Equity in the use of public services for mother and newborn child health care in Pakistan: a utilization incidence analysis
Bibliographic record
Abstract
BACKGROUND: Poor maternal and infant health indicators are mostly concentrated among low income households in Pakistan and health care expenditures - especially on medical emergencies - are the most common income shocks experienced by the poor. Public investments in health are therefore considered as pro-poor interventions by the government of Pakistan. This study employs nationally representative household data for Pakistan for 2007-08 and 2010-11 to investigate whether benefits from publicly financed services on Mother and Newborn Child Health (MNCH) are effectively captured by the poor in terms of service utilization. METHODS: The study conducts a Utilization Incidence Analysis of the use of public health services for MNCH in Pakistan. For this purpose, the utilization shares of households, ranked by economic status, are computed. The concentration curves are plotted and their dominance is tested against an equal distribution and Lorenz curves to determine whether the distribution is pro-poor and progressive. RESULTS: Although the shares of bottom income groups in the utilization of most services for MNCH have increased between 2007 and 2011, the utilization of some services such as post-natal consultation; institutional maternal delivery; and Tetanus Toxoid injections for pregnant women remains pro-rich in 2011. The utilization of pre-natal consultation, especially through lady health workers and visitors; the use of Family Panning Units; and immunization services is somewhat evenly distributed. The use of Basic Health Units (BHUs) is found to be pro-poor. The provincial analysis reveals that the province of Baluchistan depicts an unusually high level of inequity in the distribution of utilization benefits from almost all public health services. Finally, in terms of progressivity, public spending on all health services analyzed in the study is found to be progressive at the national level implying that investment in MNCH has the potential to redistribute income from rich to the poor. CONCLUSION: To target the poor effectively, the study recommends expanding the network of BHUs as well as basic reproductive and child health care services. The outreach of health facilities in Baluchistan need to be expanded while targeting the poor effectively by mitigating various access costs that prevent them from using public health services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".