While the preliminary results were encouraging [4], it was very important to assess the impact of interventions in order to determine its future course and also to look for additional measures to strengthen the ongoing malaria control operations.The purpose of improving the reporting time and providing the TABs to MPWs was to improve surveillance and make it “smart.” Smart surveillance reduces the time delay between diagnosis and registration of a new incidence for necessary action in the field to break the chain of transmission.
There was a marked reduction in malaria incidence in Mangaluru over 4 years, and also in 2020 (up to May). It is encouraging to note that the data records show cumulative reduction by 65% and continues to decline (Table 1). For the first time in the past two decades malaria incidence reduced to double digits in the 5th year as a consequence of improved surveillance and effective field work. The lower reduction in the months of July and August could be attributed to the monsoon rains and excessice mosquito breeding resulting in spread of malaria. After the introduction of malaria elimination team in June 2018, it was noted that the malaria incidence reduced even further from a peak of 1003 in July 2018 to just 294 at the end of PDY 4. The monthly closure vs incidence shows that with higher closure of cases, the incidence of malaria decreased. This indicates that smart surveillance does have an important role to play in breaking the chain of transmission.
Subsequent to smart surveillance, an important behavioral change took place among the diagnosticians at the point of diagnosis and it continued throught PDY4 wherin details of 80% of newly diagnosed cases were uploaded into the system within 48 hrs. These case records were available to field workers for sanitization and active surveillance in and around reported case (Table 3). Emphasis was laid on this process during implementation from the first year of programme and subsequently malaria elimination team was formed for rapid response. After effective implementation of control programme aided by software for 18 months, an administrative decision was taken to utilize services of MPWs for non-malarial (civic body’s) work resulting in reduced efficiency in the field. Although the community visits increased by manifold during PDY3, it was not translated to effective vector control measures and collection of smears by active surveillance reduced from 4.61 per case (PDY2) to 2.8 per case (PDY3). This resulted in slump in the work and lesser reduction of malarial incidences during PDY3. A surge in the number of cases was observed in April – May 2017 which led to increase in malarial indices. To counter this inefficiency, Complete Malaria Elimination Teams (CMETs) were formed at district malaria unit in June 2018. These teams visited each malaria case as soon as the case details were uploaded and conducted ASARC along with anti-vector activities in the locality. First visit and collection of smears from contacts and fever cases in the residences around reported case did help in brealking transmission as observed by the negative correlation between contact smear and to incidences of malaria over 4 years.
Even ward-level analysis demonstrated that the incidence in almost all the wards was reducing progressively with a good cumulative reduction in incidence(Table 2). However, it can be noted that the effect of the entire programme, its implementation and effects on malaria control was not uniform in all administrative units (wards). Most wards showed reduction ranging between 23% to 94%. The maximum reduction was noted in Kankanady-Valencia ward which had a reduction of 94% in PDY4, followed by Bajal which had a reduction of 93%. Although certain wards had low incidence, there was an increase in cases in 2 wards namely Kadri South and Cantonment. Kadri South ward showed a cumulative increase of 17% while Cantonment ward showed an increase of 7%. This is attributed to an increase in the number of cases in both these wards in PDY3 due to the repurposing of the MPWs for non-malaria work. Kadri south ward had recorded 341 cases in PDY3 compared to 159 in PDY 2 (114% increase) and Cantonment had an increasefrom 227 in PDY 2 to 292 cases in PDY 3 (29% increase). However, even these two wards do show a progressive reduction by PDY3 when the malaria elimination teams were deployed. These wards, where there was an increase in cases or very minimal reduction in cases (less than 20% in 4 years) against the expected trend as seen in other wards may probably be indicative of problematic areas. High risk categorization is based on API and such wards recorded reduction of incidence by 80% and above. Several wards converted from a high API red zone to a lesser API green or yellow zones (Fig. 3). This assumes greater significance in the light of a recent report about the asymptomatic malaria carriers in hotspots of malaria at Mangalore which indicates that these may seed transmission to the surrounding population in receptive areas [8]. There may be a role to understand geographic trends for planning the strategies at micro level and further research and review of these is warranted. Further, it may be worthwhile to look at the sociodemographic cahracteristics of people in these areas as well as the activities like construction and migration or travel [9].
Private sector contribution was higher than the public health system. According to WHO, reported cases of malaria are only from public health care facilities10,11 and hence, large number is unreported. However, even where reporting rates in the public health sector are close to a 100%, in some countries, more than 50% of malaria patients seek care in the private sector [12]. With digitization both public and private health care providers reported the malarial cases (Table 3). Private sector contribution was higher than the public health system. According to WHO reported cases of malaria are only from public health care facilities [10,11] and a large number is unreported. However, even where reporting rates in the public health sector are close to a 100%, in some countries, more than 50% of malaria patients seek care in the private sector [12]. Thus the software helped to connect people at point of diagnosis from both private and public health systems with field workers instantaneously for sanitization exercise, investigating contacts for malarial parasites and ensuring complete elimination of parasite from malarial patient. This was the biggest advantage of smart surveillance which is the essence of this software.
The action of closing the caseon day 14, which reflects accountability of field force steadily increased and it did contribute to reduction of malaria cases. The proportion of closure of cases between 15 – 30 day was also steadily maintained. There was both temporal and spatial relation to this action of field force (Figs 1 and 2). Greater the monthly closure of cases the higher was the decrease in incidence of malaria in a geographical area (Fig. 1). Hence, it ensures completion of treatment and ASARC establishes that breaking the chain of transmission and measures to reduce breeding and spread are important public health measures in control of malaria [4]. The software helped in this activity and also aided in monitoring the activities of MPWs and closure of cases with documentation.
Data from software was analyzed for changing the approach to field activities for malaria control. As described earlier, surveillance was carried out as soon as new malarial case was reported − ASARC. During analysis of new cases, clusters of new cases within a short period of one week, within a defined geographical area were identified and strategically separate programmes were carried out. One such endeavour was targeted for labourers/ 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/daily wage earners and low socioeconomic class, as they were engaged in their vocation and income generation activities during that time. Hence, a mobile 24x7 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 cases.
Smart surveillance did empower the stake hoders. Table 7 summarises perception of stakeholders and how the software was utilized and how it brought about positive change in them.
Table 7. Summary of strategies adopted based on analysis of data obtained from software and addressing the issues.
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Problem
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Action
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Reported incidence
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Timely treatment and follow up, Contact smears, information education and communication (IEC) in the surrounding along with vector control measures, follow up as required for compliance
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Identified hotspots
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Mass survey, indoor residual spray (IRS), long lasting insecticidal nets (LLINs) distribution, and vector control measures,
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Identified high risk wards
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Fortnightly mass survey for fever cases, vector identification and remedial measures along with IEC. Anti-larval activities - temephos weekly; pyriproxyfen fortnightly & lambda cyhalothrin in select areas with IRS
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Delay in diagnosis
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Mobile malaria clinic 24x7 to reduce delay
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Construction sites with focal breeding sites as well as potential residential areas
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Separate teams to visit and sanitize the construction sites. Field activities by domestic and construction team of DVBDCO to reduce the container index (CI) and house index (HI) simultaneously to reduce dengue transmission.
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Additional work responsibility to MPWs
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Complete Malaria Elimination Teams was initiated, which was dedicated for malaria control only.
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Fig. 4 describes how the information technology (IT) software functions to improve control activity. All the actions regarding treatment and vector control can be measured including the time frames for each activity. Transmission cycle is effectively broken if interventions are carried out in the first 10 days of diagnosis in addition to early diagnosis. Transmission occurs locally around a reported case and it is logical to implement effective vector control and measure that activity. Smart surveillance was able to measure many control measures which helped to change the strategies in the field.
In the 5th year post-digitization, incidence of malaria is further reduced as compared to corresponding period of previous year. Hence, with continued MCS use, other administrative measures and action taken to address issues based on data received via MCS and feedback from stakeholders, the reduction in incidence of malaria was sustained.