Geographic coverage

National Coverage

Universe

The target population is all births recorded on the NPR between 1998 and 2010 for South African citizens and permanent residents, regardless of which year the birth occurred. All births that occurred in South Africa with parents being non-South African citizens or not permanent residents were excluded.

Sampling procedure

The registration of births in South Africa is governed by the Births and Deaths Registration Act, 1992 (Act No. 51 of 1992), as amended, and is administered by the Department of Home Affairs (DHA) using Form DHA-24 (Notice of birth), which recently replaced Form BI-24 that was previously used. Notice of the birth must be given by one of the parents or; if neither parent is available to do so, the person having charge of the child or a person requested by the parents to do so. The person requested to register the birth must have a written mandate from the child's parents which must also include the reasons why neither of the parents is in a position to register the birth. The birth of a child outside the country; where at least one parent is a South African citizen; can be registered at any South African Mission abroad.Documentary proof in the form of a birth certificate of the foreign country must accompany the Notice of Birth.

The Act states that a child must be registered within 30 days of birth. Where the notice of a birth is given after the expiration of 30 days from the date of the birth, the Director-General may demand that reasons for the late notice be furnished and that the fingerprints be taken of the person whose notice of birth is given. Where the notice of a birth is given for a person aged 15 years and older, the birth shall be registered if it complies with the prescribed requirements for a late registration of birth.

Following the registration of a birth, a birth certificate is issued by the DHA. Citizens and permanent residents receive computer-printed abridged birth certificates and non-citizens receive handwritten certificates. The information of South African citizens and permanent residents is captured on the National Population Register (NPR).

The following persons and particulars are eligible to be included on the NPR:

• All children born of South African citizens and permanent residents when the notice of the birth is given within one year after the birth of the child.

• All children born of South African citizens and permanent residents when the notice of the birth is given one year after the birth of the child; together with the prescribed requirement for a late registration of birth.

• All South African citizens and permanent residents who, upon attainment of the age of 16, applied for and were granted identification cards (or books).

• All South African citizens and permanent residents who die at any age after birth.

• All South African citizens and permanent residents who depart permanently from South Africa.

The DHA captures information on places based on magisterial districts using the twelfth edition of the Standard Code List of Areas (Central Statistics Services, 1995). Stats SA then recodes the magisterial districts into district councils (DCs), metropolitan areas (metros) and provinces based on the 2011 municipal boundaries. The data sets for 1998 to 2010 have all been recoded according to the 2011 municipal boundaries.

It should be noted that the distribution of births by DCs, metros and provinces are approximate figures; as there was no perfect match of magisterial districts for all DCs, metros and provinces since some magisterial districts are situated in more than one DC, metro or province. Such magisterial districts were allocated to the district council where the majority of the land area falls (see the folder on maps). The only exception was with Nigel in Gauteng province. The majority of the land area of Nigel magisterial district is in Sedibeng district council (which is mainly farm areas and therefore sparsely populated) while the majority of the population lives in Ekurhuleni metropolitan area. As such, Nigel was classified to Ekurhuleni and not Sedibeng.

Magisterial district of birth refers to the district of birth occurrence for births registered before 15 years of age. For those that were registered from 15 years of age, district refers to the district of birth registration. Furthermore, from 2009, the processing of late birth registrations from age 15 were centralised at the DHA head office in Pretoria. As such, the late birth registrations processed in Pretoria from 15 years have a district code of Pretoria; even if they occurred in other areas. There were a few exceptional cases which were registered in Pretoria; but were not captured using the Pretoria code.

Mode of data collection

Other [oth]

Research instrument

NOTICE OF BIRTH - [Births and Deaths Registration Act 51 of 1992]

A. DETAILS OF THE CHILD

B. DETAILS OF FATHER (PARENT A)

C. DETAILS OF MOTHER (PARENT B)

D. ACKNOWLEDGEMENT OF PATERNITY OF A CHILD BORN OUT OF WEDLOCK

E. DETAILS OF THE LEGAL GUARDIAN/SOCIAL WORKER*

F. DECLARATION

G. FOR OFFICIAL USE ONLY - OFFICE OF ORIGIN

Cleaning operations

Data capturing of information on births is done by DHA officials. The data is captured directly onto the Population Register Database at Nucleus Bureau. These transactions are used to update the database of the NPR and the population register database. As soon as the DHA has captured the data; the data is made available on the mainframe. The data is then downloaded via ftp; or collected from the State Information Technology Agency (SITA) written on a CD by Stats SA. For the purpose of producing vital statistics, the following system is followed: all the civil transactions carried out at all DHA offices are written onto a cassette every day. At the end of every month, a combined set of cassettes is created containing all the transactions done for the month. These transactions are downloaded and the birth transactions are extracted for processing at Stats SA. The year in which the births are registered is the registration year. Using this information, Stats SA provides a breakdown of the registered births according to the year in which the births occurred.

While birth information sent to Stats SA is the same as that in the population register, there is a difference in the format between the two. On one hand, Stats SA’s data are based on births registered during the year (registration-based), while on the other hand, entries in the population register reflect the date of birth.

Data appraisal

Users are cautioned on the following limitations of the data:

• Father’s age had a high percentage of cases where information was unspecified or unknown for all the years.
• Data for 1998 and 1999 have incorrect information on month of birth, which could not be resolved.

Note: - Unknown : refers to cases where the answer provided is not correct or not possible given the options available. - Unspecified: refers to cases where no response was given.

Number and percentage of live births, by month of birth, 1991 to most recent year.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_6150f21b0892b3fdde546d2a1af2af82/view

The second National Family Health Survey (NFHS-2), conducted in 1998-99, provides information on fertility, mortality, family planning, and important aspects of nutrition, health, and health care. The International Institute for Population Sciences (IIPS) coordinated the survey, which collected information from a nationally representative sample of more than 90,000 ever-married women age 15-49. The NFHS-2 sample covers 99 percent of India's population living in all 26 states. This report is based on the survey data for 25 of the 26 states, however, since data collection in Tripura was delayed due to local problems in the state. IIPS also coordinated the first National Family Health Survey (NFHS-1) in 1992-93. Most of the types of information collected in NFHS-2 were also collected in the earlier survey, making it possible to identify trends over the intervening period of six and one-half years. In addition, the NFHS-2 questionnaire covered a number of new or expanded topics with important policy implications, such as reproductive health, women's autonomy, domestic violence, women's nutrition, anaemia, and salt iodization. The NFHS-2 survey was carried out in two phases. Ten states were surveyed in the first phase which began in November 1998 and the remaining states (except Tripura) were surveyed in the second phase which began in March 1999. The field staff collected information from 91,196 households in these 25 states and interviewed 89,199 eligible women in these households. In addition, the survey collected information on 32,393 children born in the three years preceding the survey. One health investigator on each survey team measured the height and weight of eligible women and children and took blood samples to assess the prevalence of anaemia. SUMMARY OF FINDINGS POPULATION CHARACTERISTICS Three-quarters (73 percent) of the population lives in rural areas. The age distribution is typical of populations that have recently experienced a fertility decline, with relatively low proportions in the younger and older age groups. Thirty-six percent of the population is below age 15, and 5 percent is age 65 and above. The sex ratio is 957 females for every 1,000 males in rural areas but only 928 females for every 1,000 males in urban areas, suggesting that more men than women have migrated to urban areas. The survey provides a variety of demographic and socioeconomic background information. In the country as a whole, 82 percent of household heads are Hindu, 12 percent are Muslim, 3 percent are Christian, and 2 percent are Sikh. Muslims live disproportionately in urban areas, where they comprise 15 percent of household heads. Nineteen percent of household heads belong to scheduled castes, 9 percent belong to scheduled tribes, and 32 percent belong to other backward classes (OBCs). Two-fifths of household heads do not belong to any of these groups. Questions about housing conditions and the standard of living of households indicate some improvements since the time of NFHS-1. Sixty percent of households in India now have electricity and 39 percent have piped drinking water compared with 51 percent and 33 percent, respectively, at the time of NFHS-1. Sixty-four percent of households have no toilet facility compared with 70 percent at the time of NFHS-1. About three-fourths (75 percent) of males and half (51 percent) of females age six and above are literate, an increase of 6-8 percentage points from literacy rates at the time of NFHS-1. The percentage of illiterate males varies from 6-7 percent in Mizoram and Kerala to 37 percent in Bihar and the percentage of illiterate females varies from 11 percent in Mizoram and 15 percent in Kerala to 65 percent in Bihar. Seventy-nine percent of children age 6-14 are attending school, up from 68 percent in NFHS-1. The proportion of children attending school has increased for all ages, particularly for girls, but girls continue to lag behind boys in school attendance. Moreover, the disparity in school attendance by sex grows with increasing age of children. At age 6-10, 85 percent of boys attend school compared with 78 percent of girls. By age 15-17, 58 percent of boys attend school compared with 40 percent of girls. The percentage of girls 6-17 attending school varies from 51 percent in Bihar and 56 percent in Rajasthan to over 90 percent in Himachal Pradesh and Kerala. Women in India tend to marry at an early age. Thirty-four percent of women age 15-19 are already married including 4 percent who are married but gauna has yet to be performed. These proportions are even higher in the rural areas. Older women are more likely than younger women to have married at an early age: 39 percent of women currently age 45-49 married before age 15 compared with 14 percent of women currently age 15-19. Although this indicates that the proportion of women who marry young is declining rapidly, half the women even in the age group 20-24 have married before reaching the legal minimum age of 18 years. On average, women are five years younger than the men they marry. The median age at marriage varies from about 15 years in Madhya Pradesh, Bihar, Uttar Pradesh, Rajasthan, and Andhra Pradesh to 23 years in Goa. As part of an increasing emphasis on gender issues, NFHS-2 asked women about their participation in household decisionmaking. In India, 91 percent of women are involved in decision-making on at least one of four selected topics. A much lower proportion (52 percent), however, are involved in making decisions about their own health care. There are large variations among states in India with regard to women's involvement in household decisionmaking. More than three out of four women are involved in decisions about their own health care in Himachal Pradesh, Meghalaya, and Punjab compared with about two out of five or less in Madhya Pradesh, Orissa, and Rajasthan. Thirty-nine percent of women do work other than housework, and more than two-thirds of these women work for cash. Only 41 percent of women who earn cash can decide independently how to spend the money that they earn. Forty-three percent of working women report that their earnings constitute at least half of total family earnings, including 18 percent who report that the family is entirely dependent on their earnings. Women's work-participation rates vary from 9 percent in Punjab and 13 percent in Haryana to 60-70 percent in Manipur, Nagaland, and Arunachal Pradesh. FERTILITY AND FAMILY PLANNING Fertility continues to decline in India. At current fertility levels, women will have an average of 2.9 children each throughout their childbearing years. The total fertility rate (TFR) is down from 3.4 children per woman at the time of NFHS-1, but is still well above the replacement level of just over two children per woman. There are large variations in fertility among the states in India. Goa and Kerala have attained below replacement level fertility and Karnataka, Himachal Pradesh, Tamil Nadu, and Punjab are at or close to replacement level fertility. By contrast, fertility is 3.3 or more children per woman in Meghalaya, Uttar Pradesh, Rajasthan, Nagaland, Bihar, and Madhya Pradesh. More than one-third to less than half of all births in these latter states are fourth or higher-order births compared with 7-9 percent of births in Kerala, Goa, and Tamil Nadu. Efforts to encourage the trend towards lower fertility might usefully focus on groups within the population that have higher fertility than average. In India, rural women and women from scheduled tribes and scheduled castes have somewhat higher fertility than other women, but fertility is particularly high for illiterate women, poor women, and Muslim women. Another striking feature is the high level of childbearing among young women. More than half of women age 20-49 had their first birth before reaching age 20, and women age 15-19 account for almost one-fifth of total fertility. Studies in India and elsewhere have shown that health and mortality risks increase when women give birth at such young ages?both for the women themselves and for their children. Family planning programmes focusing on women in this age group could make a significant impact on maternal and child health and help to reduce fertility. INFANT AND CHILD MORTALITY NFHS-2 provides estimates of infant and child mortality and examines factors associated with the survival of young children. During the five years preceding the survey, the infant mortality rate was 68 deaths at age 0-11 months per 1,000 live births, substantially lower than 79 per 1,000 in the five years preceding the NFHS-1 survey. The child mortality rate, 29 deaths at age 1-4 years per 1,000 children reaching age one, also declined from the corresponding rate of 33 per 1,000 in NFHS-1. Ninety-five children out of 1,000 born do not live to age five years. Expressed differently, 1 in 15 children die in the first year of life, and 1 in 11 die before reaching age five. Child-survival programmes might usefully focus on specific groups of children with particularly high infant and child mortality rates, such as children who live in rural areas, children whose mothers are illiterate, children belonging to scheduled castes or scheduled tribes, and children from poor households. Infant mortality rates are more than two and one-half times as high for women who did not receive any of the recommended types of maternity related medical care than for mothers who did receive all recommended types of care. HEALTH, HEALTH CARE, AND NUTRITION Promotion of maternal and child health has been one of the most important components of the Family Welfare Programme of the Government of India. One goal is for each pregnant woman to receive at least three antenatal check-ups plus two tetanus toxoid injections and a full course of iron and folic acid supplementation. In India, mothers of 65 percent of the children born in the three years preceding NFHS-2 received at least one antenatal

The Early Childhood Longitudinal Study, Birth Cohort (ECLS-B), is a study that is part of the Early Childhood Longitudinal Study program; program data is available since 1998-99 at . ECLS-B (https://nces.ed.gov/ecls/birth.asp) is a longitudinal study that is designed to provide policy makers, researchers, child care providers, teachers, and parents with detailed information about children's early life experiences. The study was conducted using multiple data collection methods (computer-assisted in-person interviews, computer-assisted telephone interviews, self-administered questionnaires, and direct observation) to collect information about children's characteristics, behaviors, development, and experiences from the adults who were important in the children's lives, including mothers, fathers, early care and education providers, and teachers. Direct child assessments were used to measure children's development, knowledge, and skills from the time the children were about 9 months old. A nationally representative sample of approximately 14,000 children born in the U.S. in 2001 was fielded. Key statistics produced from ECLS-B focus on children's health, development, care, and education during the formative years from birth through kindergarten entry.

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels. References: Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5

A growing body of research suggests that housing eviction is more common than previously recognized and may play an important role in the reproduction of poverty. The proportion of children affected by housing eviction, however, remains largely unknown. We estimate that 1 in 7 children born in large American cities in 1998–2000 experienced at least one eviction for nonpayment of rent or mortgage between birth and age 15. Rates of eviction were substantial across all cities and demographic groups studied, but children from disadvantaged backgrounds were most likely to experience eviction. Among those born into deep poverty, we estimate that about 1 in 4 were evicted by age 15. Given prior evidence that forced moves have negative consequences for children, we conclude that the high prevalence and social stratification of housing eviction are sufficient to play an important role in the reproduction of poverty and warrant greater policy attention.

The Future of Families and Child Wellbeing Study (FFCWS, formerly known as the Fragile Families and Child Wellbeing Study) follows a cohort of nearly 5,000 children born in large, U.S. cities between 1998 and 2000. The study oversampled births to unmarried couples; and, when weighted, the data are representative of births in large U.S. cities at the turn of the century. The FFCWS was originally designed to address four questions of great interest to researchers and policy makers: What are the conditions and capabilities of unmarried parents, especially fathers? What is the nature of the relationships between unmarried parents? How do children born into these families fare? How do policies and environmental conditions affect families and children? The FFCWS consists of interviews with mothers, fathers, and/or primary caregivers at birth and again when children are ages 1, 3, 5, 9, 15, and 22. The parent interviews collected information on attitudes, relationships, parenting behavior, demographic characteristics, health (mental and physical), economic and employment status, neighborhood characteristics, and program participation. Beginning at age 9, children were interviewed directly (either during the home visit or on the telephone). The direct child interviews collected data on family relationships, home routines, schools, peers, and physical and mental health, as well as health behaviors. A collaborative study of the FFCWS, the In-Home Longitudinal Study of Pre-School Aged Children (In-Home Study) collected data from a subset of the FFCWS Core respondents at the Year 3 and 5 follow-ups to ask how parental resources in the form of parental presence or absence, time, and money influence children under the age of 5. The In-Home Study collected information on a variety of domains of the child's environment, including: the physical environment (quality of housing, nutrition and food security, health care, adequacy of clothing and supervision) and parenting (parental discipline, parental attachment, and cognitive stimulation). In addition, the In-Home Study also collected information on several important child outcomes, including anthropometrics, child behaviors, and cognitive ability. This information was collected through interviews with the child's primary caregiver, and direct observation of the child's home environment and the child's interactions with his or her caregiver. Similar activities were conducted during the Year 9 follow-up. At the Year 15 follow-up, a condensed set of home visit activities were conducted with a subsample of approximately 1,000 teens. Teens who participated in the In-Home Study were also invited to participate in a Sleep Study and were asked to wear an accelerometer on their non-dominant wrist for seven consecutive days to track their sleep (Sleep Actigraphy Data) and that day's behaviors and mood (Daily Sleep Actigraphy and Diary Survey Data). An additional collaborative study collected data from the child care provider (Year 3) and teacher (Years 9 and 15) through mail-based surveys. Saliva samples were collected at Year 9 and 15 (Biomarker file and Polygenic Scores). The Study of Adolescent Neural Development (SAND) COVID Study began data collection in May 2020 following the onset of the COVID-19 pandemic. It included online surveys with the young adult and their primary caregiver. The FFCWS began its seventh wave of data collection in October 2020, around the focal child's 22nd birthday. Data collection and interviews continued through January 2024. The Year 22 wave included a young adult (YA) survey with the original focal child and a primary caregiver (PCG) survey. Data were also collected on the children of the original focal child (referred to as Generation 3, or G3). Documentation for these files is available on the FFCWS website located here. For details of updates made to the FFCWS data files, please see the project's Data Alerts page. Data collection for the Future of Families and Child Wellbeing Study was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) of the National Institutes of Health under award numbers R01HD36916, R01HD39135, and R01HD40421, as well as a consortium of private foundations.

This database is comprised of 603 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 208 males (34%) and 395 females (66%). Their ages ranged from 12 to 15 years. Their age in years at baseline is provided. The majority were born in Australia. Data were drawn from students at two Australian independent secondary schools. The data contains total responses for the following scales: The Intolerance of Uncertainty Scale (IUS-12; Short form; Carleton et al, 2007) is a 12-item scale measuring two dimensions of Prospective and Inhibitory intolerance of uncertainty. Two subscales of the Children’s Automatic Thoughts Scale (CATS; Schniering & Rapee, 2002) were administered. The Peronalising and Social Threat were each composed of 10 items. UPPS Impulsive Behaviour Scale (Whiteside & Lynam, 2001) which is comprised of 12 items. Dispositional Envy Scale (DES; Smith et al, 1999) which is comprised of 8 items. Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. Three subscales totals included were the GAD subscale (labelled SCAS_GAD), the OCD subscale (labelled SCAS_OCD) and the Social Anxiety subscale (labelled SCAS_SA). Each subscale was comprised of 6 items. Avoidance and Fusion Questionnaire for Youth (AFQ-Y; Greco et al., 2008) which is comprised of 17 items. Distress Disclosure Index (DDI; Kahn & Hessling, 2001) which is comprised of 12 items. Repetitive Thinking Questionnaire-10 (RTQ-10; McEvoy et al., 2014) which is comprised of 10 items. The Brief Fear of Negative Evaluation Scale, Straightforward Items (BFNE-S; Rodebaugh et al., 2004) which is comprised of 8 items. Short Mood and Feelings Questionnaire (SMFQ; Angold et al., 1995) which is comprised by 13 items. The Self-Compassion Scale Short Form (SCS-SF; Raes et al., 2011) which is comprised by 12 items. The subscales include Self Kindness, Self Judgment, Social Media subscales - These subscale scores were based on social media questions composed for this project and also drawn from three separate scales as indicated in the table below. The original scales assessed whether participants experience discomfort and a fear of missing out when disconnected from social media (taken from the Australian Psychological Society Stress and Wellbeing Survey; Australian Psychological Society, 2015a), style of social media use (Tandoc et al., 2015b) and Fear of Missing Out (Przybylski et al., 2013c). The items in each subscale are listed below. Pub_Share Public Sharing When I have a good time it is important for me to share the details onlinec On social media how often do you write a status updateb On social media how often do you post photosb Surveillance_SM On social media how often do you read the newsfeed On social media how often do you read a friend’s status updateb On social media how often do you view a friend’s photob On social media how often do you browse a friend’s timelineb Upset Share On social media how often do you go online to share things that have upset you? Text private On social media how often do you Text friends privately to share things that have upset you? Insight_SM Social Media Reduction I use social media less now because it often made me feel inadequate FOMO I am afraid that I will miss out on something if I don’t stay connected to my online social networksa. I feel worried and uncomfortable when I can’t access my social media accountsa. Neg Eff of SM I find it difficult to relax or sleep after spending time on social networking sitesa. I feel my brain ‘burnout’ with the constant connectivity of social mediaa. I notice I feel envy when I use social media.
I can easily detach from the envy that appears following the use of social media (reverse scored) DES_SM Envy Mean acts online Feeling envious about another person has led me to post a comment online about another person to make them laugh Feeling envious has led me to post a photo online without someone’s permission to make them angry or to make fun of them Feeling envious has prompted me to keep another student out of things on purpose, excluding her from my group of friends or ignoring them. Substance Use: Two items measuring peer influence on alcohol consumption were adapted from the SHAHRP “Patterns of Alcohol Use” measure (McBride, Farringdon & Midford, 2000). These items were “When I am with friends I am quite likely to drink too much alcohol” and “Substances (alcohol, drugs, medication) are the immediate way I respond to my thoughts about a situation when I feel distressed or upset. Angold, A., Costello, E. J., Messer, S. C., & Pickles, A. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 5(4), 237–249. Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Greco, L.A., Lambert, W. & Baer., R.A. (2008) Psychological inflexibility in childhood and adolescence: Development and evaluation of the Avoidance and Fusion Questionnaire for Youth. Psychological Assessment, 20, 93-102. https://doi.org/10.1037/1040-3590.20.2.9 Kahn, J. H., & Hessling, R. M. (2001). Measuring the tendency to conceal versus disclose psychological distress. Journal of Social and Clinical Psychology, 20(1), 41–65. https://doi.org/10.1521/jscp.20.1.41.22254 McBride, N., Farringdon, F. & Midford, R. (2000) What harms do young Australians experience in alcohol use situations. Australian and New Zealand Journal of Public Health, 24, 54–60 https://doi.org/10.1111/j.1467-842x.2000.tb00723.x McEvoy, P.M., Thibodeau, M.A., Asmundson, G.J.G. (2014) Trait Repetitive Negative Thinking: A brief transdiagnostic assessment. Journal of Experimental Psychopathology, 5, 1-17. Doi. 10.5127/jep.037813 Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in human behavior, 29(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Rodebaugh, T. L., Woods, C. M., Thissen, D. M., Heimberg, R. G., Chambless, D. L., & Rapee, R. M. (2004). More information from fewer questions: the factor structure and item properties of the original and brief fear of negative evaluation scale. Psychological assessment, 16(2), 169. https://doi.org/10.1037/10403590.16.2.169 Schniering, C. A., & Rapee, R. M. (2002). Development and validation of a measure of children’s automatic thoughts: the children’s automatic thoughts scale. Behaviour Research and Therapy, 40(9), 1091-1109. . https://doi.org/10.1016/S0005-7967(02)00022-0 Smith, R. H., Parrott, W. G., Diener, E. F., Hoyle, R. H., & Kim, S. H. (1999). Dispositional envy. Personality and Social Psychology Bulletin, 25(8), 1007-1020. https://doi.org/10.1177/01461672992511008 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5 Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43, 139–146. https://doi.org/10.1016/j.chb.2014.10.053 Whiteside, S.P. & Lynam, D.R. (2001) The five factor model and impulsivity: using a structural model of personality to understand impulsivity. Personality and Individual Differences 30,669-689. https://doi.org/10.1016/S0191-8869(00)00064-7 The data was collected by Dr Danielle A Einstein, Dr Madeleine Fraser, Dr Anne McMaugh, Prof Peter McEvoy, Prof Ron Rapee, Assoc/Prof Maree Abbott, Prof Warren Mansell and Dr Eyal Karin as part of the Insights Project. The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

All children at age 12 months who have received the complete course (3 doses) of hepatitis B vaccine within each reporting area as a percentage of all the eligible population as defined in the hepatitis B chapter of the immunisation against infectious diseases "Green Book" (have maternal Hep B positive status).RationaleInfants born to hepatitis B virus (HBV) infected mothers are at high risk of acquiring HBV infection themselves. Babies born to infected mothers are given a dose of the hepatitis B vaccine after they are born. This is followed by another two doses (with a month in between each) and a booster dose 12 months later. Around 20% of people with chronic hepatitis B will go on to develop scarring of the liver (cirrhosis), which can take 20 years to develop, and around 1 in 10 people with cirrhosis will develop liver cancer.Vaccination coverage is the best indicator of the level of protection a population will have against vaccine preventable communicable diseases. Coverage is closely correlated with levels of disease. Monitoring coverage identifies possible drops in immunity before levels of disease rise.Since April 2000 it has been recommended that all pregnant women in England and Wales should be offered testing for hepatitis B through screening for HBsAg, and that all babies of HBsAg seropositive women should be immunised (HSC 1998 127). A dose of paediatric hepatitis B vaccine is recommended for all infants born to an HBV infected mother as soon as possible after birth, then at 1 and 2, and 12 months of age ( https://www.gov.uk/government/collections/hepatitis-b-guidance-data-and-analysis ). Previous evidence shows that highlighting vaccination programmes encourages improvements in uptake levels may also have relevance for NICE guidance PH21: Reducing differences in the uptake of immunisations (The guidance aims to increase immunisation uptake among those aged under 19 years from groups where uptake is low).Definition of numeratorNumber of children at age 12 months who have received the complete course (3 doses) of hepatitis B vaccine. Numerator counts for local authorities include all people registered with practices in the local authority, and no data are available to provide resident based figures.Definition of denominatorEligible population as defined in the hepatitis B chapter of the immunisation against infectious diseases "Green Book" (have maternal Hep B positive status).Denominators for local authorities include all people registered with practices in the local authority, and no data are available to provide resident based figures.CaveatsThese statistics have been published as ‘experimental statistics’ in the NHS Digital “NHS Immunisation Statistics, England” report. There are a number of issues with the hepatitis B dataset which have either impacted on data quality or have raised potential concerns around the quality of the data. Selective neonatal hepatitis B coverage data are reported by local authority (LA) responsible population for the first time in the 2015 to 2016 publication. Many LAs could not supply complete data on infants born to hepatitis B positive mothers and for a number of other LAs there were data quality issues. It has therefore not been possible to estimate figures for those LAs or describe the quality/completeness of LA data with any accuracy. (see Quality Statement for 2015 to 2016 for more information). Office of Health Improvement and Disparities has also published data for LAs that are co terminus with former PCTs but provided data by PCT rather than LA. These data were not published or validated by NHS Digital.

Social and economic causes and consequences of divorce in the Netherlands since the 1940s. This study contains many questions about marriage, divorce, remarriage, fertility, labour force participation, life course, and social networks of the respondent, it's parental family and it's (previous) partner.Questions about relation with current and previous partner, housing, finance, family and friends, children, at start of relation, 5 years later, now (current relation). How did r. meet (previous) partner, why marry, why cohabitation / partner divorced before.Information on the current and previous household situation: division of household tasks / which part of household income earned by which partner / financial difficulties / holidays without partner / political preference / does r. (female) use family name of partner.Questions about the seperation: whose initiative, why, relation with ex-partner, alimentation / did r.'s parents, siblings, uncles and aunts divorce.Background variables of respondent, its family and (previous) partner: education / religion / where born / children.

Households, women and children indicators based on data from the DHS surveys in Benin (2001), Burkina Faso (2003), Cote D'Ivoire (1999), Ghana (2008) and Togo (1998). The following indicators have been considered to create raster datasets at 5 arcmin resolution: wealth index, age and sex of the head of household, number of dependent household members (under the age of 5), educational attainment of the respondent, occupation of the respondent, current employment status of the respondent, type and duration employment of the respondent, payment received for work by the respondent, number of sons and daughters away from home, number of years the respondent lived in the current residence, religion of the respondent. This data set has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata. This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d'Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.