Geospatial analysis on the distributions of tobacco smoking and alcohol drinking in India

Cancer trends and burden among Armed Forces personnel, veterans and their families: Cancer registry data analysis from tertiary care hospital

Background

Cancer incidence is rising across the globe. The incidence and patterns of various cancers among Armed Forces Personnel and Veterans is not known. We did the analysis of registry data maintained at our hospital.

Methods

A retrospective analysis was performed of all patients registered at our hospital cancer registry between 01st January 2017 and 31st December 2019. Patients were registered with unique identification number. Baseline demographics and cancer subtype data were retrieved. Patients with histopathologically proven diagnosis and age ≥18 years were studied. Armed Forces Personnel (AFP) were defined as those who are in active service, and Veterans as those who had retired from service at the time of registration. Patients with Acute and Chronic Leukemias were excluded.

Results

New cases registered were 2023, 2856 and 3057 in year 2017, 2018, 2019 respectively. AFP, Veterans and dependents among them were 9.6%, 17.8%, and 72.6% respectively. Haryana, Uttar Pradesh and Rajasthan represented 55% of all cases with male to female ratio 1.14:1 and median age was 59 years. The median age among AFP was 39 years. Among AFP as well as veterans, Head and Neck cancer was the most common malignancy. Cancer incidence was significantly higher in adults >40 years as compared to <40 years.

Conclusion

Seven percent rise per year of new cases in this cohort is alarming. Tobacco-related cancers were the most common. There is an unmet need to establish a prospective centralized Cancer Registry to better understand the risk factors, outcomes of treatment and strengthen the policy matters.

Nature, pattern, and changes in alcohol consumption among men in India: Insights from NFHS-4 and NFHS-5

The consumption pattern of alcohol varies between cultures and different communities. Moreover, alcohol consumption pattern depends on age, religion, education, type of drink, and other socio-economic parameters. Alcohol use has reportedly declined in most developed counties, but developing countries still witnessed an increasing trend. The study investigated socio-economic drivers, nature, and patterns of alcohol use among adult men in India. We have also investigated the state-level alcohol prevalence in India better to understand the impact of state-level alcohol prohibition policies. We have retrieved the data from the National Family Health survey's fourth and fifth-round, which was conducted in 2015-2016 and 2020-2021, respectively. We used the bi-variate technique to estimate that percentage of men who consume alcohol. Furthermore, a multivariate logistic regression model was applied to assess the association of each background characteristic with alcohol consumption. It is observed that 19 percent of men aged 15 and above currently drink alcohol, including 20 percent in rural and 17 percent in urban areas. The consumption of tadi/madi/country liquor/homemade liquor was high in rural areas than in urban areas in both rounds of the survey. Additionally, beer or wine consumption is higher in urban areas than in rural areas in NFHS-4 and NFHS-5. Among the 35-49 years of age group, around 6 and 8 times more likely to consume alcohol in NFHS-4 and NFHS-5, respectively. Rural men were 18 and one percent less likely to consume alcohol in NFHS-4 & 5 compared to urban (OR: 0.82 and 0.99 in NFHS-4 & 5 respectively). Despite an overall reduction in alcohol consumption among men, lowering alcohol consumption remains the main priority of policymakers. There is a need to target the most vulnerable socio-economic segments where alcohol consumption is still a problem, one of the primary reasons for violence against women.

Epidemiology, Hot Spots, and Sociodemographic Risk Factors of Alcohol Consumption in Indian Men and Women: Analysis of National Family Health Survey-4 (2015-16), a Nationally Representative Cross-Sectional Study

Objectives: To map the alcohol hot spots and understand the Sociodemographic Indices (SDI) affecting alcohol consumption in Indian men and women.

Methods: Data from National Family Health Survey-4 carried out from 2015 to 2016 with a sample size of 103,411 men and 699,686 women were used for Geographic Information System mapping, and hot spot identification by spatial statistics (Getis-Ord Gi^*). Bivariate analyses and multiple logistic regressions were used to analyze SDI.

Results: India has three major alcohol hot spots: (1) North-East (NE) states, (2) Eastern Peninsular states formed by Chhattisgarh, Odisha, Jharkhand, and Telangana, and (3) Southern states of Tamil Nadu and Kerala. Hot spot analysis strongly correlated with region-wise analysis of SDI. Respondents who consumed tobacco have higher odds (men adjusted odds ratio [aOR]: 5.42; women aOR: 4.30) of consuming alcohol. Except for religion and social category, other socioeconomic factors have a low to moderate effect on alcohol consumption.

Conclusions: Hot spots and high-risk districts of alcohol consumption identified in this study can guide public health policies for targeted intervention. Alcohol use is at the discretion of individual states and union territories, and stringent anti-alcohol policies strictly enforced across India are the keys to control alcohol use.

Spatial Pattern and Determinants of Tobacco Use Among Females in India: Evidence From a Nationally Representative Survey

INTRODUCTION

Tobacco use has been steadily increasing among the females in developing countries. It has led to rise in tobacco-related morbidity and mortality among females. Knowing the geographic distribution of the habit is essential to identify high-priority areas and direct the healthcare intervention. Hence, this study was done to assess the spatial patterns and determinants of tobacco consumption among females in India.

AIMS AND METHODS

Univariate and bivariate Moran's I statistic and local indicators for spatial association maps were generated to determine the spatial clustering of tobacco consumption (smoked and smokeless form). Ordinary least-square regression, spatial-lag and spatial-error models were performed to assess the determinants. Poverty (belonging to poorest and poorer quintile of wealth index), illiteracy (no formal education), marital status, ST population, tobacco use by family members, and alcohol use were the explanatory variables.

RESULTS

Univariate Moran's I was .691 suggesting positive spatial autocorrelation. High-prevalence clustering (hotspots) was maximum in the central, eastern, and northeastern states such as Chhattisgarh, Madhya Pradesh, Odisha, Bihar, Manipur, Tripura, Meghalaya, Mizoram, and Assam. This pattern was similar for both smokeless and smoked form. Results of spatial-lag and spatial-error model suggested that alcohol use, scheduled tribes, illiteracy, poverty, marital status, and tobacco use by family members were significant determinants of female tobacco consumption. The coefficient of spatial association was maximum for alcohol use (β = .20, p < .001) followed by widowed/separated/divorced (β = .12, p < .001).

CONCLUSIONS

Tobacco consumption among females in India is spatially clustered. Multisectoral coordination and targeted interventions are required in the geographical hotspots of tobacco consumption.

IMPLICATIONS

This is the first study to explore the geospatial pattern of tobacco consumption among females in India. We found that the pattern of tobacco use among females is spatially clustered in India. Clustering was predominantly found in central, eastern, and northeastern regions of the country. Tribal population in these areas and complementarities between alcohol and tobacco use contributed significantly to the high-prevalence clustering. These findings will be helpful for policymakers and planners to devise specific intervention package targeting the high-risk regions.

Spatial Evolution of Urban Expansion in the Beijing–Tianjin–Hebei Coordinated Development Region

Against the background of coordinated development of the Beijing–Tianjin–Hebei region (BTH), it is of great significance to quantitatively reveal spatiotemporal dynamics of urban expansion for optimizing the layout of urban land across regions. However, the urban expansion characteristics, types and trends, and spatial coevolution (including urban land, GDP, and population) have not been well investigated in the existing research studies. This study presents a new spatial measure that describes the difference of the main trend direction. In addition, we also introduce a new method to classify an urban expansion type based on other scholars. The results show the following: (1) The annual urban expansion area (UEA) in Beijing and Tianjin has been ahead of that in Hebei; the annual urban expansion rate (UER) gradually shifted from the highest in megacities to the highest in counties; the high–high clusters of the UEA presented an evolution from a “seesaw” pattern to a “dumbbell” pattern, while that of the UER moved first from Beijing to Tianjin and eventually to Hebei. (2) Double high speed for both UEA and UER was the main extension type; most cities presented a U-shaped trend. (3) Qinhuangdao has the largest difference between the main trend direction of spatial distribution of urban land, GDP and population; the spatial distribution of GDP is closer to that of urban land than population. (4) The area and proportion of land occupied by urban expansion varied greatly across districts/counties. BTH experienced dramatic urban expansion and has a profound impact on land use. These research results can provide a data basis and empirical reference for territorial spatial planning.

The Potential of Smartphone Apps in Informing Protobacco and Antitobacco Messaging Efforts Among Underserved Communities: Longitudinal Observational Study

Background

People from underserved communities such as those from lower socioeconomic positions or racial and ethnic minority groups are often disproportionately targeted by the tobacco industry, through the relatively high levels of tobacco retail outlets (TROs) located in their neighborhood or protobacco marketing and promotional strategies. It is difficult to capture the smoking behaviors of individuals in actual locations as well as the extent of exposure to tobacco promotional efforts. With the high ownership of smartphones in the United States—when used alongside data sources on TRO locations—apps could potentially improve tobacco control efforts. Health apps could be used to assess individual-level exposure to tobacco marketing, particularly in relation to the locations of TROs as well as locations where they were most likely to smoke. To date, it remains unclear how health apps could be used practically by health promotion organizations to better reach underserved communities in their tobacco control efforts.

Objective

This study aimed to demonstrate how smartphone apps could augment existing data on locations of TROs within underserved communities in Massachusetts and Texas to help inform tobacco control efforts.

Methods

Data for this study were collected from 2 sources: (1) geolocations of TROs from the North American Industry Classification System 2016 and (2) 95 participants (aged 18 to 34 years) from underserved communities who resided in Massachusetts and Texas and took part in an 8-week study using location tracking on their smartphones. We analyzed the data using spatial autocorrelation, optimized hot spot analysis, and fitted power-law distribution to identify the TROs that attracted the most human traffic using mobility data.

Results

Participants reported encountering protobacco messages mostly from store signs and displays and antitobacco messages predominantly through television. In Massachusetts, clusters of TROs (Dorchester Center and Jamaica Plain) and reported smoking behaviors (Dorchester Center, Roxbury Crossing, Lawrence) were found in economically disadvantaged neighborhoods. Despite the widespread distribution of TROs throughout the communities, participants overwhelmingly visited a relatively small number of TROs in Roxbury and Methuen. In Texas, clusters of TROs (Spring, Jersey Village, Bunker Hill Village, Sugar Land, and Missouri City) were found primarily in Houston, whereas clusters of reported smoking behaviors were concentrated in West University Place, Aldine, Jersey Village, Spring, and Baytown.

Conclusions

Smartphone apps could be used to pair geolocation data with self-reported smoking behavior in order to gain a better understanding of how tobacco product marketing and promotion influence smoking behavior within vulnerable communities. Public health officials could take advantage of smartphone data collection capabilities to implement targeted tobacco control efforts in these strategic locations to reach underserved communities in their built environment.

Trends in spatio-temporal dynamics of visceral leishmaniasis cases in a highly-endemic focus of Bihar, India: an investigation based on GIS tools

Background

Visceral leishmaniasis (VL) in Bihar State (India) continues to be endemic, despite the existence of effective treatment and a vector control program to control disease morbidity. A clear understanding of spatio-temporal distribution of VL may improve surveillance and control implementation. This study explored the trends in spatio-temporal dynamics of VL endemicity at a meso-scale level in Vaishali District, based on geographical information systems (GIS) tools and spatial statistical analysis.

Methods

A GIS database was used to integrate the VL case data from the study area between 2009 and 2014. All cases were spatially linked at a meso-scale level. Geospatial techniques, such as GIS-layer overlaying and mapping, were employed to visualize and detect the spatio-temporal patterns of a VL endemic outbreak across the district. The spatial statistic Moran’s I Index (Moran’s I) was used to simultaneously evaluate spatial-correlation between endemic villages and the spatial distribution patterns based on both the village location and the case incidence rate (CIR). Descriptive statistics such as mean, standard error, confidence intervals and percentages were used to summarize the VL case data.

Results

There were 624 endemic villages with 2719 (average 906 cases/year) VL cases during 2012–2014. The Moran’s I revealed a cluster pattern (P < 0.05) of CIR distribution at the meso-scale level. On average, 68 villages were newly-endemic each year. Of which 93.1% of villages’ endemicity were found to have occurred on the peripheries of the previous year endemic villages. The mean CIR of the endemic villages that were peripheral to the following year newly-endemic villages, compared to all endemic villages of the same year, was higher (P < 0.05).

Conclusion

The results show that the VL endemicity of new villages tends to occur on the periphery of villages endemic in the previous year. High-CIR plays a major role in the spatial dispersion of the VL cases between non-endemic and endemic villages. This information can help achieve VL elimination throughout the Indian subcontinent by improving vector control design and implementation in highly-endemic district.

Trends in bidi and cigarette smoking in India from 1998 to 2015, by age, gender and education

Objectives

Smoking of cigarettes or bidis (small, locally manufactured smoked tobacco) in India has likely changed over the last decade. We sought to document trends in smoking prevalence among Indians aged 15–69 years between 1998 and 2015.

Design

Comparison of 3 nationally representative surveys representing 99% of India's population; the Special Fertility and Mortality Survey (1998), the Sample Registration System Baseline Survey (2004) and the Global Adult Tobacco Survey (2010).

Setting

India.

Participants

About 14 million residents from 2.5 million homes, representative of India.

Main outcome measures

Age-standardised smoking prevalence and projected absolute numbers of smokers in 2015. Trends were stratified by type of tobacco smoked, age, gender and education level.

Findings

The age-standardised prevalence of any smoking in men at ages 15–69 years fell from about 27% in 1998 to 24% in 2010, but rose at ages 15–29 years. During this period, cigarette smoking in men became about twofold more prevalent at ages 15–69 years and fourfold more prevalent at ages 15–29 years. By contrast, bidi smoking among men at ages 15–69 years fell modestly. The age-standardised prevalence of any smoking in women at these ages was 2.7% in 2010. The smoking prevalence in women born after 1960 was about half of the prevalence in women born before 1950. By contrast, the intergenerational changes in smoking prevalence in men were much smaller. The absolute numbers of men smoking any type of tobacco at ages 15–69 years rose by about 29 million or 36% in relative terms from 79 million in 1998 to 108 million in 2015. This represents an average increase of about 1.7 million male smokers every year. By 2015, there were roughly equal numbers of men smoking cigarettes or bidis. About 11 million women aged 15–69 smoked in 2015. Among illiterate men, the prevalence of smoking rose (most sharply for cigarettes) but fell modestly among men with grade 10 or more education. The ex-smoking prevalence in men at ages 45–59 years rose modestly but was low: only 5% nationally with about 4 current smokers for every former smoker.

Conclusions

Despite modest decreases in smoking prevalence, the absolute numbers of male smokers aged 15–69 years has increased substantially over the last 15 years. Cigarettes are displacing bidi smoking, most notably among young adult men and illiterate men. Tobacco control policies need to adapt to these changes, most notably with higher taxation on tobacco products, so as to raise the currently low levels of adult smoking cessation.

Trends in bidi and cigarette smoking in India from 1998 to 2015, by age, gender and education

OBJECTIVES

Smoking of cigarettes or bidis (small, locally manufactured smoked tobacco) in India has likely changed over the last decade. We sought to document trends in smoking prevalence among Indians aged 15-69 years between 1998 and 2015.

DESIGN

Comparison of 3 nationally representative surveys representing 99% of India's population; the Special Fertility and Mortality Survey (1998), the Sample Registration System Baseline Survey (2004) and the Global Adult Tobacco Survey (2010).

SETTING

India.

PARTICIPANTS

About 14 million residents from 2.5 million homes, representative of India.

MAIN OUTCOME MEASURES

Age-standardised smoking prevalence and projected absolute numbers of smokers in 2015. Trends were stratified by type of tobacco smoked, age, gender and education level.

FINDINGS

The age-standardised prevalence of any smoking in men at ages 15-69 years fell from about 27% in 1998 to 24% in 2010, but rose at ages 15-29 years. During this period, cigarette smoking in men became about twofold more prevalent at ages 15-69 years and fourfold more prevalent at ages 15-29 years. By contrast, bidi smoking among men at ages 15-69 years fell modestly. The age-standardised prevalence of any smoking in women at these ages was 2.7% in 2010. The smoking prevalence in women born after 1960 was about half of the prevalence in women born before 1950. By contrast, the intergenerational changes in smoking prevalence in men were much smaller. The absolute numbers of men smoking any type of tobacco at ages 15-69 years rose by about 29 million or 36% in relative terms from 79 million in 1998 to 108 million in 2015. This represents an average increase of about 1.7 million male smokers every year. By 2015, there were roughly equal numbers of men smoking cigarettes or bidis. About 11 million women aged 15-69 smoked in 2015. Among illiterate men, the prevalence of smoking rose (most sharply for cigarettes) but fell modestly among men with grade 10 or more education. The ex-smoking prevalence in men at ages 45-59 years rose modestly but was low: only 5% nationally with about 4 current smokers for every former smoker.

CONCLUSIONS

Despite modest decreases in smoking prevalence, the absolute numbers of male smokers aged 15-69 years has increased substantially over the last 15 years. Cigarettes are displacing bidi smoking, most notably among young adult men and illiterate men. Tobacco control policies need to adapt to these changes, most notably with higher taxation on tobacco products, so as to raise the currently low levels of adult smoking cessation.