Mozambique Population: Gaza data was reported at 1,467,951.000 Person in 2017. This records an increase from the previous number of 1,442,094.000 Person for 2016. Mozambique Population: Gaza data is updated yearly, averaging 1,344,095.000 Person from Dec 2007 (Median) to 2017, with 11 observations. The data reached an all-time high of 1,467,951.000 Person in 2017 and a record low of 1,236,284.000 Person in 2007. Mozambique Population: Gaza data remains active status in CEIC and is reported by National Statistics Institute. The data is categorized under Global Database’s Mozambique – Table MZ.G003: Population: by Region.

Abstract

Starting in 2008, Save the Children implemented a center-based community driven preschool model in rural areas of the Gaza Province in Southern Mozambique. The project financed the construction, equipment and training for 67 classrooms in 30 communities, to provide Early Childhood Development (ECD) activities for children aged between 36 and 59 months. As part of its design, the program included an experimental impact evaluation (using Cluster-Randomized Controlled Trial) whereby the 30 intervention communities were selected at random from a pool of 76 eligible sites. Before the preschool activities initiated, a baseline survey was carried out in 2008 involving 76 communities in Gaza Province across the 3 different districts. Two years later, in 2010, the same 2,000 households participated in a mid-line survey to evaluate the impact of the program after one or two years of potential exposure to pre-school. The present data correspond to the follow-up survey that took place in 2014, namely 6-years after the beginning of the intervention when the targeted children were supposed to be in primary school. The impact evaluation has four main research questions: (1) to evaluate the efficiency of low-cost community-based preschool program in a disadvantaged rural African setting in terms of cognitive, socio-emotional skills as well as learning outcomes for the children, (2) to evaluate the effects of such intervention on school enrollment, attendance, and progress (i.e. grade promotion, repetition, dropout); (3) to assess whether parenting practices and knowledge can be durably influenced by community-based ECD program; (4) To identify potential spill-over effects of the program on health, education, productivity and labor market outcomes of siblings and parents of preschoolers. Field work was carried out from April to November 2014. In addition to household surveys and cognitive assessments of children (in literacy, numeracy and non-verbal reasoning), data from primary school directors, pre-school animators and community leaders were collected during this period. From the original 2,000 target children of the 2008 survey, more than 90% of them were successfully tracked and geo-referenced.

Geographic coverage

Three districts : Bilene, Manjacaze, Xai-xai, located in Gaza Province (Southern Mozambique).

Analysis unit

• Mothers/caregivers of the targeted children (aged between 3 to 5 in 2008)
• Targeted children aged between 3 to 5 in 2008 in sampled communities
• Siblings of the target children currently living in the same household

Kind of data

Sample survey data [ssd]

Sampling procedure

Communities sampling-process (baseline)

The design used for this impact evaluation is that of a clustered randomized control trial (C-RCT) at community levels

Stage 1: Community Eligibility. Within the three target districts, a subset of eligible communities is identified that meets two key operational requirements for implementation of the program: 1. Population size: To qualify for the intervention, communities must have a population no less than 500 and no more than 8000 people. This range was determined as operationally feasible given the community mobilization process that accompanies the establishment of each ECD center. 2. Clusters: Management of the intervention requires that the intervention be clustered in groups of 6 treatment communities that can be served by a program staff. The definition of cluster was set set by Save the Children, based on minimum criteria of operational feasibility (distance or time traveled between sites). The complete universe had 252 villages in three intervention districts. After applying eligibility criteria of population size and clustering, the sample was reduced to 167 villages in 11 clusters.

Stage 2: Clusters selections The largest clusters in each district were selected for inclusion in the sample, resulting in total of 98 villages. To achieve coverage in all three distracts, it was further agreed with the NGO that the sample would include 2 clusters each in Manjacaze and Xai Xai and one cluster in Bilene

Stage 3: Community level randomization Within clusters of communities that meet the two requirements outlined in stage 1, communities form triplets based on population size, and from each triplet a treatment community is selected at random. The two smallest villages which did not form part of a triplet were dropped. The final sample is composed of 37 treatment (7 for replacement) and 59 control villages (11 replacement), for a total sample of 96 villages. A total of 30 new intervention communities were then selected for this round of implementation through random assignment. No replacement of communities was needed.

Child level selection : In addition to randomization at the community level, there is exogenous variation in treatment within communities, based on rules of eligibility for Orphans and Vulnerable Children (OVC). ECD centers had a maximum of 3 class rooms with 35 students per class, for a maximum of 105 students per preschool. In the case of over-subscription of children to the ECD centers, Save the Children and the communities selected the children through a lottery system.

Household sample: A total of 2,000 households with preschool age children were sampled from the 76 evaluation communities at baseline. With no household listing available at the time of the survey, a census of each community was carried out to identify households with children in the age range of 36 to 59 months. Taking the list of households with at least one child in this age range, 23 households per community were planned to be selected randomly. In addition, in 4 large treatment communities where oversubscription to the program was likely, an additional 63 households were selected, yielding a total sample of 2,000 households.

Sampling deviation

In practice, some communities did not have 23 households eligible. In this case, all eligible households were sampled while in larger communities, more households than planned were sampled. Among them 1,830 targeted children were assessed in literacy, numeracy and non-verbal reasoning.

Mode of data collection

Face-to-face [f2f]

Research instrument

• Socio-economic questionnaire administrated to the mother/caregiver from the current household of the targeted child ;
• Time-use of the targeted child
• Child assessment in Literacy, Numeracy and Non-verbal reasoning
• School director questionnaire
• Community leader questionnaire
• ECD Instructor questionnaire

Response rate

The follow-up survey successfully tracked 1,875 households from baseline, representing 93.75% of the initial sample.

Socio-demographic characteristics of participants, in miners community of origin, Gaza Province, Southern of Mozambique, 2017.

人口：加沙（地中海岸港市）在12-01-2017达1,467,951.000人，相较于12-01-2016的1,442,094.000人有所增长。人口：加沙（地中海岸港市）数据按年更新，12-01-2007至12-01-2017期间平均值为1,344,095.000人，共11份观测结果。该数据的历史最高值出现于12-01-2017，达1,467,951.000人，而历史最低值则出现于12-01-2007，为1,236,284.000人。CEIC提供的人口：加沙（地中海岸港市）数据处于定期更新的状态，数据来源于Instituto Nacional De Estatistica，数据归类于Global Database的莫桑比克 – 表 MZ.G003：人口：按地区。

Demographic information of women of reproductive age in in the 12 clusters of Gaza and Maputo province in 2014.

Malaria remains a major public health concern worldwide. Malaria is endemic in Mozambique, with seasonal fluctuations throughout the country. Although the number of malaria cases in Mozambique have dropped by 11% from 2020 to 2021, there are still hotspots in the country with persistent high incidence and low insecticide-treated bed net usage. The aim of this study is to evaluate the factors associated with the use of long-lasting insecticidal nets by pregnant women and women with children under 5 years old in two hotspot districts in the Gaza province, Mozambique. A descriptive, qualitative cross-sectional study was conducted between June 15th and 21st 2022. An in-depth interview process was conducted with pregnant women and mothers with children under five years old, exploring their beliefs, experiences, and perception of messages conveyed by health professionals when long-lasting insecticidal nets were being supplied. A total of 48 women participated (24 pregnant women and 24 women with children under 5 years). Most participants recognized the protective effects of long-lasting insecticidal nets in preventing malaria, and understood that women and children were high risk groups. The nets were reported to cause side effects and difficulty breathing by 100% of pregnant women, while 54.2% of mothers with children under 5 reported no side effects. The majority of women in both groups reported that their health professionals did not educate them about how to use or handle the nets properly. Only 16.7% of mothers with children under 5 received correct handling instructions. Providing clear, culturally sensitive, and practical information on the correct use of LLINs, as well as regular monitoring of their proper use, would be a great step forward for Mozambique’s national malaria program.

Reliable statistics on maternal morbidity and mortality are scarce in low and middle-income countries, especially in rural areas. This is the case in Mozambique where many births happen at home. Furthermore, a sizeable number of facility births have inadequate registration. Such information is crucial for developing effective national and global health policies for maternal and child health. The aim of this study was to generate reliable baseline socio-demographic information on women of reproductive age as well as to establish a demographic surveillance platform to support the planning and implementation of the Community Level Intervention for Pre-eclampsia (CLIP) study, a cluster randomized controlled trial. This study represents a census of all women of reproductive age (12–49 years) in twelve rural communities in Maputo and Gaza provinces of Mozambique. The data were collected through electronic forms implemented in Open Data Kit (ODK) (an app for android based tablets) and household and individual characteristics. Verbal autopsies were conducted on all reported maternal deaths to determine the underlying cause of death. Between March and October 2014, 50,493 households and 80,483 women of reproductive age (mean age 26.9 years) were surveyed. A total of 14,617 pregnancies were reported in the twelve months prior to the census, resulting in 9,029 completed pregnancies. Of completed pregnancies, 8,796 resulted in live births, 466 resulted in stillbirths and 288 resulted in miscarriages. The remaining pregnancies had not yet been completed during the time of the survey (5,588 pregnancies). The age specific fertility indicates that highest rate (188 live births per 1,000 women) occurs in the age 20–24 years old. The estimated stillbirth rate was 50.3/1,000 live and stillbirths; neonatal mortality rate was 13.3/1,000 live births and maternal mortality ratio was 204.6/100,000 live births. The most common direct cause of maternal death was eclampsia and tuberculosis was the most common indirect cause of death. This study found that fertility rate is high at age 20–24 years old. Pregnancy in the advanced age (>35 years of age) in this study was associated with higher poor outcomes such as miscarriage and stillbirth. The study also found high stillbirth rate indicating a need for increased attention to maternal health in southern Mozambique. Tuberculosis and HIV/AIDS are prominent indirect causes of maternal death, while eclampsia represents the number one direct obstetric cause of maternal deaths in these communities. Additional efforts to promote safe motherhood and improve child survival are crucial in these communities.

Abstract

The objectives of the Smallholder Household Survey in Mozambique were to:

• Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; • Segment smallholder households in Mozambique according to the most compelling variables that emerge; • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The universe for the survey consists of smallholder households defined as households with the following criteria: 1) Household with up to 5 hectares OR farmers who have less than 50 heads of cattle, 100 goats/sheep/pigs, or 1,000 chickens 2) Agriculture provides a meaningful contribution to the household livelihood, income, or consumption.

Kind of data

Sample survey data [ssd]

Sampling procedure

The CGAP smallholder household survey in Mozambique is a nationally-representative survey with a target sample size of 3,000 smallholder households. The sample was designed to provide reliable survey estimates at the national level and for the following regions: 1. North region, comprised of the provinces of Niassa, Cabo Delgado, and Nampula; 2. Centre region, comprised of Zambezia, Tete, Maica, and Sofala, Manica; and 3. South region, consisting of Inhambane, Maputo Province, Maputo City and Gaza.

(a) SAMPLING FRAME

The sampling frame for the smallholder household survey is the 2009-2010 Census of Agriculture and Livestock (Censo Agro-Pecuário, CAP II) conducted by the Mozambique National Statistical Office (INE) and based on the 2007 Census of Population and Housing (2007 RGPH). CAP II is a large sample that was designed to be representative at the district level and its sample of enumeration areas (EAs) is considered as the master sample; for the national agricultural surveys. EAs with less than 15 agricultural households (mostly in urban areas) were excluded from the sampling frame for CAP II.

(b) SAMPLE ALLOCATION AND SELECTION

In order to take non-response into account, the target sample size was increased to 3,158 households assuming a household non-response rate of 5% observed in similar national households. The total sample size was first allocated to the three regions based on the number of agricultural households. Within each region, the resulting sample was further distributed proportionally to urban and rural areas. The sample for the smallholder survey is a stratified multistage sample. Stratification was achieved by separating urban and rural areas within each region. Since the CAP II master sample that was used as the sampling frame for the survey is stratified by district, rural and urban areas, the rural strata of the individual districts for the CAP II master sample were collapsed up to the province level, and the same for the urban strata within each province. However, the district was still used as a sorting variable in order to provide implicit stratification by district. At the first sampling stage the CAP II sample EAs were selected systematically with PPS within each district, rural and urban stratum, where the measure of size was the number of agricultural households in the census frame.

The full description of the sample design can be found in the user guide for this data set.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

During data collection, InterMedia received a weekly partial SPSS data file from the field which was analysed for quality control and used to provide timely feedback to field staff while they were still on the ground. The partial data files were also used to check and validate the structure of the data file. The full data file was also checked for completeness, inconsistencies and errors by InterMedia and corrections were made as necessary and where possible.

Response rate

85.8 percent for single respondent questionnare and 92.5 percent for household questionnaire

Sampling error estimates

The sample design for the smallholder household survey was a complex sample design featuring clustering, stratification and unequal probabilities of selection. For key survey estimates, sampling errors considering the design features were produced using either the SPSS Complex Sample module or STATA based on the Taylor series approximation method.

Data appraisal

Following the finalization of questionnaires, a script was developed using Dooblo to support data collection on smart phones. The script was thoroughly tested and validated before its use in the field.

Abstract

The Chókwè Health And Demographic Surveillance System (Chókwè HDSS), is a core research platform of Chókwè Health and Research Centre, affiliated to National Institute of Health, Ministry of Health, Mozambique. The centre is located in Chókwè District, Gaza Province, southern Mozambique., about 230 Km north of the capital Maputo, on the flood plain of the Limpopo River. It was established in June 2010, to provide a platform for implementation and evaluation of clinical trials and health intervention, especially against HIV and related diseases, based on locally generated data.

Chokwe HDSS is a member of the International Network for Demographic Evaluation of the Population and Their Health (INDEPTH), since October 2014. The surveillance area covers an area of approximately 600 square kms within a 25 kms radius of Chokwe City, including 15 (fifteen) villages of which, eight (8) villages are part of Chókwè Municipality (classified as urban) and seven (7) are rural villages namely, Lionde Administrative Post (Lionde Sede, Massavasse, Conhane) and Macarretane (Muzumuia, Matuba, Manjangue and Barragem). The population under surveillance is 97,939 inhabitants (~ 50% of total the district population), in 21,498 households.

The baseline census took place between May and July 2010 and covered all the population of Chókwè City. The census registered 56,727 inhabitants in 12,326 households and each household and individual assigned a unique permanent identification number which enables the follow up of the population. All households are geolocated and enumerated. A resident is defined as any person living in the study area or planning to stay for at least the 3 following months.

Based on recommended INDEPTH methodology for demographic surveillance, monitoring of households and members within households is undertaken in regular 6-month cycles known as 'rounds'. Since its implementation, data collection consists on demographic history including details on birth outcomes (births, still births and abortions), deaths, migration (in-migration and out-migration). The updating of demographic data is done in the three following ways: - Update rounds: In each round, a team of field interviewers and supervisors visit their assigned households to update the demographic data. - Continuous update through community informants: Local leaders report occurring demographic events in their community to the concerned HDSS team.
- Birth and death registrations in health facilities: data collectors are responsible for the daily registration of all births and deaths occurring in the health facilities in the surveillance area.

The data are double entered into a relational database designated Household Registration System (HRS1) implemented in Microsoft Visual FoxPro version 5. Consistence checking and validation are performed and resolved. Verbal autopsy using modified WHO standardized questionnaires, based on physician coded approach has been conducted on all deaths since 2012. Currently, data collection is in the process of migration to electronic data capture and interpretation with InterVA applying the WHO 2012 modified questionnaire.

The Site Leader is Dr Ricardo Thompson.

Geographic coverage

The surveillance area covers an area of approximately 600 square km around Chokwe City (-24.528981, 32.981228), including 15 (fifteen) villages of which, eight (8) villages are part of Chókwè Municipality (classified as urban) and seven (7) are rural villages namely, Lionde Administrative Post (Lionde Sede, Massavasse, Conhane) and Macarretane (Muzumuia, Matuba, Manjangue and Barragem). The population under surveillance is 97,939 inhabitants (~ 50% of total the district population), in 21,498 households.

Analysis unit

Individual

Universe

The population under surveillance is 97,939 inhabitants (~ 50% of total District population), residing in 21498 households. All households and members are assigned a unique identification number (Perm_ID). A member is defined as somebody living in the Demographic Surveillance Area (DSA) for more than three (3) months or intending to remain in the DSA for at least three months.

Kind of data

Event history data

Frequency of data collection

Round (baseline) 1 to Round 2 : once a year Round 3 to 4: twice a year Round 5 : once a year

Sampling procedure

This dataset covers the whole population int he surveillance area.

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

The core HDSS questionnaires are:
1. Household Enumeration and Member Registration Form 2. New Household Form
3. Emigration Registration Form 4. Immigration Registration Form 5. Death Registration Form 6. Pregnancy Registration Form 7. Pregnancy Outcome Registration Form

Response rate

On an average the response rate is 100% over the years for all rounds.

Sampling error estimates

Not Applicable

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate MZ021 MicroDataCleaned Starts 1 137121 137122 0, 2017-05-23 09:56
MZ021 MicroDataCleaned Transitions 0 318278 318278 0, 2017-05-23 09:56
MZ021 MicroDataCleaned Ends 137122 2017-05-23 09:56
MZ021 MicroDataCleaned SexValues 318278 2017-05-23 09:56
MZ021 MicroDataCleaned DoBValues 318278 2017-05-23 09:56