Editorial Topic : Cardiac Risk Factors Among the Very Poor

Topic: GS-2 Mains (Health) 

Question: What has been traditionally assumed about the prevalence of cardiovascular disease (CVD) risk factors among those living in extreme poverty in low and middle-income countries (LMICs)?


Question : What are some historical reasons supporting the assumption of a low prevalence of CVD risk factors among the very poor?


  • Assumed Low Prevalence:
    • Historically, extreme poverty linked to lower calorie intake, plant-based diet, and higher physical activity, reducing CVD risk.
    • Lifestyle patterns traditionally associated with decreased CVD risk factors.
  • Reality Check:
    • Recent study reveals presence of CVD risk factors (hypertension, diabetes, smoking, obesity, dyslipidaemia) among adults in extreme poverty.
    • Importance of understanding true prevalence for effective health policy and care delivery prioritization.
  • Defining Poverty and CVD Prevalence:
    • Poverty stratified by World Bank income category; extreme poverty <$1.90/day.
    • Prevalence examined across income levels: <$3.20, <$5.50, and >$5.50 per day.
    • Men show higher hypertension and smoking prevalence; income gradient evident for diabetes and obesity.
  • Access to Medicine:
    • Poor lack access to essential medications: low rates of BP-lowering, blood glucose-lowering, and statin use.
    • Hypertension, diabetes, and statin use remain low across poverty levels in low-income countries.
  • Mitigation Initiatives:
    • India’s National Programme for prevention & Control of Cancer, Diabetes, CVD, and Stroke prioritizes screening and treatment.
    • State governments supplement with regional surveillance and intervention programs.
    • Focus on primary healthcare and Universal Health Coverage aligns with 2018 Astana Declaration principles.
  • Way Forward:
    • Urgent need for vigorous public education on smoking hazards, particularly among the poor.
    • Smoking, a major risk factor for diabetes, prevalent among the poor, necessitates targeted educational interventions.
    • Evaluate effectiveness of education initiatives in modifying community behaviors and reducing CVD risk factors.



Editorial Topic : Has poverty really dropped to 5% in India?

GS-3 Mains : Economy : Poverty Estimation Methods:

Question: How do experts adjust poverty lines to account for inflation and global standards, and what implications does this have for poverty estimation?

Question: How does the large population of India affect the process of poverty estimation, and what strategies can be employed to mitigate this challenge?

Poverty Estimation Methods:

  • Estimation based on income or consumption levels assessed by committees like Tendulkar and C Rangarajan.
  • Household income or consumption below the Poverty Line is considered below the poverty line (BPL).

Tendulkar Committee Recommendations:

  • Formed in 2009, focused on shifting poverty estimation from calorie consumption.
  • Proposed uniform poverty line baskets for rural and urban areas and incorporation of private expenditure in education and health estimates.

Importance of Poverty Estimation:

  • Constitutional Requirement: Aligns with India’s promise of an equitable society.
  • Basis for Poverty Elimination: Guides government strategies to alleviate poverty.
  • Evaluating Welfare Schemes: Assists in tracking the impact of government schemes.

Challenges in Poverty Estimation:

  • Population Density: India’s large population complicates estimation.
  • Poverty Line Basket: Identifying constituents challenging due to price variations.
  • Disagreement Among Committees: Conflicting recommendations pose challenges.
  • Lifestyle Differences: Varying eating habits and lifestyle patterns complicate estimation.

Current Poverty Status:

  • NITI Aayog claims less than 5% of Indians live below the poverty line based on HCES 2022-23.
  • Experts use Tendulkar line adjusted for inflation and World Bank’s poverty line, both indicating <5% extreme poverty.

Way Forward:

  • Urgent need for poverty estimation to track government schemes and policies.
  • Addressing challenges like population density and lifestyle differences essential for accurate estimation.
  • Continued reliance on robust estimation methods crucial for guiding poverty alleviation efforts.



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