Summary box
Malaria remains a significant public health problem in India as it contributes >85%
of estimated cases in South East Asia and almost half of all Plasmodium vivax cases
globally.
There is a multiplicity of malaria stakeholders in India that include public sector
agencies, private healthcare providers, defence forces, railways, industry, independent
researchers and control programmes that at times operate in silos and this leads to
fragmented data inputs and interpretation.
Lack of an established near real-time case-based surveillance system further challenges
the ability to mount a cohesive and integrated malaria elimination agenda.
There is an urgent need to reformulate the boundaries of individual stakeholders and
initiate a system that will allow them to coalesce data into a digital integrated
platform.
Such a platform will provide near real-time epidemiological, entomological and commodity
surveillance data that will be of immediate use to all stakeholders and will allow
transparent and evidence-based formulation of malaria control policies.
Despite a substantial decline in global burden of malaria, it remains one of the most
significant public health problems in Africa and South Asia. Malaria incidence has
declined globally between 2010 and 2018 from 71 to 57 cases per 1000 population at
risk but the pace of reduction has slowed between 2014 to 2018. An estimated ~228 million
cases of malaria occurred worldwide in 2018 with WHO South East Asia region contributing
3.4% of the burden. Almost 85% of all malaria cases globally were borne by 18 African
countries and by India, that additionally accounts for nearly half (47%) of all Plasmodium
vivax cases globally.1
As malaria endemic countries shift their goal post from limiting morbidity and mortality
(malaria control) to zero-incidence of indigenous case (malaria elimination) it becomes
imperative that approaches and strategies also evolve in concert. There is thus clearly
a need for the malaria health information system to switch from use of aggregated
data to near real-time case-based surveillance.2 The malaria data collected via surveillance
systems under national programmes is de facto the basis for country’s policies, strategies
and operational activities. Incomplete data or data that are primarily used for reporting
purposes, typically as in the routine national surveillance programmes, can only prolong
malaria transmission owing to its inherent limitations for use in malaria control
efforts, especially in underserved areas.3
In India, the National Vector Borne Disease Control Programme under the Ministry of
Health and Family Welfare is a vertically implemented programme. India launched National
Framework of Malaria Elimination in 2016 with the overall goal of zero indigenous
cases in the country by 2030.
However, the robustness of the current Indian surveillance system is a matter of debate.
There is a wide discrepancy between the reported cases and deaths by national programme
of India and estimated cases by WHO and various research studies.4–6 In 2017, National
Programme reported 0.84 million cases and 194 deaths whereas WHO estimated 9.6 million
cases and 16.7K deaths during the same year.7 8 Another example of the discordance
in numbers is the surveillance-based estimation of malaria burden that was completed
by a nodal research body in India in 2015/2016 that estimated malaria incidence to
be fourfold greater than the 1 million reported by the national programme, but threefold
lesser than the 13 million estimated by the WHO. Similarly, the estimated deaths were
93-folds more than average 313 deaths reported by the national malaria programme in
2015/2016 but were comparable with that estimated by the WHO for India.6India State-Level
Disease Burden Initiative 1990 to 2016 as part of Global Burden of Disease study estimated
malaria to be responsible for 0.47% of total disability-adjusted life-years (0.19%
to 1.08%) and 0.8% of deaths in all age groups caused by neglected tropical diseases
and malaria.9
There could be multiple reasons for the above disagreement in numbers. The existing
Indian surveillance system indeed has a time-lag in reporting due to paper-based data.
It suffers from poor surveillance of the substantial mobile populations, forest workers,
transitory urban slum populations, refugees and domestic or international tourists.
In addition, it is inefficient at representing data from remote and inaccessible areas
such as tribal and hilly regions.
The marginalised section of communities afflicted by malaria, suffer not only from
neglect but also helplessness and dearth of healthcare opportunities. These communities
need enabling environment and empowerment so as to enhance their reach out to the
healthcare system and timely prevention and management. Persistent neglect and lack
of focus on malaria in pregnancy has led it to become a hidden public health problem.10
Importantly, malaria cases treated by private healthcare providers, both qualified
and unqualified, who otherwise impart approximately 70% of healthcare through hospitals,
nursing homes, clinics or general practice are not accounted for in national figures.11
Moreover, armed forces, railways and organised industries like automobiles, coal,
mines, steel, tea plantations and others with their own healthcare systems are missing
from the numbers projected by national surveillance systems. In addition to the above
missing links, the data which is routinely collected in the ambit of national programme
lacks real-time data aggregation and is bereft of desirable level of resolution such
as households. Finally, no extrapolation across geographical borders of Indian states
is currently possible either within India or outside. Adjacent districts that belong
to different states suffer from widely variable malaria incidence rates but lack of
timely data sharing makes each side vulnerable to interstate import and export of
malaria.
Further, the malaria incidence data from national programme are not available in the
public domain in terms of granularity that is below the district level. The data are
also not provided age, gender and month-wise thereby discouraging their analyses.
These constraints also prevent both the identification of infection foci and timely
adoption of corrective actions. Adding to the void, drug and insecticide efficacy
surveillance, entomological surveillance, commodity and stock management are not part
of the surveillance systems. Thus, though available in separate domains, the data
are scattered and disintegrated. (figure 1)
Figure 1
Malaria data integration. NGOs,
non-governmental organisations.
Furthermore, India, the second most populous country, has a substantial proportion
of researchers, scientists, doctors, non-government bodies, philanthropic organisations
and many other independent bodies who work in the field of malaria. Besides reading
the work in scientific literature, there is no centralised database in the country
for the active researchers and malariologists to connect with each other, missing
out on the opportunity of scientific deliberations, cross-fertilisation of research
ideas and possible collaborations. Relying on meetings, workshops and lectures as
a platform for efficient data exchange will continue to foster malaria control operations
but in silos.
Lack of a one-window platform that can provide integrated malaria data presents real
roadblocks towards enhancing our understanding of the dynamic situation of malaria
in India. Several notable instances where integration can help are; (a) high quality
data analysis and interpretation of the routine epidemiological and entomological
information gathered at national and local levels can direct any midcourse corrections
for implementation. This will need integration with data analysts and epidemiologists;
and (b) collation and analysis of data generated by multiple agencies (like private
sector, armed forces, industry, etc) and stakeholders (eg, non-governmental organisations
working in states) at one platform. This would need to be preceded by data validation
and standardisation of inputs from different sources.
Extensive training of and by the malaria programme managers and other players will
be essential for the proposed platform to provide harmonised, robust, standardised,
uniform and validated data. As much as possible, raw data can be made accessible at
the platform so as to enable offline analyses of the same. Once malaria is made a
notifiable disease, these partners would be mandated to share the real-time data.
This will allow comprehensive and cohesive analyses on which subsequent evidence-based
action can be based; (c) conglomeration of active researchers and malariologists and
(d) lessons from other countries that have successfully eliminated malaria and from
those struggling yet.
Internationally, there are examples of networks where researchers and clinical investigators
are encouraged to share and access data through secure online systems. Examples are
‘The WorldWide Antimalarial Resistance Network’ (WWARN) that is part of the Infectious
Diseases Data Observatory and is a collaborative platform to track the emergence and
spread of malaria drug resistance.12 The Worldwide Insecticide Resistance Network
(WIN) runs in a similar format.13
To meet the above exigencies of multiple, siloed and independent stakeholders we propose
that consideration should be given to ‘convergence’ of data pertaining to malaria
as a structural change to the way programmes are run currently. This convergence or
data integration will absorb all data from independent agencies mentioned in figure
1 and aim to coalesce it on a digital platform. The integrated data will provide a
single window digital platform that is lucid, inclusive, transparent, open access
and multilayered. The digital platform can be a data hub for rendering valuable data
as per the needs of the individual stakeholders. Some possible benefits are:
The digital platform can serve as a robust surveillance system wherein comprehensive,
near real-time, patient-based data collected at all levels, by public and private
sectors, on important epidemiological parameters such as demographic information including
location, diagnosis and treatment, vector control interventions and monitoring of
drug and insecticide resistance and supply chain logistics are reflected. Data capture
at the level of villages or households will also translate into immediate data-driven
control action and therefore mitigation of malaria.
The proposed digital platform will thus provide a window for ‘local data for local
action’. ‘Minimum data sets’ could be defined denoting the least possible number of
data points for standardisation and interoperability which could be useful to a range
of stakeholders14 data analysis and timely interpretations will facilitate identification
of potential hotspot of malaria and impending outbreaks. Feedback mechanisms with
in-built alert systems and decision support systems from the central to peripheral
levels of the health system and back would assist in efficiently containing new foci
of disease. Effective and timely response from health system as result of the alerts
would inform the digital information system that the activity has taken place operating
as a tool for monitoring and evaluation.15 Cloud-based database or local databases
can be selected depending on internet availability ensuring seamless reporting adhering
to the pre-decided timelines.
Data on drug and insecticide resistance, parasite and vector behaviours, standard
protocols in malaria elimination research, geospatial mapping, vector ecology, high-risk
group mapping, novel tools like newer diagnostics or vector control tools, drug treatment
trials would be valuable part of dashboard.
Data sources external to health systems, for example, census data providing population
denominators, climate data, land use data and water bodies data can be also be made
accessible. Migratory populations can be mapped for easy surveillance by using mobile
phone technology or geo-tagged devices. These together directly or indirectly impact
disease epidemiology and their merging could provide a panoramic view of vector borne
diseases like malaria.
The digital platform will serve as a database of and for malariologists. It can include
national and international consortia from different disciplines of malaria and allied
fields. This would be an excellent opportunity for researchers and scholars to develop
networks and share knowledge and resources. However, to assemble and accredit the
data integration digital platform with all the above attributes and features, India
needs to have seamless internet facility, provision for offline data entry, data server
and safety firewalls for data security. It will need adequate governance and policy
structures to ensure data privacy. Although a single window data platform can offer
consolidation of vital data, resistance to it is a possible barrier.
Hindrances such as: (a) ‘policy obstacles’ on data sharing and data security3 especially
in the context of cross-border (international) data sharing. To the neighbouring countries
of Bhutan, Nepal, Bangladesh (bordering north-eastern region of India) and Sri Lanka
import of malaria from India is a threat. However, transparent data sharing on malaria
cases, drug/insecticide resistance and intervention tools though desirable may be
stalled by bureaucratic inaction. Unfriendly political relationships between countries
alongwith biosecurity concerns may also hamper smooth exchange of the data. A possible
solution is to develop nationwide digital platforms with safeguard mechanisms like
conditional ‘writing access’ limited authorised partners but with wide and free access
and availability of data. This digital platform can be shared with neighbouring malarious
countries for adoption so as to standardise data representation and also to facilitate
exchange of vital information, if the countries so decide. Indian Council of Medical
Research’s (ICMR) initiative of bringing all malariologists and researchers at one
platform via Malaria Elimination Research Alliance-India (MERA-India) is a step towards
integration of elimination research efforts. MERA-India, steered by National Institute
of Malaria Research, is aptly placed to play this important role of international
collaborations. MERA-India is being led by Secretary (Department of Health Research)
and Director General, ICMR, the nodal research body of Indian government and supported
by Ministry of Health and Family Welfare and WHO-SEARO. MERA-India is governed by
independent experts and is a body with minimum bureaucracy involved. ICMR’s initiative
‘Regional Research Platform’, though focussed on emerging infectious diseases in its
launch year (2019) can also be harnessed to link neighbouring South East Asian countries
and support MERA-India in this proposed endeavour. (b) International donor agencies
support malaria control and elimination programmes in developing countries like India.
There may be a perceived risk that donor agencies may use the integrated data to assess
and financially audit the performance of the national malaria control programme. This
may worsen the hesitancy of the governments to fully adopt the digital platforms in
lieu of fear of losing on the funding opportunities. However, such transparency will
also provide new fillip to the national programme in terms of greater accountability.
(c) In India’s federal system, health is a state subject and thus India’s states may
be reluctant to share in near real-time the data on hotspots/outbreaks, supply chain
inefficiencies and/or poor surveillance. However, the very purpose of this proposed
digital dashboard is to bring to light national and state level data as this will
provide an opportunity for greater accountability in the running of malaria programmes.
(d) The possibility of stigmatisation and ostracisation of the areas identified as
hotspots and/or outbreak prone due to the proposed digital solution. However, as COVID-19
pandemic has taught us already, the best policy for the government and public health
officials in context of infectious diseases is utter transparency and sharing of data
that the digital platform will enable.
Data integration is necessary but is just one of the component of the overall thrust
for malaria elimination. There are several other notable gaps in the implementation
of the current national programme which may be plugged concurrently. Given its size
and complexity, India has adopted a subnational elimination plan (recommended by WHO)
wherein a particular district or state is encouraged to achieve and maintain malaria-free
status, even though transmission is likely to continue in other parts of the country
for few more years. Financial and other awards have been announced by Indian government
to give impetus to subnational malaria elimination and enthuse local health systems
and public health administration.
As we become cognizant of the ascendancy, barriers, limitations and scope of the proposed
digital data integration platform, we recognise the need for research in operationalisation
of this concept. Methods and mechanisms need to be devised, tested and validated for
each step before we embark on implementation and actual use of this digital dashboard.
An example of existing dashboard is District Health Information Software-2 (DHIS 2)
though it has its own constraints. Countries may develop their own tailor-made dashboards
with their needs fitting in the context of their health ecosystems. In India, we feel
that the ICMR can champion this cause of data integration platform. Via National Institute
of Malaria Research (NIMR) and the national programme, this integration will aid in
the malaria elimination ambition of the country. There is indeed a growing voice in
national and international communities for integrating surveillance and control programmes
that target communicable diseases. This is therefore vital and will allow optimisation
of resources and enhancement of outputs.
COVID-19, despite its paralysing effect on the world, has mobilised one and all to
share real-time data of daily cases and deaths. COVID-19 epidemiology is available
on many dashboards even within India and its states (the state of Kerala has an exceptionally
informative COVID-19 dashboard). The digital tracking of COVID-19 has empowered both
public health specialists and the general population. This sets up a good precedent
for malaria.16
As we move towards malaria elimination disruptive strategies are needed to challenge
the conventional boundaries of health and allied systems. The time is opportune to
dwell on remodelling our data collection methods, innovate on data collation and analysis
and to freely share malaria data that is generated using public funds.4