Reduction of Affected People

Outcome

% reduction in the number of affected people (experienced, expected or modelled)

This indicator estimates to what extent a DRM intervention contributed to reducing the number of people affected by a hazard, either based on observed impacts after an event (“experienced”) or based on scenario-based estimates (“expected / modelled”).

Determine the indicator value by using the following methodology. The methodology can capture changes in the number of affected people based on past events as well as future (expected or modelled) scenarios.

 

1) First, define what “affected” means for this indicator. For example, you might say that “affected” includes people who would suffer at least one of the following: injury / illness, displacement, major asset loss, house damage above a threshold, or loss of access to essential services for X days. Keep the same definition in each round of measurement so results can be compared over time.

 

2) Next, clearly define the scenario you are measuring against. Specify:

    - the hazard type (e.g. flood, drought, storm, heatwave)

    - the assumed severity (e.g. seasonal flood, 1-in-5-year event)

    - the geographic area

    - the exposed population (people living or working in the hazard-prone area)

This scenario should be realistic and relevant to the intervention.

 

3) Estimate the total number of people who would be affected if the intervention had not taken place. This is a counterfactual estimate, even if a disaster already occurred. You may base this estimate on historical data from similar hazard events in the area, a simple risk model (exposed population × typical impact rate), or structured expert judgement validated with local authorities or technical staff. Document the main assumptions used.

 

4) Estimate how many people were or are protected: Take the following two actions to estimate how many people are protected thanks to the measures supported by the project. Count only people for whom the protective measure is in place and provides at least partial protection against the defined hazard scenario:

    - Estimate coverage: determine how many people are reached by the protective measures (e.g. people living behind mitigation structures, households covered by a functioning early warning system, recipients of anticipatory support).

    - Adjust for functionality and correct use: include only measures that are completed and functional. Where behaviour matters, estimate the proportion of people who correctly adopt and use the measures, and apply this proportion to the covered population.

 

5) To calculate the indicator value:

    - deduct from the number of people who would be affected without the intervention (Step 3) the number of people who were protected by the intervention (step 4)

    - divide the resulting number by the total number of people who would be affected without the intervention

    - multiply the result by 100 to convert it to a percentage

The data can be disaggregated by gender, age group, and location, where feasible and relevant. Additional disaggregation (e.g. disability or risk exposure) may be included if it is meaningful for the intervention and data can be collected reliably.

1) Clearly state whether the estimate is based on observed evidence (measures tested during an actual event) or expected/modelled evidence (future hazard scenario).

 

2) Focus on counting people effectively protected by the intervention rather than comparing raw disaster impacts before and after an event.

 

3) The indicator relies on assumptions. Be transparent about them and explain them when reporting on the indicator value.

 

4) If several protective measures apply to overlapping populations, ensure that the same people are not counted more than once.

 

5) The indicator is one of DG ECHO’s Key Outcome Indicators.