ArticlePDF Available

Maternal and perinatal Health Research Collaboration, India (MaatHRI): methodology for establishing a hospital-based research platform in a low and middle income country setting [version 1; peer review: awaiting peer review]


Abstract and Figures

Background: Maternal and perinatal Health Research collaboration, India (MaatHRI) is a research platform that aims to improve evidence-based pregnancy care and outcomes for mothers and babies in India, a country with the second highest burden of maternal and perinatal deaths. The objective of this paper is to describe the methods used to establish and standardise the platform and the results of the process. Methods: MaatHRI is a hospital-based collaborative research platform. It is adapted from the UK Obstetric Surveillance System (UKOSS) and built on a pilot model (IndOSS-Assam), which has been extensively standardised using the following methods: (i) establishing a network of hospitals; (ii) setting up a secure system for data collection, storage and transfer; (iii) developing a standardised laboratory infrastructure; and (iv) developing and implementing regulatory systems. Results: MaatHRI was established in September 2018. Fourteen hospitals participate across four states in India – Assam, Meghalaya, Uttar Pradesh and Maharashtra. The research team includes 20 nurses, a project manager, 16 obstetricians, two pathologists, a public health specialist, a general physician and a paediatrician. MaatHRI has advanced standardisation of data and laboratory parameters, real-time monitoring of data and participant safety, and secure transfer of data. Four observational epidemiological studies are presently being undertaken through the platform. MaatHRI has enabled bi-directional capacity building. It is overseen by a steering committee and a data safety and monitoring board, a process that is not normally used, but was found to be highly effective in ensuring data safety and equitable partnerships in the context of low and middle income countries (LMICs). Conclusion: MaatHRI is the first prototype of UKOSS and other similar platforms in a LMIC setting. The model is built on existing methods but applies new standardisation processes to develop a collaborative research platform that can be replicated in other LMICs.
Content may be subject to copyright.
Open Peer Review
Maternal and perinatal Health Research Collaboration, India
(MaatHRI): methodology for establishing a hospital-based
research platform in a low and middle income country setting
[version 1; peer review: awaiting peer review]
ManishaNair , BabulBezbaruah , AmritKrishnaBora , KrishnaramBora ,
ShakuntalaChhabra , SaswatiS.Choudhury , ArupChoudhury , DipikaDeka ,
GitanjaliDeka , VijayAnandIsmavel , SwapnaD.Kakoty , RoshineM.Koshy ,
PramodKumar , PranabikaMahanta , RobinMedhi , PranoyNath ,
AnjaliRani , IndraniRoy , UshaSarma , CarolinSolomiV ,
RatnaKantaTalukdar , FarzanaZahir , MichaelHill , NimmiKansal ,
ReenaNakra , ColinBaigent , MarianKnight , JennyJ.Kurinczuk1
1 2* 3* 4*
5* 6* 7* 8*
9* 10* 11* 10*
5* 12* 11* 2*
13* 14* 9* 10*
6* 15* 16 17
17 18 1 1
07Jul2020, :683First published: 9
07Jul2020, :683Latest published: 9
Page 1 of 12
F1000Research 2020, 9:683 Last updated: 07 JUL 2020
Page 2 of 12
F1000Research 2020, 9:683 Last updated: 07 JUL 2020
ManishaNair( )Corresponding author:
 :Conceptualization,DataCuration,FormalAnalysis,FundingAcquisition,Investigation,Methodology,ProjectAdministration,Author roles: Nair M
Resources,Software,Supervision,Validation,Visualization,Writing–OriginalDraftPreparation,Writing–Review&Editing; :Bezbaruah B
Investigation,Methodology,ProjectAdministration,Resources,Validation,Writing–Review&Editing; :Investigation,Methodology,Bora AK
ProjectAdministration,Resources,Validation,Writing–Review&Editing; :Investigation,Methodology,ProjectAdministration,Resources,Bora K
Validation,Writing–Review&Editing; :Investigation,Methodology,ProjectAdministration,Resources,Validation,Writing–Review&Chhabra S
Editing; :Investigation,Methodology,ProjectAdministration,Resources,Validation,Writing–Review&Editing; :Choudhury SS Choudhury A
Investigation,Methodology,ProjectAdministration,Resources,Validation,Writing–Review&Editing; :Investigation,Methodology,ProjectDeka D
Administration,Resources,Validation,Writing–Review&Editing; :Investigation,Methodology,ProjectAdministration,Resources,Deka G
Validation,Writing–Review&Editing; :Investigation,Methodology,ProjectAdministration,Resources,Validation,Writing–Review&Ismavel VA
Editing; :Investigation,Methodology,ProjectAdministration,Resources,Validation,Writing–Review&Editing; :Kakoty SD Koshy RM
Investigation,Methodology,ProjectAdministration,Resources,Validation,Writing–Review&Editing; :Investigation,Methodology,Kumar P
ProjectAdministration,Resources,Validation,Writing–Review&Editing; :Investigation,Methodology,ProjectAdministration,Mahanta P
Resources,Validation,Writing–Review&Editing; :Investigation,Methodology,ProjectAdministration,Resources,Validation,Writing–Medhi R
Review&Editing; :Investigation,Methodology,ProjectAdministration,Resources,Validation,Writing–Review&Editing; :Nath P Rani A
Investigation,Methodology,ProjectAdministration,Resources,Validation,Writing–Review&Editing; :Investigation,Methodology,ProjectRoy I
Administration,Resources,Validation,Writing–Review&Editing; :Investigation,Methodology,ProjectAdministration,Resources,Sarma U
Validation,Writing–Review&Editing; :Investigation,Methodology,ProjectAdministration,Resources,Validation,Writing–Review&V CS
Editing; :Investigation,Methodology,ProjectAdministration,Resources,Validation,Writing–Review&Editing; :Talukdar RK Zahir F
Investigation,Methodology,ProjectAdministration,Resources,Validation,Writing–Review&Editing; :Methodology,Validation,Writing–Hill M
Review&Editing; :Methodology,Resources,Validation,Writing–Review&Editing; :Methodology,Resources,Validation,Kansal N Nakra R
Writing–Review&Editing; :Methodology,Validation,Writing–Review&Editing; :Methodology,Validation,Writing–ReviewBaigent C Knight M
&Editing; :Methodology,Validation,Writing–Review&EditingKurinczuk JJ
Nocompetinginterestsweredisclosed.Competing interests:
TheMaatHRIplatformisfundedbyaMedicalResearchCouncilCareerDevelopmentAwardtoMN(Ref:MR/P022030/1).Grant information:
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
©2020NairM .Thisisanopenaccessarticledistributedunderthetermsofthe ,whichCopyright: et al CreativeCommonsAttributionLicense
NairM,BezbaruahB,BoraAK How to cite this article: et al. Maternal and perinatal Health Research Collaboration, India (MaatHRI):
methodology for establishing a hospital-based research platform in a low and middle income country setting [version 1; peer review:
F1000Research2020, :683awaiting peer review] 9
07Jul2020, :683First published: 9
Page 3 of 12
F1000Research 2020, 9:683 Last updated: 07 JUL 2020
Maternal health is a global priority due to the large number
of women becoming pregnant every year, an estimated
211 million1, and because of the growing disparity in mater-
nal deaths across countries2,3. India has the second high-
est number of maternal deaths with ~45,000 deaths yearly2.
The rate is much higher for some states, such as Assam in
the Northeast of India. Assam has nearly half the popula-
tion of the UK, and 6 women die every day as a result of preg-
nancy and childbirth complications4 compared with around
one per week in the UK5. In addition, each year an estimated
5 million pregnant women in India experience a life-threatening
complication. To improve care and outcomes, India needs
large and robust studies to investigate the risk factors, manage-
ment and outcomes of pregnancy complications and to find
out why disease severity varies from state to state.
In a pilot project (called IndOSS-Assam) we demonstrated
the feasibility of setting up a collaborative platform for mater-
nal and perinatal health research jointly undertaken by Indian
clinical collaborators and researchers at the University of
Oxford6,7. This was modelled on the UK Obstetric Surveillance
System (UKOSS)8 and showed that a hospital-based platform
can be used to conduct large epidemiological studies and rou-
tine surveys to investigate pregnancy complications and man-
agement, and establish incidence and outcomes. UKOSS
through its work over the past decade has contributed signifi-
cantly to improving the safety and quality of care for pregnant
women8. It has inspired several high-income countries to estab-
lish obstetric surveillance and research systems, which are being
used to conduct national and multi-national studies to gener-
ate evidence to improve pregnancy care. However, there is
no such system in low-and-middle income countries (LMICs)
where more than 94% of all maternal deaths occur.
Our pilot work in India not only justified the importance
and urgency, but also demonstrated the need to further
adapt and improve the pilot model to create a standardised
collaborative platform for both research and research capac-
ity building. This led to the establishment of the Maternal and
perinatal Health Research collaboration, India (MaatHRI), a
larger standardised collaborative research platform of 14 pub-
lic and private hospitals across four states in India. The objec-
tive of this paper is to describe the methods used to establish and
standardise the platform and the results of the process. MaatHRI
means mother in Sanskrit.
MaatHRI is a hospital-based collaborative research platform
established to: (i) regularly collect data on the prevalence of
known and emerging life-threatening pregnancy complica-
tions; (ii) conduct large epidemiological studies to generate evi-
dence to improve maternal and perinatal health in India; and
(iii) develop research capacity and skills in the collaborat-
ing hospitals. It was built on the pilot system, but extensively
expanded and standardised over a period of 18 months from
May 2017 to September 2018. The following methods were used
to establish the collaborative platform:
1. Establishing a network of hospitals and clinical
2. Setting up a high-quality secure system for data collection,
storage, and transfer
3. Developing a standardised laboratory infrastructure
4. Implementing regulatory systems
Establishing a network of hospitals and clinical
Successful completion of the pilot work in two hospitals in
Assam allowed us to expand the network from two to nine
government hospitals within Assam: six teaching hospi-
tals and four district hospitals. In each hospital, we identified
a lead collaborator who were obstetricians. Through their
professional networks, we were able to reach out to other
hospitals. A hospital was included in the network based on two
criteria: (i) willingness of the hospital to participate in a large
research collaboration and (ii) a high burden of maternal
and perinatal deaths in the population covered by the hospital.
Similar to the process used in the pilot work6, we mapped the hos-
pitals to assess the spread and coverage of the population in each
Setting up a high-quality secure system for data collection,
storage and transfer
One of the major reasons for success of the pilot work was hav-
ing dedicated research staff for data collection and data entry.
A pragmatic approach was adopted to develop a high qual-
ity secure electronic system to overcome the challenges of
human resource constraints, lack of dedicated secure computer
servers for data storage in the hospitals, and securely sharing
data. The following methods were employed:
i. Research nurses were appointed in each hospital and
ii. Electronic online data collection forms were developed
for entering data
iii. Data are collated automatically in a cloud-based server
located in India
iv. Quality assurance and data security procedures were
established and implemented
Standardised laboratory infrastructure
A laboratory infrastructure was created through a partner-
ship with a private laboratory in India, Dr Lal PathLabs (LPL).
LPL has a pan-India presence with a network of sample col-
lection centres, regional laboratories and a national reference
laboratory in New Delhi, India. Their existing service delivery
model was adapted to the requirements of the MaatHRI platform
through extensive consultations between the Indian clinical col-
laborators, and experts at the University of Oxford and LPL. The
following services were agreed and are being currently used to
standardise the laboratory infrastructure:
- Service 1: Provide blood collection kits with instructions
to all study hospitals
Page 4 of 12
F1000Research 2020, 9:683 Last updated: 07 JUL 2020
- Service 2: Train MaatHRI research nurses to collect and
prepare blood samples
- Service 3: Transport samples at ambient conditions from
the hospitals to the laboratory
- Service 4: Standardise blood assays
- Service 5: Produce standardised test reports
We tested the model in a run-in phase before full
Regulatory systems
The steering committee constituted for the pilot work (IndOSS-
Assam) was expanded to form the MaatHRI steering com-
mittee. The committee includes representatives from all the
collaborating hospitals, the University of Oxford, Indian pol-
icy advocates and experts in statistics and ethics. As MaatHRI
is a research platform set up to conduct studies on a long-
term basis, an independent ‘Data safety and monitoring board’
(DSMB) was set up, including members from India and the
UK who are not associated with the MaatHRI platform. A
DSMB charter was drafted outlining the roles and responsibili-
ties of the members and how the board will function to provide
independent safety review of participants and data, and guid-
ance for observational studies during the course of the ongo-
ing projects. Since the studies currently undertaken through
the platform are observational studies, review of adverse event
data and reports of serious adverse events (SAEs) are not
currently applicable to MaatHRI DSMB. However, should
randomised controlled trials be conducted through MaatHRI
in the future we would expect the DSMB to be involved in
reviewing this type of information.
Ethics approvals
The MaatHRI platform and the ongoing studies have been
approved by the institutional review boards (IRB) of each
coordinating Indian institution, namely: Srimanta Sankara-
deva University of Health Sciences, Guwahati, Assam (No.
MC/190/2007/Pt-1/126); Nazareth hospital, Shillong, Meghalaya
(Ref No. NH/CMO/IEC/COMMUNICATIONS/18-01); Emmanuel
Hospital Association, New Delhi (Ref. Protocol No.167);
Mahatma Gandhi Institute of Medical Sciences, Sevagram,
Maharashtra (Ref No. MGIMS/IEC/OBGY/118/2017); and
the Institute of Medical Sciences, Banaras Hindu University,
Varanasi, Uttar Pradesh (No.Dean/2018/EC/290). The project
has also been approved by the Government of India’s Health
Ministry’s Screening Committee, the Indian Council of Medical
Research, New Delhi (ID number 2018-0152) and by the
Oxford Tropical Research Ethics Committee (OxTREC),
University of Oxford, UK (OxTREC Ref: 7-18).
MaatHRI network of hospitals
We were able to establish a network of 14 hospitals by
September 2018 across four states in India Assam, Meghalaya,
Uttar Pradesh, and Maharashtra. After establishing the network,
two more hospitals joined MaatHRI, but two government dis-
trict hospitals left the collaboration. A lack of interest in
research and high patient load were the main reasons given by
the lead collaborators of the departing hospitals. The MaatHRI
platform currently includes a network of 14 hospitals (11 Gov-
ernment and 3 private). Figure 1 shows the distribution of the
Figure 1. Distribution of the MaatHRI collaborating hospitals and regions covered by the hospitals across India.
Page 5 of 12
F1000Research 2020, 9:683 Last updated: 07 JUL 2020
network across India and within the state of Assam. The 14 hos-
pitals together conduct about 100,000 deliveries per year. The
network includes an Indian research team of 20 nurses, a project
manager, 16 obstetricians, two pathologists, a public health
specialist, a general physician and a paediatrician.
Setting up a high-quality and secure system for data
collection and storage
Data and biological sample collection: Depending on the
patient load and related participant recruitment rates, one or
two research nurses have been appointed in each collaborat-
ing hospital for the MaatHRI work. The nurses are responsible
for recruiting study participants, providing participant informa-
tion and obtaining informed consent, collecting data and blood
samples, and following up participants. The research nurses were
specifically trained to undertake these activities. In addition,
a project manager has been appointed to manage the research
nurses and provide supportive supervision.
Data entry and storage: Our original plan was for research
nurses to collect data in online electronic forms using tablet PCs
enabling automatic collation in the Microsoft Azure cloud com-
puting platform (Microsoft Corporation) with servers located
in India; there is no provision for storing data on the tablets.
However, after an initial trial we found that direct data entry in an
online form was not possible due to problems with internet con-
nections in several hospitals and the sensitivity associated with
a nurse standing with a tablet PC next to a very sick woman. It
was therefore decided that paper forms would be used to collect
data in these hospitals and the nurse would enter the data imme-
diately afterwards into the online data portal and then destroy
the paper forms. Each hospital has a unique Login ID and
password to access the data collection forms and their collated
data on the online portal.
Quality assurance and data security: The electronic data col-
lection forms have checks and validations to flag logical errors.
The project manager is responsible for monitoring data entry
on a day-to-day basis. Red flags are raised for errors and
incomplete forms immediately so that the research nurse can
rectify the errors before the participant is discharged from the
hospital. Data stored in the cloud server are encrypted and
password protected. Each collaborating hospital can only view
and download its own data. Identifiable information are collected
for follow-up of participants, but these can only be viewed by the
authorised hospital staff and cannot be downloaded by anyone.
Once the data collection is complete, in preparation for analy-
sis, all identifiable information is completely delinked from
the clinical data to generate pseudonymised analysis files. We
have developed secure mechanisms for transferring data within
India and between India and the UK with recommended level of
Laboratory infrastructure
Dr Lal Pathlabs (LPL) provides the laboratory infrastructure
for MaatHRI. The following services were tested in a trial
run before being fully incorporated into the platform.
Service 1: Blood collection kits with instructions to all study
hospitals. LPL provides the required blood collection kits with
specific written guidance to all study hospitals for collecting,
processing and packing the blood samples.
Service 2: Train MaatHRI research nurses to collect and
prepare blood samples. Technical experts from the labora-
tory trained the MaatHRI staff (project manager and research
nurses) to collect, centrifuge and pack samples before the start
of studies. When required, a phlebotomist from their collec-
tion centre provided supportive supervision to the research
nurses during the initial few weeks to correct or prevent any
The MaatHRI research nurses collect, centrifuge and pack blood
samples as per instructions in transportation boxes ready for
collection by LPL. A standard test requisition form for each
participant is filled in by the obstetrician caring for the par-
ticipant. This form only includes the participant ID, age and
a barcode to ensure participant confidentiality and blind-
ing to minimise reporting bias. The test results are only used
for research purposes and not for the provision of clinical
Service 3: Transport samples at ambient conditions from the
hospitals to the laboratory. A designated person from the LPL
collection centre collects the boxes from the hospital. These
are transported via road to the nearest regional laboratory where
they are checked and then shipped via air to the national labo-
ratory in New Delhi. A flow-chart describing the transporta-
tion process from the hospitals to the LPL National Reference
Laboratory is shown in Figure 2 and the network is presented
in a map in Figure 3. Time in transit is regularly monitored
by LPL and reported for each participant along with their test
Service 4: Standardising blood assays. All samples are proc-
essed and analysed in the LPL National Reference Labora-
tory based at New Delhi. The assay methods, traceability and
performance characteristics are discussed by experts from the
University of Oxford’s Wolfson laboratory and LPL before
including a test in the study. Table 1 shows the traceability and
Table 2 shows the performance characteristics for assays that
are commonly used for the epidemiological studies undertaken
using the MaatHRI platform. The details of specific tests will
be presented in subsequent publications. Traceability and assay
performance monitoring are important for standardisation of
laboratory procedures and quality control. If the quality of
a blood sample is compromised in transit, it is not proc-
essed, and the site-collaborator and research nurse are advised
to collect a fresh sample. The laboratory runs quality con-
trol checks daily for each assay (twice a day for some) and
monitors their mean coefficient of variation and standard devia-
tion. The results are shared as part of a performance moni-
toring plan during monitoring and feedback meetings. In
addition, LPL also runs a quarterly Quality Improvement
Page 6 of 12
F1000Research 2020, 9:683 Last updated: 07 JUL 2020
Figure 2. A flow-chart showing the transportation of samples from the hospital to the Dr Lal PathLabs National Reference Laboratory
for processing and analysis.
Page 7 of 12
F1000Research 2020, 9:683 Last updated: 07 JUL 2020
Table 1. Traceability of assays.
Sl No Name of test Calibrator traceability (reference
material/ reference method) Units
Calibrator uncertainty of
1 Haemoglobin
1:250 dilution in NCCLS2 recommended
reagent for the hemiglobincyanide
g/dl 12.58 1.00%
2 Hematocrit Calculated % Calculated NA
3 Platelets
A 1:101 dilution is made using a 20
μL TC pipette and 2 mL of 1% filtered
ammonium oxalate (CLSI/ formerly
thou/mm3 214.1 6.00%
4 Serum Ferritin WHO 3rd International Standard 94/572 ng/ml Low 5.44
High 953
Low 19.5
High 9.3
electrophoresis NGSP Certification for A2/F % HbF-6.6 % and
HbA2-6.7 %
HbF- Low- NA, High 1.8 %
HbA2- Low-NA, High- 3.6 %
NGSP - National Glycohemoglobin Standardization Program; CLSI – Clinical and Laboratory Standards Institute; HbF – Fetal haemoglobin; HbA2
- Haemoglobin Subunit Alpha 2; NA - Not applicable
Figure 3. Laboratory network for the MaatHRI platform.
The LPL National Reference Laboratory is accredited by the
following bodies College of American Pathologists (CAP);
National Accreditation Board for Testing and Calibration (NABL);
British Standards Institution (Quality Management System
ISO 9001: 2015, FS 60411).
Service – 5: Test reports. Test reports are securely made avail-
able to the site-collaborator in each hospital through their usual
communication channel. Data from the reports are entered
in the electronic forms by the research nurse.
Regulatory systems
MaatHRI steering committee has met biannually since the plat-
form was established in September 2018. The role of the steer-
ing committee is to guide the platform in terms of vision, scope,
equitable partnership, and research and training priorities.
Page 8 of 12
F1000Research 2020, 9:683 Last updated: 07 JUL 2020
Table 2. Assay Information and performance characteristics.
Name of test System used for
the analysis
Method information
(supplier/ method)
analytical range
Normal reference
range (adult
woman not
Uncertainty of
Haemoglobin DxH -800
(Beckman coulter) Photometric 0.1-25.5 1-25 11.50-15 g/dl 2.5 4.9
6c cell
Hematocrit DxH -800
(Beckman coulter)
calculation Not applicable Not applicable 36-46% 1.6 3.1
6c cell
Platelets DxH -800
(Beckman coulter)
principle 3-3000 10-1000 150-450 thou/
mm3 2.6 5.2
6c cell
Serum Ferritin Siemens ADVIA
Immunoassay (CLIA) 0.5 – 1650 ng/ml <0.5,
>16500 10-291ng/ml 14.2 22.5 BIO-RAD CAP PT
Variant II
testing system
High Performance
HbF-1.3-44.3 %
HbA2-1.6-18.7 %
HbA2-1.6-18.7 %
HbF- <1.5 %
HbA2-1.5-3.5 %
HbF-6.8 %
HbA2-4.5 %
HbF-13.2 %
HbA2-8.8 % BIO-RAD CAP
CAP - College of American Pathologists; CAP PT - College of American Pathologists Proficiency Testing programme; HbF – Fetal haemoglobin; HbA2 - Haemoglobin Subunit Alpha
Page 9 of 12
F1000Research 2020, 9:683 Last updated: 07 JUL 2020
It is also responsible for communicating the results of the
studies undertaken through MaatHRI to the Ministry of Health
and Family Welfare (MoHFW), Government of India.
The DSMB periodically reviews participant recruitment, data
safety and confidentiality, ethical issues and data quality, and
examines whether the overall safety and feasibility of the
MaatHRI project is acceptable. Although conventionally DSMB
is set up for individual studies, we found that setting up a DSMB
for the research platform that has oversight of all studies
undertaken through the platform could be an effective way to
ensure data safety. The DSMB has met twice since MaatHRI was
established in September 2018 and membership includes two
obstetricians (one from the UK and one from India), one paedi-
atrician (from India), one biostatistician (from the UK), and one
expert in bioethics (from India), all with prior experience and
expertise in observational epidemiological studies. They were
nominated by the study investigators.
Studies currently being undertaken through the MaatHRI
One survey and three observation studies are currently being
undertaken through the platform. A monthly survey of nine life-
threatening complications of pregnancy has been in progress
since July 2018. The complications are defined using standard
definitions and include eclampsia, pre-eclampsia, postpartum
haemorrhage, maternal peripartum infection, septic abortion,
uterine rupture, heart failure during pregnancy and postpar-
tum, transient peripheral neuropathy, and Japanese encephalitis
The epidemiological studies undertaken are informed by the
knowledge and hypothesis generated during the pilot work for
IndOSS-Assam. They include: (i) an unmatched case-control
study examining the risk factors, clinical characteristics, and
outcomes of heart failure in pregnant and postpartum women;
(ii) a prospective cohort study investigating the safety of induc-
tion and augmentation of labour in pregnant women with
anaemia; and (iii) a nested study within the prospective study
comparing the coagulation parameters in pregnant women with
and without anaemia. Of these, the nested coagulation study
is complete, and the other two studies will be completed by
June 2022. The monthly survey will continue as long as the
collaborative platform exists.
MaatHRI, a collaborative research platform, modelled on
UKOSS, was successfully established to conduct hospital-based
research to improve care and outcomes for mothers and babies
in India. It includes 14 public and private hospitals across four
states in India, which together conduct about 100,000 deliv-
eries per year. The platform is standardised in terms of data
collection, equipment, and laboratory methodology, and
employs strict measures for participant confidentiality and
data security. It is monitored by two regulatory bodies: a steer-
ing committee and an independent DSMB. One survey and
three epidemiological studies are being undertaken through the
MaatHRI is the first prototype of UKOSS and other similar
platforms9 in a low and middle income country (LMIC). Within
this setting, it covers the most deprived and vulnerable popula-
tion groups. The MaatHRI platform, although built on models
of existing surveillance and research platforms in high income
countries, is more advanced in terms of using current best
practices for standardisation of data and laboratory parameters,
monitoring data and participant safety, and secure transfer of
data within and between countries. All biological samples are
analysed at the LPL National Reference Laboratory. The pre-
cision, performance and quality of each laboratory parameter
are documented and maintained to a high level. The laboratory
partnership also benefits from subsidised costs from LPL for
each test, at a rate that is 40% less than their commercial price,
with no additional costs for transportation and project manage-
ment. The laboratory has also started tests for the MaatHRI
project, which they did not offer previously. This involved com-
pleting extensive validation processes. In addition to high quality
and standardisation of the laboratory procedures, the pseudo-
nymised laboratory model ensures confidentiality of participants
and minimises reporting bias.
Another advantage of the MaatHRI platform is the ability to
undertake long term follow-up studies of participants. Identifi-
able information collected locally from participants helps to
locate each participant by hospital staff for follow-up. All stud-
ies currently undertaken through the platform have a follow-up
component with the potential to generate participant cohorts,
based on informed consent, for long term follow-up of the
effects of pregnancy complications. Adequate measures have
been put in place for securely storing the identifiable informa-
tion and destroying it after the cohorts for long term follow-up
have been established. An independent MaatHRI DSMB
monitors data safety and participant confidentiality on an
ongoing basis, thereby ensuring confidence and trust on the
research platform.
While the platform is established and is currently running
three epidemiological studies, the process to develop capac-
ity for research and further improving pregnancy care will con-
tinue and is an integral part of the MaatHRI collaboration.
The focus is on bi-directional skills development and capacity
building through mutual learning between the collaborators in
India and the UK. The platform is also being used to develop
the research capacity of early career researchers (MSc and
PhD students and post-doctoral researchers) interested in
working in maternal and perinatal health in an LMIC setting.
Strengths and challenges
MaatHRI is a collaboration of hospitals that covers deprived
populations, some of which are located in remote rural areas of
India. While this provides the opportunity to conduct research
to improve the health of mothers and babies in areas of the
Page 10 of 12
F1000Research 2020, 9:683 Last updated: 07 JUL 2020
country that have the highest burden of maternal and perinatal
deaths, it also poses challenges related to resources and capac-
ity. Appointing new research nurses to collect data and blood
samples ensured that the MaatHRI platform was not depriving
the hospitals of their scarce human resource. This has created
an employment opportunity for nurses in the field of research,
which is not a usual job for trained nurses in India. However,
the challenge associated with this was the need for exten-
sive training and constant supervision of the nurses. Further-
more, most of the collaborating hospitals had not been involved
in a project of this scale and intensity encompassing not
just implementation, but designing, standardising and develop-
ing the project as equal partners. Therefore, it took more than
20 months of continuous engagement with staff and collabora-
tors to achieve the desired level of quality and standardisation
for the MaatHRI platform.
Within the resource constraints, a further challenge is achiev-
ing a balance between an ideal collaborative research
platform and a pragmatic solution. For example, the ideal
platform would have collected data electronically on tablets
using online forms, but this was not feasible due to a lack of
good internet connectivity in the remote hospitals and cultural
sensitivities. Therefore, paper forms are used in some hospi-
tals. However, to mitigate risks and as advised by the DSMB,
we have developed a documented process of securely storing
and destroying the paper forms within an agreed timeline for
each hospital.
Costs related to research staff, standardised laboratory param-
eters, programming data collection forms, and storing data
on Microsoft Azure make studies undertaken through the
MaatHRI platform more expensive compared with existing simi-
lar systems in the UK8, Europe and Australia9. It is our belief,
however, that the benefits of generating high quality scientific
evidence to answer important and urgent clinical research ques-
tions that will save the lives of thousands of future mothers
and babies, outweigh these additional costs.
In summary, the methods that we have used to develop the
MaatHRI platform make it a unique and high-quality research
resource using a model that can be replicated in other LMICs.
Since being established in September 2018, MaatHRI has already
secured further funding, including industry funding. One epi-
demiological study is complete and two others are in various
stages of participant recruitment and data collection. We intend
to make the data generated through the MaatHRI platform
available to researchers for secondary analysis. In addi-
tion to research impact, our approach to building the plat-
form on the premise of equitable partnership between all
collaborators and developing research capacity in the collabo-
rating institutions will further contribute to the sustainability of
Data availability
No data is associated with this article.
We thank Prof. U C Sarma, retired Vice Chancellor of Srimanta
Sankaradeva University of Health Sciences, Guwahati, Assam
for his valuable contribution in establishing the MaatHRI
platform. We also thank Prof. Hem Kanta Sarma, Professor
and Head of the Department of Obstetrics and Gynaecology,
Jorhat Medical College and Hospital, Assam for his contribution
during the initial phase of setting up MaatHRI.
A previous version of this study is available as a preprint on
1. World Health Organization: The World health report : 2005 : make every mother
and child count. Geneva: World Health Organization, 2005.
Reference Source
2. Graham W, Woodd S, Byass P, et al.: Diversity and divergence: the dynamic
burden of poor maternal health. Lancet. 2016; 388(10056): 2164–2175.
PubMed Abstract
Publisher Full Text
3. WHO, UNICEF, UNFPA,, et al.: Maternal mortality: Levels and trends 2000 to
2017. Geneva: World Health Organisation, 2019.
Reference Source
4. Office of Registrar General and Census Commissioner India: Annual Health
Survey 2012-13: Fact sheet. New Delhi: Ministry of Home Affairs, Government of
India, 2014.
5. Knight M, Bunch K, Tuffnell D, et al.: Saving Lives, Improving Mothers’ Care -
Lessons learned to inform maternity care from the UK and Ireland Confidential
Enquiries into Maternal Deaths and Morbidity 2015-17. Oxford: National
Perinatal Epidemiology Unit, University of Oxford, 2019.
Reference Source
6. Nair M, Choudhury MK, Choudhury SS, et al.: IndOSS-Assam: Investigating
the feasibility of introducing a simple maternal morbidity surveillance and
research system in Assam, India. BMJ Glob Health. 2016; 1(1):
PubMed Abstract
Publisher Full Text
Free Full Text
7. Nair M, Choudhury MK, Choudhury SS, et al.: The association between maternal
anaemia and pregnancy outcomes: a cohort study in Assam, India. BMJ Glob
Health. 2016; 1: e000026.
PubMed Abstract
Publisher Full Text
Free Full Text
8. Knight M, Lindquist A: The UK Obstetric Surveillance System: Impact on patient
safety. Best Pract Res Clin Obstet Gynaecol. 2013; 27(4): 621–30.
PubMed Abstract
Publisher Full Text
9. Knight M, INOSS: The International Network of Obstetric Survey Systems
(INOSS): benefits of multi-country studies of severe and uncommon maternal
morbidities. Acta Obstet Gynecol Scand. 2014; 93(2): 127–31.
PubMed Abstract
Publisher Full Text
Page 11 of 12
F1000Research 2020, 9:683 Last updated: 07 JUL 2020
The benefits of publishing with F1000Research:
Your article is published within days, with no editorial bias
You can publish traditional articles, null/negative results, case reports, data notes and more
The peer review process is transparent and collaborative
Your article is indexed in PubMed after passing peer review
Dedicated customer support at every stage
For pre-submission enquiries, contact
Page 12 of 12
F1000Research 2020, 9:683 Last updated: 07 JUL 2020
Full-text available
Background: Common mental disorders (CMD) are among the largest contributors to global maternal morbidity and mortality. Although research on perinatal mental health in India has grown in recent years, important evidence gaps remain, especially regarding CMD. Our study aims to improve understanding of CMD among perinatal and non-perinatal women of reproductive age across two settings in India: Bangalore (Karnataka) and Tanda (Himachal Pradesh). Methods: The study is embedded within the Maternal and Perinatal Health Research Collaboration India (MaatHRI). This mixed-methods observational study comprises three consecutive phases: (i) focus group discussions and individual interviews to explore women's knowledge and seek feedback on CMD screening tools; (ii) validation of CMD screening tools; and (iii) prospective cohort study to identify CMD incidence, prevalence and risk factors among perinatal and non-perinatal women. Results of the three phases will be analyzed using inductive thematic analysis, psychometric analysis and multivariable regression analysis, respectively. Conclusion: Improving understanding, detection and management of CMD among women is key to improving women's health and promoting gender equality. This study will provide evidence of CMD screening tools for perinatal and non-perinatal women in two diverse Indian settings, produce data on CMD prevalence, incidence and risk factors and enhance understanding of the specific contribution of the perinatal state to CMD.
Full-text available
Objectives To investigate the association between coagulation parameters and severity of anaemia (moderate anaemia: haemoglobin (Hb) 7–9.9 g/dL and severe anaemia: Hb <7 g/dL) during pregnancy and relate these to postpartum haemorrhage (PPH) at childbirth. Design A prospective cohort study of pregnant women recruited in the third trimester and followed-up after childbirth. Setting Ten hospitals across four states in India. Participants 1342 pregnant women. Intervention Not applicable. Methods Hb and coagulation parameters: fibrinogen, D-dimer, D-dimer/fibrinogen ratio, platelets and international normalised ratio (INR) were measured at baseline. Participants were followed-up to measure blood loss within 2 hours after childbirth and PPH was defined based on blood loss and clinical assessment. Associations between coagulation parameters, Hb, anaemia and PPH were examined using multivariable logistic regression models. Outcomes measures Adjusted OR with 95% CI. Results In women with severe anaemia during the third trimester, the D-dimer was 27% higher, mean fibrinogen 117 mg/dL lower, D-dimer/fibrinogen ratio 69% higher and INR 12% higher compared with women with no/mild anaemia. Mean platelets in severe anaemia was 37.8×10 ⁹ /L lower compared with women with moderate anaemia. Similar relationships with smaller effect sizes were identified for women with moderate anaemia compared with women with no/mild anaemia. Low Hb and high INR at third trimester of pregnancy independently increased the odds of PPH at childbirth, but the other coagulation parameters were not found to be significantly associated with PPH. Conclusion Altered blood coagulation profile in pregnant women with severe anaemia could be a risk factor for PPH and requires further evaluation.
Full-text available
Background The SARS-CoV-2 pandemic in India has adversely affected many aspects of population health. We need detailed evidence of the impact on reproductive health in India so that lessons can be learnt. Methods Hospital-based repeated monthly survey of nine severe maternal complications and death in 15 hospitals across five states in India covering a total of 202,986 hospital births, December-2018 through to May-2021. We calculated incidence rates (with 95% confidence intervals (CIs)) per 1000 hospital births, case-fatality and rate ratios (RR) with 95% CIs. Linear regression was used to examine the association between the Government Response Stringency Index (GRSI) for India and changes in hospital births, incidence and case-fatality. Findings There was a significant decrease in hospital births per month during the pandemic period with a 4.8% decrease per 10% increase in the GRSI scores (p < 0.001). The overall incidence of severe complications in the pandemic period was not significantly different from the pre-pandemic period, but hospital admissions from septic abortion was 56% higher (RR=1.56; 95% CI=1.22–1.99; p < 0.001). The overall case-fatality of complications increased by 23% (RR=1.23; 95% CI=1.03–1.46; p = 0.022) and remained high across the different phases of the pandemic with a notable significant increase in deaths from heart failure in pregnancy. Interpretation Our study supports the legitimacy of the calls made to maintain sexual and reproductive health services as essential services during the pandemic. Lessons learnt should be used to avert the ongoing reproductive health crisis while India plans to manage a third wave of the pandemic. Funding The MaatHRI platform and this study are funded by a Medical Research Council Career Development Award to MN (Ref:MR/P022030/1). The funder has no role in the study design, data collection, analysis, or writing the paper
Full-text available
Objective To assess the feasibility of establishing a simple maternal morbidity surveillance system in Assam (Indian Obstetric Surveillance System-Assam (IndOSS-Assam)) to investigate the incidence and trends in severe maternal complications. This study presents the surveillance platform of IndOSS-Assam. Design Four tasks were undertaken: (1) setting up of a steering committee; (2) establishing priorities for the region; (3) mapping of surveillance sites; (4) piloting case-notification systems in selected centres. Setting Two government tertiary hospitals in the state. Study population Pregnant women delivering in the hospitals between March and August 2015. Main outcome measures Incidence and case fatality rates with 95% CIs. Results Local stakeholder ownership and a simple uncomplicated anonymous system for case notification were the key strengths of this project. Cases and deaths were reported for six conditions: eclampsia, postpartum haemorrhage, puerperal sepsis, septic abortion, uterine rupture and anaemic heart failure. Among 10 475 women delivering over 6 months, 402 had one of these conditions and 66 died (case fatality 16%). The incidence of eclampsia was 17 per 1000 deliveries (95% CI 14 to 19), postpartum haemorrhage was 11 per 1000 deliveries (95% CI 10 to 13) and anaemic heart failure was 3 per 1000 deliveries (95% CI 2 to 5). For each of the other three conditions—puerperal sepsis, septic abortion and uterine rupture—the incidence rate was 2 per 1000 deliveries. Conclusions IndOSS-Assam was shown to be a feasible and simple system for ongoing surveillance of maternal morbidity that can be used to monitor the trends in the incidence of specific severe life-threatening conditions during pregnancy.
Full-text available
Objectives To examine the association between maternal anaemia and adverse maternal and infant outcomes, and to assess the feasibility of conducting epidemiological studies through the Indian Obstetric Surveillance System–Assam (IndOSS-Assam). Design Retrospective cohort study using anonymised hospital records. Exposure: maternal iron deficiency anaemia; outcomes: postpartum haemorrhage (PPH), low birthweight, small-for-gestational age babies, perinatal death. Setting 5 government medical colleges in Assam. Study population 1007 pregnant women who delivered in the 5 medical colleges from January to June 2015. Main outcome measures ORs with 95% CIs to estimate the association between maternal iron deficiency anaemia and the adverse maternal and infant outcomes. Potential interactive roles of infections and induction of labour on the adverse outcomes were explored. Results 35% (n=351) pregnant women had moderate–severe anaemia. Women with severe anaemia had a higher odds of PPH (adjusted OR (aOR) =9.45; 95% CI 2.62 to 34.05), giving birth to low birthweight (aOR=6.19; 95% CI 1.44 to 26.71) and small-for-gestational age babies (aOR=8.72; 95% CI 1.66 to 45.67), and perinatal death (aOR=16.42; 95% CI 4.38 to 61.55). Odds of PPH increased 17-fold among women with moderate–severe anaemia who underwent induction of labour, and 19-fold among women who had infection and moderate–severe anaemia. Conclusions Maternal iron deficiency anaemia is a major public health problem in Assam. Maternal anaemia was associated with increased risks of PPH, low birthweight, small-for-gestational age babies and perinatal death. While the best approach is prevention, a large number of women present with severe anaemia late in pregnancy and there is no clear guidance on how these women should be managed during labour and delivery.
Maternal health is a big issue and is central to sustainable development. Each year, about 210 million women become pregnant and about 140 million newborn babies are delivered-the sheer scale of maternal health alone makes maternal well being and survival vital concerns. In this Series paper, we adopt primarily a numerical lens to illuminate patterns and trends in outcomes, but recognise that understanding of poor maternal health also warrants other perspectives, such as human rights. Our use of the best available evidence highlights the dynamic burden of maternal health problems. Increased diversity in the magnitude and causes of maternal mortality and morbidity between and within populations presents a major challenge to policies and programmes aiming to match varying needs with diverse types of care across different settings. This diversity, in turn, contributes to a widening gap or differences in levels of maternal mortality, seen most acutely in vulnerable populations, predominantly in sub-Saharan Africa. Strong political and technical commitment to improve equity-sensitive information systems is required to monitor the gap in maternal mortality, and robust research is needed to elucidate major interactions between the broad range of health problems. Diversity and divergence are defining characteristics of poor maternal health in the 21st century. Progress on this issue will be an ultimate judge of sustainable development.
The International Network of Obstetric Survey Systems (INOSS) is a multi-country collaboration formed to facilitate studies of uncommon and severe complications of pregnancy and childbirth. Collaborations such as INOSS offer many benefits in the study of rare complications. The use of uniform case definitions, common datasets, specifically collected detailed data and prospectively agreed comparative and combined analyses all add to the validity of studies and their utility to guide policy and clinical practice and hence improve the quality of care. Such multi-national collaborations allow for the conduct of robust studies less subject to many of the biases attributed to typical observational studies. For very rare conditions such collaborations may provide the only route to providing high quality evidence to guide practice. Clinicians and researchers conducting studies into rare and severe complications should consider working through a network such as INOSS to maximize the value of their research.
The UK Obstetric Surveillance System is a national system that allows for the collection of information on a range of rare disorders of pregnancy, enabling national descriptive, case-control and cohort studies. The population-based nature of the studies conducted renders them less susceptible to the biases typically associated with observational studies. Data collected using The UK Obstetric Surveillance System and similar systems can be used to address a range of patient safety issues. These include assessing the safety of different treatment options, using the data as an aid to service planning, as part of ongoing quality-improvement initiatives, as a benchmark against which to compare hospital-level disease incidence and outcomes, to inform and audit national guidelines, and to monitor the effect of changes in practice or policy. Studies can be introduced rapidly in response to newly arising safety concerns. International comparisons can further enhance the utility of these data for improving patient safety.
Reference Source 4. Office of Registrar General and Census Commissioner India: Annual Health Survey 2012-13: Fact sheet
  • Unicef Who
  • Unfpa
WHO, UNICEF, UNFPA,, et al.: Maternal mortality: Levels and trends 2000 to 2017. Geneva: World Health Organisation, 2019. Reference Source 4. Office of Registrar General and Census Commissioner India: Annual Health Survey 2012-13: Fact sheet. New Delhi: Ministry of Home Affairs, Government of India, 2014.
Saving Lives, Improving Mothers' Care -Lessons learned to inform maternity care from the UK and Ireland Confidential Enquiries into Maternal Deaths and Morbidity 2015-17
  • M Knight
  • K Bunch
  • D Tuffnell
Knight M, Bunch K, Tuffnell D, et al.: Saving Lives, Improving Mothers' Care -Lessons learned to inform maternity care from the UK and Ireland Confidential Enquiries into Maternal Deaths and Morbidity 2015-17. Oxford: National Perinatal Epidemiology Unit, University of Oxford, 2019. Reference Source 6.
Saving Lives, Improving Mothers’ Care - Lessons learned to inform maternity care from the UK and Ireland Confidential Enquiries into Maternal Deaths and Morbidity 2015-17
  • M Knight