Background
Internal validity the extent to which the research tool is really measuring what it purports to measure [46] | |
External validity the extent to which the research findings can be generalised to the wider population of interest and applied to different settings [46] | |
Confounding a situation in which the estimate of association between an exposure and an outcome is distorted because of the association of the exposure with another factor that is also associated with the outcome. Confounding factors can be controlled for in certain analyses [47] | |
Effect modification variation in the effect of the exposure on an outcome across values of another factor (effect modifier). Stratification allows for visualising the effect: rather than controlling for it, the effect of the exposure on the outcome would need to be reported separately for different values of the effect modifier [47] |
Context factors external to the intervention which may influence its implementation, or whether its mechanisms of impact act as intended [34] | |
Process evaluation a study which aims to understand the functioning of an intervention, by examining implementation, mechanisms of impact, and contextual factors [34] |
Main text
Methods/design
Process outline
Defining context and selecting contextual factors
Define context | |
Determine list of relevant contextual themes and specific factors to obtain | |
Categorise contextual factors to assign frequency of data needed for source to be appropriate | |
Determine appropriate time frame for contextual data collection (period of time source documents collected). This should be aligned with programme implementation and timing of evaluation surveys. For example in an evaluation of a programme conducted from 2010 to 2012, baseline contextual factors should have been collected before 2010 and subsequent time periods ideally aligned with timing of evaluation surveys and/or following period of implementation | |
Determine level of contextual factor aggregation most useful for evaluation (district, subnational, etc.) | |
Share and adapt tools with country experts | |
Conduct desk review to identify sources | |
Extract data from sources | |
Compile metadata on sources to understand frequency of availability, time frame of reference, geographic coverage and level of aggregation | |
Iterative reviews of data | |
Prepare maternal and newborn health policy summary to serve as baseline to assess policy changes over time | |
Develop checklist for primary data collection | |
Populate checklist with as much publicly available data as possible | |
Circulate to research team to capture tacit knowledge | |
Identify appropriate key informants with country specific experts and leads | |
Conduct interviews using the populated checklist to verify existing information and fill gaps | |
On an annual basis: update desk review and assess need for primary data collection | |
Develop data analysis plan | |
Integrate analysis into interpretations of study findings |
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Demographics and socio-economics
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Epidemiological profile
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Health systems
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Health service uptake
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Infrastructure
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Education
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Environmental
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Politics, policy and governance.
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Maternal and Newborn health policy and implementation
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Category 1: Structural Factors that were unlikely to change rapidly over the course of the evaluation. Examples include: religion or ethnicity of people living in a given geography. Leichter [11] refers to these as “slow changing” or “structural”.
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Category 2: Situational Factors hypothesized to change relatively quickly and thus require more frequent review, or as Leichter calls them, “situational,” that is, particular to a specific point in time [11]. Additionally, those of particular relevancy to understanding maternal and newborn health outcomes are also included in this category. Examples of this category include health programmes in the area, number of health care workers, vaccination campaigns, political instability or natural disasters.
Code | Contextual factor | Category 1 Structural 2 Situational |
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Demographic profile | ||
Dem 1 | Total population | 1 |
Dem 2 | % Rural | 1 |
Dem 3 | % Urban | 1 |
Dem 4 | % Female | 1 |
Dem 5 | % Male | 1 |
Dem 6 | Population density (population/km2) | 1 |
Dem 7 | Fertility rate | 2 |
Dem 8 | Average family size | 2 |
Dem 9 | Religion | 1 |
Dem 10 | Ethnicity | 1 |
Epidemiological profile | ||
Epi 1 | Under-5-mortality rate | 2 |
Epi 2 | Maternal mortality rate | 2 |
Epi 3 | Newborn mortality rate | 2 |
Epi 4 | Infant mortality rate | 2 |
Epi 5 | Prevalence of malnutrition | 2 |
Epi 6 | % underweight | 2 |
Epi 7 | % stunting | 2 |
Epi 8 | % severe acute malnutrition | 2 |
Epi 9 | HIV-prevalence | 1 |
Epi 10 | Malaria transmission intensity | 2 |
Health service provision | ||
HSP1 | Number of family planning new users | 2 |
HSP2 | Number of family planning repeat users | 2 |
HSP3 | Number of women attending ANC (1st visit) | 2 |
HSP4 | Number of pregnant women attend 3 or more ANC visits | 2 |
HSP5 | Number of ANC clients receiving HIV test | 2 |
HSP6 | HIV-prevalence in pregnant women | 2 |
HSP7 | Number of pregnant women enrolled in HIV care | 2 |
HSP8 | Number of women delivering in a health facility | 2 |
HSP9 | Number of deliveries attended by skilled birth attendant | 2 |
HSP10 | Number of births protected against NNT | 2 |
HSP11 | Number of institutional maternal deaths | 2 |
HSP12 | Number of institutional neonatal deaths | 2 |
HSP13 | Number of first postnatal attendance | 2 |
Health system | ||
HS 1 | Number of hospitals | 2 |
HS 2 | Number of health centres (or equivalent) | 2 |
HS 3 | Number of health posts (or equivalent) | 2 |
HS 4 | Number of specialised doctor | 2 |
HS 5 | Number of general practioners | 2 |
HS 6 | Number of health officer | 2 |
HS 7 | Number of clinical nurse degree and diploma | 2 |
HS 8 | Number of midwife nurse degree and diploma | 2 |
HS 9 | Number of frontline health workers | 2 |
HS 10 | Number of rural frontline health workers | 2 |
HS 11 | Number of urban frontline health workers | 2 |
HS 12 | Number of ambulances available | 2 |
HS 13 | Overall health budget (allocated) | 2 |
HS 14 | Health budget per capita | 2 |
HS 15 | Available health budget (disbursed) | 2 |
HS 16 | % of budget allocated for maternal and child health | 2 |
HS 17 | % of population within 5 km of a health facility | 2 |
Economics | ||
Eco 1 | Ownership of assets-land/house | 1 |
Eco 2 | Employment rate | 1 |
Eco 3 | Coverage of electricity service | 1 |
Eco 4 | Wealth index (include definition) | 1 |
Infrastructure | ||
Com 1 | Mobile telephone coverage rate | 1 |
Com 2 | Mobile telephone subscriptions | 1 |
Tran 1 | Kilometers of all weather roads | 1 |
Tran 2 | % of local area connected to all weather roads | 1 |
Wat 1 | Proportion of population using improved drinking water source | 2 |
Wat 2 | Proportion of population using improved sanitation facilities | 2 |
Education | ||
Ed 1 | Number of primary schools | 1 |
Ed 2 | Number of secondary schools | 1 |
Ed 3 | Primary school net enrolment rate | 1 |
Ed 4 | % Male | 1 |
Ed 5 | % Female | 1 |
Ed 6 | Adult literacy rate | 1 |
Ed 7 | Female literacy rate | 1 |
Environment | ||
Env 1 | Average rainfall (annual in mm) | 1 |
Env 2 | Area affected by floods and rain (in hectares) | 1 |
Env 3 | Area affected by drought (in hectares) | 1 |
Env 4 | Total land mass (in hectares) | 1 |
Contextual data checklist | Source of information | ||||||||
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Code | Contextual factor | Local area 1 | |||||||
Circle yes/no | Circle coverage (language TBD) 1 = limited coverage 2 = 3 = 4 = 5 = all of local area | ||||||||
Other health-related factors | |||||||||
Have major health programmes, beyond those normally planned, been implemented in the following areas? | |||||||||
OH 1 | Malaria | Yes | No | 1 | 2 | 3 | 4 | 5 | |
OH 2 | Micronutrient supplementation | Yes | No | 1 | 2 | 3 | 4 | 5 | |
OH 3 | Nutrition | Yes | No | 1 | 2 | 3 | 4 | 5 | |
OH 4 | Immunization campaign | Yes | No | 1 | 2 | 3 | 4 | 5 | |
OH 5 | Other health programmes? Describe: | Yes | No | 1 | 2 | 3 | 4 | 5 | |
Are the following NGOs active? (Ethiopia/Nigeria)/What are the most active NGOs? (India) | |||||||||
OH 6 | NGO 1 | Yes | No | 1 | 2 | 3 | 4 | 5 | |
OH 7 | NGO 2 | Yes | No | 1 | 2 | 3 | 4 | 5 | |
OH 8 | NGO 3 | Yes | No | 1 | 2 | 3 | 4 | 5 | |
OH 9 | NGO 4 | Yes | No | 1 | 2 | 3 | 4 | 5 | |
OH 10 | NGO 5 | Yes | No | 1 | 2 | 3 | 4 | 5 | |
OH 11 | NGO 6 | Yes | No | 1 | 2 | 3 | 4 | 5 | |
Epidemiological | |||||||||
OB 1 | Have there been any major outbreaks? If yes, specify Disease, time period and proportion of area affected (coverage) for each outbreak below | Yes | No | ||||||
OB 2 | Disease: time period (approx) | 1 | 2 | 3 | 4 | 5 | |||
OB 3 | Disease: time period (approx) | 1 | 2 | 3 | 4 | 5 | |||
OB 4 | Disease: time period (approx) | 1 | 2 | 3 | 4 | 5 | |||
Health system | |||||||||
HSYS 1 | Have there been any MNH policy changes since X policy (refer to desk review)? | Yes | No | ||||||
Have there been any stockouts of the following commodities (add timeframe)? What proportion of the local area was affected | |||||||||
HSYS 2 | Vaccines | Yes | No | 1 | 2 | 3 | 4 | 5 | |
HSYS 3 | Antibiotics | Yes | No | 1 | 2 | 3 | 4 | 5 | |
HSYS 4 | Medication (list to be specified) | Yes | No | 1 | 2 | 3 | 4 | 5 | |
Infrastructure | |||||||||
INF 1 | Has there been construction of new roads? | Yes | No | 1 | 2 | 3 | 4 | 5 | |
INF 2 | Has there been construction of improved water supply? | Yes | No | 1 | 2 | 3 | 4 | 5 | |
INF 3 | Have sanitation facilities been improved? | Yes | No | 1 | 2 | 3 | 4 | 5 | |
Environment | |||||||||
DIS 1 | Have there been any major environmental events (droughts/floods, etc.?) | Yes | No | 1 | 2 | 3 | 4 | 5 | |
Political, policy and governance | |||||||||
POL 1 | Have there been any major political events? | Yes | No | 1 | 2 | 3 | 4 | 5 | |
POL 2 | Have there been any major government policy changes? | Yes | No | 1 | 2 | 3 | 4 | 5 | |
Other contextual factors | |||||||||
OTH 1 | Is there anything else you would like to mention that could be influencing maternal and child health in X time period in these areas? | Yes | No | ||||||
Additional questions as needed |
Secondary data extraction
Policy summary
Primary data collection
Contextual data use
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Interpret patterns in the quantitative and qualitative data used to evaluate if and how maternal and newborn health programmes increase coverage of life-saving interventions.
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Gather supplemental information to further understand explanations for how and why scale up happens and if these scaled programmes increase coverage of life-saving interventions
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Provide an opportunity through targeted interviews to ask questions of key informants about the preliminary findings to assist in interpretation.
Preliminary results
Implementation status
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Policy memo development was changed to become a two-staged process with a draft emerging following documentary review and a final version following primary data collection capturing policy awareness and implementation. This then further evolved from a one-off general maternal and newborn health policy memo to a more specific dashboard. The dashboard lists key maternal and newborn health care services and interventions and documents supportive policies and the degree of implementation for each country. Please see Additional file 1: Annex 1 for a list of the specific services and interventions screened for inclusion in existing policies and strategies.
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Frequency of secondary data extraction was changed to no longer be annual. Given the limited secondary sources in Nigeria and the delays in accessing data in the other geographies this frequency was impractical. Many of the data sources were only available in hard copy and some online platforms are not reliable. In the absence of updated central repositories it was not possible to implement this as envisioned.