Forty years (1980-2019) of visceral leishmaniasis in Nepal: trends and elimination challenges.

BACKGROUND: Visceral leishmaniasis (VL) is an important re-emerging neglected tropical disease associated with poverty. Despite the elimination initiative started in 2005, VL cases have been expanding into geographic areas in Nepal. The present study aims at exploring the trends of VL from 1980 to 2019. METHODS: This retrospective analysis covers 40 y of VL cases reported by the Epidemiology Diseases Control Division, Nepal. Subgroup analyses for annual incidence were performed by age, sex, seasons, districts and provinces, and VL cases were visualized on in-country maps. RESULTS: A total of 34 564 cases and 584 deaths of VL were reported during 1980-2019. VL persistently increased until 2006 and was reported from all seven provinces of the country. The highest number of confirmed cases (n=2229) was reported in 2003 and the lowest (n=60) in 1983. VL cases expanded from 12 to 23 endemic districts. The key components of the VL elimination program are early diagnosis; enhanced surveillance; integrated vector management; social mobilization; research and treatment. CONCLUSIONS: Expansion of VL towards the hilly and mountain regions of Nepal has posed challenges to the elimination program. Urgent VL control measures are required to achieve the elimination goals.

Psychological Status of Nepalese Young Adults During The End of First Wave of COVID-19 Pandemic.

BACKGROUND: Psychological problems increased during the period of COVID-19. Lockdown" is adopted in many countries of the world. It has also been seen that COVID-19 has led not only to an increase of infection and death but also vast change in the lifestyles of every individual especially in young adults causing various mental health issues. However, in Nepal, there are limited studies to address this issue. The main objective of this study is to generate evidence on the prevalence of symptoms of post-traumatic stress disorder, anxiety, and depression among young adults and the factors contributing to these outcomes in Nepal. METHODS: Cross-sectional methods were employed using an online structured questionnaire in January 2021, among 1229 participants. Three logistic regression models were performed to examine the significant COVID-19 factors. RESULTS: The prevalence of Depression, Anxiety and post-traumatic stress disorder related symptoms in this study were 255(20.4%), 240(19.2%)and 162(13.2%) respectively relate.COVID-19 diagnosis, COVID-related stress and exposure was significantly related to depression. Similarly, COVID-19 diagnosis, change in income during COVID-19, being exposed to 4 or more exposure factors had high chances of getting anxiety. Also, income change during COVID-19 and stressors during COVID-19 were related to post-traumatic stress disorder. CONCLUSIONS: This research shows that various COVID-19 related factors have contributed to the high prevalence of these outcomes.

Spatial Variations and Determinants of Anemia among Under-five Children in Nepal, DHS (2006-2016).

Anemia among under-five children is the major health problem in Nepal. The lack of nutritional supplementation and lack of healthcare facilities are influential factors of anemia. Thus, the main objective of this study is to explore spatial variations and determinants of anemia among under-five children in Nepal. Nepal Demographic and Health Survey (NDHS) data from 2006 to 2016 were used in this study, which includes: household and individual-level data of 8555 under-five children, whose anemia was measured. In addition, a total of 260 (2006), 281 (2011), and 383 DHS clusters (2016) were taken in consideration for spatial analysis. The overall prevalence of anemia was 48.9%, 46.4%, and 52.2% in 2006, 2011, and 2016 respectively. The spatial analysis revealed a nonrandom spatial distribution, where statistically significant hotspots and coldspots were detected in different parts of the country. The results also identified mother's age, mother's educational level, socioeconomic status of household, number of under-5 children, household size, birth weight, underweight, stunting, diarrhea, and fever as associated factors of anemia among under-5 children. These findings may provide assistance to concerned health officials in adopting anemia-related programs and policies to address the anemia problems that plague Nepalese children under the age of five.

Empirical dynamic modeling of the association between ambient PM2.5 and under-five mortality across 2851 counties in Mainland China, 1999-2012.

BACKGROUND: Ambient fine particulate matter (PM2.5) pollution has been associated with mortality from various diseases, however, its association with under-five mortality rate (U5MR) has remained largely unknown. METHODS: Based on the U5MR data across 2851 counties in Mainland China from 1999 to 2012, we employed approximate Bayesian latent Gaussian models to assess the association between ambient PM2.5 and U5MR at the county level for the whole nation and sub-regions. GDP growth rate, normalized difference vegetation index (NDVI), temperature, and night-time light were included as covariates using a smoothing function. We further implemented an empirical dynamic model (EDM) to explore the potential causal relationship between PM2.5 and U5MR. RESULTS: We observed a declining trend in U5MR in most counties throughout the study period. Spatial heterogeneity in U5MR was observed. Nationwide analysis suggested that each 10 µg/m3 increase in annual concentration of PM2.5 was associated with an increase of 1.2 (95% CI: 1.0 - 1.3) per 1000 live births in U5MR. Regional analyses showed that the strongest positive association was located in the Northeastern part of China [1.8 (95% CI: 1.4 - 2.1)]. The EDM showed a significant causal association between PM2.5 and U5MR, with an embedding dimension of 5 and 7, and nonlinear values θ of 4 and 6, respectively. CONCLUSION: China exhibited a downward trend in U5MR from 1999 to 2012, with spatial heterogeneity observed across the country. Our analysis reveals a positive association between PM2.5 and U5MR, which may support a causal relationship.

Ambient air pollution exposure associated with glucose homeostasis during pregnancy and gestational diabetes mellitus.

BACKGROUND: To investigate the effects of air pollution exposure during pregnancy on the indicators of glucose homeostasis and gestational diabetes mellitus (GDM). METHODS: We conducted a birth cohort study in Foshan, China during 2015-2019. Oral glucose tolerance test (OGTT) was administered to each participant during pregnancy. GDM was defined according to the International Association of Diabetes and Pregnancy Study Groups criteria (IADPSG). Air pollutant (fine particulate matter (PM2.5), particulate matter with an aerodynamic diameter of 10 µm or less (PM10), sulfate dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3)) concentrations from the air monitoring stations in Foshan were used to estimate individual air pollutant exposure during the first two trimesters. Linear and logistic regression models were employed to estimate the associations between air pollution exposure during the first two trimesters and OGTT glucose levels and GDM. RESULTS: Of 12,842 pregnant women, 3055 (23.8%) had GDM. A 10 µg/m3 increase in PM2.5, PM10 and SO2 during trimester 1, trimester 2 and two trimesters were associated with 0.07 mmol/L to 0.29 mmol/L increment in OGTT-fasting glucose levels in single-pollutant model. A 10 µg/m3 increase in NO2 and O3 during two trimesters were associated with 0.15 mmol/L and 0.12 mmol/L decrease in OGTT-fasting glucose in single-pollutant model. However, no significant or weaker effects of O3 during two trimesters on OGTT-fasting glucose were observed in two-pollutant models. Moreover, exposure to PM2.5, PM10 and SO2 were associated with increased risk of GDM in both single- and two-pollutant models. CONCLUSIONS: Our study suggests PM2.5, PM10 and SO2 exposure during the first two trimesters might increase the risk of GDM.

The mediation effect of maternal glucose on the association between ambient air pollution and birth weight in Foshan, China.

Maternal blood glucose level is associated with fetal growth, therefore, its role in the associations between air pollution and birth weight deserves investigation. We examined the mediation effect of maternal blood glucose on the associations between maternal air pollution exposure and birth weight. A total of 10,904 pregnant women in Foshan, China during 2015-2019 were recruited. Oral glucose tolerance test (OGTT) was administered to each participant after late trimester 2. Air pollution data at the monitoring stations in residential districts was used to estimate exposures of each participant during trimester 1 and trimester 2. Mixed-effects linear models were used to estimate the associations between air pollution and birth weight. After controlling for ten covariates, the direct effect of PM2.5 and SO2 (each 10 µg/m3 increment) on birth weight was -15.7 g (95% CI: -29.4, -4.8 g) and -83.6 g (95% CI: -134.8, -33.0 g) during trimester 1. The indirect effect of PM2.5 and SO2 (each 10 µg/m3 increment) on birth weight by increasing maternal fasting glucose level was 6.6 g (95% CI: 4.6, 9.1 g) and 22.0 g (95% CI: 4.1, 44.0 g) during trimester 1. Our findings suggest that air pollution might affect the birth weight through direct and indirect pathway, and the indirect effect might be mediated by maternal blood glucose.

How longer can people live by achieving the daily ambient fine particulate pollution standards in the Pearl River Delta region, China?

BACKGROUND: Previous research has reported the effects of long-term fine particulate matter (PM2.5) pollution on years of life lost (YLL), but these effects may not represent the full impact. This study aims to estimate potential benefits in life time from adhering to daily ambient PM2.5 concentration standards/guidelines. METHODS: This study evaluated the relationship between daily ambient PM2.5 level and YLL using a two-stage approach with generalized additive models and meta-analysis. Potential life expectancy gains were then estimated by presuming that daily PM2.5 levels were in compliance with the Chinese and WHO standards. In addition, the attributable fraction of YLL due to excess PM2.5 exposure was also calculated. RESULTS: During 2013-2016, 459,468 non-accidental deaths were recorded in the six cities of Pearl River Delta, China. Each 10 µg/m3 increment in four-day average (lag03) level of PM2.5 was related to an increment of 13.31 [95% confidence interval (CI): 5.74, 20.87] years of life lost. Implementation of the WHO guidelines might avoid 180,980.83 YLLs (95% CI: 78,116.07, 283,845.60), which corresponded to 0.39 (95% CI: 0.17, 0.62) years of increased life time per death. Additionally, an estimated 0.15% (95% CI: 0.06%, 0.23%) or 2.04% (95% CI: 0.88%, 3.20%) of YLLs could be attributed to PM2.5 exposures higher than the Chinese or WHO guidelines, respectively. CONCLUSIONS: This study suggests that people might live longer by controlling daily PM2.5 concentration and highlights the need to adopt stricter standards in China.

Maternal air pollution exposure associated with risk of congenital heart defect in pre-pregnancy overweighted women.

OBJECTIVES: Prenatal exposure to air pollutant has been associated with congenital heart defect (CHD). However, no study has investigated this effect in pre-pregnancy overweighted women. This study aimed to evaluate gestational exposure to particulate pollutant (PM2.5) and gaseous air pollutants (O3 and NO2) on the risk of CHD, and explore the potential effect modifiers including maternal age, pre-pregnancy BMI and pregestational diseases. METHODS: In this birth cohort study, a total of 63,213 pregnant women in Foshan, China were initially recruited and followed from their first hospital visit for pregnancy to delivery during 2015-2019. CHD cases were confirmed by the records in hospital- and population- based birth defect surveillance systems. Air pollutant exposures were estimated by the daily concentrations measured in air monitoring stations in each participant's residential county. Mixed-effects regression models, adjusted for potential confounding factors were applied to estimate the associations between air pollutant and CHD during the first three months of the pregnancy. RESULTS: A total of 985 (1.6%) newborns were identified as CHD cases. For each 10 µg/m3 increase in ambient O3 during the 1st month, the OR values for CHD were 1.03 (95% CI: 0.94, 1.13) in pre-pregnancy normal weighted women and 1.24 (95% CI: 1.01, 1.53) in pre-pregnancy overweighted women. For each 10 µg/m3 increase in NO2 during the 3rd month, the OR values for CHD were 1.09 (95% CI: 1.01, 1.18) in pre-pregnancy normal weighted women and 1.27 (95% CI: 1.07, 1.51) in pre-pregnancy overweighted women. No significant associations were found between PM2.5 exposure and CHD in our analysis. CONCLUSIONS: This study demonstrates that gaseous air pollutants (O3 and NO2) exposure during the cardiac embryogenesis period is associated with an increased risk of CHD, particularly for pre-pregnancy overweighted women.

Potential gains in life expectancy by attaining daily ambient fine particulate matter pollution standards in mainland China: A modeling study based on nationwide data.

BACKGROUND: Ambient fine particulate matter pollution (PM2.5) is one leading cause of disease burden, but no study has quantified the association between daily PM2.5 exposure and life expectancy. We aimed to assess the potential benefits in life expectancy by attaining the daily PM2.5 standards in 72 cities of China during 2013-2016. METHODS AND FINDINGS: We applied a two-stage approach for the analysis. At the first stage, we used a generalized additive model (GAM) with a Gaussian link to examine the city-specific short-term association between daily PM2.5 and years of life lost (YLL); at the second stage, a random-effects meta-analysis was used to generate the regional and national estimations. We further estimated the potential gains in life expectancy (PGLE) by assuming that ambient PM2.5 has met the Chinese National Ambient Air Quality Standard (NAAQS, 75 µg/m3) or the ambient air quality guideline (AQG) of the World Health Organization (WHO) (25 µg/m3). We also calculated the attributable fraction (AF), which denoted the proportion of YLL attributable to a higher-than-standards daily mean PM2.5 concentration. During the period from January 18, 2013 to December 31, 2016, we recorded 1,226,849 nonaccidental deaths in the study area. We observed significant associations between daily PM2.5 and YLL: each 10 µg/m3 increase in three-day-averaged (lag02) PM2.5 concentrations corresponded to an increment of 0.43 years of life lost (95% CI: 0.29-0.57). We estimated that 168,065.18 (95% CI: 114,144.91-221,985.45) and 68,684.95 (95% CI: 46,648.79-90,721.11) years of life lost can be avoided by achieving WHO's AQG and Chinese NAAQS in the study area, which corresponded to 0.14 (95% CI: 0.09-0.18) and 0.06 (95% CI: 0.04-0.07) years of gain in life expectancy for each death in these cities. We observed differential regional estimates across the 7 regions, with the highest gains in the Northwest region (0.28 years of gain [95% CI: 0.06-0.49]) and the lowest in the North region (0.08 [95% CI: 0.02-0.15]). Furthermore, using WHO's AQG and Chinese NAAQS as the references, we estimated that 1.00% (95% CI: 0.68%-1.32%) and 0.41% (95% CI: 0.28%-0.54%) of YLL could be attributable to the PM2.5 exposure at the national level. Findings from this study were mainly limited by the unavailability of data on individual PM2.5 exposure. CONCLUSIONS: This study indicates that significantly longer life expectancy could be achieved by a reduction in the ambient PM2.5 concentrations. It also highlights the need to formulate a stricter ambient PM2.5 standard at both national and regional levels of China to protect the population's health.

Mapping Environmental Suitability of Scrub Typhus in Nepal Using MaxEnt and Random Forest Models.

Being a globally emerging mite-borne zoonotic disease, scrub typhus is a serious public health concern in Nepal. Mapping environmental suitability and quantifying the human population under risk of the disease is important for prevention and control efforts. In this study, we model and map the environmental suitability of scrub typhus using the ecological niche approach, machine learning modeling techniques, and report locations of scrub typhus along with several climatic, topographic, Normalized Difference Vegetation Index (NDVI), and proximity explanatory variables and estimated population under the risk of disease at a national level. Both MaxEnt and RF technique results reveal robust predictive power with test The area under curve (AUC) and true skill statistics (TSS) of above 0.8 and 0.6, respectively. Spatial prediction reveals that environmentally suitable areas of scrub typhus are widely distributed across the country particularly in the low-land Tarai and less elevated river valleys. We found that areas close to agricultural land with gentle slopes have higher suitability of scrub typhus occurrence. Despite several speculations on the association between scrub typhus and proximity to earthquake epicenters, we did not find a significant role of proximity to earthquake epicenters in the distribution of scrub typhus in Nepal. About 43% of the population living in highly suitable areas for scrub typhus are at higher risk of infection, followed by 29% living in suitable areas of moderate-risk, and about 22% living in moderately suitable areas of lower risk. These findings could be useful in selecting priority areas for surveillance and control strategies effectively.

Hourly associations between ambient air pollution and emergency ambulance calls in one central Chinese city: Implications for hourly air quality standards.

BACKGROUND: Most studies on the short-term health effects of air pollution have been conducted on a daily time scale, while hourly associations remain unclear. METHODS: We collected the hourly data of emergency ambulance calls (EACs), ambient air pollution, and meteorological variables from 2014 to 2016 in Luoyang, a central Chinese city in Henan Province. We used a generalized additive model to estimate the hourly effects of ambient air pollutants (PM2.5, PM10, SO2, and NO2) on EACs for all natural causes and cardiovascular and respiratory morbidity, with adjustment for potential confounding factors. We further examined the effect modification by temperature, relative humidity, wind speed, and atmospheric pressure using stratified analyses. RESULTS: In the single-pollutant models, PM2.5, PM10, SO2, and NO2 were associated with an immediate increase in all-cause morbidity at 0, 0, 12, 10 h, separately, after exposure to these pollutants (excess risks: 0.19% (95% confidence interval (CI): 0.03%, 0.35%), 0.13% (95% CI: 0.02%, 0.24%), 0.28% (95% CI: 0.01%, 0.54%) and 0.52% (95% CI: 0.06%, 0.99%), respectively). These effects remained generally stable in two-pollutant models. SO2 and NO2 were significantly associated with an immediate increase in risk of cardiovascular morbidity, but the effects on respiratory morbidity were relatively more delayed. The stratified analyses suggested that temperature could modify the association between PM2.5 and EACs, humidity and atmospheric pressure could modify the association between SO2 and EACs. CONCLUSIONS: Our study provides new evidence that higher concentrations of PM2.5, PM10, SO2, and NO2 may have transiently acute effects on all-cause morbidity and subacute effects on respiratory morbidity. SO2 and NO2 may also have immediate effects on cardiovascular morbidity. Findings of this study have important implications for the formation of hourly air quality standards.

Ambient fine particulate matter and ozone higher than certain thresholds associated with myopia in the elderly aged 50 years and above.

Although myopia has been largely ignored among the elderly population, there is an increased risk of myopia with advancing age. Ambient air pollution is one potential contributor to vision impairments, but few epidemiological studies have demonstrated such an association. This cross-sectional survey collected the information of 33,626 subjects aged ≥50 years in six developing countries during 2007-2010. Myopia was identified based on questions related to symptoms of myopia. The annual concentrations of fine particulate matter (PM2.5) and ozone (O3) were estimated with the satellite data and chemical transport model. We examined the associations between the two pollutants and myopia using mixed-effect Poisson regression models with robust variance estimation (sandwich estimation). We observed J-shaped associations between the two pollutants and myopia, and identified 12 and 54 µg/m3 as the threshold concentrations. The adjusted prevalence ratio was 1.12 (95% CI: 1.05, 1.21) and 1.26 (95% CI: 1.14, 1.38) for each standard deviation (SD) increase in PM2.5 and O3 concentrations above their threshold, respectively. In addition, the interaction analysis suggested a synergistic interaction of these two pollutants on myopia in the additive model, with a synergistic index of 1.81 (Bootstrapping 95% CI: 0.92, 4.94). Our results indicate that long-term exposures to PM2.5 and O3 might be important environmental risk factors of myopia in the elderly, and suggest that more efforts should be taken to reduce airborne PM2.5 and O3 levels to protect vision health.

Applying the concept of "number needed to treat" to the formulation of daily ambient air quality standards.

The World Health Organization sets up the Ambient Air Quality Guidelines mainly based on short-term and long-term health effects of air pollution. Previous studies, however, have generally revealed a non-threshold concentration-response relationship between air pollution and health, making it difficult to determine a concentration, below which no obvious health effects can be observed. Here we proposed a novel approach based on the concept of "number needed to treat", specifically, we calculated the reduction in air pollution concentrations needed to avoid one death corresponding to different hypothetical concentration standards; the one with the smallest value would be the most practical concentration standard. As an example, we applied this approach to the daily standard of ambient PM2.5 (particulate matter with aerodynamic diameter ≤2.5 µm) in four Chinese cities. The calculation was based on the association between daily mortality and ambient PM2.5, which was examined by a generalized additive model with adjustment of important covariates. Significant associations were observed between PM2.5 and mortality. Our analyses suggested that it is appropriate to have 50 µg/m3 as the daily standard of ambient PM2.5 for the study area, compared to the current standard of which were directly adopted from the national standard of 75 µg/m3. This novel approach should be considered when planning and/or revising the ambient air quality guidelines/standards.

Modeling the spatially varying risk factors of dengue fever in Jhapa district, Nepal, using the semi-parametric geographically weighted regression model.

Dengue fever is expanding rapidly in many tropical and subtropical countries since the last few decades. However, due to limited research, little is known about the spatial patterns and associated risk factors on a local scale particularly in the newly emerged areas. In this study, we explored spatial patterns and evaluated associated potential environmental and socioeconomic risk factors in the distribution of dengue fever incidence in Jhapa district, Nepal. Global and local Moran's I were used to assess global and local clustering patterns of the disease. The ordinary least square (OLS), geographically weighted regression (GWR), and semi-parametric geographically weighted regression (s-GWR) models were compared to describe spatial relationship of potential environmental and socioeconomic risk factors with dengue incidence. Our result revealed heterogeneous and highly clustered distribution of dengue incidence in Jhapa district during the study period. The s-GWR model best explained the spatial association of potential risk factors with dengue incidence and was used to produce the predictive map. The statistical relationship between dengue incidence and proportion of urban area, proximity to road, and population density varied significantly among the wards while the associations of land surface temperature (LST) and normalized difference vegetation index (NDVI) remained constant spatially showing importance of mixed geographical modeling approach (s-GWR) in the spatial distribution of dengue fever. This finding could be used in the formulation and execution of evidence-based dengue control and management program to allocate scare resources locally.

Spatiotemporal Distribution and Geospatial Diffusion Patterns of 2013 Dengue Outbreak in Jhapa District, Nepal.

This study describes spatiotemporal distribution and geospatial diffusion patterns of dengue outbreak of 2013 in Jhapa district, Nepal. Laboratory-confirmed dengue cases were collected from the District Public Health Office, Government of Nepal. Choropleth mapping technique, Global Moran's Index, SaTScan, and standard deviational ellipse were used to map and quantify the outbreak dynamics. The results revealed heterogeneous distribution and globally autocorrelated patterns. Local clusters were observed in 3 major urban centers. The standard deviational ellipse demonstrated the outbreak occurred from the east and diffused to the west along the east-west highway in different weeks. The results of this study could be useful to public health authorities to plan and execute dengue control strategies.

Present and Future of Dengue Fever in Nepal: Mapping Climatic Suitability by Ecological Niche Model.

Both the number of cases of dengue fever and the areas of outbreaks within Nepal have increased significantly in recent years. Further expansion and range shift is expected in the future due to global climate change and other associated factors. However, due to limited spatially-explicit research in Nepal, there is poor understanding about the present spatial distribution patterns of dengue risk areas and the potential range shift due to future climate change. In this context, it is crucial to assess and map dengue fever risk areas in Nepal. Here, we used reported dengue cases and a set of bioclimatic variables on the MaxEnt ecological niche modeling approach to model the climatic niche and map present and future (2050s and 2070s) climatically suitable areas under different representative concentration pathways (RCP2.6, RCP6.0 and RCP8.5). Simulation-based estimates suggest that climatically suitable areas for dengue fever are presently distributed throughout the lowland Tarai from east to west and in river valleys at lower elevations. Under the different climate change scenarios, these areas will be slightly shifted towards higher elevation with varied magnitude and spatial patterns. Population exposed to climatically suitable areas of dengue fever in Nepal is anticipated to further increase in both 2050s and 2070s on all the assumed emission scenarios. These findings could be instrumental to plan and execute the strategic interventions for controlling dengue fever in Nepal.

Spatiotemporal analysis of dengue fever in Nepal from 2010 to 2014.

BACKGROUND: Due to recent emergence, dengue is becoming one of the major public health problems in Nepal. The numbers of reported dengue cases in general and the area with reported dengue cases are both continuously increasing in recent years. However, spatiotemporal patterns and clusters of dengue have not been investigated yet. This study aims to fill this gap by analyzing spatiotemporal patterns based on monthly surveillance data aggregated at district. METHODS: Dengue cases from 2010 to 2014 at district level were collected from the Nepal government's health and mapping agencies respectively. GeoDa software was used to map crude incidence, excess hazard and spatially smoothed incidence. Cluster analysis was performed in SaTScan software to explore spatiotemporal clusters of dengue during the above-mentioned time period. RESULTS: Spatiotemporal distribution of dengue fever in Nepal from 2010 to 2014 was mapped at district level in terms of crude incidence, excess risk and spatially smoothed incidence. Results show that the distribution of dengue fever was not random but clustered in space and time. Chitwan district was identified as the most likely cluster and Jhapa district was the first secondary cluster in both spatial and spatiotemporal scan. July to September of 2010 was identified as a significant temporal cluster. CONCLUSION: This study assessed and mapped for the first time the spatiotemporal pattern of dengue fever in Nepal. Two districts namely Chitwan and Jhapa were found highly affected by dengue fever. The current study also demonstrated the importance of geospatial approach in epidemiological research. The initial result on dengue patterns and risk of this study may assist institutions and policy makers to develop better preventive strategies.