Sex Ratio at Birth: Female per 1000 Male: Punjab: Urban data was reported at 932.000 NA in 2020. This records an increase from the previous number of 918.000 NA for 2019. Sex Ratio at Birth: Female per 1000 Male: Punjab: Urban data is updated yearly, averaging 878.000 NA from Dec 2006 (Median) to 2020, with 15 observations. The data reached an all-time high of 932.000 NA in 2020 and a record low of 800.000 NA in 2006. Sex Ratio at Birth: Female per 1000 Male: Punjab: Urban data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAJ001: Memo Items: Sex Ratio at Birth.

The statistic displays the literacy rate in Punjab in India between 1991 and 2011, broken down by gender. In 2001, ** percent of the female population in Punjab knew how to read or write. India's literacy rate from 1981 through 2011 can be found here.

Sex Ratio at Birth: Female per 1000 Male: Punjab: Rural data was reported at 874.000 NA in 2020. This stayed constant from the previous number of 874.000 NA for 2019. Sex Ratio at Birth: Female per 1000 Male: Punjab: Rural data is updated yearly, averaging 861.000 NA from Dec 2006 (Median) to 2020, with 15 observations. The data reached an all-time high of 878.000 NA in 2018 and a record low of 813.000 NA in 2006. Sex Ratio at Birth: Female per 1000 Male: Punjab: Rural data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAJ001: Memo Items: Sex Ratio at Birth.

The total gross enrollment ratio of students from the pre-primary to second grade across the state of Punjab in India during financial year 2024 was around ** percent. The enrollment ratio of students from sixth grade to eighth grade was higher among female students compared to male students that year.

The catalog contains the data related to decadal sex ratio (number of females per one thousand males) in Punjab.

Population by sex and sex ratio at districts level in urban area Punjab, Census 2017 (Population in Thousand)

India General Election: Punjab: Number of Voters: Male to Female Ratio data was reported at 1.103 % in 2014. This records an increase from the previous number of 1.096 % for 2009. India General Election: Punjab: Number of Voters: Male to Female Ratio data is updated yearly, averaging 1.219 % from Mar 1962 (Median) to 2014, with 14 observations. The data reached an all-time high of 1.486 % in 1992 and a record low of 1.096 % in 2009. India General Election: Punjab: Number of Voters: Male to Female Ratio data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s General Election – Table IN.GEF029: General Election: Loksabha: Number of Voters: Punjab.

The catalog contains the data related to number of male, female and total population (rural and urban) in Punjab.

The catalog contains the data related to Area in sq. kilometer, male and female population and population density per square kilometer in Punjab.

Percentage distribution of population by sex in rural areas, Punjab, Pakistan Census 2017

In 2018, the northern state of Punjab in India had a higher share of disabled females with multiple disabilities than males at 2.8 percent. According to the 76th round of the NSO survey conducted between July and December 2018, a higher percentage of disabled men than disabled women were present in India. The National Statistical Office (NSO) is the statistical wing of the Ministry of Statistics and Programme Implementation (MOSPI), mainly responsible for laying down standards for statistical analysis, data collection, and implementation.

The statistic presents the literacy rate in rural and urban regions of Punjab in India in 2011, with a breakdown by gender. In that year, the literacy rate among males living in rural areas in Punjab was around 77 percent. India's literacy rate from 1981 through 2011 can be found here.

The density-dependent prophylaxis hypothesis predicts that risk of pathogen transmission increases with increase in population density, and in response to this, organisms mount a prophylactic immune response when exposed to high density. This prophylactic response is expected to help organisms improve their chances of survival when exposed to pathogens. Alternatively, organisms living at high densities can exhibit compromised defense against pathogens due to lack of resources and density associated physiological stress; the crowding stress hypothesis. We housed adult Drosophila melanogaster flies at different densities and measured the effect this has on their post-infection survival and resistance to starvation. We find that flies housed at higher densities show greater mortality after being infected with bacterial pathogens, while also exhibiting increased resistance to starvation. Our results are more in line with the density-stress hypothesis that postulates a compromised immune system when hosts are subjected to high densities. Methods This file ("Adult_density_experiment.xlsx") was generated in 2019-20 by Paresh Nath Das and others at the Evolutionary Biology Lab, IISER Mohali. GENERAL INFORMATION 1. Title of Dataset: "Increasing adult density compromises anti-bacterial defense in Drosophila melanogaster" 2. Author Information A. Principal Investigator Contact Information Name: Prof. N. G. Prasad Institution: Indian Institute of Science Education and Research, Mohali Address: IISER Mohali, Sector 81, Knowledge City, SAS Nagar, Punjab - 140306, India. Email: prasad@iisermohali.ac.in B. Associate or Co-investigator Contact Information Name: Paresh Nath Das Institution: Indian Institute of Science Education and Research, Mohali Address: IISER Mohali, Sector 81, Knowledge City, SAS Nagar, Punjab - 140306, India. Email: pareshnathd@gmail.com C. Associate or Co-investigator Contact Information Name: Aabeer Kumar Basu Institution: Indian Institute of Science Education and Research, Mohali Address: IISER Mohali, Sector 81, Knowledge City, SAS Nagar, Punjab - 140306, India. Email: aabeerkbasu@gmail.com 3. Duration of data collection: September 2019 - March 2020 4. Geographic location of data collection: Mohali, Punjab, India 5. Information about funding sources that supported the collection of the data: IISER Mohali, MHRD, Govt. of India. SHARING/ACCESS INFORMATION Links to publications that cite or use the data: bioRxiv: https://doi.org/10.1101/2022.01.02.474745 Journal of Insect Physiology (in press version): https://doi.org/10.1016/j.jinsphys.2022.104415 METHODOLOGICAL INFORMATION A. Details of fly populations Blue Ridge Baseline (BRB) population: BRB2 is a lab-adapted, large, outbred, wild-type population of Drosophila melanogaster, maintained on a 14-day discrete generation cycle, on standard banana-jaggery-yeast medium. The BRB population was originally derived by hybridising 19 iso-female lines caught from the wild population at Blue Ridge Mountains, USA. The experiments reported were conducted after 200 generations of lab-adaptation. B. Effect of density, 8 adults vs. 32 adults, on immune function and starvation resistance.

Experimental treatments: 2-3 day old adult flies were randomly distributed into two density treatments.

a. 8 adults per vial (1:1 sex ratio) b. 32 adults per vial (1:1 sex ratio) Vilas of both treatments had equal amout of standard fly food (1.5-2 ml). Flies were housed like this for 48 hours, and thereafter assayed for immune function and starvation resistance.

Immune function assay: Flies of both treatments (described above) were assayed separately for resistance against two entomopathogenic bacteria, Enterococcus faecalis and Erwinia c. carotovora, with two independent replicates per pathogen.

Within each replicate experiment, 80 males and 80 females from each treatment (described above) were subjected to infection, and 40 males and 40 females were subjected to sham-infections. Post-infection mortality was recorded for 120 hours; during this period, flies of both treatments were housed at equal density (4 males and 4 females per vial).

Starvation resistance assay: Flies of both treatments (described above) were assayed for starvation resistance; experiment independently replicated twice.

Within each replicate experiment, 80 males and 80 females from each treatment (described above) were subjected to starvation in vials with non-nutritive agar gel only. Post-starvation mortality was recorded till the last fly died; during this period, flies of both treatments were housed at equal density (4 males and 4 females per vial). C. Effect of density, 50 adults vs. 200 adults, on immune function and starvation resistance.

Experimental treatments: 2-3 day old adult flies were randomly distributed into two density treatments.

a. 50 adults per vial (1:1 sex ratio) b. 200 adults per vial (1:1 sex ratio) Vilas of both treatments had equal amout of standard fly food (1.5-2 ml). Flies were housed like this for 48 hours, and thereafter assayed for immune function and starvation resistance.

Immune function assay: Flies of both treatments (described above) were assayed separately for resistance against two entomopathogenic bacteria, Enterococcus faecalis and Erwinia c. carotovora, with two independent replicates per pathogen.

Within each replicate experiment, 80 males and 80 females from each treatment (described above) were subjected to infection, and 40 males and 40 females were subjected to sham-infections. Post-infection mortality was recorded for 120 hours; during this period, flies of both treatments were housed at equal density (4 males and 4 females per vial).

Starvation resistance assay: Flies of both treatments (described above) were assayed for starvation resistance; experiment independently replicated twice.

Within each replicate experiment, 80 males and 80 females from each treatment (described above) were subjected to starvation in vials with non-nutritive agar gel only. Post-starvation mortality was recorded till the last fly died; during this period, flies of both treatments were housed at equal density (4 males and 4 females per vial).

Sex Ratio at Birth: Female per 1000 Male: Punjab: Urban在2020达932.000NA，相较于2019的918.000NA有所增长。Sex Ratio at Birth: Female per 1000 Male: Punjab: Urban数据按每年更新，2006至2020期间平均值为878.000NA，共15份观测结果。该数据的历史最高值出现于2020，达932.000NA，而历史最低值则出现于2006，为800.000NA。CEIC提供的Sex Ratio at Birth: Female per 1000 Male: Punjab: Urban数据处于定期更新的状态，数据来源于Office of the Registrar General & Census Commissioner, India，数据归类于India Premium Database的Demographic – Table IN.GAJ001: Memo Items: Sex Ratio at Birth。

Sex Ratio at Birth: Female per 1000 Male: Punjab: Rural在2020达874.000NA，相较于2019的874.000NA保持不变。Sex Ratio at Birth: Female per 1000 Male: Punjab: Rural数据按每年更新，2006至2020期间平均值为861.000NA，共15份观测结果。该数据的历史最高值出现于2018，达878.000NA，而历史最低值则出现于2006，为813.000NA。CEIC提供的Sex Ratio at Birth: Female per 1000 Male: Punjab: Rural数据处于定期更新的状态，数据来源于Office of the Registrar General & Census Commissioner, India，数据归类于India Premium Database的Demographic – Table IN.GAJ001: Memo Items: Sex Ratio at Birth。

Abstract

The MICS Punjab, 2017-18 has as its primary objectives:

• To provide high quality data for assessing the situation of children, adolescents, women and households in Punjab;

• To furnish data needed for monitoring progress toward national goals, as a basis for future action;

• To collect disaggregated data for the identification of disparities, to inform policies aimed at social inclusion of the most vulnerable;

• To validate data from other sources and the results of focused interventions;

• To generate data on national and global SDG indicators;

• To generate internationally comparable data for the assessment of the progress made in various areas, and to put additional efforts in those areas that require more attention;

• To generate behavioural and attitudinal data not available in other data sources.

Geographic coverage

The sample for the MICS Punjab, 2017-18 was designed to provide estimates for a large number of indicators on the situation of children and women at the Punjab level, for urban and rural areas, and for all 36 districts of Punjab.

Analysis unit

• Individuals

• Households

Universe

The survey covered all de jure household members (usual residents), all women age 15-49 years, all men age 15-49 years, all children under 5 and children age 5-17 years living in the household.

Sampling procedure

The urban and rural areas within each district were identified as the main sampling strata, and the sample of households was selected in two stages. Within each stratum, a specified number of census enumeration areas were selected systematically with probability proportional to size. Using the listing of households from the Census 2017 for each sample enumeration area, provided by Pakistan Bureau of Statistics, a systematic sample of 20 households was drawn in each sample enumeration area1. The total sample size was 53,840 households in 2,692 sample clusters. All the selected enumeration areas were visited during the fieldwork period. As the sample is not self-weighting, sample weights are used for reporting survey results.

Mode of data collection

Face-to-face [f2f]

Research instrument

Six questionnaires were used in the survey: 1) a household questionnaire to collect basic demographic information, the household, and the dwelling; 2) a water quality testing questionnaire administered in three households in each cluster of the sample; 3) a questionnaire for individual women administered in each household to all women age 15-49 years; 4) a questionnaire for individual men administered in every second household to all men age 15-49 years; 5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and 6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household.

Cleaning operations

Data were received at the Bureau of Statistics, Punjab via Internet File Streaming System (IFSS) integrated into the management application on the supervisors' tablets. Whenever logistically possible, synchronisation was daily. The central office communicated application updates to field teams through this system.

During data collection and following the completion of fieldwork, data were edited according to the editing process described in detail in the Guidelines for Secondary Editing, a customised version of the standard MICS6 documentation.

Data were analysed using the Statistical Package for Social Sciences (SPSS) software, Version 24 Model syntax and tabulation plan developed by UNICEF were customised and used for this purpose.

Response rate

Response rate (Households): 97.9%

Sex Ratio at Birth: Female per 1000 Male: Punjab在2020达897.000NA，相较于2019的891.000NA有所增长。Sex Ratio at Birth: Female per 1000 Male: Punjab数据按每年更新，2006至2020期间平均值为867.000NA，共15份观测结果。该数据的历史最高值出现于2020，达897.000NA，而历史最低值则出现于2006，为808.000NA。CEIC提供的Sex Ratio at Birth: Female per 1000 Male: Punjab数据处于定期更新的状态，数据来源于Office of the Registrar General & Census Commissioner, India，数据归类于India Premium Database的Demographic – Table IN.GAJ001: Memo Items: Sex Ratio at Birth。

BackgroundHepatitis C virus (HCV) infections are amongst the leading public health concerns in Pakistan with a high disease burden. Despite the availability of effective antiviral treatments in the country the disease burden in general population has not lowered. This could be attributed to the asymptomatic nature of this infection that results in lack of diagnosis until the late symptomatic stage. To better estimate and map HCV infections in the country a population-based analysis is necessary for an effective control of the infection.MethodsSerologic samples of ~66,000 participants from all major cities of the Punjab province were tested for anti-HCV antibodies. The antibody-based seroprevalence was associated with socio-demographic variables including geographical region, age, gender and sex, and occupation.ResultsOverall serological response to HCV surface antigens was observed in over 17% of the population. Two of the districts were identified with significantly high prevalence in general population. Analysis by occupation showed significantly high prevalence in farmers (over 40%) followed by jobless and retired individuals, laborers and transporters. A significant difference in seroprevalence was observed in different age groups amongst sex and genders (male, female and transgender) with highest response in individuals of over 40 years of age. Moreover, most of the tested IDUs showed positive response for anti-HCV antibody.ConclusionThis study represents a retrospective analysis of HCV infections in general population of the most populated province of Pakistan to identify socio-demographic groups at higher risk. Two geographical regions, Faisalabad and Okara districts, and an occupational group, farmers, were identified with significantly high HCV seroprevalence. These socio-demographic groups are the potential focused groups for follow-up studies on factors contributing to the high HCV prevalence in these groups towards orchestrating effective prevention, control and treatment.

Abstract

The Pakistan Integrated Household Survey (PIHS) was conducted jointly by the Federal Bureau of Statistics (FBS), Government of Pakistan, and the World Bank. The survey was part of the Living Standards Measurement Study (LSMS) household surveys that have been conducted in a number of developing countries with the assistance of the World Bank. The purpose of these surveys is to provide policy makers and researchers with individual, household, and community level data needed to analyze the impact of policy initiatives on living standards of households.

The Pakistan Integrated Household Survey was carried out in 1991. This nationwide survey gathered individual and household level data using a multi-purpose household questionnaire. Topics covered included housing conditions, education, health, employment characteristics, selfemployment activities, consumption, migration, fertility, credit and savings, and household energy consumption. Community level and price data were also collected during the course of the survey.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Communities

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the PIHS was drawn using a multi-stage stratified sampling procedure from the Master Sample Frame developed by FBS based on the 1981 Population Census.

SAMPLE FRAME:

This sample frame covers all four provinces (Punjab, Sindh, NWFP, and Balochistan) and both urban and rural areas. Excluded, however, are the Federally Administered Tribal Areas, military restricted areas, the districts of Kohistan, Chitral and Malakand and protected areas of NWFP. According to the FBS, the population of the excluded areas amounts to about 4 percent of the total population of Pakistan. Also excluded are households which depend entirely on charity for their living.

The sample frame consists of three main domains: (a) the self-representing cities; (b) other urban areas; and (c) rural areas. These domains are further split up into a number of smaller strata based on the system used by the Government to divide the country into administrative units. The four provinces of Pakistan mentioned above are divided into 20 divisions altogether; each of these divisions in turn is then further split into several districts. The system used to divide the sample frame into the three domains and the various strata is as follows: (a) Self-representing cities: All cities with a population of 500,000 or more are classified as self-representing cities. These include Karachi, Lahore, Gujranwala, Faisalabad, Rawalpindi, Multan, Hyderabad and Peshawar. In addition to these cities, Islamabad and Quetta are also included in this group as a result of being the national and provincial capitals respectively. Each self-representing city is considered as a separate stratum, and is further sub-stratified into low, medium, and high income groups on the basis of information collected at the time of demarcation or updating of the urban area sample frame. (b) Other urban areas: All settlements with a population of 5,000 or more at the time of the 1981 Population Census are included in this group (excluding the self-representing cities mentioned above). Urban areas in each division of the four provinces are considered to be separate strata. (c) Rural areas: Villages and communities with population less than 5,000 (at the time of the Census) are classified as rural areas. Settlements within each district of the country are considered to be separate strata with the exception of Balochistan province where, as a result of the relatively sparse population of the districts, each division instead is taken to be a stratum.

Main strata of the Master Sample frame

Domain / Punjab / Sindh / NWFP / Balochistan / PAKISTAN Self-representing cities / 6 / 2 / 1 / 1 / 10 Other urban areas / 8 / 3 / 5 / 4 / 20 Rural areas / 30 / 14 / 10 / 4 / 58 Total 44 / 19 / 16 / 9 / 88

As the above table shows, the sample frame consists of 88 strata altogether. Households in each stratum of the sample frame are exclusively and exhaustively divided into PSUs. In urban areas, each city or town is divided into a number of enumeration blocks with welldefined boundaries and maps. Each enumeration block consists of about 200-250 households, and is taken to be a separate PSU. The list of enumeration blocks is updated every five years or so, with the list used for the PIHS having been modified on the basis of the Census of Establishments conducted in 1988. In rural areas, demarcation of PSUs has been done on the basis of the list of villages/mouzas/dehs published by the Population Census Organization based on the 1981 Census. Each of these villages/mouzas/dehs is taken to be a separate PSU. Altogether, the sample frame consists of approximately 18,000 urban and 43,000 rural PSUs.

SAMPLE SELECTION:

The PIHS sample comprised 4,800 households drawn from 300 PSUs throughout the country. Sample PSUs were divided equally between urban and rural areas, with at least two PSUs selected from each of the strata. Selection of PSUs from within each stratum was carried out using the probability proportional to estimated size method. In urban areas, estimates of the size of PSUs were based on the household count as found during the 1988 Census of Establishments. In rural areas, these estimates were based on the population count during the 1981 Census.

Once sample PSUs had been identified, a listing of all households residing in the PSU was made in all those PSUs where such a listing exercise had not been undertaken recently. Using systematic sampling with a random start, a short-list of 24 households was prepared for each PSU. Sixteen households from this list were selected to be interviewed from the PSU; every third household on the list was designated as a replacement household to be interviewed only if it was not possible to interview either of the two households immediately preceding it on the list.

As a result of replacing households that could not be interviewed because of non-responses, temporary absence, and other such reasons, the actual number of households interviewed during the survey - 4,794 - was very close to the planned sample size of 4,800 households. Moreover, following a pre-determined procedure for replacing households had the added advantage of minimizing any biases that may otherwise have arisen had field teams been allowed more discretion in choosing substitute households.

SAMPLE DESIGN EFFECTS:

The three-stage stratified sampling procedure outlined above has several advantages from the point of view of survey organization and implementation. Using this procedure ensures that all regions or strata deemed important are represented in the sample drawn for the survey. Picking clusters of households or PSUs in the various strata rather than directly drawing households randomly from throughout the country greatly reduces travel time and cost. Finally, selecting a fixed number of households in each PSU makes it easier to distribute the workload evenly amongst field teams. However, in using this procedure to select the sample for the survey, two important matters need to be given consideration: (a) sampling weights or raising factors have to be first calculated to get national estimates from the survey data; and (b) the standard errors for estimates obtained from the data need to be adjusted to take account for the use of this procedure.

Mode of data collection

Face-to-face [f2f]

Research instrument

The PIHS used three questionnaires: a household questionnaire, a community questionnaire, and a price questionnaire.

HOUSEHOLD QUESTIONNAIRE:

The PIHS questionnaire comprised 17 sections, each of which covered a separate aspect of household activity. The various sections of the household questionnaire were as follows: 1. HOUSEHOLD INFORMATION 2. HOUSING 3. EDUCATION 4. HEALTH 5. WAGE EMPLOYMENT 6. FAMILY LABOR 7. ENERGY 8. MIGRATION 9. FARMING AND LIVESTOCK 10. NON-FARM ENTERPRISE ACTIVITIES 11. NON-FOOD EXPENDITURES AND INVENTORY OF DURABLE GOODS 12. FOOD EXPENSES AND HOME PRODUCTION 13. MARRIAGE AND MATERNITY HISTORY 14. ANTHROPOMETRICS 15. CREDIT AND SAVINGS 16. TRANSFERS AND REMITTANCES 17. OTHER INCOME

The household questionnaire was designed to be administered in two visits to each sample household. Apart from avoiding the problem of interviewing household members in one long stretch, scheduling two visits also allowed the teams to improve the quality of the data collected.

During the first visit to the household (Round 1), the enumerators covered sections 1 to 8, and fixed a date with the designated respondents of the household for the second visit. During the second visit (Round 2), which was normally held two weeks after the first visit, the enumerators covered the remaining portion of the questionnaire and resolved any omissions or inconsistencies that were detected during data entry of information from the first part of the survey.

Since many of the sections of the questionnaire pertained specifically to female members of the household, female interviewers were included in conducting the survey. The household questionnaire was split into two parts (Male and Female). Sections such as SECTION 3: EDUCATION, which solicited information on all individual members of the household (male as well as female) were included in both parts of the questionnaire. Other sections such as SECTION 2: HOUSING and SECTION 12: FOOD EXPENSES AND HOME PRODUCTION , which collected data at the aggregate household level, were included in either the male questionnaire or the female questionnaire, depending upon which member of the household was more likely

Abstract

The 2012-13 Pakistan Demographic and Health Survey was undertaken to provide current and reliable data on fertility and family planning, childhood mortality, maternal and child health, women’s and children’s nutritional status, women’s empowerment, domestic violence, and knowledge of HIV/AIDS. The survey was designed with the broad objective of providing policymakers with information to monitor and evaluate programmatic interventions based on empirical evidence.

The specific objectives of the survey are to: • collect high-quality data on topics such as fertility levels and preferences, contraceptive use, maternal and child health, infant (and especially neonatal) mortality levels, awareness regarding HIV/AIDS, and other indicators related to the Millennium Development Goals and the country’s Poverty Reduction Strategy Paper • investigate factors that affect maternal and neonatal morbidity and mortality (i.e., antenatal, delivery, and postnatal care) • provide information to address the evaluation needs of health and family planning programs for evidence-based planning • provide guidelines to program managers and policymakers that will allow them to effectively plan and implement future interventions

Geographic coverage

National coverage

Analysis unit

• Household
• Ever married women age 15-49
• Ever married men age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The primary objective of the 2012-13 PDHS is to provide reliable estimates of key fertility, family planning, maternal, and child health indicators at the national, provincial, and urban and rural levels. NIPS coordinated the design and selection of the sample with the Pakistan Bureau of Statistics. The sample for the 2012-13 PDHS represents the population of Pakistan excluding Azad Jammu and Kashmir, FATA, and restricted military and protected areas. The universe consists of all urban and rural areas of the four provinces of Pakistan and Gilgit Baltistan, defined as such in the 1998 Population Census. PBS developed the urban area frame. All urban cities and towns are divided into mutually exclusive, small areas, known as enumeration blocks, that were identifiable with maps. Each enumeration block consists of about 200 to 250 households on average, and blocks are further grouped into low-, middle-, and high-income categories. The urban area sampling frame consists of 26,543 enumeration blocks, updated through the economic census conducted in 2003. In rural areas, lists of villages/mouzas/dehs developed through the 1998 population census were used as the sample frame. In this frame, each village/mouza/deh is identifiable by its name. In Balochistan, Islamabad, and Gilgit Baltistan, urban areas were oversampled and proportions were adjusted by applying sampling weights during the analysis.

A sample size of 14,000 households was estimated to provide reasonable precision for the survey indicators. NIPS trained 43 PBS staff members to obtain fresh listings from 248 urban and 252 rural survey sample areas across the country. The household listing was carried out from August to December 2012.

The second stage of sampling involved selecting households. At each sampling point, 28 households were selected by applying a systematic sampling technique with a random start. This resulted in 14,000 households being selected (6,944 in urban areas and 7,056 in rural areas). The survey was carried out in a total of 498 areas. Two areas of Balochistan province (Punjgur and Dera Bugti) were dropped because of their deteriorating law and order situations. Overall, 24 areas (mostly in Balochistan) were replaced, mainly because of their adverse law and order situation.

Refer to Appendix B in the final report for details of sample design and implementation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2012-13 PDHS used four types of questionnaires: Household Questionnaire, Woman’s Questionnaire, Man’s Questionnaire, and Community Questionnaire. The contents of the Household, Woman’s, and Man’s Questionnaires were based on model questionnaires developed by the MEASURE DHS program. However, the questionnaires were modified, in consultation with a broad spectrum of research institutions, government departments, and local and international organizations, to reflect issues relevant to the Pakistani population, including migration status, family planning, domestic violence, HIV/AIDS, and maternal and child health. A series of questionnaire design meetings were organized by NIPS, and discussions from these meetings were used to finalize the survey questionnaires. The questionnaires were then translated into Urdu and Sindhi and pretested, after which they were further refined. The questionnaires were presented to the Technical Advisory Committee for final approval.

The Household Questionnaire was used to list the usual members and visitors in the selected households. Basic information was collected on the characteristics of each person listed, including age, sex, marital status, education, and relationship to the head of the household. Data on current school attendance, migration status, and survivorship of parents among those under age 18 were also collected. The questionnaire also provided the opportunity to identify ever-married women and men age 15-49 who were eligible for individual interviews and children age 0-5 eligible for anthropometry measurements. The Household Questionnaire collected information on characteristics of the dwelling unit as well, such as the source of drinking water; type of toilet facilities; type of cooking fuel; materials used for the floor, roof, and walls of the house; and ownership of durable goods, agricultural land, livestock/farm animals/poultry, and mosquito nets.

The Woman’s Questionnaire was used to collect information from ever-married women age 15-49 on the following topics: • Background characteristics (education, literacy, native tongue, marital status, etc.) • Reproductive history • Knowledge and use of family planning methods • Fertility preferences • Antenatal, delivery, and postnatal care • Breastfeeding and infant feeding practices • Vaccinations and childhood illnesses • Woman’s work and husband’s background characteristics • Infant and childhood mortality • Women’s decision making • Awareness about AIDS and other sexually transmitted infections • Other health issues (e.g., knowledge of tuberculosis and hepatitis, injection safety) • Domestic violence

Similarly, the Man’s Questionnaire, used to collect information from ever-married men age 15-49, covered the following topics: • Background characteristics • Knowledge and use of family planning methods • Fertility preferences • Employment and gender roles • Awareness about AIDS and other sexually transmitted infections • Other health issues

The Community Questionnaire, a brief form completed for each rural sample point, included questions about the availability of various types of health facilities and other services, particularly transportation, education, and communication facilities.

All elements of the PDHS data collection activities were pretested in June 2012. Three teams were formed for the pretest, each consisting of a supervisor, a male interviewer, and three female interviewers. One team worked in the Sukkur and Khairpur districts in the province of Sindh, another in the Peshawar and Charsadda districts in Khyber Pakhtunkhwa, and the third in the district of Rawalpindi in Punjab. Each team covered one rural and one urban non-sample area.

Cleaning operations

The processing of the 2012-13 PDHS data began simultaneously with the fieldwork. Completed questionnaires were edited and data entry was carried out immediately in the field by the field editors. The data were uploaded on the same day to enable retrieval in the central office at NIPS in Islamabad, and the Internet File Streaming System was used to transfer data from the field to the central office. The completed questionnaires were then returned periodically from the field to the NIPS office in Islamabad through a courier service, where the data were again edited and entered by data processing personnel specially trained for this task. Thus, all data were entered twice for 100 percent verification. Data were entered using the CSPro computer package. The concurrent processing of the data offered a distinct advantage because of the assurance that the data were error-free and authentic. Moreover, the double entry of data enabled easy identification of errors and inconsistencies, which were resolved via comparisons with the paper questionnaire entries. The secondary editing of the data was completed in the first week of May 2013.

As noted, the PDHS used the CAFE system in the field for the first time. This application was developed and fully tested before teams were deployed in the field. Field editors were selected after careful screening from among the participants who attended the main training exercise. Seven-day training was arranged for field editors so that each editor could enter a sample cluster’s data under the supervision of NIPS senior staff, which enabled a better understanding of the CAFE system. The system was deemed efficient in capturing data immediately in the field and providing immediate feedback to the field teams. Early transfer of data back to the central office enabled the generation of field check tables on a regular basis, an efficient tool for monitoring the fieldwork.

Response rate

A total of 13,944 households were selected for the sample, of which