The Perils of Over-Testing, Why Delhi’s ANMOL Initiative for Newborn Screening Is Flawed

The Perils of Over-Testing, Why Delhi’s ANMOL Initiative for Newborn Screening Is Flawed

India’s states are often criticised for chronically underinvesting in health. Yet, Delhi has long stood apart, consistently allocating one of the highest proportions of its budget to the health sector. In the latest state budget, one of the key announcements is the launch of the Advanced Newborn Monitoring for Optimal Lifecare (ANMOL) initiative—a programme that proposes to conduct as many as 56 blood tests on every newborn. At first glance, this appears to be a bold decision, a testament to Delhi’s commitment to child health. The proposed panel includes conditions such as congenital hypothyroidism, phenylketonuria, congenital adrenal hyperplasia, G6PD deficiency, galactosemia, biotinidase deficiency, cystic fibrosis, and a range of metabolic and genetic disorders. Several of these tests are clinically justified in well-defined contexts; however, when applied universally and without prioritisation, they raise serious concerns about scientific validity and policy prudence. Screening in the absence of assured treatment is not prevention—and could be a source of distress. Delhi’s prescription for newborn testing is flawed, and unless recalibrated, it risks causing more harm than good.

The Principles of Screening: What Every Policymaker Should Know

The foundational principles of screening are clear and time-tested. They were articulated by the World Health Organization and have been refined over decades of public health practice. A condition should be sufficiently prevalent to justify population-wide testing. Its natural history must be well understood. The screening test should be reliable, affordable, and acceptable. And—most critically—early detection should offer a clear advantage over later diagnosis. There must be an effective treatment or intervention that can be offered to those who test positive. Screening without assured treatment is not prevention; it is a cruel exercise that identifies problems without offering solutions.

Assessed against these criteria, the proposal to screen every newborn for 56 conditions appears excessive. Many of the disorders included are exceedingly rare, with incidence rates so low that the cost of identifying a single case becomes disproportionately high. For example, conditions like biotinidase deficiency or certain organic acidemias affect fewer than 1 in 50,000 or even 1 in 100,000 newborns. To find one case, the health system would need to screen tens of thousands of babies—most of whom will be healthy. The resources consumed in this process could have been used for other, more impactful interventions.

More troubling is the uneven capacity of Delhi’s health system to provide comprehensive and lifelong care for many of these conditions. Diagnosing a rare metabolic disorder in a newborn may be scientifically impressive, but it offers little practical benefit if families cannot access specialised diets, long-term therapies, or genetic counselling. Many of these conditions require lifelong management by multidisciplinary teams—metabolic physicians, nutritionists, genetic counsellors, physical therapists—and access to expensive medical foods and medications. Delhi’s public health system, despite its relatively high budget allocation, is not equipped to provide this level of care for every diagnosed newborn. The result will be a diagnosis without a cure, information without intervention, and anxiety without resolution.

The Hidden Harms: False Positives, Anxiety, and Opportunity Cost

There are less visible consequences of large screening panels. False positives are an inevitable by-product of any screening test. No test is 100 per cent specific; even a test with 99.9 per cent specificity will generate false positives when applied to a large population. When applied to thousands of newborns, even small error rates translate into large numbers of families being subjected to anxiety, repeated testing, and the psychological burden of uncertainty. The experience of a false-positive result for a serious metabolic disorder is traumatic. Parents are told that their apparently healthy newborn may have a life-threatening condition. They undergo weeks of worry, further testing, and specialist consultations, only to be told eventually that the result was a mistake. This is not harmless; it is a form of iatrogenic harm.

For the health system, this results in additional costs, increased workload, and the diversion of scarce resources from more pressing priorities. The same laboratory technicians, machines, and specialists who are occupied with chasing down false positives could have been used to strengthen essential newborn care: ensuring timely vaccinations, promoting breastfeeding, managing neonatal infections, or screening for conditions that are truly common and treatable. Every rupee spent on over-testing is a rupee not spent on more effective interventions.

The Signalling Effect: How Policy Drives Private Sector Over-Testing

Equally relevant is the signalling effect of such a policy. When the government of the national capital territory endorses an extensive panel of tests as part of routine newborn care, it implicitly communicates that all these tests are essential. The private healthcare market, highly responsive to both policy signals and consumer anxieties, is likely to amplify this trend. Hospitals and diagnostic centres may begin routinely offering similar panels, thereby increasing the cost of delivery and newborn care without commensurate health benefits.

In a country where households already bear a substantial share of healthcare expenditure (over 60 per cent of health spending is out-of-pocket), this cascading effect could deepen financial vulnerability under the guise of preventive care. A middle-class family that delivers a baby in a private hospital may be presented with a bill for newborn screening—not just for the few tests that are evidence-based, but for the entire 56-test panel. They may not know which tests are essential and which are superfluous. They may be told that “this is what the government recommends.” They may pay out of fear. This is not healthcare; it is rent-seeking enabled by policy.

A Cautionary Precedent: Universal Lipid Screening in Children

This pattern is not without precedent. The ongoing policy discourse in India around universal lipid screening for children at ages seven and 17 offers a useful parallel. Advocates argue that early identification of dyslipidaemia (abnormal lipid levels) can help prevent cardiovascular disease later in life. There is some merit in this argument, particularly for detecting familial hypercholesterolaemia, a genetic condition where early intervention with statins can indeed be life-saving. However, extending this approach to universal screening remains contentious.

Lipid levels in children are dynamic, influenced by growth, diet, and hormonal changes. A single elevated cholesterol reading in a seven-year-old is a poor predictor of adult heart disease. The ability of childhood lipid levels to predict adult disease is far from definitive. Labelling large numbers of children as “at risk” risks medicalising normal biological variation, leading to unnecessary follow-up tests, dietary restrictions, and, in some cases, premature pharmacological interventions. The harms—anxiety, labelling, cost—may outweigh the benefits.

The growing use of advanced lipid markers, such as lipoprotein(a) and apolipoprotein A, further illustrates the slippery slope of over-testing. While these biomarkers have a role in specific clinical situations (e.g., a strong family history of premature heart disease), their repeated use in the general population is not supported by strong evidence. Yet, once introduced into clinical practice, such tests tend to proliferate—driven by a combination of clinical uncertainty, patient expectations, and commercial incentives. The proposed ANMOL screening policy risks triggering a similar cascade, normalising the idea that more testing automatically translates into better care.

The Alternative: Targeted, Evidence-Based Screening

This is not to suggest that newborn screening should be abandoned. On the contrary, targeted newborn screening programmes have been among the most successful public health interventions globally. Early detection of conditions such as congenital hypothyroidism (which affects about 1 in 2,500 newborns) and phenylketonuria (about 1 in 10,000) has prevented severe intellectual disability and transformed countless lives. A baby with congenital hypothyroidism who is diagnosed within the first few weeks and started on thyroid hormone replacement will develop normally; a baby who is missed will suffer permanent brain damage. These conditions meet all the criteria for screening: they are relatively common, have a well-understood natural history, have reliable and affordable tests, and have effective treatments that must be started early.

India would benefit from strengthening and expanding such focused programmes, ensuring high coverage, quality assurance, and strong linkage to treatment and follow-up care. The real question is how much to screen, for whom, and with what purpose. A more rational approach would ground screening policy in local epidemiology, the availability of treatment, and health system capacity. Risk stratification—based on family history, regional disease patterns, and clinical indicators—offers a more nuanced and effective pathway than blanket testing.

For example, screening for cystic fibrosis (which has a higher incidence in certain populations) could be targeted to groups at higher risk. Screening for G6PD deficiency (which is more common in certain regions and ethnic groups) could be targeted accordingly. Universal screening should be reserved for conditions that are truly common (e.g., congenital hypothyroidism) or where early intervention is critical and treatment is universally available.

Transparency and Accountability

Equally important is transparency. Policymakers must clearly articulate the evidence supporting each test, along with its expected benefits, potential harms, and cost implications. They should publish the incidence rates of each condition in Delhi’s population, the specificity and sensitivity of the tests being used, the positive predictive value (the probability that a positive test truly indicates disease), and the availability of treatment for each condition. They should also conduct a pilot study before rolling out the programme statewide, to assess feasibility, acceptability, and impact.

Public health interventions derive legitimacy not from their scale, but from their scientific integrity and ethical soundness. A 56-test panel may sound impressive, but if it is not evidence-based, it is not good medicine. It may be worse than no screening at all.

The Opportunity for Recalibration

There remains an opportunity to recalibrate. As the operational guidelines for the ANMOL initiative are developed, they can incorporate a more discerning and evidence-based framework—prioritising essential tests, linking screening to assured treatment pathways, and avoiding unnecessary expansion. Resources thus saved could be redirected towards strengthening neonatal care systems, improving follow-up mechanisms, and supporting families of affected children.

In the final analysis, this would go beyond simply providing a list of possible tests to choose from. Instead, it would require a careful consideration of the potential benefits and drawbacks of each option, taking into account the unique needs and circumstances of individual families. A one-size-fits-all approach to newborn screening is neither scientifically sound nor ethically defensible. Delhi’s prescription is flawed—but it can be fixed.

Q&A: Newborn Screening and the ANMOL Initiative

Q1: What is the ANMOL initiative proposed by the Delhi government, and what are its stated goals?

A1: The Advanced Newborn Monitoring for Optimal Lifecare (ANMOL) initiative proposes to conduct 56 blood tests on every newborn in Delhi. The panel includes conditions such as congenital hypothyroidism, phenylketonuria, congenital adrenal hyperplasia, G6PD deficiency, galactosemia, biotinidase deficiency, cystic fibrosis, and a range of metabolic and genetic disorders. The stated goal is to enable early detection and intervention for these conditions. However, the article argues that the proposal is excessive and not grounded in the foundational principles of screening.

Q2: What are the foundational principles of screening, and why does the ANMOL proposal fail against them?

A2: The foundational screening principles are:

• The condition should be sufficiently prevalent to justify population-wide testing.

• Its natural history must be well understood.

• The screening test should be reliable, affordable, and acceptable.

• Early detection should offer a clear advantage over later diagnosis (i.e., effective treatment must exist).
  The ANMOL proposal fails because:

• Most of the 56 conditions are exceedingly rare (e.g., biotinidase deficiency affects fewer than 1 in 50,000-100,000 newborns). The cost of identifying a single case is disproportionately high.

• Delhi’s health system lacks the capacity to provide comprehensive, lifelong care for many conditions (specialised diets, therapies, genetic counselling, multidisciplinary teams).

• Screening without assured treatment is not prevention; it creates anxiety without solutions.

Q3: What are the hidden harms of large screening panels like ANMOL?

A3: The hidden harms include:

• False positives: No test is 100% specific. Even a 99.9% specific test generates false positives when applied to thousands of newborns. Families experience weeks of anxiety, repeated testing, and psychological burden only to be told the result was a mistake—a form of iatrogenic harm.

• Cost and workload diversion: Resources (lab technicians, machines, specialists) spent chasing false positives are diverted from more pressing priorities like essential newborn care (vaccinations, breastfeeding support, neonatal infection management).

• Signalling effect: When the government endorses a 56-test panel, it implicitly signals that all tests are essential. The private sector amplifies this, leading to routine offering of similar panels. This increases the cost of delivery and newborn care without health benefits, deepening out-of-pocket expenditure.

• Financial vulnerability: Families may be presented with bills for the full panel without knowing which tests are essential, paying out of fear.

Q4: What is the precedent of universal lipid screening in children, and what lesson does it offer for newborn screening?

A4: The ongoing policy discourse around universal lipid screening for children at ages 7 and 17 offers a parallel. While early detection of familial hypercholesterolaemia (genetic condition) can be life-saving with statins, universal screening remains contentious because:

• Lipid levels in children are dynamic, influenced by growth, diet, and hormonal changes.

• A single elevated reading in a child is a poor predictor of adult heart disease.

• Labelling children as “at risk” medicalises normal biological variation, leading to unnecessary follow-up tests, dietary restrictions, and even premature drug use.
  The lesson is that the proliferation of advanced markers (lipoprotein(a), apolipoprotein A) once introduced tends to spread, driven by clinical uncertainty, patient expectations, and commercial incentives. The ANMOL proposal risks a similar cascade, normalising the idea that “more testing equals better care.”

Q5: What does the article recommend as a more rational approach to newborn screening in Delhi?

A5: The article recommends:

• Targeted, not universal, screening based on local epidemiology, treatment availability, and health system capacity.

• Risk stratification: Base screening on family history, regional disease patterns, and clinical indicators, rather than blanket testing of all newborns for all conditions.

• Focus on conditions that meet all screening criteria: relatively common (e.g., congenital hypothyroidism affects 1 in 2,500; untreated, it causes permanent brain damage, but early thyroid hormone replacement prevents this), well-understood, with reliable tests and assured treatment.

• Transparency: Policymakers must publish evidence for each test: incidence rates, test specificity/sensitivity, positive predictive value, and treatment availability. Conduct pilot studies.

• Recalibration of ANMOL: As operational guidelines are developed, prioritise essential tests, link screening to assured treatment pathways, and avoid unnecessary expansion. Redirect saved resources to strengthening neonatal care systems, improving follow-up, and supporting affected families.
  The article concludes that a “one-size-fits-all approach to newborn screening is neither scientifically sound nor ethically defensible.” Delhi’s prescription is flawed, but it can be fixed.