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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