Where being a woman is a pre-existing condition

Originally published at Benfell Blog. Please leave any comments there.

For my class project in my Power of Partnership class this semester, I’ve been putting World Health Organization mortality statistics through a wringer. It hasn’t been easy. The sheer volume of data is tremendous. WHO warns:

The files available here do not constitute a user-friendly data collection which the average user can download and access. These are the basic underlying raw data files, together with the necessary instructions, file structures, code reference tables, etc. which can be used by institutions and organizations which need access at this level of detail mainly for research purposes AND have available the required information technology (IT) resources to use this information.

Okay, so I’m not an average user. I earned an A.A. degree in Business Data Processing in 1979 and worked for six years as a computer programmer before burning out. That doesn’t help me much with modern anything; the Python programming language, for example, implements concepts that are simply beyond my grasp. And frankly, there are times when I really wish I was back on a DEC PDP-11/70 running RSTS/E with a stack of documentation I actually understood, a CPU instruction set that irritates me for the failure of any processor since to implement anything nearly so elegant, and programming languages (including TECO, nominally a text editor) that I could pretty much make do anything.

My background also leaves me ill-prepared for modern databases. Here’s WHO again:

Due to the large size of these files, they are provided in ASCII (comma separated values) format to facilitate the download process. You should import these data files into a Database Management System rather than spreadsheets. . . . However users are strongly recommended not to try to import the data into spreadsheets because of the excessive number of records. There are over 1 million records in one data file.

So I had to deal with this monster file on a laptop which I had not purchased with serious number or data crunching in mind using, of course, a spreadsheet. I’ve limited my analysis to five countries, the Netherlands, the Philippines, Sweden, Thailand, and the United States, but the amount of work involved was still enormous.

I’ve just finished assembling the data. And as I began to look at what I have, I recalled U.S. Speaker of the House Nancy Pelosi’s remarks upon passage of the health care bill that “no longer will being a woman be considered a pre-existing condition.”

I’m wondering what Filipino physicians might say in response. Here are the top two causes of death, sorted by mortalities per one million population, for all five countries in my analysis:

cause total mortalities, per million total
of death year gender all ages population population
Philippines O99 2003 female 343,929 8,420.5 40,844,000
Philippines F33 2003 female 325,006 7,957.2 40,844,000

“O99” is the ICD-10 code for “Other maternal diseases classifiable elsewhere but complicating pregnancy, childbirth and the puerperium” within the taxonomic hierarchy for “Ch. XV [O00-O99] Pregnancy, childbirth and the puerperium – [O94-O99] Other obstetric conditions, not elsewhere classified.” “F33” is the code for “Recurrent depressive disorder” within “Ch. V [F00-F99] Mental and behavioural disorders – [F30-F39] Mood [affective] disorders.”

Self-harm is in a separate category; I wasn’t aware that depression could, in and of itself, be lethal. And while I’ve noticed that a lot of cause of death codes include the words “unspecified,” “other,” “ill-defined,” and “unknown,” or the phrase “not elsewhere classified,” for such a diagnosis to be the leading cause of death for either gender raises questions about maternity care in the Philippines. And for depression to be listed as a second leading cause of death, whether or not these diagnoses are accurate, certainly raises questions about attitudes towards women amongst Filipino physicians.

There’s much more to come. I have to think about my next step in analyzing this data. And I have a paper to write which is due on the 11th.