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Table 1 Summary of estimation steps

From: A method for small-area estimation of population mortality in settings affected by crises

Step Description Sub-steps Data requirements Depends on
Data collection and management steps
1 Identify existing ground mortality data and prepare them for analysis Identify all available estimates
Extract meta-data for each estimate
Clean and re-analyse datasets
Grade estimate quality
Describe data coverage and crude patterns in key demographic indicators
Raw datasets of surveys or other estimation exercises
Survey reports
Official administrative data, shape files for geographic boundaries
2 Reconstruct population denominators
[not presented in this paper]
Identify and curate alternative population datasets. Grade their robustness
Identify and curate displacement data
Make appropriate assumptions on population and displacement dynamics
Reconstruct population for each \(kt\) stratum as an average of alternative estimates
Population datasets
Remote sensing estimates
Internal and refugee displacement data
Explanatory accompanying documents and reports
3 Capture predictor variable data and prepare them for analysis Identify possible sources of data based on a conceptual framework
Capture and curate predictor datasets
Ascertain missingness and perform any appropriate imputation
Convert absolute figures into population rates, smooth time series and create lags if appropriate
Predictor datasets
Explanation of variable meanings/variable dictionaries
Steps 1–2
Analysis steps
4 Fit a statistical model to predict the death rate as a function of the predictors Explore correlation among predictors
Do univariate analysis
Fit alternative multivariate models and select the most appropriate one
  Steps 1–3
5 Apply the model to estimate excess mortality while propagating known sources of error Specify a set of counterfactual scenarios:
Agree on what key deviations from normal define the crisis being analysed
Arbitrarily define alternative (e.g. most likely, best-case, worst-case) scenarios for what values the model predictors would have taken in the absence of a crisis
Construct counterfactual predictor datasets accordingly
Apply counterfactual death rates and assumptions on displacement to reconstruct corresponding counterfactual population denominators
Set up statistical simulation that implements Eq. (1) for each \(kt\) stratum while drawing from known error distributions of each parameter
Compute excess death toll estimates overall and for sub-populations/periods of interest
Extensive contextual knowledge
Mortality and predictor data for periods as long as possible before the crisis (recommended)
Steps 2–4
6 Conduct sensitivity analyses of interest Explore how possible bias or uncertainty in key parameters affect the estimates, by running the analysis with alternative data or assumptions   Step 5