Abstract

The survey was specifically designed to meet the following objectives: -to assess the current situation in Moldova concerning fertility, abortion, contraception and various other reproductive health issues; -to enable policy makers, program managers, and researchers to evaluate and improve existing programs and to develop new strategies; -to measure changes in fertility and contraceptive prevalence rates and study factors that affect these changes, such as geographic and socio-demographic factors, breast-feeding patterns, use of induced abortion, and availability of family planning; -to provide data necessary to develop sex education and health promotion programs; -to obtain data on knowledge, attitudes, and behavior of young adults 15-24 years of age; -to provide information on the level of knowledge about AIDS transmission and prevention; -to identify and focus further reproductive health studies toward high risk groups.

The survey provides data that will assist the Moldovan Government in improving services related to the health of women and children and was proposed in conjunction with the UNFPAsponsored reproductive health (RH) activities in Moldova, which consist of several components intended to increase the use of effective contraception, reduce the reliance on induced abortion as a means of fertility control, and, more generally, to improve RH. Specific projects supported by UNFPA in Moldova include ongoing support to the Government for developing a national RH plan, provisions of contraceptives, and training of family planning providers. In addition, the national RH plan is receiving support from USAID (family planning logistics management, information/ education/communication activities), IPPF (provision of contraceptives), and UNICEF.

Geographic coverage

The 1997 MRHS was designed to collect information from a representative sample of women of reproductive age throughout Moldova.

Universe

The universe from which the respondents were selected included all females between the ages of 15 and 44, regardless of marital status, who were living in Moldova when the survey was carried out.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey employed a three-stage probability sample design and successfully interviewed 5,412 (98%) of 5,543 women identified in sample households as eligible for interview.

The survey employed a three-stage sampling design using two sampling frames (one for urban areas and one for rural areas) provided by the MSDS. The urban sampling frame was based on the 1989 census, whereas the rural sampling frame consisted of a list of the 1,607 villages in the country, recently updated for household composition in January-April 1997 for an agricultural registry.

In the first stage, 128 census sectors in urban areas and 122 villages were selected as Primary Sampling Units (PSUs) with probability proportional to the number of households in each census sector/village. In the second stage of sampling, clusters of households were randomly selected in each census sector/village chosen in the first stage. Before second-stage selection in urban areas, the Census Division of the MSDS redefined each 1989 census sector selected as a PSU for street boundaries, converted the maps and listings from Russian to Moldavian, and updated the sector's household composition in collaboration with personnel from the local health care units. A cluster of households was randomly selected from the updated sector lists of the PSUs in urban areas and from the household listings in the villages selected as PSUs in the first stage. (Since there were roughly equal numbers of urban and rural households, the sample was designed to be geographically self-weighting.) In each sample strata, urban and rural, the third stage consisted of the random selection of one woman if there were two or more eligible women (aged 15-44 years) living in the same household.

Cluster size determination was based on the number of households required to obtain an average of 20 interviews per cluster. The total number of households in each cluster took into account estimates of unoccupied households, average number of women 15-44 per household, the interview of only one woman per household, and an estimated response rate of 90% in urban areas and 92% in rural areas. In urban areas, the cluster size with a yield of 20 interviews, on average, was determined to be 45 households. In rural areas, because the average number of women 15-44 per household varies considerably by raion, the average number of households needed to obtain 20 complete interviews varied from 42 to 60.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was first drafted by CDC/DRH consultants based on a core questionnaire used in the 1993 Romanian Reproductive Health Survey. This core questionnaire was reviewed and modified by Moldovan experts in reproductive health and family planning, as well as by USAID and UNFPA. Based on these reviews, a pretest questionnaire was developed and field-tested in April 1997. The questionnaire, developed in Romanian, was translated into Russian after the pretest. All interviewers spoke these two languages.

The questionnaire had two components: (1) A short household questionnaire used to collect residential and geographic information, select information about all women of childbearing age living in sampled households, and information on interview status. This module was also used to randomly select one respondent when there was more than one eligible woman in the household; (2) The longer individual questionnaire collected information on the topics mentioned above.

The major reproductive health topics on which information was collected were: pregnancies and childbearing (a complete history of all pregnancies, including planning status of pregnancies in the last five years, a detailed history of abortions within the last five years, including postabortion counseling, and the history of all births within the last five years, including the patterns of utilization of health services during pregnancy, maternal morbidity, infant health and breast-feeding); family planning (knowledge and history of use of methods of preventing pregnancy, current use of contraception, source of contraception, reasons for not using, reasons for use of less effective methods of contraception, future fertility preferences and intentions to use voluntary sterilization); women's health (health behavior and use of women's health services, tobacco and alcohol use); reproductive health knowledge and attitudes (especially regarding birth control pills, condoms, and IUDs); knowledge about HIV/AIDS transmission and prevention; domestic violence, including violence during the most recent pregnancy; history of sexual abuse; and socioeconomic characteristics of women and their husbands/families. The young women (15-24 years of age) were asked additional questions on sex education, age and contraceptive use at first sexual intercourse, and sexual behaviors.

Most issues have been examined by geographic, demographic, and socio-economic characteristics, making it possible to identify the segments of the population with specific health needs or problems.

Response rate

Of the 11,506 households selected, 5,543 were found to include at least one 15-44 year-old woman. Of these women, 5,412 were successfully interviewed, for a response rate of 97.6%. Less than one percent of selected women refused to be interviewed, while another 1.3% could not be located. Response rates were slightly better in rural areas (98%) than in municipalities and other urban areas (97%). In Chisinau (not shown), the response rate was 96%; nearly 3% of women selected in the sample could not be located.

Data appraisal

The geographic distribution of the sample, by residence and region, is very close to official figures of the population distribution for 1996, estimated by the Moldovan State Department for Statistics.

The percent distribution of women in the sample by five-year age groups is compared with the 1994 official estimates (the most recent estimates by age group) in Table 2.3. Compared with these estimates, the survey sample has slightly over-represented adolescent women (15-19 yearolds) and under-represented women aged 40-44 by about two percentage points. However, several factors may have contributed to the differences observed: first, there is a three-year difference between the time the official estimates were calculated and the survey was implemented; second, the official estimates are projections of the age composition recorded by the 1989 census and thus dependent on assumptions used in projecting the aging of a cohort; finally, official estimates include any possible age misreporting that occured in the census.

Moldova MD: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 4.900 % in 2012. This records a decrease from the previous number of 9.100 % for 2005. Moldova MD: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 7.000 % from Dec 2005 (Median) to 2012, with 2 observations. The data reached an all-time high of 9.100 % in 2005 and a record low of 4.900 % in 2012. Moldova MD: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank.WDI: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

Moldova MD: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data was reported at 1.800 % in 2012. This records a decrease from the previous number of 5.600 % for 2005. Moldova MD: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 3.700 % from Dec 2005 (Median) to 2012, with 2 observations. The data reached an all-time high of 5.600 % in 2005 and a record low of 1.800 % in 2012. Moldova MD: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank.WDI: Health Statistics. Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

Abstract

The Republic of Moldova Multiple Indicator Cluster Survey was carried out in 2012 (hereinafter the 2012 Moldova MICS) by the National Centre of Public Health of the Ministry of Health in collaboration with the National Bureau of Statistics, the Scientific Research Institute of Mother and Child Health Care, the Ministry of Labour, Social Protection and Family, the Ministry of Education, the National Centre for Health Management, and the National Centre for Reproductive Health and Medical Genetics. Financial and technical support was provided by the United Nations Children’s Fund (UNICEF), with additional contribution of the Swiss Agency for Development and Cooperation and the World Health Organization Regional Office for Europe within the EU supported project on technical assistance to the health sector.

The Multiple Indicator Cluster Survey (MICS) is an international household survey programme developed by UNICEF. The 2012 Moldova MICS was conducted as part of the fourth global round of MICS surveys (MICS4). MICS provides up-to-date information on the situation of children and women and measures key indicators that allow countries to monitor progress towards the Millennium Development Goals (MDGs) and other internationally agreed upon commitments.

The 2012 Moldova MICS was based on a nationally representative probability sample, stratified in two stages and consisting of about 12,500 households. Fieldwork was carried out between April 17 and June 30, 2012 using four Questionnaires – the Household Questionnaire, the Questionnaire for Individual Women aged 15-49 years, the Questionnaire for Children Under Five, the Questionnaire for Individual Men aged 15-49 years, as well as a Questionnaire Form for Vaccination Records at the Health Facility.

In addition to the administration of questionnaires, fieldwork teams tested the salt used for cooking in the households for its iodate content, observed the place used for handwashing, measured the weights and heights of children under the age of five, as well as the haemoglobin levels in women aged 15-49 years and children aged 6-59 months. The household response rate was 97 percent, with 89 percent, 77 percent and 96 percent response rates calculated for the women’s, men’s and under-5’s interviews respectively.

Geographic coverage

National

Analysis unit

• individuals
• households

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

A probability-based stratified sample was selected in two stages for the 2012 Moldova MICS. Considering that the 2004 Population Census cartographic materials were discarded, it became impossible to use them as a source of data for the sampling frame. Thus, the decision was to use the 2005 Moldova DHS sample for the first stage (PSU - Primary Sampling Unit) and for the second stage a probability-based sample of the households has been selected from each PSU.

Coverage The reference population for the 2012 Moldova MICS depends on the particular indicators and is defined as follows (the size estimates are presented in Table SD.1): 1. Households; 2. Women aged 15-49 years; 3. Men aged 15-49 years; 4. Children under 5 years of age. Geographically, the reference population is placed within the administrative borders of Moldova's territorial units which are located on the western side of the Nistru (Dniester River); the population living in the eastern side (left bank of the Dniester River and the Bender municipality -Transnistrian region) are not a part of 2012 Moldova MICS.

Sample representativeness The 2012 Moldova MICS sample ensured representativeness at a national level (excluding Transnistrian region) and, like in the case of the 2005 Moldova DHS, at the level of residential areas - urban and rural. Although at the first sampling stage no stratification by zone was used, the results of the 2005 Moldova DHS survey indicate that the level of precision of the zone level estimates is acceptable.

Sample size The sample size is determined, on the one hand, by the precision expected to be achieved for the key indicators, and on the other hand, by the availability of human and financial resources. The precision of a sample survey's results is liable to be affected by two types of errors: sampling and non-sampling errors. The level of the sampling errors is inversely proportional to the square root of the sample size, whereas the nonsampling errors are affected by an increase in the sample size. Consequently, the larger the sample is, the smaller the sampling errors, and the greater the non-sampling errors are. Therefore it is important that the size of the sample is balanced so as to ensure both an acceptable precision and a minimum level of non-sampling errors.

Taking into account the limitations due to the lack of maps of census sectors, which made it impossible to select a new sample of PSUs, it was decided to use the same sample of PSUs that was used for the 2005 Moldova DHS, which included 400 census sectors. The final sample size was 12,500 households, a figure obtained by selecting respective number of households from each of the 400 PSUs drawn at the first sampling stage.

PSU (cluster) size The average number of households per PSU is around 90 in rural areas and approximately 120 in urban areas. These sizes were determined so as to ensure a reasonable workload for the enumerators involved in general 2004 Population Census conducted by the NBS. This also made the PSUs practical for updating the list of the households for the purpose of providing a sampling frame for the 2012 Moldova MICS second sampling stage in a timely and cost-effective manner.

Sampling frame The sampling frame at the first sampling stage was built on the census sectors defined for the purposes of the 2004 Population Census carried out by the NBS. This included the list of all the census sectors, put into digital form, accompanied by variables for the identification of the sectors in the 2004 PC, information on areas of residence and geographical zones, and their measure of size expressed in number of persons.

Sample Selection Procedures At the first stage of sampling, PSUs within each stratum were systematically drawn with probabilities proportional to their size (number of population based on the 2004 PC data). Prior to sampling, the census sectors in each stratum were sorted in geographical order from north to south, in order to provide an additional level of implicit stratification based on the geographic criterion. At the second sampling stage, a sample of 30 households was selected from each PSU. The selection was done in each PSU based on the lists of households registered following the update, using a simple systematic sampling procedure.

The sampling procedures are more fully described in "Moldova Multiple Indicator Cluster Survey 2012 - Final Report" pp.143-147.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered in each household, which collected various information on household members including sex, age and relationship. The household questionnaire includes household information panel, household listing form, education, water and sanitation, household characteristics, child discipline, hand washing and salt iodization.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49, children under age five and men age 15-49. For children, the questionnaire was administered to the mother or primary caretaker of the child.

The women's questionnaire includes woman's information panel, woman's background, access to mass media and ICT, child mortality -birth history, desire for last birth, maternal and newborn health, post-natal health checks, illness symptoms, contraception, unmet need, attitudes toward domestic violence, marriage/union, sexual behavior, HIV/AIDS, tuberculosis, tobacco and alcohol use, life satisfaction and haemoglobin measurement.

The children's questionnaire includes child's age, birth registration, early childhood development, breastfeeding, care of illness, immunisation, anthropometry and haemoglobin measurement.

The men's questionnaire includes man's information panel, man's background, access to mass media and ICT, child mortality, attitudes toward domestic violence, marriage/union, sexual behavior, HIV/AIDS, tuberculosis, tobacco and alcohol use and life satisfaction.

MICS fourth round model questionnaires were customized based on the country’s needs so as to reflect relevant issues which are present in the Republic of Moldova in terms of children’s, women’s and men’s health, education, child protection, migration, HIV/AIDS, tuberculosis, anaemia, etc. Following content approval by the Steering Committee members, the questionnaires were translated from English and Russian into Romanian and were subsequently pre-tested (in Romanian and Russian).

Cleaning operations

Data were entered using the CSPro software on 12 computers by 12 previously trained data-entry clerks. A supervisor and an expert in data processing and analysis were responsible for the quality of data entry. Completed questionnaires were returned each week from the field to the NCPH office in Chisinau for additional editing by two office editors. In order to ensure quality control, all

Moldova MD: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data was reported at 5.800 % in 2012. This records a decrease from the previous number of 11.000 % for 2005. Moldova MD: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data is updated yearly, averaging 8.400 % from Dec 2005 (Median) to 2012, with 2 observations. The data reached an all-time high of 11.000 % in 2005 and a record low of 5.800 % in 2012. Moldova MD: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank.WDI: Health Statistics. Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

Moldova MD: Prevalence of Underweight: Weight for Age: % of Children Under 5 data was reported at 2.200 % in 2012. This records a decrease from the previous number of 3.200 % for 2005. Moldova MD: Prevalence of Underweight: Weight for Age: % of Children Under 5 data is updated yearly, averaging 2.700 % from Dec 2005 (Median) to 2012, with 2 observations. The data reached an all-time high of 3.200 % in 2005 and a record low of 2.200 % in 2012. Moldova MD: Prevalence of Underweight: Weight for Age: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank: Health Statistics. Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

The footnotes in the table are represented in brackets. The first footnote does not appear in the table.Footnotes: 1 For the 2011 National Household Survey (NHS) estimates, the global non-response rate (GNR) is used as an indicator of data quality. This indicator combines complete non-response (household) and partial non-response (question) into a single rate. The value of the GNR is presented to users. A smaller GNR indicates a lower risk of non-response bias and as a result, lower risk of inaccuracy. The threshold used for estimates' suppression is a GNR of 50% or more. For more information, please refer to the National Household Survey User Guide, 2011.2 Includes persons who are stateless.3 Includes persons who are stateless.4 The official name of Bolivia is Plurinational State of Bolivia.5 The official name of Venezuela is Bolivarian Republic of Venezuela.6 Includes countries such as Bonaire, Saint Eustatius and Saba; Falkland Islands (Malvinas); Greenland; Saint Barthélemy; Saint Martin (French part); and South Georgia and the South Sandwich Islands.7 The official name of Moldova is Republic of Moldova.8 The official name of United Kingdom is United Kingdom of Great Britain and Northern Ireland. United Kingdom includes Scotland, Wales, England and Northern Ireland (excludes Isle of Man, the Channel Islands and British Overseas Territories).9 The official name of Kosovo is Republic of Kosovo.10 Known as Former Yugoslav Republic of Macedonia in the United Nations and other international bodies.11 Includes countries such as Åland Islands; Andorra; Holy See (Vatican City State); Liechtenstein; San Marino; and Svalbard and Jan Mayen Island.12 The official name of Tanzania is United Republic of Tanzania.13 The official name of Libya is Libyan Arab Jamahiriya.14 Includes countries such as Mayotte; Saint Helena; Sao Tome and Principe; and Western Sahara.15 The official name of Iran is Islamic Republic of Iran.16 The official name of Syria is Syrian Arab Republic.17 West Bank and Gaza Strip are the territories referred to in the Declaration of Principles, signed by Israel and the Palestine Liberation Organization in 1993. Palestine refers to pre-1948 British mandate Palestine.18 China excludes Hong Kong Special Administrative Region and Macao Special Administrative Region.19 The official name of North Korea is Democratic People's Republic of Korea.20 The official name of South Korea is Republic of Korea.21 The official name of Laos is Lao People's Democratic Republic.22 The official name of Viet Nam is Socialist Republic of Viet Nam.23 Includes countries such as British Indian Ocean Territory; Maldives; and Timor-Leste.24 Includes countries such as American Samoa; Christmas Island; Cocos (Keeling) Islands; Cook Islands; Guam; Kiribati; Marshall Islands; Micronesia, Federated States of; Nauru; Niue; Norfolk Island; Northern Mariana Islands; Palau; Pitcairn; Solomon Islands; Tokelau; Tuvalu; United States Minor Outlying Islands; Vanuatu; and Wallis and Futuna.

Moldova MD: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 3.700 % in 2012. This records a decrease from the previous number of 9.300 % for 2005. Moldova MD: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 6.500 % from Dec 2005 (Median) to 2012, with 2 observations. The data reached an all-time high of 9.300 % in 2005 and a record low of 3.700 % in 2012. Moldova MD: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank.WDI: Health Statistics. Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

Moldova MD: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 4.100 % in 2024. This records an increase from the previous number of 3.700 % for 2023. Moldova MD: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 5.100 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 8.900 % in 2002 and a record low of 3.300 % in 2021. Moldova MD: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

Moldova MD: Prevalence of Severe Wasting: Weight for Height: Male: % of Children under 5 data was reported at 0.500 % in 2012. This records a decrease from the previous number of 2.700 % for 2005. Moldova MD: Prevalence of Severe Wasting: Weight for Height: Male: % of Children under 5 data is updated yearly, averaging 1.600 % from Dec 2005 (Median) to 2012, with 2 observations. The data reached an all-time high of 2.700 % in 2005 and a record low of 0.500 % in 2012. Moldova MD: Prevalence of Severe Wasting: Weight for Height: Male: % of Children under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank: Health Statistics. Prevalence of severe wasting, male, is the proportion of boys under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

Moldova MD: Prevalence of Stunting: Height for Age: Female: % of Children Under 5 data was reported at 7.000 % in 2012. This records a decrease from the previous number of 11.500 % for 2005. Moldova MD: Prevalence of Stunting: Height for Age: Female: % of Children Under 5 data is updated yearly, averaging 9.250 % from Dec 2005 (Median) to 2012, with 2 observations. The data reached an all-time high of 11.500 % in 2005 and a record low of 7.000 % in 2012. Moldova MD: Prevalence of Stunting: Height for Age: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank.WDI: Health Statistics. Prevalence of stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

Moldova MD: Prevalence of Stunting: Height for Age: % of Children Under 5, Modeled Estimate data was reported at 4.200 % in 2024. This stayed constant from the previous number of 4.200 % for 2023. Moldova MD: Prevalence of Stunting: Height for Age: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 6.800 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 12.900 % in 2000 and a record low of 4.200 % in 2024. Moldova MD: Prevalence of Stunting: Height for Age: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank.WDI: Social: Health Statistics. Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.