Using data to indicate adverse selection in a community health insurance programme.

1. DATA FOR BETTER LIVES: A NEW SOCIAL CONTRACT
PROJECT TOPIC: USING DATA TO INDICATE ADVERSE SELECTION IN A
COMMUNITY HEALTH INSURANCE
DATE: 13th
NOVEMBER, 2021
NAME: ERNEST IKEH
COUNTRY: NIGERIA
Introduction:
In general, data is any set of characters that is gathered and translated for some purpose,
usually analysis. If data is not put into context, it doesn't do anything to a human or computer.
On the other hand, data are individual facts, statistics, or items of information, often numeric.
In a more technical sense, data are a set of values of qualitative or quantitative variables about
one or more persons or objects, while a datum (singular of data) is a single value of a single
variable. Although the terms "data" and "information" are often used interchangeably.
Overview:
Data as a tool for Sustainable Development it poised to transform the way governments,
citizens, and companies do business and to inform better planning and excellent decision
making. The revolution is being defined by the explosion in availability of data resources and
rapidly evolving technologies. Low-cost data collection tools, ranging from crowdsourcing
to satellite imagery, are changing the way we do business and increasing the availability of
data all around us. The creation and implementation of data governance and the Sustainable
Development Goals (SDGs) offers a unique opportunity to ensure that the benefits of the data
revolution are extended to the countries and communities most in need, leaving no one
behind.
More generally, the way data are produced and consumed has been fast evolving over the
past decade. Against this backdrop, it is important to understand how well national statistical
systems can meet requirements, old and new, and to improve their capacities to do so. To
respond to these challenges, the World Bank has replaced its highly used Statistical Capacity
Indicators and Index (SCI) with the new Statistical Performance Indicators (SPI). The SPI
framework focuses on 5 key new areas, also called pillars in measuring a country’s statistical
performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data
infrastructure.

2. Each pillar consists of four or five dimensions; in all, there are 22 dimensions by which
to evaluate a country’s statistical performance. This offers a 'dashboard’-type architecture
that unpacks a country’s overall statistical performance and helps identify areas
for improvement.
To measure up with the above learnings from this course, it is imperative to relate the use of
data development purpose in my current Community Programme. The Bonny Community
Health Insurance Programme (BCHIP). A programme aimed at applying a Public Private
People Partnership (PPPP) philosophy that pools participants’ resources and provides access
to affordable sustainable, scalable, and robust quality healthcare delivery system to tackle
health challenges without the need of beneficiaries making out of pocket expenses when
accessing health care services.
In order ensure effective data framework for better supervision and monitor of activities the
programme Management Team (PMT) engaged the services of an IT professional to build a
scalable and robust software for data entries which include registration of enrolees, choice of
service providers and utilisation of service.
The use of this data framework was able to indicate Adverse Selection which describes a
situation where individuals with pre-existing medical conditions buy health insurance more
than healthy individuals which is a major risk in community health insurance. With adverse
selection, premiums are fixed at the average risk in the population are not enough to cover all
claims. Hence, the financial sustainability of the community health insurance programme
becomes jeopardized, putting it at the risk of winding up. Nevertheless, with the availability
of reliable data a random selection survey of consumption pattern by clients with pre-existing
chronic ailments and clients without pre-existing chronic ailments that accessed various
treatments under the programme, to establish the degree of negative impact of adverse
selection on the programme finances as represented by quantity of drugs consumed by clients
with pre-existing chronic ailments to total cost of drugs consumed by all clients in a given
period.

3. Review for clients & Clients with existing ailments (Chronic ailments)
Table 1.0 Drug Consumption Review for clients & Clients with existing ailments (Chronic ailments),
S/N Month/
Years
Total
Clients
Treated
No of
pre-
existing
Ailments
Consumption
by Clients
with Chronic
Ailments (N)
Drug
Consumption by
All Clients (N)
Drugs Utilization
rate by clients
with Chronic
conditions (%).
1 Jan. 20 207 102 141,634 253,801 56%
2 Feb.20 192 84 100,262 199,447 50%
3 Mar.20 213 94 133,530 214,254 62%
4 Apr.20 173 96 213,411 332,158 64%
5
May.2
0 209 93 264,409 397,231 67%
6 Jun.20 137 56 209,588 305,644 69%
7 Jul.20 182 103 297,743 385,747 77%
8 Aug.20 310 131 295,584 459,106 64%
9 Sep.20 331 137 474,337 733,892 65%
10 Oct.20 208 111 298,162 587,619 51%
11 Nov.20 269 115 328,705 528,383 62%
12 Dec.20 326 151 514,450 927,199 55%
Findings:
From the table above, it could be seen that the use of data has helped to indicate that clients
with pre-existing ailments averagely account for at least half of total clients that accessed
treatments across the months reviewed. Also, in terms of quantity of drugs consumed each
month, clients with pre-exiting ailments consume more than 50% of the drugs dispensed. For
instance, in the month of September, clients with chronic conditions consumed drugs worth
N474,337 which accounts for 65% of the month’s drug consumption cost. Also, for the month
of November, this set of clients consumed drugs worth N328,705 which is 62.2% of the total
worth of drugs consumed for the month. On the average, it could be inferred that clients with
pre-existing ailments (mostly hypertension and diabetes) consumed about 62% of total drug
consumption for the period under review.
Conclusion:
The use of data clearly shows that people with pre-existing medical conditions buy health
insurance more than healthy individuals is a major risk in community health insurance. As a
result, clients with pre-existing ailments averagely account for at least half of total clients that
accessed treatments thereby affecting the quality of care being rendered to enrollees and
threatens the financial sustainability of the programme. Therefore, the need for proper
planning, and informed decisions making is required through the use of pillars in measuring
statistical performance