Abstract - Confex

1

Completeness of Death Registration under CRS and Construction a Life

Table with Adjusted ASDR for India and States

Anurag Verma1*, Gyan Prakash Singh1, Abhinav Singh2

1. Dept. of Community Medicine, IMS, BHU, Varanasi, India

2. Dept. of Statistics, University of Allahabad, Allahabad, India

*presenting author, e-mail:[email protected]

Abstract

The main objective of the present study was to estimate the completeness of deaths registration

under Civil Registration System in India and its major states during 2001-2010. In the present project

methods proposed by Bennett and Horiuchi (1983) have been used to estimate the completeness of

death registration. The next aim of this study is to construct a Life Table for Adjusted ASDR. The

analysis of life table was done using adjusted ASDR which gives an accurate measure of life

expectancy in comparison to another method. The completeness of death registration in India is 49%

and 39% in males and females respectively. The adjusted Life Expectancy at birth as 66 and 69 years

for males and females respectively in India. Finally, The National mortality statistics from civil

registration were rated unsatisfactory for coverage and completeness of death registration in India.

Keywords: Mortality Statistics; Vital Statistics; Life Expectancy; Indirect Estimation Technique;

MortPak.

Introduction

The value of good-quality mortality data for public health is widely acknowledged. WHO

comparative assessments rated the quality of Indian mortality data as present is low. Since then,

focused initiatives were introduced to improve Civil Registration and Vital Statistics. Furthermore,

Indian mortality data are widely used by researchers and international development agencies as the

basis for making policy and program. It is hence important to assess the quality of more recent Indian

mortality data. Evaluating the quality of national mortality statistics from civil registration is important

[1] not only for its role in the estimation of intercensal populations but also in the construction of life

table which is used in many other demographic estimation procedures. [1,2].

The importance of reliable and valid as well as comparable mortality data for measuring and

improving population health with the value of good quality mortality data for public health is widely

2

acknowledged, [3,4] while effective civil registration system remains the “gold standard” source for

continuous mortality measurement [5].

Kingsley Davis talked of the “Amazing Decline” of mortality in underdeveloped countries [6].

Since the developing countries continue in their effects to enhance their Civil Registration & Vital

Statistics are particularly beneficial to identify biases in mortality data and it is important for them to

have the ability to evaluate their progress by periodic evaluations of mortality statistics. A couple of

low and middle revenue countries have such data [3, 9]. Among the few countries which have carried

out such as exercise in the developing world are Brazil [7], china [8], & India, [9]. According to WHO

report, India rank third, next to just Myanmar and Nepal, among all south Asian countries ordered by

adult death rate in 2002[10]. One of the greatest human achievements in the decline in mortality that’s

occurred during the modern era [11].

Civil Registration System(CRS), a unified process of continuous, permanent and compulsory

recording of the vital events (livebirth, deaths, fetal deaths, marriages, and divorces) and

characteristics thereof, as provide through the legal requirements of the country. [12] In India, the

Registration of Births and Deaths act, 1969, provides for the compulsory registration of births &

deaths. Registrar general of India initiated a scheme of Sample Registration of birth and death in India,

rural in 1964-65 on pilot basis. The scheme of sample registration become operational on full scale

from 1969-70 and is popularly known as SRS. [13, 14] The difference in approach of data collection

between CRS and SRS, a comparison of vital rates based on these two sources helps in evaluation the

performance of CRS over SRS. The level of registration helps in reviewing the registration system and

defining measures that would be necessary to improve registration levels across the country. [15]

According to Bhat [16], the omission rate is common is the registration system and even in

census of developing countries and therefore it is necessary to check their completeness.

The assessment of the mortality level of a population is often based on information including

the number of registered deaths. In many developing nations, like India, however, deaths are under

registered by a significant margin, which in turn many lead to a biased estimate of the level of

mortality [16,17].

Various methods have been developed for estimating the completeness of death registration.

Each of them assumes that the population is closed to migration and that ages at death were correctly

reported. In addition, Brass’s sectional growth balance method, 1975[18] biased by declining

mortality; Preston and Coale method,1980[19], sensitive to growth rate chosen and biased by

overstatement of age at death; Bourgeais-Pichat method,1984, Limited experience with method seems

to overestimate completeness because of age overstatement at high ages; the other methods which

relax the assumption of stability are, Forward projection method, The modified growth balance

3

method, 1979[20], Preston and Hill method, 1980, [21]; United Nation method,1979, and Bennett and

Horiuchi method, 1981[22-23];

Excellent summary of these techniques has been given by Preston, 1984. The method that do

not assume stability are a generalization of the methods assuming stability. One of these non-stable

methods, the United Nation method, has been much used and has shown to give erratic results [24].In

the modified growth balance method, Martin, 1980[25] sought the Brass estimate of completeness, for

mortality decline. The procedure requires the knowledge of the rate and duration of mortality decline.

Estimation of these factors involves some circularity because the estimate of the mortality condition is

the aim of the exercise in the first place.

The Preston and Hill method and the forward projection methods are related to the size of

cohorts in two censuses. They become awkward to use irregular intercensal periods. For every

developing countries where census are conducted irregular as well as regular intercensal periods. For

such a purpose, one is left with the Bennett-Horiuchi method.

In India, the assessment of mortality level in population can be done by CRS and SRS. Since

2002[16], there has not been any attempt to examine the quality of CRS due to unavailability of age

specific mortality data. In this context, present paper is an attempt to assess the quality of national as

well as states level mortality statistics from Civil Registration in India. The next aim of this study is to

construct the life table with Adjusted ASDR which is given by Bennett-Horiuchi Method.

Materials and Methods

Data Sources The analysis is based on the data available from four sources. The first data source is the

annual statistical report of the Civil Registration system for the period 2000 through 2010. The second

data source is the annual statistical report of the Simple Registration system for the period 2000

through 2010. The third data source used in the present analysis is the abridged life tables for the

period 2002-2006 which are based on the age specific death rates available through the sample

registration system. Finally, the fourth data source used is the population by age and sex enumerated at

2001 and 2011 population census.

The Bennett- Horiuchi method is basically the extension of the methodology given by Preston

and Coale [19] with the important development which does not require the assumption of stability of

the population. Beside the advantage of discards the assumption of stability, this method can also be

used for irregular intercensal periods. For stable population Preston et al. (1980) employed the

relationship:

4

N a = 𝐷∗ 𝑥 exp 𝑟 𝑥 − 𝑎 𝑑𝑥∞

𝑎, (1)

Where N(a) denotes the number of persons aged a, r is the growth rate for stable population

and 𝐷∗ 𝑥 is the true number of deaths experienced by persons aged x in the current population. If the

completeness of the deaths registration is constant at age a and above, then

𝐷∗ 𝑥 = 𝑘𝐷 𝑥 , 𝑓𝑜𝑟 𝑎𝑙𝑙 𝑥 ≥ 𝑎, (2)

Where D(x) is the number of registered deaths to persons aged x and k is the inverse of the

completeness of death registration. There for from (1) & (2), we obtain:

N a = k 𝐷 𝑥 exp 𝑟 𝑥 − 𝑎 𝑑𝑥∞

𝑎, (3)

If we define,

N a = 𝐷 𝑥 exp 𝑟 𝑥 − 𝑎 𝑑𝑥∞

𝑎, (4)

Then the estimates of completeness are derived from the median of a series of age specific

completeness of death which are ratio of estimated to observed population (N a / N a ), when the

number of registered deaths by age, the number of living person by age and the growth rate of the

population are provided. This median is then used to calculate an adjusted set of age specific death

rates(ASDR) and the Life expectancy for ages 5 and above [22-23,37-38].

Bennett-Horiuchi method is based on four assumptions. First, the population was not exposed

to migration during the intercensal period. Second, both censuses have the same degree of

completeness. Third, age misreporting occurs only after age 50 years, and Fourth, degree of

completeness of death registration is uniform above age 5 years.

The following procedure was adopted to apply the Bennett-Horiuchi method:

The total number of registered deaths by sex was taken from the CRS report [26].

The percentage distribution of deaths by sex and 5-years age-group was taken from SRS report

[27].

Then the total number of registered deaths by sex and 5-year age group in CRS was estimated

by using above SRS data.

Similarly, it is done for all Annual Reports on Vital Statistics of India and its states based on

CRS 2001-2010.

Summing the number of registered death by sex and age group for 2001-2010 which is

obtained by above procedure for calculating the number of intercensal deaths between 2001-

2010.

5

Total population by sex available for the years 2001[28] and 2011[29] was used to estimate

population of India and selected states for different years of the intercensal period using

Newton forward and backward interpolation methods. This procedure provided estimates of

population for years 2010.

Finally, to estimate the completeness of death registration, population of two censuses 2001

and 2010 with age groups and total deaths during the period has been used in BENHR program

in MORTPAK software [30] which gives completeness of death registration for each age

group, median value for all age groups and life expectancy at age 5 and above.

Construction of life table

To constructing a life table, the usual method, the death rates for 0-1, 1-5, and other remaining

five years’ age group are required. But, the Bennett-Horiuchi provided adjusted ASDR starting

by age 5 and above. Then m0-5 values are converted into the m0-1 & m1-5 with the help of qn-x by

employing the following steps:

First, construct a life table based on average SRS ASDR from the period 2001 to 2010, and

then we get three estimated values q0-1, q1-5, q0-5.

Next, we have (m0-5) adjusted ASDR given by Bennett-Horiuchi method. Now estimated

Adjusted ASDR m0-1 and m1-5 with the help of above SRS estimated qn-x values as follows:

The adj. ASDR in 𝑚0−1 is =𝑞0−1

𝑞0−5∗ 𝑚0−5 (5)

The adj. ASDR in 𝑚1−5 is =𝑞1−5

𝑞0−5∗ 𝑚0−5 (6)

Finally, we have Adj. ASDR with starting 0-1, 1-5, and other remaining five years’ age group

and up to age 75+. Then LIFTB program used for the construction of a life table based on a set

of adjusted ASDR in the MORTPAK software package for mortality measurement.

Results and Discussions

Table 1 provides the estimated value of completeness of death registration in India and major

states. The national level coverage of median deaths completeness is 49% male and 39% in female.

The major states have been categorized into 5 group as per their level of registration of death for year,

2001-2010. The states, Bihar (15.3%) show that the minimum level of completeness and Haryana is

the maximum (95.8%) as well as on states where the level of completeness is above 90% in Total

(both male and female). The reasons behind, the Haryana states took bold step and shifted the

registration of birth and death from police to Primary Health Center (PHC) with encouraging trends

6

though the states have not yet achieved 95% registration of births and deaths but substantial

impartments have been made.

Table 1: Paste Here

Table 2 provides the adjusted ASDR and Life Expectancy in India and is selected major states.

With the help of theses Abridged adjusted ASDR can construct a complete life table by using

MORTPAK or other software package. The national life expectancy at birth is 66 years and 69 years

in males and females respectively. The state, Andhra Pradesh show that the minimum 60 years and

states Kerala show that the maximum 70 years of life expectancy at birth in male. The state, Assam

shows that the minimum 64 years and states Kerala show that the maximum 74 years of life

expectancy at birth in females. The 10-years age difference between the states in males as well as

females, it shown that wide variation present in mortality situation in India.

Table 2: Paste Here

Figure 1 gives the Completeness of death registration in India and its major selected states.

The data are arranged in ascending order in the Completeness of death registration. The national level

of completeness in India is 45% which show very less amount of completeness which means the half

of the death is under registered. Haryana is an only one state in India where the death registration

(95.8%) is above 90%. In Haryana, to ensure better implementation of various provisions of RBD Act,

it was decided to get the work of registration done through PHC instead of police stations [31]. This

policy change was also meant to increase the number of registration center so as to make registration

more accessible to people. Bihar, is states where the minimum level of completeness of death

registration and it is only 15.3 %, in this state as well as other states in India where the completeness is

less than 90%, it is very important to improve the process of death registration by taking bold steps,

getting the example of Haryana state.

Figure 1. Estimates of Completeness of death registration for India and States, 2001-2010. The data are arranged in

ascending order in the Completeness of death registration.

15.330.2

45 50.1 54.5 55.3 60.3 67.4 70.480.9 80.9 85.1 86.3 86.8 95.8

020406080

100

Leve

l of

Co

mp

lete

ne

ss

Place

Level of Completeness

7

Life expectancy at birth reflects the overall mortality of a population. It summarizes the

mortality pattern that prevails across all age group- children and adolescents, adults and the elderly

[32]. The Life Table has been constructing for India and states separately for male and female for the

period 2001-2010. Figure 2 gives the estimates of Life Expectancy at birth by sex for India and States

from the period 2001 to 2010. The data are arranged for female life expectancy at births. The national

level, India Life Expectancy at birth is 66 year in males and 69 year in females. Bihar is a state where

the mortality condition in male and female more are less equal that means both are equal risk. On the

other hand, Andhra Pradesh is a state where the difference in Life Expectancy in males and female is

high, it show that the males is higher mortality risk condition.

Figure 2. Estimates of Life Expectancy at birth by sex, India and States, 2001-2010. The data are arranged in

descending order in the Life Expectancy at birth in females.

Life Expectancy is regularly used to measure the overall health of the society. Life expectancy

at age 65 is frequently used as a measure of a health status of adult population. Changes in life

expectancy are repeatedly used to explain trends in mortality. Then we able to predict how populations

will age have enormous implications for the planning and provision of services and supports. As the

life expectancy of population lengthens, the numbers of people living with chronic illnesses are more

common among older person. Figure 3 gives the estimates of Life Expectancy at age 65+ by sex for

India and States from the period 2001 to 2010. The data are arranged for female life expectancy at

births.

KERALAHARYA

NAMAHARASTHRA

RAJASTHAN

PUNJABTAMIL NADU

KARNATAK

MADHYA

PRADESH

WEST BANGAL

GUJRAT INDIA

ANDHRA

PRADESH

BIHAR ODISSA ASSAM

Male 69.69 67.89 68.3 67.89 67.62 67.79 66.95 67.59 67.38 66.4 66.66 60.19 67.79 65.54 62.51

Female 74.33 71.8 71.65 71.39 71.36 70.6 70.3 69.8 69.45 69.34 69 68.34 66.57 65.75 64.14

01020304050607080

Life

Exp

ect

ancy

at

Bir

th

8

Figure 3. Estimates of Life Expectancy at age 65 by sex, India and States, 2001-2010. The data are arranged in

descending order in the Life Expectancy at age 65+ in females.

In developed countries, male’s riskier unhealthy behaviors are a major reason they die younger.

[33] Their higher rates of cigarette smoking, heavy drinking, gun use, employment in hazardous

occupations, and risk taking in recreation and driving are responsible for male’s higher death rate due

to lung cancer, accidents, suicide, and homicide. [34] Making a difference in Life Expectancy have

helped decrease deaths related to unhealthy behaviors, particularly those associated with male’s

deaths. In the, US life expectancy difference in males for female is 5.01 years, whereas in France it

was 7.8 years and in the U.K., 4.3 years, difference in Russia reaching more than 12 years, whereas in

China with 3-year difference[35]. The diversity in worldwide longevity alone indicates that the

difference in mortality between the sexes is not purely biological and that there are intervening social

factors. Also from, Figure 4, show that the estimates difference in female and male Life Expectancy at

birth for India and States from the period, 2001-2010. It helps us to measure the difference in mortality

condition by sex. Wide gap in life expectancy lead to need more focus in health policy and try to

improvement in health situation to reduce wide gap. In the addition, from the Figure 4, it is clearly

observed that Bihar is on state where male life expectancy is higher than female, it so that in the state

of Bihar where female is much higher mortality risk condition. On the other hand, more than 8 years’

difference in life expectancy in the state Andhra Pradesh in show that a male’s is much higher risk

mortality stage due to social factor with also unhealthy life style.

HARYANA

PUNJAB KERALAMAHARASTHRA

KARNATAK

RAJASTHAN

MADHYA

PRADESH

GUJRATTAMIL NADU

INDIAWEST

BANGAL

ANDHRA

PRADESH

ODISSA ASSAM BIHAR

Male 17.35 16.91 15.83 16.45 16.42 15.64 16.12 15.5 16.19 15.81 15.46 15.2 16.09 14.56 15.62

Female 19.1 18.1 17.89 17.76 17.69 17.49 17.35 17.08 16.93 16.79 16.7 16.44 16.11 15.91 15.11

0

5

10

15

20

25Li

fe E

xpe

ctan

cy a

t ag

e 6

5

9

Figure 4. Estimates the difference in female and male Life Expectancy at birth, India and States, 2001-2010. The

data are arranged in ascending order in the difference in Life Expectancy at birth in females and males.

Figure 5 show that the Intercensal and Adjusted ASDR for total (Both male and female) for

three selected states of India with their level of completeness of death registration. Its show that, form

fig. 5(a) the state Bihar where the difference in death rates is increases with age it shows that that

death registration is highly under registered with age increase which leads to biasness is present in

Bihar mortality data. In the states, Haryana more than 90 % coverage of death registration is indicates

the good quality of mortality statistics. Also from fig. 5(c) which show that there is no difference in

death rates. Whereas in the Gujarat is less biasness in mortality data form fig. 5(b) it is clear observed.

Figure 5. Adjusted ASDR & Intercensal Death Rates for selected state Bihar, Gujarat & Haryana, 2001-2010

-1.22

0.21

1.63 2.07 2.21 2.34 2.81 2.94 3.35 3.35 3.5 3.74 3.914.64

8.15

-2

0

2

4

6

8

10

Dif

f. in

Li

fe E

xpe

ctan

cy a

t b

irth

Difference in years

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0-5

5 - 10

10 - 15

15 - 20

20 - 25

25 - 30

30 - 35

35 - 40

40 - 45

45 - 50

50 - 55

55 - 60

60 - 65

65 - 70

70 - 75 75+

Fig.5(a). Estimates of Adj. ASDR and ASDR (Bihar)

Intercensal Death Rate Adj. Death Rate

0

0.02

0.04

0.06

0.08

0.1

0.12

0-5

5 - 10

10 - 15

15 - 20

20 - 25

25 - 30

30 - 35

35 - 40

40 - 45

45 - 50

50 - 55

55 - 60

60 - 65

65 - 70

70 - 75 75+

Fig.5(b). Estimates of Adj. ASDR and ASDR (Gujarat)


0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0-5

5 - 10

10 - 15

15 - 20

20 - 25

25 - 30

30 - 35

35 - 40

40 - 45

45 - 50

50 - 55

55 - 60

60 - 65

65 - 70

70 - 75 75+

Fig.5(c). Estimates of Adj. ASDR and ASDR (Haryana)


10

Figure 6 provides that the difference between estimated (based on Adjusted ASDR for 2001-

2010) and SRS (2004-2008) [36] Life Expectancy at birth. Figure shows that for some states SRS give

over estimates and for some states SRS give under estimates compare estimated of Life Expectancy at

age birth.

Figure 6. Estimates of difference between (Estimated 2001-2010 & SRS 2004-2008) Life Expectancy at birth, India

and States. The data are arranged in ascending order in the difference in Life Expectancy at birth in females and males

between (Estimate & SRS).

Figure 7 provides that the difference between estimated (based on Adjusted ASDR for 2001-

2010) and SRS (2004-2008) Life Expectancy at age 5. In this figure its so that for all most every states

SRS give over estimates compare to estimate of Life Expectancy at age 5 based on adjusted ASDR.

Also from the figure 6 & 7 it shows there is a need to improvement in SRS procedure and reviewing

the registration system and defining measure that would be necessary to improve registration levels

across the country.

Figure 7. Estimates of difference between (Estimated 2001-2010 & SRS 2004-2008) Life Expectancy at age 5, India

and States. The data are arranged in ascending order in the difference in Life Expectancy at age 5 in females and males

between (Estimate & SRS).

KERALAWEST

BANGAL

MAHARASHTR

A

ANDHRA

PRADESH

PUNJAB

TAMIL NADU

GUJRATKARNATAKA

ASSAM BIHAR ODISHAHARYA

NAINDIA

RAJASTHAN

MADHYA

PRADESH

Male -1.51 0.68 0.7 -3.31 0.12 1.19 2.1 2.55 2.51 3.59 3.44 2.59 2.06 3.59 7.29

Female -3.07 -0.85 0.05 0.24 0.26 0.5 0.64 1.1 1.84 1.87 2.75 2.8 3.05 3.99 7.1

-4

-2

0

2

4

6

8

Dif

f. in

Lif

e E

xpe

ctan

cy

KERALA BIHARRAJAST

HAN

ANDHRA

PRADESH

GUJRAT

WEST BANGA

L

PUNJAB

INDIAMAHARASHT

RA

KARNATAKA

TAMIL NADU

ASSAMHARYA

NAODISH

A

MADHYA

PRADESH

Male -2.27 -1.1 -2.46 -2.52 -0.95 -1.39 -2.14 -1.05 -1.13 -0.15 -0.88 -0.96 -0.75 -0.56 0.72

Female -3.79 -3.47 -3.13 -2.8 -2.71 -2.71 -2.53 -2.37 -1.9 -1.6 -1.57 -1.51 -1.36 -1.12 -0.15

-4.5-4

-3.5-3

-2.5-2

-1.5-1

-0.50

0.51

Dif

f. in

Lif

e E

xpe

ctan

cy

Male Female

11

Conclusion

In India, CRS give reliable estimate of vital events, but this has some problem regarding

completeness in adult deaths. As a result, the death rate implied by the reported deaths is usually an

underestimate of the true death rate then some method of adjustment is required to transform the

reported death rate into a better estimate of true mortality conditions. Therefore, it is necessary to

check the completeness of death registration over time. This study finds the gap in actual death and

registered death of by CRS. Therefore from this study, it is can be concluded that there is a need to

review the CRS procedure and updating of adults deaths registration system. That will give better and

more reliable estimates of deaths at national and states level by sex. On the whole it may be concluded

that there is a need of incredible change in completeness of deaths registration process. Thus the paper

suggests for considering death completeness while estimating completeness of deaths situation or

representing gender differential in completeness of death at any place. However, it is necessary to

implement policy changes to reform in CRS.

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14

Appendix

Table 1. Estimated of death registration (Application of Bennett-Horiuchi Technique) India and

States, 2001-2010

Groups (as per their

level of death

registration)

Bennett-Horiuchi technique

Total Male Female

Above 90 1 States (Haryana)

5 States (Haryana, Kerala,

Punjab, Karnataka, Tamil

Nadu) No States

80-90

5 States

(Kerala, Punjab, Karnataka,

Maharashtra, Tamil Nadu)

1 State

(Maharashtra)

2 States

(Haryana, Punjab)

50-80

6 States

(Orrisha, Gujarat, Rajasthan,

Madhya Predesh, Andhrya

Pradesh, West Bengal)

6 States

(Orrisha, Gujarat, Rajasthan,

Madhya Predesh, Andhrya

Pradesh, West Bengal)

7 States

(Kerala, Maharashtra, Tamil

Nadu, Karnataka, Orrrisha,

Gujarat, Andhra Pradesh)

25-50 1 States

(India*, Assam)

1 States

(India, Assam)

3 States

(India, Madhya Pradesh,

Rajasthan, West Bengal)

Below 25 1 States

(Bihar)

1 States

(Bihar)

2 States

(Assam, Bihar)

* Uttar Pradesh, Tripura, Uttarakhand excluded from the measure of the level of completeness of death

registration in India due to unavailability of registered death.

15

Table 2. Estimates of Adjusted ASDR and Life Expectancy in India and States, 2001-2010.

INDIA ANDHRA PRADESH ASSAM

AGE Male Female Male Female Male Female

GROUP Adj. ASDR E(X) Adj. ASDR E(X) Adj. ASDR E(X) Adj. ASDR E(X) Adj. ASDR E(X) Adj. ASDR E(X) 0-1 0.0134 66.06 0.0011 69.95 0.0548 60.19 0.0148 68.34 0.0157 62.51 0.0156 64.14 1-5 0.0035 65.94 0.0004 69.03 0.0076 62.51 0.0020 68.35 0.0055 62.49 0.0061 64.15 5 - 10 0.0013 62.85 0.0015 65.13 0.0008 60.38 0.0007 64.90 0.0021 59.84 0.0019 61.69 10 - 15 0.0010 58.23 0.0010 60.59 0.0008 55.62 0.0007 60.13 0.0013 55.43 0.0014 57.25 15 - 20 0.0015 53.50 0.0019 55.88 0.0017 50.83 0.0020 55.33 0.0023 50.78 0.0030 52.63 20 - 25 0.0021 48.87 0.0025 51.39 0.0027 46.24 0.0024 50.85 0.0025 46.33 0.0031 48.39 25 - 30 0.0026 44.36 0.0023 47.01 0.0036 41.83 0.0023 46.43 0.0033 41.89 0.0031 44.10 30 - 35 0.0033 39.91 0.0024 42.52 0.0050 37.55 0.0025 41.93 0.0036 37.54 0.0040 39.75 35 - 40 0.0042 35.54 0.0026 38.01 0.0061 33.42 0.0025 37.42 0.0045 33.18 0.0041 35.50 40 - 45 0.0056 31.23 0.0035 33.48 0.0076 29.37 0.0039 32.85 0.0073 28.88 0.0053 31.19 45 - 50 0.0077 27.04 0.0046 29.02 0.0102 25.41 0.0045 28.45 0.0100 24.86 0.0070 26.95 50 - 55 0.0110 22.99 0.0082 24.63 0.0151 21.60 0.0093 24.03 0.0144 21.00 0.0120 22.82 55 - 60 0.0191 19.14 0.0134 20.55 0.0253 18.09 0.0153 20.05 0.0274 17.38 0.0212 19.07 60 - 65 0.0238 15.81 0.0175 16.79 0.0274 15.20 0.0177 16.44 0.0294 14.56 0.0187 15.91 65 - 70 0.0409 12.49 0.0319 13.09 0.0475 12.06 0.0353 12.72 0.0524 11.47 0.0433 12.22 70 - 75 0.0525 9.75 0.0499 9.91 0.0557 9.62 0.0540 9.67 0.0581 9.17 0.0647 9.56 75+ 0.1009 6.93 0.0968 7.00 0.1031 6.92 0.1008 6.89 0.1138 6.42 0.1001 7.28

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