Dashboard: Effects of medical diagnoses on health and socio-economic outcomes

Based on
Crego, J. A., Karpati, D., Soerlie Kvaerner, J., & Renneboog, L. (2025). Health Risk and Economic Value: Quantifying the Insurance, Financial, and Fiscal Implications of Reducing Disease Burden.

Methodology

Effects are estimated using an event-study design: individuals diagnosed with the same condition in a later calendar year serve as a control group for those diagnosed earlier.

Mortality is an exception — later-year cohorts cannot serve as controls by construction. It is estimated via multivariate regression controlling for all 333 other diagnoses and an age polynomial.

Diagnosis graph — Links represent one-year Granger-causal relationships. For each diagnosis, a Lasso regression predicts the probability of receiving that diagnosis in year T+1 using indicators for all 334 diagnoses in year T, age, age², and permanent income tercile dummies. The link strength is the estimated increase in probability (in percentage points).


For full details see Crego et al. (2025).

Dashboard tabs

Effects explorer Event-study plots by diagnosis, outcome & subgroup
Rank diagnoses Rank by effect size, weighted or unweighted by incidence
Scatter plots Cross-outcome or cross-group scatter across all 334 diagnoses
Diagnoses ICD-10 correspondence, incidence & treatment setting
Diagnosis graph Directed Granger-causal graph between diagnoses

Dimensions

Sex Male or female, from Statistics Netherlands municipal registers
Age group Category based on age in the year of diagnosis (e.g., 25–44)
Income Permanent income tercile (low / medium / high), from lifetime labor earnings adjusted for age
Relative year Years since diagnosis (Dx−1, Dx+0, Dx+1, …)

Not all sex–age–income combinations are available for every outcome. For example, labor participation is not estimated for ages 65+; nursing home use is only available for ages 65+. Some estimates use aggregated groups (e.g., sex–age only, or full population).

Outcomes

Medical expenses Annual expenses under the Dutch basic health insurance scheme, in €
Mortality Cumulative death indicator (1 if died in or before given year)
Physical limitations Age-standardized index (1 SD = 1 unit); mapped from medicine use & expenditure to OECD functional limitation items
Mental limitations Age-standardized index (1 SD = 1 unit); mapped to Kessler K10 distress scale items
Labor participation 1 if main income source is employment earnings
Log labor earnings Log of annual gross employment earnings (€)
Full disability 1 if receiving WIA/WGA benefit at ≥ 80% assessed disability
Nursing home use 1 if residing in a nursing home in the given year
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You can select up to two diagnoses, and up to two sex, age, and income groups (in total maximum 16 lines). Some sex-age-income groups are not available, e.g., medical expenses are not split by income group.
Lines = all combos of selected diagnoses × sex × age × income (up to 16). Hover over the lines to see estimate, confidence interval, p-value, CI, and the number of diagnosed individuals in the sex-age-income group underlying the estimates in the Jan 2013 - Sep 2017 period (n).
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Diagnosis group Diagnosis Estimate Cohort-entry annual burden CI low CI high p Annual cases
Annual cases: number of cases (in the demographic group) between January 2013 and September 2017 divided by 4.75. Cohort-entry annual burden = estimate × annual cases
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Select series X and Series Y (outcome + sex + age + income + year). Each point is a diagnosis Points with missing X or Y are excluded.
Series X (x-axis)
Series Y (y-axis)
Marker size reflects number of total cases in sample (geometric mean for series X and Y). Hover shows estimates, confidence intervals, p-values, and number of cases for both series.
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Search diagnoses; click on a diagnosis to open details.
Internal id
ICD
ICD title
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Pick a diagnosis, then sex and age group, then show outgoing or incoming edges.
Hover edges for ΔP(T+1) in percentage points; line width reflects |link_strength|.