diff --git a/nnmm/mods/mortality.py b/nnmm/mods/mortality.py
index a86a6b2..7c673c2 100644
--- a/nnmm/mods/mortality.py
+++ b/nnmm/mods/mortality.py
@@ -37,7 +37,8 @@ def init( model ):
         # Find index of first value after eula_age
         split_index = np.searchsorted(model.population.age, eula_age)
         # Get list of indices we would EULA-ify
-        dod_filtered = model.population.dod[0:split_index]
+        # If pop is age order, lowest to highest, we keep the "left chunk" and eula-ify the middle chunk
+        dod_filtered = model.population.dod[split_index:model.population.count]
         # Convert these all to "simulation year of death"
         death_years = dod_filtered // 365
         # Now invoke function in Population class to calculate the expected deaths by simulation year
@@ -88,7 +89,11 @@ def do_non_disease_deaths(model, tick):
     year = tick // 365
     if model.population.expected_new_deaths_per_year is not None:
         for nodeid in range(len(model.population.expected_new_deaths_per_year)):
-            model.nodes.population[nodeid,tick+1] -= (model.population.expected_new_deaths_per_year[nodeid][year]/365)
+            # converting yearly number to daily basically sucks
+            deaths = (model.population.expected_new_deaths_per_year[nodeid][year]/365)
+            model.nodes.population[nodeid,tick+1] -= deaths 
+            # Reporting only
+            model.nodes.deaths[nodeid,year] += deaths
 
     pq = model.nddq
     while len(pq) > 0 and pq.peekv() <= tick:
diff --git a/src/idmlaser/numpynumba/population.py b/src/idmlaser/numpynumba/population.py
index 308e5a7..8c1bf36 100644
--- a/src/idmlaser/numpynumba/population.py
+++ b/src/idmlaser/numpynumba/population.py
@@ -327,7 +327,7 @@ def expected_deaths_over_sim(self, death_years, split_index, sim_years=10):
         death_years[death_years > sim_years] = sim_years  # Cap deaths at years-1
 
         # Initialize the expected_new_deaths array
-        nodeids_filtered = self.nodeid[0:split_index]
+        nodeids_filtered = self.nodeid[split_index:self.count]
         unique_nodeids = np.unique(nodeids_filtered) # slow way of calculating node count
         nodeid_indices = {nodeid: i for i, nodeid in enumerate(unique_nodeids)}
         self.expected_new_deaths_per_year = np.zeros((len(unique_nodeids), sim_years+1), dtype=int)