# Stunting, Day Care Centers and Early Reading Across 1,639 municipalities, models link more stunting with slightly more struggling readers; day care center gaps show no consistent link. This is the Markdown copy of [Stunting, Day Care Centers and Early Reading](https://nash.com.ph/blog/stunting-day-care-and-reading), an article on the NASH Reviewer Blog by Mark Anthony Llego, published Oct 27, 2026, updated Nov 4, 2026. Topics: DepEd data, Learning outcomes. NASH Reviewer is an independent practice platform not affiliated with DepEd, BHROD, or NEAP. Across 1,639 Philippine municipalities and cities, places with more stunting among children aged 0–2 have slightly more struggling readers in Grades 1–3; places with more barangays that lack a child development center (CDC, the barangay day care center) do not. The stunting link is qualified: the rank correlation alone, 0.05 (95% interval -0.06 to 0.17), is consistent with no association or a weak positive one, but all four regression models, including those that allow for provinces and for neighboring places, find a small positive association whose intervals exclude zero. For CDCs there is no consistent association: the rank correlation is 0.01 (95% interval -0.10 to 0.12). The reading data are the Comprehensive Rapid Literacy Assessment (CRLA) results for public schools at the end of SY 2025-26, as of 10 May 2026, from the [EDCOM II early-childhood (ECCD) dashboard](https://edcom2.gov.ph/eccddashboard/), which also carries the stunting and CDC data. A weak link between places is expected even if early nutrition matters a great deal for each child: the toddlers measured in 2025 are not the learners assessed in SY 2025-26, and a municipal average blurs what happens to individual children. Everything below describes places, not children. ## Key findings - OPT Plus 2025 measured 3,007,695 children aged 0–2 in the municipalities analyzed, and 129,690 of them (4.3% of those measured) were stunted; the median municipality is at 4.3%, and one in ten is above 14.5%. - The DILG's CBMS list names 4,581 barangays without a CDC in the municipalities analyzed, while 684 municipalities and cities (41.7%) have a CDC in every barangay. - In the multilevel model with province effects, municipalities with more stunting have slightly more struggling readers: the odds ratio per standard deviation (SD) of stunting is 1.05 (95% interval 1.02 to 1.08). On the log-odds scale, the stunting interval excludes zero in all four models, including the two that allow for neighboring places resembling each other. - The rank correlation between stunting and struggling readers is 0.05 (95% interval -0.06 to 0.17), consistent with no association or a weak positive one; within regions it is positive in 13 of 18. - The CDC gap shows no consistent association: its odds ratio in the multilevel model is 0.99 (95% interval 0.96 to 1.02), and only the simple ordinary least squares (OLS) model, which ignores neighbors, gives an interval that excludes zero. - The share of struggling readers is 10.4% where every barangay has a CDC and 10.1% where more than half have none, but places where up to a quarter of barangays lack one are lower than both, so there is no steady rise. - All of these are associations between places, measured in different cohorts of children; they cannot show what happens to any individual child. ## Stunting and child development center data for each municipality ### Stunting among children aged 0–2 in OPT Plus 2025 Operation Timbang Plus (OPT Plus) is the yearly weighing and measuring of young children in every barangay, done by barangay nutrition scholars and health workers under the Department of Health and the [National Nutrition Council](https://nnc.gov.ph/). A child is stunted when he or she is too short for his or her age by the World Health Organization growth standards, a sign of long-term undernutrition and repeated illness. The figures here are OPT Plus 2025 results for children aged 0–2, as of 2 March 2026. In the municipalities analyzed, OPT Plus 2025 measured 3,007,695 children aged 0–2, and 129,690 of them were stunted, or 4.3% of those measured. This is a share among the children actually measured, not a survey prevalence, and coverage differs between barangays. Places differ widely: the median municipality is at 4.3%, and one in ten is above 14.5%. ### Barangays without a child development center A child development center is the barangay center, run by the local government, where child development workers care for and teach young children before Kindergarten. Under RA 6972 of 1990, every barangay is meant to have one. The [DILG](https://www.dilg.gov.ph/) Community-Based Monitoring System (CBMS) list of barangays without a CDC, as of 17 September 2025, names 4,581 barangays in the municipalities analyzed. The measure used here is the share of each municipality's barangays on that list. In 684 municipalities and cities (41.7% of those analyzed), every barangay has a CDC. The two measures barely move together (rank correlation 0.03), so the models below can separate them. ## Map of stunting among young children by municipality The map shades each municipality and city by its stunting share, in seven classes that each hold about the same number of places. The regions with the most municipalities in the darkest class are Region VIII (56), Region V (31), BARMM (23). ![Choropleth map of stunting among children aged 0–2 in 1,639 municipalities and cities, OPT Plus 2025, in seven equal-count classes from 0.0% to 66.8%; the regions with the most places in the darkest class are Region VIII (56), Region V (31), BARMM (23).](https://nash.com.ph/blog/_astro/stunting-map-municipalities-2025.C12D8heg_Z1NYQw4.webp) *Figure 1. Stunting among children aged 0–2 in each municipality and city, OPT Plus 2025, in seven classes with about equal numbers of places; darker shading means a higher share of measured children were stunted. Chart: © 2026 Mark Anthony Llego, NASH Reviewer. [Download the full-resolution chart (PNG)](https://nash.com.ph/blog/charts/deped-data/stunting-map-municipalities-2025.png).* Those three regions already show how loose the link with reading is. Region VIII, with the most places in the darkest class, has a share of struggling readers below the national figure: 7.2% against 10.0%. Region V is above it at 12.1%, and BARMM, at 23.4%, has the highest share of the 18 regions. ## Stunting and early reading across municipalities Nationally, 511,753 of the 5,127,670 public school learners assessed in Grades 1–3 (10.0%) are struggling readers, at the Low Emerging or High Emerging level of the CRLA. Each learner is counted once: Grade 1 in the Mother Tongue, Grade 2 in Filipino and Grade 3 in English. In the scatter plot, each dot is a municipality or city, sized by the number of learners assessed. The blue line joins the pooled share of struggling readers (all struggling readers over all learners assessed) in each tenth of places, sorted from least to most stunting. ![Scatter plot of 1,639 municipalities and cities: stunting among children aged 0–2 (OPT Plus 2025) against the estimated share of Grade 1–3 learners struggling (end of SY 2025-26), with pooled shares for each tenth of places by stunting. The rank correlation is 0.05.](https://nash.com.ph/blog/_astro/crla-stunting-vs-struggling-readers.BrFbj37z_11iOcj.webp) *Figure 2. Each municipality and city by stunting among children aged 0–2 (OPT Plus 2025) and the estimated share of Grade 1–3 learners struggling at the end of SY 2025-26; the line joins pooled shares for each tenth of places by stunting. Chart: © 2026 Mark Anthony Llego, NASH Reviewer. [Download the full-resolution chart (PNG)](https://nash.com.ph/blog/charts/deped-data/crla-stunting-vs-struggling-readers.png).* The line rises gently from the least-stunted tenth of places to the most-stunted, with a bump among the low-stunting tenths. Around it, the dots spread widely at every level of stunting: some places with very little stunting have many struggling readers, and some high-stunting places have few. The Spearman rank correlation sums this up. It ranks the municipalities on stunting and on struggling readers and measures how well the two orders agree: zero means no agreement, and values near one mean the orders almost match. Ranks keep a few extreme places from pulling the result around. The correlation is 0.05, with a 95% interval of -0.06 to 0.17 from a bootstrap that draws whole provinces at random, with replacement, many times and recomputes the correlation each time. Whole provinces are drawn because municipalities in the same province, many of them neighbors, resemble each other; counting each one as an independent case would overstate how much evidence the data hold. The interval includes zero, so the correlation alone is consistent with no association or a weak positive one. ### Stunting and struggling readers within each region A national correlation can come from differences between regions rather than between nearby places. Computed separately inside each of the 18 regions, the correlation is positive in 13 of 18 regions. The table lists the regions from the highest correlation to the lowest. Even the largest is moderate at most, and regions with fewer places give noisier values; these figures are descriptive and carry no intervals. Many regional correlations exceed the national one. | Region | Municipalities and cities | Spearman correlation, stunting and struggling | |:------------|----------------------------:|------------------------------------------------:| | Region IV-A | 142 | 0.38 | | Region VII | 101 | 0.3 | | Region XII | 49 | 0.29 | | Region VI | 101 | 0.27 | | Region II | 93 | 0.25 | | BARMM | 107 | 0.2 | | NCR | 17 | 0.11 | | CAR | 77 | 0.1 | | Region V | 114 | 0.1 | | Region X | 93 | 0.08 | | MIMAROPA | 73 | 0.07 | | Region VIII | 143 | 0.03 | | Region XI | 49 | 0.01 | | NIR | 63 | -0.01 | | Region III | 130 | -0.02 | | Region I | 125 | -0.08 | | Caraga | 73 | -0.1 | | Region IX | 89 | -0.11 | ## Day care centers and struggling readers The table groups municipalities and cities by the share of their barangays without a CDC and gives the pooled share of struggling readers in each group. | Barangays without a CDC | Municipalities and cities | Learners assessed | Share struggling | |:--------------------------|----------------------------:|--------------------:|:-------------------| | None | 684 | 1,706,534 | 10.4% | | Up to 10% | 529 | 1,830,691 | 9.6% | | 10–25% | 279 | 928,474 | 9.3% | | 25–50% | 120 | 518,559 | 11.1% | | Over 50% | 27 | 143,397 | 10.1% | Where every barangay has a CDC, 10.4% of learners are struggling, across 684 places. The share is lower where some barangays, up to a quarter, lack one (9.6% and 9.3%), and rises again where more than a quarter do: 11.1% across 120 places and 10.1% across 27 places. ![Bar chart of the pooled share of learners struggling by the share of a municipality's barangays with no child development center: 10.4% where every barangay has one (684 places) and 10.1% where more than half have none (27 places).](https://nash.com.ph/blog/_astro/crla-cdc-coverage-vs-struggling-readers.DdpYpmFG_Z1D9LYJ.webp) *Figure 3. Pooled share of Grade 1–3 learners struggling at the end of SY 2025-26, by the share of a municipality's barangays with no child development center; the label on each bar gives the number of places in the group. Chart: © 2026 Mark Anthony Llego, NASH Reviewer. [Download the full-resolution chart (PNG)](https://nash.com.ph/blog/charts/deped-data/crla-cdc-coverage-vs-struggling-readers.png).* The pattern is not a gradient. Places with full CDC coverage do not have the fewest struggling readers, and the group with the largest gaps is small enough for a few provinces to move its share. The rank correlation between the CDC gap and struggling readers is 0.01 (95% interval -0.10 to 0.12). That interval includes zero, so the data cannot tell this association apart from none. ## Stunting, day care centers and early reading in four models The models look at stunting and the CDC gap together, and they deal with two features of the data: nearby places resemble each other, and places in the same province share much of their history and economy. Each estimate is the change linked to one standard deviation (SD) of the factor, a typical distance of a place from the average: 6.6 percentage points of stunting, or 12.3 percentage points of barangays without a CDC. ### A multilevel model with province effects This model counts struggling readers out of the learners assessed in each municipality. It is a beta-binomial model, which allows places to vary more than chance alone would. Province effects give each province its own starting level, so municipalities are compared mainly with others in the same province. An odds ratio of 1 means no association, and values above 1 mean higher odds of struggling. For stunting, the odds ratio per SD is 1.05 (95% interval 1.02 to 1.08): slightly above 1, with an interval that stays above 1. For the CDC gap it is 0.99 (95% interval 0.96 to 1.02), an interval that includes 1. ### OLS, spatial lag and spatial error models The other three models describe each municipality's share of struggling readers on the log-odds scale, a standard scale for modeling shares; on it, a coefficient of zero means no association. Ordinary least squares (OLS) is the familiar straight-line regression, and it treats every municipality as an independent case. That fails here: after OLS, the leftover differences still cluster strongly on the map. Moran's I of the residuals, a measure of how much neighbors resemble each other, is 0.58, where zero would mean no clustering. OLS then counts one regional pattern many times over, so its intervals are too narrow and a regional pattern can pass for a link. Two spatial models correct for this. The spatial lag model adds the neighbors' average share of struggling readers as a predictor; its rho, how closely a place's share follows its neighbors' average, is 0.67. Its coefficients hold the neighbors' share fixed, so they are not directly comparable with OLS. The spatial error model keeps the OLS form but lets the unexplained part of each share resemble that of its neighbors; its lambda, how strongly those leftovers move together, is 0.67. Both fit far better than OLS by AIC, a score that balances fit against complexity where lower is better: 3116 and 3116 against 3977. The coefficients per SD, on the log-odds scale, are: - Stunting: +0.057 (95% interval +0.017 to +0.096) in OLS, +0.031 (95% interval +0.002 to +0.059) in the spatial lag model and +0.037 (95% interval +0.006 to +0.068) in the spatial error model. - Barangays without a CDC: +0.084 (95% interval +0.044 to +0.123) in OLS, +0.025 (95% interval -0.004 to +0.053) in the spatial lag model and +0.008 (95% interval -0.026 to +0.042) in the spatial error model. The lower end of the spatial lag interval for stunting is just above zero. The table gives every estimate from the four models to three decimals, with the multilevel results on the same log-odds scale. | Model | Exposure (per SD) | Log-odds change (95% interval) | |:------------------------------|:------------------------|:---------------------------------| | OLS | Stunting, ages 0–2 | +0.057 (+0.017 to +0.096) | | OLS | Barangays without a CDC | +0.084 (+0.044 to +0.123) | | Spatial lag | Stunting, ages 0–2 | +0.031 (+0.002 to +0.059) | | Spatial lag | Barangays without a CDC | +0.025 (-0.004 to +0.053) | | Spatial error | Stunting, ages 0–2 | +0.037 (+0.006 to +0.068) | | Spatial error | Barangays without a CDC | +0.008 (-0.026 to +0.042) | | Multilevel (province effects) | Stunting, ages 0–2 | +0.049 (+0.022 to +0.076) | | Multilevel (province effects) | Barangays without a CDC | -0.010 (-0.040 to +0.019) | ![Dot plot with 95% intervals of the association between each exposure and struggling readers across 1,639 municipalities and cities, in four models: OLS, spatial lag, spatial error and a multilevel model with province effects. For stunting the log-odds change per SD ranges from +0.03 to +0.06, and every interval excludes zero; for barangays without a CDC the estimates range from -0.01 to +0.08 and not every interval excludes zero.](https://nash.com.ph/blog/_astro/crla-stunting-cdc-model-estimates.BezxvBnS_SmoRB.webp) *Figure 4. Change in the log-odds of struggling per SD of stunting and per SD of barangays without a CDC, with 95% intervals, in the OLS, spatial lag, spatial error and multilevel models; the dashed line marks no association. Chart: © 2026 Mark Anthony Llego, NASH Reviewer. [Download the full-resolution chart (PNG)](https://nash.com.ph/blog/charts/deped-data/crla-stunting-cdc-model-estimates.png).* Whether each association has an interval that excludes zero in all four models: - Stunting among children aged 0–2: yes. - Barangays without a CDC: no. The stunting estimate is smaller in the spatial error model than in OLS, and its interval stays above zero in every model. The multilevel and spatial models separate it from zero, unlike the correlation alone, because they compare places mainly within their own province or with their neighbors. That sets aside regional contrasts, such as Region VIII's high stunting and low share of struggling readers, which pull the national correlation toward zero. The CDC estimate has an interval above zero only in OLS. That is consistent with CDC gaps and struggling readers both being more common in some parts of the country, for reasons that may have little to do with the centers. All of these are small: near the national share of 10.0%, a change of a few hundredths in log-odds moves the share of struggling readers by well under a percentage point. ## Why a weak link between places is expected Studies that follow the same children over the years have linked early stunting with later learning. This analysis cannot test that, and a weak association between places does not contradict it. A municipality's reading results depend on many things besides nutrition: the language of instruction, teachers, class sizes, home literacy, poverty and how the CRLA was given. Both measures are also noisy, and noise weakens the correlation that can be seen even when a real relationship is there. The stunting figure is a stand-in, too: it assumes that places with high stunting now also had high stunting when today's Grade 1–3 learners were small. Finally, an association between places can be weaker or stronger than the one among children, and can even point the other way. ## What this means for school heads A municipality's stunting share is not a forecast of a school's reading results, and the link is too weak to target schools with. Start from the school's own CRLA results; [CRLA Reading Hotspots: A Map of Struggling Readers](https://nash.com.ph/blog/crla-reading-hotspots-map) shows where struggling readers cluster. The weak link is no reason to set early childhood aside. CDCs and nutrition programs stand on their own evidence about child health and development, and this analysis neither strengthens nor weakens that case. [Literacy and Nutrition Programs Compared With Need](https://nash.com.ph/blog/literacy-nutrition-programs-coverage) looks at how those programs line up with need. The practical step is partnership with the local government unit. Barangay nutrition scholars and child development workers know the children who will enter Kindergarten in the coming years. A school head can invite them to Kindergarten enrollment and transition activities, ask the municipal or city nutrition office about local OPT Plus results, and coordinate school feeding with barangay nutrition programs. [Local Education Models in EDCOM II](https://nash.com.ph/blog/local-education-reform-models-edcom-ii) covers the local nutrition and early childhood programs the EDCOM II reports present as models, and the evidence behind them. ## How these numbers were made The OPT Plus file has 1,642 rows, of which 3 match no municipality or city. Of the 1,639 municipalities and cities with CRLA results, 1,639 have both a stunting figure and a CDC count. Stunting is stunted children aged 0–2 divided by those measured; the CDC gap is barangays on the DILG list divided by all barangays. Regions follow the PSGC's 18 regions, with Sulu in Region IX and the Negros Island Region (NIR) as its own region. Rank-correlation intervals come from a bootstrap that resamples whole provinces. Municipal shares of struggling readers for the correlations and the OLS and spatial models are empirical Bayes estimates: each raw share is pulled toward the national share, more strongly for places with few learners, so small places do not land at extremes by chance. The binned line and the CDC table use pooled raw counts. The spatial models treat places that share a border or a corner as neighbors, and island municipalities are linked to their nearest place (k = 1). The multilevel model is Bayesian and also allows for the number of learners assessed. Its largest R-hat is 1.004, below 1.01, and its smallest effective sample size is 665, above 400, so its estimates are stable. ## Limits **Different children.** The children aged 0–2 measured in 2025 are not the learners assessed in Grades 1–3 in SY 2025-26. Current stunting is only a marker of a place's longer-running conditions. **Places, not children.** This is ecological inference. It cannot show whether stunted children read less well, or whether children who attended a CDC read better. **What OPT Plus measures.** OPT Plus counts the children who were actually weighed and measured, and coverage varies between barangays, so a municipality's share can be too high or too low. **What a CDC count measures.** The DILG list says whether a barangay has a CDC, not how good it is, how many children attend or how far families live from it. Private preschools are not counted. **A small association.** The stunting association is small, and the rank correlation alone cannot rule out no association. **Other differences between places.** The models do not adjust for poverty, language or school resources. The estimates are associations, not effects. ## Frequently asked questions ### Does stunting affect how well children learn to read? Studies that follow the same children have linked early stunting with later learning. These data cannot test that, because they compare places and different cohorts. Between places, the models find a small positive association: the multilevel odds ratio per SD of stunting among children aged 0–2 is 1.05 (95% interval 1.02 to 1.08), while the rank correlation, 0.05 (95% interval -0.06 to 0.17), is consistent with no association or a weak positive one. ### Do municipalities with more day care centers have fewer struggling readers? Not consistently. The rank correlation between the CDC gap and the share of struggling readers is 0.01 (95% interval -0.10 to 0.12), and the multilevel odds ratio is 0.99 (95% interval 0.96 to 1.02). A CDC's presence also says nothing about its quality or enrollment. ### Is the OPT Plus stunting rate the same as the national stunting rate? No. The 4.3% figure is the share stunted among the 3,007,695 children aged 0–2 measured in OPT Plus 2025 in the municipalities analyzed. National nutrition surveys sample households with a survey design, so their prevalence figures are not directly comparable. ### How can school heads work with barangay nutrition and day care workers? Invite barangay nutrition scholars and child development workers to Kindergarten enrollment and transition activities, and ask the local nutrition office about OPT Plus results for the school's barangays. Use the school's own CRLA results, not the municipality's stunting share, to decide which learners need reading help. ## Download the data The cleaned EDCOM II dashboard data behind this series are free to download for personal study; to reuse them, email [support@nash.com.ph](mailto:support@nash.com.ph) for written permission first. [How the EDCOM Dashboard Data Was Cleaned](https://nash.com.ph/blog/edcom-eccd-dashboard-data-cleaned) explains every table and cleaning rule. The files are at data.nash.com.ph: start with the [README](https://data.nash.com.ph/edcom-data/v1/README.md), then the [school table with CRLA results and school inputs](https://data.nash.com.ph/edcom-data/v1/edcom_crla_2026.parquet) (Parquet), the [school estimates](https://data.nash.com.ph/edcom-data/v1/school_estimates.csv.gz) (CSV), the [division estimates](https://data.nash.com.ph/edcom-data/v1/division_estimates.csv) (CSV) and the [municipal table](https://data.nash.com.ph/edcom-data/v1/municipalities.csv) (CSV). The DepEd school files used for school inputs are described in [Download Cleaned DepEd School Data](https://nash.com.ph/blog/download-cleaned-deped-school-data). Every file, with its rows, columns and checksum, is listed on the [EDCOM II CRLA data page](https://nash.com.ph/edcom-data). To cite the data: Llego, M. A. (2026). *EDCOM II early-childhood dashboard data, cleaned, version 1*. NASH Reviewer. https://nash.com.ph/edcom-data ## More in this series - [CRLA End-of-Year Results SY 2025-26: All 18 Regions](https://nash.com.ph/blog/crla-end-of-year-results-sy-2025-2026) - [CRLA Start vs End of SY 2025-26 in the Same Schools](https://nash.com.ph/blog/crla-start-vs-end-of-year-sy-2025-2026) - [CRLA by Language: Mother Tongue, Filipino and English](https://nash.com.ph/blog/crla-mother-tongue-filipino-english) - [How to Read a School's CRLA Results Fairly](https://nash.com.ph/blog/how-to-read-school-crla-results) - [CRLA Variation: School, Division or Region?](https://nash.com.ph/blog/crla-school-division-region-variation) - [Where Struggling Readers Are Concentrated](https://nash.com.ph/blog/where-struggling-readers-are-concentrated) - [School Resources and CRLA Reading Results](https://nash.com.ph/blog/school-resources-and-crla-reading) - [CRLA Results in Schools With and Without a Principal](https://nash.com.ph/blog/schools-with-and-without-principal-crla) - [Action Research and Thesis Topics on Reading (CRLA)](https://nash.com.ph/blog/action-research-topics-reading-crla) - [CRLA Reading Hotspots: A Map of Struggling Readers](https://nash.com.ph/blog/crla-reading-hotspots-map) - [Stunting, Day Care Centers and Early Reading](https://nash.com.ph/blog/stunting-day-care-and-reading) - [Literacy and Nutrition Programs Compared With Need](https://nash.com.ph/blog/literacy-nutrition-programs-coverage) - [CRLA Schools With No Struggling Readers: A Data Check](https://nash.com.ph/blog/crla-schools-with-no-struggling-readers) - [How the EDCOM Dashboard Data Was Cleaned](https://nash.com.ph/blog/edcom-eccd-dashboard-data-cleaned) - Regional reports: [NCR](https://nash.com.ph/blog/crla-results-sy-2025-2026-ncr), [CAR](https://nash.com.ph/blog/crla-results-sy-2025-2026-car), [Region I](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-1-ilocos), [Region II](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-2-cagayan-valley), [Region III](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-3-central-luzon), [Region IV-A](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-4a-calabarzon), [MIMAROPA](https://nash.com.ph/blog/crla-results-sy-2025-2026-mimaropa), [Region V](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-5-bicol), [Region VI](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-6-western-visayas), [NIR](https://nash.com.ph/blog/crla-results-sy-2025-2026-negros-island-region), [Region VII](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-7-central-visayas), [Region VIII](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-8-eastern-visayas), [Region IX](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-9-zamboanga-peninsula), [Region X](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-10-northern-mindanao), [Region XI](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-11-davao), [Region XII](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-12-soccsksargen), [Caraga](https://nash.com.ph/blog/crla-results-sy-2025-2026-region-13-caraga), [BARMM](https://nash.com.ph/blog/crla-results-sy-2025-2026-barmm) - Earlier reports: [CRLA Results SY 2025-2026 (DepEd's selected-school file)](https://nash.com.ph/blog/crla-results-sy-2025-2026), [Do the Same DepEd Schools Lag on the ELLNA, NAT and CRLA?](https://nash.com.ph/blog/deped-schools-lag-ellna-nat-crla), [DepEd School Inputs vs Test Results](https://nash.com.ph/blog/deped-school-inputs-vs-test-results), [EDCOM II Final Report: Guide for Aspiring School Heads](https://nash.com.ph/blog/edcom-ii-final-report), [DepEd Data for Aspiring School Heads: Where to Start](https://nash.com.ph/blog/deped-data-aspiring-school-heads) *Analysis and charts by Mark Anthony Llego for NASH Reviewer. Data: EDCOM II early-childhood dashboard and Department of Education public datasets. NASH Reviewer is not affiliated with EDCOM II or DepEd. Charts may be reused only with written permission from support@nash.com.ph.* --- © 2026 Mark Anthony Llego, NASH Reviewer. All rights reserved. You may quote up to 100 words from this article with the author's name, its title and a link. In research, cite it as: Llego, M. A. (2026, October 27). *Stunting, Day Care Centers and Early Reading*. NASH Reviewer. https://nash.com.ph/blog/stunting-day-care-and-reading. Any other reuse of the article, its charts or its data needs written permission: email support@nash.com.ph. Terms of Service: https://nash.com.ph/terms#website-content-and-data