Insect and Pest Management for Sustaining Crop Production Under Changing Climatic Patterns of Drylands

Climate change is alarming, particularly for agriculturists as it severely impacts the development, distribution, and survival of insects and pests, affecting crop production globally. Over time, climate change is drastically tumbling the crop productivity in all the cropping systems, whereas the dryland agriculture with existing low productivity is immensely hit. While all the existing species in drylands, including humans, are coping with extreme climate variations for millennia, future climate change predictions put dryland agriculture in a threat zone. Drylands support 38% of the world’s population; therefore, climate change coupled with population growth and global food security draws the attention of scientists towards sustainable crop production under changing trends. The intermingling and intermixing of various biological, hydrological, and geographical systems plus the anthropogenic factors continuously amplify the changes in the dryland systems. All of this brings us to one challenge: developing pest management strategies suitable for changing climatic patterns. In this complex agrology framework, integrated pest management (IPM) strategies, especially those involving early monitoring of pests using prediction models, are a way to save the show. In this chapter, we will summarize the direct and indirect effects of climate change on crop production, the biology of insect pests, the changing pest scenarios, the efficacy of current pest management tactics, and the development of next-generation crop protection products. Finally, we will provide a perspective on the integration of best agronomic practices and crop protection measures to achieve the goal of sustainable crop production under changing climatic trends of drylands

COVID-19 in Punjab, India: Epidemiological patterns, laboratory surveillance and contact tracing of COVID-19 cases, March–May 2020

Background: In Punjab, first COVID-19 case was detected on March 5, 2020 followed by multiple clusters. Understanding the epidemiology of reported COVID-19 cases helps decision makers in planning future responses. We described the epidemiological patterns, laboratory surveillance and contact tracing of COVID-19 cases in Punjab. Methods: We analysed state's COVID-19 data from March–May 2020 to describe time, place and person distribution. We analysed the laboratory surveillance and contact tracing reports to calculate frequency of testing, sample positivity rate (PR) and contacts traced per case. Findings: A total of 2256 cases were reported from March–May 2020 (attack rate 75 cases/million and case fatality rate 2%). Attack rate was higher among males (81 cases/million males) and maximum affected age group was 60–69 years (164∙5 cases/million). Five of 22 districts reported almost half cases in May's first week. Mortality rate was highest among individuals >60 years (six deaths/million) and males (two deaths/million males). Of 45 deaths, 41 reported comorbidities [(hypertension (42%), diabetes (40%)]. COVID-19 testing increased from 46 samples/day (PR: 2%) in March's first week to 4000 samples/day (PR: 2∙5%) by May's end (2752 tests/million). Amritsar conducted 2035 tests/million (highest PR: 6∙5%) while Barnala conducted 4158 tests/million (lowest PR: 1%). For 2256 cases, 19,432 contacts were traced (nine contacts/case) with 11% positivity rate. Interpretation: COVID-19 in Punjab mostly affected males, >60 years of age and individuals with comorbid conditions. Many districts with less testing and contact tracing had higher positivity rate. We recommended to implement and ensure adequate testing and contact tracing in all the districts of Punjab

An evaluation of malaria surveillance system in Punjab, India, 2020

Background: India accounted for 6% of global burden of malaria with 95% population residing in malaria endemic areas. However, Punjab is in the malaria elimination phase with annual parasite incidence (API) <1/1000 population. Objectives: We evaluated malaria surveillance system in Punjab using CDC's updated guidelines for evaluating public health surveillance systems to provide recommendations for strengthening the existing system and to overcome the challenges in the path of malaria free Punjab. Methods: We chose two districts of Punjab, Amritsar (lowest API) and Mansa (highest API), interviewed stakeholders, and performed a retrospective desk review. We evaluated the overall usefulness of the system and assessed seven attributes at state, district, health facility, and village level during July–August 2020. Results: In Punjab, there was progressive decline in the malaria cases from 2,955 cases in 2009 to 1,140 in 2019 and no malaria deaths since 2011. Regarding various attributes, overall score for flexibility was good (85.9%); average for simplicity (77%), acceptability (74%), data quality (74%), and timeliness (70%); and poor for representativeness (59%) and stability (57%). Conclusions: Malaria surveillance system was useful in analyzing the trends of morbidity and mortality and for generating data to drive policy decisions. To improve stability, representativeness, and acceptability, surveillance staff should not be engaged in supplemental work, and reports from private sector must be ensured. Supportive supervision and regular trainings should be carried out regarding reporting formats, guidelines, and timely epidemiological investigations to improve timeliness, data quality, and simplicity

Cost-effectiveness and budget impact analysis of facility-based screening and treatment of hepatitis C in Punjab state of India

10.1136/bmjopen-2020-042280BMJ Open112e04228

Real-world cost-effectiveness of pan-genotypic Sofosbuvir-Velpatasvir combination versus genotype dependent directly acting anti-viral drugs for treatment of hepatitis C patients in the universal coverage scheme of Punjab state in India.

BackgroundWe undertook this study to assess the incremental cost per quality adjusted life year (QALY) gained with the use of pan-genotypic sofosbuvir (SOF) + velpatasvir (VEL) for HCV patients, as compared to the current treatment regimen under the universal free treatment scheme in Punjab state.MethodologyA Markov model depicting natural history of HCV was developed to simulate the progression of disease. Three scenarios were compared: I (Current Regimen)-use of SOF + daclatasvir (DCV) for non-cirrhotic patients and ledipasvir (LDV) or DCV with SOF ± ribavirin (RBV) according to the genotype for cirrhotic patients; II-use of SOF + DCV for non-cirrhotic patients and use of SOF+VEL for compensated cirrhotic patients (with RBV in decompensated cirrhosis patients) and III-use of SOF+VEL for both non-cirrhotic and compensated cirrhotic patients (with RBV in decompensated cirrhosis patients). The lifetime costs, life-years and QALYs were assessed for each scenario, using a societal perspective. All the future costs and health outcomes were discounted at an annual rate of 3%. Finally, the incremental cost per QALY gained was computed for each of scenario II and III, as compared to scenario I and for scenario III as compared to II. In addition, we evaluated the lifetime costs and QALYs among HCV patients for each of scenario I, II and III against the counterfactual of 'no universal free treatment scheme' scenario which involves patients purchasing care in routine setting of from public and private sector.ResultsEach of the scenarios I, II and III dominate over the no universal free treatment scheme scenario, i.e. have greater QALYs and lesser costs. The use of SOF+VEL only for cirrhotic patients (scenario II) increases QALYs by 0.28 (0.03 to 0.71) per person, and decreases the cost by ₹ 5,946 (₹ 1,198 to ₹ 14,174) per patient, when compared to scenario I. Compared to scenario I, scenario III leads to an increase in QALYs by 0.44 (0.14 to 1.01) per person, and is cost-neutral. While the mean cost difference between scenario III and I is-₹ 2,676 per patient, it ranges from a cost saving of ₹ 14,835 to incurring an extra cost of ₹ 3,456 per patient. For scenario III as compared II, QALYs increase by 0.16 (0.03 to 0.36) per person as well as costs by ₹ 3,086 per patient which ranges from a cost saving of ₹ 1,264 to incurring an extra cost of ₹ 6,344. Shift to scenario II and III increases the program budget by 5.5% and 60% respectively.ConclusionOverall, the use of SOF+VEL is highly recommended for the treatment of HCV infection. In comparison to the current practice (scenario I), scenario II is a dominant option. Scenario III is cost-effective as compared to scenario II at a threshold of one-time GDP per capita. If budget is an important constraint, velpatasvir should be given to HCV infected cirrhotic patients. However, if no budget constraint, universal use of velpatasvir for HCV treatment is recommended

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

Abstract Introduction Febrile illnesses (FI) represent a typical spectrum of diseases in low-resource settings, either in isolation or with other common symptoms. They contribute substantially to morbidity and mortality in India. The primary objective was to study the burden of FI based on Integrated Disease Surveillance Programme (IDSP) data in Punjab, analyze geospatial and temporal trends and patterns, and identify the potential hotspots for effective intervention. Methods A retrospective ecological study used the district-level IDSP reports between 2012 and 2019. Diseases responsible for FI on a large scale, like Dengue, Chikungunya, Malaria (Plasmodium Falciparum, P. Vivax), Enteric fever, and Pyrexia of Unknown Origin (PUO), were included in the analysis. The digital map of Punjab was obtained from GitHub. Spatial autocorrelation and cluster analysis were done using Moran’s I and Getis-Ord G* to determine hotspots of FI using the incidence and crude disease numbers reported under IDSP. Further, negative binomial regression was used to determine the association between Spatio-temporal and population variables per the census 2011. Stable hotspots were depicted using heat maps generated from district-wise yearly data. Results PUO was the highest reported FI. We observed a rising trend in the incidence of Dengue, Chikungunya, and Enteric fever, which depicted occasional spikes during the study period. FI expressed significant inter-district variations and clustering during the start of the study period, with more dispersion in the latter part of the study period. P.Vivax malaria depicted stable hotspots in southern districts of Punjab. In contrast, P. Falciparum malaria, Chikungunya, and PUO expressed no spatial patterns. Enteric Fever incidence was high in central and northeastern districts but depicted no stable spatial patterns. Certain districts were common incidence hotspots for multiple diseases. The number of cases in each district has shown over-dispersion for each disease and has little dependence on population, gender, or residence as per regression analysis. Conclusions The study demonstrates that information obtained through IDSP can describe the spatial epidemiology of FI at crude spatial scales and drive concerted efforts against FI by identifying actionable points