The Data Space

Input data shared by every by-policy projection.

The seven input CSVs are read here, once per model, and referenced from Projection as data. Projection is parameterized by point_id, so each Projection[N] is a separate ItemSpace with its own cells cache; if the readers lived there, every model point would re-read every file. Holding them in an unparameterized Space reads each file once no matter how many policies are projected.

Inputs are external files: plain CSVs in the model folder’s parent directory, products/cancer/, rather than data stored inside the model. The model folder therefore holds nothing but formulas — no _data/, no IOSpec, no embedded values — so a diff of the model shows logic changes only. This follows annuallife.TradLife_A; contrast basiclife.BasicTerm_S, which keeps its inputs inside the model through modelx’s IOSpec machinery.

The consequence worth knowing: the model is not portable on its own. Copying the Cancer_JP_S folder without its parent’s CSVs produces a model that reads and then fails on first evaluation.

input_dir() resolves the directory from _model.path.parent at run time, so the model works wherever the repository is checked out. Each table has a filename Reference and a reader Cells:

Reference	Cells	File
model_point_file	model_point_table()	model_point_table.csv
mort_table_file	mort_table()	mort_table.csv
incidence_table_file	incidence_table()	incidence_table.csv
sex_factor_file	sex_factor_table()	sex_factor_table.csv
survival_table_file	survival_table()	survival_table.csv
lapse_table_file	lapse_table()	lapse_table.csv
hosp_stay_file	hosp_stay_table()	hosp_stay_table.csv

The mortality table is a construction, not a copy

mort_table.csv is a [std] construction. 第三分野標準生命表2018 is published by 日本アクチュアリー会 at a stable public URL, free and in full, and can be retrieved and checked by anyone — but the publisher’s site terms prohibit reproduction, alteration and transmission to third parties without written consent, so this library must not ship a copy of it. What is shipped instead is the library-wide [std] construction built on the union of the individual rates jplib’s products quote and attribute — 22 anchor rows across the two sexes, 男 q(40) = 0.00076 among them, and the terminal rows 男 q(116) = 1.00000 and 女 q(118) = 1.00000 — graduated log-linearly (geometrically) between adjacent anchors, q(x) = q(a) (q(b)/q(a))^((x-a)/(b-a)). That graduation reproduces every quoted rate exactly, which is the property a fitted curve does not have, and it is locally the Gompertz family the publisher itself uses at the older ages. Every product that reads this table ships the same file, so one cell carries one value and one provenance string library-wide; each row’s provenance says whether it is an anchor or an interpolation. The model reproduces the quoted rates exactly and asserts nothing else about the IAJ table. The copy here is cut to the ages this model can reach — male 20-116, female 20-118 — which are the issue-age range and the two terminal ages. Drop a licensed extract in over the same schema — sex, age, mort_rate — and no formula changes.

incidence_table.csv is the opposite case and the contrast is the point: the age-banded 罹患率 of 全国がん登録 are public, freely downloadable and reproduced here verbatim with their attribution in provenance. What is [std] about the incidence basis is only the sex split, which lives in sex_factor_table.csv as the two sourced ratios the notes interpolate between.

hosp_stay_table.csv carries two bases: all_ages, the sourced 14.4-day mean stay used in the base run, and age_band, the sourced four-band age gradient that the hosp_age_gradient switch reads instead.

Cells Descriptions

input_dir()

The directory holding the input CSVs: the model folder’s parent.

Inputs are external files, not data stored inside the model, so the model folder is pure formulas. The path is resolved at run time from where the model was read, following annuallife.TradLife_A.

model_point_table()

The model point table, read from model_point_table.csv.

Indexed by point_id; point_id = 1 is the technical notes’ worked-example anchor cell. premium is an input on this product in a stronger sense than on any other in the library: no carrier publishes a rate table for a cancer main contract and the 算出方法書 is not a published document, so every premium in the table is a [std] modelling value.

mort_table()

The [std] mortality table by sex and age, from mort_table.csv.

A log-linear graduation of the 第三分野標準生命表2018 rates the library quotes and attributes, 男 q(40) = 0.00076 among them, not a copy of that table; see the Space docstring for why the distinction is load-bearing. Read as the valuation-basis rate, which Projection.mort_rate scales by mort_be_factor.

incidence_table()

全国がん登録 first-diagnosis 罹患率 by five-year age band, from incidence_table.csv.

Both sexes combined, all sites C00-C96, crude rate per 100,000, 2023 diagnoses. Indexed by band_start; the last row is the 100+ open band. These are sourced values reproduced with their attribution, in deliberate contrast to mort_table().

sex_factor_table()

The two sourced male / both-sexes incidence ratios, from sex_factor_table.csv.

72.92 / 132.21 at the 35-39 band midpoint 37.5 and 2,684.60 / 1,948.71 at the 70-74 band midpoint 72.5. Projection.sex_factor interpolates linearly in age between them; replacing this two-row file and the interpolation with the by-sex age-band grid from the same workbook is the first thing a serious user should do.

survival_table()

全国がん登録 5年相対生存率 by sex, from survival_table.csv.

All sites, 2018 diagnoses. Relative survival nets out background mortality, so it converts into an excess hazard added to the baseline rather than a replacement for it; Projection.mu_ex does that conversion.

lapse_table()

The [std] annual lapse rates by policy year, from lapse_table.csv.

Shared unchanged with the medical chassis so the two third-sector products do not disagree about persistency. The only published industry-wide figure is a sum-assured-weighted 解約・失効率 on a book dominated by death cover, which a がん保険 with no sum assured cannot enter; the shipped curve averages 5.5% over its first ten years against that 5.6%.

hosp_stay_table()

患者調査 mean stay in days for 悪性新生物 discharges, from hosp_stay_table.csv.

Two bases in one file, selected by the basis column: all_ages is the sourced 14.4-day figure the base run uses, and age_band is the sourced four-band gradient that Projection.hosp_age_gradient switches to.