Literature DB >> 26769992

Estimating mortality using data from civil registration: a cross-sectional study in India.

Mamta Gupta1, Chalapati Rao2, P V M Lakshmi1, Shankar Prinja1, Rajesh Kumar1.   

Abstract

OBJECTIVE: To analyse the design and operational status of India's civil registration and vital statistics system and facilitate the system's development into an accurate and reliable source of mortality data.
METHODS: We assessed the national civil registration and vital statistics system's legal framework, administrative structure and design through document review. We did a cross-sectional study for the year 2013 at national level and in Punjab state to assess the quality of the system's mortality data through analyses of life tables and investigation of the completeness of death registration and the proportion of deaths assigned ill-defined causes. We interviewed registrars, medical officers and coders in Punjab state to assess their knowledge and practice.
FINDINGS: Although we found the legal framework and system design to be appropriate, data collection was based on complex intersectoral collaborations at state and local level and the collected data were found to be of poor quality. The registration data were inadequate for a robust estimate of mortality at national level. A medically certified cause of death was only recorded for 965,992 (16.8%) of the 5,735,082 deaths registered.
CONCLUSION: The data recorded by India's civil registration and vital statistics system in 2011 were incomplete. If improved, the system could be used to reliably estimate mortality. We recommend improving political support and intersectoral coordination, capacity building, computerization and state-level initiatives to ensure that every death is registered and that reliable causes of death are recorded - at least within an adequate sample of registration units within each state.

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Mesh:

Year:  2015        PMID: 26769992      PMCID: PMC4709797          DOI: 10.2471/BLT.15.153585

Source DB:  PubMed          Journal:  Bull World Health Organ        ISSN: 0042-9686            Impact factor:   9.408


Introduction

Vital statistics are essential for tracking population dynamics, assessing health risks and evaluating health programmes. In India, a national civil registration and vital statistics system – hereafter called the vital statistics system – is still under development. Estimates of mortality are based on alternate data sources – e.g. censuses, the sample registration system and specific projects. Fragmentary data from such sources have been used recently to derive national estimates of mortality by age, sex and cause.– Inconsistency between estimates has hampered the evaluation of burdens posed by malaria, human immunodeficiency virus and road traffic collisions.– Before the introduction of the Registration of Births and Deaths Act in 1969, registration was non-uniform across states in India. The current system is supported by a national agency – the Office of the Registrar General of India– and registrars at state and local level. The accuracy of the system, particularly in relation to cause of death, is limited. In 1964–1965, the Office of the Registrar General introduced the sample registration system – as a separate entity from the vital statistics system – to measure fertility and mortality rates at national and state level in both urban and rural areas. In 2013, the sample registration system covered 7597 primary registration units with a combined population of 7.52 million. In urban areas, the Registration of Births and Deaths Act of 1969 requires attending physicians to certify causes of deaths under the medical certification of cause of death scheme – hereafter called the certification scheme. Although this scheme’s coverage has gradually expanded over the last four decades, it remains patchy. Between 1962 and 1999, causes of rural deaths were investigated in several surveys implemented by the Office of the Registrar General in collaboration with state health ministries. In selected primary health centres, paramedical staff used disease-specific algorithms and a structured questionnaire on symptoms and signs to assign causes of death. The job of ascertaining causes of deaths in rural areas was transferred to the sample registration system in 1999. Since then, the sample registration system has piloted verbal autopsy procedures and reported national summaries of causes of death as part of the Million Deaths Study., Each year, this system captures barely 0.5% of the estimated deaths in India. The current sample is sufficiently powered to measure infant mortality reliably but is inadequate to provide accurate measures of child, maternal or adult mortality or life expectancy at state or district level. Given its nationwide coverage, a strengthened vital statistics system could meet the growing needs for detailed, timely and reliable data on mortality. Therefore, we investigated the system’s design and functional status to facilitate the system’s development into an accurate and reliable source of mortality data.

Methods

We used several sources to assess the design of the vital statistics system and to analysis of broad characteristics of the system’s performance at national and subnational level (Box 1).– The quality of coverage was assessed in terms of the completeness of death registration and the quality of the recorded causes of death. The Registration of Births and Deaths Act, 1969. Model registration rules for Punjab state, 1999. Registrar General of India’s report on vital statistics, 2011. Population by age and sex for each state from 2011 census. Report on life tables from the sample registration system. Report on medical certification of cause of death. Registration data for Punjab state and districts, 2012., Vital statistics of India based on the civil registration system 2008, 2009 and 2010.– The completeness of death registration for individuals aged at least six years at time of death was estimated by two methods: (i) using the number of deaths reported to the vital statistics system and the Brass growth balance indirect demographic technique; and (ii) applying the sample registration system’s state-level mortality rates for 2011 to the state populations recorded in the 2011 census, to estimate the total mortality at national scale (the denominator). The number of deaths registered in the civil registration system is the numerator. Dividing the numerator by the denominator gives the percentage completeness of death registration. In this way, we analysed the trend in the completeness of death registration for the civil registration system from 1999 to 2011.– State specific estimates were computed from life tables based on the 2011 census population and the 2011 vital statistics system reported deaths, without any adjustments for incomplete death registration. The quality of the medically certified causes of death was considered to be good if the proportion of underlying causes assigned codes representing only symptoms, signs and ill-defined conditions was less than 10%. The vital statistics system’s functional aspects were assessed in three districts of Punjab state, where the health sector is responsible for the system’s implementation. For the year 2012, the districts of Fatehgarh Sahib, Bathinda and Amritsar were selected to represent three levels of completeness of death registration: less than 80%, 80–90% and more than 90%, respectively. Information on the infrastructure was collected from a stratified random sample of 21 primary registration units that, together, covered primary, secondary and tertiary health facilities in both urban and rural settings. In each registration unit, a preliminary questionnaire was used to record information on registration operations. The target sample size for the evaluation of the completeness and accuracy of the recorded data – i.e. 300 records each for births, deaths and causes of death – was based on the expectation that, within a 10% margin of error at a 95% level of confidence, 70% of the records would be accurate and complete. A sample of 15 birth and death reports for 2013 was randomly selected from each of the 21 study registration units before the corresponding entries in the other relevant registers and reports were scrutinized. In addition, 80 certification scheme forms from each district hospital in the study area and 100 from the teaching hospital in the study area were evaluated. Documents were evaluated for the completeness, accuracy and consistency of the recorded information across individual reports, registers and monthly returns. A record of a live birth or stillbirth was only considered complete if it noted the maternal age, date and place of birth, sex, parity, birth weight and address of the mother. A death record was only considered complete if it noted the date, place and cause of death and the type of medical attention. We did semi-structured interviews with seven registrars, four medical officers and four medical staff who did coding, to evaluate their knowledge of, attitudes towards, and practices within the vital statistics system.

Results

Administrative aspects

The Registration of Births and Deaths Act of 1969 mandates compulsory registration, provides definitions of vital events and key terms and recommends registration formats and processes for statistical compilation. At state level, model registration rules elaborate on the operational aspects. In general, the head of the affected household and/or the village headman is responsible for notification of births and deaths in the community whereas the attendant physicians are responsible for notification of such events when they occur in health facilities. However, the Act mentions several additional notifiers – e.g. community health staff and local police – who may be among the first people to observe a death. The dead person’s usual place of residence – as well as the place of birth or death – should be noted. There are fines for non-compliance., While the legal framework is comprehensive, it permits the registration of death after cremation or burial, fails to call for periodic standardized inspections of the primary registration units, and fails to provide adequate detail on data standards – particularly in terms of the registration of causes of death., India’s vital statistics system follows a model of decentralization with multiple-level administration. Although the system is led by the Office of the Registrar General, each state has an independent structure led by a nominated state registrar from India’s administrative or health sectors or the Statistics Office. Within each state, registration is delegated to local government units from different sectors (Table 1). Only Punjab (Fig. 1) and eight other states have a single sector that holds responsibility for the vital statistics system at both state and local level. Funding for some of the vital statistics system’s components – e.g. training, stationery, computers and photocopiers – is provided by the State Directorate of Census Operations. The latter is a subordinate office of the Office of the Registrar General that does not have any operational role in the vital statistics system. The complexity of the system’s organizational structure underscores the need for close intersectoral collaboration.
Table 1

Design and functional status of death registration systems, India, 2011

AreaPopulation (millions)Sector implementing system
No. of deaths registered by CRVSSEstimates for males/females
CRVSS reporting coverage in rural/urban areas, %bEstimates of CRVSS completeness, % of deaths
Medically certified deaths
State levelSub-district levelAdult mortality,a deaths per 1000 populationLife expectancy at birth, years
Based on CRVSS datacBased on SRS datadTotal no.No. of ill-defined causes (%)
Based on CRVSS dataBased on SRS data
Major statee
Andhra Pradesh84.6HealthAdmin420 646120/9076/8464/6872/100466666 4426 179 (9.3)
Arunachal Pradesh1.4StatisticsAdmin1 560NANANANANA2250712 (2.4)
Assam31.2HealthHealth111 054NANA61/6391/100NA4516 1600 (0.0)
Bihar104.1StatisticsAdminf155 176NANA65/66NANA247 429334 (4.5)
Chhattisgarh25.5StatisticsAdmin114 842NANANA73/100NA609 550974 (10.2)
Delhi16.8StatisticsHealth112 142211/12162/70NA100/1004010063 61117 302 (27.2)
Goa1.5StatisticsAdmin11 326209/7566/73NA100/100509511 3211 574 (13.9)
Gujarat60.4HealthAdmin324 080148/7568/7665/69100/100548270 27515 461 (22.0)
Haryana25.4HealthHealth153 530NANA67/69100/100NA92NANA
Himachal Pradesh6.9HealthAdmin42 524134/7269/7668/72100/10059935 014266 (5.3)
Jammu and Kashmir12.5HealthPolice35 42555/5791/10069/7160/5638552440 (0.0)
Jharkhand33.0StatisticsAdminf116 615NANANANANA5441228 (6.8)
Karnataka61.1StatisticsAdmin384 745171/8867/7565/7095/975791123 2211 972 (1.6)
Kerala33.4AdminAdmin244 295129/4969/7672/77100/1005410029 252380 (1.3)
Madhya Pradesh72.6StatisticsAdminf351 621137/11274 / 8261/6496/99556037 1319 877 (26.6)
Maharashtrag112.4HealthAdmin633 206262/14770/7668/7292/955388215 618 24 365 (11.3)
Manipur2.9HealthHealthh4 253NANANA57/40NA422 215128 (5.8)
Meghalaya3.0HealthHealth14 848170/12670/79NA100/9142721 681178 (10.6)
Mizoram1.1AdminEducation5 484196/8069/79NA87/841002 537104 (4.1)
Nagaland2.0AdminEducation6 961114/10591/86NA93/9437931620 (0.0)
Odisha42.0HealthHealth277 484149/11568/7262/64NA578033 9752 582 (7.6)
Punjab27.7HealthHealth187 675167/10268/7667/72100/100699919 9201 614 (8.1)
Rajasthan68.5StatisticsEducation360 560134/9269/7565/6896/100807942 4171 824 (4.3)
Sikkim0.6HealthHealth3 094162/10470/77NA100/10044901 27531 (2.4)
Tamil Nadu72.1HealthAdmin476 709183/9068/7567/7199/994395149 9463 059 (20.4)
Tripura3.7HealthAdmin8 911NANANA100/100NA494 553961 (21.1)
Uttar Pradesh199.8HealthHealth751 596NANA62/64NANA47NANA
Uttarakhand10.1HealthHealth29 300NANANA75/78NA473 800319 (8.4)
West Bengal91.3HealthAdmin371 079NANA67/7188/75NA6721 484494 (2.3)
All India1210.2Admin5 735 082NANA65/6892/96NA67965 992118 817 (12.3)

Admin: administrative; CRVSS: civil registration and vital statistics system; NA: not available; SRS: sample registration system.

a Probability of dying between ages 15–60 years per 1000 population.

b Defined as the percentage of all rural/urban registration units throughout the state that submitted monthly and annual reports.

c Estimated using the Brass growth balance indirect demographic technique.

d Estimated by applying SRS state-level mortality rates to the state populations recorded in 2011 census.

e This excludes the Union Territories of India.

f With the health sector in a secondary role, supporting the administrative sector.

g The CRVSS mortality data reported here for Maharashtra were collected in 2010.

h With the administrative sector in a secondary role, supporting the health sector.

Fig. 1

Organizational flowchart of the civil registration and vital statistics system to report deaths in Punjab, India, 2013

Admin: administrative; CRVSS: civil registration and vital statistics system; NA: not available; SRS: sample registration system. a Probability of dying between ages 15–60 years per 1000 population. b Defined as the percentage of all rural/urban registration units throughout the state that submitted monthly and annual reports. c Estimated using the Brass growth balance indirect demographic technique. d Estimated by applying SRS state-level mortality rates to the state populations recorded in 2011 census. e This excludes the Union Territories of India. f With the health sector in a secondary role, supporting the administrative sector. g The CRVSS mortality data reported here for Maharashtra were collected in 2010. h With the administrative sector in a secondary role, supporting the health sector. Organizational flowchart of the civil registration and vital statistics system to report deaths in Punjab, India, 2013 In 2001, the Office of the Registrar General issued separate instructions from the Registration of Births and Deaths Act and the sample registration system, on the compilation of vital statistics and called for detailed tabulations of deaths by age and sex from each state. Guidelines and formats for the standardized coding and compilation of data on causes of death have also been issued as part of the certification scheme.

Technical perspectives

Notifications for live births, stillbirths, deaths and causes of death conform to international standards. Medically certified causes of death are coded according to the International Classification of Diseases, tenth revision (ICD-10)., In practice, there are several areas in which data completeness or quality could be improved. For 2–5% of registered deaths, for example, sex and/or age is not recorded. In India, the oldest age group considered in the summary statistics on deaths is older than 70 years whereas international standards require it to be older than 85 years. Although place of death is usually recorded, place of usual residence is often missing., The data from Punjab revealed that, although rural health centres generally appeared to be adequately staffed for registration, less than 50% of the statistical staff positions available within the state’s health sector were filled. Most (3/4) of the hospitals we investigated had no staff designated for the ICD-10 coding of causes of death. Most of the local registrars we interviewed had an inadequate understanding about the filling of forms, the registration of stillbirths and the processing of delayed registrations. Fifteen (71%) of the 21 study registration units had never received a visit by a district or state official who wished to assess the quality of their registration data. The field personnel we interviewed generally believed that the training programmes associated with the vital statistics system were too theoretical and lacked practical field-based exercises. Many of the nurses and pharmacists trained in coding causes of death had subsequently found themselves to be uninvolved in such coding.

Data quality

Close to six million deaths – i.e. more than two-thirds of the 8 503 372 deaths estimated to occur annually – were registered in India’s vital statistics system during 2011. Table 1 presents summary mortality indicators for India’s major states. The operational functionality of the system is indicated by the high levels of reporting coverage across India. The detailed data needed for the construction of life tables were available for two-thirds of the states. The mortality estimates indicated a gradual improvement in the completeness of death registration between 1999 and 2011 (Fig. 2) – but these were fairly crude as they took no account of the variations arising from sample distribution, sampling error or sex or age differentials. Estimates of life expectancy based on the vital statistics system were sometimes implausibly high – and often much higher than the estimates based on the sample registration system – probably because of the relative incompleteness of the data in the vital statistics system.
Fig. 2

The completeness of death registration, India, 1999–2011

The completeness of death registration, India, 1999–2011 Notes: Completeness was calculated as the percentage of the estimated number of deaths occurring in each year. This number was estimated using data recorded by the sample registration system. The results of the district-level analysis from Punjab showed that (Table 2) indicate disproportionately high numbers of registered deaths in Amritsar, Faridkot, Jalandhar and Ludhiana, probably because of the preferential utilization of tertiary care in these cities by people from the surrounding districts. The death-related data in the vital statistics system were sufficiently complete to allow estimates of the age- and sex-specific levels of mortality even at district level (Table 2). However, there were more registered deaths among men than women and there were problems with the quality of the registration documents. For example, the data for most stillbirths and almost half of the live births were recorded incompletely (Fig. 3). The recorded information for key variables was found accurate in 95% (285/300) of the birth register’s forms and in 83% (249/300) of the death register’s forms. For neonatal and infant deaths, it was rare to record age at death in terms of months, days and hours. On death reports, the columns for recording information on specific factors – e.g. pregnancy, smoking and alcohol use – were usually left empty. Even if such information was recorded on a death form, there was no space for it on the corresponding death register. Death registers only captured the legal variables required for the issuance of death certificates. Of the certification scheme forms that we evaluated, over half (163/300) recorded an ill-defined condition – e.g. heart failure or cardiopulmonary arrest – as the underlying cause of death.
Table 2

Mortality and life expectancy in the districts of Punjab, India, 2012

DistrictPopulation (millions)No. of deaths registered by CRVSSEstimates for males/females
CRVSS completeness, %
Adulta deaths per 1000 populationLife expectancy at birth, yearsb
Amritsar2.521 473172/12464/6994
Bathinda1.48768149/9071/8165
Barnala0.63862161/9670/7666
Faridkot0.65251222/14067/7366
Fatehgarh Sahib0.63378140/7172/7781
Ferozepur2.08346108/7274/8049
Gurdaspur2.314 529121/7770/7681
Hoshiarpur1.611 836174/10968/7566
Jalandhar2.219 356199/17263/6971
Kapurthala0.84897129/8974/8457
Ludhiana3.526 538307/22169/7745
Mansa0.84960193/6869/7673
Moga1.07028111/9070/7488
Muktsar0.95435128/12569/6670
Patiala1.913 38074/5468/7296
Roopnagar0.74597150/6369/7569
Sahibzada Ajit Singh Nagar1.05870154/9570/7658
Sangrur1.711 069147/9370/7673
Shahid Bhagat Singh Nagar0.65281219/13364/7561
Tarn Taran1.18028210/12866/7382

CRVSS: civil registration and vital statistics system.

a Probability of dying between ages 15–60 years per 1000 population.

b Based on the CRVSS data.

c Estimated from the CRVSS data, using the Brass growth balance indirect demographic technique.

Fig. 3

Completeness of the vital statistics records in three districts of Punjab, India, 2013

CRVSS: civil registration and vital statistics system. a Probability of dying between ages 15–60 years per 1000 population. b Based on the CRVSS data. c Estimated from the CRVSS data, using the Brass growth balance indirect demographic technique. Completeness of the vital statistics records in three districts of Punjab, India, 2013 MCCD: medical certification of causes of death. Notes: A record of a live birth or stillbirth was only considered complete if it noted the maternal age, date and place of birth, sex, parity, birth weight and address. A death record was only considered complete if it noted the date, place and cause of death and the type of medical attention. A medically certified cause was recorded for just 965 992 (16.8%) of the 5 735 082 deaths registered in 2011 (Table 1). The most recent data available on causes of death – including the leading causes for 2011 (Table 3) – come from the certification scheme. In 2011, vague or ambiguous categories such as “ill-defined cause”, “septicaemia” and “other heart disease” accounted for nearly a quarter of all medically certified causes. In several states more than 10% of deaths were assigned to ill-defined causes (Table 1). The failure to assign an accurate cause of death reduces the value of all of the data on cause of death. Furthermore, since most of the deaths with medically certified causes occurred in health facilities, the data collected on causes of death may not reflect broader national trends.
Table 3

Leading causes of death recorded within the medical certification of causes of death scheme, India, 2011

RankMales (n = 587 375)
Females (n = 364 403)
Recorded causeICD-10 codes% of medically certified deathsRecorded causeICD-10 codes% of medically certified deaths
1Other heart diseaseI26–I5110.6Other heart diseaseI26–I5111.1
2Ischaemic heart diseaseI20–I259.2Ischaemic heart diseaseI20–I258.7
3Perinatal conditionP00–P967.1Perinatal conditionP00–P967.7
4Cerebrovascular diseaseI60–I694.6Cerebrovascular diseaseI60–I694.5
5Respiratory tuberculosisA15–A164.1SepticaemiaA40–A414.5
6SepticaemiaA40–A413.9Hypertensive diseaseI10–I143.9
7Diseases of the liverK70–K763.8Diabetes mellitusE10–E143.8
8Chronic lower respiratory diseaseJ40–J473.6Chronic lower respiratory diseaseJ40–J472.9
9Diabetes mellitusE10–E143.4Respiratory tuberculosisA15–A162.8
10Hypertensive diseasesI10–I143.3Renal failureN17–N192.3
11Symptoms or ill-defined conditionR00–R9912.4Symptoms or ill-defined conditionsR00–R9912.7

ICD-10: International Classification of Diseases, tenth revision.

ICD-10: International Classification of Diseases, tenth revision. An attempt was made to identify and evaluate the major sources of information on the causes of deaths occurring in Punjab (Table 4). None of the five identified sources was found to be adequate.
Table 4

Sources of cause of death statistics in Punjab, India, 2013

Characteristic  Data source
Civil registration and vital statistics system
National Health Mission’s report to Ministry of Health and WelfarePunjab Health System Corporation’s health facility reportSample registration system’s verbal autopsy reports
Death reportsMedical certifications
Completeness,% of deaths (n = 189 571)99.0 (187 675)10.3 (19 620)35.6 (67 538)5.0 (9 433)1.8 (3 378)
Most recent data available, year20112010201220092001–2003
Source of cause of deathLay individualsAttending physiciansLay individuals or paramedical staffMedical record cover sheetsPhysician review of verbal autopsies
% of deaths attributed to ill-defined causes (no. ill-defined causes/all causes)NAa7.4 (1 452/19620)50.0 (33 769/67538)8.0 (755/9433)9.9 (338/3378)
Limitations of data sourceCause given in non-medical termsNot representative of either urban or rural population. Lacking a defined source populationConfined to rural settingsExcludes deaths at homeDistrict-level and block-level data not available

NA: not available.

a Computerized data from Shahid Bhagat Singh Nagar district indicates that 60% of the deaths recorded in the district were assigned as ill-defined causes.

NA: not available. a Computerized data from Shahid Bhagat Singh Nagar district indicates that 60% of the deaths recorded in the district were assigned as ill-defined causes.

Societal perspectives

The Indian Government has undertaken several initiatives to strengthen the vital statistics system. In some states, the certification scheme requires all health facilities to register in-facility deaths. The government-led computerization of all birth and death reports, which has been initiated in some districts of a few states, offers the possibility of linking data from different sources. National and state-level interdepartmental committees have been established to review the performance of the vital statistics system annually. At community level, we did not come across any direct evidence of targeted campaigns to increase public awareness of the vital statistics system except for a few public notices. Although communities increasingly recognize the need for death certificates for adults – e.g. to effect property transfers and other legal or financial transactions – they appear to be less inclined to seek death certificates for infants or appreciate the importance of the accurate reporting of causes of death.

Discussion

For several states, mortality data for 2011 from the vital statistic system were available in sufficient detail to enable life-table analyses – albeit with known biases. Despite considerable heterogeneity in the vital statistics system’s administrative structure and organization – both among and within states – the data collected by the system indicate that functional intersectoral collaboration exists but needs to be strengthened, perhaps through re-alignment. For example, there was a considerable increase in data capture after Haryana state transferred responsibilities for birth and death registration from the police to the health sector in 2005.To enhance coordination, the Ministry of Health and Family Welfare – in collaboration with the Office of the Registrar General – convened an interdepartmental coordination committee. The members of this committee, who met several times in 2012, made several important recommendations that could strengthen the vital statistics system (Box 2). Any such strengthening activities need to emphasize the roles and responsibilities of private health facilities and personnel in the registration of births, deaths and causes of death. We did not present any findings on deaths among children younger than six years because few deaths were registered in this age group. A protocol for the routine review of maternal and child deaths has recently been developed to identify the relevant gaps in health service delivery., This initiative should be integrated with the local operations of the vital statistics system, to generate routine, low-cost, local measures of mortality. The observation of more registered deaths among men than among women needs to be further investigated. It remains unclear if this represents a relatively low probability of registration for women and/or true sex differences in the levels of mortality. There are limitations in our estimates of registration completeness, because the indirect demographic technique we used to estimate the completeness is based on several assumptions – e.g. constant fertility and mortality and zero net migration – that do not hold at state level in India. Some of the life expectancies estimated from the data in the vital statistics system are implausibly high – probably because of incomplete death registration. The life expectancies estimated from the sample registration system should be considered more reliable, given that system’s rigorous internal processes for verification and follow-up of death recording in each primary sampling unit. Incomplete registration was identified as a problem in previous assessments of civil registration in India. While the vital statistics system’s reporting coverage is high, greater attention is required to ensure the quality of the coverage in each registration unit. The reporting of causes of death appears to be a major weakness of the vital statistics system. There is scope to increase the coverage of the certification scheme in several states, as well as to improve the reporting compliance of both government and private health facilities. Also, classification of all deaths by place of usual residence should improve the derivation of mortality indicators at state, district and even community levels. Greater emphasis is required for improving the quality of the medical certification of causes of death and reducing the large numbers of deaths that – despite the availability of a detailed manual on cause of death certification – are assigned ill-defined causes. Poor cause of death certification has even been observed in India’s teaching hospitals., Comparisons between the causes of death reported on the certification scheme’s forms and those derived via an expert physician review of medical records are needed. The current design and operational status of the vital statistics system provide a suitable platform for launching a programme to improve the data available for mortality measurement even at district level. Given its central role in the notification of vital events, determination of causes of death and compilation and use of the registration of data, India’s health sector could and should have a key role in strengthening the national vital statistics system. We need further analyses of the system’s performance to guide the system’s strengthening. Previous research has indicated that, for a population with India’s demographic characteristics and mortality patterns, detailed information on approximately 20 000 deaths per state to measure cause-specific mortality reliably by age and sex, is needed. Therefore, detailed information on about 0.7 million deaths for India as a whole is needed. Such a sample is potentially available within the existing sampling frame of the vital statistics system’s registration units. In rural areas, a representative sample of primary health centres could be selected in each state. All home and in-facility deaths registered from the catchment areas of the selected health centres could then be followed up to ascertain causes of death – via verbal autopsies by health centre staff and by examination of the forms of the certification scheme, respectively. For urban areas, the sample could comprise a selection of municipal wards. In the future, computerization of the vital statistics system and the records of the certification scheme should enhance data compilation and analysis, especially when combined with the accurate recording of place of usual residence. If we are to have reliable mortality statistics for India, state-level plans need to be supported by greater intersectoral coordination, improvements in the training of human resources and the general strengthening of infrastructure. Mandate completion of death reports before cremation or burial and for maintenance of records at all cremation and burial grounds; emphasize that death reports must be completed before the cremation or burial of fetuses, infants and children. Ask Office of the Registrar General of India to instruct state chief registrars to monitor civil registration and vital statistics system compliance across all districts. (Recommendation proposed in the 2012 Report of the Committee on Strengthening of Civil Registration System.) In each state, ask Office of the Registrar General of India’s state-level director of census operations to depute one official to liaise and coordinate civil registration and vital statistics system activities with the state chief registrar. (Recommendation proposed in the 2012 Report of the Committee on Strengthening of Civil Registration System.) Use the National Health Mission’s resources – e.g. personnel, information technology equipment, printing services and public awareness campaigns – to support civil registration and vital statistics system operations in all states, whether or not the health sector is responsible for registration at any level. Establish interdepartmental coordination committees at state and district level to monitor intersectoral collaboration, evaluate performance and implement strengthening mechanisms for the civil registration and vital statistics system. Design protocols for the reporting, registration and ascertainment of cause of death of individuals found dead on arrival at hospitals. Provide resources and protocols for household enquiries into cause of death in follow-up to death reporting and registration; such enquiries should use standardized formats and be conducted by designated government health staff. Develop standardized requirements for coding and classification of causes of death in the medical certification of cause of death scheme. Produce and supply standardized civil registration and vital statistics system software at district level for data entry, data archiving and processing for all stillbirths, live births and deaths. (Recommendation proposed in the 2012 Report of the Committee on Strengthening of Civil Registration System.) Develop revised standards for statistical compilation in terms of age group, place of occurrence of event, place of usual residence and, where applicable, multiple causes per death. Develop standard framework to evaluate data quality at district level – including reporting coverage, timeliness and data accuracy. Promote the triangulation of district-level civil registration and vital statistics system data on vital events with related data from other sources – e.g. police or health programme records. Encourage feedback on local vital statistics to registration units and state-level registrar, to strengthen and monitor data quality. Strengthen the civil registration and vital statistics system supervisory role of sub-district and district-level registrars and designated health sector officials in states where the health department is not directly responsible for civil registration and vital statistics system operations. (Recommendation proposed in the 2012 Report of the Committee on Strengthening of Civil Registration System.) Design and implement operations research activities at state level, for the empirical evaluation of the completeness of death registration and the validity of causes of death. Initiate collection and compilation of vital statistics in adequate samples for robust mortality measurement at state level on annual basis. Make qualified personnel and information technology infrastructure available at all levels – but especially in registration units and the offices of district registrars. (Recommendation proposed in the 2012 Report of the Committee on Strengthening of Civil Registration System.) At all levels, promote training to support reforms in structure, system design and data management processes. Train field staff in household cause-of-death enquiries, medical certification and coding of causes of death using International Classification of Diseases. At national level, engage with the Unique Identification Authority of India to enhance civil registration and vital statistics system performance. Conduct workshops for health bureaucrats and planning department staff on civil registration and vital statistics system data quality and vital statistics, and gain political support and advocacy for directing resources for civil registration and vital statistics system reforms and strengthening initiatives. Invite participation and collaboration from development partners and other stakeholders with interest in the civil registration and vital statistics system. (Recommendation proposed in the 2012 Report of the Committee on Strengthening of Civil Registration System.) Organize special registration events – with suitable local publicity – to facilitate completion of delayed registration. Promote local networks of notifiers for death registration – particularly for stillbirths, deaths among infants, women and the elderly – who could liaise with bereaved relatives and assist in their compliance with registration requirements. Mobilize participation in birth registration as an official requirement for school enrolment. (Recommendation proposed in the 2012 Report of the Committee on Strengthening of Civil Registration System.)
  10 in total

1.  Cause of death reporting systems in India: a performance analysis.

Authors:  P Mahapatra; P V Chalapati Rao
Journal:  Natl Med J India       Date:  2001 May-Jun       Impact factor: 0.537

2.  Design options for sample-based mortality surveillance.

Authors:  Stephen Begg; Chalapati Rao; Alan D Lopez
Journal:  Int J Epidemiol       Date:  2005-05-23       Impact factor: 7.196

3.  The use of cause-of-death statistics for health situation assessment: national and international experiences.

Authors:  L T Ruzicka; A D Lopez
Journal:  World Health Stat Q       Date:  1990

4.  The quality of police data on RTC fatalities in India.

Authors:  Magdalena Z Raban; Lalit Dandona; Rakhi Dandona
Journal:  Inj Prev       Date:  2014-04-15       Impact factor: 2.399

5.  Educational intervention to improve death certification at a teaching hospital.

Authors:  Himanshu Pandya; Neeta Bose; Ripal Shah; Nayanjeet Chaudhury; Ajay Phatak
Journal:  Natl Med J India       Date:  2009 Nov-Dec       Impact factor: 0.537

6.  HIV mortality and infection in India: estimates from nationally representative mortality survey of 1.1 million homes.

Authors:  Prabhat Jha; Rajesh Kumar; Ajay Khera; Madhulekha Bhattacharya; Paul Arora; Vendhan Gajalakshmi; Prakash Bhatia; Derek Kam; Diego G Bassani; Ashleigh Sullivan; Wilson Suraweera; Catherine McLaughlin; Neeraj Dhingra; Nico Nagelkerke
Journal:  BMJ       Date:  2010-02-23

7.  Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.

Authors: 
Journal:  Lancet       Date:  2014-12-18       Impact factor: 79.321

8.  Impact of policy initiatives on civil registration system in haryana.

Authors:  Pravin Kumar Singh; Manmeet Kaur; Nidhi Jaswal; Rajesh Kumar
Journal:  Indian J Community Med       Date:  2012-04

9.  Prospective study of one million deaths in India: rationale, design, and validation results.

Authors:  Prabhat Jha; Vendhan Gajalakshmi; Prakash C Gupta; Rajesh Kumar; Prem Mony; Neeraj Dhingra; Richard Peto
Journal:  PLoS Med       Date:  2005-12-20       Impact factor: 11.069

10.  Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010.

Authors:  Rafael Lozano; Mohsen Naghavi; Kyle Foreman; Stephen Lim; Kenji Shibuya; Victor Aboyans; Jerry Abraham; Timothy Adair; Rakesh Aggarwal; Stephanie Y Ahn; Miriam Alvarado; H Ross Anderson; Laurie M Anderson; Kathryn G Andrews; Charles Atkinson; Larry M Baddour; Suzanne Barker-Collo; David H Bartels; Michelle L Bell; Emelia J Benjamin; Derrick Bennett; Kavi Bhalla; Boris Bikbov; Aref Bin Abdulhak; Gretchen Birbeck; Fiona Blyth; Ian Bolliger; Soufiane Boufous; Chiara Bucello; Michael Burch; Peter Burney; Jonathan Carapetis; Honglei Chen; David Chou; Sumeet S Chugh; Luc E Coffeng; Steven D Colan; Samantha Colquhoun; K Ellicott Colson; John Condon; Myles D Connor; Leslie T Cooper; Matthew Corriere; Monica Cortinovis; Karen Courville de Vaccaro; William Couser; Benjamin C Cowie; Michael H Criqui; Marita Cross; Kaustubh C Dabhadkar; Nabila Dahodwala; Diego De Leo; Louisa Degenhardt; Allyne Delossantos; Julie Denenberg; Don C Des Jarlais; Samath D Dharmaratne; E Ray Dorsey; Tim Driscoll; Herbert Duber; Beth Ebel; Patricia J Erwin; Patricia Espindola; Majid Ezzati; Valery Feigin; Abraham D Flaxman; Mohammad H Forouzanfar; Francis Gerry R Fowkes; Richard Franklin; Marlene Fransen; Michael K Freeman; Sherine E Gabriel; Emmanuela Gakidou; Flavio Gaspari; Richard F Gillum; Diego Gonzalez-Medina; Yara A Halasa; Diana Haring; James E Harrison; Rasmus Havmoeller; Roderick J Hay; Bruno Hoen; Peter J Hotez; Damian Hoy; Kathryn H Jacobsen; Spencer L James; Rashmi Jasrasaria; Sudha Jayaraman; Nicole Johns; Ganesan Karthikeyan; Nicholas Kassebaum; Andre Keren; Jon-Paul Khoo; Lisa Marie Knowlton; Olive Kobusingye; Adofo Koranteng; Rita Krishnamurthi; Michael Lipnick; Steven E Lipshultz; Summer Lockett Ohno; Jacqueline Mabweijano; Michael F MacIntyre; Leslie Mallinger; Lyn March; Guy B Marks; Robin Marks; Akira Matsumori; Richard Matzopoulos; Bongani M Mayosi; John H McAnulty; Mary M McDermott; John McGrath; George A Mensah; Tony R Merriman; Catherine Michaud; Matthew Miller; Ted R Miller; Charles Mock; Ana Olga Mocumbi; Ali A Mokdad; Andrew Moran; Kim Mulholland; M Nathan Nair; Luigi Naldi; K M Venkat Narayan; Kiumarss Nasseri; Paul Norman; Martin O'Donnell; Saad B Omer; Katrina Ortblad; Richard Osborne; Doruk Ozgediz; Bishnu Pahari; Jeyaraj Durai Pandian; Andrea Panozo Rivero; Rogelio Perez Padilla; Fernando Perez-Ruiz; Norberto Perico; David Phillips; Kelsey Pierce; C Arden Pope; Esteban Porrini; Farshad Pourmalek; Murugesan Raju; Dharani Ranganathan; Jürgen T Rehm; David B Rein; Guiseppe Remuzzi; Frederick P Rivara; Thomas Roberts; Felipe Rodriguez De León; Lisa C Rosenfeld; Lesley Rushton; Ralph L Sacco; Joshua A Salomon; Uchechukwu Sampson; Ella Sanman; David C Schwebel; Maria Segui-Gomez; Donald S Shepard; David Singh; Jessica Singleton; Karen Sliwa; Emma Smith; Andrew Steer; Jennifer A Taylor; Bernadette Thomas; Imad M Tleyjeh; Jeffrey A Towbin; Thomas Truelsen; Eduardo A Undurraga; N Venketasubramanian; Lakshmi Vijayakumar; Theo Vos; Gregory R Wagner; Mengru Wang; Wenzhi Wang; Kerrianne Watt; Martin A Weinstock; Robert Weintraub; James D Wilkinson; Anthony D Woolf; Sarah Wulf; Pon-Hsiu Yeh; Paul Yip; Azadeh Zabetian; Zhi-Jie Zheng; Alan D Lopez; Christopher J L Murray; Mohammad A AlMazroa; Ziad A Memish
Journal:  Lancet       Date:  2012-12-15       Impact factor: 79.321

  10 in total
  20 in total

1.  A cross-sectional study of awareness and practices regarding animal bites in rural community, North India.

Authors:  Tarundeep Singh; Shuchi Mahajan; Neha Dahiya
Journal:  J Family Med Prim Care       Date:  2020-06-30

2.  Comparing pattern of musculoskeletal injuries prior to and during COVID-19 lockdown: A time-trend case study from a tertiary level Trauma Center of Northern India.

Authors:  Anshul Dahuja; Kapil Bansal; Nikhil Gupta; Sagar Arora; Radhe Shyam Garg; Mamta Gupta
Journal:  J Family Med Prim Care       Date:  2021-01-30

3.  Comparative performance of verbal autopsy methods in identifying causes of adult mortality: A case study in India.

Authors:  Mamta Gupta; P V M Lakshmi; Shankar Prinja; Tarundeep Singh; Titiksha Sirari; Chalapati Rao; Rajesh Kumar
Journal:  Indian J Med Res       Date:  2021-04       Impact factor: 5.274

4.  Global Burden of Childhood Tuberculosis.

Authors:  Helen E Jenkins
Journal:  Pneumonia (Nathan)       Date:  2016-11-24

5.  The global burden of tuberculosis mortality in children: a mathematical modelling study.

Authors:  Peter J Dodd; Courtney M Yuen; Charalambos Sismanidis; James A Seddon; Helen E Jenkins
Journal:  Lancet Glob Health       Date:  2017-09       Impact factor: 26.763

6.  Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden of Disease Study 2016.

Authors: 
Journal:  Lancet       Date:  2017-09-16       Impact factor: 79.321

7.  Evaluation of data sources and approaches for estimation of influenza-associated mortality in India.

Authors:  Venkatesh Vinayak Narayan; Angela Danielle Iuliano; Katherine Roguski; Partha Haldar; Siddhartha Saha; Vishnubhatla Sreenivas; Shashi Kant; Sanjay Zodpey; Chandrakant S Pandav; Seema Jain; Anand Krishnan
Journal:  Influenza Other Respir Viruses       Date:  2017-12-02       Impact factor: 4.380

8.  Identification of publicly available data sources to inform the conduct of Health Technology Assessment in India.

Authors:  Laura Downey; Neethi Rao; Lorna Guinness; Miqdad Asaria; Shankar Prinja; Anju Sinha; Rajni Kant; Arvind Pandey; Francoise Cluzeau; Kalipso Chalkidou
Journal:  F1000Res       Date:  2018-02-28

9.  Etiology and mode of presentation of chronic liver diseases in India: A multi centric study.

Authors:  Partha S Mukherjee; Sreenivas Vishnubhatla; Deepak N Amarapurkar; Kausik Das; Ajit Sood; Yogesh K Chawla; Chundamannil E Eapen; Prabhakar Boddu; Varghese Thomas; Subodh Varshney; Diamond Sharma Hidangmayum; Pradip Bhaumik; Bhaskar Thakur; Subrat K Acharya; Abhijit Chowdhury
Journal:  PLoS One       Date:  2017-10-26       Impact factor: 3.240

10.  Risk factors for maternal mortality among 1.9 million women in nine empowered action group states in India: secondary analysis of Annual Health Survey data.

Authors:  Geneviève Horwood; Charles Opondo; Saswati Sanyal Choudhury; Anjali Rani; Manisha Nair
Journal:  BMJ Open       Date:  2020-08-20       Impact factor: 2.692

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