Updating 2022 risk coefficients group mapping for HHS

.gitignore

@@ -3,6 +3,7 @@
 build/
 dist/
 *.egg-info
+.vscode/
 
 # Compiled source #
 ###################

README.md

@@ -1,14 +1,40 @@
+# NOTICE - Dec 13, 2024
+
+# 📢 Repository Status Update
+
+This repository has been succeeded by a new version!
+We've created an enhanced version of this project with new features and improvements. You can find it here:
+🔗 github.com/mimilabs/hccinfhir
+
+# What This Means
+
+This repository is no longer actively maintained
+No new features or updates will be added here
+For the latest features and support, please use the new repository
+
+Thank you for your interest in this project! We encourage you to migrate to the new version to access the latest improvements.
+
+---
+
 # hccpy 
 
 Hierachical Condition Categories Python Package.
 
 This module implements the [Hierachical Condition Categories](https://www.cms.gov/cciio/resources/forms-reports-and-other-resources/downloads/ra-march-31-white-paper-032416.pdf) that are used for adjusting risks for the Medicare population.
 The original SAS implementation can be found [here](https://www.nber.org/data/cms-risk-adjustment.html).
-Currently, hccpy supports CMS-HCC V22, V23, V24, ESRD, and HHS-HCC Year 2019.
 
-NOTE: This package does not support for ICD-9.
-NOTE: The latest version is 0.1.5 - updated on 10/20/2022.
-NOTE: The latest version is 0.1.4 - updated on 09/13/2022.
+The latest version is 0.1.10 which was released on 04/10/2024.
+
+Currently, hccpy supports:
+* CMS-HCC V22
+* CMS-HCC V23
+* CMS-HCC V24
+* CMS-HCC V28
+* CMS-HCC ESRD
+* HHS-HCC 2019 (V05)
+* HHS-HCC 2022 (V07)
+
+Note that hccpy does not have support for ICD-9.
 
 ## Installing
 
@@ -131,6 +157,49 @@ To get a HCC profile from a list of diagnosis codes (in ICD-10):
 >>>
 ```
 
+### HCC for the new model, V28
+
+Please use "V28" when initializing the engine.
+
+```python
+>>> from hccpy.hcc import HCCEngine
+>>> he = HCCEngine("28")
+```
+
+Also, see the `test_v23()` examples in `tests/hcc_tests.py`.
+
+### Coding Intensity Factor and Normalization Factors
+
+You can add normalization factors and coding intensity factors to directly calculate the adjusted risk score.
+
+By default, these two parameters are set as:
+
+```python
+cif = 0.059, # coding intensity factor.
+norm_params={ 
+  "C": 1.015, # community/institution models
+  "D": 1.022, # ESRD Dialysis
+  "G": 1.028 # ESRD Graft
+}
+```
+
+You can overwrite these parameters. For example, this setting below would not adjust the raw risk score.
+
+```python
+HCCEngine(version="28", cif = 0, norm_params={"C": 1})
+```
+
+To see the adjusted risk scores, 
+```python
+>>> from hccpy.hcc import HCCEngine
+>>> he = HCCEngine("28")
+>>> rp = he.profile(["E1169", "I5030", "I509", "I211", "I209", "R05"],
+                    age=70, sex="M", elig="CNA") 
+>>> rp["risk_score_adj"]
+```
+
+Also, see the `test_norm_factors()` examples in `tests/hcc_tests.py`.
+
 ### HCC-Profiling a New Member
 
 If a member is new, then provide the `elig="NE"` in the input:

hccpy/_E2118P1M.py

@@ -0,0 +1,69 @@
+from collections import Counter
+
+def create_interactions(cc_lst, DISABL, age):
+
+    x = Counter(cc_lst) 
+    z = Counter()
+
+    # payable HCC's count interaction
+    hcccnt = len([k for k in x.keys() if "HCC" in k])
+    if hcccnt > 9:
+        x["D10P"] = 1
+    elif hcccnt > 0:
+        x["D{}".format(hcccnt)] = 1
+
+    # diagnostic categories
+    z["CANCER"] = max(x["HCC8"], x["HCC9"], x["HCC10"], 
+                        x["HCC11"], x["HCC12"])
+    z["DIABETES"] = max(x["HCC17"], x["HCC18"], x["HCC19"])
+    z["IMMUNE"] = x["HCC47"]
+    z["CARD_RESP_FAIL"] = max(x["HCC82"], x["HCC83"], x["HCC84"])
+    z["CHF"] = x["HCC85"]
+    z["COPD"] = max(x["HCC110"], x["HCC111"])
+    z["RENAL"] = max(x["HCC134"], x["HCC135"], x["HCC136"], 
+                            x["HCC137"], x["HCC138"], x["HCC139"],
+                            x["HCC140"], x["HCC141"])
+    z["COMPL"] = x["HCC176"]
+    z["SEPSIS"] = x["HCC2"]
+
+    # community model interactions
+    x["SEPSIS_CARD_RESP_FAIL"] =  z["SEPSIS"] * z["CARD_RESP_FAIL"]
+    x["CANCER_IMMUNE"] = z["CANCER"] * z["IMMUNE"]
+    x["DIABETES_CHF"] = z["DIABETES"] * z["CHF"]
+    x["CHF_COPD"] = z["CHF"] * z["COPD"]
+    x["CHF_RENAL"] = z["CHF"] * z["RENAL"]
+    x["COPD_CARD_RESP_FAIL"] = z["COPD"] * z["CARD_RESP_FAIL"]
+
+    # institutional model interactions
+    x["NONAGED_HCC6"] = DISABL * x["HCC6"]
+    x["NONAGED_HCC34"] = DISABL * x["HCC34"]
+    x["NONAGED_HCC46"] = DISABL * x["HCC46"]
+    x["NONAGED_HCC54"]  = DISABL * x["HCC54"]
+    x["NONAGED_HCC55"]  = DISABL * x["HCC55"]
+    x["NONAGED_HCC110"] = DISABL * x["HCC110"]
+    x["NONAGED_HCC176"] = DISABL * x["HCC176"]
+
+    x["PRESSURE_ULCER"] = max(x["HCC157"], x["HCC158"], 
+                            x["HCC159"], x["HCC160"])
+    x["SEPSIS_PRESSURE_ULCER"] = z["SEPSIS"] * x["PRESSURE_ULCER"]
+    x["SEPSIS_ARTIF_OPENINGS"] = z["SEPSIS"] * x["HCC188"]
+    x["ART_OPENINGS_PRESSURE_ULCER"] = x["HCC188"] * x["PRESSURE_ULCER"]
+    x["DIABETES_CHF"] = z["DIABETES"] * z["CHF"]
+    x["COPD_ASP_SPEC_BACT_PNEUM"] = z["COPD"] * x["HCC114"]
+    x["ASP_SPEC_BACT_PNEUM_PRES_ULC"] = x["HCC114"] * x["PRESSURE_ULCER"]
+    x["SEPSIS_ASP_SPEC_BACT_PNEUM"] = z["SEPSIS"] * x["HCC114"]
+    x["SCHIZOPHRENIA_COPD"] = x["HCC57"] * z["COPD"]
+    x["SCHIZOPHRENIA_CHF"] = x["HCC57"] * z["CHF"]
+    x["SCHIZOPHRENIA_SEIZURES"] = x["HCC57"] * x["HCC79"]
+
+    x["NONAGED_HCC85"] = DISABL * x["HCC85"]
+    x["NONAGED_PRESSURE_ULCER"] = DISABL * x["PRESSURE_ULCER"]
+    x["NONAGED_HCC161"] = DISABL * x["HCC161"]
+    x["NONAGED_HCC39"] = DISABL * x["HCC39"]
+    x["NONAGED_HCC77"] = DISABL * x["HCC77"]
+
+    cc_lst = [k for k, v in x.items() if v > 0]
+
+    return cc_lst
+
+

hccpy/_E2118P1P.py

@@ -31,6 +31,12 @@ def get_risk_dct(coefn, hcc_lst, age, sex):
 
     # build hcc factor strings and add to risk_dict
     for hcc in hcc_lst:
+        # NOTE: Other Eligibilities are not implemented
+        # DI: dialysis model 
+        # DNE: dialysis new enrollees
+        # GC: graft community model
+        # GI: graft institutional model
+        # GNE: graft new enrollee
         elig_hcc = "DI_" + hcc
         risk_dct[elig_hcc] = coefn.get(elig_hcc, 0.0)
 

hccpy/_V0519F3P.py

@@ -12,7 +12,7 @@ def get_risk_dct(coefn, hcc_lst, agesexvar, agegroup, enroll_dur, plate):
     for hcc in hcc_lst:
         if agegroup+"_"+hcc in coefn:
             risk_dct[hcc] = coefn[agegroup+"_"+hcc][plate]
-
+    
     return risk_dct
 
 

hccpy/_V2218O1M.py

@@ -37,7 +37,7 @@ def create_interactions(cc_lst, DISABL):
     x["DIABETES_CHF"] = z["DIABETES"] * z["CHF"]
     x["gCopdCF_ASP_SPEC_BACT_PNEUM"]  = z["gCopdCF"] * x["HCC114"]
     x["ASP_SPEC_BACT_PNEUM_PRES_ULC"] = x["HCC114"]*x["PRESSURE_ULCER"]
-    x["SEPSIS_ASP_SPEC_BACT_PNEUM"] = x["SEPSIS"] * x["HCC114"]
+    x["SEPSIS_ASP_SPEC_BACT_PNEUM"] = z["SEPSIS"] * x["HCC114"]
     x["SCHIZOPHRENIA_gCopdCF"] = x["HCC57"] * z["gCopdCF"]
     x["SCHIZOPHRENIA_CHF"] = x["HCC57"] * z["CHF"]
     x["SCHIZOPHRENIA_SEIZURES"] = x["HCC57"] * x["HCC79"]

hccpy/_V2318P1M.py

@@ -39,7 +39,7 @@ def create_interactions(cc_lst, DISABL, age):
     x["DIABETES_CHF"] = z["DIABETES"] * z["CHF"]
     x["gCopdCF_ASP_SPEC_B_PNEUM"]  = z["gCopdCF"] * x["HCC114"]
     x["ASP_SPEC_BACT_PNEUM_PRES_ULC"] = x["HCC114"]*x["PRESSURE_ULCER"]
-    x["SEPSIS_ASP_SPEC_BACT_PNEUM"] = x["SEPSIS"] * x["HCC114"]
+    x["SEPSIS_ASP_SPEC_BACT_PNEUM"] = z["SEPSIS"] * x["HCC114"]
     x["SCHIZOPHRENIA_gCopdCF"] = x["HCC57"] * z["gCopdCF"]
     x["SCHIZOPHRENIA_CHF"] = x["HCC57"] * z["CHF"]
     x["SCHIZOPHRENIA_SEIZURES"] = x["HCC57"] * x["HCC79"]

hccpy/_V2419P1M.py

@@ -46,7 +46,7 @@ def create_interactions(cc_lst, DISABL, age):
     x["ART_OPENINGS_PRESSURE_ULCER"] = x["HCC188"]*x["PRESSURE_ULCER"]
     x["gCopdCF_ASP_SPEC_B_PNEUM"]  = z["gCopdCF"] * x["HCC114"]
     x["ASP_SPEC_BACT_PNEUM_PRES_ULC"] = x["HCC114"]*x["PRESSURE_ULCER"]
-    x["SEPSIS_ASP_SPEC_BACT_PNEUM"] = x["SEPSIS"] * x["HCC114"]
+    x["SEPSIS_ASP_SPEC_BACT_PNEUM"] = z["SEPSIS"] * x["HCC114"]
     x["SCHIZOPHRENIA_gCopdCF"] = x["HCC57"] * z["gCopdCF"]
     x["SCHIZOPHRENIA_CHF"] = x["HCC57"] * z["CHF"]
     x["SCHIZOPHRENIA_SEIZURES"] = x["HCC57"] * x["HCC79"]

hccpy/_V2823T2M.py

@@ -0,0 +1,57 @@
+from collections import Counter
+
+def create_interactions(cc_lst, DISABL, age):
+
+    x = Counter(cc_lst) 
+    z = Counter()
+
+    # payable HCC's count interaction
+    hcccnt = len([k for k in x.keys() if "HCC" in k])
+    if hcccnt > 9:
+        x["D10P"] = 1
+    elif hcccnt > 0:
+        x["D{}".format(hcccnt)] = 1
+
+    # diagnostic categories
+    z["CANCER"] = max(x["HCC17"], x["HCC18"], x["HCC19"], 
+                        x["HCC20"], x["HCC21"], x["HCC22"], x["HCC23"])
+    z["DIABETES"] = max(x["HCC35"], x["HCC36"], x["HCC37"], x["HCC38"])
+    z["CARD_RESP_FAIL"] = max(x["HCC211"], x["HCC212"], x["HCC213"])
+    z["HF"] = max(x["HCC221"], x["HCC222"], x["HCC223"], 
+                x["HCC224"], x["HCC225"], x["HCC226"])
+    z["CHR_LUNG"] = max(x["HCC276"], x["HCC277"], x["HCC278"], 
+                        x["HCC279"], x["HCC280"])
+    z["KIDNEY"] = max(x["HCC326"], x["HCC327"], x["HCC328"], x["HCC329"])
+    z["SEPSIS"] = x["HCC2"]
+    z["gSubUseDisorder"] = max(x["HCC135"], x["HCC136"], x["HCC137"],
+                            x["HCC138"], x["HCC139"])
+    z["gPsychiatric"] =  max(x["HCC151"], x["HCC152"], 
+                                x["HCC153"], x["HCC154"], x["HCC155"])
+    z["NEURO"] = max(x["HCC180"], x["HCC181"], x["HCC182"], 
+                        x["HCC190"], x["HCC191"], x["HCC192"],
+                        x["HCC195"], x["HCC196"], x["HCC198"],
+                        x["HCC199"])
+    z["ULCER"] = max(x["HCC379"], x["HCC380"], x["HCC381"], x["HCC382"]) 
+
+    # community model interactions
+    # NOTE: updated interaction codes to match V24hcccoefn.csv; values changed from V23 --> V24
+    x["DIABETES_HF"] = z["DIABETES"] * z["HF"]
+    x["HF_CHR_LUNG"] = z["HF"] * z["CHR_LUNG"]
+    x["HF_KIDNEY"] = z["HF"] * z["KIDNEY"]
+    x["CHR_LUNG_CARD_RESP_FAIL"] = z["CHR_LUNG"] * z["CARD_RESP_FAIL"]
+    x["HF_HCC238"] = z["HF"] * x["HCC238"]
+    x["gSubUseDisorder_gPsych"] = (z["gSubUseDisorder"] *
+                                        z["gPsychiatric"])
+
+    # institutional model interactions
+    x["DISABLED_CANCER"] = DISABL * z["CANCER"]
+    x["DISABLED_NEURO"] = DISABL * z["NEURO"]
+    x["DISABLED_HF"] = DISABL * z["HF"]
+    x["DISABLED_CHR_LUNG"] = DISABL * z["CHR_LUNG"]
+    x["DISABLED_ULCER"] = DISABL * z["ULCER"]
+
+    cc_lst = [k for k, v in x.items() if v > 0]
+
+    return cc_lst
+
+

hccpy/_V28I0ED1.py

@@ -0,0 +1,64 @@
+
+# contribution by @ronnie-canopy
+
+def apply_agesex_edits(cc_dct, age, sex):
+    # age/sex edits. see "V28I0ED1.TXT"
+
+    # Factor VIII or XI deficiency
+    e_set0 = {"D66", "D67"}
+
+    e_set1 = {
+            "J410", "J411", "J418", "J42", "J430", 
+            "J431", "J432", "J438", "J439", "J440", 
+            "J441", "J449", "J982", "J983"
+            }
+
+    e_set2 = {
+            "C50011", "C50012", "C50019", "C50021",
+            "C50022", "C50029", "C50111", "C50112",
+            "C50119", "C50121", "C50122", "C50129",
+            "C50211", "C50212", "C50219", "C50221",
+            "C50222", "C50229", "C50311", "C50312",
+            "C50319", "C50321", "C50322", "C50329",
+            "C50411", "C50412", "C50419", "C50421",
+            "C50422", "C50429", "C50511", "C50512",
+            "C50519", "C50521", "C50522", "C50529",
+            "C50611", "C50612", "C50619", "C50621",
+            "C50622", "C50629", "C50811", "C50812",
+            "C50819", "C50821", "C50822", "C50829",
+            "C50911", "C50912", "C50919", "C50921",
+            "C50922", "C50929"
+              }
+
+    e_set3 = {
+                "P040",  "P041",  "P0411", "P0412", 
+                "P0413", "P0414", "P0415", "P0416", 
+                "P0417", "P0418", "P0419", "P041A", 
+                "P042",  "P043",  "P0440", "P0441", 
+                "P0442", "P0449", "P045",  "P046",  
+                "P048",  "P0481", "P0489", "P049",  
+                "P270",  "P271",  "P278",  "P279",  
+                "P930",  "P938",  "P961",  "P962"
+                }
+
+    if sex == "F":
+        # for dx in (dx for dx in elst0 if dx in cc_dct):
+        for dx in (e_set0 & cc_dct.keys()):
+            cc_dct[dx] = ["HCC112"] # Immune Thrombocytopenia and Specified Coagulation Defects and Other Specified Hematological Disorders
+
+    if age < 18:
+        # for dx in (dx for dx in elst1 if dx in cc_dct):
+        for dx in (e_set1 & cc_dct.keys()):
+            cc_dct[dx] = ["HCCNA"] # Respiritry codes
+
+    if age < 50 :
+        # for dx in (dx for dx in elst2 if dx in cc_dct):
+        for dx in (e_set2 & cc_dct.keys()):
+            cc_dct[dx] = ["HCC22"] # Bladder, Colorectal, and Other Cancers
+
+    if age >= 2 :
+        # for dx in (dx for dx in elst3 if dx in cc_dct):
+        for dx in (e_set3 & cc_dct.keys()):
+            cc_dct[dx] = ["HCCNA"] # Newborn codes
+
+    return cc_dct