Digitized smart surveillance and micromanagement using information technology for malaria elimination in Mangaluru, India: an analysis of five-year post-digitization data

Smart surveillance was initiated from October 2014 to September 2015, and was considered as digitization year (DY) [9]; October 2015 to September 2016 as post-digitization year 1 (PDY 1); October 2016 to September 2017 as post-digitization year 2 (PDY 2) [9]; October 2017 to September 2018 as post-digitization year 3 (PDY 3); October 2018 to September 2019 as post-digitization year 4 (PDY 4) and October 2019 to September 2020 as post-digitization 5 (PDY 5).

Closure and closure time of each positive case and vector interventions were analysed. Community visits, contact smears during active surveillance around reported case (ASARC), vector control activities were analysed along with malaria indices such as Annual Blood Examination Rate (ABER), Slide Positivity Rate (SPR), Slide Falciparum Rate (SFR) and Annual Parasite Incidence (API). Monthly trends of malaria at each level were also plotted in relation to closure of cases. Fischer F test was applied to find the significance in reduction of malaria cases. Time series analysis was done for plotting the trends of closure rate of cases against the incidence of cases of malaria. Bonferroni t test was used to test the statistical significance of inter time interval. A p value of < 0.05 was considered statistically significant.

Malariometric indices showed significant changes over 5 years. The incidence of both Plasmodium vivax and Plasmodium falciparum gradually decreased. Initially, ABER increased significantly with predominant contribution from passive surveillance. Stagnation between second and third year after implementing of MCS was a consequence of administrative decision to utilize MPWs for non-malarial work. In PDY 3, CMETs were formed to supplement the active surveillance and the results can be seen during PDY 4 and PDY 5. During COVID-19 pandemic, active surveillance could not be carried out efficiently resulting in decreased ABER and increased SPR. However, the incidence of malaria and API continued to decrease without any rebound increase in the ‘post-lockdown’ period. Thus, there is a need to have comprehensive approach for malaria elimination since it is a dual host disease with wide ranging R_O factor, dormant stage in humans and resistance to various strategies adopted for control or elimination. The ultimate goal of all strategies is to reduce API in the area and reduce the size of malaria map. A dedicated, user-friendly system which captures data with timeline will assist in micromanaging multiple strategies.

Reporting of malaria cases was mandatory under the communicable disease act of 1969; notifiable disease act of Government of Karnataka, and Karnataka Private Medical Establishment Act 2007. Prior to the introduction of MCS, malaria cases were reported late by the private sector health institutions via email or never reported in spite of statutory requirements. Subsequent to the introduction of MCS, ‘smart surveillance’, training programmes were conducted for private hospitals. Monitoring and appraisal on quality of reporting system was carried out periodically. In the absence of newly diagnosed cases, hospitals were required to provide `zero malaria case’ report. With persistent motivation, behavioural changes were observed with respect to timely reporting of malaria cases by the diagnosticians, and it continued through PDY 5. Details of 89% of newly diagnosed cases were uploaded into the system within 48 h. Both public and private health care providers reported the malaria cases (Table 3). All these were passive case detection (PCD) from health facilities with exception of cases reported by ASARC and DVBDCO. Very high rate of passive case detection reflects ‘health-seeking behaviour’ of the population and is probably one of the reasons for decrease in incidence even during COVID-19 pandemic. Private sector contribution was higher than public health system and is an indication of definite compliance to non-reporting from private health system which was a major hurdle for malaria control in India [7]. As per World Health Organization, cases of malaria are reported only from public health care facilities, and hence a large number of cases are unreported thereby facilitating transmission [14, 15]. However, even where reporting rates in the public health sector are close to a 100% in some countries, more than 50% of malaria patients sought health care in the private sector [12]. Hence, reporting from private sector is crucial for malaria control.

Figure 2 indicates average time taken to report from the time of diagnosis. Capturing the case details and transferring this information to the health workers in the field is the key to initiate control activities. Robust reporting of PCD initiated active case detection (ACD), which is a very important factor for malaria elimination. It has been observed that early reporting from the diagnosticians continued even during COVID-19 epidemic thus resulting in disruption of transmission cycle.

Efficient participation of multiple stakeholders is crucial for effective control measures. Multi-pronged, integrated approach is critical for containment of malaria and elimination thereafter. ‘Smart surveillance’ helped to connect all stakeholders with necessary information for anticipated response in the field to break the chain of transmission. ‘Time-bound’ field response i.e. active case detection and anti-mosquito measures in the geographical risk areas were carried out simultaneously. Immediate contact smears and identification of positive cases helped to reduce parasite pool available for transmission. Importance of time bound programme has been reported from China [16].

In the initial year after MCS, an increase in incidence was documented suggesting improved surveillance. In subsequent years, there was a gradual reduction in incidence of malaria. This reduction was not uniform throughout the year. Although during and immediately after monsoon rains (June to October) there have been variable spikes in incidence, the number of cases gradually reduced during same year on year period (Fig. 1). Surveillance, early case detection, treatment and vector control measures were done as per the NVBDCP guidelines with variable results. With the introduction of MCS, the surveillance was robust time-bound and ‘incidence-centric’. Quick transfer of information from point-of-diagnosis to the field workers and surveillance thereafter contributed to 1.8% of reported cases of malaria in the city in PDY 5. Albeit small in number, it is of high significance for breaking the transmission cycle. Rapid reporting and information of geolocation have been the strength of malaria control system in Zanzibar and Swaziland [17‐20]

All required field activities were recorded directly in the programme by MPWs using predesigned dropdown menu. This shift from manual documentation to MCS ensured appropriate field response including mosquito control measures by the MPWs. In the initial days after introduction of MCS the field movement of MPWs were monitored using GPS. Such monitoring and feedback brought about behavioural changes among health care providers in the preceding years. Transmission of malaria depends on Ro which in turn is determined by patient factors (PR or parasite ratio) and mosquito behaviour related to entomological inoculation rate (EIR) [21]. Therefore, it is imperative to prevent transmission of parasite from malaria patient to mosquito. An infected mosquito can continue to transmit sporozoites to many healthy individuals for a longer period. The risk of transmission to surrounding population can be minimized with anti-adult or anti-larval measures in houses around the residence of active malaria cases. Effective source reduction management happened over 5 years with gradual reduction of active breeding habitats (Fig. 4). Measures to reduce breeding and spread are important public health measures in malaria elimination operation [9].

Eighteen months after digitization an administrative decision was taken to utilize the services of MPWs for non-malarial work resulting in diminishing efficiency in the field. Although the community visits increased by manifold during PDY 3, it was not translated to effective vector control measures and collection of smears as active surveillance reduced from 4.61 per incidence (PDY 2) to 2.8 per incidence (PDY 3). This resulted in a slump in the work and non-reduction of malaria incidence during PDY 3. A surge in the number of cases was observed in April–May 2017 which led to increase in malaria indices. To counter this inefficiency, CMETs were formed at district malaria unit in June 2018. The CMETs conducted ASARC along with anti-vector activities in the locality. Subsequent to CMETs surveillance, reduction of cases was observed in the fourth year. During PDY 5, because of COVID-19 pandemic, the entire nation was under lockdown, and the public health system was engaged in fighting this new disease. However, the CMETs continued carrying out the visits to malarial houses. This activity is probably the main reason for reduction of malaria cases during PDY 5.

The global effort of malaria control is in line with the ‘One World One Health’ concept, but then a globally defined ‘one-size-fits-all’ malaria control strategy would be inefficient in endemic areas [22, 23]. Introduction of MCS did aid in local modification of strategies. During analysis of new cases, clusters of new cases within a short period of one week, within a defined geographical area were identified (hot-spots) and strategically separate programmes were carried out. One such endeavour was targeted for labourers and daily wage earners. Generally, malaria clinics are open from 9 AM and to 5 PM, which were underutilized as it was not convenient for the manual labourers and daily wage earners and low socioeconomic class, as they were engaged in their income generation activities during that time. Hence, a mobile 24 × 7 clinic using a van and health care workers was introduced so that it could visit various places and could also be sent to the site if there was a phone call made to the central malaria helpline number. This helped in not only enhancing the diagnosis, but also treatment and prompt reporting of malaria in migrant population.

It has been a long-standing concern for epidemiologists to quantify and stratify risk for malaria. Risk categorization for strategies and programme management is the key to success of malaria eradication and elimination [21]. MCS captured data on real time basis for spatial risk classification. Geographical high-risk categorization is based on API, and 43 such wards recorded reduction of incidence by 80% and above over 5 years. Several wards converted from a high API red zone to a lesser API green or yellow zones (Fig. 3). Risk prediction model was applied for malaria elimination process [6, 20]. There is a role to understand geographic trends for planning the strategies at micro-level and further research and review are warranted. Moreover, it may be worthwhile to look at the socio-demographic characteristics of people in these areas as well as the activities like construction and migration or travel [22].

In PDY 5, COVID-19 emerged as a major global public health challenge and disrupted malaria control programme in general. While February 2020 was mainly focused on preparation to plan strategies to control COVID-19, nationwide lockdown recorded a decrease in number of cases of all diseases as the hospitals were converted to Covid-19 facilities and care centres. Diversion of health care workforce towards COVID-19 management, total lockdown of the entire country, non-availability of transportation, closure or limited working hours of health facilities hampered anti-malarial activities for a short period of 5 to 6 months. However, active surveillance i.e. ASARC continued uninterrupted, routine house visits were reduced, but closure of cases continued quite effectively.

Strengthening of field work force and capacity building is essential in any public health programme. MCS did empower the field workers and it also helped in data-based micromanagement by the administrators as well as field workers. A bidirectional accountability was also observed i.e. from field force to administration and vice-versa. ASARC, time bound action in the geographical area surrounding the new malarial case, continuity in control measures especially during low transmission period (non-monsoon period). The necessity of closing the case on day 14, and its measure reflects functional accountability by the field work force. Closure of cases steadily increased and contributed to reduction of malaria incidence. There were delays in closure of cases as a result of multiple factors, like non-working days, non-availability of the patient upon visit to home, migration, etc. Nevertheless, over 90% cases were tracked and closed subsequently. An inverse relation between closure and malarial incidence was observed (Fig. 2). Hence, the function of ‘close a case’ ascertained complete treatment and parasite clearance thereby contributing to transmission control.

The five-year data indicated that technology has a major role to play in evaluating epidemiology of malaria as well as malariogenic factors. Learning from MCS application should help to upgrade functions, incorporate analytical and predictive output, warning and alarm systems for compliance in the field. Ideally, there is a need to design IT system driven field response for both treatment and vector control analytics and predictions. Since most control measures are similar for all vector borne diseases they should be brought under the purview of independent system to manage vector borne diseases or even other infectious diseases.

Malaria elimination is being envisaged by 2030 in India. An excellent information system should be at the core of malaria elimination programmes to ensure that all cases are detected and responded to an effective and timely manner. Investment in robust, response-focused systems is essential to achieve malaria elimination. The operational manual elaborates the strategies. However, these strategies need to be structured with ‘time-bound’ interventions. Figure 6 provides functional description of MCS for good micromanagement which is essential for malaria elimination [5, 6]. All micromanagement data regarding treatment and vector control measures can be quantified in relation to ‘time frames’ for each action. Transmission cycle is broken effectively if field interventions are carried out in the first 7 days of diagnosis. Transmission occurs locally around a reported case, and it is logical to implement effective vector control activity and measure this activity simultaneously. A standardized surveillance system landscaping was conducted in 16 countries between 2015 and 2017 in collaboration with governmental malaria programmes. The landscaping analysis identified multiple gaps in current malaria surveillance systems [24]. Nema et al. also pointed out these gaps [25], and suggested for a robust digital health care service in India [26]. Smart surveillance is able to measure and micromanage control measures for designing local strategies.