# Presentation: 681169

## ICE Injury Statistics International meeting Washington DC, September 7-8, 2006  

**Pan American Health Organization **
**Injury Statistics Activities**

**Notes:**
Thanks for the opportunity 
Again Yvette is here and Maria Isabel....Dr becker and Laura a fellow, will share the presentation 

## PAHO’s Current Projects

- Morbidity data
- Hospital injury surveillance
- Mortality data
- Observatorios Violencia
- Interpersonal violence
-  Intimate partner violence, sexual violence, child maltreatment, youth violence, suicides, etc

## Morbidity data 

- PAHO-CDC-MOH-CISALVA “Injury Surveillance Systems in emergency rooms in Nicaragua, El Salvador and Colombia ”
- Technical assistance in different LAC countries
- Existing ISS as found in Jamaica, Peru, Brasil, Honduras, Bolivia, Chile, Argentina, Paraguay, etc
- MOH in Central America : “Family violence and sexual violence surveillance”

**Notes:**
partners: MOH and Universities :CISALVA , Colombia & CIDS, Nicaragua
Challengue the MOH wants to extend to all hospitals + MOH have focal points ( EL salvador and Nicaragua ) 
Countries in the region received very well the methods that PAHO- CDC are using in the 3 countries like Ecuador, Costa Rica, etc 
Existing Injury Surveillance Systems also in Jamaica ( CDC and PAHO supported now are the queens of the ISS ) ...with different methods...o in some cities ...

## Hospital description : Injury Surveillance System

**Hospital description : Injury Surveillance System**

**Data analysis  **

**Statistician & Epidemiologist **
**Data entry and quality control **

**Identification of injured person**** **

**Fill out Form**

**Dissemination of the results  **

**Intervention for Injury Prevention **

**Notes:**
Participatory Approach 
1. Promote a technical committee in every hospital 
2. Agreement to develop the forms to be complete in Emergency Rooms 
3. Sources of information and stakeholders
4. Selection of local technical coordinator
5. Timelines
6. Training

I want to introduce Laura, she will do the next slides about a case study in Cali, Colombia 

----------------------------------
So how does an emergency room injury surveillance system work?

First, the patient arrives in the hospital emergency room. They may have first had an interaction with the police or social workers who directed them to the hospital. 

Once they arrive in the emergency room, the patient is identified as injured. They may have been injured in a road accident, through an act of violence, drowning, burns, self-inflicted wounds or a myriad of other violent or unintentional means. 

Once the doctors triage the injured patient, they will complete a standardized national injury form. In many cases, this form is included in hospital’s admissions form, for convenience. 

A statistician in each hospital will collect all the forms on a daily basis, and input data into a basic computer statistics program such as EpiInfo. The variables they will consider include age, sex, type of injury, cause of injury, and location of injury. They may also include what the patient was doing at the time of the injury. 

This data will all be collected and analyzed by the epidemiologist, who will then work with the hospital to provide it to the Ministry of Health in a timely manner. The analyzed data will also be sent back to the doctors who collected it originally, because it may be useful to them as they plan hospital priorities. The data may be published in a national bulletin or journal, and also shared at national and international conferences. 

The results of this data can then be used by public health officials and governments to make policies that can reduce injuries. For example, in El Salvador, once it was discovered that a large percentage of their motor-vehicle related injuries were occurring along the same stretch of road, they were able to put up barriers and reduce the number of injuries in that area. 

The injury surveillance system should continue to function autonomously, providing monthly data that can be used to uncover seasonal or larger annual trends. 

## Cali, Colombia: a case study in data cleaning and coding 

- Challenges:

- Incomplete codebook

- Format

- Miscodes

- before

- after

- Missing data

- Excess of variables

**Notes:**
This data, from 2 hospitals in Cali, Colombia, was presented at a previous conference by Dr. Clavel.  I will be discussing the various challenges of working with this dataset, going over our progress and looking to further uses for this dataset.

The database initially over 150 variables, few of which had a complete codebooks entry.  This has been edited down to 60 complete variables, each of which are included in a compressive codebook. Another major challenge was the format of both the variables and the dataset.   However, the last two items, miscodes and recodes and missing data, posed the greatest challenge.  To deal with the the former, data was systemically sorted by intention and then mechanism of injury and then reviewed to ensure that each entry told a consisted and comprehensive story, of the patient from time of injury to when they left the ED. Varaibles were also combined or deleted. For example, four variables describing the date and time of the injury and the data and time of entry into the ER were combined into one variable: time elapse between event and injury. We can see a clear example of the recoding here. The before slide shows the first five entries as we received them. To the left we see the exact same patients, after both the variables and the data itself has been recoded.

The last challenge, missing data is our greatest threat to validity. 

## Cali, Colombia: a case study in data cleaning and coding

- Missing Data
- Asses amount of missing data
- Review and Reconstruct

**Notes:**
There were two specific efforts within missing data.  The first was to fully asses the amount of missing data in each variable.  This varies widely, from none to 83% within the education variable.  This information advised the overall utility of the variable; clearly if the majority was missing the variable was not useful. 

 Our second step was to minimize the amount of missing data by reviewing all those marked missing and attempting to reconstruct the information. Even internality, one of our best variables, has benefited from this.  We can see from these before and after pie charts. This shows that those data entries missing the intentionality variable have decreased. However, missing data still remains of course. We were careful to impute the value of variable only when there was significant evidence that this was in fact the correct value.  In the end, approximately 12% of the data were recoded. 

## Cali, Colombia: a case study in data cleaning and coding

- Predictors of missing data
- Hospital
- Doctor
- Presence of alcohol
- Hour of admission
- Pre hospital care

- The future.....?

**Notes:**
Right now we are in the process of running a series of logistic regressions looking at what may have caused these data issues on a systemic level.  Currently the largest predictor of a missing ICD10 for example, is in fact what hospital you attended, however, other risk and protective factors include certain doctors, if the patient was under the influence of alcohol, and if the patient receive pre-hospital care. Here you can actually see some of our output from STATA.  

So, where are we going with all this? Well, we are aiming to expand and increase out analysis of missing data, with the end goal of improving our surveillance system. Then we are going to examine the information itself, with a focus on unintentional injuries, particularly road traffic injuries and falls. 

## Achievements 

- Injury data collection in ED is possible
- Health authorities are interested in:
- Extension of injury surveillance
- Creation of injury prevention programs
- Promotion of intersectorial injury mortality surveillance
- Data is being used to make decisions

**Notes:**
Is possible with quality 
Health authorities are interested to expand and to create injury prevention programs like in El Salvador approved “ Injury Prevention Program “ inside the MOH in Nov 2004. MOH in Nicaragua wants to extend the system to all public hospitals 32

## Challenges  

- “Parallel” systems in the MOH persist
- Incomplete data in :
- alcohol abuse and other drugs, relationship between perpetrators and victims, precipitating factors in self-inflicted, etc
- Lack of consensus in measures of severity
-  Insufficient dissemination of data to the authorities

**Notes:**
Cont

## Challenges- Opportunities  

- Consolidating existing health information systems, and promoting the integration of ISS in these systems :
- Ex SILEX- SIMMOW ( El Salvador )
- Expert collaborations ( ICE, IACPV, etc )
- strengthening quality of data, minimum data set, measures of severity, classification, etc
- New Training Injury Tools
- Teach- VIP ( WHO ) ; Injury Surveillance Training Manual ( CDC) ; etc

**Notes:**
Injury Training Manual CDC ; TEACH- VIP 

## Recommendations 

- Regulate injury surveillance in the MOH
- Increase injury research and training (mentoring) in association with national & international universities
- Disseminate opportunely data to the authorities
- Strengthen coordination at the national and local level to achieve best practices for injury prevention
- Diversify funds to continue technical assistance

**Notes:**
Strengthen the quality of data : In place “ adapted “ Manual of procedures 
Minimum data set : alcohol use, injury modules ( relationship , road user, precipitating factors ? ) 
Methodology must be flexible
Improved data dissemination : Increase the users of data 
Use of System to identify the research questions, Initiated Cost studies
Include the system inside the health public agenda ( Cali, Colombia ) 
Include the evaluation inside the interventions 
Best practices , for the priorities in every country road traffic, violence, suicide,...including the evaluation in the process 

## Mortality data 

- “Observatorios Municipales del Delito“  CISALVA, Colombia
- Colombia began in 1992
- PAHO – IACPV : “ Pilot the Observatorios in Central America in some municipalities : data to action
- Linking data from mortality- morbidity ( where the hospital systems exist ) – family violence data ( Pasto, Colombia )

## Contacts in the countries   

**Colombia CISALVA **
- Maria Isabel Gutierrez : mgutierr@univalle.edu.co; cisalva@univalle.edu.co
- Andrés Fandiño : fandino.andres@gmail.com
- www.cisalva.univalle.edu.co
**El Salvador**
- Silvia Moran de Garcia : _[smoran@mspas.gob.sv](mailto:smoran@mspas.gob.sv)_
- Janette Ortiz de Montoya : _[janetteom@gmail.com](mailto:janetteom@gmail.com)_
- Rafael Chacon : epidemiologia@hospitalbloom.gob.sv
- _[www.mspas.gob.sv](http://www.mspas.gob.sv/)_
**Nicaragua **
- Maribel Rivas : _[viglesiones@minsa.gob.nic](mailto:viglesiones@minsa.gob.nic)_
- Julio Rocha : injury12003@yahoo.com
- Francisco Tercero  cidsft@yahoo.es
- _[www.minsa.gob.ni](http://www.minsa.gob.ni/)_

## THANKS  

**Alberto Concha- Eastman**
- 	e-mail : _[conchaal@paho.org](mailto:conchaal@paho.org)_
- 		  _[www.paho.org](http://www.paho.org/)_
**Roberto Becker**
- 	e-mail : _[beckerro@paho.org](mailto:beckerro@paho.org)_
**Carme Clavel-Arcas**
- 	e-mail: _[zgn4@cdc.gov](mailto:zgn4@cdc.gov)_
- 		_[www.cdc.gov/ncipc](http://www.cdc.gov/ncipc)_
**Laura Fraade- Blanar**
- 	e-mail : _lfraade@jhsph.edu_
**Eugenia M. Rodrigues**
- 	e-mail eugenia@bra.ops-oms.org

**Notes:**
Alberto Concha Eastman
Regional Advisor for injury and violence prevention
Roberto Becker
PAHO/WHO Regional Advisor for the WHO Family of International Classifications
 Carme Clavel Arcas
Injury Surveillance Project coordinator for PAHO- CDC 
Laura Fraade-Blanar 
Hopkins Fellowship
Eugenia Maria Rodrigues, PAHO Regional Advisor for Road Safety in Brazil since 2005