Specify timezone when converting date to datetime

When parsing clinical data, several date values are converted to datetime data type which
by default are interpretted as midnight UTC time. Indicating the timezone when these
conversions occur to ensure that date values are converted to midnight local time when a
time component is being added.

lib/seattleflu/id3c/cli/command/clinical.py

@@ -275,9 +275,11 @@ def parse_sch(sch_filename, manifest_format, output):
     clinical_records = clinical_records[columns_to_keep]
 
     # Convert dtypes
-    clinical_records["encountered"] = pd.to_datetime(clinical_records["encountered"])
+    # Incoming `encountered` value is typically just date but is cast to datetime with timezone in postgres. Timezone is
+    # being specified here to ensure values are set to midnight local time instead of UTC.
+    clinical_records["encountered"] = pd.to_datetime(clinical_records["encountered"]).dt.tz_localize('America/Los_Angeles')
 
-    # Reformat vaccination dates
+    # Reformat vaccination dates. Values are immediately stripped of time component, so don't need timezone specified.
     if manifest_format in ['year3', 'year4']:
         clinical_records["FluShotDate"] = pd.to_datetime(clinical_records["FluShotDate"]).dt.strftime('%Y-%m-%d')
         clinical_records["CovidShot1Date"] = pd.to_datetime(clinical_records["CovidShot1Date"]).dt.strftime('%Y-%m-%d')
@@ -387,7 +389,7 @@ def parse_kp(kp_filename, kp_specimen_manifest_filename, manifest_format, output
     clinical_records = clinical_records[column_map.values()]
 
     # Convert dtypes
-    clinical_records["encountered"] = pd.to_datetime(clinical_records["encountered"])
+    clinical_records["encountered"] = pd.to_datetime(clinical_records["encountered"]).dt.tz_localize('America/Los_Angeles')
 
     # Insert static value columns
     clinical_records["site"] = "KP"
@@ -491,8 +493,9 @@ def parse_phskc(phskc_filename: str, phskc_specimen_manifest_filename: str, geoc
     # localize encounter timestamps to pacific time
     clinical_records['encountered'] = clinical_records['collect_ts'].dt.tz_localize('America/Los_Angeles')
 
-    # calculate age based on sample collection date and birth day
-    clinical_records['birth_date'] = pd.to_datetime(clinical_records['birth_date'])
+    # calculate age based on sample collection date and birth day. Localize birth date datetime value to ensure accurate
+    # delta with local collection datetime.
+    clinical_records['birth_date'] = pd.to_datetime(clinical_records['birth_date']).dt.tz_localize('America/Los_Angeles')
     clinical_records['age'] = clinical_records.apply(
         lambda row: age_ceiling(
                 relativedelta(