Abstract
India’s maternal mortality and morbidity statistics remain contested due to methodological ambiguities and systemic data gaps. While the Maternal Mortality Ratio (MMR) is internationally standardized as deaths per 100,000 live births, confusion persists with the Maternal Mortality Rate, which uses women of reproductive age as the denominator. In India, estimates are derived largely from the Sample Registration System (SRS), household surveys such as the National Family Health Survey (NFHS), and facility-based reporting under the Health Management Information System (HMIS). These sources, however, are periodic, sample-based, and often modeled, rather than real-time.
The PCPNDT Act presumes sex-selective abortion and female infanticide as the primary causes of skewed sex ratios, yet medical realities reveal a broader spectrum of female fetal loss — including spontaneous abortions, stillbirths, and medically indicated terminations — that household surveys cannot capture. Despite extensive digitization of hospitals, the absence of mandatory ICD coding and real-time reporting undermines the reliability of morbidity and mortality data. Consequently, policy formulations risk being based on self-supporting guesstimates rather than robust, coded health statistics.
This paper argues for a shift from politically framed digitization claims to mandatory ICD-coded, real-time hospital reporting, centrally collated and analyzed state-wise. Only such a system can provide India with reliable health statistics, ensure medico-legal accountability, and support evidence-based policy in maternal and child health.
Q. What Is The Difference Between Maternal Mortality Ratio And Maternal Mortality Rate?
Great question — the terms Maternal Mortality Ratio and Maternal Mortality Rate are often confused because both are abbreviated as MMR, but they measure slightly different things.
Maternal Mortality Ratio (MMR)
- Definition: The number of maternal deaths per 100,000 live births.
- Focus: It relates maternal deaths to the risk associated with each pregnancy and delivery.
- Interpretation: A measure of obstetric risk — how dangerous pregnancy and childbirth are in a given population.
- Example: If a country has 150 maternal deaths per 100,000 live births, its MMR is 150.
Maternal Mortality Rate
- Definition: The number of maternal deaths per 100,000 women of reproductive age (usually 15–49 years) in a given year.
- Focus: It relates maternal deaths to the general population of women who could become pregnant.
- Interpretation: A measure of population-level burden — how maternal deaths affect the overall female population.
- Example: If 20 maternal deaths occur per 100,000 women aged 15–49 in a year, the maternal mortality rate is 20.
Key Difference
- Ratio → Denominator is live births (risk per pregnancy).
- Rate → Denominator is women of reproductive age (burden in population).
| Measure | Denominator | Represents |
|---|---|---|
| Maternal Mortality Ratio (MMR) | Live Births | Risk Per Pregnancy |
| Maternal Mortality Rate | Women Aged 15–49 Years | Population Burden |
Q. How Are The Maternal Mortality Ratio And Maternal Mortality Rate Calculated?
Here’s how both are calculated — the distinction lies entirely in the denominator used:
Maternal Mortality Ratio (MMR)
Formula:
MMR = (Number of maternal deaths in a given time period ÷ Number of live births in the same period) × 100,000
- Numerator: Maternal deaths (during pregnancy or within 42 days of termination, from pregnancy-related causes).
- Denominator: Live births.
- Multiplier: 100,000 (to standardize across populations).
- Purpose: Measures the risk of death per pregnancy.
Example
If a country records 250 maternal deaths and 200,000 live births in a year:
MMR = (250 ÷ 200,000) × 100,000 = 125
So, the maternal mortality ratio is 125 per 100,000 live births.
Maternal Mortality Rate
Formula:
Maternal Mortality Rate = (Number of maternal deaths in a given time period ÷ Number of women of reproductive age (15–49 years)) × 100,000
- Numerator: Maternal deaths (same definition as above).
- Denominator: All women of reproductive age (not just those who gave birth).
- Multiplier: 100,000 (to standardize).
- Purpose: Measures the population burden of maternal deaths.
Example
If the same country has 250 maternal deaths and 50 million women aged 15–49:
Rate = (250 ÷ 50,000,000) × 100,000 = 0.5
So, the maternal mortality rate is 0.5 per 100,000 women of reproductive age.
Summary Table
| Indicator | Numerator | Denominator | Multiplier | Meaning |
|---|---|---|---|---|
| Maternal Mortality Ratio (MMR) | Maternal Deaths | Live Births | ×100,000 | Risk Per Pregnancy |
| Maternal Mortality Rate | Maternal Deaths | Women Aged 15–49 Years | ×100,000 | Burden In Population |
How Are the Required Data – Numerator and Denominator – for the Two Generated and Reported in India?
In India, maternal mortality data are generated primarily through the Sample Registration System (SRS) run by the Office of the Registrar General of India. The numerator (maternal deaths) is identified through continuous household surveys and verbal autopsies, while the denominator differs for the two indicators:
- Maternal Mortality Ratio (MMR): Uses the number of live births as the denominator.
- Maternal Mortality Rate: Uses the number of women aged 15–49 years as the denominator.
These figures are reported in periodic SRS Bulletins and are used by the Ministry of Health & Family Welfare (MoHFW) and the National Health Mission (NHM) to monitor maternal health outcomes and progress.
How the Numerator (Maternal Deaths) Is Generated
Definition of Maternal Death
A maternal death is defined as the death of a woman while pregnant or within 42 days of the termination of pregnancy, from causes related to or aggravated by the pregnancy or its management, excluding accidental or incidental causes.
Sources of Maternal Death Data
- Sample Registration System (SRS): A large-scale demographic survey covering a nationally representative sample of households across India.
- Verbal Autopsy (VA): For every reported death, trained field staff interview family members to determine the probable cause of death. This process helps accurately identify and classify maternal deaths.
Coverage and Verification
The SRS follows a continuous dual-record system involving enumerators and supervisors. This mechanism ensures cross-verification of births and deaths, improving the reliability of maternal mortality estimates.
How the Denominators Are Generated
1. Maternal Mortality Ratio (MMR)
The Maternal Mortality Ratio measures the risk of maternal death per live birth.
| Component | Details |
|---|---|
| Denominator | Number of live births in the same population and time period |
| Source | Live births recorded through SRS household surveys and cross-checked with civil registration data |
| Purpose | Measures the risk of maternal death associated with pregnancy and childbirth |
Calculation Formula
MMR = (Maternal Deaths ÷ Live Births) × 100,000
Reporting
The Maternal Mortality Ratio is published in SRS Special Bulletins. According to the latest estimates, India’s MMR declined to 87 maternal deaths per 100,000 live births during 2022–24.
2. Maternal Mortality Rate
The Maternal Mortality Rate measures the burden of maternal deaths among women of reproductive age.
| Component | Details |
|---|---|
| Denominator | Number of women aged 15–49 years in the population |
| Source | Population estimates derived from census projections and SRS demographic data |
| Purpose | Measures the overall population burden of maternal mortality |
Calculation Formula
Maternal Mortality Rate = (Maternal Deaths ÷ Women Aged 15–49 Years) × 100,000
Reporting
The Maternal Mortality Rate is also reported in SRS Bulletins, although it receives less emphasis in policy discussions than the Maternal Mortality Ratio.
Institutional Reporting and Use
| Institution | Role |
|---|---|
| Registrar General of India (RGI) | Publishes official MMR and Maternal Mortality Rate estimates through SRS Bulletins. |
| Ministry of Health & Family Welfare (MoHFW) | Uses the data to monitor progress under the National Health Mission (NHM) and initiatives such as SUMAN (Surakshit Matritva Aashwasan). |
| WHO, UNICEF, UNFPA, and the World Bank | Use India’s SRS data for international maternal mortality comparisons and global reporting. |
Key Takeaways
- Numerator (Maternal Deaths): Identified through the Sample Registration System (SRS) and Verbal Autopsy (VA).
- Denominator for Maternal Mortality Ratio (MMR): Number of live births, reflecting the risk associated with pregnancy and childbirth.
- Denominator for Maternal Mortality Rate: Women aged 15–49 years, reflecting the population burden of maternal mortality.
- India’s Current MMR (2022–24): 87 maternal deaths per 100,000 live births, indicating a steady decline.
- Policy Preference: The Maternal Mortality Ratio is more commonly used in health policy discussions than the Maternal Mortality Rate.
What Is Risk Estimates for Maternal Mortality? How Is It Done in India?
In India, maternal mortality risk estimates are derived mainly from the Sample Registration System (SRS), which uses large-scale household surveys, continuous monitoring of births and deaths, and verbal autopsies to identify maternal deaths. These estimates are then modeled and triangulated with other sources like the Health Management Information System (HMIS), Census data, and the National Family Health Survey (NFHS) to produce district, state, and national-level risk estimates.
How Risk Estimates Are Done in India
1. Sample Registration System (SRS)
- Primary source for maternal mortality estimates.
- Covers a nationally representative sample of households.
- Uses a dual-record system (continuous enumeration + independent survey) to capture births and deaths.
- Verbal autopsy (VA) is conducted for each death to determine cause, including maternal deaths.
- Provides direct estimates of Maternal Mortality Ratio (MMR) and Maternal Mortality Rate.
2. Health Management Information System (HMIS)
- Routine facility-based reporting of maternal deaths.
- Data triangulated with SRS and Census to generate district-level estimates.
- Found to be a cost-effective and reliable source for monitoring maternal mortality at local levels.
3. National Family Health Survey (NFHS)
- Provides maternal health indicators (antenatal care, institutional delivery, skilled birth attendance, postnatal care).
- Used in Bayesian spatio-temporal models to estimate risk patterns and identify hotspots.
- Helps quantify system-level risk factors influencing maternal mortality.
Risk Estimation Methods
| Method | Description |
|---|---|
| Direct Estimation (SRS) | Maternal deaths per 100,000 live births. |
| Model-Based Estimation | Combines SRS, HMIS, NFHS, and Census data to account for underreporting and spatial heterogeneity. |
| Bayesian Spatio-Temporal Models | Used to identify district-level risk clusters (e.g., Assam, Bihar, and parts of Uttar Pradesh are high-risk hotspots). |
Key Risk Factors Identified
- High fertility levels and early maternal age.
- Poor health infrastructure and lack of skilled birth attendance.
- Low education and poor nutrition among mothers.
- Socio-economic disadvantage (poverty, rural residence).
- Regional disparities: North-eastern, eastern, and central states show higher risk compared to southern states.
Maternal Mortality Data Sources Summary
| Data Source | Primary Role | Coverage |
|---|---|---|
| SRS | Official maternal mortality estimation | National and State Level |
| HMIS | Facility-based maternal death reporting | District and Local Level |
| NFHS | Maternal health indicators and risk-factor analysis | National, State, and District Level |
| Census Data | Population and demographic validation | National Coverage |
Takeaway
- India’s official maternal mortality risk estimates come from SRS, but district-level granularity is achieved by triangulating HMIS, NFHS, and Census data.
- Risk estimation is not just about counting deaths — it involves modeling socio-demographic and health system factors to identify vulnerable regions and populations.
- Current national MMR (2022–24) is 87 per 100,000 live births, but district-level hotspots still exceed 200, highlighting persistent inequities.
What Is Real-Time Health Statistics?
Q. What is Real Time health statistics? Is sample survey data, Like Family Heath Survey by House Hold Survey, a valid substitute?
What Are Real-Time Health Statistics?
- Definition: Real-time health statistics are data collected and reported continuously or near-instantaneously, often through digital platforms, electronic health records (EHRs), civil registration systems, and facility-based reporting.
- Purpose: They allow ongoing monitoring of health indicators (like maternal deaths, births, disease outbreaks) without waiting for periodic surveys.
Examples of Real-Time Health Statistics in India
| System | Purpose |
|---|---|
| HMIS (Health Management Information System) | Facility-based reporting of maternal deaths, deliveries, and complications. |
| Civil Registration System (CRS) | Continuous registration of births and deaths. |
| Integrated Disease Surveillance Programme (IDSP) | Real-time reporting of communicable diseases. |
Sample Surveys (e.g., NFHS, Household Surveys)
- Nature: Conducted periodically (every 5–7 years for NFHS).
- Method: Household interviews, retrospective recall, and sampling.
Strengths
- Provide representative national and state-level estimates.
- Capture socio-demographic and health behavior data (e.g., antenatal care, institutional delivery).
Limitations
- Not real-time — data reflect conditions at the time of survey, not current events.
- Subject to recall bias and sampling error.
- Cannot capture sudden spikes (e.g., maternal deaths during epidemics).
Can Sample Surveys Substitute Real-Time Statistics?
- No, not fully.
- Household surveys like NFHS are valid for long-term trend analysis and policy planning.
- But they cannot substitute real-time systems for monitoring maternal mortality or health emergencies.
Complementary Role
| System Type | Primary Role |
|---|---|
| Real-time systems (HMIS, CRS) | Continuous monitoring and immediate response. |
| Sample surveys (NFHS, DLHS) | Broader context, determinants, and long-term evaluation. |
Triangulated Health Data Framework in India
In India, SRS + HMIS + NFHS together provide a triangulated picture:
- SRS → Official maternal mortality estimates.
- HMIS → Real-time facility-based monitoring.
- NFHS → Population-level determinants and coverage indicators.
Takeaway
| Real-Time Statistics | Sample Surveys |
|---|---|
| Continuous | Periodic |
| Immediate | Representative |
| Actionable | Contextual |
| Supports rapid intervention | Supports policy planning and evaluation |
- Real-time statistics = continuous, immediate, actionable.
- Sample surveys = periodic, representative, contextual.
- In India, they are not substitutes but complements — surveys validate and enrich real-time data, while real-time systems ensure timely monitoring and intervention.
Labor Room Data, Institutional Deliveries and Sex Ratio at Birth
Q. Are Labor Room data from all the hospitals, now that institutional delivery has become a norm in the country being collected? Sex Ratio at birth is based on sex at birth of live, still born, spontaneous abortions and medically aborted children, this cannot be collected from house hold surveys. The data is essential to assess the causes of Female Fetal loss that skews the sex ratio. Is there any firm basis to presume Sex Selected Abortions and Female Infanticide to be the only clause under the PCPNDT Act?
You’ve raised a very important point about the limits of household surveys and the medico-legal assumptions embedded in the PCPNDT Act.
Labor Room Data & Institutional Deliveries
- With institutional delivery now the norm in India (over 89% according to NFHS-5), labor room records from hospitals are indeed the most direct source for sex ratio at birth.
- These records capture live births, stillbirths, spontaneous abortions, and medically terminated pregnancies — data that household surveys cannot reliably collect because families often underreport or misclassify such events.
- However, national reporting still relies heavily on sample surveys (NFHS, SRS) and facility-based HMIS data. The completeness of labor room data varies by state, depending on how rigorously hospitals feed into HMIS and Civil Registration System (CRS).
Sex Ratio at Birth & Female Fetal Loss
- Sex ratio at birth (SRB) is ideally calculated from all births (live + still).
- Household surveys like NFHS only capture live births reported by families, missing stillbirths and abortions.
- This means female fetal loss due to spontaneous or medically indicated abortions is not fully reflected in survey-based SRB.
- To assess causes of skewed SRB, one needs facility-based data (labor room registers, CRS, HMIS), not household surveys.
PCPNDT Act & Its Assumptions
- The Pre-Conception and Pre-Natal Diagnostic Techniques (PCPNDT) Act, 1994 was enacted to prevent misuse of ultrasound and diagnostic techniques for sex-selective abortion.
- The Act presumes that skewed sex ratios are primarily due to:
- Sex-selective abortions (female fetuses terminated after prenatal sex determination).
- Female infanticide (post-birth killing of female infants).
- But this is a narrow framing. Other contributors to female fetal loss — such as spontaneous abortions, higher vulnerability of female fetuses, or medically indicated terminations — are not addressed in the Act.
- Thus, while PCPNDT is crucial for curbing sex determination misuse, it does not provide a comprehensive medico-legal framework for all causes of female fetal loss.
Key Takeaways
- Labor room data is essential for accurate sex ratio analysis; household surveys are insufficient.
- Female fetal loss has multiple causes beyond sex-selective abortion and infanticide.
- The PCPNDT Act’s presumption is legally strong but medically incomplete — it targets diagnostic misuse, not the broader epidemiology of fetal loss.
- For policy and medico-legal advocacy, India needs integrated facility-based reporting (HMIS + CRS) to complement surveys.
Structured Framework Contrasting PCPNDT’s Legal Assumptions with Medical Realities of Female Fetal Loss
Here’s a structured framework contrasting the legal assumptions of the PCPNDT Act with the medical realities of female fetal loss. This format can serve as an advocacy and sensitization tool for judicial, policy, and hospital audiences:
PCPNDT Act vs. Medical Realities of Female Fetal Loss
1. Legal Assumptions Under PCPNDT Act
- Premise: Skewed sex ratio is primarily due to sex-selective abortions and female infanticide.
- Focus: Prevent misuse of diagnostic techniques (especially ultrasound) for prenatal sex determination.
- Mechanism:
- Regulates registration of genetic clinics, laboratories, and ultrasound centers.
- Criminalizes sex determination and sex-selective abortion.
- Presumes that diagnostic misuse is the dominant cause of female fetal loss.
- Limitations: Narrow scope — does not address spontaneous or medically indicated female fetal loss.
2. Medical Realities of Female Fetal Loss
- Multiple Causes Beyond Sex Selection:
- Spontaneous abortions: Higher vulnerability of female fetuses in early gestation.
- Stillbirths: Often due to maternal health conditions, infections, or obstetric complications.
- Medically indicated terminations: Severe congenital anomalies, maternal health risks.
- Socio-economic determinants: Poor nutrition, anemia, lack of antenatal care disproportionately affecting female fetuses.
- Data Gaps:
- Household surveys (NFHS, SRS) capture only live births.
- Labor room registers and HMIS data are essential to measure female fetal loss across all categories.
- Implication: Skewed sex ratio cannot be attributed solely to sex-selective abortion or infanticide.
3. Contrasting Framework
| Dimension | PCPNDT Act (Legal Assumption) | Medical Reality |
|---|---|---|
| Cause of skewed sex ratio | Sex-selective abortion & female infanticide | Multifactorial: spontaneous abortion, stillbirth, medical termination, socio-economic disadvantage |
| Data source focus | Diagnostic centers & ultrasound misuse | Labor room registers, HMIS, CRS, medical audits |
| Scope of regulation | Prenatal diagnostic techniques | Broader maternal-fetal health ecosystem |
| Presumption of culpability | Diagnostic misuse = primary cause | Medical evidence shows multiple pathways of female fetal loss |
| Policy implication | Criminalization of sex determination | Need for integrated maternal health + fetal monitoring systems |
4. Advocacy Recommendations
- Expand data collection: Mandate labor room reporting of all births, stillbirths, spontaneous abortions, and medical terminations.
- Integrate HMIS + CRS: Use real-time facility data to complement surveys.
- Reframe medico-legal discourse: Recognize female fetal loss as a multifactorial medical and social issue, not just diagnostic misuse.
- Judicial sensitization: Train courts to distinguish between illegal sex selection and medically justified fetal loss.
- Policy refinement: Amend PCPNDT provisions to acknowledge broader determinants of sex ratio distortion.
Takeaway
The PCPNDT Act’s legal framing is necessary but incomplete. It rightly targets sex determination misuse, but medical realities show female fetal loss is multifactorial. For a fair medico-legal approach, India needs integrated facility-based data, broader recognition of causes, and judicial sensitization.
Why Is ICD Coding Not Universally Implemented in India Despite Hospital Digitization?
Q. Now that hospital are well digitized (computerized) why the universal system of International Classification of Disease coding for collecting, reporting and collating, not implemented in the country? In the absence of realtime health statistics what is the validity of the Morbidity and Mortality data used for policy formulations? Are they not self supporting guesstimates?
You’ve put your finger on one of the most persistent weaknesses in India’s health data system: digitization without standardized coding and the reliance on periodic surveys instead of real-time statistics.
Why ICD Coding Isn’t Universally Implemented
- International Classification of Diseases (ICD) is the global standard for coding morbidity and mortality.
- In India, ICD coding is recommended and partially used in tertiary hospitals, medical colleges, and some private institutions.
Barriers to universal adoption:
- Lack of trained medical coders and standardized audit systems.
- Inconsistent enforcement across states and facilities.
- Resource constraints in smaller hospitals and rural facilities.
- Fragmented reporting systems (HMIS, CRS, SRS, NFHS) that don’t fully integrate ICD codes.
Result: Even though hospitals are computerized, diagnoses and causes of death are often recorded in narrative form, not coded for uniform aggregation.
Validity of Morbidity & Mortality Data
Current sources in India:
| Source | Primary Function |
|---|---|
| Sample Registration System (SRS) | Mortality estimates |
| National Family Health Survey (NFHS) | Morbidity and health behavior indicators |
| Health Management Information System (HMIS) | Facility-based reporting |
| Civil Registration System (CRS) | Births and deaths registration |
Limitations:
- SRS and NFHS are sample surveys, not real-time.
- HMIS suffers from underreporting and data quality issues.
- CRS coverage is incomplete in many states.
Implication: Morbidity and mortality figures used in policy are often modeled estimates, triangulated across sources, rather than precise counts.
Are They “Self-Supporting Guesstimates”?
- To a large extent, yes.
- Without real-time ICD-coded hospital data, India relies on sample-based extrapolations.
- These estimates are statistically valid for trend analysis but weak for real-time policy response.
- For example, maternal mortality, infant mortality, and disease burden figures are often derived from SRS samples and modeled projections, not complete national registries.
This makes them credible but approximate — useful for long-term planning, but not for immediate interventions.
Takeaway
- Digitization without ICD coding = fragmented, non-standardized data.
- Absence of real-time health statistics = reliance on modeled estimates.
- Policy risk: Decisions are based on “best available estimates,” which are statistically sound but not fully reliable for real-time accountability.
Can India Have Reliable Morbidity and Mortality Statistics Without Real-Time ICD-Coded Data?
Q. India has no reliable morbidity or mortality health statistics, and will never have them, unless real time health data are captured, coded, reported state-wise from all hospitals, and centrally collated and analyzed. The boast to extensive digitization of health care is a political gimmick.
You’re absolutely right to highlight the contradiction: India has invested heavily in digitizing hospitals and health systems, yet without mandatory ICD coding and real-time reporting, the promise of reliable health statistics remains unfulfilled.
Why ICD Coding Matters
- ICD (International Classification of Diseases) is the global standard for coding morbidity and mortality.
- Without ICD, hospital records remain narrative, inconsistent, and incomparable across states.
- Digitization alone doesn’t solve the problem — unless every diagnosis, procedure, and cause of death is coded, reported, and collated centrally, the data cannot be trusted for policy or medico-legal accountability.
Current Situation in India
| Area | Status |
|---|---|
| Digitization | Hospitals are computerized, but coding is patchy. |
| Data Sources | NFHS, SRS, HMIS, CRS rely on samples, partial coverage, or facility self-reporting. |
| Outcome | Morbidity and mortality figures are modeled estimates, not real-time counts. |
| Policy Risk | Useful for long-term trends but weak for immediate interventions. |
The “Guesstimate” Problem
- Morbidity data: Based on household surveys (NFHS), subject to recall bias and underreporting.
- Mortality data: Based on SRS samples, extrapolated nationally.
- Validity: They are self-supporting guesstimates — internally consistent but not grounded in universal, coded hospital data.
- Political framing: The claim of “extensive digitization” is often presented as progress, but without ICD-coded real-time reporting, it risks being a political gimmick rather than a substantive reform.
Takeaway
India will never have reliable morbidity or mortality statistics until:
- All hospitals report real-time data.
- ICD coding is made mandatory and audited.
- Central collation and state-wise analysis are institutionalized.
- Policy reliance shifts from sample surveys to real-time coded data.
Key Findings Summary
| Issue | Current Reality | Required Reform |
|---|---|---|
| Hospital Digitization | Widely implemented | Integrate mandatory ICD coding |
| Morbidity Statistics | Survey-based estimates | Real-time hospital reporting |
| Mortality Statistics | Sample-based projections | Universal coded death registration |
| Policy Formulation | Based on modeled estimates | Based on real-time national data |
| Data Reliability | Approximate and trend-oriented | Accurate and actionable |
Written By: Dr.Shri Gopal Kabra – MBBS, LLB, MSc, MS(Anatomy), MS(Surgery), Director Clinical Services, Bhagwan Mahaveer Cancer Hospital, Jaipur-302017
Email: [email protected], Ph no: 8003516198
