Statistical Analysis Plan · DTP Diphtheria, Tetanus, and Pertussis vaccine DTPa Diphtheria, Tetanus and acellular Pertussis vaccine DTPw Diphtheria, Tetanus and whole-cell Pertussis

Statistical Analysis Plan

Study alias & e-track number: EPI-PERTUSSIS-034 BOD CO DB (201521)

FORM-9000026972-01 Statistical Analysis Plan Template Effective date: 01 June 2014 GSK SOP Reference: SOP-9000026972 Form Owner: VVHS Biometrics,

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Detailed Title: An observational, retrospective, ecological database study to assess the impact of BoostrixTM maternal vaccination on morbidity and mortality of pertussis disease in infants 6 weeks of age, in Bogota, Colombia.

SAP version Version 1

SAP date 13-Nov-2015

Scope: All data pertaining to the above study.

Co-ordinating author:

Other author(s):

Adhoc reviewers:

Approved by: < (Director and Regional Epidemiologist)>

< (Regional Epidemiologist)>

< (Lead Statistician)>

< (Scientific Writer)>

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TABLE OF CONTENTS

PAGE

LIST OF ABBREVIATIONS ............................................................................................. 3

1. DOCUMENT HISTORY ............................................................................................ 4

2. STUDY DESIGN ...................................................................................................... 4

3. OBJECTIVES ........................................................................................................... 7 3.1. Primary objective .......................................................................................... 7 3.2. Secondary objectives .................................................................................... 7

4. ENDPOINTS ............................................................................................................ 7 4.1.1. Primary endpoint ............................................................................ 7 4.1.2. Secondary endpoints ..................................................................... 7

5. STUDY POPULATION ............................................................................................. 8 5.1.1. Total cohort .................................................................................... 8

6. STATISTICAL METHODS ........................................................................................ 8 6.1.1. Analysis of demographics/baseline characteristics ........................ 8 6.1.2. Analysis of primary and secondary objectives ................................ 8

7. STATISTICAL CALCULATIONS ............................................................................ 12 7.1. Derived and transformed data ..................................................................... 12 7.2. Handling missing data ................................................................................. 13 7.3. Methodology for computing CI .................................................................... 13 7.4. Number of decimals: ................................................................................... 13

8. CONDUCT OF ANALYSES .................................................................................... 14 8.1. Sequence of analyses ................................................................................. 14 8.2. Statistical considerations for interim analyses ............................................. 14

9. CHANGES FROM PLANNED ANALYSES/PROTOCOL ........................................ 14

10. REFERENCES ....................................................................................................... 15

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The complete statistical analysis plan and results presentation is divided into 2 parts: the first part detailing the analyses to be performed (known as SAP, current document) and a second part, annex (called TFL) describing the flow and format of tables, figures and listings to be annexed to the SR.

LIST OF ABBREVIATIONS

ATP According-To-Protocol

ALRTI Acute Lower Respiratory Tract Infection

CI Confidence Interval

CSV Comma Separated Values

DB Database

DTP Diphtheria, Tetanus, and Pertussis vaccine

DTPa Diphtheria, Tetanus and acellular Pertussis vaccine

DTPw Diphtheria, Tetanus and whole-cell Pertussis vaccine

GSK GlaxoSmithKline

LL Lower Limit of the confidence interval

SAP Statistical Analysis Plan

SAS Statistical Analysis System

SD Standard Deviation

SDD SAS Drug and Development

SR Study Report

TFL Tables Figures and Listing template annexed to SAP

UL Upper Limit of the confidence interval

UMV Universal Mass Vaccination

ZIP Zero-inflated Poisson

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1. DOCUMENT HISTORY

Date Description Protocol Version

13-NOV-2015 Version 1 Final – 31-MAR-2015

2. STUDY DESIGN

Type of study: Self-contained.

Type of design: An observational, retrospective, ecological database study.

Systematic screening of the national databases of Bogota (described above) will be performed for the following aggregated data:

pertussis cases (as per case definition) in infants 12 months of age,

pertussis related morbidity (including hospitalisations) in infants 12 months of age,

pertussis associated deaths in infants 12 months of age,

ALRTI cases in infants 12 months of age

vaccination coverage of Boostrix in pregnant women and primary pertussis vaccination coverage in children 12 months of age.

population projections, demographic data on infants, number of pregnancies reported during post vaccination period.

Data on vaccination coverage for the mothers will be extracted from March 2013 to December 2015 by the principal investigator and transferred to data management in an Excel or comma separated values (CSV) format. Rest of the data needed for the study will be extracted from January 2005- December 2015 and transferred in the same format.

The period before introduction of Boostrix in the UMV program will be considered as pre-vaccination or baseline period (January 2005-February 2013), the year when Boostrix was introduced will be considered as transition period (March to December 2013) and the later period will be considered as post-vaccination period (January to December 2015).

Period of data collection: Data will be collected retrospectively for cases identified through surveillance in Bogota from January 2005 until December 2015. Data on vaccination coverage for the mothers will be collected from March 2013 to December 2015.

Epoch 001: Retrospective data collection.

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The following group names will be used for the statistical analyses:

Group order in tables

Group label in tables Group definition for footnote

Vaccination Time Period (Main analysis) 1 Pre-vaccination Pre-vaccination period from January 2011 to

February 2013

2 Transition Transition period from March to December 2013 3 Post-vaccination Post-vaccination period from January 2014 to

December 2015 Vaccination Time Period (Sensitivity analysis) 1 Pre-vaccination Pre-vaccination period from January 2005 to

February 2013

2 Transition Transition period from March to December 2013 3 Post-vaccination Post-vaccination period from January 2014 to

December 2015 Age group

1 0-<1m Infants with <1 month of age

2 1m-<2m Infants from 1 month to <2 months of age

3 2m-<3m Infants from 2 months to <3 months of age

4 3m -<6m Infants from 3 months to <6 months of age


6 9m-≤12m Infants from 9 months to ≤12 months of age

Combined Age group

1 0-<6w or 0-<2m Infants with <6 weeks of age or Infants with <2 months of age

2 6w-≤12m or 2m-≤12m Infants from 6 weeks to ≤12 months of age or Infants from 2 months to ≤12 months of age

Year 1 2005 Year 2005

2 2006 Year 2006

3 2007 Year 2007

4 2008 Year 2008

5 2009 Year 2009

6 2010 Year 2010

7 2011 Year 2011

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8 2012 Year 2012

9 2013 Year 2013

10 2014 Year 2014

11 2015 Year 2015

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3. OBJECTIVES

3.1. Primary objective

• To describe trends on the morbidity and mortality caused by pertussis disease in infants 6 weeks of age before and after introduction of pertussis maternal immunisation in Bogota, Colombia from January 2005-December 2015.

3.2. Secondary objectives

• To describe trends on the morbidity and mortality caused by pertussis disease in infants 7 weeks to 12 months of age (stratified into different age groups by month), before and after pertussis maternal immunisation in Bogota, Colombia from January 2005-December 2015.

• To describe trends on ALRTI in infants 6 weeks of age and 7 weeks to 12 months of age, following the introduction of pertussis maternal immunisation in Bogota, Colombia from January 2005 -December 2015.

• To describe pertussis vaccination coverage in pregnant women since the introduction of Boostrix as part of the UMV program in Bogota, and the vaccine uptake from March 2013 to December 2015.

• To describe primary pertussis vaccination coverage in infants 12 months of age in Bogota from January 2005-December 2015.

4. ENDPOINTS

4.1.1. Primary endpoint

• Occurrence of reported cases of hospitalisations and deaths due to pertussis in infants 6 weeks of age, in post-vaccination period compared to pre-vaccination period of Boostrix vaccination (i.e. from January 2005-December 2015).

4.1.2. Secondary endpoints

• Occurrence of reported cases of hospitalisations and deaths due to pertussis in infants 7 weeks to 12 months of age, in post-vaccination period compared to pre-vaccination period of Boostrix vaccination (i.e. from January 2005-December 2015).

• Occurrence of ALRTI in infants 6 weeks of age and 7 weeks to 12 months of age, following the introduction of pertussis maternal immunisation in Bogota, Colombia (i.e. from January 2005-December 2015).

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• Description of the total number of doses of Boostrix administered in pregnant women, after its introduction into the UMV program in Bogota (i.e. from March 2013-December 2015).

• Description of the total number of doses of primary pertussis vaccine administered to infants 12 months of age in Bogota from January 2005-December 2015.

5. STUDY POPULATION

5.1.1. Total cohort

The Total cohort will include all subjects included in the study. Only aggregated anonymised information will be collected for these subjects.

6. STATISTICAL METHODS

6.1.1. Analysis of demographics/baseline characteristics

The distribution of subjects by month and year or year (as applicable) and age group will be tabulated separately for pertussis cases (i.e. overall, Probable, Suspected, lab confirmed or clinically confirmed), hospitalisations and deaths, and if possible for the corresponding denominator population. Any potential changes in the denominator population will be presented and discussed (in relation to potential changes in the catchment population of each of the databases). In addition to summary statistics such as mean, median, standard deviation, minimum and maximum, baseline versus post-vaccination percent change in mean and median will also be calculated for both the vaccination time periods (main analysis and sensitivity analysis).

For each outcome (counts and/or incidence rates), the Percent change in Mean or Median will be computed using the below formula:

%Change_mean or median = {(Mean or median counts/incidence for baseline period – counts/Incidence in the given post-vac year) ÷ Mean or median counts/incidence for baseline period}*100

6.1.2. Analysis of primary and secondary objectives

As the primary objective is to describe the trends on the morbidity and mortality caused by pertussis disease in infants 6 weeks (before and after introduction of maternal immunisation), all the suspected and confirmed cases, hospitalisations and deaths due to pertussis will be tabulated for infants ≤6 weeks of age before and after introduction of

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pertussis maternal immunisation by year and overall. Also the incidence rate of pertussis cases (i.e. overall, probable, suspected, lab confirmed or clinically confirmed), proportion of hospitalisations and mortality rate due to pertussis in infants will be calculated with 95% confidence interval (CI) before and after introduction of pertussis maternal immunisation by year and overall.

To describe trends on the morbidity and mortality caused by pertussis disease and to assess the vaccine impact before and after pertussis maternal immunisation, an age-period Poisson (in case of equi-dispersion) or negative binomial regression (in case of over-dispersion) model will be fitted by considering rate or/and number of cases (i.e. overall, probable, suspected, lab confirmed or clinically confirmed) as outcome variable during the years 2011 to 2015 (as there were less number of cases before the year 2011 and age of the first dose has changed over time) depending on the availability of population data. This model includes month and year as secular trend, age group of the infants and vaccination time period as dummy/indicator variable (i.e. a three-valued variable that contains values 0 denoting pre-vaccination period, 1 denoting transition period and 2 denoting post-vaccination period) as covariates by including a minimum of 5 subjects per stratum. The same model will be fitted by replacing vaccination time period with vaccine coverage (for pregnant women) as one of the variable.

Note: The adequacy of the model fit (whether the data is equi- or over dispersed) will be checked by assessing the goodness of fit criteria related to deviance and/or Pearson chi-square. If the Poisson regression model is adequate then the expected value of both the deviance and Pearson chi-square is equal (or close) to 1. Otherwise negative binomial regression model will be used.

Also, as part of sensitivity analysis, the above model will also be performed using the data from the years 2005 to 2015. Further the graphical representation (line/bar chart) for number of pertussis cases, hospitalisations due to pertussis and deaths will be provided by age group and year. Based on the disease trend, the age group will be classified (especially with different category of first age group as the timeliness of vaccination in new-borns are not available) for example as <1 month, 1 to <2 months, 2 to <3 months, 3 to <6 months, 6 to <9 months, 9 months to ≤12 months and calendar period as monthly, quarterly, etc.,

Similarly, the trends on ALRTI in infants 12 months will be assessed using the above specified age-period regression model considering the before and after pertussis maternal immunisation periods.

All these models will be generated only if there will be sufficient numbers of cases available. If applicable, alternative regression models such as zero-inflated models or age-period count regression models using parametric smooth functions based on natural splines will be considered.

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The vaccination coverage in pregnant women, since the introduction of Boostrix into the UMV program, will be summarised per calendar year (after vaccine introduction, i.e., March 2013 onwards) using frequency and percentage.

The pertussis vaccination history or coverage (DTPw or DTPa vaccine) for infants 12 months of age will be tabulated by year (from January 2005 – December 2015) and overall.

Age-period (AP) Poisson / Negative binomial regression models: The AP Poisson model has the following form:

log(E(yij)) = µ + αi + βj + log(Nij)

The AP Negative binomial model has the following form:

log(E(yij)) = µ + αi + βj + log(Nij) + ɛij

The number of pertussis cases in age group i at the time period j is denoted yij

Nij - number of persons at risk in age group i at the time period j (population data)

Where αi’s are the age effects,

βj’s are period effects,

log(Nij ) is an offset (only applicable when we have population at risk. i.e. denominator data to model the outcome as rate)

E(yij ) expected number of pertussis cases And

ɛij represents random error

To describe trends over time on reported pertussis cases and the impact of the vaccine, the following independent variables will be included in the model

• Month and year as secular trend: It will be a continuous variable indicating time in month and year (i.e. t=0 for the first month and year, t=1 for the second month and year etc.,).

• Age group (1 month, 2 to 3 months, 4 to 6 months, 7 to 9 months, 10 months to 12 months)

• Vaccine coverage (for pregnant women): Vaccine coverage will be expressed as an percentage by year

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• Vaccination time period as dummy/indicator variable (i.e. a three-valued variable that contains values 0, denoting pre-vaccination period, 1 denoting transition period, and 2 denoting post-vaccination period)

The above model will also be fitted to describe the trends on ALRTI in infants 12 months considering the before and after pertussis maternal immunisation periods.

The multi colinearity effect for the regression model can be statistically tested using the Variance Inflation Factor (VIF). In case of multi colinearity, the regression model will be adjusted by dropping certain correlated variables from the model.

Note: The reference category for each categorical variable will be either fixed at the time of TFL or during the analysis.

Zero-inflated Poisson / Negative binomial regression models:

If applicable, an alternative regression model which is capable of dealing with excess zero counts (pertussis cases) will be fitted to assess the disease trend and vaccination impact.

The zero-inflated regression models (Mullahy 1986; Lambert 1992, Cameron and Trivedi 1998, 2005) are two-component mixture models combining a point mass at zero with a count distribution. Hence they generates two separate models and then combines them

• First, a Binary logit model is generated for the certain zero pertussis cases

• A poisson model is generated to predict the dependent variable with no certain zeros.

SAS codes:

PROC GENMOD DATA = <data>;

MODEL <pertussis cases> = <agr_grp> <vac_status> /DIST=ZIP;

ZEROMODEL <month_year> /LINK = LOGIT ;

RUN;

Age-period count regression models using parametric smooth functions based on natural splines

Standard regression techniques specify a functional form (such as a straight line) to describe the relation between the predictor and response variables. Smoothing methods

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take a more flexible approach, allowing the data points themselves to determine the form of the fitted curve.

If applicable, as an alternative regression model, smoothing spline poisson (negative binomial) regression will be fitted.

A spline is a piece-wise polynomial with pieces defined by a sequence of knots such that the pieces join smoothly at the knots.

Let Y be a response random variable and X1, X2,.., Xp be a set of predictor variables.

E(Y |X1, X2,..., Xp) = s0 + s1(X1) + s2(X2) + ... + sp(Xp)

Where si(X); i = 1, 2,...,p are smooth functions.

For example, natural cubic spline with 3 knots will be used: S(x) = βo + β1x + β2x 2 + β3x3

SAS codes: PROC GAM DATA=<data>;

MODEL < pertussis cases > =SPLINE(month_year, df=3) SPLINE(age_grp, df= 3) SPLINE(vac_status, df=3) / DIST=POISSON METHOD=GCV;

RUN;

7. STATISTICAL CALCULATIONS

7.1. Derived and transformed data

• Vaccination time period: For the main analysis, the period of January 2011-February 2013 will be considered as baseline or pre-vaccination period, March to December 2013 will be considered as transition period and January 2014 to December 2015 will be considered as post-vaccination period. For the sensitivity analysis the period of January 2005-February 2013 will be considered as baseline or pre-vaccination period, March to December 2013 will be considered as transition period and January 2014 to December 2015 will be considered as post-vaccination period.

• Pertussis cases: ICD 10 code A37

• ALRTI cases: ICD 10 codes J13, J14, J15, J16, J17, J18, J20, J21 and J22

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7.2. Handling missing data

• The percentages will be computed by using only available information. Only frequencies will be provided for unknown or missing or not applicable categories.

• Any measurements omitted or non-evaluable for a particular subject or analysis will not be replaced. Therefore, subjects with omitted or non-evaluable measurements will be excluded from the analysis.

7.3. Methodology for computing CI

All CI will be 2 sided 95% CI.

• The exact 95% CIs for a proportion within a group will be calculated [Clopper 1934].

• The exact 95% Poisson CIs for an Incidence rate will be calculated [Ulm, 1990] If n is the number of subjects presenting a given characteristic among these Ny subjects per year, the true incidence rate can be estimated by (n/Ny)*1000. Its exact (1- )% confidence interval is obtained from: CINV( /2,2*n)/2/Ny*1000 as the lower boundary and CINV((1- /2),2*(n+1))/2/Ny*1000 as the upper boundary.

where CINV(probability, degrees of freedom) returns the inverse of the chi-squared probability distribution and is the type I error rate.

• Two sided 95% Wald CI will be calculated for Poisson or Negative binomial or Binomial regression coefficients. It will be calculated as Estimate (β) ± (zα/2)*(Standard Error (1.96)), where zα/2 is a critical value on the standard normal distribution. The type one error (or α-error) will be fixed at 0.05 level of significance.

7.4. Number of decimals:

The following decimal description from the decision rules will be used Display Table Parameters Number of

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decimal digits

Demographic characteristics

Mean, median, minimum and maximum

1

Demographic characteristics

SD (age) 2

All summaries % of count, including LL & UL of CI 1

All summaries % of difference/incidence rate, including LL & UL of CI

2

All summaries p-value 3 or 4

8. CONDUCT OF ANALYSES

8.1. Sequence of analyses

There will be an interim analysis on data extracted and verified from January 2005 until December 2014, followed by a final analysis at the end of the study. A final study report will be written when all the data for the study are extracted and verified (i.e, data from January 2005 to December 2015).

Description Analysis ID (SDD & CARS sub-folder)

Interim analysis E1_01 Final analysis E1_02

8.2. Statistical considerations for interim analyses

All the analyses with respect to the primary and secondary endpoints will be descriptive/ exploratory. As the results of the interim analyses will not be used to alter the study conduct, no statistical adjustment for interim analyses is required

9. CHANGES FROM PLANNED ANALYSES/PROTOCOL

• As mentioned in the protocol, there will be an extension in the recommendation of Boostrix for pregnant women in Bogota and the study period (post-vaccination period) will be extended until December 2015. Therefore the study design, objectives, endpoints and statistical methods were updated accordingly to incorporate these changes.

• Since the data on infant’s age will be available in months (not weeks), all the age groups were re-defined accordingly.

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10. REFERENCES

• Clopper CJ, Pearson ES. The use of confidence or fiducial limits illustrated in the case of binomial. Biometrika. 1934; 26:404-413.

• Ulm K. A simple method to calculate the confidence interval of a standardized mortality ratio. American Journal of Epidemiology 1990;131(2):373-375.

• Mullahy J (1986). “Specification and Testing of Some Modified Count Data Models.” Journal of Econometrics, 33, 341–365.

• Lambert D (1992). “Zero-inflated Poisson Regression, With an Application to Defects in Manufacturing.” Technometrics, 34, 1–14.

• Cameron AC, Trivedi PK (1998). Regression Analysis of Count Data. Cambridge University Press, Cambridge.

• Cameron AC, Trivedi PK (2005). Microeconometrics: Methods and Applications. Cambridge University Press, Cambridge.

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Detailed Title: An observational, retrospective, ecological database study to assess the impact of BoostrixTM maternal vaccination on morbidity and mortality of pertussis disease in infants 6 weeks of age, in Bogota, Colombia.

SAP version Version 2

SAP date 09-Jan-2017

Scope: All data pertaining to the above study.

Co-ordinating author:

Other author(s):

Adhoc reviewers: < (Director, EPI/HO LATAM Epidemiologist)>

Approved by: < (Regional Epidemiologist)>

< (Lead Statistician)>

< (Scientific Writer)>

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TABLE OF CONTENTS

PAGE

LIST OF ABBREVIATIONS ............................................................................................. 3

1. DOCUMENT HISTORY ............................................................................................ 4

2. STUDY DESIGN ...................................................................................................... 4

3. OBJECTIVES ........................................................................................................... 7 3.1. Primary objective .......................................................................................... 7 3.2. Secondary objectives .................................................................................... 7

4. ENDPOINTS ............................................................................................................ 7 4.1.1. Primary endpoint ............................................................................ 7 4.1.2. Secondary endpoints ..................................................................... 7

5. STUDY POPULATION ............................................................................................. 8 5.1.1. Total cohort .................................................................................... 8

6. STATISTICAL METHODS ........................................................................................ 8 6.1.1. Analysis of demographics/baseline characteristics ........................ 8 6.1.2. Analysis of primary and secondary objectives ................................ 8

7. STATISTICAL CALCULATIONS ............................................................................ 11 7.1. Derived and transformed data ..................................................................... 11 7.2. Handling missing data ................................................................................. 11 7.3. Methodology for computing CI .................................................................... 12 7.4. Number of decimals: ................................................................................... 12

8. CONDUCT OF ANALYSES .................................................................................... 13 8.1. Sequence of analyses ................................................................................. 13 8.2. Statistical considerations for interim analyses ............................................. 13

9. CHANGES FROM PLANNED ANALYSES/PROTOCOL ........................................ 13

10. REFERENCES ....................................................................................................... 14

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The complete statistical analysis plan and results presentation is divided into 2 parts: the first part detailing the analyses to be performed (known as SAP, current document) and a second part, annex (called TFL) describing the flow and format of tables, figures and listings to be annexed to the SR.

LIST OF ABBREVIATIONS

ATP According-To-Protocol

ARI Acute Respiratory Infection

CI Confidence Interval

CSV Comma Separated Values

DB Database

DTP Diphtheria, Tetanus, and Pertussis vaccine

DTPa Diphtheria, Tetanus and acellular Pertussis vaccine

DTPw Diphtheria, Tetanus and whole-cell Pertussis vaccine

GSK GlaxoSmithKline

LL Lower Limit of the confidence interval

SAP Statistical Analysis Plan

SAS Statistical Analysis System

SD Standard Deviation

SDD SAS Drug and Development

SR Study Report

TFL Tables Figures and Listing template annexed to SAP

UL Upper Limit of the confidence interval

UMV Universal Mass Vaccination

ZIP Zero-inflated Poisson

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1. DOCUMENT HISTORY

Date Description Protocol Version

13-NOV-2015 Version 1 Final – 31-MAR-2015

09-JAN-2017 Version 2 (updated based on the available data for interim analysis)

Final – 31-MAR-2015

2. STUDY DESIGN

Type of study: Self-contained.

Type of design: An observational, retrospective, ecological database study.

Systematic screening of the national databases of Bogota (described above) will be performed for the following aggregated data:

pertussis cases (as per case definition) in infants <12 months of age,

pertussis related morbidity (including hospitalisations) in infants <12 months of age,

pertussis associated deaths in infants <12 months of age,

ARI cases in infants <12 months of age

vaccination coverage of Boostrix in pregnant women and primary pertussis vaccination coverage in children <12 months of age.

population projections, demographic data on infants, number of pregnancies reported during post vaccination period.

Data will be extracted by the principal investigator and transferred to data management in an Excel or comma separated values (CSV) format for the following time periods:

• Vaccination coverage (PAI database) in infants : from January 2005 to December 2015 Note: the interim (pre-extension) analysis will only include data up to December 2014

• Pertussis cases and deaths (SIVIGILA database): from January 2005 to December 2016 Note: the interim (pre-extension) analysis will only include data up to December 2015

• Pertussis associated deaths and ARI associated deaths in infants (DANE database): from January 2005 to December 2015 Note: the interim (pre-extension) analysis will only include data up to December 2014

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• Vaccination coverage for the mothers: from January 2013 to December 2016 Note: the interim (pre-extension) analysis will only include data up to December 2014

The period before introduction of Boostrix in the UMV program will be considered as pre-vaccination or baseline period (January 2011-December 2012 for main analysis and January 2005-December 2012 for sensitivity analysis), the year when Boostrix was introduced will be considered as transition period (January to December 2013) and the later period will be considered as post-vaccination period (January 2014 to December 2016 for SIVIGILA data and January 2014 to December 2015 for DANE data).

Period of data collection: Data will be collected retrospectively for cases identified through surveillance in Bogota from January 2005 until December 2016. Data on vaccination coverage for the mothers will be collected from March 2013 to December 2016.

Epoch 001: Retrospective data collection.

The following group names will be used for the statistical analyses:

Group order in tables

Group label in tables Group definition for footnote

Vaccination Time Period (Main analysis) 1 Pre-vaccination Pre-vaccination period from January 2011 to

December 2012

2 Transition Transition period from January to December 2013 3 Post-vaccination Post-vaccination period from January 2014 to

December 2016 for SIVIGILA database Post-vaccination period from January 2014 to December 2015 for DANE database

Vaccination Time Period (Sensitivity analysis) 1 Pre-vaccination Pre-vaccination period from January 2005 to

December 2012

2 Transition Transition period from January to December 2013 3 Post-vaccination Post-vaccination period from January 2014 to

December 2016 for SIVIGILA database Post-vaccination period from January 2014 to December 2015 for DANE database

Age group

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1 0-<1m Infants with <1 month of age

2 1m-<6m Infants from 1 month to <6 months of age


4 12m-<60m Infants from 12 months to <60 months of age (5 yrs)

Combined Age group (only for the final analysis) 1 0-≤6w or 0-<2m Infants with <6 weeks of age or Infants with <2

months of age

2 7w-<12m or 2m-<12m Infants from 6 weeks to <12 months of age or Infants from 2 months to <12 months of age

Combined Age group (for both interim and final analysis)

1 0-<12m or 0-<1y Infants with <12 months of age or Infants with <1 year of age

2 12m-<60m or 1y-<5y Infants from 12 months to <60 months of age or children from 1 year to < 5 year of age

Year 1 2005 Year 2005

2 2006 Year 2006

3 2007 Year 2007

4 2008 Year 2008

5 2009 Year 2009

6 2010 Year 2010

7 2011 Year 2011

8 2012 Year 2012

9 2013 Year 2013

10 2014 Year 2014

11 2015 Year 2015

12 2016 Year 2016

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3. OBJECTIVES

3.1. Primary objective

• To describe trends on the morbidity and mortality caused by pertussis disease in infants 6 weeks of age before and after introduction of pertussis maternal immunisation in Bogota, Colombia from January 2005-December 2016.

Note: the interim (pre-extension) analysis will only include children < 12 months of age and only data up to December 2015

3.2. Secondary objectives

• To describe trends on the morbidity and mortality caused by pertussis disease in infants 7 weeks to <12 months of age (stratified into different age groups by month), before and after pertussis maternal immunisation in Bogota, Colombia from January 2005-December 2016. Note: the interim (pre-extension) analysis will not include this age stratified data

• To describe trends on ARI in infants 6 weeks of age and 7 weeks to <12 months of age, following the introduction of pertussis maternal immunisation in Bogota, Colombia from January 2005 -December 2016.

Note: the interim (pre-extension) analysis will not include ARI morbidity data

• To describe pertussis vaccination coverage in pregnant women since the introduction of Boostrix as part of the UMV program in Bogota, and the vaccine uptake from January 2013 to December 2016.

• To describe primary pertussis vaccination coverage in infants <12 months of age in Bogota from January 2005-December 2016.

4. ENDPOINTS

4.1.1. Primary endpoint

• Occurrence of reported cases of hospitalisations and deaths due to pertussis in infants 6 weeks of age, in post-vaccination period compared to pre-vaccination period of Boostrix vaccination (i.e. from January 2005-December 2016).

4.1.2. Secondary endpoints

• Occurrence of reported cases of hospitalisations and deaths due to pertussis in infants 7 weeks to <12 months of age, in post-vaccination period compared to pre-vaccination period of Boostrix vaccination (i.e. from January 2005-December 2016).

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• Occurrence of ARI in infants 6 weeks of age and 7 weeks to <12 months of age, following the introduction of pertussis maternal immunisation in Bogota, Colombia (i.e. from January 2005-December 2016).

• Description of the total number of doses of Boostrix administered in pregnant women, after its introduction into the UMV program in Bogota (i.e. from March 2013-December 2016).

• Description of the total number of doses of primary pertussis vaccine administered to infants <12 months of age in Bogota from January 2005-December 2015.

5. STUDY POPULATION

5.1.1. Total cohort

The Total cohort will include all subjects included in the study. Only aggregated anonymised information will be collected for these subjects.

6. STATISTICAL METHODS

6.1.1. Analysis of demographics/baseline characteristics

The distribution of subjects by month and year or year (as applicable) and age group will be tabulated separately for pertussis cases (i.e. overall, Probable, Suspected, lab confirmed or clinically confirmed, when available), hospitalisations and deaths, and if possible for the corresponding denominator population. Any potential changes in the denominator population will be presented and discussed (in relation to potential changes in the catchment population of each of the databases). In addition, the baseline versus post-vaccination percent change in mean and median will also be calculated for both the vaccination time periods (main analysis and sensitivity analysis).

For each outcome (counts and/or incidence rates), the Percent change in Mean or Median will be computed using the below formula:

%Change_mean or median = {(Mean or median counts/incidence for baseline period – counts/Incidence in the given post-vac year) ÷ Mean or median counts/incidence for baseline period}*100

6.1.2. Analysis of primary and secondary objectives

As the primary objective is to describe the trends on the morbidity and mortality caused by pertussis disease in infants 6 weeks (before and after introduction of maternal

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immunisation), all the suspected and confirmed cases, hospitalisations and deaths due to pertussis will be tabulated for infants ≤6 weeks of age before and after introduction of pertussis maternal immunisation by year and overall. Also the incidence rate of pertussis cases i.e. overall, probable, suspected, lab confirmed or clinically confirmed, when available, proportion of hospitalisations and mortality rate due to pertussis in infants will be calculated with 95% confidence interval (CI) before and after introduction of pertussis maternal immunisation by year and overall.

To describe trends on the morbidity and mortality caused by pertussis disease and to assess the vaccine impact before and after pertussis maternal immunisation, an age-period Poisson (in case of equi-dispersion) or negative binomial regression (in case of over-dispersion) model will be fitted by considering rate or/and number of cases (i.e. overall, probable, suspected, lab confirmed or clinically confirmed, when available) as outcome variable during the years 2011 to 2015 (as there were less number of cases before the year 2011 and age of the first dose has changed over time) depending on the availability of population data. This model includes year, age group of the infants and vaccination time period as dummy/indicator variable (i.e. a three-valued variable that contains values 0 denoting pre-vaccination period, 1 denoting transition period and 2 denoting post-vaccination period) as covariates by including a minimum of 5 subjects per stratum. The same model will be fitted by replacing vaccination time period with vaccine coverage (for pregnant women) as one of the variable.

Note: The adequacy of the model fit (whether the data is equi- or over dispersed) will be checked by assessing the goodness of fit criteria related to deviance and/or Pearson chi-square. If the Poisson regression model is adequate then the expected value of both the deviance and Pearson chi-square is equal (or close) to 1. Otherwise negative binomial regression model will be used.

Also, as part of sensitivity analysis, the above model will also be performed using the data from the years 2005 to 2015. Further the graphical representation (line/bar chart) for number of pertussis cases, hospitalisations due to pertussis and deaths will be provided by age group and year. Based on the disease trend, the age group will be classified (especially with different category of first age group as the timeliness of vaccination in new-borns are not available) for example as <1 month, 1 to <6 months, 6 to <12 months, 12 to <60 months.

Similarly, the trends on ARI in infants < 12 months will be assessed using the above specified age-period regression model considering the before and after pertussis maternal immunisation periods.

The vaccination coverage in pregnant women, since the introduction of Boostrix into the UMV program, will be summarised per calendar year (after vaccine introduction, i.e., March 2013 onwards) using frequency and percentage.

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The pertussis vaccination history or coverage (DTPw or DTPa vaccine) for infants < 12 months of age will be tabulated by year (from January 2005 – December 2015) and overall.

Age-period (AP) Poisson / Negative binomial regression models: The AP Poisson model has the following form:

log(E(yij)) = µ + αi + βj +γk+ log(Nijk)

The AP Negative binomial model has the following form:

log(E(yij)) = µ + αi + βj + γk+ log(Nijk) + ɛijk

The number of pertussis cases in age group i at the time period j is denoted yij

Nij - number of persons at risk in age group i at the time period j (population data)

Where αi’s are the age effects,

βj’s are period effects,

γk’s are the vaccination effect (vaccine coverage or vaccination time period)

log(Nijk ) is an offset (only applicable when we have population at risk. i.e. denominator data to model the outcome as rate)

E(yij ) expected number of pertussis cases And

ɛij represents random error

To describe trends over time on reported pertussis cases and the impact of the vaccine, the following independent variables will be included in the model

• Year: It will be a continuous variable indicating calendar year.

• Age group, when available (<1 month, 1 to <6 months, 6 to <12 months, 12 to <60 months)

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• Vaccine coverage (for pregnant women): Vaccine coverage will be expressed as an percentage by year

OR

• Vaccination time period as dummy/indicator variable (i.e. a three-valued variable that contains values 0, denoting pre-vaccination period, 1 denoting transition period, and 2 denoting post-vaccination period)

The above model will also be fitted to describe the trends on ARI in infants < 12 months considering the before and after pertussis maternal immunisation periods.

Note: The reference category for each categorical variable will be either fixed at the time of TFL or during the analysis.

7. STATISTICAL CALCULATIONS

7.1. Derived and transformed data

• Vaccination time period: For the main analysis, the period of January 2011-February 2013 will be considered as baseline or pre-vaccination period, January to December 2013 will be considered as transition period and January 2014 to December 2016 will be considered as post-vaccination period. For the sensitivity analysis the period of January 2005-February 2013 will be considered as baseline or pre-vaccination period, January to December 2013 will be considered as transition period and January 2014 to December 2016 will be considered as post-vaccination period.

• Pertussis cases: ICD 10 code A37 (For DANE database), case-definitions as per the protocol used for the SIVIGILA database

ARI cases: ICD 10 codes J00-J06, J09-J18, J20-J22

7.2. Handling missing data

• The percentages will be computed by using only available information. Only frequencies will be provided for unknown or missing or not applicable categories.

• Any measurements omitted or non-evaluable for a particular subject or analysis will not be replaced. Therefore, subjects with omitted or non-evaluable measurements will be excluded from the analysis.

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7.3. Methodology for computing CI

All CI will be 2 sided 95% CI.

• The exact 95% CIs for a proportion within a group will be calculated [Clopper 1934].

• The exact 95% Poisson CIs for an Incidence rate will be calculated [Ulm, 1990] If n is the number of subjects presenting a given characteristic among these Ny subjects per year, the true incidence rate can be estimated by (n/Ny)*1000. Its exact (1- )% confidence interval is obtained from: CINV( /2,2*n)/2/Ny*1000 as the lower boundary and CINV((1- /2),2*(n+1))/2/Ny*1000 as the upper boundary.

where CINV(probability, degrees of freedom) returns the inverse of the chi-squared probability distribution and is the type I error rate.

• Two sided 95% Wald CI will be calculated for Poisson or Negative binomial or Binomial regression coefficients. It will be calculated as Estimate (β) ± (zα/2)*(Standard Error (1.96)), where zα/2 is a critical value on the standard normal distribution. The type one error (or α-error) will be fixed at 0.05 level of significance.

7.4. Number of decimals:

The following decimal description from the decision rules will be used

Display Table Parameters Number of decimal digits

All summaries % of count, including LL & UL of CI 1

All summaries % of difference/incidence rate, including LL & UL of CI

2

All summaries p-value 3 or 4

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8. CONDUCT OF ANALYSES

8.1. Sequence of analyses

There will be an interim analysis (pre-extension) on data extracted and verified from January 2005 until December 2015, followed by a final analysis at the end of the study. A final study report will be written when all the data for the study are extracted and verified (i.e, data from January 2005 to December 2016).

Description Analysis ID (SDD & CARS sub-folder)

Interim analysis (pre-extension) E1_01 Final analysis E1_02

8.2. Statistical considerations for interim analyses

All the analyses with respect to the primary and secondary endpoints will be descriptive/ exploratory. As the results of the interim analyses will not be used to alter the study conduct, no statistical adjustment for interim analyses is required

9. CHANGES FROM PLANNED ANALYSES/PROTOCOL

• As per the note provided in the protocol, there will be an extension in the recommendation of Boostrix for pregnant women in Bogota and the study period (post-vaccination period) will be extended until December 2016. Therefore the study design, objectives, endpoints and statistical methods were updated accordingly to incorporate these changes.

• There will be a pre-extension (interim) analysis planned considering the data up to 2015 and the study objectives, time period for this interim analysis (pre-extension) was updated based on the availability of data. All the sections related to this analysis were updated accordingly.

• One of the secondary objectives, to describe trends on ALRTI in infants was modified to ARI cases.

• In all the applicable sections, infants ≤12 months of age was updated to <12 months of age.

• Transition period was updated as January to December 2013 instead of March to December 2013.

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• The alternative regression models such as zero-inflated models or age-period count regression models using parametric smooth functions based on natural splines mentioned in the statistical analysis section will not be considered assuming the sufficient number of cases available for each year from 2005 to 2016.

10. REFERENCES

Clopper CJ, Pearson ES. The use of confidence or fiducial limits illustrated in the case of binomial. Biometrika. 1934; 26:404-413.

Ulm K. A simple method to calculate the confidence interval of a standardized mortality ratio. American Journal of Epidemiology 1990;131(2):373-375.

Mullahy J (1986). “Specification and Testing of Some Modified Count Data Models.” Journal of Econometrics, 33, 341–365.

Lambert D (1992). “Zero-inflated Poisson Regression, With an Application to Defects in Manufacturing.” Technometrics, 34, 1–14.

Cameron AC, Trivedi PK (1998). Regression Analysis of Count Data. Cambridge University Press, Cambridge.

Cameron AC, Trivedi PK (2005). Microeconometrics: Methods and Applications. Cambridge University Press, Cambridge.

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