RESUMEN
Introduction The burden of non-communicable diseases (NCDs) is fast changing across the world, especially in the context of rapid urbanization, adoption of Western lifestyles, and an aging multi-morbid population. Over the last three decades, India has undergone a significant demographic and socioeconomic transition. For effective targeting of health system resources and services, it is essential to understand how the prevalence of NCDs varies among population groups across India. We set out to understand the distribution of NCDs and co-morbidities in urban and rural West Bengal. Methods As part of a service improvement project, data was collected from four urban and four rural community-based clinics across West Bengal, India. The reason for visiting the healthcare center was recorded as the primary diagnosis and co-morbidities were recorded per the Elixhauser comorbidity scoring criteria. Associations between all the demographic variables and NCDs were studied using the Poisson regression model and multivariate analysis. Demographic profile, co-morbidities, and Elixhauser comorbidity index were expressed as frequency (%), mean (standard deviation, SD), or median (interquartile range, IQR) as appropriate. Results We obtained data from 1244 patients of which 886 (71%) were from urban areas and 358 (29%) were from rural areas. Patients were mostly female (61%) and had a mean (SD) age of 53 (11) years. There was a positive correlation between living in an urban residence and age, body mass index (BMI), hypertension, cardiovascular disease (CVD), and respiratory disease. There was a positive correlation between CVD and age, male sex, living in an urban residence, and hypertension but did not correlate positively with diabetes. BMI positively correlated with living in an urban residence, hypertension, diabetes, and musculoskeletal disorders. We observed a significantly higher prevalence of musculoskeletal (p=0.002) and psychological diseases (p<0.001) in the rural population, while the prevalence of hypertension (p<0.001) and respiratory diseases among the participants living in urban areas was higher (p<0.001). There was no statistically significant difference in the prevalence of diabetes between urban and rural areas (p=0.38). In the multivariable analyses, we observed that increased age, being overweight, and living in urban areas were associated with hypertension (prevalence ratio (PR): 1.40, 1.30, and 1.30, respectively; all p-values <0.05). An interaction between sex and living area was associated with a lower prevalence of musculoskeletal diseases (PR: 0.34; 95%CI: 0.18-0.66), i.e., musculoskeletal diseases were less prevalent in males living in urban areas (p=0.002). Conclusion There is a rise in multimorbidity with changing demographic patterns and a narrowing of the urban-rural gap in disease distribution. More investment is required in risk factor prevention, screening, and treatment, with greater accessibility of healthcare resources for those in rural areas. Further work needs to be done to study the trends and distribution of NCDs in West Bengal to inform healthcare policy.
RESUMEN
PURPOSE: Respiratory conditions remain a source of morbidity globally. As such, this study aimed to explore factors associated with the development of airflow obstruction (AFO) in a rural Indian setting and, using spirometry, study whether underweight is linked to AFO. METHODS: Patients > 35 years old attending a rural clinic in West Bengal, India, took a structured questionnaire, had their body mass index (BMI) measured, and had spirometry performed by an ancillary health care worker. RESULTS: In total, 416 patients completed the study; spirometry was acceptable for analysis of forced expiratory volume in 1 second in 286 cases (69%); 16% were noted to exhibit AFO. Factors associated with AFO were: increasing age (95% confidence interval (CI) 0.004-0.011; P = 0.005), smoking history (95% CI 0.07-0.174; P = 0.006), male gender (95% CI 0.19-0.47; P = 0.012), reduced BMI (95% CI 0.19-0.65; P = 0.02), and occupation (95% CI 0.12-0.84; P = 0.08). The mean BMI in males who currently smoked (n = 60; 19.29 kg/m(2); standard deviation [SD] 3.46) was significantly lower than in male never smokers (n = 33; 21.15 kg/m(2) SD 3.38; P < 0.001). AFO was observed in 27% of subjects with a BMI <18.5 kg/m(2), falling to 13% with a BMI ≥18.5 kg/m(2) (P = 0.013). AFO was observed in 11% of housewives, 22% of farm laborers, and 31% of cotton/jute workers (P = 0.035). CONCLUSION: In a rural Indian setting, AFO was related to advancing age, current or previous smoking, male gender, reduced BMI, and occupation. The data also suggest that being under-weight is linked with AFO and that a mechanistic relationship exists between low body weight, smoking tobacco, and development of AFO.