population - pureportal.strath.ac.uk · web viewhow much influence does word-of-mouth have? ......

CHI Bangladesh Model DocumentationPopulation

Juvenileaging to 15birth

time juvenilebirth rate

Fecundaging to 50

time fecundfecund dying

nm mort ratefecund

juvenile dying

nm mort ratejuvenile

Matureaging to 60

time mature

Over 60

over 60 dying

nm mort rateover 60

mature dying

nm mort ratemature

<Fecund>

Under 5aging to 5

time under 5

under 5 dying

nm mort rateunder 5

mort rate under 5mort rate juvenile mort rate fecund mort rate mature

mort rate over 60

<effect of improvedhealthcare on mortality

rate>

Population simulates how many people are born and die each year:

Population deathsbirths+

+

+

-

The figure shows that as Population increases, births increase. And as Population increases, deaths increase but that causes Population to decrease. Thus, the “births loop” continuously increases Population, and the “deaths loop” brings Population back into balance.

The principal output of Population is the Population Insured Status. As the Population increases or decreases, it affects the number of people who are insured or noninsured.

The principal inputs to Population are birth and mortality rates and the “effect of effect of improved healthcare on mortality rate”.

There are two “initial conditions” that can be selected. One is called dynamic equilibrium in which the total population is distributed across age groups and the birth rate is computed to maintain the same total population over time. The other is called dynamic disequilibrium in which the initial value of each age group reflects the 2010 census. Although the dynamic equilibrium is purely hypothetical, it helps users to isolate the effect of changes in other parts of the healthcare system.

CHI Bangladesh Model Documentation

Enrolment

target enrolmentmarket fraction of target

market remaining

PotentialPrimary

EnrolmentPrimary

Enrolmentprimary enrolling

Prime enrollee contactwith potential enrollees

contacts withenrollees

sociability

enrolment by wordof mouth

rate of persuasionfrom enrollee

enrolment bysolicitation

disenrolling

normaldisenrolment rate

month to start chiinitiaitive change in potential

primary enrolment

time to changepotential primary

enrolment

<PrimaryEnrolment>

rate of persuasionfrom solicitation

average physicianaccess

time to averageavailability

disenrolment rate

<CHI Availability>

<CHI Accessibility>

fecund informalworkers

mature informalworkers

over 60 informalworkers

<CHIAttractiveness>

target marketattracted to offering

<Time>

<CHI Awareness>

total primaryenrolment

Because one of our goals is to understand how the market for healthcare insurance can emerge, we simulate enrolment in the proposed health plan. The target market is informal workers, most of whom are between the ages of 15 years and 60 years old. In the subject area, we estimate there are about 27,000 informal workers, and about twice as many men are classified informal workers as women. We assume people enroll because of two influences: advertising (solicitation) and word-of-mouth:

<Population>

informal workers+

target marketattracted to offering

+

CHI Attractiveness

+

PotentialPrimary

Enrolment

+

enrolling+

-

PrimaryEnrolment

+

+

primary enrolleecontact with

potential members

+

+

word-of-mouthinfluence

+

advertisinginfluence

+

disenrolling

+

disenrolment rate

+

AccessibilityAvailability

--

There are important questions to consider:

How much influence does advertising have? How much influence does word-of-mouth have? How does the attractiveness of the offering affect the size of the market? What causes the rate of disenrollment to change?

Because there is no comparable marketplace to study in Bangladesh, the model allows us to explore a wide range of assumptions that can change outcomes. For example, we can change the influence

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of both advertising and word-of-mouth to see how differently the market can grow. Or we can consider how access to medical care might change the disenrollment rate.

The principal inputs to Enrolment are Population, Attractiveness, Accessibility and Availability.

The principal output is Primary Enrolment.

Insured Status

Juvenile byInsured Status

Fecund byInsured Status

Mature byInsured Status

Over 60 byInsured Status

population by sexand insured status

time to enrol<time to enrol> <time to enrol> <time to enrol>

<PrimaryEnrolment><target enrolment

market>

fraction of informalworkers enrolled

<fecund informalworkers>

<mature informalworkers>

<over 60 informalworkers>

insured status

juvenile informalworkers

<Fecund><Juvenile>

<Mature> <Over 60><Under 5>

Under 5 byInsured Status

under 5 informalworkers

<under 5 dependentsof informal workers>

<juvenile dependentsof informal workers>

<fecund dependentsof informal workers>

<mature dependentsof informal workers> <over 60 dependents

of informal workers>

Insured status is highlighted in the model because we can assume that only informal workers are allowed in the health plan or we can assume that informal workers may enroll their dependents:

<informal workers>

portion of informalworkers enrolled

population by sexand insured status

dependents ofinformal workers

dependency ratio

In the model, we distinguish between ages and sexes because the healthcare utilization rates are very different amongst ages and sexes. In the full model, we simulate by male and female gender and age groups (cohorts) for children (under age 5 and juveniles under age 15 years), people of child-bearing years (fecund), those who are economically productive but no longer having children (mature), and those over age 60 years.

The principal inputs are informal workers, fraction of informal workers enrolled, and the dependency ratio.

The principal output is the population by sex and insured status.

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Dependencies (Dependents of informal workers)

under 5 dependentsof informal workers

juvenile dependentsof informal workers

fecund dependents ofinformal workers

mature dependents ofinformal workers over 60 dependents

of informal workers

dependency ratio

total dependents ofinformal workers

<targetenrolmentmarket>

<Fecund><Juvenile> <Mature> <Over 60><Under 5>

switch on dependentsof informal workers

<total population>

The Group estimates there are 3.5 dependents for each informal worker – the dependency ratio. The target enrolment market comprises all informal workers. Thus,

total dependents of informal workers =

dependency ratio

* target enrolment market

Units: people

When switch on dependents of informal workers = 1, total dependents of informal workers are distributed to the age and gender cohorts in the proportion of the total population:

under 5 dependentsof informal workers

juvenile dependentsof informal workers

fecund dependents ofinformal workers

mature dependents ofinformal workers over 60 dependents

of informal workers

dependency ratio

total dependents ofinformal workers

<targetenrolmentmarket>

<Fecund><Juvenile> <Mature> <Over 60><Under 5>

switch on dependentsof informal workers

<total population>

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Demand

<Juvenile byInsuredStatus>

<Fecund byInsuredStatus>

<Mature byInsuredStatus>

<Over 60 byInsuredStatus>

average juvenilenurse need

average fecundnurse need

average maturenurse need

average over 60nurse need

juvenile demand fornurse by status

average juvenilephysician need

likelihood of juvenileseeking care

juvenile demand forphysicians by status

likelihood of fecundseeking care

average fecundphysician need

fecund demand fornurse by status

<effect of adverseselection>

fecund demand forphysicians by status

<effect of adverseselection> <effect of adverse

selection>


average maturephysician need

average over 60physician need

likelihood of matureseeking care

likelihood of over 60seeking care

mature demand fornurse by status

mature demand forphysicians by status

over 60 demand fornurse by status

over 60 demand forphysicians by status

<Under 5 byInsuredStatus>

likelihood of under 5seeking care

average under 5physician need

average under 5nurse need


under 5 demand fornurse by status under 5 demand for

physicians by status

Demand is simulated as a perceived medical need. We do not have measured data to say what medical needs are, but we can look to Bangladesh or similar countries (in this case, Indonesia) to estimate how many times per year people of the same age and gender will seek care from a physician, nurse or community healthcare worker. We can assume the perceived need for medical care is the same regardless of insured status, but the likelihood of the people with needs is definitely influenced by their insured status. People with insured access to care seek it more frequently.

At the beginning of any large health plan, the most likely people to enroll are those with a perceived medical need. In economic terms, this is called “adverse selection”. That is, the selection is adverse to the operation of the health plan because the early insured people demand more care than those who sign up later.

<Population byInsuredStatus>

average medic need

likelihood ofseeking care

averagephysician need

demand formedics by status

demand forphysicians by status

effect of adverseselection

adverse selectionmodifier

switch on adverseselectionHistorical

Fraction ofPopulationEnrolled

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As the Historical Fraction of Population Enrolled increases, the effect of adverse selection decreases. In essence, the healthier population is less likely to enroll early and only gradually the impact of adverse selection. As the fraction enrolled approaches 100%, adverse selection no longer effects the utilization rate.

The inputs to demand are the average (by age and gender) perceived medical need for physician or medic care, the likelihood of people to seek care based on their insured status, the modification we expect for adverse selection and the historical fraction of enrolled population.

The outputs are demand for physician or medic (nurse) care.

Adverse Selection Effect

effect of adverseselection

adverse selectionmodifier

<fraction of informalworkers enrolled>

HistoricalFraction ofPopulationEnrolled

time to averagehistorical pop enrol

change in histfract pop enrol

switch on adverseselection

The group assumes the possibility of adverse selection: low risk patients opt out of the scheme (or do not join in the first place) with the result that a scheme contains only patients with expensive needs, i.e. above average demand for healthcare. The group further assumes that those with the greatest perceived needs will enrol earliest. Hence, demand per enrolee can be estimated to be highest when enrolment is least, and demand per enrolee will decrease as more people enrol.

To implement these assumptions:

effect of adverse selection[sex] =

( 1

+ adverse selection modifier

* ( 1

- Historical Fraction of Population Enrolled ) )

Units: Dimensionless

Where:

Historical Fraction of Population Enrolled is a simple exponential smooth over 6 months of fraction of informal workers enrolled, and

fraction of informal workers enrolled =

6


insured status[insured] * Primary Enrolment / target enrolment market


The adverse selection modifier = 1, selected by the group to estimate that earliest enrolment will have twice the needs of the total population.

Therefore, as actual enrolment approaches total potential enrolment, the effect of adverse selection diminishes to zero.

Health Status

PrimaryEnrolment

totalenrollment

time

<primary enrolling> <disenrolling>

averageenrollment time

decreaseenrolment time

gainingenrolment time

rate of gainingenrolment time

<average life spanby sex>

average populationlife span

<total populationby sex>

<population by sexand insured status>

total enrolment time ofinsured population

total populationexperience

<total population>

months perperson per year

fractionalimprovement inmortality rate

effect of improvedhealthcare onmortality rate

max improvement inmortality rate from

healthcare

time to realizeimprovement inmortality rate

switch on improvedmortality rate

The model permits healthcare to affect health status. To approximate this effect, the group assumed that as more of the population maintains membership in the healthcare scheme, the population health status will improve over baseline. To that end, a coflow measures average enrolment time.

PrimaryEnrolment

totalenrollment

time

<primary enrolling> <disenrolling>

averageenrollment time

decreaseenrolment time

gainingenrolment time

rate of gainingenrolment time

<average life spanby sex>

<total populationby sex>

<averageenrollment time>

This structure tracks enrolment and rate of gaining time in the stock, less the number of those who disenrol times the average enrolment time to keep a rolling average, which is applied to the population:

total enrolment time of insured population =

average enrollment time

* population by insured status

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Units: Month

The result is then compared to the total population experience:

fractional improvement in mortality rate =

total enrolment time of insured population

/ total population experience


Where:

total population experience =

total population

* average population life span

* months per person per year

Units: Month

The fractional improvement in mortality rate is applied to the insured population in a fixed delay of 60 months:

effect of improved healthcare on mortality rate =

fractional improvement in mortality rate

* max improvement in mortality rate from healthcare


Where:

max improvement in mortality rate from healthcare = 0.1


The effect of a more inclusive healthcare system is expected to be more productive work time. That is, actual morbidity is assumed to remain constant but the time away from economic production can increase over the baseline:

informal worker days per month =

initial informal worker days per month

+ max ave productive days gained

* fraction target enrolled

Units: day/(Month*person)

Where:

initial informal worker days per month = 24

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max ave productive days gained = 2


fraction target enrolled =

Primary Enrolment

/ target enrolment market


Treatment Capacity

<juvenile demand forphysicians by status>

<fecund demand forphysicians by status>

<mature demand forphysicians by status>

<over 60 demand forphysicians by status>

total physiciandemand by status

fraction of insuredphysician demand met

PhysicianQueuemonthly demand

for physicianreceiving phys

treatment

quitting wait forphysician

<max physiciancapacity>

phys quitting rate

average unmetphysician demand

time to averageunmet demand

average physicianwaiting time

normal physquit rate

fract physiciandemand by status

<under 5 demand forphysicians by status>

time to quitqueue

<fraction of chidemand covered>

The capacity to meet the demand for healthcare is limited or constrained by the number of physicians and nurses. We make no assumption about the severity of medical conditions, but we assume people have a limit to their willingness to wait for care. Thus, as waiting times increase, the number of people who simply quit waiting increases too.

Queue forCare

<demand forphysicians by status>

+

physician visitcapacity

+

receiving treatment

+

-

quitting the queue

+

9


Patients waiting for treatment cannot know how much longer they will have to wait, but they can know how long they have waited. In the model we simulate this by comparing the size of the queue with the number of people currently being treated:

Queue forCare

<demand forphysicians by status>

+

physician visitcapacity

+

receiving treatment

+

-

quitting the queue+

perceivedwaiting time

+

+-

Receiving treatment and quitting the queue have the same effect on the Queue for Care: an increase in either reduces the Queue and future waiting time.

We assume that physician and nurse caregivers do not distinguish between insured and noninsured patients. That is, they allocate their visit time based on the presenting patient. However, demand by insured patients is likely to be greater than noninsured patients (measured patient by patient), so that available capacity will skew to insured patients.

The principal inputs are demand and physician visit capacity. The principal output is receiving treatment.

CHI AttractivenessThe marketplace judges whether a health plan is attractive. In the model, we assume several variables influence that judgement.

People perceive:

the health plan is available – Awareness there is treatment capacity to meet their demand – Availability treatment is within reach – they can get to treatment with ease – Accessibility the health plan is affordable – it does not use too much monthly income – Affordability expenditures to treat their medical conditions will be paid by the health plan – Coverage

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CHI Awareness

CHI Coverage

CHI Affordability

CHIAttractiveness

CHI Availability

switch on InsuranceAttractiveness function

BankAttractiveness

Weight on BankAttractiveness

InsuranceAttractiveness

We add one more element of attractiveness: Bank Attractiveness. That is, our plan is connected to a bank that offers e-payments by telephone text. The members make small monthly deposits to savings that permit them to borrow money for approved needs. The Bank may be highly attractive if it offers low cost loans, above-market savings rates, and financial stability.

Inputs include advertising impact, physician and nurse demand met, relative distance to treatment, portion of monthly income expended for premiums, and portion of medical conditions covered.

Outputs affect market size and enrolment, and in the case of the bank, membership in the bank customer community.

Expenditures Per Member Per Month

physician visit price

<receiving nursetreatment>

<receiving phystreatment>

nurse visit price

nurse visitclaims BDT

physician visitclaims BDT

total visit claims

enrolmentwaiting period

physician nurseprice ratio

initial physicianvisit price

total claims paid

<fraction ofclaims denied>

The principal expenditures per member are the sum of monthly visits to care venues times the prices charged for each visit:

11


total visit claims =

nurse visit claims BDT + physician visit claims BDT

Units: BDT/Month

Where:

nurse visit claims BDT =

nurse visit price

* receiving nurse treatment[insured]

Units: BDT/Month

physician visit claims BDT =

physician visit price

* receiving phys treatment[insured]

Units: BDT/Month

Claims Processing

<receiving nursetreatment>

<receiving phystreatment>

<enrolmentwaiting period>

claims per visit

ClaimProcessors

ClaimsAwaiting

Processingtotal claims made claims processed

time to processclaims

expected claims

forecast horizon

claim averagingtime

hiring and trainingclaim processors

claim processorsleaving

average claimprocessor career

claims processorproductivity

time to hire and trainclaim processor

pending claims

fraction ofclaims denied

PendedClaimspended claims

resubmitted

time toprocessappeal

fract claimsresubmitted

pended claimsdenied

desired claimsprocessors

<pending claims>

normal claimsprocessor productivity

effect of automatedclaim processing

Health plans provide structure to the health care system. Not every expenditure claimed as a covered expense is covered by the plan. Some noncovered expenditures include certain illnesses, say, very costly cancers or very common upper respiratory tract infections. Claims are usually submitted for payment by the provider and contain date of rendered service, identification of the insured person, a description of the condition and service provided, and amount of the claim. The claims are bundled by provider and the covered claims are paid.

The processing of claims can be entirely manual or electronically assisted. In large plans, claims are submitted electronically and processed without intervention by a claims processor. This automated claims processing is very inexpensive but can be prone to erroneous payments to providers. The question is whether the cost of auditing many claims is less than the amount that would be saved by catching overbillings.

Unacceptable claims are “pended”, meaning the provider is told the claim will not be paid without additional justification. This is a relatively time-consuming process because the human intervention

12


is extensive. Some pended claims will be reprocessed and paid; some pended claims will not be pursued by the provider.

In considering the claims processing function, processor productivity is influenced by electronic assistance such as automated claims processing. The denial rate influences the number of times a claim will circulate through the system before it closes.

ClaimProcessors

ClaimsAwaiting

Processing

PendedClaims

claims made

+

claims processed+

claim processorproductivity

+ +-

pendingclaims

+

+-

-

resubmittingclaims

+

+

forecasted claims

+

-

+

pended claimsdenied

+-

denial rate

+

Principal inputs include claims made, claim processor productivity and claims denial rates.

The output is claims processed.

Monthly Premium

claim processormonthly wages

general andmarketing expense

CBHI

general expensemultiple

medical costreserve


indicatedpremium pmpm

GeneralExpensechg gen exp

time to chg gen exp

<month to startchi initiaitive>

<Time>

time over which tochange forecastedmonthly premium

ForecastedMonthly Premium

premium forecasthorizon

MonthlyPremium

Computedchange in monthlypremium

<ClaimProcessors>

<CHI advertisingbudget>

<total claims paid>

Monthly Premiumfor Game

<switch on MonthlyPremium for Game>

Monthly Premium

13


To breakeven the monthly premium covers medical and administrative costs. The challenge facing any healthcare plan is to set a stable premium in advance of incurring those expenses and hold that rate constant for a year. Health plans employ actuaries to analyze historical expenditures and guess at which trends will dominate in the coming year.

Our model simulates that activity by accumulating medical, general and administrative expenses that indicate a monthly premium which is then analyzed for its trend over time. The trend-adjusted monthly premium is then projected over a time horizon that represents how confident the forecaster is that trends will continue.

indicated medicalcosts pmpm

<nurse visitclaims BDT>

<physician visitclaims BDT>

claim processormonthly wages

general andadministrative

expensemedical costreserve

indicatedpremium pmpm

GeneralExpense

ForecastedMonthly Premium

premium forecasthorizon

MonthlyPremium

<ClaimProcessors>

Inputs are claims from medical practice, claims processor wages paid and general expenses of running the business. The output is the monthly premium.

14


Insurance Firm Accounts

CBHI Cash

monthly premiums

interest earnedCBHI

CBHI P and L

CBHI ShortTerm

Investments orBorrowing


<investmentearning rate>

target cash CBHI

CBHI cash reserve

Change in STinvestments or

borrowing time in which toinvest st cbhi

<monthlypremiums>

<interest earnedCBHI>

<general and marketingexpense CBHI>

<general and marketingexpense CBHI>

Initial Equity CBHI

<total visit claims>

total claims paid

<fraction ofclaims denied>

<total claims paid>

<monthlyinsurance income>

<switch onInsurance Initiative>

<MonthlyPremium>

Accounting for health plan operations is relatively simple, although companies in the industry use sophisticated management tools to maximize invested sums.

CBHI Cash

CBHI interest earned

Change in ST investments

general and administrative expense

Initial Equity

monthly premiums

total claims paid

monthly insurance income

CBHI interest earned

general and administrative expense

monthly premiums

total claims paid

The principal outputs are monthly insurance income, Cash and Short-term Investments.

15


Microfinance Structure

Bank ShortTerm

Investments

Bank Cash

<lending>

<depositing>

<repaying>

<withdrawals>

<interest earnedbank>

<administrativeexp bank>

Initial Equity bank

change ininvestments

time to changeinvested cash

target cash

cash reserve ratio

<Bank Deposits>

<switch on BankInitiative>

Banks are boxes of cash coming in and going out. The accounts reflect those flows.

Inputs:

Bank Loans

lending

loans defaulting

repaying

Bank Deposits

deposit interest accrued

depositing

withdrawals

16


Bank Cash

interest earned

administrative exp

cash reserve ratio

change in investments

depositing

initial equity investment

lending

repaying

withdrawals

Deposits may be mandatory for membership, and that is a choice yet to be made.

depositingpopulation participating in microfinance

savings rate

Outputs include loan defaults and equity that influence the Bank’s Reputation.

Bank ReputationBank Equity

loans defaulting

Bank AttractivenessBank Attractiveness is represented by perceived Awareness, Accessibility, Affordability and Reputation.

Awareness is a function of advertising and the word of mouth in the Health Plan. Accessibility is measured as the number of bank locations relative to the number of customers. Affordability is measured as the interest rate spread in its loans. Reputation is a function of loan defaults and the Bank Equity.

17


Bank Awareness

Bank Reputation

Bank Affordability

BankAttractiveness

<loans defaulting>

<BankEquity>

<interest ratespread>

Bank Accessibility

bank locations

Inputs are target market awareness, bank locations, loan interest rate spread, loan defaults and bank equity:

Bank Attractiveness =

Bank Accessibility

* Bank Affordability

* Bank Awareness

* Bank Reputation ,


Where:

Bank Reputation =

MAX (loans defaulting /

Bank Equity ,

0) ,

Units: Dmnl

Bank Affordability =

f( interest rate spread) ,


Bank Accessibility =

f( bank locations

/ max bank location density)


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population - pureportal.strath.ac.uk · web viewhow much influence does word-of-mouth have? ......

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