Using Mobile Phones text messaging to

improve Maternal and Newborn

Healthcare Services, An economic

evaluation of a Deployment and a

Randomized Control Trial (RCT)

By Honore B. Youfegnuy

Presentation of a Protocol

Outline

Acknowledgements

Background

Frontlinesms Architecture

Limitation of the study

Why it is important to do this study

Primary Objective

Adverse Effects of the Intervention

Research Questions

Phases of the study

Sample Size

Data Collection

Analysis Plan

Abbreviations

SMS : Short Message Service

ANC: Antenatal Care

IWC: Infant Welfare Clinic

RCT: Randomized Control Trial

ADRA: Adventist Development and Relief Agency

MMR: Maternal Mortality Rate

EmOC: Emergency Obstetrics Care

MNCH: Maternal, Newborn, and Child Health

NWR: North West Region

DALY: Disability Adjusted Life Years

STDs: Sexually Transmitted Diseases

Background

Maternal mortality rate (MMR) in Cameroon has increased from 430 in 2000 to 670 in 2010 per 100,000 live births (UNICEF, 2000, 2012).

In the North West region, an average of 22.8%, of women give birth at the hospital, with Batibo, Fundong and Nwa having less than 10% (Tchakountouo, 2008).

A study by the Cameroon ministry of Public Health reported that 65% of women with obstetric complications do not reach the designated health structures, that only 6.1% of deliveries takes place in a health facility that offers emergency obstetrics care (EmOC) (MINSANTE 2010).

Background (cont’d)

in 2009, The Ministry of Health of Bhutan [Asia] created

awareness about the usefulness of eHealth and considers

it as an effective strategy to meet the health care needs

of population living in rural and remote areas, and

improve the quality and sustainability of services

(International Telecommunication Union 2010).

The World Health Organization (WHO) has prioritized

the use of new technologies to assist health delivery in

resource-limited settings (WHO 2010).

Health Related Technology and

Population in developing countries

(millions).

Source: Vital Wave Consulting, Business Monitor International (BMI), International

Telecommunications Union, World Bank’s World Development Indicators, and the

United Nations

Continuum of Care

The continuum of care has become a rallying call to reduce the yearly toll of half a million maternal deaths, 4 million neonatal deaths, and 6 million child deaths (Kerber 2007). Attention should be shifting towards a maternal, newborn, and child health (MNCH) continuum of care.

The focus is on universal coverage of effective interventions, integrating care throughout the lifecycle and building a comprehensive and responsive health system.

The mobile platform can provide continuous interactive relationship in a two way communication between the health care worker and the patient

Continuum of Care

In Cameroon, GSM coverage was 58, 67, and 68 percent of the population respectively for 2007, 2008, 2009 (The World Bank 2013).

44 out of every 100 people own at least one mobile phone (The World Bank 2013, Lester 2008).

Mobile phone technology is widely used and acceptable in Cameroon, and most households possess at least one mobile phone. This makes it a very useful tool for communication (Mbuagbaw, 2012).

GSM can then offer an increased accessibility of health care as a supportive layer of services to traditional face to face healthcare and such layer of services provided by the GSM can reduce discontinuity of care.

Example

For example in 2010, in cooperation with the Rwanda

Ministry of Health, UNICEF in collaboration with WHO

and UNFPA, supported the development and testing of a

cell phone-based operational model (called RapidSMS)

with which Community Health Workers track

pregnancies and newborn health at community level to

audit and reduce maternal and newborn mortality.

FRONTLINESMS ARCHITECTURE

Source: frontlinesms.com

Hubs of SMS based network

Using the ubiquitous nature of SMS, this study would

want to clarify if the use of SMS within frontlineSMS can

improve maternal and neonatal health.

FrontlineSMS has the advantage of easier implementation

without prior computing expertise.

If proven useful, Mobile phones will be used to instantly

communicate between a pool of clients and healthcare

worker at very low cost, forming hubs of network and

could be networked among themselves at a national or

even broader level.

Limitation of the study

The effects of an intervention on maternal mortality are

extremely difficult to measure.

This is because, as National Research Council (2000)

citing Henry Mosley noted, maternal mortality is a

relatively rare demographic event.

In particular, Mosley reveals that if the maternal mortality

ratio is about 400 deaths per 100 000 live births, one

would have to follow an average population of 500 000

annually for five years to observe 400 maternal deaths.

Why it is important to do this study

According to Kamrul (2006), one woman dies per minute in childbirth around the globe. Almost half of these deaths occur in Sub-Saharan Africa.

It is estimated that rural women in Africa produce 80 per cent of the continent’s food supply (Kamrul, 2006). Maternal mortality can jeopardize social harmony and economic productivity; it also increases costs and burdens to families, communities, service providers and the Treasury.

This study is important in that it will provide evidence on the feasibility, efficacy and cost of using mobile phone technology to increase access to maternal and newborn care in rural Cameroon. This will be useful evidence for policy makers, civil society and development agencies

Primary Objective

1. To investigate the effectiveness of the current m-health projects in the world that have been scaled up above a pilot level.

2. To assess the relative effectiveness of Plan International m-health project for maternal and newborn care in Wum.

3. To deploy, install and configure Frontlinesms as an SDMS platform to promote healthy behaviour for maternal and newborn care, and measure the economic effect of the program on the Fundong Health District, North West Region, Cameroon.

4. To make an economic evaluation of the effects of Frontlinesms mobile phone text messaging on reproductive health information services and the overall health of pregnant women and newborn in the Fundong Health District, in the North West Region of Cameroon.

ADVERSE EFFECTS OF THE

INTERVENTION

Adverse effects of this technology include the risk of

inaccurate data input and this inaccurate data being acted

upon.

Data protection is another issue, as is the psychological

and social impact of using the mobile phone in this way

Research Questions To what extend is the use of mobile phone effective in

improving healthcare services, specifically, how effective are the

current world m-health projects that have been scaled up

above a pilot project?

What are the gains in healthcare quality and operational

efficiency derived from the Plan International Deployment of

m-health using mobile phone text messages to support

maternal and newborn care in the Wum Health district?

What is the effect of mobile short message service (SMS) on

Well being derived from maternal and newborn health

counseling when using Frontlinesms as an SMS gateway for m-

health application in maternal and newborn care in Fundong

Health district in the NWR of Cameroon?

Research Questions

What is the effect of informative mobile phone text

messaging on reproductive health services and the overall

health of pregnant women and the newborn in Fundong

Health district in the NWR of Cameroon?

Participants for this study will be pregnant women and

their families, Community Health workers and Healthcare

workers (for best practices reminders) selected from the

Wum and Fundong Health district in the North West

Region of Cameroon.

Phases of the study

A 1st phase that will measure standard maternal health

indicators through a cross-sectional observational study of an

mHealth deployment of Plan International in the Wum district

which implement a mobile phone-based tool for community

health workers to use with their clients.

Here, the study will set up a desktop based SMS platform that

will send and received SMS to women receiving maternal care

and to community health workers to relay the information to

women without phone. The messages sent will aim at

promoting reproductive health, hygiene and healthy eating and

habits among pregnant women and newborns. The content of

the messages will be vetted by professional experts.

DESIGN OF THE SECOND PHASE

The intervention will be for 6 months and indicators will

be measured before, during and at the end of the

intervention.

This phase will target 430 women or all the population in

the Health district receiving maternal and child care

during the period of study: 215 receiving the intervention

and the other 215 will constitute the control group.

Variances and Regression analysis of data from the two

groups will be run to ascertain if the results can be

attributed to the intervention.

Indicators

Available indicators of maternal and infant health include

number and recalled content of antenatal care visits, birth

weight, breastfeeding, newborn immunisation, number of STDs

(Sexually Transmitted Diseases), Well being derived from

nutrition counselling, no of healthy birth, no of low birth

weight, number of domestic violence, no of cigarettes smokers

or indirect smokers, no of postpartum depression, no of

substance abuse. While the first phase will measure indicators

such as maternal and newborn illness and site of care-seeking;

and maternal, newborn and infant mortality; no of visits for

prenatal care; rates of consultations and referrals. Our idea in

the second phase is to target behaviour change indicators that

information can influence, while health indicators will be

measured from the observational study (first phase)

Cost effectiveness studies

This will analyse cost for implementing mHealth versus

the benefits in terms of lives saved and immunisations

gained.

Cost effectiveness and Analysis data: DALY, etc

Informative SMS

Source: Mbah et al , 2013, Using Mobile Phones to Promote Utilisation of

Reproductive Healthcare Services, The Lagdo mHealth Pilot Study: A Protocol,

unpublished

Overview of Outcome Measures

Name Type SPSS Measure Unit Measured Analysis Method

Scale of Measurement Format

ANC attendance rate Ratio Percentage Scale % appointments scheduled and

attended per trimester >95%

Chi-squared test

Reproductive health

knowledge

Ratio Percentage Scale Change in knowledge, beliefs,

perceived barriers

Paired and Independent T-Tests,

Wilcoxon Sign rank test

Partner visits Ratio Percentage Scale % schedules attended with partner Chi-squared test

APGAR score at birth Interval Numeric Scale Comparison of APGAR scores

between intervention and control

group

Independent T-test

Estimated blood loss

(EBL)

Interval Numeric Scale Comparison of EBL between

intervention and control group

Independent t-test

Intention to exclusively

breastfeed (EBF)

Ordinal Categorical Ordinal Comparison of Intention to EBF

between intervention and control

group

Wilcoxon Sign Test

Intention to practice family

planning (FP)

Ordinal Categorical Ordinal Comparison of Intention to practice FP

between intervention and control group

Wilcoxon Sign Test

Baby’s weight at birth Ratio Numeric Scale Comparison of baby’s weight at birth between

intervention and control group

Independent t-test

Adherence rate to

immunisation protocols

Ratio Numeric Scale Comparison of adherence to immunization

protocols between control and intervention

group

Chi-square

Baby’s growth Ratio Numeric Scale Comparison of baby’s growth between control

and intervention group

Independent t-test

Phase 1 only below

Time to see a skilled birth

attendant

Interval Time Scale Bip time (time from when mother noticed

problem to when she sees a skilled birth

attendant)

Kaplan-Meier Survival

analysis/Independent t-test

Maternal mortality rates

(MMR)

Ratio Numeric Scale Comparison of MMR between control and

intervention groups

Independent t-test

Neonatal mortality Ratio Numeric Scale Comparison of NMR between control and

intervention groups

Independent t-test

Sample Size

For the first phase a convenience sampling targeting the entire consenting population that the research will encounter during the period of research at exit pools and all the Community Health Workers in the program. In the second phase, an estimate of 430 women will be recruited and the selection criteria will base on them receiving maternal and newborn care or that they are approached by the community health workers on the ground that the community health worker is providing maternal care to the women. Data will be collected via questionnaires, using mobile phone SMS and also from source documents in the Health centres.

The study based the sample size calculation on the formulae used in comparing two proportions which is n = [(Zα/2 + Zβ)

2 × {(p1 (1-p1) + (p2 (1-p2))}]/(p1 - p2)2

where

n = sample size required in each group, is approximately 200. (To account for attrition, a total of 430 women will be recruited).

p1 = proportion of death before the intervention = 0.06

p2 = proportion of death after the intervention = 0.15

p1-p2 = clinically significant difference = 0.09

Zα/2: This depends on level of significance, for 5% this is 1.96

Zβ: This depends on power, for 80% this is 0.84

Estimate data used to calculate the sample size was based

on the results of a study of a system (similar to what Plan

International has deployed for the population of this

inquiry) used in Rwanda by UNICEF Rwanda Country

Office (2010), titled Rwanda Rapidsms - Protecting Pregnant

Women And Newborn in Rwanda: District Model Developed,

Tested, Documented and Ready for National Scale Up Across

Rwanda.

Sample Size

Data Collection

We will use questionnaires in paper and in Frontlinesms

forms to collect baseline data, follow-up data (at the

beginning of the study, at 3 months, and 6 months),

district hospital ANC, maternity and IWC registers and

district health service registers for immunisation.

Data collectors who are blinded to randomisation will fill

out questionnaires on paper and on mobile phone

Frontlinesms forms as the participants present at the

concerned services.

Analysis Plan

The data will be analysed using SPSS after they have been imported from Frontlinesms and entered from paper questionnaire and the data analyst working with the software will be blinded to the study groups.

Results of patient demographics and baseline outcome variables (both primary and secondary) will be analysed using descriptive summary measures: expressed as mean (standard deviation) or median (minimum-maximum) for continuous variables and number (percentage) for categorical variables.

We will use the T-test for comparing groups on continuous outcomes and the chi-squared test for binary outcomes.

All statistical tests will be performed using two-sided tests at the 0.05 level of significance.

. For all group comparisons, the results will be expressed as effect (or risk ratio for binary outcomes), corresponding two sided 95% confidence intervals and associated p-values.

Wum m-Health Flow Diagram

www

Assessed for eligibility (n=430)

Female and pregnant

> 18 years

Owns a mobile phone

CHW can serve as link

A family member owns a mobile phone

ddddd

Exclusion

Does not fulfill eligibility criteria

Woman or family member unable to use mobile phone

Refusal to participate

Have no access to CHW

ddddd

Target Enrolment n= 430

(Assume attrition of 30)

ddddd

Enrolment

ddddd

Randomisation

ddddd

Allocation

ddddd

Allocated to Control

Receive standard maternal care

No information SMS

No access interactive 2 way communication for feedback

ddddd

Allocated to Intervention (m-health)

Receive standard maternal care

Receive informative SMS

Has access to interactive 2 way communication for feedback

ddddd

Follow Up

Baseline Assessment

ddddd

Baseline Data Collection

Socio demographic

Obstetric Data

Compliance with ANC appointments and medications

Reproductive Health Knowledge, Beliefs, Perceive Barriers

ddddd

Baseline Data Collection

Socio demographic data including

Obstetric Data

Compliance with ANC appointments and medications

Reproductive Health Knowledge, Beliefs, Perceived Barriers

ddddd

Follow UP Data Collection

Socio demographic

Obstetric Data

Compliance with ANC appointments and medications

Reproductive Health Knowledge, Beliefs, Perceived Barriers

Time to see a healthcare worker when/if needed

Intrapartum events

Post-partum events

Follow Up Data Collection

Socio demographic

Obstetric Data

Compliance with ANC appointments and medications

Reproductive Health Knowledge, Beliefs, Perceived Barrierrs

Time to see a healthcare worker when/if needed

Intrapartum events

Post-partum events

Reference

This Protocol was adapted and modified from a previous

work done by Okwen Patrick Mbah (OPM) et al

(December 2012), Senior Health Expert, Adventist

Development and Relief Agency (ADRA), unpublished

Thank you