211 extractive question-and-answer pairs built from Sarvekshana changes in hospitalization, published by iipsindia.ac.in. Every answer is a verbatim span of text the source prints, and each row carries the passage it sits in, its offset in that passage, the source quote, the page and the location in the document, so any row can be checked against the original. 164 of the 211 pairs (77.7%) are explanatory questions and 47 restate a figure. 100.00% of rows pass the corpus quality gate.
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First 10 of 211 rows
| question | answer | context | answer_start | question_type | knowledge_quality_score | source_quote | source_page | source_location | confidence | validation_status |
|---|---|---|---|---|---|---|---|---|---|---|
| What is the purpose of the Ayushman Bharat- Pradhan Mantri Jan Arogya Yojana? | to provide health insurance coverage to poor and vulnerable families | The government of India has launched various initiatives to improve access to healthcare, including the National Health Mission (NHM) and the Ayushman Bharat- Pradhan Mantri Jan Arogya Yojana (PMJAY), which aims to provide health insurance coverage to poor and vulnerable families (National Health Authority, 2022). Despite these efforts, much work remains to be done to ensure that all Indians have access to quality and affordable inpatient care treatment. In that context, monitoring of changes in hospitalisation is seen as a key indicator for assessment of the contribution of the ongoing transformation in the health care delivery system towards ensuring equitable access to health care. Studies assessing the changes in income-related inequalities based on nationally representative data in India remain scarce. | 212 | definition | 1.000 | The government of India has launched various initiatives to improve access to healthcare, including the National Health Mission (NHM) and the Ayushman Bharat- Pradhan Mantri Jan Arogya Yojana (PMJAY), which aims to provide health insurance coverage to poor and vulnerable families (National Health Authority, 2022). | 5 | page=5,block=1 | 0.700 | valid |
| What does the concentration index (CI) assess in a population? | the degree of inequality in the distribution of health outcomes across a population | Further, the concentration index (CI) and concentration curve (CC) were calculated to determine income-related inequality in hospitalisation. The CI is a widely used measure of health inequality that provides a summary of the degree of inequality in the distribution of health outcomes across a population. It is based on the notion of the Lorenz curve, which plots the cumulative distribution of health outcomes against the cumulative distribution of the population. The overall CI measures the degree of deviation of the Lorenz curve from a line of equality, which represents perfect equality in the distribution of health outcomes. It ranges from -1 to +1, with the sign indicating the direction of the relationship and the magnitude reflecting its strength. A value of zero implies complete equality, with no inequality present. | 222 | definition | 1.000 | The CI is a widely used measure of health inequality that provides a summary of the degree of inequality in the distribution of health outcomes across a population. | 7 | page=7,block=7 | 0.700 | |
| What does the Lorenz curve represent in relation to health outcomes? | the cumulative distribution of health outcomes against the cumulative distribution of the population | Further, the concentration index (CI) and concentration curve (CC) were calculated to determine income-related inequality in hospitalisation. The CI is a widely used measure of health inequality that provides a summary of the degree of inequality in the distribution of health outcomes across a population. It is based on the notion of the Lorenz curve, which plots the cumulative distribution of health outcomes against the cumulative distribution of the population. The overall CI measures the degree of deviation of the Lorenz curve from a line of equality, which represents perfect equality in the distribution of health outcomes. It ranges from -1 to +1, with the sign indicating the direction of the relationship and the magnitude reflecting its strength. A value of zero implies complete equality, with no inequality present. | 366 | definition | 1.000 | It is based on the notion of the Lorenz curve, which plots the cumulative distribution of health outcomes against the cumulative distribution of the population. | 7 | page=7,block=7 | 0.700 | |
| What is represented by the first term in the decomposition of inequality? | the explained component of inequality, derived from the elasticities of the explanatory variables and their respective concentration indices | The first term represents the explained component of inequality, derived from the elasticities of the explanatory variables and their respective concentration indices, while the second term represents the unexplained component captured by the residual. This shows that the CI consists of explained and residual components. When all factors remain constant, a positive contribution from a factor reduces socioeconomic inequality, while a negative contribution increases it. All statistical analyses were conducted using Stata v16.0, and appropriate sampling weights were used in the estimations. 3. Results 3. Bivariate analysis of differentials in hospitalisations in 1995-96 and 2017-18 | 26 | definition | 1.000 | The first term represents the explained component of inequality, derived from the elasticities of the explanatory variables and their respective concentration indices, while the second term represents the unexplained component captured by the residual. | 8 | page=8,block=3 | 0.700 | |
| What is represented by the second term in the decomposition of inequality? | the unexplained component captured by the residual | The first term represents the explained component of inequality, derived from the elasticities of the explanatory variables and their respective concentration indices, while the second term represents the unexplained component captured by the residual. This shows that the CI consists of explained and residual components. When all factors remain constant, a positive contribution from a factor reduces socioeconomic inequality, while a negative contribution increases it. All statistical analyses were conducted using Stata v16.0, and appropriate sampling weights were used in the estimations. 3. Results 3. Bivariate analysis of differentials in hospitalisations in 1995-96 and 2017-18 | 201 | definition | 1.000 | The first term represents the explained component of inequality, derived from the elasticities of the explanatory variables and their respective concentration indices, while the second term represents the unexplained component captured by the residual. | 8 | page=8,block=3 | 0.700 | valid |
| What does a positive percentage contribution indicate in the context of hospitalisation inequality? | a factor contributes to the increase in observed socioeconomic gaps in hospitalisation | Year Concentration Index 95% CI Std. Error p-value 1995-96 Urban 0. (0.176, 0.206) 0.00790 <0. Rural 0. (0.374, 0.405) 0.00780 <0. Total 0. (0.336, 0.358) 0.00573 <0.001 2017-18 Urban 0. (0.046, 0.072) 0.00669 <0. Rural 0. (0.185, 0.210) 0.00641 <0. Total 0. (0.162, 0.181) 0.00470 <0. CI - Confidence Interval 3. Decomposition of inequalities in hospitalisation Table 5 shows the decomposition of hospitalisation inequality for 1995-96 and 2017-18. The positive or negative direction of the CI indicates whether the factors were more prevalent in the wealthy or poor group. The percentage contribution indicates how much each variable in the model contributes to socioeconomic disparities as a whole. A positive percentage contribution indicates that a factor contributes to the increase in observed socioeconomic gaps in hospitalisation. | 752 | definition | 1.000 | A positive percentage contribution indicates that a factor contributes to the increase in observed socioeconomic gaps in hospitalisation. | 15 | page=15,block=2 | 0.700 | |
| What does the positive or negative direction of the Concentration Index indicate? | whether the factors were more prevalent in the wealthy or poor group | Year Concentration Index 95% CI Std. Error p-value 1995-96 Urban 0. (0.176, 0.206) 0.00790 <0. Rural 0. (0.374, 0.405) 0.00780 <0. Total 0. (0.336, 0.358) 0.00573 <0.001 2017-18 Urban 0. (0.046, 0.072) 0.00669 <0. Rural 0. (0.185, 0.210) 0.00641 <0. Total 0. (0.162, 0.181) 0.00470 <0. CI - Confidence Interval 3. Decomposition of inequalities in hospitalisation Table 5 shows the decomposition of hospitalisation inequality for 1995-96 and 2017-18. The positive or negative direction of the CI indicates whether the factors were more prevalent in the wealthy or poor group. The percentage contribution indicates how much each variable in the model contributes to socioeconomic disparities as a whole. A positive percentage contribution indicates that a factor contributes to the increase in observed socioeconomic gaps in hospitalisation. | 505 | definition | 1.000 | The positive or negative direction of the CI indicates whether the factors were more prevalent in the wealthy or poor group. | 15 | page=15,block=2 | 0.700 | valid |
| What does a negative percentage contribution indicate about a component's effect on socioeconomic inequalities in hospitalisation? | a component that is anticipated to reduce socioeconomic inequalities connected to hospitalisation | A negative percentage contribution, on the other hand, indicates a component that is anticipated to reduce socioeconomic inequalities connected to hospitalisation. In 1995-96, approximately 91% of the observed inequality was explained by factors such as household economic status (MPCE quintile), education, region, and household type, with MPCE alone accounting for over 50% of the inequality. Education and region also contributed significantly, indicating that hospitalization was concentrated among wealthier, more educated individuals and in more developed regions like the South. by 2017-18, the share of explained inequality declined substantially to 40%, suggesting that a growing proportion of inequality is attributable to unobserved or structural factors. | 65 | definition | 1.000 | A negative percentage contribution, on the other hand, indicates a component that is anticipated to reduce socioeconomic inequalities connected to hospitalisation. | 15 | page=15,block=2 | 0.700 | |
| What does the variable R represent in the formula involving the concentration index? | the fractional rank of individuals in the socioeconomic distribution (ranked from poorest to richest) | where C is the concentration index, yi is the outcome variable, and cov denotes covariance. R represents the fractional rank of individuals in the socioeconomic distribution (ranked from poorest to richest), and is the mean of the outcome variable. | 105 | definition | 1.000 | R represents the fractional rank of individuals in the socioeconomic distribution (ranked from poorest to richest), and is the mean of the outcome variable. | 8 | page=8,block=1 | 0.700 | valid |
| What does the endowment effect represent in the decomposition of changes in hospitalisation rates? | the portion of the change attributable to differences in the distribution of characteristics across the two time points | Specifically, the decomposition separates the total change in the outcome into two components: Endowment effect (also called "composition effect"): This captures the portion of the change attributable to differences in the distribution of characteristics across the two time points. if a larger share of the population is elderly in 2017 18 than in 1995 96, and elderly individuals tend to use more hospital services, this will increase hospitalisation rates through the endowment effect. Coefficient effect (also called "rate effect"): This represents the part of the change due to shifts in the influence or "returns" of characteristics. if having higher education had a stronger positive association with hospitalisation in 1995 96 than in 2017 18, this change in effect contributes to the coefficient effect. | 162 | definition | 1.000 | Specifically, the decomposition separates the total change in the outcome into two components: Endowment effect (also called "composition effect"): This captures the portion of the change attributable to differences in the distribution of characteristics across the two time points. |
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