and number of positive cultures during the first 3 days of outbreak, population density, travel time to the nearest town, coverage of OCV campaigns between 2012 and 2014 OCV campaigns, and semesters.

Consequently, the crude CATI effectiveness in reducing the number of cases from the 4th day of outbreak (cCE5) was estimated to be −228% (95% CI, −353 to −138), and after adjusting for potential confounders (aCE5), −411% (−638 to −254) (Appendix 2-table 2). The crude CATI effectiveness on the duration of outbreak (cCE6) was −319% (−457 to −216), and after adjusting for potential confounders (aCE6), −300% (−441 to −196) (Appendix 2—table 3). This confirmed the significant confounding by indication, which explained why outbreaks that were responded to paradoxically exhibited a worse outcome than outbreaks that received no response (Remschmidt et al., 2015). This may be explained by the fact that numerous little outbreaks ended automatically, often before mobile teams arrived for the response. In absence of randomization, response teams likely tended to give priority to initially more severe outbreaks.

Appendix 3

Sensitivity analyses of CATI effectiveness

To assess the potential impact of our choices of definitions for outbreaks and for complete CATIs, we conducted several sensitivity analyses of CATI effectiveness according to different outbreak definitions, response definitions, and methods of model adjustment.

Appendix 3.1

Alternative outbreak definitions

In the main analyses, a cholera outbreak was defined by the occurrence, within the same locality, of at least two suspected cholera cases with at least one severely dehydrated case or positive culture, during a three-day time window, and after a refractory period of at least 21 days with no case. Outbreak onset was defined as the date of the first suspected case or positive culture, and outbreak end as the date of the last case or positive culture before a refractory period of at least 21 days. The three-day time window was initially chosen as it roughly corresponds to twice the median time incubation period of cholera (Azman et al., 2013). The 21 day refractory period was initially chosen as it roughly corresponds to twice the maximum incubation period (Azman et al., 2013) after the end of symptoms in the last case.

Nevertheless, using various thresholds of suspected cases, severely dehydrated cases, positive cultures, time window, and refractory period, we thus tested several alternative outbreak definitions summarized in Appendix 3—table 1.

Appendix 3—table 1
Alternative outbreak definitions.
Cholera outbreakDefinitionRemarkNo. of outbreaks
Outbreak A• suspected cholera cases ≥ 2
• (severely dehydrated case + positive culture)≥1
• onset time window = 3 days
• refractory period = 21 days
Scenario 1
Main manuscript
Appendix 2
Appendix 3.2
Appendix 3.3
452
Outbreak Cu0• suspected cholera cases ≥ 2
• irrespective of severely dehydrated cases and positive cultures
• onset time window = 3 days
• refractory period = 21 days
Scenario 22043
Outbreak Cu1• suspected cholera cases ≥ 2
• positive culture ≥ 1
• irrespective of severely dehydrated cases
• onset time window = 3 days
• refractory period = 21 days
Scenario 364
Outbreak Ca1• suspected cholera cases ≥ 1
• (severely dehydrated case + positive culture)≥1
• onset time window = 3 days
• refractory period = 21 days
Scenario 41514
Outbreak T1• same as Outbreak A except:
• onset time window = 1 day
Scenario 5267
Outbreak T2• same as Outbreak A except:
• onset time window = 2 days
Scenario 6394
Outbreak T4• same as Outbreak A except:
• onset time window = 4 days
Scenario 7494
Outbreak T5• same as Outbreak A except:
• onset time window = 5 days
Scenario 8535
Outbreak R7• same as Outbreak A except:
• refractory period = 7 days
Scenario 9519
Outbreak R14• same as Outbreak A except:
• refractory period = 14 days
Scenario 10486

In order to estimate the confounding by indication, we then compared outbreak outcome between outbreaks that were and were not responded to, as described in Appendix 2. In order to estimate CATI effectiveness, we compared outbreak outcome between classes of response promptness and between classes of response intensity, as described in the main manuscript.

Overall, all outbreak definitions led to a significant confounding by indication (Appendix 3—table 2). When considering only outbreaks that were responded to, CATI effectiveness according to response promptness and response intensity on the reduction of accumulated cases and on the reduction of outbreak duration remained consistent, irrespective of the adopted outbreak definition (Appendix 3—table 2). Some alternative outbreak definitions even brought higher effectiveness estimates than the definition used in the main manuscript.

Appendix 3—table 2
Sensitivity analysis on outbreak and CATI definitions.
Scenario12345678910
Outbreak definition*Outbreak AOutbreak Cu0Outbreak Cu1Outbreak Ca1Outbreak T1Outbreak T2Outbreak T4Outbreak T5Outbreak R7Outbreak R14
Response definitionCATIc7CATIc7CATIc7CATIc7CATIc7CATIc7CATIc7CATIc7CATIc7CATIc7
No. of outbreaks4522043641514267394494535519486
No. of CATIs3596359635963596359635963596359635963596
No. of CATIs during outbreaks (%)633
(18%)
1445
(40%)
153
(4%)
1000
(28%)
386
(11%)
540
(15%)
670
(19%)
717
(20%)
497
(14%)
576
(16%)
No. of outbreaks that were responded to (%)238
(53%)
730
(36%)
45
(70%)
500
(33%)
152
(57%)
211
(54%)
256
(52%)
276
(52%)
242
(47%)
240
(49%)
Comparison between outbreaks that were and were not responded to
No. of cases during the first 3 days of outbreak, Odds ratio (95% CI)1.22
(1.04 to 1.43)
1.61
(1.43 to 1.8)
24.69
(1.4 to 435.42)
1.69
(1.47 to 1.96)
1.1
(0.85 to 1.43)
1.15
(0.96 to 1.37)
1.19
(1.04 to 1.36)
1.19
(1.05 to 1.34)
1,25
(1,07 to 1,45)
1,24
(1,06 to 1,45)
No. of positive culture during the first 3 days of outbreak, Odds ratio (95% CI)1.64
(1.12 to 2.39)
2.29
(1.63 to 3.21)
1.82
(0.64 to 5.21)
2.16
(1.61 to 2.9)
1.35
(0.77 to 2.36)
1.6
(1.06 to 2.41)
1.85
(1.26 to 2.72)
1.85
(1.29 to 2.65)
2,08
(1,42 to 3,06)
1,91
(1,3 to 2,8)
CATI effectiveness according to the response promptness
≤1 day vs >7 days crude estimate of CATI effectiveness on accumulated cases (95% CI) (cCE1)83%
(71 to 90)
85%
(79 to 89)
40%
(−86 to 81)
89%
(83 to 92)
73%
(48 to 87)
79%
(63 to 88)
86%
(86 to 87)
84%
(70 to 91)
89%
(69 to 96)
86%
(72 to 93)
≤1 day vs >7 days crude estimate of CATI effectiveness on outbreak duration (95% CI) (cCE2)59%
(36 to 74)
74%
(66 to 80)
22%
(−81 to 66)
78%
(69 to 84)
48%
(18 to 67)
54%
(29 to 70)
61%
(40 to 75)
58%
(34 to 73)
65%
(39 to 80)
62%
(43 to 75)
CATI effectiveness according to the response intensity
≥1 vs <0.25 CATIs per week crude estimate of CATI effectiveness on accumulated cases (95% CI) (cCE3)74%
(44 to 88)
77%
(62 to 85)
74%
(−122 to 97)
69%
(43 to 84)
28%
(−85 to 72)
69%
(33 to 86)
82%
(60 to 92)
83%
(61 to 92)
87%
(86 to 87)
66%
(−10 to 89)
≥1 vs <0.25 CATIs per case crude estimate of CATI effectiveness on outbreak duration (95% CI) (cCE4)76%
(54 to 88)
89%
(85 to 92)
55%
(−155 to 92)
91%
(86 to 94)
89%
(79 to 95)
81%
(66 to 89)
76%
(55 to 87)
75%
(54 to 86)
78%
(59 to 88)
81%
(68 to 88)

Appendix 3.2

Alternative response definitions

In the main analyses, cholera outbreaks were considered as responded to if they received at least one complete CATI (i.e. a case-area targeted intervention with at least education, house decontamination by spraying and distribution of chlorine tablets) within 7 days after the last recorded case of the outbreak.

We thus tested several alternative response definitions summarized in Appendix 3—table 3.

Appendix 3—table 3
Alternative response definitions.
Cholera outbreakDefinitionRemarkNo. of CATIsNo. of CATIs during outbreaks (%)
CATIc7• Complete CATI (mobile teams reported at least education, house decontamination by spraying and distribution of chlorine tablets)
• Implemented within 7 days after the last recorded case of the outbreak
Scenario 1
Main manuscript
Appendix 2
Appendix 3.1
Appendix 3.3
3596633 (18%)
CATIc0• Complete CATI
• implemented before the last recorded case of the outbreak
Scenario 113596501 (14%)
CATI7• All CATI (irrespective of activities reported by mobile teams
• Implemented within 7 days after the last recorded case of the outbreak
Scenario 123887681 (18%)
CATIcEMIRA7• Complete CATI
• Conducted by NGO mobile teams in tandem with EMIRA staff
• Implemented within 7 days after the last recorded case of the outbreak
Scenario 132539458 (18%)
CATIcATB7• Complete CATI and reported antibiotic prophylaxis
• Implemented within 7 days after the last recorded case of the outbreak
Scenario 14
Main manuscript
1922350 (18%)
  1. EMIRA, cholera rapid response team of the Ministry of health.

In order to estimate the confounding by indication, we then compared outbreak outcome between outbreaks that were and were not responded to, as described in Appendix 2. In order to estimate CATI effectiveness, we compared outbreak outcome between classes of response promptness and between classes of response intensity, as described in the main manuscript.

Overall, all response definitions led to a significant confounding by indication (Appendix 3—table 4). When considering only outbreaks that were responded to, CATI effectiveness according to response promptness and response intensity on the reduction of accumulated cases and on the reduction of outbreak duration remained consistent, irrespective of the adopted response definition (Appendix 3—table 4). Some alternative response definitions even brought higher effectiveness estimates than the definition used in the main manuscript.

Appendix 3—table 4
Sensitivity analysis on outbreak and CATI definitions.
Scenario111121314
Outbreak definition*Outbreak AOutbreak AOutbreak AOutbreak AOutbreak A
Response definitionCATIc7CATIc0CATI7CATIcEMIRA7CATIcATB7
No. of outbreaks452452452452452
No. of CATIs35963596388725391922
No. of CATIs during outbreaks (%)633
(18%)
501
(14%)
681
(18%)
458
(18%)
350
(18%)
No. of outbreaks that were responded to (%)238
(53%)
172
(38%)
244
(54%)
201
(44%)
160
(35%)
Comparison between outbreaks that were and were not responded to
No. of cases during the first 3 days of outbreak, Odds ratio (95% CI)1.22
(1.04 to 1.43)
1.49
(1.24 to 1.80)
1.22
(1.04 to 1.43)
1.21
(1.04 to 1.40)
1.09
(0.97 to 1.24)
No. of positive culture during the first 3 days of outbreak, Odds ratio (95% CI)1.64
(1.12 to 2.39)
1.63
(1.14 to 2.32)
1.62
(1.11 to 2.37)
2.12
(1.44 to 3.11)
1.92
(1.35 to 2.73)
CATI effectiveness according to the response promptness
≤1 day vs >7 days crude estimate of CATI effectiveness on accumulated cases (95% CI) (cCE1)83%
(71 to 90)
85%
(76 to 91)
83%
(71 to 90)
89%
(81 to 94)
90%
(81 to 94)
≤1 day vs >7 days crude estimate of CATI effectiveness on outbreak duration (95% CI) (cCE2)59%
(36 to 74)
65%
(44 to 78)
57%
(33 to 72)
76%
(61 to 85)
84%
(74 to 90)
CATI effectiveness according to the response intensity
≥1 vs <0.25 CATIs per week crude estimate of CATI effectiveness on accumulated cases (95% CI) (cCE3)74%
(44 to 88)
81%
(64 to 90)
70%
(35 to 86)
77%
(49 to 90)
85%
(64 to 94)
≥1 vs <0.25 CATIs per case crude estimate of CATI effectiveness on outbreak duration (95% CI) (cCE4)76%
(54 to 88)
54%
(−14 to 82)
75%
(53 to 86)
86%
(72 to 93)
86%
(71 to 93)

Appendix 3.3

Alternative adjustment methods for effectiveness estimates

In the main analyses, we adjusted CATI effectiveness estimates for covariates for which p-values were less than 0.25 at the initial univariate step (Mickey and Greenland, 1989).

We thus tested two alternative methods of confounder selection: adjusting on all eight covariates (number of cases and number of positive cultures during the first 3 days of outbreak, rainfall, population density, travel time to the nearest town, accumulated case incidence between 2010 and 2014, coverage of OCV campaigns between 2012 and 2014 OCV campaigns, and number of semesters since the beginning of the study); and minimizing the Akaike information criterion (AIC) of the models in order to avoid overfitting (Appendix 3—table 5).

Appendix 3—table 5
Alternative adjustment methods for CATI effectiveness estimates.
Crude estimatesEstimates adjusted for covariates selected by p-valuesEstimates adjusted for all covariatesEstimates adjusted for covariates selected by AIC*
No. of covariates
(AIC*)
cCE*No. of covariates
(AIC*)
aCE*No. of covariates
(AIC*)
aCE*No. of covariates
(AIC*)
aCE*
CATI effectiveness according to the response promptness
≤1 day vs >7 days estimate of CATI effectiveness on accumulated cases (95% CI) (CE1)0
(1102.35)
83%
(71 to 90)
5
(1 096.91)
76%
(59 to 86)
8
(1073.75)
77%
(62 to 87)
6
(1072.51)
79%
(65 to 88)
≤1 day vs >7 days crude estimate of CATI effectiveness on outbreak duration (95% CI) (CE2)0
(956.25)
59%
(36 to 74)
5
(933.42)
61%
(41 to 75)
8
(929.93)
65%
(46 to 77)
5
(924.20)
65%
(46 to 77)
CATI effectiveness according to the response intensity
≥1 vs <0.25 CATIs per week estimate of CATI effectiveness on accumulated cases (95% CI) (CE3)0
(1123.91)
74%
(44 to 88)
2
(1112.44)
58%
(8 to 81)
8
(1093.38)
62%
(18 to 82)
7
(1092.17)
62%
(22 to 81)
≥1 vs <0.25 CATIs per case estimate of CATI effectiveness on outbreak duration (95% CI) (CE4)0
(945.29)
76%
(54 to 88)
3
(949.59)
73%
(49 to 86)
8
(931.29)
76%
(56 to 87)
3
(925.52)
54%
(27 to 71)
  1. *AIC, Akaike information criterion; CATI, case-area targeted intervention; cCE, crude CATI effectiveness estimates; aCE, adjusted CATI effectiveness estimates.

    Covariates for which p-values were less than 0.25 at the initial univariate step (Tables 1 and 2).

Overall, all adjustment methods of models led to consistent CATI effectiveness estimates (Appendix 3—table 5). Inclusion of all covariates did not bring important overfitting.

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Decision letter

  1. Neil M Ferguson
    Senior Editor; Imperial College London, United Kingdom
  2. Mark Jit
    Reviewing Editor; London School of Hygiene & Tropical Medicine, and Public Health England, United Kingdom
  3. Virginia E Pitzer
    Reviewer; Yale School of Public Health, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This manuscript addresses an important and timely question around the effectiveness of case-area targeted interventions (CATIs) against cholera in Haiti. The cholera epidemic in Haiti has been swift and devastating. It has led to substantial scientific study of the spatio-temporal dynamics of the disease, but also to fundamental debates on transmission routes and implications for best approaches to control and possibly elimination. The detailed monitoring of CATIs in the Centre Department provides an opportunity for real, on-the-ground, assessment of this strategy. With a set of robust statistical analyses, the manuscript presents evidence for the effectiveness and importance of CATIs. It finds that a prompter response (measured in categories of days since the first case was reported) and a more intense response (measured in the number of CATIs per week and/or case of the outbreak) were effective at reducing the number of cases and duration of cholera outbreaks.

Decision letter after peer review:

Thank you for submitting your article "Estimating effectiveness of case-area targeted response interventions against cholera in Haiti" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Neil Ferguson as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Virginia E. Pitzer.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Overall, we feel that the results are novel and important for justifying the continued (and expanded) use of CATIs in response to cholera. The statistical analysis was also felt to be through and well handled. However, we have concerns about the methodological approach, interpretation of findings and presentation of results.

Essential revisions:

1) Our major concern was the choice of CATIs to include. For instance, according to the manuscript, only 15% of the CATI interventions were done in response to an "outbreak" detected (a-posteriori). Thus it seems like CATI that weren't implemented within an outbreak were excluded from the analysis but it was maybe just the effect of those CATI that kept the number of cases small enough to stay below the authors definition of an "outbreak". This potentially adds important bias to the reported results. While this limitation might be inherent in a retrospective study, the manuscript needs to acknowledge this limitation more clearly. In particular, you should discuss how these excluded CATIs may affect the results.

2) If the data allow to differentiate between different types of CATI (for instance antibiotics) then an additional stratified analysis of CATIs with or without prophylactic antibiotics should be included. For instance, the findings of the study would be considerably strengthened by including an additional analysis of the association between the promptness and intensity of the response and the outbreak size and duration stratified by whether or not the CATIs included the distribution of prophylactic antibiotics to household members of identified cases. Based on the results presented in Appendix 3—table 4, it would seem that such an analysis would be feasible and straightforward to perform, and would have reasonable power to detect a potential difference in the effect estimates. Given the use of antibiotic prophylaxis to prevent cholera is somewhat controversial compared to the other components of the CATIs, it would be very interesting and potential impactful to have an estimate of its effectiveness from a real-world implementation setting.

3) More details on the CATIs would also be useful, for example: Do you know how many households or people have been targeted with exactly what intervention? Or in what geographical area around cases the interventions have been applied? The same applies to case data (you mention that some confirmed cases are not mentioned in the line list, and that many lab results are negative for V. cholerae).

4) Regarding the analysis of confounders and subsequent control for confounding in the adjusted models of intervention effectiveness: it's not clear why you went to all the trouble to analyse which covariates may be potential confounders of the relationship between response promptness (Table 1) and response intensity (Table 2) and outcomes of interest (outbreak size and duration), but then subsequently included all of the potential covariates in the adjusted models. By definition, any covariate that is not associated with both the exposure and outcome of interest is not a potential confounder. Furthermore, including covariates that are downstream of other confounders has the potential to inadvertently introduce bias into an analysis (see Hernan et al., Epidemiology, 2004). The sensitivity analysis presented in Appendix 3—table 5 shows that the results are very similar when only adjusting for the covariates that are significantly associated with the exposure and/or outcome. Nevertheless, these should be presented in the main text, and the model including all potential covariates could be included as a sensitivity analysis.

5) The presentation of results in the main text would be clearer and more concise if you focused the analysis of the association between the intensity of the response and the outbreak size and duration on only one of the exposure-outcome combinations. As is, you present the results for the association between response intensity as measured by the number of CATIs per week and the outcome as measured by the cumulative number of cholera cases in the outbreak in Table 5, and the association between response intensity as measured by the number of CATIs per case and the outcome as measured by the outbreak duration in Table 6. Why were these exposure-outcome combinations chosen and not the other two alternatives (e.g. the association between the number of CATIs per week and outbreak duration, and the association between the number of CATIs per case and outbreak size)? In general, the results were quite similar (although there was little difference between the effectiveness of having 0.25 to <0.5 CATIs per case and the baseline comparator of >0 to <0.25 CATIs per case). Therefore, just one of the results should be presented in the main text, and the other 3 exposure-outcome combinations presented in the Appendix, with only the qualitative differences described in the main text. Additionally, the notation used in the tables and figures for these categories is quite confusing and unnecessary.

6) Another concern is in regard to the interpretation of the potential impact of the under-diagnosis of cases and misclassification of non-cholera diarrhoea cases in paragraph four of the Discussion. Here, it is stated that both a high number of unrecorded cases and the misclassification of outbreaks due to inconsistent culture confirmation would lead to an underestimation of the effectiveness of CATIs. However, we do not believe there is any reason to conclude that the direction of the bias would be to underestimate the effect of the intervention in either instance. In order for the first statement to be true, cases would have to be differentially underdiagnosed more often when the CATIs are implemented earlier compared to when they were introduced >7 days after the outbreak started, or else cases would have to be underdiagnosed more often when the control efforts were more intense. Neither situation seems likely. While misclassification of non-cholera diarrhoea outbreaks could potentially attenuate the effectiveness estimate for the interventions (as suggested by the alternative outbreak definition "Outbreak Cu1" examined in Appendix 3—table 2), this was not true for most of the other outbreak definitions, and again is unlikely to be true more generally. At the very least, more justification should be provided for these statements.

7) The effectiveness of CATIs has implications well beyond the specific control question at hand, on the still poorly agreed importance of routes of transmission in cholera. In turn, these routes and in particular the role of an environmental reservoir continue to be the subject of substantial debate, and also influence views on approaches to control and sustain the gains of control in the long run. We understand that the paper is not meant to resolve these matters but it would be valuable to add some discussion of related implications even if briefly. These will broaden the scientific value of the findings.

8) There is concern about the choice of "outbreaks", which the authors define post-hoc based on reported incidence, as the unit of analysis. Some discussion would be useful of the implications if the unit of analysis is either an individual or a household, targeted with interventions or not.

9) Outbreaks that haven't been responded to by a targeted intervention have been excluded from the analysis because the authors have detected major bias ("confounding by indication"), e.g. larger outbreaks are more likely to be targeted by > 1 intervention. Doesn't the same bias exist between outbreaks that have been responded to more or less promptly or with a higher or lower intensity?

https://doi.org/10.7554/eLife.50243.sa1

Author response

Essential revisions:

1) Our major concern was the choice of CATIs to include. For instance, according to the manuscript, only 15% of the CATI interventions were done in response to an "outbreak" detected (a-posteriori). Thus it seems like CATI that weren't implemented within an outbreak were excluded from the analysis but it was maybe just the effect of those CATI that kept the number of cases small enough to stay below the authors definition of an "outbreak". This potentially adds important bias to the reported results. While this limitation might be inherent in a retrospective study, the manuscript needs to acknowledge this limitation more clearly. In particular, you should discuss how these excluded CATIs may affect the results.

We thank the reviewers for raising the concern of CATIs conducted outside identified outbreaks.

To clarify this point, we included the number and proportion of CATIs during outbreaks in Appendix 3—table 1, in Appendix 3—table 3 and in Appendix 3—table 4.

CATIs conducted outside identified outbreaks were implemented in response to sporadic cases that did not meet outbreak definition criteria, as illustrated by the higher proportions of CATIs within outbreaks found when using less stringent outbreak definitions (up to 40% in scenario 2, Appendix 3—table 2).

However, effectiveness sensitivity analyses using alternative outbreak definitions that included higher proportions of CATIs, did not exhibit substantially different results (Appendix 3—table 2).

Sporadic CATIs may theoretically have prevented, delayed or attenuated the emergence of certain outbreaks. They may also be associated with the propensity of future outbreak response. In our revised effectiveness analyses, we thus included the frequency of previous complete CATIs conducted between the beginning of the study period and the outbreak onset as a new covariate (see Table 1 and 2). Because previous CATIs may be dependent of the previous number of cases to be responded to, we also included the previous incidence of suspected cholera cases recorded between the beginning of the study period and the outbreak onset as another new covariate. However, we found no significant association between these covariates and response promptness or intensity. We now address this concern in the revised Discussion section.

Finally, sporadic CATIs may prevent the occurrence of future outbreaks, or attenuate outbreaks that may thus end before being responded. Therefore, the real impact of CATIs against cholera may in fact be broader than suggested by our analyses, which specifically assessed the effectiveness of prompt and intense CATIs to reduce the size and duration of outbreaks that received a response.

2) If the data allow to differentiate between different types of CATI (for instance antibiotics) then an additional stratified analysis of CATIs with or without prophylactic antibiotics should be included. For instance, the findings of the study would be considerably strengthened by including an additional analysis of the association between the promptness and intensity of the response and the outbreak size and duration stratified by whether or not the CATIs included the distribution of prophylactic antibiotics to household members of identified cases. Based on the results presented in Appendix 3—table 4, it would seem that such an analysis would be feasible and straightforward to perform, and would have reasonable power to detect a potential difference in the effect estimates. Given the use of antibiotic prophylaxis to prevent cholera is somewhat controversial compared to the other components of the CATIs, it would be very interesting and potential impactful to have an estimate of its effectiveness from a real-world implementation setting.

Our study initially did not aim at evaluating the respective effectiveness of each activity included in CATIs. Besides, stratified analyses on house decontamination, education and chlorine distribution was not possible as nearly all CATIs included these activities.

However, we thank reviewers for suggesting us to include stratified analyses on antibiotic prophylaxis, which are now described in a separate part in the Results section. We conducted additional comparisons of outbreak size or outbreak duration according to the response promptness or to the response intensity, stratified by whether all complete CATIs or none of complete CATIs conducted within outbreaks included antibiotic prophylaxis. We found that most of effectiveness estimates appeared higher when all CATIs included antibiotic prophylaxis.

We also discuss these results, including the risk of bacterial resistance selection when using antibiotics as selected chemoprophylaxis.

Nevertheless, we would like to point out that the methodology used in this study is probably not the most appropriate to accurately measure the impact of an antibiotic prophylaxis delivered during CATIs. Other approaches, such as randomization of antibiotic distribution during CATIs, would help to better understand the additional impact of this measure. The circumstances in which we conducted our study did not allow such randomization. This is why we insisted at the end of the Discussion on the interest of conducting additional research to better define the optimal modalities for the implementation of CATIs.

3) More details on the CATIs would also be useful, for example: Do you know how many households or people have been targeted with exactly what intervention? Or in what geographical area around cases the interventions have been applied? The same applies to case data (you mention that some confirmed cases are not mentioned in the line list, and that many lab results are negative for V. cholerae).

Following the reviewers’ request, we added a supplementary Table describing baseline characteristics of suspected cholera cases, cholera stool cultures and CATIs (Appendix 1—table 1), including the number of households targeted by decontamination or chlorine distribution and the number of people who were educated and who received antibiotic chemoprophylaxis.

4) Regarding the analysis of confounders and subsequent control for confounding in the adjusted models of intervention effectiveness: it's not clear why you went to all the trouble to analyse which covariates may be potential confounders of the relationship between response promptness (Table 1) and response intensity (Table 2) and outcomes of interest (outbreak size and duration), but then subsequently included all of the potential covariates in the adjusted models. By definition, any covariate that is not associated with both the exposure and outcome of interest is not a potential confounder. Furthermore, including covariates that are downstream of other confounders has the potential to inadvertently introduce bias into an analysis (see Hernan et al., Epidemiology, 2004). The sensitivity analysis presented in Appendix 3—table 5 shows that the results are very similar when only adjusting for the covariates that are significantly associated with the exposure and/or outcome. Nevertheless, these should be presented in the main text, and the model including all potential covariates could be included as a sensitivity analysis.

We agree with the reviewers’ suggestion and therefore reintegrated adjusted estimates for covariates for which P-values were less than 0.25 at the initial univariate step in the main manuscript. We now only present the model including all potential covariates as a sensitivity analysis (Appendix 3—table 5). We also kept models minimizing the Akaike information criterion (AIC) as a sensitivity analysis (Appendix 3—table 5). Results between the three model groups are indeed very similar.

We also totally agree that including covariates that are downstream of other confounders has the potential to inadvertently introduce bias into an analysis. We believe that the four final models presented in the main text do not present such bias.

5) The presentation of results in the main text would be clearer and more concise if you focused the analysis of the association between the intensity of the response and the outbreak size and duration on only one of the exposure-outcome combinations. As is, you present the results for the association between response intensity as measured by the number of CATIs per week and the outcome as measured by the cumulative number of cholera cases in the outbreak in Table 5, and the association between response intensity as measured by the number of CATIs per case and the outcome as measured by the outbreak duration in Table 6. Why were these exposure-outcome combinations chosen and not the other two alternatives (e.g. the association between the number of CATIs per week and outbreak duration, and the association between the number of CATIs per case and outbreak size)? In general, the results were quite similar (although there was little difference between the effectiveness of having 0.25 to <0.5 CATIs per case and the baseline comparator of >0 to <0.25 CATIs per case). Therefore, just one of the results should be presented in the main text, and the other 3 exposure-outcome combinations presented in the Appendix, with only the qualitative differences described in the main text. Additionally, the notation used in the tables and figures for these categories is quite confusing and unnecessary.

We thank the reviewers for these comments and better explained in the Materials and method section why we used two different proxies of response intensity when comparing outbreak size or outbreak duration. Indeed, we had to avoid that cases or duration be included both within outcome and exposure variables: we could neither compare the number of cases accumulated from the 4th day of outbreak between classes of CATIs per case, nor compare the duration of outbreaks between classes of CATIs per week. We thus approximated response intensity by the number of complete CATIs per week ratio when comparing the number of cases accumulated from the 4th day of outbreak (CE3), and by the number of complete CATIs per case ratio when comparing the duration of outbreak (CE4).

Considering that we independently evaluated CATI effectiveness by comparing outbreak outcome defined by two different variables (outbreak size and outbreak duration), between classes of two exposure variables (response promptness and response intensity), we believed important to keep all four groups of results within the main manuscript. The fact that all effectiveness estimates were of the same order of magnitude suggests that our results are consistent.

Finally, we clarified the notation for classes of response intensity used in the tables and figures, as suggested by the reviewers.

6) Another concern is in regard to the interpretation of the potential impact of the under-diagnosis of cases and misclassification of non-cholera diarrhoea cases in paragraph four of the Discussion. Here, it is stated that both a high number of unrecorded cases and the misclassification of outbreaks due to inconsistent culture confirmation would lead to an underestimation of the effectiveness of CATIs. However, we do not believe there is any reason to conclude that the direction of the bias would be to underestimate the effect of the intervention in either instance. In order for the first statement to be true, cases would have to be differentially underdiagnosed more often when the CATIs are implemented earlier compared to when they were introduced >7 days after the outbreak started, or else cases would have to be underdiagnosed more often when the control efforts were more intense. Neither situation seems likely. While misclassification of non-cholera diarrhoea outbreaks could potentially attenuate the effectiveness estimate for the interventions (as suggested by the alternative outbreak definition "Outbreak Cu1" examined in Appendix 3—table 2), this was not true for most of the other outbreak definitions, and again is unlikely to be true more generally. At the very least, more justification should be provided for these statements.

We thank the reviewers for this comment and acknowledge that our CATI effectiveness may have been over or under estimated depending on the differential distribution of missing values between classes of response promptness and intensity. We modified our Discussion accordingly.

Despite missing data, we believe that our sensitivity analyses using very different outbreak definitions provide sufficient confidence that prompt and intense CATIs have the potential to effectively reduce cholera outbreak size and duration.

7) The effectiveness of CATIs has implications well beyond the specific control question at hand, on the still poorly agreed importance of routes of transmission in cholera. In turn, these routes and in particular the role of an environmental reservoir continue to be the subject of substantial debate, and also influence views on approaches to control and sustain the gains of control in the long run. We understand that the paper is not meant to resolve these matters but it would be valuable to add some discussion of related implications even if briefly. These will broaden the scientific value of the findings.

We agree with the reviewers that better understanding the respective importance of cholera transmission routes and the potential role of an environment reservoir can impact strategic choices in epidemiological surveillance and control. But as we believe that household water treatment, sanitation and hygiene promotion, as well as antibiotic prophylaxis may theoretically prevent both human-to-human and environment-to-human cholera transmission pathways, we initially did not discuss this important issue within the specific context of this paper dedicated to case-area-targeted interventions. Nevertheless, following the reviewers’ suggestion, we now discuss this important scientific issue in our conclusion.

8) There is concern about the choice of "outbreaks", which the authors define post-hoc based on reported incidence, as the unit of analysis. Some discussion would be useful of the implications if the unit of analysis is either an individual or a household, targeted with interventions or not.

Together with the reviewers, we are concerned that our effectiveness estimates may be biased by the fact that our retrospectively defined outbreaks may be an approximate unit of analysis in term of space, time and population. This is why we conducted a sensitivity analysis using alternative definitions, including systematically lab-confirmed cholera outbreaks, which showed consistent and robust estimates (Appendix 3.1). As suggested by the reviewers, another CATI effectiveness studies at the household level is underway in Haiti.

We believe that our Discussion section now better acknowledges this potential bias.

9) Outbreaks that haven't been responded to by a targeted intervention have been excluded from the analysis because the authors have detected major bias ("confounding by indication"), e.g. larger outbreaks are more likely to be targeted by > 1 intervention. Doesn't the same bias exist between outbreaks that have been responded to more or less promptly or with a higher or lower intensity?

We shared the same concern as the reviewers regarding the possibility of a residual confounding by indication in the group of outbreaks that received a CATI response.

However, proxies of outbreak initial severity (no. of cases and no. of positive cultures during the first three days) were not systematically higher in classes of better response promptness or intensity. In addition, our models were adjusted for potential confounders and took into account the heterogeneity between localities. This quasi-experimental study was also stratified on response promptness and on response intensity, which classes yielded consistent response effectiveness estimates.

Anyway, in the event a significant but hidden confounding by indication remained, this should have underestimated CATI effectiveness, which all appeared significantly positive.

https://doi.org/10.7554/eLife.50243.sa2

Article and author information

Author details

  1. Edwige Michel

    Ministry of Public Health and Population, Directorate of Epidemiology Laboratory and Research, Port-au-Prince, Haiti
    Contribution
    Data curation, Formal analysis, Investigation
    Competing interests
    No competing interests declared
  2. Jean Gaudart

    Aix-Marseille Université, APHM, INSERM, IRD, SESSTIM, Hop Timone, BiSTIC, Biostatistics and ICT, Marseille, France
    Contribution
    Conceptualization, Software, Supervision, Validation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9006-5729
  3. Samuel Beaulieu

    United Nations Children's Fund, Port-au-Prince, Haiti
    Contribution
    Funding acquisition, Validation, Investigation
    Competing interests
    No competing interests declared
  4. Gregory Bulit

    United Nations Children's Fund, Port-au-Prince, Haiti
    Contribution
    Funding acquisition, Validation, Investigation
    Competing interests
    No competing interests declared
  5. Martine Piarroux

    Centre d'Épidémiologie et de Santé Publique des Armées, Service de Santé des Armées, Marseille, France
    Contribution
    Supervision, Investigation, Methodology
    Competing interests
    No competing interests declared
  6. Jacques Boncy

    Ministry of Public Health and Population, National Laboratory of Public Health, Delmas, Haiti
    Contribution
    Funding acquisition, Investigation, Project administration
    Competing interests
    No competing interests declared
  7. Patrick Dely

    Ministry of Public Health and Population, Directorate of Epidemiology Laboratory and Research, Port-au-Prince, Haiti
    Contribution
    Supervision, Funding acquisition, Project administration
    Competing interests
    No competing interests declared
  8. Renaud Piarroux

    Sorbonne Université, Sorbonne Université, INSERM, Institut Pierre-Louis d’Epidémiologie et de Santé Publique (IPLESP), AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
    Contribution
    Conceptualization, Supervision, Funding acquisition, Validation, Investigation, Methodology
    For correspondence
    renaud.piarroux@aphp.fr
    Competing interests
    No competing interests declared
  9. Stanislas Rebaudet

    1. APHM, Hôpital Européen, Aix Marseille Université, INSERM, IRD, SESSTIM, IPLESP, Marseille, France
    2. Sorbonne Université, INSERM, Institut Pierre-Louis d’Epidémiologie et de Santé Publique, Paris, France
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Project administration
    For correspondence
    stanreb@gmail.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5099-1947

Funding

UNICEF

  • Samuel Beaulieu
  • Gregory Bulit
  • Stanislas Rebaudet

Assistance Publique - Hôpitaux de Marseille

  • Stanislas Rebaudet

Assistance Publique - Hôpitaux de Paris

  • Renaud Piarroux

The funders of this study (UNICEF, APHM, APHP) had staff (co-authors of this manuscript) who had a role in data collection, analyses and writing of the report. However, the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgements

We are grateful to the staff of MSPP, UNICEF, DINEPA and NGOs, who cared for patients, implemented and coordinated field responses, analysed stool cultures, gathered epidemiological and intervention data. We thank Bevan Hurley for editing the manuscript.

Ethics

Human subjects: All analyses retrospectively included routinely collected cholera surveillance and control data. The study protocol received authorization #1718-24 from the National Bioethics Committee of Haiti Ministry of Health (MSPP). The study only analysed anonymised data. Informed consent from patients and from people who benefited from a response intervention was therefore not required for this study.

Senior Editor

  1. Neil M Ferguson, Imperial College London, United Kingdom

Reviewing Editor

  1. Mark Jit, London School of Hygiene & Tropical Medicine, and Public Health England, United Kingdom

Reviewer

  1. Virginia E Pitzer, Yale School of Public Health, United States

Publication history

  1. Received: July 16, 2019
  2. Accepted: December 20, 2019
  3. Accepted Manuscript published: December 30, 2019 (version 1)
  4. Version of Record published: February 25, 2020 (version 2)

Copyright

© 2019, Michel et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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