Mapping the stability of febrile illness hotspots in Punjab from 2012 to 2019- a spatial clustering and regression analysis

This is among the few papers from India that have presented such an analysis using the routinely collected IDSP data. Our study yielded the following key findings. First, PUO depicted the highest incidence among all the FI, while Malaria had the lowest annual incidence. We observed a rising incidence of Dengue and Enteric fever, while Chikungunya depicted an occasional spike during the study period. Second, there were significant inter-district variations in the burden of all FI. The FI expressed clustering at the start of the study period, with more dispersion in the latter part. P. Vivax was seen with high incidence in southern districts of Punjab, especially Bathinda. Enteric Fever incidence is high in central and north-eastern districts, with Nawanshahr and Jalandhar being significant hotspots of the disease. Hoshiarpur and Nawanshahr are also hotspots of PUO during the duration of the study. Lastly, the number of cases in each district has shown over-dispersion for each disease with little dependence on population characteristics.

The rising trends of FI observed in our study are coherent with the reports from Low-middle-income and high-income countries [39]. Two distinct trends can explain these escalated estimates: increasing vector-borne disease incidence and interspersed, infrequent outbreaks attributed to a widening spectrum of domestic and imported VBD. This rising trend can be attributed to changes in human activities like trade and travel, rising urbanization, a rise in population, and increasing encroachment of wildlands. These activities have also increased anthropogenic greenhouse gases, leading to abrupt climatic alterations that can affect the mechanics of disease transmission, geographic spread, and re-emergence of VBD through multiple pathways [40, 41]. Also, climatic change directly affects vector species and their ecosystem (including urban habitats), in which vectors may or may not survive. Because the vectors are poikilothermic, global warming will further increase the vector's abundance, survival, and feeding activity, and in similar proportions to the pathogen's development rate [42].

The state is amongst the top five states of India in terms of Dengue burden and also suffered a Chikungunya outbreak around 2016 [12]. For Dengue, Fatehgarh Sahib was identified as a consistent "Low–High" outlier over the years, indicating that this district, with a low value, is surrounded by districts with higher values. There were no stable hotspots, coldspots, or "High-Low" outliers for Dengue. Chikungunya did not exhibit any stable spatial patterns, and no districts were identified as consistent hotspots, cold spots, or outliers, similar to an analysis from Barbados [11]. The two are arboviral diseases, and two principal vector species, Aedes aegypti, and Aedes albopictus, are known for transmission, and outbreaks can be mitigated commonly [43]. Dengue and Chikungunya control also calls for advocacy and health awareness campaigns as the vector. While Dengue is largely endemic to the region, the Chikungunya outbreak was uncommon until the last decade. The first outbreak was recorded in 1963 in Calcutta, but the disease re-emerged in India in 2005 [44]. There have been Chikungunya outbreaks in other parts of Northern India between 2013–17, similar to what was observed in Punjab [13, 45]. Previous studies demonstrated that age-specific seroprevalence has been uniform across age groups in this region, suggesting an epidemic transmission pattern, susceptibility of the population to the virus, and the absence of herd immunity [46]. In the absence of established human-to-human transmission, the infrequent spikes can be attributed to the introduction of the mutated strains of the virus through extensive human movements, as seen in other countries [47]. The Chikungunya outbreaks in North India during the study period were mainly attributed to the Indian Ocean Lineage of the East-Central South African CHIKV genotype, which increased the adaptability of Chikungunya virus to Aedes albopictus mosquitoes [48]. Such mutations increase the ability of the Chikungunya virus to adapt to a new vector and expand its geographical distribution, making it a dynamic pathogen of global public health concern [49]. With a common vector, improved Dengue and Chikungunya control can be achieved through reliable epidemic forecasting systems that detect temporal anomalies in disease incidence [50]. The mosquito mates, feeds, rests, and lays eggs in and around urban human habitation. So, getting infected with Dengue and Chikungunya is due to inappropriate public health measures around the patient only. Control of vectors is warranted by source reduction elimination of container habitats that are favorable for vector breeding [51].

P. Falciparum malaria depicted no stable spatial patterns, with no districts emerging as hotspots, cold spots, or outliers. But for P. Vivax, Bathinda stood out as a stable hot spot, suggesting a high disease incidence in this district and its neighboring areas. On the other hand, Hoshiarpur was identified as a stable cold spot, indicating a consistently low disease incidence compared to its neighboring districts. No outliers were observed for P. Vivax. The incidence of Malaria due to P. Falciparum was observed to be < 1 case/1000 population at risk, while it was a little higher for P. Vivax and is therefore categorized in category one, where the states/UTs have the Annual Parasite Incidence of less than one and all the districts in the state with API less than one., and thus it can be considered that the state is already on the lines to eliminate Malaria within the timelines stipulated by National Framework for Malaria Elimination in India (2016–2030) [30]. More effective control strategies that affect entomological and epidemiological endpoints are required. Google trends have been effective in identifying outbreaks early and helping control them effectively [13].

The estimates of Enteric fever were as high as 116 cases/lakh population and are slightly lower than recent modeled estimates from India [18, 52]. It was also the most widely spread disease, with about 5 districts depicting hotspots, but exhibited no stable spatial patterns, with no districts consistently identified as hotspots, coldspots, or outliers. Enteric fever is majorly influenced by the Water and Sanitation Hygiene (WASH) indicators that depend on the social determinants of health [53]. Such a high burden also necessitates the introduction of typhoid vaccine for children under five to prevent unwarranted morbidity and mortality [17]. Similarly, the PUO was the most common type of FI reported in Punjab, but no districts consistently showed any form of spatial autocorrelation, whether it be hotspots, coldspots, or outliers. The absence of distinct hot or cold regions suggests that interventions may need a broader, more generalized approach for PUO. The high burden of PUO also calls for health strengthening, as the most commonly cited factors associated with PUO diagnosis include the year of evaluation, physician experience, quality of referral center, and the clinical characteristics of the fever itself [20, 54]. Apart from the regional socioeconomic factors, such as healthcare access and variations in practice patterns, geographical factors may also play a significant role [55]. The diagnosis of PUO is complex. It is elaborated as a persistent fever above 38.3 °C (100°F) that remains undiagnosed for at least three weeks, of which one week should be investigated following hospitalization. Due to inadequate diagnostic facilities, primary care physicians tend to label patients with PUO if they are negative for available laboratory tests. However, previous studies have reported that various infections, including Tuberculosis, are responsible for only 43%-53% of PUO cases, and that too in the hospital setting [56, 57]. High prevalence of PUO points toward other infectious diseases prevalent in the area, like bacteremia, scrub typhus, leptospirosis, or miss-diagnosis of the known causes. This calls for a review of the management practices of health professionals regarding communicable diseases.

We observed that Nawanshahr was a common incidence hotspot for Dengue, Malaria (P. Falciparum), Enteric fever, and PUO, followed by Rupnagar, which was a common hotspot for Dengue, Malaria (P. Falciparum), and Enteric Fever, and pose a risk of outbreaks in neighboring districts as well. Spatial mapping of the districts and identifying hot spots could help policymakers and program officers identify districts that require more focus. A rapid review of literature in the Indian health context revealed that the GIS tool had been extensively used to control vector-borne diseases, outbreaks, and disease surveillance [28, 58‐60]. These studies have identified hot spots for prioritizing public health interventions. The significant inter-district variations can be attributed to vector abundance, geographical determinants like transitional swamps and unmanaged pasture proportions, and population demographic composition [26]. The availability of water bodies (natural in rural and artificial in urban areas) significantly affected the clustering of FI. Our study reinforces the hypothesis developed by previous studies that Malaria and Dengue tend to cluster with specific geographic units significantly [61]. Our study calls for augmented actions in such districts, and stricter implementation of public health interventions can impact the overall indicators of the state. Thus, spatial heterogeneity is common in several health issues, including FI, vector-borne diseases, malnutrition, etc. Against the background of these spatial mapping exercises, we recommend a GIS visualization platform with regular real-time data updation under the IDSP to monitor various illnesses so that it is highlighted if a localized spurt of disease cases occurs.

The study's specific strengths and limitations need to be acknowledged. District-wise data collected for eight years using standard case definitions helped us build robust estimates about the disease trends and hotspots. The results emanating from the study will be crucial in designing future disease containment measures. A rigorous statistical analysis makes the results reliable and reasonably valid. Some important caveats to the data used are essential to note during interpreting results. It was an ecological study, and we did not have patient-level data. In addition, the absence of access to environmental variables such as climate change, population growth, vector density, and patient-level data prevented us from making a more robust model supporting our hypothesis related to hot spots. We also cannot comment upon the accuracy of the disease reporting system as some cases might be due to the inaccessibility of health systems. We could not include the patients diagnosed and treated in the private sector.

To conclude, the present study demonstrated the stable hot spots for certain FI reported under IDSP and their relation with the demographic composition. Specific policy implications are emerging from the study. We have observed hot spots for most of the studies FI. This can significantly push the VBD control program already in place. Identifying the spatial clusters of infection is crucial for health planning and re-distributing the available resources as and when required. The present study demonstrates that information obtained through IDSP can describe the spatial epidemiology of FI at crude spatial scales. Still, our only challenge for the next few years is to develop effective interventions that allow real-time identification of the local spatial heterogeneity using existing surveillance data and are affordable simultaneously.