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The design of Conditional Cash Transfer Schemes in India

A small baby smiles from within a red velvet hammock
Photo by Pranav Kumar Jain on Unsplash

A recent paper by von Haaren and Klonner compares two different conditional cash transfer (CCT) schemes being offered by the Indian government for the purposes of increasing maternal and infant welfare. The Janani Suraksha Yojana (JSY) was started in 2005 and focuses on increasing institutional delivery, and the second scheme, the Pradhan Mantri Matru Vandana Yojana (PMMVY) which incentivises a much broader set of behaviours, was started in 2011.

The authors find that the PMMVY was able to incentivise a number of actions, such as the increase of complete immunisations, the increase in the delay between subsequent births, increased contact with health workers, and an increase in other immunisations like the one for measles. Unfortunately, its coverage was less comprehensive than the JSY's. At the same time, it did not have any adverse effects on fertility rates, which increased under the JSY.

The JSY was designed as a one-time payment contingent on either institutional delivery or by a certified professional at home. This caused an uptick in institutional deliveries, as was expected. It also resulted in an increase in the number of babies being breastfed and the number of families in contact with healthcare professionals. However, what was not expected was the increase in fertility rates (though it has been seen in numerous conditional cash transfer schemes across the world), and the lack of any reduction in either maternal or neonatal mortality. It also resulted in a substitution away from private healthcare providers.

The PMMVY is a second generation benefit scheme which has been implemented after learning from the first generation. It incentivises a larger number of behaviours and features a number of training programmes. It covers a longer period of around 9 months around delivery and additional supply-side financing. During its pilot phase, eligibility for its cash transfers were given for the first and second child, but post-2017 when the programme rolled out to everyone in India the benefits were confined to the first child.

"Consistent with IGMSY's incentives, we find that polio, DPT and BCG vaccinations increase. As an indirect effect, measles immunizations, which are administered well beyond the period covered by the scheme, also increase. As a consequence, complete infant immunizations increase by 9%. We also document two positive side-effects: mothers of once eligible children report 14% more contacts with the government health system 3 to 4 years later. Moreover, there are no adverse effects on fertility, and birth intervals increase by 11% on average and by 17% between the first two parities, which are covered by the program. On the other hand, similar to JSY, we find no robust evidence of increased breastfeeding or gains in health outcomes, albeit some of our results suggest improvements in breastfeeding duration, child mortality and weight-related outcomes for both children and mothers." - von Haaren and Klonner

At this moment, this is one of the first papers to compare different Indian conditional cash transfer schemes. The authors point out that it seems that the PMMVY was a better designed scheme as compared to the JSY, for it had fewer side effects. For example, as mentioned earlier, fertility rates did not go up after its introduction. It also prioritised training new mothers about breastfeeding and nutrition leading to improvements in child mortality, neonatal weight, and weight-related outcomes for mothers. However, the authors state that the effects were extremely small, which tells us that more work needs to be done to design these programmes properly for Indian conditions.

Some misc. interesting papers

The effects of pollution on neonatal health: It is common wisdom that excessive pollution in developing countries leads to excess mortality. However, a group of researchers decided to look at whether the relationship really holds or not. They wished to see whether it was the pollution in developing countries which led to babies dying or whether it was the limited ability in populations to manage the effects of pollution which caused excess neonatal mortality. The study looks at the case of Hong Kong, which is a high-income city with a high concentration of particulate matter (PM). The effects of PM concentration on birthweight, the number of low birthweight babies born, and neonatal mortality were observed across 2001 - 2019.

It was found that while seasonal variations, even marginal ones, in PM concentration caused measurable changes in birthweight and in the number of low birthweight babies, it had no measurable effect on neonatal mortality. The authors also performed a comparative analysis to check whether their results hold up or not. In the authors' own words, "In light of our conceptual framework, these comparative results suggest that vulnerability to particulate matter exposure may be more important than particulate matter exposure itself in explaining differences in marginal mortality damages across countries. To be clear, we do not assert that baseline particulate matter exposure does not matter but rather that marginal mortality damages may be linear in exposure."

Identifying high-cost users of CKD: An exercise was described by Sowa et. al. in which they attempted to identify differences between two different populations suffering from Chronic Kidney Disease (CKD). The 90th percentile in terms of cost of treatment (High Cost Users, or HCU of in-patient care) and everyone else. The study specifically focuses on the first year of patients coming into specialist care in Australia. They found there to be no difference between the populations when it came to gender or ethnicity (indigenous vs White Australian). However, the median age of the latter population was higher than non-HCU patients. They were also, unsurprisingly, more prone to being at stages 4 and 5. Diabetic neuropathy was also more common in the HCU population, as was the presence of co-morbidities such as diabetes, cardiovascular disease, and hypertension. It was also seen that HCU patients were far more likely to have been admitted after an episode change. They were also much more likely to be readmitted within a 30-day period.

In addition, "HCUs were at an increased risk of admissions due to issues of the nervous system (RR: 1.94; 95% CI 1.74 to 2.15), factors influencing health status (FIHS) (1.92; 1.74 to 2.09), circulatory (1.24; 1.14 to 1.34) and respiratory system (1.2; 1.03 to 1.37). HCUs were at a lower risk of admissions caused by digestive system issues (0.71; 0.56 to 0.87) or other MDCs (0.73; 0.66 to 0.81). Key FIHS that distinguished HCU from non-HCU involved the use of rehabilitation procedures (Z50, 47.4% vs 20.8%, respectively) and problems related to medical facilities and other healthcare (Z75, 13.5% vs 1.5%, respectively)."

Cost Benefit analyses of returning incidental findings in genomic research: An interesting question posed to advocates of whole genome sequencing and exome sequencing is, "Is it worthwhile reporting incidental findings (IFs) back to patients?" In other words, should doctors tell patients about findings of dubious clinical significance, or findings which may have clinical significance but were not the ones for which the doctors were actually screening? There is a rich debate in the community about this question, but there is no definite answer. Marx et. al. sidestep ethical questions and focus on performing a systematic review of economic analyses surrounding this work.

The results they get are inconclusive. This is not a question pursued with much rigor in the literature. The authors wished to focus on Africa, and thus found only seven studies which satisfied all their criteria. The authors state that while five of those studies actually provided economic evaluation results, they do not give enough evidence to judge the practice of sharing IFs. Marx et. al. aren't the only ones to come to such conclusions. An older scoping review found that the costs and benefits of sharing IFs aren't being considered in most studies dealing with whole genome sequencing.

However, one finding which the authors highlight is that the costs of sharing IFs probably vary with the primary health condition affecting a particular patient.

"For instance, Christensen et al. found that the lifetime average health care cost of returning IFs for a cardiomyopathy patient is around $90, compared with $325 for a colorectal cancer patient, and $440 for each healthy individual. Christensen et al. results show that omitting or limiting the types of IFs results that patients in cardiology and primary care receive can save on average $69 and 182, respectively. Hart et al. add to the discussion that returning IFs to patients will increase healthcare resource utilization and the cost on average is $421 (range $141–1,114) up to 1-year post-result return of the IFs." - Marx et. al.

The authors go on to state that the community seems to be settling on the answer it might not be worth sharing IFs with patients, especially if there is no diagnostic support available to them. There are often mixed psychological reactions to sharing genetic data ranging from absolutely ignoring results to being very scared of them. Sharing incidental findings might further complicate such reactions.

Including survivor costs in economic evaluations: The economic effects of the saving of a life are typically not taken into account by studies conducting economic analyses from the societal perspective. However, recent American guidelines have recommended taking these costs into account. Kellerborg et. al. say that the inclusion of these costs has an impact on ICERs (incremental cost-effectiveness ratios) as well. They state that the inclusion of these costs leads to an increase in the numerator of the ICER, making it higher. This increase is the lowest for people from a lower socioeconomic status which makes interventions targeting them look more economically favourable. At the same time, people from a higher socioeconomic status both lived longer and had a higher quality of life at all ages, therefore their quality adjusted life expectancy was higher than that of people from low socioeconomic status. While the effect of including these consequences is substantial, the effect across educational groups is much smaller. They relate this to two issues:

  • People with a lower educational background enjoy a lower quality of life than those with a higher educational background
  • People with fewer years of education tend to form single-person households in their later years, implying higher consumption costs compared to people in a multi-person household

The major finding from this study, however, is that the differences in consumption seem to be mitigated by the concomitant differences in quality of life and household size.